Paper Info
Title
Crisp-Esnep: Towards A Data-Driven Knowledge Discovery Process For Electronic Social Networks
Abstract
Value in big data is created when insights are mined to support business processes. On social networks (SNs), big data, coupled with the operational mechanisms of such networks presents challenging, yet interesting perspectives to generate insights. A key limitation to big data research on SNs is the lack of a concise methodological model that drives conceptual and analytical questions. We add specificity to existing Knowledge Discovery and Data Mining (KDDM) frameworks by proposing a methodology for analyzing big data on electronic SNs. Particularly, we propose the Cross Industry Standard Process for Electronic Social Network Platforms (CRISP-eSNeP) not only as an extension to the CRISP-DM model, but also as an advancement of the knowledge on KDDM methods. Our method emphasizes the efficient management of large semi-structured and unstructured data that reflects a specific SN. We present the results of our process development using Gregor and Hevner's design science research schema.
Year
DOI
Venue
2019
10.1080/12460125.2019.1696614
JOURNAL OF DECISION SYSTEMS
Keywords
DocType
Volume
Analytics, CRISP-DM, CRISP-eSNeP, knowledge discovery, social networks
Journal
28
Issue
ISSN
Citations 
4
1246-0125
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Daniel Adomako Asamoah1164.41
Ramesh Sharda2106398.91