Bank of America Corporation

Deviprasad Tatti

Gaurav Bansal

Sandeep Kumar Chauhan

Shekhar Singh Mehra

Ashwin Kumar Yedte

Suresh V Kamboji

Vibhash K Jha

Embodiments of the invention are directed to systems, methods and computer program products for utilizing resources from multiple sources to complete a resource distribution. The invention may receive a distribution request from an entity, where the distribution request includes information identifying a first resource distribution device provided by a user to the entity, a first source associated with the user, and a request amount of resources. The invention may determine whether the requested amount of resources is greater than a first amount of resources in the first source, determine whether the first resource distribution device is associated with one or more other sources associated with the user, and determine whether a sum of the first amount of resources in the first source and a second amount of resources in the one or more other sources is greater than or equal to the requested amount of resources.

System for utilizing resources from multiple sources to complete a resource distribution

An apparatus includes a database and a processor. The database stores a set of columns and rules assigned to each column. The rules are used to assess the quality of the data stored in the columns. The processor determines, based in part on the set of rules, the set of columns, and metadata and statistical properties of the columns, a machine learning policy adapted to generate a set of candidate rules for a given column. The processor further determines those columns of the set of columns that are similar to a subject column based on the names of the columns and the names of the tables storing the columns. The processor applies the machine learning policy to the subject column of data, rules of the similar columns, and metadata and statistical properties of the subject column to determine a set of candidate rules for the subject column.

Data quality tool

A memory stores a first log and a collection of queries. A processor accesses a second log that includes a first, second, third and fourth historical query, determines that the first and second historical queries belong to a first similarity group and the third and fourth historical queries belong to a second similarity group, and ranks the queries within groups based on static and dynamic features. The processor further determines the third historical query is frequently submitted and copies the highest ranked similar queries into the collection. The processor receives a first database query, stores it in both logs, determines it is not similar to queries stored in memory, further determines that it belongs to the first similarity group, copies the highest ranked similar queries to the first log, and returns a report containing the similar queries. The processor further receives and executes a second query selected from the report.

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Hyderabad Telangana, India

Deviprasad Tatti