July 2013 - UPDATE

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Trends and Applications

Oracle is advancing the role of Java for IoT (Internet of Things) with the latest releases of its Oracle Java Embedded product portfolio - Oracle Java ME Embedded 3.3 and Oracle Java ME Software Development Kit (SDK) 3.3, a complete client Java runtime and toolkit optimized for microcontrollers and other resource-constrained devices. Oracle is also introducing the Oracle Java Platform Integrator program to provide partners with the ability to customize Oracle Java ME Embedded products to reach different device types and market segments. "We see IoT as the next big wave that will hit the industry," Oracle's Peter Utzschneider, vice president of product management, explained during a recent interview.

SAP has launched Sybase ASE (Adaptive Server Enterprise) 15.7 service pack 100 (SP100) to provide higher performance and scalability as well as improved monitoring and diagnostic capabilities for very large database environments. "The new release adds features in three areas to drive transactional environments to even more extreme levels. We really see ASE moving increasingly into extreme transactions and to do that we have organized the feature set around the three areas," said Dan Lahl, vice president, Database Product Marketing, SAP, in an interview with 5 Minute Briefing.

While unstructured data may represent one of the greatest opportunities of the big data revolution, it is one of its most perplexing challenges. In many ways, the very core of big data is that it is unstructured, and many enterprises are not yet equipped to handle this kind of information in an organized, systematic way. Most of the world's enterprise databases—based on a model designed in the 1970s and 1980s that served enterprises well in the decades since—suddenly seem out-of-date, and clunky at best when it comes to managing and storing unstructured data. However, insights from these disparate data types—including weblog, social media, documents, image, text, and graphical files—are increasingly being sought by the business.

A new science called "data persona analytics" (DPA) is emerging. DPA is defined as the science of determining the static and dynamic attributes of a given data set so as to construct an optimized infrastructure that manages and monitors data injection, alteration, analysis, storage and protection while facilitating data flow. Each unique set of data both transient and permanent has a descriptive data personality profile which can be determined through analysis using the methodologies of DPA.

Join DBTA and MarkLogic for a webcast on Wednesday, July 31, to learn about the essential technologies and approaches to succeeding with predictive analytics on Big Data. In a recent survey of Database Trends and Applications subscribers, predictive analytics was cited as the greatest opportunity that big data offers to their organizations. The reason is simple — whether you're fighting crime, delivering healthcare, scoring credit or fine-tuning marketing, predictive analytics is the key to identifying risks and opportunities and making better decisions. However, to leverage the power of predictive analytics, organizations must possess the right technology and skills.