Applications of Generalized Difference Method for Hypothesis Generation to Social Big Data in Concept and Real Spaces
Pages 44 - 55
Abstract
Analytic methodology as to generation of integrated hypotheses is necessary for applications involving different sources of social big data. In this paper, first, we introduce an abstract data model for integrating data management and data mining by using mathematical concepts of families, collections of sets to facilitate reproducibility and accountability required for social big data applications. Next, we describe generalized difference methods as a methodology for generating integrated hypotheses. Finally, we validate our proposal by applying them to three use cases involving data in concept and real spaces by using our data model as their description guided by generalized difference methods.
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Index Terms
- Applications of Generalized Difference Method for Hypothesis Generation to Social Big Data in Concept and Real Spaces
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Published In
November 2019
350 pages
ISBN:9781450362382
DOI:10.1145/3297662
- General Chairs:
- Richard Chbeir,
- Yannis Manolopoulos,
- Program Chairs:
- Sergio Ilarri,
- Apostolos Papadopoulos
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Published: 10 January 2020
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MEDES '19
MEDES '19: 11th International Conference on Management of Digital EcoSystems
November 12 - 14, 2019
Limassol, Cyprus
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MEDES '19 Paper Acceptance Rate 41 of 102 submissions, 40%;
Overall Acceptance Rate 267 of 682 submissions, 39%
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