Posts Tagged ‘SPARQL City’

Cambridge Semantics and SPARQL City Forge Partnership to Offer Interactive Big Data Search

Cambridge SemanticsCAMBRIDGE, Mass. (PRWEB) December 10, 2014 — Cambridge Semantics, the leading provider of smart data solutions driven by Semantic Web technology, and SPARQL City, providers of a high performance scalable Hadoop-based graph database infrastructure, today announced a collaboration to jointly offer an integrated graph analytics solutions with semantic understanding to help enterprises get better understanding and value from big data.

Cambridge Semantics offers the award-winning Anzo Smart Data Platform (Anzo SDP), leveraged by customers and partners for building interactive Smart Data solutions that help enterprises rapidly discover, understand, combine, analyze, link and manage data from diverse sources, both from within and across organizational boundaries. SPARQL City’s Hadoop-based graph analytics engine provides a simple and powerful way for people to query semi-structured and structured data and find more nuanced relationships within and across these datasets in easy-to-grasp graphical representations. Read more

SPARQL City’s Benchmark Results Showcase New Possibilities in Enterprise Graph Analytics

Solution demonstrates 10x+ the performance while running on 100x the data

Enterprise meet Graph Analysis - SPARQLcity.comNoSQL Now 2014 & SemTechBiz 2014

San Diego – August 20, 2014 – SPARQL City, which introduced its scalable graph analytic engine to market earlier this year, today announced that it has successfully run the SP2 SPARQL benchmark on 100 times the data volume as other graph solution providers, while still delivering an order of magnitude better performance on average compared to published results.

SPARQL City ran the SP2 Benchmark against 2.5 billion triples/edges on a sixteen node cluster on Amazon EC2. Average query response time for the set of seventeen queries was about 6 seconds, with query 4, the most data intensive query involving the entire dataset taking approximately 34 seconds to run. By comparison, the best reported query 4 result by other graph solution providers has been around 15 seconds, but this is when running against 25 million triples/edges, or 1/100th of the data volume in SPARQL City’s benchmark test. This level of performance, combined with the ability to easily scale out the solution on a cluster when required, makes easy to use interactive graph analytics on very large datasets possible for the first time. Detailed benchmark results can be found on our website.

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SPARQL And NoSQL: A Match On Many Levels

site-header-10th-blog-304x200Is SPARQL the SQL for NoSQL? The question will be discussed at this month’s Semantic Technology & Business Conference in San Jose by Arthur Keen, vp of solution architecture of startup SPARQL City.

It’s not the first time that the industry has considered common database query languages for NoSQL (see this story at our sister site Dataversity.net for some perspective on that). But as Keen sees it, SPARQL has the legs for the job. “What I know about SPARQL is that for every database [SQL and NoSQL alike] out there, someone has tried to put SPARQL on it,” he says, whereas other common query language efforts may be limited in database support. A factor in SPARQL’s favor is query portability across NoSQL systems. Additionally, “you can achieve much higher performance using declarative query languages like SPARQL because they specify the ‘What’ and not the ‘How’ of the query, allowing optimizers to choose the best way to implement the query,” he explains.

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