An Ambient Service Model for Providing Structured Web Information Based on User-Contexts

Users often visit many stores while they compare merchandise in order to purchase the merchandise or merchandise related to it. Given a service providing information such as location of these stores, they can reduce their time and effort spent by wandering around them because they can go straight to these stores. And they can obtain new purchase opportunities because they can know what kinds of related stores are around them. Thus, they can do efficient purchasing activities. In this paper, we discuss service providing information of related stores efficiently in order to support users’ efficient purchasing activities. And we propose an ambient service model that consists of three layers: structured (purchase-related) information space, real-life space, and ambient information space. In this model, a lot of store information collected from the web is grouped and structured automatically by relation in terms of purchasing. And users search information of related stores by using an ambient query that is automatically created by their context in real-life space. Finally, they obtain information of related stores in the form of hierarchy structure through map interface. Then, they can search other kinds of information of related stores additionally by using the hierarchy structure. We implemented a system based on this model by using Radio-Frequency Identification (RFID), map-based, location-based and ontology technology. The implemented system was performed in specific shopping region (Ilsan Lapesta shopping mall, Goyang-city Gyeonggi-do, Korea). And we confirmed that users can efficiently obtain information of related stores through the system. We expect that this model can be used for developing services that provide information of objects related to various objects besides stores.

[1]  Hani Hagras,et al.  A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[2]  Svetha Venkatesh,et al.  Multi-modal emotive computing in a smart house environment , 2007, Pervasive Mob. Comput..

[3]  Diane J. Cook,et al.  Author's Personal Copy Pervasive and Mobile Computing Ambient Intelligence: Technologies, Applications, and Opportunities , 2022 .

[4]  Asunción Gómez-Pérez,et al.  Ontology Specification Languages for the Semantic Web , 2002, IEEE Intell. Syst..

[5]  Oliver Brdiczka,et al.  Learning to detect user activity and availability from a variety of sensor data , 2004, Second IEEE Annual Conference on Pervasive Computing and Communications, 2004. Proceedings of the.

[6]  Albrecht Schmidt,et al.  Mediacups: experience with design and use of computer-augmented everyday artefacts , 2001, Comput. Networks.

[7]  Andy Hopper,et al.  The Anatomy of a Context-Aware Application , 1999, Wirel. Networks.

[8]  Guanling Chen,et al.  A Survey of Context-Aware Mobile Computing Research , 2000 .

[9]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.

[10]  Bing Liu,et al.  Web data extraction based on partial tree alignment , 2005, WWW '05.

[11]  Bernhard Mitschang,et al.  A Model-Based, Open Architecture for Mobile, Spatially Aware Applications , 2001, SSTD.

[12]  Tatsuo Nakajima,et al.  Towards system software for physical space applications , 2005, SAC '05.