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
Published on 25. April 2019
Book
Paperback/Softback
IX, 137 pages
978-3-030-19142-9 (ISBN)
Description
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.
More details
Series
Edition
2019 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
19 farbige Abbildungen, 26 s/w Abbildungen
IX, 137 p. 45 illus., 19 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 9 mm
Weight
236 gr
ISBN-13
978-3-030-19142-9 (9783030191429)
DOI
10.1007/978-3-030-19143-6
Schweitzer Classification
Other editions
Additional editions
Hakim Hacid | Quan Z. Sheng | Tetsuya Yoshida
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
E-Book
04/2019
Springer
€53.49
Available for download
Persons
Content
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.