pwclogoSherlock, a Urika graph-analytics appliance,” says Nick Nystrom, The Pittsburgh Supercomputing Center director of strategic applications, “provides a unique capability for discovering new patterns and relationships in data.”

White Papers

Have your cake and eat it too: Improve efficiency and turbocharge your threat discovery
(Compliance Risk Concepts, August 2013)
Compliance organizations have had good success leveraging new technologies to improve efficiency, but recent trends as discussed will increasingly force compliance leaders to take action to mitigate the risks arising from the data and regulation explosion. Those leaders that act on these challenges by deploying solutions to achieve the twin pillars of increased efficiency and improved detection effectiveness will see significant and lasting returns on their investment.

Bloor Research: Urika, an InDetail Paper
(Philip Howard, July 2012)
In our view, graph databases offer a better solution for relevant applications than other database technologies and we expect them to make a major impact on the market. Urika is an appliance for graph analytics which contains a graph data­base, but what exactly is a graph database or, for that matter, a graph, and why should you care?

Graph Analytics: Beyond a Small Circle of Friends
(Ovum, Tony Baer, July 2012)
Big Data has raised the bar for analytics: organizations need no longer make the tradeoff between depth of analysis and breadth of data (“rich vs. reach”). New platforms and frameworks are making possible the ability to solve new kinds of problems, such as analyzing real world scenarios involving networks of people, places, or things connected by many-to-many relationships that are impacted by events, economics, natural phenomena, or other trends.

Discovering Big Data’s Value with Graph Analytics
(ESG, Evan Quinn, April 2013)
ESG believes that “big data value” will largely be realized through discovery analytics. Though discovery analytics may seem more abstract initially, the results often produce insights that carry more strategic impact than BI or basic business analytics. To reach those insights, the discovery analytics process typically involves more diverse and potentially voluminous data, and requires of queries and searches for patterns in the data relationships that eventually yield strategic insight. “Discovery” implies a less concrete initial grasp of the outcome and the data that will drive the outcome. With BI and basic business analytics, the answer design and the underlying data are usually already well understood.