Multiple Data Quality Evaluation and Data Cleaning on Imprecise Temporal Data

Abstract

With data currency issues draw the attentions of both researchers and engineers, temporal data, which describes real world events with time tags in database, is playing a key role in data warehouse, data mining, and etc. At the same time, 4V features of big data give rise to the difficulties in comprehensive data quality management and data cleaning. On one hand, entity resolution methods are faced with challenges when dealing with temporal data. On another hand, multiple problems existing in data records are hard to be captured and repaired. Motivated by this, we address data quality evaluation and data cleaning issues in imprecise temporal data. This project aims to solve three key problems in temporal data quality improvement and cleaning: (1) Determining currency on imprecise temporal data, (2) Entity resolution on temporal data with incomplete timestamps, and (3) Data quality improvement on consistency and completeness with data currency. The purpose of this paper is to address the problem definitions and discuss the procedure framework and the solutions of improving the effectiveness of temporal data cleaning with multiple errors.