We caught up with Bernie Spang, IBM’s Director, Strategy and Marketing, Database Software and Systems, to discuss the latest release of its enterprise data products DB2 and InfoSphere. Version 10 of both products have just been released. DB2 is used by thousands of organizations worldwide and comes in flavors ranging from a free version that maxes out at 2GB storage to systems that support large enterprises (Coca-Cola was an early adopter of DB2 version 10, and is already reporting cost-savings of over $1 Million).

The latest version of DB2 is the first in four years and represents a significant release, according to Spang, “This is a culmination of four years of effort by hundreds of engineers in IBM Research and Software Development Labs around the world; we also had more than 100 clients and over 200 business partners involved in the ‘early access program’ to help deliver this software. With the fundamental goal of delivering faster, easier, lower-cost data management.”

The early testing is showing positive results, with customers reporting “up to 10x faster data warehouse queries; freeing up to 90% of storage space using compression; and 98% code compatibility with Oracle Database, which makes it easier to migrate from Oracle to IBM software without changing data or retraining staff.”

For our readers, though, one of the more intriguing new features of DB2 is its built-in support for RDF. While semantics is not new to IBM — IBM Watson has gained particular fame — the appearance of RDF support in such a widely used, stable, enterprise database system is exciting.
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SemTechBiz is Less Than 2 Weeks Away

The Semantic Tech & Business Conference (SemTechBiz) is coming to San Francisco on June 3-7! Join us for case studies, innovative panels, tutorials, and keynotes that will provide you with practical advice, hands-on guidance, and breakthrough approaches to solving business problems with semantic technology. Passes go up $200 at the door. Sign up now and save !

I’m very happy to announce that the World Wide Web Consortium’s RDB2RDF Working Group, in which I participate as an Invited Expert,  has published two Candidate Recommendations: R2RML: RDB to RDF Mapping Language and A Direct Mapping of Relational Data to RDF. This has been a long road and we still have some ways to go. The standardization process goes back to the W3C Workshop on RDF Access to Relational Databases, which took place in October 2007. The W3C RDB2RDF Incubator Group followed afterwards. After almost 5 years, we are on track to have a standard. However, what is this standard bringing to the table?

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When it comes to Semantic Web technologies, there are some business-technology leaders that see value in moving rapidly forward. For some, it’s critical if they’re to live up to their image as technologically advanced enterprises. For others, it’s a matter of hearing that competitors are doing it, so they need to get on board too. There’s also the case to be made that there the amount of data to deal with already is overwhelming, and it’s only going to get worse, creating a world that mere humans and current information technology tools simply can’t keep up with.

At the (quickly) upcoming Semantic Tech & Business Conference in Washington D.C., Janet Millenson, principal of advisory firm Two Crows Consulting, will hit those high notes. But expect also to hear about what remains to grapple with in order to get executive support for what is still a new idea in many organizations.

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Researchers at the University of Texas – Pan American have found that HBase “has the edge in data management for next generation Internet and cloud computing users.” The article states, “An open-source, non-relational database written in Java that can scale to thousands of servers, HBase makes many features of Google’s proprietary, high-performance distributed storage system BigTable available to the programming community. It also features a fail-safe library that runs ‘on top of’ a server cluster — a global architecture that detects and handles failures at the local level before they spread.” Read more

With all the hullabaloo around Big Data, I’ve been a little surprised that there hasn’t been more talk about how to consume the vast petabytes that people are talking about…until I realized that there are really two Big Data problems out there!

Roughly speaking, the two primary ways in which data scales is by adding depth and by adding breadth.  The first is what most people mean when they refer to Big Data.  Want to run analytics on every single transaction that Wal*Mart has done over 10 years to analyze trends?  THAT is vertical scale.  Technically, you can characterize it as having lots and lots of similarly structured data.  That is where technologies like Hadoop and column-based data storage make a big difference.

Horizontal Big Data, on the other hand, is like the Linked Data Cloud.   It has all kinds of random information that ranges from highly structured and numeric to highly unstructured.  Significantly, it tends to change quite a bit over time with increasing heterogeneity.  That’s a completely different kind of scale, and one that is not well solved by using highly structured, vertically scaling technologies.

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SPARQL is the standardized query language for RDF, the same way SQL is the standardized query language for relational databases. If this is the first time you look at SPARQL, but you’re familiar with SQL, you will see some similarities because it shares several keywords such as SELECT, WHERE, etc. It also has new keywords that you have never seen if you come from a SQL world such as OPTIONAL, FILTER and much more.

Recall that RDF is a triple comprised of a subject, predicate and object. A SPARQL query consists of a set of triples where the subject, predicate and/or object can consist of variables. The idea is to match the triples in the SPARQL query with the existing RDF triples and find solutions to the variables. A SPARQL query is executed on a RDF dataset, which can be a native RDF database, or on a Relational Database to RDF (RDB2RDF) system, such as Ultrawrap.  These databases have SPARQL endpoints which accept queries and return results via HTTP.

A basic example

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 The U.K. is moving ahead with plans to introduce more transparency and accountability into the public agenda, through efforts such as the data.gov.uk initiative to make public data more easily available. Often,  governmental agencies and semi-governmental bodies are getting onboard with the open data movement by exporting information from databases or spreadsheets into CSV files and putting them up in that format on their website.

But so much more can be accomplished if they head in the direction of Linked Data, expressing their data in RDF and using dereferenceable URIs to identify the things in those databases and spreadsheets, so that ultimately their information can be meshed with other Linked Data sets in what hopefully will be useful applications for the citizenry.  

 That, however, represents a technological hurdle for many of these organizations – one that PublishMyData would like to help them through with what it likens to a content management system that’s geared up for Linked Data. Its hosted service will translate these organizations’ information into Linked Data and look after all the infrastructure issues that go along with it, such as managing triple stores.  

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In order for the Semantic Web to become a reality and success, there needs to be data on the web published as Linked Data. However, data on the web is not a new thing. People have been publishing raw data for a long time as XML, CSV or even spreadsheets. Data can also be accessed through APIs.  But where does most of the data on the web come from? Relational Databases!

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— DR. JANS AASMAN, ROBERT STANLEY

Semantic Universe editor Tony Shaw recently spoke with Jans Aasman, CEO of Franz Inc., and Robert Stanley, President & CEO of IO Informatics, about the announcement of their new strategic partnership to deliver 'fit for purpose' applications created by an innovative Semantic application framework. Their partnership has already seen success with the Pfizer IDEA pilot, which serves as a real-world example of using a semantic application in the pharma industry. This pilot was used to integrate data for compound purity verification and drug product stability analysis. The IDEA project was originally expected to take four to six months to produce results, but by using the AllegroGraph-Sentient framework, it was completed in only six weeks.

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This is the second of a two-part series discussing how Semantic Web Technology can enable Dynamic Business Applications in the enterprise. Read Part 1 of the article here.

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