TY - GEN
T1 - Event cube – A conceptual framework for event modeling and analysis
AU - Li, Qing
AU - Ma, Yun
AU - Yang, Zhenguo
PY - 2017/1/1
Y1 - 2017/1/1
N2 - The publicly available data such as the massive and dynamically updated news and social media data streams (a.k.a. big data) covers the various aspects of social activities, personal views and expressions, which points to the importance of understanding and discovering the knowledge patterns underlying the big data, and the need of developing methodologies and techniques to discover real-world events from such big data, to manage and to analyze the discovered events in an effective and elegant way. In this paper we present an event cube (EC) model which is devised to support various queries and analysis tasks of events; such events include those discovered by techniques of untargeted event detection (UED) and targeted event detection (TED) from multi-sourced data. Specifically, based on the essential event elements of 5W1H (i.e., When, Where, Who, What, Why, and How), the EC model is developed to organize the discovered events from multiple dimensions, to operate on the events at various levels of granularity, so as to facilitate analyzing and mining hidden/inherent relationships among the events effectively. Case studies are provided to illustrate the usages and show the benefits of EC facilities in on-line analytical processing of events and their relationships.
AB - The publicly available data such as the massive and dynamically updated news and social media data streams (a.k.a. big data) covers the various aspects of social activities, personal views and expressions, which points to the importance of understanding and discovering the knowledge patterns underlying the big data, and the need of developing methodologies and techniques to discover real-world events from such big data, to manage and to analyze the discovered events in an effective and elegant way. In this paper we present an event cube (EC) model which is devised to support various queries and analysis tasks of events; such events include those discovered by techniques of untargeted event detection (UED) and targeted event detection (TED) from multi-sourced data. Specifically, based on the essential event elements of 5W1H (i.e., When, Where, Who, What, Why, and How), the EC model is developed to organize the discovered events from multiple dimensions, to operate on the events at various levels of granularity, so as to facilitate analyzing and mining hidden/inherent relationships among the events effectively. Case studies are provided to illustrate the usages and show the benefits of EC facilities in on-line analytical processing of events and their relationships.
KW - Event cube
KW - Event modeling
KW - Event relationship analysis
KW - On-line analytical processing
UR - http://www.scopus.com/inward/record.url?scp=85031415354&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-68783-4_34
DO - 10.1007/978-3-319-68783-4_34
M3 - Conference article published in proceeding or book
AN - SCOPUS:85031415354
SN - 9783319687827
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 499
EP - 515
BT - Web Information Systems Engineering – WISE 2017 - 18th International Conference, Proceedings
A2 - Chen, Lu
A2 - Bouguettaya, Athman
A2 - Klimenko, Andrey
A2 - Dzerzhinskiy, Fedor
A2 - Klimenko, Stanislav V.
A2 - Zhang, Xiangliang
A2 - Li, Qing
A2 - Gao, Yunjun
A2 - Jia, Weijia
PB - Springer-Verlag
T2 - 18th International Conference on Web Information Systems Engineering, WISE 2017
Y2 - 7 October 2017 through 11 October 2017
ER -