OAKLAND, Calif. — April 23, 2013 – Franz Inc., the leading supplier of Graph Database technology for the Semantic Web, today announced Gruff v5.0, the industry’s leading RDF browser to view graph data, explore connections within data, and visually build queries without writing code.

The free-form nature of Graph style data offers a lot of flexibility for connecting data, but that freedom can also make it more challenging to find interesting patterns or simply navigate through your data. It has become typical for RDF data sets to contain thousands of classes and relationship types, making it hard to even formulate the analytics and queries you want to perform.

Gruff, a visual analytics and discovery tool, was developed by Franz to specifically address these Graph data challenges in large data sets. Gruff lets you intelligently explore graphs in multiple views:

• Graphical View – See the shape and density of your graph data
• Tabular view – Understand objects as a whole
• Outline view – Explore the often hierarchical nature of graphs
• Query view – Write Prolog or SPARQL queries
• Graphical Query Builder – Create queries visually via drag and drop

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A key feature of many triplestores is the ability to do inference. It is important to note that a DBMS typically offers the capacity to deal with concurrency, security, logging, recovery, and updates, in addition to loading and storing data. Not all Triplestores offer all these capabilities (yet).

Triplestore Implementations

Triplestores can be broadly classified in three types categories: Native triplestores, RDBMS-backed triplestores and NoSQL triplestores. continued…

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A struggle for pharma companies, Bulusu notes, sits in driving standards for data that exists across system silos, so it is broadly applicable across groups. A transaction like creating a batch of materials, doing analytical testing on it and enabling clinical trial releases is the work of multiple groups of people in departments like R&D entering data across different systems.

The foundational layer needed to support data aggregation in a persistent graph semantic database and visualization with collaborative, semantic knowledge maps “is all about data already in transactional, silo’d systems,” Bulusu says. “We want to make sure that across those systems, key data is entered consistently for entities.” That means limiting them to selecting via a drop-down list from a vocabulary that is consistently managed and published from a single source to all these transaction systems, so the same entity is called by the same name as it traverses systems to support analytics and other requirements. That, he says, “is where we directly impact the day-to-day operational work of users.”

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Ecosystem image via Shutterstock

As the Semantic Web Blog recently noted, 3RoundStones’ Callimachus Enterprise emerged as the winner of the Top Semantic Technology Start-up competition that was held at the Semantic Tech & Business Conference (#SemTechBiz) in San Francisco a few weeks ago. The commercially supported Linked Data Management system, now being piloted by eight companies, will this summer be released to the general public as Version 1.0.

Callimachus Enterprise is distinguished not only by its technology, but by CTO David Wood’s presentation that spoke to the real business needs of the enterprise today – including rapidly demonstrating value, in its case around exposing, connecting and visualizing disparate enterprise content – and also in that it provides a way for organizations to deal with their enterprise information in an entirely cloud-based solution. It leverages the Amazon cloud.

“A lot of companies are using cloud-based solutions for travel and expense tracking,” says CEO Bernadette Hyland. “But this is the beginning of a new wave.”

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The e-commerce agent, Hadfield told attendees at SemTech in San Francisco last week, was the company’s own foray into figuring out what it needed to add to the platform to make it easier to build apps that bring semantic technologies and Big Data together.

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I’m talking of course about Big Data and NoSQL.  Given that I’ve already given my take on how Semantic Web technology can help with the Big Data problem on SemanticWeb.com, this time around I’ll tackle NoSQL and the Semantic Web.

After all, we gave up SQL more than a decade ago.  We should be part of the discussion.  Heck, even the XQuery guys got in on the action early!

Check out this Google Trends diagram.

Semantic Web vs. NoSQL on Google Trends

NoSQL came out of nowhere in 2009, and now dominates much of the database conversation on the web.  Document stores like MongoDB and CouchDB, distributed, key-value stores such as Riak and Cassandra, and other weird stores like Hadoop-as-database (never understood that usage myself) now dominate the conversation as the alternative to traditional, SQL databases.

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What makes MongoDB appealing to JavaScript programmers working with JSON objects at these and other organizations is its simplicity. If all that’s desired is to have an easy-to-use database where you can add or retrieve JSON objects – the main data type for Javascript developers – it remains an attractive option.

But Franz Inc. proposes an alternative for those who want more sophisticated functionality: Use the semantic power of its AllegroGraph Web 3.0 database to deal with complicated queries, via MongoGraph, a MongoDB API to AllegroGraph technology.

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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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