Emc IP Holding Company LLC

Dell Products Inc.

Oshry Ben Harush

Ofir Ezrielev

Amihai Savir

A search engine responding to a user query to find relevant data assets in a federation business data lake (FBDL) system based on interactions of known users interacting with data assets in the FBDL system. Predicted data usage for a population of possible users is derived by training a generative model that uses reconstructive self-supervised learning (SSL) techniques to generate possible values for missing data usage features of the possible users. The predicted usage is then used to generate similarity scores that are combined with those of the known users to return relevant results to a target user. The predicted usage for possible users is processed in a consensus mechanism to produce integrated recommendations that are iteratively optimized using a chosen usage by the target user an a optimizer using a genetic algorithm to change consensus parameters to derive a final recommendation to the target user.

Search engine using predictive models based on partial information for a population of possible users through an optimized consensus mechanism

A search engine responding to a user query to find relevant data assets in a federation business data lake (FBDL) system. The search engine receives a search query from an unprivileged user or a user not having sufficient privileges to access the FBDL. It returns initial results to the unprivileged user including a first data asset recommendation responsive to the search query. It then determines a causal reason that the first data asset was recommended, and uses a similarity engine conditioned on the causal reason to return a replacement data asset in response to the search query.

Search engine using causal replacement of search results for unprivileged access rights

A search engine responding to a user query to find relevant data assets in a federation business data lake (FBDL) system by monitoring and recording interactions of known users interacting with data assets in the FBDL system. Predicted data usage for unknown or new users is derived by training a generative model that uses reconstructive self-supervised learning (SSL) techniques to generate possible values for missing data usage features of the unknown users. The predicted usage is then used to generate similarity scores that are combined for those of the known users to help inform the search engine processing to return relevant results to a target user.

Search engine using self-supervised learning and predictive models for searches based on partial information

Embodiments for automatically optimizing and persisting database views by receiving queries made to a database, wherein each query generates a respective database view, and generating a set of database maintained views generated by the queries. The system obtains, for each generated view, certain telemetry information about a respective view including latency, memory space utilization, and processor utilization, among other factors. It then scores each view of the generated views based on an base score modified by the obtained information to determine which one or more of the generated views to make persistent, and maintains the one or more persistent views to produce an optimized persistent set of database views. It further adapts later queries to use the optimized persistent views.

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Cedar Park, TX, United States of America

Oshry Ben Harush