Sinequa has announced the general availability of Sinequa ES Version 10 which offers a cognitive search and analytics platform that uses advanced natural language processing (NLP) and machine learning algorithms to extract insights from structured and unstructured data.
This latest version of the Sinequa self-learning platform offers enhanced analytics of contents and user behavior through the integration of the Spark platform in its distributed architecture and implemented machine learning algorithms on Spark within the core of the platform.
Sinequa’s machine learning algorithms continually analyze and enrich the content of the Sinequa Logical Data Warehouse. In this update, new machine learning capabilities include collaborative filtering and recommendations, classification by example, clusterization and similarity calculations for unstructured contents, and predictive analysis.
The platform currently offers 150 plus ready-to-use connectors as Sinequa continues to grow and broaden connectivity for extraction of insights from enterprise applications, Hadoop and cloud environments. 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 such as 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.