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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Semantic Tech & Business Conference Returns to San Francisco

Semantic Tech & Business Conference returns to San Francisco in June! Join us from June 3-7 for complete coverage of Big Data, Linked Data, Extreme Information Management, and Semantic Web. From breakthrough approaches to solving business problems to the big data implications of fast–evolving technologies, SemTechBiz provides you with an unparalleled interactive experience and delivers tangible business value. We're offering a special early rate when you register by February 17. Sign up now!

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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W3C announces the new RDB2RDF Working Group, whose mission is to standardize a language for mapping relational data and relational database schemas into RDF and OWL, tentatively called the RDB2RDF Mapping Language, R2RML. From the beginning of the deployment of the Semantic Web there has been increasing interest in mapping relational data to the Semantic Web. This is to allow relational data to be combined with other data on the Web, to link semantics directly to relational data and to aid in enterprise data integration. The creation of this Working Group follows the work of a previous W3C Incubator Group in this area. Read the RDB2RDF Working Group Charter

There comes a point in most programmers careers where they make a startling realization. Computer programming has nothing to do with mathematics, and everything to do, ultimately, with language. It’s a sobering thought. The art of computer programming largely involves the creation of and manipulation of text at the level of the individual character, at the level of the word, the line, the paragraph – and from there to the next level of abstraction:

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