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Rafiki: A Semantic and Collaborative Approach to Community Health-Care in Underserved Areas

Fri, 09/19/2014 - 12:26



Primal Pappachan, Roberto Yus, Anupam Joshi and Tim Finin, Rafiki: A Semantic and Collaborative Approach to Community Health-Care in Underserved Areas, 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, 22-15 October2014, Miami.

Community Health Workers (CHWs) act as liaisons between health-care providers and patients in underserved or un-served areas. However, the lack of information sharing and training support impedes the effectiveness of CHWs and their ability to correctly diagnose patients. In this paper, we propose and describe a system for mobile and wearable computing devices called Rafiki which assists CHWs in decision making and facilitates collaboration among them. Rafiki can infer possible diseases and treatments by representing the diseases, their symptoms, and patient context in OWL ontologies and by reasoning over this model. The use of semantic representation of data makes it easier to share knowledge related to disease, symptom, diagnosis guidelines, and patient demography, between various personnel involved in health-care (e.g., CHWs, patients, health-care providers). We describe the Rafiki system with the help of a motivating community health-care scenario and present an Android prototype for smart phones and Google Glass.

Taming Wild Big Data

Thu, 09/18/2014 - 01:36



Jennifer Sleeman and Tim Finin, Taming Wild Big Data, AAAI Fall Symposium on Natural Language Access to Big Data, Nov. 2014.

Wild Big Data is data that is hard to extract, understand, and use due to its heterogeneous nature and volume. It typically comes without a schema, is obtained from multiple sources and provides a challenge for information extraction and integration. We describe a way to subduing Wild Big Data that uses techniques and resources that are popular for processing natural language text. The approach is applicable to data that is presented as a graph of objects and relations between them and to tabular data that can be transformed into such a graph. We start by applying topic models to contextualize the data and then use the results to identify the potential types of the graph’s nodes by mapping them to known types found in large open ontologies such as Freebase, and DBpedia. The results allow us to assemble coarse clusters of objects that can then be used to interpret the link and perform entity disambiguation and record linking. v1.91: Offer/price documentation fixes, cleanup and community contributions.

Fri, 09/12/2014 - 07:52


RDF has been updated to v1.91.

From the release notes:

  • Updated text of the price property to include practical usage guidance, alongside links to information from GS1 for the gtin-related Offer properties.
  • Updated all our examples to follow that guidance; primarily by using priceCurrency and the content= attribute.
  • Noted our thanks to the OpenDomain project for our domain name.
  • Updated the text of the 'image' property to match its expected types. Thanks, Dan Scott!
  • Changed spelling of 'supercededBy' to the more conventional supersededBy. Thanks, Sachini Aparna Herath!
  • Noted that 'logo' and 'photo' are sub-properties of 'image'. Thanks, Sachini Aparna Herath, again!
  • Fixed two syntax errors in examples (Store opening hours RDFa; Book, PublicationVolume Microdata). Thanks, Gregg Kellogg!
  • Added Tolkien-based examples for exampleOfWork/workExample. Thanks, Dan Scott, again!
  • Fixed a bug with our UTF-8 support. Thanks, Richard Wallis!
See the releases page in our documentation for details of previous updates.

Major UMBEL Release: 1.10

Tue, 09/09/2014 - 14:57


After more than 2 years, we are now finally releasing a new version of the UMBEL ontology and reference concept structure. One might think that we haven’t worked on the project all that time, but it is not strictly true.

We did improve the mapping to external vocabularies/ontologies, we worked much on linking Wikipedia pages to the UMBEL structure, but we haven’t had time to release a new version… until now!

For people new to the ontology, UMBEL is a general reference structure of about 28,000 reference concepts, which provides a scaffolding to link and interoperate other datasets and domain vocabularies. Its main purpose is to have a coherent conceptual structure that we can use to link and interoperate unrelated data sources. But it can also be used as a conceptual structure to be used to describe information like any other ontologies.

What is new with the ontology?

The major change in UMBEL is not the structure itself, but the piece of software used to generate it. In fact, the previous system we developed for generating UMBEL was about 7 years old. It was a bit clunky and really not that easy to work with.

Based on our prior experience with UMBEL, we choose to dump it and to create a brand new UMBEL reference structure generator. This new generator has been developed in Clojure and uses the latest version of the OWL API. It makes the management of the structure much simpler, which means that it will help in releasing new UMBEL version more regularly. We also have a suite of tools to analyze the structure and to pinpoint possible issues.

Other than that, we updated the, DBpedia Ontology and Geonames Ontology mappings to UMBEL. This is a major effort undertaken by Mike for this new version. The mappings are composed of:

  • 754 rdfs:subClassOf relationships between classes and UMBEL reference concepts
  • 688 rdfs:subClassOf relationships between DBpedia Ontology classes and UMBEL reference concepts
  • 682 rdfs:subClassOf relationships between Geonames Ontology classes and UMBEL reference concepts

These new mappings will help manage data instances that use these external ontologies/schemas in a broader conceptual structure (which is UMBEL). This enables us to be able to reason over this external data using the UMBEL conceptual structure even if these external data sources didn’t originally use UMBEL to describe their data. That is one of the main features of UMBEL.

We also managed to add a few hundred UMBEL reference concepts. Most of them were added to create these new linkages with the external ontologies. Others have been added because they were improving the overall structure.

A few weeks back, we found an issue with the umbel:superClassOf assignations, which has also now been resolved in version 1.10.

In the previous versions of UMBEL, the preferred labels were not unique. There were a few hundred of the concepts that were having the same preferred labels. This was not an issue in itself, but this was not a best practice to create an ontology. We managed to remove all these non-distinct preferred labels and to make all of them unique.

We added a few skos:broader and skos:narrower relationships between some of the reference concepts. In the previous versions, all the relationships were skos:broaderTransitive and skos:narrowerTransitive properties only.

Finally we made sure that the entire UMBEL reference structure (Core + the Geo module) was absent of any inconsistencies and that it was satisfiable.

What is new with the portal and web services?

This new version of UMBEL also led us to create a few new features to the UMBEL website. The most apparent feature is the new External Linkage section that may appear at the top of a reference concept page (obviously, it will not appear if there are no external links for a given reference concept). This section shows you the linkage between the UMBEL reference concept and other external classes:

Another feature that you will notice on this screenshot is the Core blue tag at the right of the URI of the reference concept. This tag is used to tell you from where the reference concept is coming. Another tag that you may encounter is the green Geo tag, which tells you that the reference concept comes from the UMBEL Geo module. The same tags appear in the search resultsets:

What is next?

Because UMBEL is an ontology, by nature it will always evolve over time. Things change, and the way we see the World can always improve.

For the next version of UMBEL, we will analyze the entire UMBEL reference concept structure using different algorithms, heuristics and other techniques to analyze the conceptual structure and to find conceptual gaps in it. The goal of this analysis is to tighten the structure, to have a better conceptual hierarchy and a more fine-grained one.

Other things we want to do in other coming versions are to improve the Super Types structure of UMBEL. As you may know, many of the Super Types are non disjoint because some of the concepts belong to multiple Super Type classes. What we want to do here is to create new Super Types classes that are the intersection between two, or more, Super Types that will be used to categorize these concepts that belong to multiple Super Types. That way, we will end-up with a better classification of the UMBEL reference concepts from a Super Types standpoint.

Another thing we want to do related to the UMBEL web services is to update them such that you can query the linkage to the external ontologies. For now, you can see the linkage when querying the sub-classes and super-classes of a reference concept. But you cannot query the web services this way: give me all the sub-classes-of the class, for example.

As you can see, the UMBEL ontology and web services will continue to evolve over time to enable new ways to leverage the conceptual structure and external data sources.

SEMANTiCS 2014 (part 3 of 3): Conversations

Mon, 09/08/2014 - 20:11



I was asked for an oracular statement about the future of relational database (RDBMS) at the conference. The answer, without doubt or hesitation, is that this is forever. But this does not mean that the RDBMS world would be immutable, quite the opposite.

The specializations converge. The RDBMS becomes more adaptable and less schema-first. Of course the RDBMS also take new data models beside the relational. RDF and other property graph models, for instance.

The schema-last-ness is now well in evidence. For example, PostgreSQL has an hstore column type which is a list of key-value pairs. Vertica has a feature called flex tables where a column can be added on a row-by-row basis.

Specialized indexing for text and geometries is a well established practice. However, dedicated IR systems, often Lucene derivatives, can offer more transparency in the IR domain for things like vector-space-models and hit-scoring. There is specialized faceted search support which is quite good. I do not know of an RDBMS that would do the exact same trick as Lucene for facets, but, of course, in the forever expanding scope of RDB, this is added easily enough.

JSON is all the rage in the web developer world. Phil Archer even said in his keynote, as a parody of the web developer: " I will never touch that crap of RDF or the semantic web; this is a pipe dream of reality ignoring academics and I will not have it. I will only use JSON-LD."

XML and JSON are much the same thing. While most databases have had XML support for over a decade, there is a crop of specialized JSON systems like MongoDB. PostgreSQL also has a JSON datatype. Unsurprisingly, MarkLogic too has JSON, as this is pretty much the same thing as their core competence of XML.

Virtuoso, too, naturally has a JSON parser, and mapping this to the native XML data type is a non-issue. This should probably be done.

Stefano Bertolo of the EC, also LOD2 project officer, used the word Cambrian explosion when talking about the proliferation of new database approaches in recent years.

Hadoop is a big factor in some environments. Actian Vector (née VectorWise), for example, can use this as its file system. HDFS is singularly cumbersome for this but still not impossible and riding the Hadoop bandwagon makes this adaptation likely worthwhile.

Graphs are popular in database research. We have a good deal of exposure to this via LDBC. Going back to an API for database access, as is often done in graph database, can have its point, especially as a reaction to the opaque and sometimes hard to predict query optimization of declarative languages. This just keeps getting more complex, so a counter-reaction is understandable. APIs are good if crossed infrequently and bad otherwise. So, graph database APIs will develop vectoring, is my prediction and even recommendation in LDBC deliverables.

So, there are diverse responses to the same evolutionary pressures. These are of initial necessity one-off special-purpose systems, since the time to solution is manageable. Doing these things inside an RDBMS usually takes longer. The geek also likes to start from scratch. Well, not always, as there have been some cases of grafting some entirely non-MySQL-like functionality, e.g. Infobright and Kickfire, onto MySQL.

From the Virtuoso angle, adding new data and control structures has been done many times. There is no reason why this cannot continue. The next instances will consist of some graph processing (BSP, or Bulk Synchronous Processing) in the query languages. Another recent example is an interface for pluggable specialized content indices. One can make chemical structure indices, use alternate full text indices, etc., with this.

Most of this diversification has to do with physical design. The common logical side is a demand for more flexibility in schema and sometimes in scaling, e.g., various forms of elasticity in growing scale-out clusters, especially with the big web players.

The diversification is a fact, but the results tend to migrate into the RDBMS given enough time.

On the other hand, when a new species like the RDF store emerges, with products that do this and no other thing and are numerous enough to form a market, the RDBMS functionality seeps in. Bigdata has a sort of multicolumn table feature, if I am not mistaken. We just heard about the wish for strict schema, views, and triggers. By all means.

From the Virtuoso angle, with structure awareness, the difference of SQL and RDF gradually fades, and any advance can be exploited to equal effect on either side.

Right now, I would say we have convergence when all the experimental streams feel many of the same necessities.

Of course you cannot have a semantic tech conference without the matter of the public SPARQL end point coming up. The answer is very simple: If you have operational need for SPARQL accessible data, you must have your own infrastructure. No public end points. Public end points are for lookups and discovery; sort of a dataset demo. If operational data is in all other instances the responsibility of the one running the operation, why should it be otherwise here? Outsourcing is of course possible, either for platform (cloud) or software (SaaS). To outsource something with a service level, the service level must be specifiable. A service level cannot be specified in terms of throughput with arbitrary queries but in terms of well defined transactions; hence the services world runs via APIs, as in the case of Open PHACTS. For arbitrary queries (i.e., analytics on demand), with the huge variation in performance dependent on query plans and configuration of schema, the best is to try these things with platform on demand in a cloud. Like this, there can be a clear understanding of performance, which cannot be had with an entirely uncontrolled concurrent utilization. For systems in constant operation, having one's own equipment is cheaper, but still might be impossible to procure due to governance.

Having clarified this, the incentives for operators also become clearer. A public end point is a free evaluation; a SaaS deal or product sale is the commercial offering.

Anyway, common datasets like DBpedia are available preconfigured on AWS with a Virtuoso server. For larger data, there is a point to making ready-to-run cluster configurations available for evaluation, now that AWS has suitable equipment (e.g., dual E5 2670 with 240 GB RAM and SSD for USD 2.8 an hour). According to Amazon, up to five of these are available at a time without special request. We will try this during the fall and make the images available.

SEMANTiCS 2014 Series

SEMANTiCS 2014 (part 2 of 3): RDF Data Shapes

Mon, 09/08/2014 - 19:22



The first keynote of Semantics 2014 was by Phil Archer of the W3C, entitled "10 Years of Achievement." After my talk, in the questions, Phil brought up the matter of the upcoming W3C work group charter on RDF Data Shapes. We had discussed this already at the reception the night before and I will here give some ideas about this.

After the talk, my answer was that naturally the existence of something that expressed the same sort of thing as SQL DDL, with W3C backing, can only be a good thing and will give the structure awareness work by OpenLink in Virtuoso and probably others a more official seal of approval. Quite importantly, this will be a facilitator of interoperability and will raise this from a product specific optimization trick to a respectable, generally-approved piece of functionality.

This is the general gist of the matter and can hardly be otherwise. But underneath is a whole world of details, which we discussed at the reception.

Phil noted that there was controversy around whether a lightweight OWL-style representation or SPIN should function as the basis for data shapes.

Phil stated in the keynote that the W3C considered the RDF series of standards as good and complete, but would still have working groups for filling in gaps as these came up. This is what I had understood from my previous talks with him at the Linking Geospatial Data workshop in London earlier this year.

So, against this backdrop, as well as what I had discussed with Ralph Hodgson of Top Quadrant at a previous LDBC TUC meeting in Amsterdam, SPIN seems to me a good fit.

Now, it turns out that we are talking about two different use cases. Phil said that the RDF Data Shapes use case was about making explicit what applications required of data. For example, all products should have a unit price, and this should have one value that is a number.

The SPIN proposition on the other hand, as Ralph himself put it in the LDBC meeting, is providing to the linked data space functionality that roughly corresponds to SQL views. Well, this is one major point, but SPIN involves more than this.

So, is it DDL or views? These are quite different. I proposed to Phil that there was in fact little point in fighting over this; best to just have two profiles.

To be quite exact, even SQL DDL equivalence is tricky, since enforcing this requires a DBMS; consider, for instance, foreign key and check constraints. At the reception, Phil stressed that SPIN was certainly good but since it could not be conceived without a SPARQL implementation, it was too heavy to use as a filter for an application that, for example, just processed a stream of triples.

The point, as I see it, is that there is a wish to have data shape enforcement, at least to a level, in a form that can apply to a stream without random access capability or general purpose query language. This can make sense for some big data style applications, like an ETL-stage pre-cooking of data before the application. Applications mostly run against a DBMS, but in some cases, this could be a specialized map-reduce or graph analytics job also, so no low cost random access.

My own take is that views are quite necessary, especially for complex query; this is why Virtuoso has the SPARQL macro extension. This will do, by query expansion, a large part of what general purpose inference will do, except for complex recursive cases. Simple recursive cases come down to transitivity and still fit the profile. SPIN is a more generic thing, but has a large intersection with SPARQL macro functionality.

My other take is that structure awareness needs a way of talking about structure. This is a use case that is clearly distinct from views.

A favorite example of mine is the business rule that a good customer is one that has ordered more than 5 times in the last year, for a total of more than so much, and has no returns or complaints. This can be stated as a macro or SPIN rule with some aggregates and existences. This cannot be stated in any of the OWL profiles. When presented with this, Phil said that this was not the use case. Fair enough. I would not want to describe what amounts to SQL DDL in these terms either.

A related topic that has come up in other conversations is the equivalent of the trigger. One use case of this is enforcement of business rules and complex access rights for updates. So, we see that the whole RDBMS repertoire is getting recreated.

Now, talking from the viewpoint of the structure-aware RDF store, or the triple-stream application for that matter, I will outline some of what data shapes should do. The triggers and views matter is left out, here.

The commonality of bulk-load, ETL, and stream processing, is that they should not rely on arbitrary database access. This would slow them down. Still, they must check the following sorts of things:

  • Data types
  • Presence of some required attributes
  • Cardinality — e.g., a person has no more than one date of birth
  • Ranges — e.g., a product's price is a positive number; gender is male/female; etc.
  • Limited referential integrity — e.g., a product has one product type, and this is a subject of the RDF type product type.
  • Limited intra-subject checks — e.g.. delivery date is greater-than-or-equal-to ship date.

All these checks depend on previous triples about the subject; for example, these checks may be conditional on the subject having a certain RDF type. In a data model with a join per attribute, some joining cannot be excluded. Checking conditions that can be resolved one triple at a time is probably not enough, at least not for the structure-aware RDF store case.

But, to avoid arbitrary joins which would require a DBMS, we have to introduce a processing window. The triples in the window must be cross-checkable within the window. With RDF set semantics, some reference data may be replicated among processing windows (e.g., files) with no ill effect.

A version of foreign key declarations is useful. To fit within a processing window, complete enforcement may not be possible but the declaration should still be possible, a little like in SQL where one can turn off checking.

In SQL, it is conventional to name columns by prefixing them with an abbreviation of the table name. All the TPC schemas are like that, for example. Generally in coding, it is good to prefix names with data type or subsystem abbreviation. In RDF, this is not the practice. For reuse of vocabularies, where a property may occur in anything, the namespace or other prefix denotes where the property comes from, not where it occurs.

So, in TPC-H, l_partkey and ps_partkey are both foreign keys that refer to part, plus that l_partkey is also a part of a composite foreign key to partsupp. By RDF practices, these would be called rdfh:hasPart. So, depending on which subject type we have, rdfh:hasPart is 30:1 or 4:1. (distinct subjects:distinct objects) Due to this usage, the property's features are not dependent only on the property, but on the property plus the subject/object where it occurs.

In the relational model, when there is a parent and a child item (one to many), the child item usually has a composite key prefixed with the parent's key, with a distinguishing column appended, e.g., l_orderkey, l_linenumber. In RDF, this is rdfh:hasOrder as a property of the lineitem subject. In SQL, there is no single part lineitem subject at all, but in RDF, one must be made since everything must be referenceable with a single value. This does not have to matter very much, as long as it is possible to declare that lineitems will be primarily accessed via their order. It is either this or a scan of all lineitems. Sometimes a group of lineitems are accessed by the composite foreign key of l_partkey, l_suppkey. There could be a composite index on these. Furthermore, for each l_partkey, l_suppkey in lineitem there exists a partsupp. In an RDF translation, the rdfh:hasPart and rdfh:hasSupplier, when they occur in a lineitem subject, specify exactly one subject of type partsupp. When they occur in a partsupp subject, they are unique as a pair. Again, because names are not explicit as to where they occur and what role they play, the referential properties do not depend only on the name, but on the name plus included data shape. Declaring and checking all this is conventional in the mainstream and actually useful for query optimization also.

Take the other example of a social network where the foaf:knows edge is qualified by a date when this edge was created. This may be by reification, or more usually by an "entitized" relationship where the foaf:knows is made into a subject with the persons who know each other and the date of acquaintance as properties. In a SQL schema, this is a key person1, person2 -> date. In RDF, there are two join steps to go from person1 to person2; in SQL, 1. This is eliminated by saying that the foaf:knows entity is usually referenced by the person1 Object or person2 Object, not the Subject identifier of the foaf:knows.

This allows making the physical storage by O, S, G -> O2, O3, …. A secondary index with S, G, O still allows access by the mandatory subject identifier. In SQL, a structure like this is called a clustered table. In other words, the row is arranged contiguous with a key that is not necessarily the primary key.

So, identifying a clustering key in RDF can be important.

Identifying whether there are value-based accesses on a given Object without making the Object a clustering key is also important. This is equivalent to creating a secondary index in SQL. In the tradition of homogenous access by anything, such indexing may be on by default, except if the property is explicitly declared of low cardinality. For example, an index on gender makes no sense. The same is most often true of rdfs:type. Some properties may have many distinct values (e.g., price), but are still not good for indexing, as this makes for the extreme difference in load time between SQL and the all-indexing RDF.

Identifying whether a column will be frequently updated is another useful thing. This will turn off indexing and use an easy-to-update physical representation. Plus, properties which are frequently updated are best put physically together. This may, for example, guide the choice between row-wise and column-wise representation. A customer's account balance and orders year-to-date would be an example of such properties.

Some short string valued properties may be frequently returned or used as sorting keys. This requires accessing the literal via an ID in the dictionary table. Non-string literals, numbers, dates, etc., are always inlined (at least in most implementations), but strings are a special question. Bigdata and early versions of Virtuoso would inline short ones; later versions of Virtuoso would not. So specifying, per property/class combination, a length limit for an inlined string is very high gain and trivial to do. The BSBM explore score at large scales can get a factor of 2 gain just from inlining one label. BSBM is out of its league here, but this is still really true and yields benefits across the board. The simpler the application, the greater the win.

If there are foreign keys, then data should be loaded with the referenced entities first. This makes dimensional clustering possible at load time. If the foreign key is frequently used for accessing the referencing item (for example, if customers are often accessed by country), then loading customers so that customers of the same country end up next to each other can result in great gains. The same applies to a time dimension, which in SQL is often done as a dimension table, but rarely so in linked data. Anyhow, if date is a frequent selection criterion, physically putting items in certain date ranges together can give great gains.

The trick here is not necessarily to index on date, but rather to use zone maps (aka min/max index). If nearby values are together, then just storing a min-max value for thousands of consecutive column values is very compact and fast to check, provided that the rows have nearby values. Actian Vector's (VectorWise) prowess in TPC-H is in part from smart use of date order in this style.

To recap, the data shapes desiderata from the viewpoint of guiding physical storage is as follows:

(I will use "data shape" to mean "characteristic set," or "set of Subjects subject to the same set of constraints." A Subject belonging to a data shape may be determined either by its rdfs:type or by the fact of it having, within the processing window, all or some of a set of properties.)

  • All normal range, domain, cardinality, optionality, etc. Specifically, declaring something as single valued (as with SQL's UNIQUE constraint) and mandatory (as with SQL's NOT NULL constraint) is good.
  • Primary access path — The Properties whose Objects are dominant access criteria is important
  • No-index — Declare that no index will be made on the Object of a Property within a data shape.
  • Inlined string — String values of up to so many characters in this data shape are inlined
  • Clustering key — The Subject identifiers will be picked to be correlated with the Object of this Property in this data shape. This can be qualified by a number of buckets (e.g., if dates are from 2000 to 2020, then this interval may be 100 buckets), with an exception bucket for out of range values.
  • No full text index — A string value will not need to be full text indexed in this Property even if full text indexing is generally on.
  • Full text index desired — This means that if the value of the property is a string, then the row must be locatable via this string. The string may or may not be inlined, but an index will exist on the literal ID of the string, e.g., POSG.
  • Co-location — This is akin to clustering but specifies, for a high cardinality Object, that the Subject identifier should be picked to fall in the same partition as the Object. The Object is typically a parent of the Subject being loaded; for example, the containing assembly of a sub-assembly. Traversing the assembly created in this way will be local on a scale-out system. This can also apply to geometries or text values: If primary access is by text or geo index, then the metadata represented as triples should be in the same partition as the entry in the full text/geo index.
  • Update group — A set of properties that will often change together. Implies no index and some form of co-location, plus update-friendly physical representation. Many update groups may exist, in which case they may or may not be collocated.
  • Composite foreign/primary key. A data shape can have a multicolumn foreign key, e.g., l_partkey, l_suppkey in lineitem with the matching primary key of ps_partkey, ps_suppkey in partsupp. This can be used for checking and for query optimization: Looking at l_partkey and l_suppkey as independent properties, the guess would be that there hardly ever exists a partsupp, whereas one does always exist. The XML standards stack also has a notion of a composite key for random access on multiple attributes.

These things have the semantic of "hint for physical storage" and may all be ignored without effect on semantics, at least if the data is constraint-compliant to start with.

These things will have some degree of reference implementation through the evolution of Virtuoso structure awareness, though not necessarily immediately. These are, to the semanticist, surely dirty low-level disgraceful un-abstractions, some of the very abominations the early semanticists abhorred or were blissfully ignorant of when they first raised their revolutionary standard.

Still, these are well-established principles of the broader science of database. SQL does not standardize some of these, nor does it have much need to, as the use of these features is system-specific. The support varies widely and the performance impacts are diverse. However, since RDF excels as a reference model and as a data interchange format, giving these indications as hints to back-end systems cannot hurt, and can make a difference of night and day in load and query time.

As Phil Archer said, the idea of RDF Data Shapes is for an application to say that "it will barf if it gets data that is not like this." An extension is for the data to say what the intended usage pattern is so that the system may optimize for this.

All these things may be learned from static analysis and workload traces. The danger of this is over-fitting a particular profile. This enters a gray area in benchmarking. For big data, if RDF is to be used as the logical model and the race is about highest absolute performance, never mind what the physical model ends up being, all this and more is necessary. And if one is stretching the envelope for scale, the race is always about highest absolute performance. For this reason, these things will figure at the leading edge with or without standardization. I would say that the build-up of experience in the RDBMS world is sufficient for these things to be included as hints in a profile of data shapes. The compliance cost will be nil if these are ignored, so for the W3C, these will not make the implementation effort for compliance with an eventual data shapes recommendation prohibitive.

The use case is primarily the data warehouse to go. If many departments or organizations publish data for eventual use by their peers, users within the organization may compose different combinations of extractions for different purposes. Exhaustive indexing of everything by default makes the process slow and needlessly expensive, as we have seen. Much of such exploration is bounded by load time. Federated approaches for analytics are just not good, even though they may work for infrequent lookups. If datasets are a commodity to be plugged in and out, the load and query investment must be minimized without the user/DBA having to run workload analysis and manual schema optimization. Therefore, bundling guidelines such as these with data shapes in a dataset manifest can do no harm and can in cases provide 10-50x gains in load speeds and 2-4x in space consumption, not to mention unbounded gains in query time, as good and bad plans easily differ by 10-100x, especially in analytics.

So, here is the pitch:

  • Dramatic gains in ad hoc user experience
  • Minimal effort by data publishers, as much of the physical guidelines can be made from workload trace and dataset; the point is that the ad hoc user does not have to do this.
  • Great optimization potential for system vendors; low cost for initial compliance
  • Better understanding of the science of performance by the semantic community

To be continued...

SEMANTiCS 2014 Series

SEMANTiCS 2014 (part 1 of 3): Keynote

Mon, 09/08/2014 - 17:17



I was invited to give a keynote at SEMANTiCS 2014 in Leipzig, Germany last Thursday. I will here recap some of the main points, and comment on some of the ensuing controversy. The talk was initially titled Virtuoso, the Prometheus of RDF. Well, mythical Prometheus did perform a service but ended up paying for it. Still, the mythical reference is sometimes used when talking of major breakthroughs and big-gain ambitions. In the first slide, I changed it to Linked Data at Dawn, which is less product specific and more a reflection on the state of the linked data enterprise at large.

The first part of the talk was under the heading of the promise and the practice. The promise we know well and find no fault with: Schema-last-ness, persistent unique identifiers, self-describing data, some but not too much inference. The applications usually involve some form of integration and often have a mix of strictly structured content with semi-structured or textual content.

These values are by now uncontroversial and embraced by many; however, most instances of this embracing do not occur in the context of RDF as such. For example, the big online systems on the web: all have some schema-last (key-value) functionality. Applications involving long-term data retention have diverse means of having persistent IDs and self description, from UUIDs to having the table name in a column so that one can tell where a CSV dump came from.

The practice involves competing with diverse alternative technologies: SQL, key-value, information retrieval (often Lucene-derived). In some instances, graph databases occur as alternatives: Young semanticist, do or die.

In this race, linked data is often the prettiest and most flexible, but gets a hit on different aspects of performance and scalability. This is a database gig, and database is a performance game; make no mistake.

After these preliminaries we come to the "RDF tax," or the more or less intrinsic overheads of describing all as triples. The word "triple" is used by habit. In fact, we nearly always talk about quads, i.e., subject-predicate-object-graph (SPOG). The next slide is provocatively titled the Bane of the Triple, and is about why having all as triples is, on the surface, much like relational, except it makes life hard, where tables make it at least manageable, if still not altogether trivial.

The very first statement on the tax slide reads "90% of bad performance comes from non-optimal query plans." If one does triples in the customary way (i.e., a table of quads plus dictionary tables to map URIs and literal strings to internal IDs), one incurs certain fixed costs.

These costs are deemed acceptable by users who deploy linked data. If these costs were not acceptable, the proof of concept would have already disqualified linked data.

The support cases that come my way are nearly always about things taking too much time. Much less frequently, are these about something unambiguously not working. Database has well defined semantics, so whether something works or not is clear cut.

So, support cases are overwhelmingly about query optimization. The problems fall in two categories:

  • The plan is good in the end, but it takes much longer to make the plan than to execute it.
  • The plan either does the wrong things or does things in the wrong order, but produces a correct result.

Getting no plan at all or getting a clearly wrong result is much less frequent.

If the RDF overheads incurred with a good query plan were show stoppers, the show would have already stopped.

So, let's look at this in more detail; then we will talk about the fixed overheads.

The join selectivity of triple patterns is correlated. Some properties occur together all the time; some occur rarely; some not at all. Some property values can be correlated, i.e., order number and order date. Capturing these by sampling in a multicolumn table is easy; capturing this in triples would require doing the join in the cost model, which is not done since it would further extend compilation times. When everything is a join, selectivity estimation errors build up fast. When everything is a join, the space of possible graph query plans explodes as opposed to tables; thus, while the full plan space can be covered with 7 tables, it cannot be covered with 18 triple patterns. This is not factorial (number of permutations). For different join types (index/hash) and the different compositions of the hash build side, this is much worse, in some nameless outer space fringe of non-polynomiality.

TPC-H can be run with success because the cost model hits the right plan every time. The primary reason for this is the fact that the schema and queries unambiguously suggest the structure, even without foreign key declarations. The other reason is that with a handful of tables, all plans can be reviewed, and the cost model reliably tells how many rows will result from each sequence of operations.

Try this with triples; you will know what I mean.

Now, some people have suggested purely rule-based models of SPARQL query compilation. These are arguably faster to run and more predictable. But the thing that must be done, yet will not be done with these, is the right trade-off between index and hash. This is the crux of the matter, and without this, one can forget about anything but lookups. The choice depends on reliable estimation of cardinality (number of rows, number of distinct keys) on either side of the join. Quantity, not pattern matching.

Well, many linked data applications are lookups. The graph database API world is sometimes attractive because it gives manual control. Map reduce in the analytical space is sometimes attractive for the same reason.

On the other hand, query languages also give manual control, but then this depends on system specific hints and cheats. People are often black and white: Either all declarative or all imperative. We stand for declarative, but still allow physical control of plan, like most DBMS.

To round off, I will give a concrete example:

{ ?thing rdfs:label ?lbl . ?thing dc:title ?title . ?lbl bif:contains "gizmo" . ?title bif:contains "widget" . ?thing a xx:Document . ?thing dc:date ?dt . FILTER ( ?dt > "2014-01-01"^^xsd:date ) }

There are two full text conditions, one date, and one class, all on the same subject. How do you do this? Most selective text first, then get the data and check, then check the second full text given the literal and the condition, then check the class? Wrong. If widgets and gizmos are both frequent and most documents new, this is very bad because using a text index to check for a specific ID having a specific string is not easily vectorable. So, the right plan is: Take the more selective text expression, then check the date and class for the results, put the ?things in a hash table. Then do the less selective text condition, and drop the ones that are not in the hash table. Easily 10x better. Simple? In the end yes, but you do not know this unless you know the quantities.

This gives the general flavor of the problem. Doing this with TPC-H in RDF is way harder, but you catch my drift.

Each individual instance is do-able. Having closer and closer alignment between reality and prediction will improve the situation indefinitely, but since the space is as good as infinite there cannot be a guarantee of optimality except for toy cases.

The Gordian Knot shall not be defeated with pincers but by the sword.

We will come to this in a bit.

Now, let us talk of the fixed overheads. The embarrassments are in the query optimization domain; the daily grind, relative cost, and provisioning are in this one.

The overheads come from:

  • Indexing everything
  • Having literals and URI strings via dictionary
  • Having a join for every attribute

These all fall under the category of having little to no physical design room.

In the indexing everything department, we load 100 GB TPC-H in 15 minutes in SQL with ordering only on primary keys and almost no other indexing. The equivalent with triples is around 12 hours. This data can be found on this blog (TPC-H series and Meeting the Challenges of Linked Data in the Enterprise). This is on the order of confusing a screwdriver with a hammer. If the nail is not too big, the wood not too hard, and you hit it just right — the nail might still go in. The RDF bulk load is close to the fastest possible given the general constraints of what it does. The same logic is used for the record-breaking 15 minutes of TPC-H bulk load, so the code is good. But indexing everything is just silly.

The second, namely the dictionary of URIs and literals, is a dual edge. I talked to Bryan Thompson of SYSTAP (Bigdata RDF store) in D.C. at the ICDE there. He said that they do short strings inline and long ones via dictionary. I said we used to do the same but stopped in the interest of better compression. What is best depends on workload and working-set-to-memory ratio. But if you must make the choice once and for all, or at least as a database-wide global setting, you are between a rock and a hard place. Physical vs. logical design, again.

The other aspect of this is the applications that do regexps on URI strings or literals. Doing this is like driving a Formula 1 race in reverse gear. Use a text index. Always. This is why most implementations have one even though SPARQL itself makes no provisions for this. If you really need regexps, and on supposedly opaque URIs at that, tokenize them and put them in a text index as a text literal. Or if an inverted-file-word index is really not what you need, use a trigram one. So far, nobody has wanted one hard enough for us to offer this, even though this is easy enough. But special indices for special data types (e.g., chemical structure) are sometimes wanted, and we have a generic solution for all this, to be introduced shortly on this blog. Again, physical design.

I deliberately name the self-join-per-attribute point last, even though this is often the first and only intrinsic overhead that is named. True, if the physical model is triples, each attribute is a join against the triple table. Vectored execution and right use of hash-join help, though. The Star Schema Benchmark SQL to SPARQL gap is only 2.5x, as documented last year on this blog. This makes SPARQL win by 100+x against MySQL and lose by only 0.8x against column store pioneer MonetDB. Let it be said that this is so far the best case and that the gap is wider in pretty much all other cases. This gap is well and truly due to the self-join matter, even after the self-joins are done vectored, local, ordered; in one word, right. The literal and URI translation matter plays no role here. The needless indexing hurts at load but has no effect at query time, since none of the bloat participates in the running. Again, physical design.

Triples are done right, so?

In the summer of 2013, after the Star Schema results, it became clear that maybe further gains could be had and query optimization made smoother and more predictable, but that these would be paths of certain progress but with diminishing returns per effort. No, not the pincers; give me the sword. So, between fall 2013 and spring 2014, aside from doing diverse maintenance, I did the TPC-H series. This is the proficiency run for big league databases; the America's Cup, not a regatta on the semantic lake.

Even if the audience is principally Linked Data, the baseline must be that of the senior science of SQL.

It stands to reason and has been demonstrated by extensive experimentation at CWI that RDF data, by and large, has structure. This structure will carry linked data through the last mile to being a real runner against the alternative technologies (SQL, IR, key value) mentioned earlier.

The operative principles have been mentioned earlier and are set forth on the slides. In forthcoming articles I will display some results.

One important proposal for structure awareness was by Thomas Neumann in an RDF3X paper introducing characteristic sets. There, the application was creation of more predictable cost estimates. Neumann correctly saw this as possibly the greatest barrier to predictable RDF performance. Peter Boncz and I discussed the use of this for physical optimization once when driving back to Amsterdam from a LOD2 review in Luxembourg. Pham Minh Duc of CWI did much of the schema discovery research, documented in the now published LOD2 book (Linked Open Data -- Creating Knowledge Out of Interlinked Data). The initial Virtuoso implementation had to wait for the TPC-H and general squeezing of the quads model to be near complete. It will likely turn out that the greatest gain of all with structure awareness will be bringing optimization predictability to SQL levels. This will open the whole bag of tricks known to data warehousing to safe deployment for linked data. Of course, much of this has to do with exploiting physical layout; hence it also needs the physical model to be adapted. Many of these techniques have high negative impact if used in the wrong place; hence the cost model must guess right. But they work in SQL and, as per Thomas Neumann's initial vision, there is no reason why these would not do so in a schema-less model if adapted in a smart enough manner.

All this gives rise to some sociological or psychological observations. Jens Lehmann asked me why now, why not earlier; after all, over the years many people have suggested property tables and other structured representations. This is now because there is no further breakthroughs within an undifferentiated physical model.

For completeness, we must here mention other approaches to alternative, if still undifferentiated, physical models. A number of research papers mention memory-only, pointer-based (i.e., no index, no hash-join) implementations of triples or quads. Some of these are on graph processing frameworks, some stand-alone. Yarc Data is a commercial implementation that falls in this category. These may have higher top speeds than column stores, even after all vectoring and related optimizations. However the space utilization is perforce larger than with optimum column compression and this plus the requirement of 100% in memory makes these more expensive to scale. The linked data proposition is usually about integration, and this implies initially large data even if not all ends up being used.

The graph analytics, pointer-based item will be specially good for a per-application extraction, as suggested by Oracle in their paper at GRADES 13. No doubt this will come under discussion at LDBC, where Oracle Labs is now a participant.

But back to physical model. What we have in mind is relational column store — multicolumn-ordered column-wise compressed tables — a bit like Vertica and Virtuoso in SQL mode for the regular parts and quads for the rest. What is big is regular, since a big thing perforce comes from something that happens a lot, like click streams, commercial transactions, instrument readings. For the 8-lane-motorway of regular data, you get the F1 racer with the hardcore best in column store tech. When the autobahn ends and turns into the mountain trail, the engine morphs into a dirt bike.

This is complex enough, and until all the easy gains have been extracted from quads, there is little incentive. Plus this has the prerequisite of quads done right, plus the need for top of the line relational capability for not falling on your face once the speedway begins.

Steve Buxton of MarkLogic gave a talk right before mine. Coming from a document-centric world, it stands to reason that MarkLogic would have a whole continuum of different mixes between SPARQL and document oriented queries. Steve correctly observed that some users found this great; others found this a near blasphemy, an unholy heterodoxy of confusing distinct principles.

This is our experience as well, since usage of XML fragments in SPARQL with XPath and such things in Virtuoso is possible but very seldom practiced. This is not the same as MarkLogic, though, as MarkLogic is about triples-in-documents, and the Virtuoso take is more like documents-in-triples. Not to mention that use of SQL and stored procedures in Virtuoso is rare among the SPARQL users.

The whole thing about the absence of physical design in RDF is a related, but broader instance of such purism.

In my talk, I had a slide titled The Cycle of Adventure, generally philosophizing on the dynamics of innovation. All progress begins with an irritation with the status quo; to mention a few examples: the No-SQL rebellion; the rejection of parallel SQL database in favor of key-value and map-reduce; the admission that central schema authority at web scale is impossible; the anti-ACID stance when having wide-area geographies to deal with. The stage of radicalism tends to discard the baby with the bathwater. But when the purists have their own enclave, free of the noxious corruption of the rejected world, they find that life is hard and defects of human character persist, even when all subscribe to the same religion. Of course, here we may have further splinter groups. After this, the dogma adapts to reality: the truly valuable insights of the original rebellion gain in appreciation, and the extremism becomes more moderate. Finally there is integration with mainstream, which becomes enriched by new content.

By the time the term Linked Data came to broad use, the RDF enterprise had its break-away colonies that started to shed some of the initial zeal. By now, we have the last phase of reconciliation in its early stages.

This process is in principle complete when linked data is no longer a radical bet, but a technology to be routinely applied to data when the nature of the data fits the profile. The structure awareness and other technology discussed here will mostly eliminate the differential in deployment cost.

The spreading perception of an expertise gap in this domain will even-out the cost in terms of personnel. The flexibility gains that were the initial drive for the movement will be more widely enjoyed when these factors fuel broader adoption.

To help this along, we have LDBC, the Linked Data Benchmark Council, with the agenda of creating industry consensus on measuring progress across the linked data and graph DB frontiers. I duly invited MarkLogic to join.

There were many other interesting conversations at the conference, I will later comment on these.

To be continued...

SEMANTiCS 2014 Series

SEMANTiCS – the emergence of a European Marketplace for the Semantic Web

Mon, 09/08/2014 - 11:34



SEMANTiCS conference celebrated its 10th anniversary this September in Leipzig. And this year’s venue has been capable of opening a new age for the Semantic Web in Europe - a marketplace for the next generation of semantic technologies was born.

As Phil Archer stated in his key note, the Semantic Web is now mature, and academia and industry can be proud of the achievements so far. And exactly that fact gave the thread for the conference: Real world use cases demonstrated by industry representatives, new and already running applied projects presented by the leading consortia in the field and a vivid academia showing the next ideas and developments in the field. So this years SEMANTiCS conference brought together the European Community in Semantic Web Technology – both from academia and industry.

  • Papers and Presentations: 45 (50% of them industry talks)
  • Posters: 10 (out of 22)
  • A marketplace with 11 permanent booths
  • Presented Vocabularies at the 1st Vocabulary Carnival: 24
  • Attendance: 225
  • Geographic Coverage: 21 countries

This year’s SEMANTiCS was co-located and connected with a couple of other related events, like the German ISKO, the Multilingual Linked Open Data for Enterprises (MLODE 2014) and the 2nd DBpedia Community Meeting 2014. This wisely connected gatherings brought people together and allowed transdisciplinary exchange.

Recapitulatory speaking: This SEMANTiCS has opened up new sights on Semantic Technologies, when it comes to

  • industry use
  • problem solving capacity
  • next generation development
  • knowledge about top companies, institutes and people in the sector

A layer cake of spatial data, and in a jigsaw puzzle style

Thu, 09/04/2014 - 20:43



During a lunch at the GeoData 2014 workshop, Boulder, CO, USA, June 2014, people sitting around the table began to chat about topics relevant to data sharing, data format, interoperability – all those topics relevant to geoscience data – well, inter-agency data interoperability was the central topic of that workshop. When someone rose up the topic of comparing data sharing policies in USA with those in Europe and China, a few people (those who know me) looked at me and began to smile. Yes, I am confident to say that I have some comments on the geoscience data sharing in Europe.

Before I came to USA I spent about four and half years in the Netherlands working for a PhD degree on geoscience data interoperability . When I looked back, it seems very interesting because I knew nothing about what was happening on data sharing in Europe before I headed to ITC. But the world is a really small cycle. At the second year of my PhD study, I got in contact with a colleague in the Commission for Management and Application of Geoscience Information of the International Union of Geological Sciences, and he worked at the Geological Survey of the Netherlands at Utrecht. I visited him several times and from him I also came to know about the giant data sharing initiative of EU, the Infrastructure for Spatial Information in Europe (INSPIRE).

Initially, what attracted me is some technical details in INSPIRE, especially those surrounding the works on vocabulary modeling and web map services. INSPIRE covers 34 data themes, among which geology is my favorite because geological data is the topic of my PhD work at ITC. And I really appreciated the data specification working group of the Geology theme in INSPIRE, as colleagues in that group offered me so many fresh technical ideas. Then, in my fourth ITC year, when I began to prepare my PhD dissertation and the defense, a guideline ‘Don’t get lost in details, look at the big picture’ inspired me review the INSPIRE from another angle and discuss my ideas with advisors and colleagues at ITC.

I forgot to mention that many such discussions happened during coffee breaks or lunch breaks at ITC (Well, there is no such a culture in the USA). And then, one day, during such a coffee break chat, a view came into my brain – a jigsaw puzzle layer cake – a nice analog of the INSPIRE initiative: the 34 data themes represent 34 layers and the 27 EU nations (in 2011) represent 27 puzzle pieces. The data specifications and implementation rules of INSPIRE are the recopies for making cakes, and the public agencies in EU nations are the cake cooks.

This ‘cake’ view sounds like a jest, but I took it seriously and I know in GIScience people used to call data as layer cakes. I drafted a manuscript to describe my view immediately after that coffee break chat, but it was out of my plan that the short article was not published until four years later – actually, just one month before the lunch table meeting at GeoData 2014, and
EU has 28 nations now (Croatia joined in 2013). The article is accessible at

The INSPIRE initiative is combination of bottom-up and top-down approaches. The bottom-up approach is reflected in the works of data specification drafting and technical infrastructure constructions, which represent the consensus of experts from the EU nations. The top-down approach is reflected in the formally issued EU directive for the INSPRE, which makes it a de jure initiative, that is, EU member nations are required to comply with the INSPIRE data specifications and implementation rules when build their national spatial data infrastructures.

USA has a different administrative system comparing with EU. That, more or less, is also reflected in the geoscience data sharing policies and technologies. However, people here also build such data cakes. What can USA benefit from the EU experience and what suggestions can it provide based on its own work? I do not have a single answer now but I hope I will have some comments a few years later. Fortunately, similar to my encounter with the colleague at the Geological Survey of the Netherlands, now I also come to know colleagues at NASA, USGS, NOAA, EPA, USGCRP, and more, who are showing me the picture of geoscience data issues in the USA. Support for Bibliographic Relationships and Periodicals

Tue, 09/02/2014 - 17:12


[Guest post by Richard Wallis, OCLC & Dan Scott, Laurentian University]
With the addition of three new types, the latest version of introduces support for describing the relationship between, Articles and the Periodicals in which they were published, along with potentially related PublicationIssues & PublicationVolumes. For example:
  • The article "The semantic web" was published in May 2001, in volume 284, issue 5 of Scientific american on pages 28 through 37.
  • That issue of Scientific American contained 33 other articles listed at
  • The editors for that issue included Mark Alpert, Steve Ashley, and Carol Ezzell.
You can now also describe creative works that span multiple parts using the hasPart and isPartOf properties, and you can express relationships between a conceptual representation of a creative work and physical examples of that work using the exampleOfWork and workExample properties. For example:
  • The Lord of the Rings is a trilogy consisting of three separate books.
  • One edition of the first book, The Fellowship of the Ring, was published by HarperCollins in 1974 with ISBN .
  • Another edition of the first book was published by Ballantine Books in 1984 with ISBN 0345296052.
  • The movie J.R.R. Tolkien's The Lord of the Rings, directed by Ralph Bakshi and released in 1978, was based on the first book in the trilogy.
  • The movie The Lord of the Rings: The Fellowship of the Ring, directed by Peter Jackson and released in 2001, was also based on the first book in the trilogy.
These extensions were developed by the W3C Schema Bib Extend Community Group, and were designed in the spirit of to provide an easily published and widely consumable vocabulary for creative works. Many other modelling and vocabulary initiatives, such as RDA and BIBFRAME, continue to work towards offering the additional layers of granularity of description desired by many in the bibliographic metadata world, and these extensions hope to complement those efforts. Where possible, we aligned our work with the Bibliographic Ontology, and acknowledge their leadership in tackling many of these issues.

We are pleased with the outcome of working with the broader W3C Web Schemas Task Force community to refine these extensions, which also helped address similar concepts and relationships required by a number of associated domains such as TV, Radio and Music Recording. One outcome of this discussion was the elevation of position to a general superproperty of properties such as issueNumber, volumeNumber, seasonNumber, and episodeNumber. Combined with the recent addition of the Role type, now has the flexible, generic framework to address the specialized needs of other domains such as Comics.

We welcome the acceptance, refinement and introduction of these proposals by, which greatly enhances the capability for describing creative works in general, and bibliographic resources in particular.

Sharing Files With Whomever Is Simple

Tue, 09/02/2014 - 09:25



Dropbox, Google Drive, OneDrive, – they all allow you to share files with others. But they all do it via the strange concept of public links. Anyone who has this link has access to the file. On first glance this might be easy enough but what if you want to revoke read access for just one of those people? What if you want to share a set of files with a whole group?

I will not answer these questions per se. I will show an alternative based on

Final Program published for DC-2014

Fri, 08/29/2014 - 23:59



29, The Texas Digital Library and the Conference Committee of DC-2014 in Austin, Texas on 8-11 October have published the final program of the DCMI International Conference at Join us in Austin for an exciting agenda including 46 papers, project reports and best practice posters and presentations. Parallel with the peer reviewed program is an array of special sessions of panels and discussions on key metadata issues, challenges and new opportunities. Pre- and post-conference workshops and tutorials round out the program by providing 1/2 day to full day instruction. Every year the DCMI community gathers for both its Annual Meeting and its International Conference on Dublin Core & Metadata Applications. The work agenda of the DCMI community is broad and inclusive of all aspects of innovation in metadata design, implementation and best practices. While the work of the Initiative progresses throughout the year, the Annual Meeting and Conference provide the opportunity for DCMI "citizens" as well as students and early career professionals studying and practicing the dark arts of metadata to gather face-to-face to share experiences. In addition, the gathering provides public- and private-sector initiatives beyond DCMI engaged in significant metadata work to come together to compare notes and cast a broader light into their particular metadata domain silos. Through such a gathering of the metadata "clans", DCMI advances its "first goal" of promoting metadata interoperability and harmonization. Visit the DC-2014 conference website at for additional information and to register. It is a meeting you will not want to miss.

LOD2 Finale (part 3 of n): The 500 Giga-triple Runs

Fri, 08/29/2014 - 16:49



In the evening of day 8, we have kernel settings in the cluster changed to allow more mmaps. At this point, we notice that the dataset is missing the implied types of products; i.e., the most specific type is given but its superclasses are not directly associated with the product. We have always run this with this unique inference materialized, which is also how the data generator makes the data, with the right switch. But the switch was not used. So a further 10 Gt (Giga-triples) are added, by running a SQL script to make the superclasses explicit.

At this point, we run BSBM explore for the first time. To what degree does the 37.5 Gt predict the 500 Gt behavior? First, there is an overflow that causes a query plan cost to come out negative if the default graph is specified. This is a bona fide software bug you don't get unless a sample is quite large. Also, we note that starting the databases takes a few minutes due to disk. Further, the first query takes a long time to compile, again because of sampling the database for overall statistics.

The statistics are therefore gathered by running a few queries, and then saved. Subsequent runs will reload the stats from the file system, saving some minutes of start time. There is a function for this, stat_import and stat_export. These are used for a similar purpose by some users.

On day 10, Wednesday August 20, we have some results of BSBM explore.

Then, we get into BSBM updates. The BSBM generator makes an update dataset, but it cannot be made large enough. The BSBM test driver suite is by now hated and feared in equal measure. Is it bad in and of itself? Depends. It was certainly not made for large data. Anyway, no fix will be attempted this time. Instead, a couple of SQL procedures are made to drive a random update workload. These can run long enough to get a steady state with warm cache, which is what any OLTP measurement needs.

On day 12, some updates are measured, with a one hour ramp-up to steady-state, but these are not quite the right mix, since these are products only and the mix needs to contain offers and reviews also. The first steady-state rate was 109 Kt/s, a full 50x less than the bulk load, but then this was very badly bound by latency. So, the updates are adjusted to have more variety. The final measurement was on day 17. Now the steady-state rate is 2563 Kt/s, which is better but still quite bound by network. By adding diversity to the dataset, we get slammed by a sharp rise in warm-up time (now 2 hours to be at 230 Kt/s), at which point we launch the explore mix to be timed during update. Time is short and we do not want to find out exactly how long it takes to get the plateau in insert rate. As it happens, the explore mix is hardly slowed down by the updates, but the updates get hit worse, so that the rate goes to about 1/3 of what it was, then comes back up when the explore is finished. Finally, half an hour after this, there is a steady state of 263 Kt/s update rate.

Of course, the main object of the festivities is still the business intelligence (BI) mix. This is our (specifically, Orri's) own invention from years back, subsequently formulated in SPARQL by FU Berlin (Andreas Schultz). Well, it is already something to do big joins with 150 Gt, all on index and vectored random access, as was done in January 2013, the last time results were published on the CWI cluster. You may remember that there was an aborted attempt in January 2014. So now, with the LOD2 end date under two weeks away, we will take the BI racer out for a spin with 500 Gt. This is now a very different proposition from Jan 2013, as we have by now done the whole TPC-H work documented on this blog. This serves to show, inter alia, that we can run with the best in the much bigger and harder mainstream database sports. The full benefits of this will be realized for the semantic data public still this year, so this is more than personal vanity.

So we will see. The BI mix is not exactly TPC-H, but what is good for one is good for the other. Checking that the plans are good on the 37 Gt scale model is done around day 12. On day 13, we try this on the larger cluster. You never know — pushing the envelope, even when you know what you are doing and have written the whole thing, is still a dive in the fog. Claiming otherwise would be a lie lacking credibility. The iceberg which first emerges is overflow and partition skew. Well, there can be a lot of messages if all messages go via the same path. So we make the data structure different and retry and now die from out of memory. On the scale model, this looks like a little imbalance you don't bother to notice; at 13x scale, this kills. So, as is the case with most database problems, the query plan is bad. Instead of using a PSOG index, it uses a POSG index, and there is a constant for O. Partitioning is on either S or O, whichever is first. Not hard to fix, but still needs a cost-model adjustment to penalize low-cardinality partition columns. This is something you don't get with TPC-H, where there are hardly any indices. Once this is fixed there are other problems, such as Q5, which we ended up leaving out. The scale model is good; the large one does not produce a plan, because some search-space corner is visited that is not visited in the scale model, due to different ratios of things in the cost model. Could be a couple of days to track; this is complex stuff. So we dropped it. It is not a big part of the metric, and its omission is immaterial to the broader claim of handling 500 Gt in all safety and comfort. The moral is: never get stuck; only do what is predictable, insofar as anything in this shadowy frontier is such.

So, on days 15 and 16, the BI mix that is reported was run. The multiuser score was negatively impacted by memory skew, so some swapping on one of the nodes, but the run finished in about 2 hours anyway. The peak of transient memory consumption is another thing that you cannot forecast with exact precision. There is no model for that; the query streams are in random order, and you just have to try. And it is a few hours per iteration, so you don't want to be stuck doing that either. A rerun would get a higher multiuser BI score; maybe one will be made but not before all the rest is wrapped up.

Now we are talking 2 hours, versus 9 hours with the 150 Gt set back in January 2013. So 3.3x the data, 4.5x less time, 1.5x the gear. This comes out at one order of magnitude. With a better score from better memory balance and some other fixes, a 15x improvement for BSBM BI is in the cards.

The final explore runs were made on day 18, while writing the report to be published at the LOD2 deliverables repository. The report contains in depth discussion on the query plans and diverse database tricks and their effectiveness.

The overall moral of this trip into these uncharted spaces is this: Expect things to break. You have to be the designer and author of the system to take it past its limits. You will cut it or you won't, and nobody can do anything about it, not with the best intentions, nor even with the best expertise, which both were present. This is true of the last minute daredevil stuff like this; if you have a year full time instead of the last 20 days of a project, all is quite different, and these things are more leisurely. This might then become a committee affair, though, which has different problems. In the end, the Virtuoso DBMS has never thrown anything at us we could not handle. The uncertainty in trips of this sort is with the hardware platform, of which we had to replace 2 units to get on the way, and with how fast you can locate and fix a software problem. So you pick the quickest ones and leave the uncertain aside. There is another category of rare events like network failures that in theory cannot happen. Yet they do. So, to program a cluster, you have to have some recovery things for these. We saw a couple of these along the way. Duplication of these can take days, and whether this correlates with specific links or is a bona fide software thing is time consuming to prove, and getting into this is a sure way to lose the race. These seem to be load peaks outside of steady-state; steady-state is in fact very steady once it is there. Except at the start, network glitches were not a big factor in these experiments. The bulk of these went away after replacing a machine. After this we twice witnessed something that cannot exist but knew better than to get stuck with that. Neither incident happened again. This is days of running at a cross sectional 1 GB/s of traffic. These are the truly unpredictable, and, in a crash course like this, can sink the whole gig no matter how good you are.

Thanks are due to CWI and especially Peter Boncz for providing the race track as well as advice and support.

In the next installments of this series, we will look at how schema and characteristic sets will deliver the promise of RDF without its cost. All the experiments so far were done with a quads table, as always before. So we could say that the present level is close to the limit of the achievable within this physical model. The future lies beyond the misconception of triples/quads as primary physical model.

To be continued...

LOD2 Finale Series

Upgrade complete

Mon, 08/18/2014 - 22:50


All systems back to normal? Well, no, probably not. There's tremendous room for error...

LOD2 Finale (part 2 of n): The 500 Giga-triples

Mon, 08/18/2014 - 20:54



No epic is complete without a descent into hell. Enter the historia calamitatum of the 500 Giga-triples (Gt) at CWI's Scilens cluster.

Now, from last time, we know to generate the data without 10 GB of namespace prefixes per file and with many short files. So we have 1.5 TB of gzipped data in 40,000 files, spread over 12 machines. The data generator has again been modified. Now the generation was about 4 days. Also from last time, we know to treat small integers specially when they occur as partition keys: 1 and 2 are very common values and skew becomes severe if they all go to the same partition; hence consecutive small INTs each go to a different partition, but for larger ones the low 8 bits are ignored, which is good for compression: Consecutive values must fall in consecutive places, but not for small INTs. Another uniquely brain-dead feature of the BSBM generator has also been rectified: When generating multiple files, the program would put things in files in a round-robin manner, instead of putting consecutive numbers in consecutive places, which is how every other data generator or exporter does it. This impacts bulk load locality and as you, dear reader, ought to know by now, performance comes from (1) locality and (2) parallelism.

The machines are similar to last time: each a dual E5 2650 v2 with 256 GB RAM and QDR InfiniBand (IB). No SSD this time, but a slightly higher clock than last time; anyway, a different set of machines.

The first experiment is with triples, so no characteristic sets, no schema.

So, first day (Monday), we notice that one cannot allocate more than 9 GB of memory. Then we figure out that it cannot be done with malloc, whether in small or large pieces, but it can with mmap. Ain't seen that before. One day shot. Then, towards the end of day 2, load begins. But it does not run for more than 15 minutes before a network error causes the whole thing to abort. All subsequent tries die within 15 minutes. Then, in the morning of day 3, we switch from IB to Gigabit Ethernet (GigE). For loading this is all the same; the maximal aggregate throughput is 800 MB/s, which is around 40% of the nominal bidirectional capacity of 12 GigE's. So, it works better, for 30 minutes, and one can even stop the load and do a checkpoint. But after resuming, one box just dies; does not even respond to ping. We change this to another. After this, still running on GigE, there are no more network errors. So, at the end of day 3, maybe 10% of the data are in. But now it takes 2h21min to make a checkpoint, i.e., make the loaded data durable on disk. One of the boxes manages to write 2 MB/s to a RAID-0 of three 2 TB drives. Bad disk, seen such before. The data can however be read back once the write is finally done.

Well, this is a non-starter. So, by mid-day of day 4, another machine has been replaced. Now writing to disk is possible within expected delays.

In the afternoon of day 4, the load rate is about 4.3 Mega-triples (Mt) per second, all going in RAM.

In the evening of day 4, adding more files to load in parallel increases the load rate to between 4.9 and 5.2 Mt/s. This is about as fast as this will go, since the load is not exactly even. This comes from the RDF stupidity of keeping an index on everything, so even object values where an index is useless get indexed, leading to some load peaks. For example, there is an index on POSG for triples were the predicate is rdf:type and the object is a common type. Use of characteristic sets will stop this nonsense.

But let us not get ahead of the facts: At 9:10 PM of day 4, the whole cluster goes unreachable. No, this is not a software crash or swapping; this also affects boxes on which nothing of the experiment was running. A whole night of running is shot.

A previous scale model experiment of loading 37.5 Gt in 192 GB of RAM, paging to a pair of 2 TB disks, has been done a week before. This finishes in time, keeping a load rate of above 400 Kt/s on a 12-core box.

At 10AM on day 5 (Friday), the cluster is rebooted; a whole night's run missed. The cluster starts and takes about 30 minutes to get to its former 5 Mt/s load rate. We now try switching the network back to InfiniBand. The whole ethernet network seemed to have crashed at 9PM on day 4. This is of course unexplained but the experiment had been driving the ethernet at about half its cross-sectional throughput, so maybe a switch crashed. We will never know. We will now try IB rather than risk this happening again, especially since if it did repeat, the whole weekend would be shot, as we would have to wait for the admin to reboot the lot on Monday (day 8).

So, at noon on day 5, the cluster is restarted with IB. The cruising speed is now 6.2 Mt/s, thanks to the faster network. The cross sectional throughput is about 960 MB/s, up from 720 MB/s, which accounts for the difference. CPU load is correspondingly up. This is still not full platform since there is load unbalance as noted above.

At 9PM on day 5, the rate is around 5.7 Mt/s with the peak node at 1500% CPU out of a possible 1600%. The next one is under 800%, which is just to show what it means to index everything. In specific, the node that has the highest CPU is the one in whose partition the bsbm:offer class falls, so that there is a local peak since one of every 9 or so triples says that something is an offer. The stupidity of the triple store is to index garbage like this to begin with. The reason why the performance is still good is that a POSG index where P and O are fixed and the S is densely ascending is very good, with everything but the S represented as run lengths and the S as bitmaps. Still, no representation at all is better for performance than even the most efficient representation.

The journey consists of 3 different parts. At 10PM, the 3rd and last part is started. The triples have more literals, but the load is more even. The cruising speed is 4.3 Mt/s down from 6.2, but the data has a different shape, including more literals.

The last stretch of the data is about reviews. This stretch of the data has less skew. So we increase parallelism, running 8 x 24 files at a time. The load rate goes above 6.3 Mt/s.

At 6:45 in the morning of day 6, the data is all loaded. The count of triples is 490.0 billion. If the load were done in a single stretch without stops and reconfiguration, it would likely go in under 24h. The average rate for a 4 hour sample between midnight and 4AM of day 6 is 6.8 MT/s. The resulting database files add up to 10.9 TB, with about 20% of the volume in unallocated pages.

At this time, noon of day 6, we find that some cross-partition joins need more distinct pieces of memory than the default kernel settings allow per process. A large number of partitions makes a large number of sometimes long messages which makes many mmaps. So we will wait until morning of day 8 (Monday) for the administrator to set these. In the meantime, we analyze the behavior of the workload on the 37 Gt scale model cluster on my desktop.

To be continued...

LOD2 Finale Series

LOD2 Finale (part 1 of n): RDF Before The Dawn

Mon, 08/18/2014 - 20:54



The LOD2 FP7 ends at the end of August, 2014. This post begins a series that will crown the project with a grand finale, another decisive step towards the project’s chief goal of giving RDF and linked data performance parity with SQL systems.

In a nutshell, LOD2 went like this:

  1. Triples were done right, taking the best of the column store world and adapting it to RDF. This is now in widespread use.

  2. SQL was done right, as I have described in detail in the TPC-H series. This is generally available as open source in v7fasttrack. SQL is the senior science and a runner-up like sem-tech will not carry the day without mastering this.

  3. RDF is now breaking free of the triple store. RDF is a very general, minimalistic way of talking about things. It is not a prescription on how to do database. Confusing these two things has given rise to RDF’s relative cost against alternatives. To cap off LOD2, we will have the flexibility of triples with the speed of the best SQL.

In this post we will look at accomplishments so far and outline what is to follow during August. We will also look at what in fact constitutes the RDF overhead, why this is presently so, and why this does not have to stay thus.

This series will be of special interest to anybody concerned with RDF efficiency and scalability.

At the beginning of LOD2, I wrote a blog post discussing the RDF technology and its planned revolution in terms of the legend of Perseus. The classics give us exemplars and archetypes, but actual histories seldom follow them one-to-one; rather, events may have a fractal nature where subplots reproduce the overall scheme of the containing story.

So it is also with LOD2: The Promethean pattern of fetching the fire (state of the art of the column store) from the gods (the DB world) and bringing it to fuel the campfires of the primitive semantic tribes is one phase, but it is not the totality. This is successfully concluded, and Virtuoso 7 is widely used at present. Space efficiency gains are about 3x over the previous, with performance gains anywhere from 3 to 100x. As pointed out in the Star Schema Benchmark series (part 1 and part 2), in the good case one can run circles in SPARQL around anything but the best SQL analytics databases.

In the larger scheme of things, this is just preparation. In the classical pattern, there is the call or the crisis: Presently this is that having done triples about as right as they can be done, the mediocre in SQL can be vanquished, but the best cannot. Then there is the actual preparation: Perseus talking to Athena and receiving the shield of polished brass and the winged sandals. In the present case, this is my second pilgrimage to Mount Database, consisting of the TPC-H series. Now, the incense has been burned and libations offered at each of the 22 stations. This is not reading papers, but personally making one of the best-ever implementations of this foundational workload. This establishes Virtuoso as one of the top-of-the-line SQL analytics engines. The RDF public, which is anyway the principal Virtuoso constituency today, may ask what this does for them.

Well, without this step, the LOD2 goal of performance parity with SQL would be both meaningless and unattainable. The goal of parity is worth something only if you compare the RDF contestant to the very best SQL. And the comparison cannot possibly be successful unless it incorporates the very same hard core of down-to-the-metal competence the SQL world has been pursuing now for over forty years.

It is now time to cut the Gorgon’s head. The knowledge and prerequisite conditions exist.

The epic story is mostly about principles. If it is about personal combat, the persons stand for values and principles rather than for individuals. Here the enemy is actually an illusion, an error of perception, that has kept RDF in chains all this time. Yes, RDF is defined as a data model with triples in named graphs, i.e., quads. If nothing else is said, an RDF Store is a thing that can take arbitrary triples and retrieve them with SPARQL. The naïve implementation is to store things as rows in a quad table, indexed in any number of ways. There have been other approaches suggested, such as property tables or materialized views of some joins, but these tend to flush the baby with the bathwater: If RDF is used in the first place, it is used for its schema-less-ness and for having global identifiers. In some cases, there is also some inference, but the matter of schema-less-ness and identifiers predominates.

We need to go beyond a triple table and a dictionary of URI names while maintaining the present semantics and flexibility. Nobody said that physical structure needs to follow this. Everybody just implements things this way because this is the minimum that will in any case be required. Combining this with a SQL database for some other part of the data/workload hits basically insoluble problems of impedance mismatch between the SQL and SPARQL type systems, maybe using multiple servers for different parts of a query, etc. But if you own one of the hottest SQL racers in DB city and can make it do anything you want, most of these problems fall away.

The idea is simple: Put the de facto rectangular part of RDF data into tables; do not naively index everything in places where an index gives no benefit; keep the irregular or sparse part of the data as quads. Optimize queries according to the table-like structure, as that is where the volume is and where getting the best plan is a make or break matter, as we saw in the TPC-H series. Then, execute in a way where the details of the physical plan track the data; i.e., sometimes the operator is on a table, sometimes on triples, for the long tail of exceptions.

In the next articles we will look at how this works and what the gains are.

These experiments will for the first time showcase the adaptive schema features of the Virtuoso RDF store. Some of these features will be commercial only, but the interested will be able to reproduce the single server experiments themselves using the v7fasttrack open source preview. This will be updated around the second week of September to give a preview of this with BSBM and possibly some other datasets, e.g., Uniprot. Performance gains for regular datasets will be very large.

To be continued...

LOD2 Finale Series

Scheduled maintenance

Fri, 08/15/2014 - 13:55


I'm going to upgrade my "production" server this weekend. Expect some downtime...