The Pacific Northwest National Laboratory recently reported on Phys.org, “As computing tools and expertise used in conducting scientific research continue to expand, so have the enormity and diversity of the data being collected. Developed at Pacific Northwest National Laboratory, the Graph Engine for Multithreaded Systems, or GEMS, is a multilayer software system for semantic graph databases. In their work, scientists from PNNL and NVIDIA Research examined how GEMS answered queries on science metadata and compared its scaling performance against generated benchmark data sets. They showed that GEMS could answer queries over science metadata in seconds and scaled well to larger quantities of data.” Read more
Posts Tagged ‘graph database’
Research firm Forrester at the end of September issued its Forrester Wave: NoSQL Key-Value Databases, Q3 2014 report. The report looked at seven enterprise-class vendors in the space: Amazon Web Services, Aerospike, Basho Technologies, Couchbase, DataStax, MapR Technologies, and Oracle.
Noting that the current adoption of NoSQL is at 20 percent and is likely to double by 2017, Forrester principal analyst and report author Noel Yuhanna and his co-authors explain that top use cases for key-value database include social and mobile apps, scale-out apps, Web 2.0, line-of-business apps, big data apps, and operational and analytical apps.
That said, he also notes that the lines between key-value store, document database and graph database NoSQL solutions are blurring, as vendors look to satisfy broader enterprise needs and better appeal to app developers. “Relational database management system vendors, such as Oracle, IBM, Microsoft and SAP, will broaden their current relational database products to include key-value, graph and document features and functionality to deliver more comprehensive data management platforms in the coming years,” the report states.
Apigee wants the development community to be able to seamlessly take advantage of predictive analytics in their applications.
“One of the biggest things we want to ensure is that the development community gets comfortable with powering their apps with data and insights,” says Anant Jhingran, Apigee’s VP of products and formerly CTO for IBM’s information management and data division. “That is the next wave that we see.”
Apigee wants to help them ride that wave, enabling their business to better deal with customer issues, from engagement to churn, in more personal and contextual ways. “We are in the business of helping customers take their digital assets and expose them through clean APIs so that these APIs can power the next generation of applications,” says Jhingran. But in thinking through that core business, “we realized the interactions happening through the APIs represent very powerful signals. Those signals, when combined with other contextual data that may be in the enterprise, enable some very deep insights into what is really happening in these channels.”
With today’s announcement of a new version of its Apigee Insights big data platform, all those signals generated – through API and web channels, call centers, and more – can come together in the service of predictive analytics for developers to leverage.
WASHINGTON, D.C. – SYSTAP, LLC. today announced that Syapse, the leading provider of software for enabling precision medicine, has selected Bigdata® as its backend semantic database. Syapse, which launched the Precision Medicine Data Platform in 2011, will use the Bigdata® database as a key element of their semantic platform. The Syapse Precision Medicine Data Platform integrates medical data, omics data, and biomedical knowledge for use in the clinic. Syapse software is delivered as a cloud-based SaaS, enabling access from anywhere with an internet connection, regular software updates and new features, and online collaboration and delivery of results, with minimal IT resources required. Syapse applications comply with HIPAA/HITECH, and data in the Syapse platform are protected according to industry standards.
Syapse’s Precision Medicine Data Platform features a semantic layer that provides powerful data modeling, query, and integration functionality. According to Syapse CTO and Co-Founder, Tony Loeser, Ph.D., “We have adopted SYSTAP’s graph database, Bigdata®, as our RDF store. Bigdata’s exceptional scalability, query performance, and high-availability architecture make it an enterprise-class foundation for our semantic technology stack.”
Barak Michener, Software Engineer, Knowledge NYC has posted on the Google Open Source Blog about “Cayley, an open source graph database.”: “Four years ago this July, Google acquired Metaweb, bringing Freebase and linked open data to Google. It’s been astounding to watch the growth of the Knowledge Graph and how it has improved Google search to delight users every day. When I moved to New York last year, I saw just how far the concepts of Freebase and its data had spread through Google’s worldwide offices. I began to wonder how the concepts would advance if developers everywhere could work with similar tools. However, there wasn’t a graph available that was fast, free, and easy to get started working with. With the Freebase data already public and universally accessible, it was time to make it useful, and that meant writing some code as a side project.”
The post continues: “Cayley is a spiritual successor to graphd; it shares a similar query strategy for speed. While not an exact replica of its predecessor, it brings its own features to the table:RESTful API, multiple (modular) backend stores such as LevelDB and MongoDB, multiple (modular) query languages, easy to get started, simple to build on top of as a library, and of course open source. Cayley is written in Go, which was a natural choice. As a backend service that depends upon speed and concurrent access, Go seemed like a good fit.”
A new report from the Securities Technology Analysis Center (STAC), Big Data Cases in Banking and Securities, looks to understand big data challenges specific to banking by studying 16 projects at 10 of the top global investment and retail banks.
According to the report, about half the cases involved e petabyte or more or data. That includes both natural language text and highly structured formats that themselves presented a great deal of variety (such as different departments using the same field for a different purpose or for the same purpose but using a different vocabulary) and therefore a challenge for integration in some cases. The analytic complexity of the workloads studied, the Intel-sponsored report notes, covered everything from basic transformations at the low end to machine learning at the high-end.
Penny Wolf of IT News in Australia reports, “Yahoo7 is rolling out a semantic publishing platform across the web sites that promote Channel 7’s local free to air programs, building its new content systems atop an open source graph database. Craig Penfold, CTO Yahoo7 was inspired by a similar project undertaken by the BBC, which initially launched semantic web publishing for the channel’s 2010 World Cup website to increase viewer engagement. After conducting research and discussions with BBC and Yahoo, the Yahoo7 team chose to change the datastore from a standard SQL database to a graph database.” Read more
Every picture tells a story, don’t it? Well, turns out that’s true in the enterprise as much as on our Facebook pages. In this case, the picture is the enterprise graph of the workforce – who interacts with whom, when, in what context. And the story is what the patterns of interactions revealed by the graph may say about employee engagement, influence, and how to better leverage all that to the business’ – and the employees’ — benefit.
When Marie Wallace, IBM analytics strategist, looks at social and collaborative networks and other sources of enterprise communications and channels for business processes, such as CRM systems, “I am interested in the narrative,” she told an audience at the Sentiment Analytics Symposium earlier this month. “There is a lot of information in CRM systems – who met with whom, what industry the client is in, what products were presented. All this is valuable and contributes to the enterprise graph.”
Startup Elementum wants to take supply chains into the 21st century. Incubated at Flextronics, the second largest contract manufacturer in the world, and launching today with $44 million in Series B funding from that company and Lightspeed Ventures, its approach is to get supply chain participants – the OEMs that generate product ideas and designs, the contract manufacturers who build to those specs, the component makers who supply the ingredients to make the product, the various logistics hubs to move finished product to market, and the retail customer – to drop the one-off relational database integrations and instead see the supply chain fundamentally as a complex graph or web of connections.
“It’s no different thematically from how Facebook thinks of its social network or how LinkedIn thinks of what it calls the economic graph,” says Tyler Ziemann, head of growth at Elementum. Built on Amazon Web Services, Elementum’s “mobile-first” apps for real-time visibility, shipment tracking and carrier management, risk monitoring and mitigation, and order collaboration have a back-end built to consume and make sense of both structured and unstructured data on-the-fly, based on a real-time Java, MongoDB NoSQL document database to scale in a simple and less expensive way across a global supply chain that fundamentally involves many trillions of records, and flexible schema graph database to store and map the nodes and edges of the supply chain graph.
“Relational database systems can’t scale to support the types of data volumes we need and the flexibility that is required for modeling the supply chain as a graph,” Ziemann says.
Recent updates to YarcData’s software for its Urika analytics appliance reflect the fact that the enterprise is starting to understand the impact that semantic technology has on turning Big Data into actual insights.
The latest update includes integration with more enterprise data discovery tools, including the visualization and business intelligence tools Centrifuge Visual Network Analytics and TIBCO Spotfire, as well as those based on SPARQL and RDF, JDBC, JSON, and Apache Jena. The goal is to streamline the process of getting data in and then being able to provide connectivity to the tools analysts use every day.
As customers see the value of using the appliance to gain business insight, they want to be able to more tightly integrate this technology into wider enterprise workflows and infrastructures, says Ramesh Menon, YarcData vice president, solutions. “Not only do you want data from all different enterprise sources to flow into the appliance easily, but the value of results is enhanced tremendously if the insights and the ability to use those insights are more broadly distributed inside the enterprise,” he says. “Instead of having one analyst write queries on the appliance, 200 analysts can use the appliance without necessarily knowing a lot about the underlying, or semantic, technology. They are able to use the front end or discovery tools they use on daily basis, not have to leave that interface, and still get the benefit of the Ureka appliance.”
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