Sinequa announced the general availability of Sinequa ES Version 10. Powered by Machine Learning capabilities at its core, this ground breaking version helps deliver deep analytics of contents and user behavior, offering information with continually improving relevance to users in their work environments. The self-learning platform makes information self-service a reality, even for non-experts, while keeping it simple for administrators to manage.
Cognitively enabled applications, especially those dealing with unstructured data, are clearly the future for better information delivery to users in a wide range of industries and work environments,” said David Schubmehl, Research Director at IDC. “By 2020, 50% of all business analytics software will include prescriptive analytics built on cognitive computing functionality.”
In order to achieve the quantum leap into the world of Cognitive Computing with this new version, Sinequa has integrated the Spark platform in its distributed architecture and implemented Machine Learning algorithms on Spark within the core of its product.
Our law firm has built a system leveraging Sinequa’s Search & Analytics platform to get insight about our experience from millions of records and documents including attorney biographies, subject matter summaries, time notes/billable hours and more. We are excited about this new version with Machine Learning based on Spark and look forward to the added value it can provide to us,” said Harris Tilevitz, CTO at Skadden, Arps, Slate, Meagher & Flom LLP. With nearly 1,700 attorneys, Skadden, Arps, Slate, Meagher & Flom LLP is one of the highest-grossing law firms in the world.
Sinequa has implemented and fine-tuned these algorithms to ensure high performance and the best possible integration with its platform. The Machine Learning algorithms continually analyze and enrich the content of the Sinequa Logical Data Warehouse. The new Cognitive Search & Analytics platform offers better insights and more relevant information to meet users’ expectations. As always with Sinequa, the new functionalities require minimal user efforts while offering intelligent tuning capabilities to administrators.
This new version is a leap forward into the era of ‘cognitive computing’ or ‘insight engines,’ to use the terminology coined by leading market analysts,” stated Alexandre Bilger, CEO, Sinequa. “In dealing with Big Data and its rapid growth, leading data-driven organizations need to rely on intelligent and self-learning systems to analyze data and find valuable information for their employees, thus increasing their productivity and job satisfaction, and the company’s competitiveness. Our Machine Learning capabilities achieve these goals by including Collaborative Filtering and Recommendations, Classification by Example, Clusterization and Similarity calculations for unstructured contents, and Predictive Analysis.”
The new Sinequa ES V10 also includes innovative features that have proved useful in customer projects and that represent strong emerging trends: Sinequa is now a “native resident” of cloud platforms, such as Amazon Web Services and Microsoft Azure, optimizing the use of resources of those platforms. Industry specific dictionaries and ontologies from partners like Scibite and Linguamatics have been integrated for customers in Life Science and Health Care. Google Vision and Microsoft Azure Media Services are also leveraged in order to deal more effectively with images as well as videos. Google Translate is used for automated translation between over 100 languages. With 150+ ready-to-use connectors and growing, Sinequa continues to broaden connectivity for rapid extraction of valuable insights from Enterprise Applications, Hadoop and Cloud environments.
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