Paper Info
Title
A Real-time Complex Event Discovery Platform for Cyber-Physical-Social Systems
Abstract
We are living in the Internet of Things (IoT) era where all the (smart) objects around us are connected and communicated with each other to serve our life better without the need of explicit instruction. Soon we have to cope with trillions of heterogeneous data streams coming from IoT. Since data is not information, methods for discovering useful and correlative information from data and utilising them for the better life, in real-time mode, are the utmost requirements. In order to tackle this problem, we introduce an Event Information Management platform (EvIM) that can be used to develop applications run as cyber-physical-social systems. EvIM includes two components (1) EventWarehouse: is built for harvesting, storing, and analysing data coming from large scale heterogeneous sensors, and (2) EventShop: plays as a real-time complex spatio-temporal event processing system. Differ from conventional systems that use data-driven or pre-defined event-based approaches, the proposed platform can alleviate the burden of human intervention meanwhile increase the scalability, robustness, feasibility, and applicability by offering series of services that not only automatically discover correlations among sensors' data to extract useful information but also can help users design and monitor situations visually, efficiently and effectively, under on-fly mode.
Year
DOI
Venue
2014
10.1145/2578726.2578755
ICMR
Keywords
Field
DocType
real-time complex spatio-temporal event,analysing data,heterogeneous data stream,complex event discovery platform,on-fly mode,heterogeneous sensor,proposed platform,better life,correlative information,event information management platform,cyber-physical-social systems,useful information,complex event processing,internet of things
Data stream mining,Computer science,Computer security,Robustness (computer science),Human–computer interaction,Cyber-physical system,Artificial intelligence,Data stream management system,Information management,Complex event processing,Social system,Machine learning,Scalability
Conference
Citations 
PageRank 
References 
7
0.90
10
Authors
6
Name
Order
Citations
PageRank
Minh-son Dao19321.42
Siripen Pongpaichet2526.46
Laleh Jalali3406.19
Kyoung-Sook Kim42414.07
Ramesh Jain576301861.65
Koiji Zettsu671.24