I was invited to the ESWC 2013 "Semantic Technologies for Big Data Analytics: Opportunities and Challenges" panel on 29th May 2013 in Montpellier, France. The panel was moderated by Marko Grobelnik (JSI), with panelists Enrico Motta (KMi), Manfred Hauswirth (NUIG), David Karger (MIT), John Davies (British Telecom), José Manuel Gómez Pérez (ISOCO) and Orri Erling (myself).

Marko opened the panel by looking at the Google Trends search statistics for big data, semantics, business intelligence, data mining, and other such terms. Big data keeps climbing its hype-cycle hill, now above semantics and most of the other terms. But what do these in fact mean? In the leading books about big data, the word semantics does not occur.

I will first recap my 5 minute intro, and then summarize some questions and answers. This is from memory and is in no sense a full transcript.


Over the years we have maintained that what the RDF community most needs is good database. Indeed, RDF is relational in essence and, while it requires some new datatypes and other adaptations, there is nothing in it that is fundamentally foreign to RDBMS technology.

This spring, we came through on the promise, delivering Virtuoso 7, packed full of all the state-of-the-art tricks in analytics-oriented databasing, column-wise compressed storage, vectored execution, great parallelism, and flexible scale-out.

At this same ESWC, Benedikt Kaempgen and Andreas Harth presented a paper (No Size Fits All -- Running the Star Schema Benchmark with SPARQL and RDF Aggregate Views) comparing Virtuoso and MySQL on the star schema benchmark at 1G scale. We redid their experiments with Virtuoso 7 at 30x and at 300x the scale.

At present, when running the star schema benchmark in SQL, we outperform column-store pioneer MonetDB by a factor of 2. When running the same star schema benchmark in SPARQL against triples as opposed to tables, we see a slowdown of 5x. When scaling from 30 to 300G and from one to two machines, we get linear increase in throughput, 5x longer for 10x more data.

Coming back to MySQL, the run with 1G takes about 60 seconds. Virtuoso SPARQL does the same on 30x the data in 45 seconds. Well, you could say that we should go pick on somebody in our series and not MySQL, being not relevant for this. Comparing with MonetDB and other analytics column stores is of course more relevant.

For cluster scaling, one could say that star schema benchmark is easy, and so it is, but even with harder ones, which do joins across partitions all the time, like the BSBM BI workload, we get scaling that is close to linear.

So, for analytics, you can use SPARQL in Virtuoso, and run circles around some common SQL databases.

The difference between SQL and SPARQL comes from having no schema. Instead of scanning aligned columns in a table, you do an index lookup for each column. This is not too slow if there is locality, as there is, but still a lot more than when talking about a multicolumn column-compressed table. With more execution tricks, we can maybe cut this to 3x.

The beach-head of workable RDF-based analytics on schema-less data has been attained. Medium-scale data, to the single-digit terabytes, is OK on small clusters.

What about the future?

First, Big Data means more than querying. Before meaningful analytics can be done, the data must generally be prepared and massaged. This means fast bulk load and fast database-resident transformation. We have that via flexible, expressive, parallelizable stored procedures and run time hosting. One can do everything one does in MapReduce right inside the database.

Some analytics cannot be expressed in a query language. For example, graph algorithms like clustering generate large intermediate states and run in many passes. For this, bulk synchronous processing frameworks like Giraph are becoming popular. We can again do this right inside the DBMS, on RDF or SQL tables. There is great platform utilization and more flexibility than in strict BSP, while being able to do any BSP algorithm.

The history of technology is one of divergence followed by reintegration. New trends, like Column stores, RDF databases, key value stores, or MapReduce, start as one-off special-purpose products, and the technologies then find their way back into platforms addressing a broader functionality.

The whole semantic experiment might be seen as a break-away from the web, if also a little from database, for the specific purpose of exploring schemaless-ness, universal referenceability of data, self-describing data, and some inference.

With RDF, we see lasting value in globally consistent identifiers. The URI "superkey" is the ultimate silo-breaker. The future is in integrating more and more varied data and a schema-first approach is cost-prohibitive. If data is to be preserved over extended lengths of time, self-description is essential; the applications and people that produced the data might not be around. Same for publishing data for outside reuse.

In fact, many of these things are right now being pursued in mainstream IT. Everybody is reinventing the triple, whether by using non-first normal form key-value pairs in an RDB, tagging each row of a table with the name of the table, using XML documents, etc. The RDF model provides all these desirable features, but most applications that need these things do not run on RDF infrastructure.

Anyway, by revolutionizing RDF store performance, we make this technology a cost-effective alternative in places where it was not such before.

To get much further in performance, physical storage needs to adapt to the data. Thus, in the long term, we see RDF as a lingua franca of data interchange and publishing, supported by highly scalable and adaptive databases that exploit the structure implicit in the data to deliver performance equal to the best in SQL data warehousing. When we get the schema from the data, we have schema-last flexibility and schema-first performance. The genie is back in the bottle, and data models are unified.

Questions and Answers

Q: Is the web big data?

David Karger: No, the shallow web (i.e., static web pages for purposes of search) is not big data. One can put it in a box and search. But for purposes of more complex processing, like analytics on the structure of the whole web, this is still big data.

Q: I bet you still can't do analytics on a fast stream.

Orri Erling: I am not sure about that, because when you have a stream -- whether this is network management and denial of service detection, or managing traffic in a city -- you know ahead of time what peak volume you are looking at, so you can size the system accordingly. And streams have a schema. So you can play all the database tricks. Vectored execution will work there just as it does for query processing, for example.

Q: I did not mean storage, I meant analysis.

Orri Erling: Here we mean sliding windows and constant queries. The triple vs. row issue also seems the same. There will be some overhead from schema-lastness, but for streams, I would say each has a regular structure.

John Davies: For example, we gather gigabytes a minute of traffic data from sensors in the road network and all this data is very regular, with a fixed schema.

Manfred Hauswirth: Or is this always so? The internet of things has potentially huge diversity in schema, with everything producing a stream. The user of the stream has no control whatever on the schema.

Marko Grobelnik: Yes, we have had streams for a long time -- on Wall Street, for example, where these make a lot of money. But high frequency trading is a very specific application. There is a stream, some analytics, not very complicated, just fast. This is one specific solution, with fixed schema and very specific scope, no explicit semantics.

Q: What is big data, in fact?

David Karger: Computer science has always been about big data; it is just the definition of big that changes. Big data is something one cannot conveniently process on a computer system. Not without unusual tricks, where something trivial, like shortest path, becomes difficult just because of volume. So it is that big data is very much about performance, and performance is usually obtained by sacrificing the general for the specific. The semantic world on the other hand is after something very general and about complex and expressive schema. When data gets big, the schema is vanishingly small in comparison with the data, and the schema work gets done by hand; the schema is not the problem there. Big data is not very internetty either, because the 40 TB produced by the telescope are centrally stored and you do not download them or otherwise transport them very much.

Q: Now, what do each of you understand with semantics?

Manfred Hauswirth: The essential aspect is that data is machine interpretable, with sufficient machine readable context.

David Karger: Semantics has to do with complexity or heterogeneity in the schema. Big data has to do with large volume. Maybe semantic big data would be all the databases in the world with a million different schemas. But today we do not see such applications. If the volume is high, the schema is usually not very large.

Manfred Hauswirth: This is not so far as that, for example a telco has over a hundred distinct network management systems and each has a different schema.

Orri Erling: From the data angle, we have come to associate semantic with

  • schema-lastness
  • globally-resolvable identifiers
  • self-description
When people use RDF as a storage model, they mostly do so because of schema flexibility, not because of expressive schemas or inference. Some use a little inference, but inference or logics or considerations of knowledge representation do not in our experience drive the choice.


In conclusion, the event was rather peaceful, with a good deal of agreement between the panelists and audience and no heated controversy. I hoped to get some reaction when I said that semantics was schema flexibility, but apparently this has become a politically acceptable stance. In the golden days of AI this would not have been so. But then Marko Grobelnik did point out that the whole landscape has become data driven. Even in fields like natural language, one looks more at statistics than deep structure: For example, if a phrase is often found on Google, it is proper usage.