TY - GEN
T1 - Multiple Data Quality Evaluation and Data Cleaning on Imprecise Temporal Data
AU - Ding, Xiaoou
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Data currency
KW - Data quality
KW - Multiple data cleaning
KW - Temporal data
UR - https://www.scopus.com/pages/publications/85055421197
U2 - 10.1007/978-3-030-01391-2_14
DO - 10.1007/978-3-030-01391-2_14
M3 - 会议稿件
AN - SCOPUS:85055421197
SN - 9783030013905
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 69
EP - 75
BT - Advances in Conceptual Modeling - ER 2018 Workshops Emp-ER, MoBiD, MREBA, QMMQ, SCME, 2018, Proceedings
A2 - Li, Zhanhuai
A2 - Ling, Tok Wang
A2 - Li, Guoliang
A2 - Lu, Jiaheng
A2 - Woo, Carson
A2 - Lee, Mong Li
PB - Springer Verlag
T2 - 37th International Conference on Conceptual Modeling, ER 2018 Workshops Emp-ER, MoBiD, MREBA, QMMQ, SCME
Y2 - 22 October 2018 through 25 October 2018
ER -