International Business Machines Corporation

Amazon Technologies, Inc.

Informatica Corporation

Atreyee Dey

Prasad Manikarao Deshpande

Sriram Kolinjivadi Padmanabhan

Rajeev Gupta

Salil Joshi

Sanjeev K Gupta

Prasan Roy

Songhua Xing

Gaurav Bhatnagar

Debasish Das

Sanjay Kaluskar

Udayakumar Dhansingh

Systems, methods, and computer-readable media are disclosed for systems and methods for anonymization of user data for privacy across distributed computing systems. Example methods may include determining, by a first computer system, a request for content to present at a user device, wherein the request for content is associated with a user account, determining a first search query associated with the user account, and determining a first keyword associated with the first search query. Some methods may include generating a first hash value for the first keyword, sending the first hash value to a second computer system for identification of first content for presentation at the user device, and causing the second computer system to send the first content to the user device for presentation, wherein the first computer system does not receive the first content.

Anonymization of user data for privacy across distributed computing systems

A data analysis server maintains database operation history data and context data for database operations performed on tables by a set of training users. The data analysis server builds predictive models for using the maintained data to recommend database operations and operands to a set of guided users. The data analysis server trains the predictive models by determining and weighting features derived from context data that are predictive of performing database operations to tables with similar context data. Using the predictive model, the data analysis server generates recommended database operations and operands based on context data received from a data analysis application of a guided user and sends the recommendations to the data analysis application for presentation to the guided user.

Learning from historical logs and recommending database operations on a data-asset in an ETL tool

Entity mappings that produce matching entities for a first data asset having attributes and a second data asset having attributes are generated by: generating entity mappings that produce matching entities for a first data asset having attributes with attribute values and a second data asset having attributes with attribute values by: matching the attribute values of the attributes of the first data asset with the attribute values of the attributes of the second data asset, using the matching attribute values to generate matching attribute pairs, and using the matching attribute pairs to identify entity mappings; computing an entity mapping score for each of the entity mappings based on a combination of factors; ranking the entity mappings based on each entity mapping score; and using some of the ranked entity mappings to determine whether a same real-world entity is described by the first data asset and the second data asset.

Identifying entity mappings across data assets

Embodiments of the present invention disclose a method, computer program product, and system for identifying matching products relative to a reference product. A reference product is identified from a received product query and a query is generated based on the reference product. A generated query comprises of an ontology, at least one word appearing in a title of the reference product, and a set of key words appearing in social media data associated with the reference product. A database is searched using the generated query to find matching product sets and the results are returned and filtered. Results are filtered by calculating a relationship score between the reference product and one or more matching products in the set of matching products, and/or by filtering a subset of the set of matching products based on a customer profile. The filtered subset of results are communicated to a recipient.

Product recommendations over multiple stores

Generation of synthetic database data includes annotated query subplans for a multiple table query workload that includes a desired cardinality for nodes (v) in the subplans. The subplans may be merged and represented by a direct acyclic graph (DAG). The maximum entropy joint probability distribution for each attribute (x) for each node (v) is determined as: for each node v, where wis a weight of node v, fis a conjunct of predicates in a subplan rooted at node v, and Z is a normalization factor. This distribution is determined such that the desired cardinality, and selectivities for each node v determined from the desired cardinality, are satisfied. The data for a plurality of tables are generated by sampling the maximum entropy joint probability distribution for a domain of attributes (x) of a plurality of tables. Data may be efficiently generated for multiple table queries and for DAGs.

Method for synthetic data generation for query workloads

Methods and arrangements for identifying related data in different data sets to assist in searching the data sets. A first data asset and a second data asset are accessed. Common entities are identified between the first and second data assets. A score is determined for the relationship between the first and second data assets, based on the identified common entities. One or more relationship contexts are determined for the relationship between the first and second data assets, and the relationship score and one or more relationship contexts are used to join at least a portion of each of the first and second data assets as a basis for subsequent searching. Other variants and embodiments are broadly contemplated herein.

Contextualization of entity relationships

Generation of synthetic database data includes annotated query subplans for a multiple table query workload that includes a desired cardinality for nodes (v) in the subplans. The subplans may be merged and represented by a direct acyclic graph (DAG). The maximum entropy joint probability distribution for each attribute (x) for each node (v) is determined as: for each node &#x3bd;, where wis a weight of node v, fis a conjunct of predicates in a subplan rooted at node v, and Z is a normalization factor. This distribution is determined such that the desired cardinality, and selectivities for each node v determined from the desired cardinality, are satisfied. The data for a plurality of tables are generated by sampling the maximum entropy joint probability distribution for a domain of attributes (x) of a plurality of tables. Data may be efficiently generated for multiple table queries and for DAGs.

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Bengaluru, India

Atreyee Dey