Hybrid: A Large-Scale In-memory Image Analytics Engine

ABSTRACT Analytical image/video processing tasks such as scene/face/activity recognition are historically performed externally of most relational database management systems. Relational engines are optimized for relational data and therefore, have weaker support for non-relational data such as images or video. We have been working on Hybrid, a high-velocity in-memory analytics engine, which supports the advanced access capabilities for both image/video contents and structured data via SQL or JSON. This allows the user to query both relational (rows and columns of a table) and video/image contents (objects, activities, scene attributes) in a single SQL or hybrid SQL/JSON statemen

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Structural text search and comparison using automaticall

Text and structured data fusion in data tamer at scale

Large-scale text data research has recently started to regain momentum [1]-[10], because of the wealth of up to date information communicated in unstructured format. For example, new information in online media (e.g. Web blogs, Twitter, Facebook, news feeds, etc) becomes instantly available and is refreshed regularly, has very broad coverage and other valuable properties unusual for other data sources and formats. Therefore, many enterprises and individuals are interested in integrating and using unstructured text in addition to their structured data