Posts Tagged ‘graph database’

Syapse Selects SYSTAP’s Bigdata® as Semantic Database for Precision Medicine Data Platform

Syapse and BigData logosFor immediate release: 8/19/2014

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.”

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New Open Source Graph Database Cayley Unveiled (Video – Part 2)

Cayley Logo[Editor's note: This is Part 2 of a 3 part series. See Part 1 and Part 3]

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.”

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Big Data Challenges In Banking And Securities

Photo courtesy: Johan Hansson, https://www.flickr.com/photos/plastanka/

Photo courtesy: Johan Hansson, https://www.flickr.com/photos/plastanka/

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.

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Yahoo7 Upgrades to Bigdata Open Source Graph Database

bigdataPenny 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

Let Your Enterprise Graph Tell You A Story

entgrafEvery 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.”

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The Supply Chain Is One Big Graph In Start-up Elementum’s Platform

rsz_elementum_transport_appStartup 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.

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YarcData Software Update Points Out That The Sphere Of Semantic Influence Is Growing

YarcDataRecent 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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Decibel Makes Sweet Music With Neo4j Graph Database

At Decibel, provider of metadata-driven music discovery APIs, Neo4j has a featured role in a learning project that is the start of a plan to replace the relational database of 5 million tracks from 1.1 million albums by 300,000 artists – and the world of connections around that data – with a NoSQL graph database. With Decibel’s APIs, customers like the Blue Note Records jazz label, in partnership with developer Groovebug, have turned their record collections into a virtual record store, including track listings, individual track participations, recording session venues and dates.

With the APIs that tap into Decibel and fold into their own programs, developers at record labels, MP3 services and other digital music/entertainment or other venues can connect everything from the debut date of the bootleg Thin Lizzy album, Remembering Part 1, to its number of tracks on it to Sade’s 2011 coverage of Still in Love With You, to accommodate music-lovers’ search and discovery experiences. Or they’ll be able to surface that a piece of classical music in German is the same as another piece referenced by its French name, or that a musician that has gone by three different names in his career is one and the same.

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The Arches Project Puts A Semantic And Geo-Spatial Spin On Cultural Heritage

Inventorying and managing cultural heritage data turns out to be a pretty complicated undertaking. The construction of a famous site may have lasted across different time periods, and its present location may span multiple districts. Buildings may be associated not only with famous architects but also with well-known residents. Or structures may have been constructed atop pre-existing entities.

Helping sort it all out is the work of The Arches Project, collaboration between the Getty Conservation Institute (GCI) and World Monuments Fund (WMF). The Arches effort grew out of GCI’s and WMF’s work to develop MEGA-Jordan, a purpose-built geographic information system (GIS) to inventory and manage archaeology sites at a national level for that country. But for this more generic and open-source take at accommodating any country, region or other institution worldwide responsible for the protection of immovable cultural heritage, the focus expanded from the geo-spatial to the semantic.

“We became very familiar with the CIDOC Conceptual Reference Model ontology,” says Alison Dalgity, who manages the Arches project on GCI’s side. The CRM provides definitions and a formal structure for describing the implicit and explicit concepts and relationships used in cultural heritage documentation. “We realized we needed something like that. Now, the GIS piece is only part of this – it’s nice to know where something is, but all the other relationships – the who, how, what and when and so on – have to be represented, too.”

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Graphs Make The World Of Data Go Round

“We want to help the world make sense of data and we think graphs are the best way of doing that.”

That’s the word from Emil Eifrem, CEO of Neo Technology, which makes the open-source Neo4j NoSQL graph database. He’s not talking in terms of RDF-centric solutions, even though he says he’s 100 percent in agreement with the vision of the semantic web and machine readability. “The world is a graph,” Eifrem says, “and RDF is a great way of connecting things. I’m all in agreement there.” The problem, in his opinion, is that execution on the software end there has been lacking.

“This comes down to usability,” he says, and the average developer, he believes, finds the semantic web-oriented tools largely incomprehensible. Eifrem says he’s speaking from real-world experiences, having worked directly with RDF and taught classes on the semantic web layers. Where it took a week to get students up to speed on things like Jena and Sesame, they ‘get’ the property graph and graph databases in half-a-day, he says. Neo4j stores data in nodes connected by directed, typed relationships with properties on both – also known as a property graph.

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