Oracle said it has added digital twins, workflow threads between enterprise systems between the Internet of Things (IoT) to corporate data and embedded artificial intelligence to its IoT Cloud applications. The company has been stepping up its IoT efforts, with an aim to easing deployments and delivering more business value. Like Salesforce and SAP, Oracle is arguing that melding corporate and customer data with the Internet of Things and analytics drives business value. To Oracle, the IoT Cloud will ultimately blend augmented and virtual reality for efforts like preventative maintenance and inventory tracking. A demo highlighted how IoT, Oracle systems, and augmented and virtual reality can co-mingle.

Amazon has unveiled a machine learning-based tool aimed at securing sensitive data held in the cloud, after a number of high-profile data leaks involving customers of Amazon Web Services (AWS). The tool, called Macie, was announced at the AWS New York Summit event along with an automated extract, transform and load (ETL) service and a unified repository of AWS' data migration tools. The announcement follows several data breaches in which major companies were found to have stored sensitive data on AWS Simple Storage Service (S3) in a way that left it publicly accessible. Last month it was disclosed that Verizon had exposed data on about 6 million customers in this way, and similar incidents have affected voter information held by the Republican National Committee (RNC) and customer data exposed by wrestling entertainment company WWE. The RNC breach, disclosed in June, affected more than 198 million people, or about 61 percent of the US population, and was the country's largest-ever voter data exposure.

Motivation: The rapid growth of diverse biological data allows us to consider interactions between a variety of objects, such as genes, chemicals, molecular signatures, diseases, pathways and environmental exposures. Often, any pair of objects--such as a gene and a disease--can be related in different ways, for example, directly via gene-disease associations or indirectly via functional annotations, chemicals and pathways. Different ways of relating these objects carry different semantic meanings. However, traditional methods disregard these semantics and thus cannot fully exploit their value in data modeling. Results: We present Medusa, an approach to detect size-k modules of objects that, taken together, appear most significant to another set of objects. Medusa operates on large-scale collections of heterogeneous data sets and explicitly distinguishes between diverse data semantics. It advances research along two dimensions: it builds on collective matrix factorization to derive different semantics, and it formulates the growing of the modules as a submodular optimization program. Medusa is flexible in choosing or combining semantic meanings and provides theoretical guarantees about detection quality. In a systematic study on 310 complex diseases, we show the effectiveness of Medusa in associating genes with diseases and detecting disease modules. We demonstrate that in predicting gene-disease associations Medusa compares favorably to methods that ignore diverse semantic meanings. We find that the utility of different semantics depends on disease categories and that, overall, Medusa recovers disease modules more accurately when combining different semantics.

Technology integration is complex and an evergreen business challenge for IT teams. Enterprises setup manual & point to point connections to exchange data from business partners. However, data disruptions emerge when the business IT systems expand and prevents organizations from sharing data with partners. Information exchange with this approach requires more resources and manpower is required. For such scenarios, information solution experts recommend to use the B2B data exchange tool to support the Electronic Data Interchange.

The analysis of data sets arising from multiple sensors has drawn significant research attention over the years. Traditional methods, including kernel-based methods, are typically incapable of capturing nonlinear geometric structures. We introduce a latent common manifold model underlying multiple sensor observations for the purpose of multimodal data fusion. A method based on alternating diffusion is presented and analyzed; we provide theoretical analysis of the method under the latent common manifold model. To exemplify the power of the proposed framework, experimental results in several applications are reported.

"The Striim platform is a full end-to-end streaming integration and analytics platform that is middleware that covers a lot of different use cases," explained Steve Wilkes, Founder and CTO at Striim, in this SYS-CON.tv Cloud computing is now being embraced by a majority of enterprises of all sizes. Yesterday's debate about public vs. private has transformed into the reality of hybrid cloud: a recent survey shows that 74% of enterprises have a hybrid cloud strategy. Meanwhile, 94% of enterprises are using some form of XaaS - software, platform, and infrastructure as a service. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation.

Integrating AI and marketing can help build profitable long-term relationships, but security, privacy, and data integration can be roadblocks. Join our free VB Live event to learn how to join marketing insight with AI to significantly boost customer loyalty. "A lot of what makes AI and bots powerful is the ability to know your customer," says Scott Horn, chief marketing officer at [24]7, an intent-driven customer engagement solutions company. "But you've got to have great algorithms, and you've got to have a lot of domain-relevant data." That's one of the reasons why marketers are excited about the potential of chatbots in the customer service arena.

If you ask anyone what they think that the future will hold and Artificial Intelligence or AI will definitely be a part of their fantasy. AI is the next big thing in technology and a recent report by IDC seems to suggest the same thing. The report, called Data Age 2025 is sponsored by Seagate is all about the state of the global datasphere by the year 2025. Among other startling facts like how data creation is expected to swell to 163 Zettabytes in less than a decade, the report also talks about how AI systems will change the technology landscape of the future. IDC estimates that by 2025, a whopping two-thirds of global financial firms will integrate cognitive data to improve customer experience through targeted product and service offerings, as well as fraud protection.

This post is a brief review of leading Data Integration tools in the market. Heavily referencing from the Gartner 2016 report and peer reviews from my circle. The data integration tool market was worth approximately $2.8 billion at the end of 2015, an increase of 10.5% from the end of 2014 [2016 Gartner Report – Data Integration Tools].

Abstract--The increasing complexity of the power grid, due to higher penetration of distributed resources and the growing availability of interconnected, distributed metering devices requires novel tools for providing a unified and consistent view of the system. A computational framework for power systems data fusion, based on probabilistic graphical models, capable of combining heterogeneous data sources with classical state estimation nodes and other customised computational nodes, is proposed. The framework allows flexible extension of the notion of grid state beyond the view of flows and injection in bus-branch models, and an efficient, naturally distributed inference algorithm can be derived. An application of the data fusion model to the quantification of distributed solar energy is proposed through numerical examples based on semisynthetic simulations of the standard IEEE 14-bus test case. The electrical grid is going through a significant transformation towards a more distributed architecture for demand-supply balancing, due to a higher penetration of distributed sources of renewable generation, storage and demand flexibility. Internet-of-Things (IOT) technologies are an integral part of the transformation, with energy utilities availing of more and more highly-distributed intelligent devices which produce an ever-increasing amount of heterogeneous data significantly different in terms of format, resolution and quality [1], [2].