Description-Based Semantic Prefetching Scheme for Data Management in Location-Based Services
- Authors
- Kang, Sang-Won; Gil, Joon-Min; Kim, Jongwan; Im, SeokJin; Lee, Sangkeun
- Issue Date
- 11월-2008
- Publisher
- INST INFORMATION SCIENCE
- Keywords
- semantic prefetching; range query; cache replacement; locality; preference; query saving ratio; location-based services
- Citation
- JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, v.24, no.6, pp.1799 - 1820
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF INFORMATION SCIENCE AND ENGINEERING
- Volume
- 24
- Number
- 6
- Start Page
- 1799
- End Page
- 1820
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/122465
- ISSN
- 1016-2364
- Abstract
- Many recent studies in the field of data transmission have considered location-based services. Prefetching and caching are exemplary techniques for data transmission, and offer advantages in user-centric services in location-dependent data environments. However, in mobile environments, prefetching and caching inevitably require frequent uplink requests because the data that is needed in the clients' current location has to be transmitted from a server. To overcome this drawback, this paper presents a semantic prefetching scheme, with descriptions and cache replacement policies, based on range query processing algorithms. To decrease frequent uplink requests, when a client enters a new service area, that is, a semantic prefetching area, our scheme fetches the necessary descriptions and data from the server in advance. The client maintains the descriptions in its own cache using proposed semantic least recently used, semantic least frequently used, and preference priority replacement policies. Range queries can get their results from the cache, with only a few requests to the server, regardless of mobility and query patterns. Experimental results show that our semantic prefetching scheme is more efficient than the existing scheme in terms of cache efficiency and data accessibility.
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Collections - Graduate School > Department of Artificial Intelligence > 1. Journal Articles
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