Fidelity Information Services, LLC

John C Platais

Ranadhir Ghosh

A method for finding historically similar incidents is disclosed. The method includes receiving a data object indicating an occurrence of a current incident associated with a configurable item, the data object including current incident metadata, the current incident metadata including a configurable item identifier (ID), a configurable item name, and a description of the current incident; receiving a plurality of historical data objects corresponding to a plurality of previous incidents; determining one or more historical data objects, of the plurality of historical data objects, that are similar to the data object based on a comparison of current incident metadata to the previous incident metadata; generating a score for each of the one or more historical data objects based on the comparison of the current incident metadata to the previous incident metadata; and outputting the one or more historical data objects that are similar to the data object to a user.

Systems and methods for finding historically similar incidents

A computer implemented method for processing data through a data pipeline is disclosed. The method includes: receiving, by a collection point, data from one or more data sources, the collection point being configured to at least one of extract, transform, or load the data; transferring the data from the collection point to a front gate processor, the front gate processor being configured to process the data; transferring the processed data from the front gate processor to a data storage system, the data storage system being configured to store the processed data; transferring the processed data from the front gate processor to a processing platform; and transferring the processed data from the processing platform to one or more data sink layers, each of the one or more data sink layers being configured to provide short term storage of the processed data.

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Menomonee Falls, WI, United States of America

John C Platais