Derrick Harris of GigaOM reports, “WibiData — the big data startup from Cloudera Co-founder Christophe Bisciglia and Aaron Kimball — doesn’t have overly big plans. It only wants to become one of the first, if not the first, company selling off-the-shelf software that lets other companies build valuable, customer-facing applications on Hadoop. On Thursday, WibiData announced $15 million in Series B funding from Canaan Partners, as well as existing investors NEA and Google Chairman Eric Schmidt, to help make the goal a reality.” continued…

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Ritka Puri of Business2Community reports, “Big data is transforming industries. From education to tech, retail, and healthcare, sophisticated analytics are helping organizations make intelligent decisions to maximize key goals. ‘From Facebook to Netflix, companies are tracking and analyzing our searches, our purchases, and just about every other online activity that will give them more insight into what we are and who we want,’ wrote Jim Fruchterman for Harvard Business Review. ‘The more we use technology in our education and health systems, the more data we collect about how people learn and what keeps us healthy or what makes us sick.’ For financial firms that face challenges of increased governance, risk, strict compliance guidelines, and worldwide economic instability, the need for data-driven decisions is even more crucial.” continued…

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Robin Wauters of The Next Web reports, “Seattle startup GraphLab claims it is building the ‘fastest machine-learning analytics engine for graph datasets’, based on the popular open-source distributed graph computation framework with the same name, and it has just raised capital to come through on its promise. Founded by scientists from the University of Washington, Carnegie Mellon and UC Berkeley, GraphLab today announced that it has secured $6.75 million in a financing round led by Madrona and NEA.” continued…

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The Enterprise Linked Data Cloud Needs Semantics, And More

Another exploration of Big Data’s intersection with semantic technology will take place at this session, where Dr. Giovanni Tummarello, senior research fellow at DERI and CTO of SindiceTech, will talk about the former becoming an enabler for the latter to be really useful in enterprises. “A lot of people say it’s via Big Data that semantic technologies like RDF will see a coming of age and clear applications in certain industries,” he says. There’s value to adding data first and understanding it later, and to that end, “semantic technologies give you the most agile tool to deal with data you don’t know, where there’s a lot of diversity, and you don’t know what of it particularly will be useful.”

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The opening keynote, for example, will be given by Abhishek Gattani, senior director at Walmart in the WalmartLabs. In a conversation in advance of the event, Gattani told The Semantic Web Blog that he’ll be focusing on the idea that businesses should embrace the mindset of using external data – social and web data – to solve internal problems. “This is what happens when you run an enterprise – external factors influence your market,” he says, whether that’s a new product being launched or a lower-priced competitor coming into play or a natural disaster or economic event taking place. The data about those things exist outside your own company’s realm, but combining your information with that could lead to interesting prospects and extraordinary results.

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David Ramel of ADTmag writes, “What the heck are you doing reading this article? You should be boning up on your Big Data developer skills. Well, if you like making the big bucks, that is. Yes, the Big Data skills shortage shows no signs of shrinking even after several years of hype. That means great opportunities for data developers. ‘By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions,’ stated a recent McKinsey Global Institute report. And where there’s hype, there’s money. ‘Salaries reported by those who regularly use Hadoop, NoSQL, and Mongo DB are all north of $100,000,’ claimed a recent report from the 2013-2012 Dice Salary Survey.” continued…

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Prachi Patel of IEEE Spectrum reports, “Farmers today produce three times as much food as they did 50 years ago using just 12 percent more land, thanks to new technologies and better farming practices. But the global playing field isn’t level. In Africa, farmers produce a fraction of what they could, according to the Forum for Agricultural Research in Africa, and most barely get by, struggling against infertile soil, drought, and diseases. Helping farmers—in Africa and elsewhere—produce more will be key to lifting millions out of poverty and sustainably feeding a world population of 9 billion in 2050. Food-policy experts believe that a crucial step toward that goal is to give farmers, scientists, and entrepreneurs unhindered access to agricultural data which is generated at research centers worldwide.” continued…

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Industry engineers waste a significant amount of time searching for data that they require for their core tasks. When informed about potential problems, diagnosis engineers at Siemens Energy Services, an integrated business unit which runs service centers for power plants, need to access several terabytes of time-stamped sensor data and several gigabytes of event data, including both raw and processed data. These engineers have to respond to about 1,000 service requests per center per year, and end up spending 80% of their time on data gathering alone. What makes this problem even worse is that their data grows at a rate of 30 gigabytes per day. Similarly, at Statoil Exploration, geology and geographic experts spend between 30 and 70% of their time looking for and assessing the quality of some 1,000 terabytes of relational data using diverse schemata and spread over 2,000 tables and multiple individual databases [1]. In such scenarios, it may take several days to formulate the queries that satisfy the information needs of the experts, typically involving the assistance of experienced IT experts who have been working with the database schemata for years.

Siemens and Statoil Exploration are hardly the only companies faced with time-wasting Big Data issues, but the root of these issues is not simply the “big” aspect of their data. The real challenge is finding a way to efficiently and effectively mine data for value and insight, regardless of its volume.

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London, UK – May 8, 2013: Today, Ontology Systems, the semantic search company for structured enterprise application data, announce their nomination as finalist in the Big Data and Analytics category for the Pipeline COMENT Innovation Awards 2013.

The recognition comes as Ontology is being increasingly adopted by CSPs and enterprise across other industries, such as financial services, for its innovative uses of semantic search across large, complex data estates as a faster, more cost-effective, more resilient and more accurate alternative to traditional data integration approaches. continued…

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NEWBURYPORT, MA–(Marketwired – May 6, 2013) – Entagen, a fast-growing software company providing Big Data analytics and collaboration solutions across the enterprise, announced today that the company has earned a spot on Gartner’s prestigious list of Cool Vendors in Life Sciences for 2013 according to the Gartner report published May 2^nd, 2013⁽¹⁾. Entagen was recognized for its TripleMap & Extera software platforms, which help life science & healthcare companies “Connect the Dots in Big Data.” continued…

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David Hill of Network Computing recently shared his theory on the three transformations of IT. He writes, “The first was the digitization of business. The second is the continuing digitization of human experience. The third stage is the digitization of machines. Each transformation is ongoing, builds upon the others, and may overlap. Thus, some technologies that formed a foundation earlier are still active. For example, the mainframe is still alive and well, even in the time of mobile computing. Even though specific technologies provide a frame of reference, these transformations span a broad perspective and are not dependent upon any one technology. Please also note that there is not a smooth transition to each transformation, but that elements of a later transformation may be present while the key transformation of an earlier era is still more prominent.” continued…

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David Meyer of GigaOM reports, “Amazon has announced the launch of a new development center for cloud technologies in Germany, with locations in both Berlin and Dresden. According to a statement from the company, the 70-plus engineers that Amazon will hire will work on technologies for supporting various hypervisors, management tools and operating systems. This is effectively a major expansion of the development team Amazon has already had in Germany since buying Berlin-based Peritor last year – a purchase that led to the release of the OpsWorks devops toolkit this February.” continued…

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Taking second place in the competition, the work of Adam Lugowski, Dr. John Gilbert, and Kevin Dewesse, of the University of California at Santa Barbara, leveraged a dataset created for the Mayo Clinic Smackdown project, that has the same structure and property types – and scale – as the medical organization’s actual Big Data sets around autism, but which uses publicly available data in place of the real thing. The team can’t use the real data because it includes private information about patients, diagnosis, prescriptions, and the like.

But the actual data deployed for the project doesn’t matter, says Lugowski . “The goal is to find relationships we have never thought of before, and this way it doesn’t prejudice the algorithm,” he says. Using YarcData’s uRIKA graph analytics appliance, the algorithm queries the Smackdown dataset – which in its smallest version has almost 40 million RDF triples and in its largest is about 100 times bigger, mirroring the size of all the Mayo Clinic’s actual autism data – to discover commonalities among the data, mimicking how the real data sets could be queried in search of common precursors among clusters of patients with the diagnosis.

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Mare Lucas of Wired.com reports

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Don Clark of the Wall Street Journal writes, “Big data has become one of tech’s biggest buzz phrases, despite varying definitions about what it really is. But the financial bets being placed are pretty substantial, with Skytree emerging as the latest example. The Silicon Valley startup on Tuesday is announcing it has raised $18 million in Series A funding, a fairly hefty amount these days for an early-stage software company with less than 20 employees. U.S. Venture Partners, a venerable name in the venture-capital world, led the round. Scott McNealy, the former chief executive of Sun Microsystems, is also participating.” continued…

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