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Mobile Information Systems
Volume 10, Issue 1, Pages 105-125
http://dx.doi.org/10.3233/MIS-130173

Constructing the Web of Events from Raw Data in the Web of Things

Yunchuan Sun,1 Hongli Yan,1 Cheng Lu,2 Rongfang Bie,2 and Zhangbing Zhou3,4

1Business School, Beijing Normal University, Beijing, China
2College of Information Science and Technology, Beijing Normal University, Beijing, China
3Schoolof Information Engineering, China University of Geosciences, Beijing, China
4Computer Science Department, Institute Mines-TELECOM/TELECOM SudParis, Paris, France

Received 23 July 2013; Accepted 23 July 2013

Copyright © 2014 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

An exciting paradise of data is emerging into our daily life along with the development of the Web of Things. Nowadays, volumes of heterogeneous raw data are continuously generated and captured by trillions of smart devices like sensors, smart controls, readers and other monitoring devices, while various events occur in the physical world. It is hard for users including people and smart things to master valuable information hidden in the massive data, which is more useful and understandable than raw data for users to get the crucial points for problems-solving. Thus, how to automatically and actively extract the knowledge of events and their internal links from the big data is one key challenge for the future Web of Things. This paper proposes an effective approach to extract events and their internal links from large scale data leveraging predefined event schemas in the Web of Things, which starts with grasping the critical data for useful events by filtering data with well-defined event types in the schema. A case study in the context of smart campus is presented to show the application of proposed approach for the extraction of events and their internal semantic links.