US Patent Application 17662083. HANDLING DATA GAPS IN SEQUENTIAL DATA simplified abstract
Contents
HANDLING DATA GAPS IN SEQUENTIAL DATA
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
Larisa Shwartz of Greenwich CT (US)
Ruchi Mahindru of Elmsford NY (US)
Yu Deng of YORKTOWN HEIGHTS NY (US)
HANDLING DATA GAPS IN SEQUENTIAL DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 17662083 titled 'HANDLING DATA GAPS IN SEQUENTIAL DATA
Simplified Explanation
- The patent application describes a method, computer program product, and computer system for handling a data gap in sequential data. - The method involves receiving sequential data for a specific time period. - The data gap in the sequential data is identified at a particular timestamp. - A sliding window is determined based on the timestamp, which includes dependent data that the data gap relies on. - Extracted patterns are generated based on the dependent data of the sliding window, if the dependent data includes at least one window data gap. - These extracted patterns are used to mask the window data gap. - A prediction model is used to determine a prediction that can fill the data gap. - The prediction model takes modified data, which is based on the dependent data and the extracted patterns, as input.
Original Abstract Submitted
A method, a computer program product, and a computer system handle a data gap in sequential data. The method includes receiving the sequential data for a period of time. The method includes selecting the data gap in the sequential data at a timestamp. The method includes determining a sliding window associated with the data gap based on the timestamp for a duration of time. The sliding window includes dependent data from which the data gap depends. The method includes, as a result of the dependent data of the sliding window including at least one window data gap, generating extracted patterns based on the dependent data to mask the at least one window data gap. The method includes determining a prediction to fill the data gap using a prediction model that takes as input modified data based on the dependent data and the extracted patterns.
