TY  - BOOK
AU  - Hacid,Hakim
AU  - Sheng,Quan Z.
AU  - Yoshida,Tetsuya
AU  - Sarkheyli,Azadeh
AU  - Zhou,Rui
ED  - QUAT (Workshop)
ED  - WISE Society.
TI  - Data quality and trust in big data: 5th International Workshop, QUAT 2018, held in conjunction with WISE 2018, Dubai, UAE, November 12-15, 2018, revised selected papers 
T2  - Lecture notes in computer science,
SN  - 9783030191436
AV  - QA76.76.A65 Q38 2018
U1  - 005.7 23
PY  - 2019///]
CY  - Cham, Switzerland
PB  - Springer
KW  - Big data
KW  - Congresses
KW  - Information storage and retrieval systems
KW  - Information Storage and Retrieval
KW  - Données volumineuses
KW  - Congrès
KW  - Systèmes d'information
KW  - Artificial intelligence
KW  - fast
KW  - Congress
KW  - proceedings (reports)
KW  - aat
KW  - Conference papers and proceedings
KW  - lcgft
KW  - Actes de congrès
KW  - rvmgf
N1  - "ISSN 1611-3349 (electronic)--Title page verso; "The 5th WISE workshop on Data Quality and Trust in Big Data, QUAT 2018, was held in conjunction with WISE 2018, Dubai, UAE, during November 12-15, 2018"--Preface; Includes bibliographical references and index; A Novel Data Quality Metric for Minimality -- Automated Schema Quality Measurement in Large-scale Information Systems -- Email Importance Evaluation in Mailing List Discussions -- SETTRUST: Social Exchange Theory Based Context- Aware Trust Prediction in Online Social Networks -- CNR: Cross-Network Recommendation Embedding User's Personality -- Firefly Algorithm with Proportional Adjustment Strategy -- A Formal Taxonomy of Temporal Data Defects -- Data-intensive Computing Acceleration with Python in Xilinx FPGA -- Delone and McLean IS Success Model for Evaluating Knowledge Sharing
N2  - This book constitutes revised selected papers from the International Workshop on Data Quality and Trust in Big Data, QUAT 2018, which was held in conjunction with the International Conference on Web Information Systems Engineering, WISE 2018, in Dubai, UAE, in November 2018. The 9 papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with novel ideas and solutions related to the problems of exploring, assessing, monitoring, improving, and maintaining the quality of data and trust for Big Data
UR  - https://link.springer.com/10.1007/978-3-030-19143-6
ER  -