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  1. Proceedings of the 1st international workshop on Mobile location-based service (MLBS '11)
  2. Preference-based top-k spatial keyword queries
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Learning techniques in social and location-based service recommendation
Extractable mobile photo tags
Toward better indoor localization: cooperative localization and estimation fusion
Efficient relative camera orientation detection for mobile applications
Location-based topic evolution
Guided by touch: tactile pedestrian navigation
Fusion of Wi-Fi and bluetooth for indoor localization
Social linking and physical proximity in a mobile location-based service
Inertial sensors aided image alignment and stitching for panorama on mobile phones
Temporal sensitivity for location disclosure through mobile photo-sharing
Preference-based top-k spatial keyword queries
History trajectory privacy-preserving through graph partition
XINS: the anatomy of an indoor positioning and navigation architecture
Privacy-aware mobile location-based systems

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Preference-based top-k spatial keyword queries

Content Provider ACM Digital Library
Author Meng, Xiaofeng Zhang, Jinzeng Liu, Dongqi
Abstract With proliferation of geo-positioning and geo-tagging, spatial keyword query has been an attractive and challenging topic that blooms various interesting applications in spatial databases. However, spatial keyword queries in those fields do not always satisfy users requirements. One reason for those queries contain fuzzy linguistic labels that often embody users preference, e.g., finding hotels near Zhongguancun with moderate price. In this paper, we define Preference-based Top-k Spatial Keyword (PTkSK) query for coping with fuzzy labels. Existing works only consider on spatial locations, textual descriptions and overlook users preference. In order to answer (PTkSK) queries efficiently, we propose a hybrid index structure called AIR-tree(Attribute-Inverted file R-tree), which combines location proximity with preference similarity and textual relevance. Given a query with fuzzy labels, we propose an efficient strategy of computing users preference to transform these labels into the [0,1] interval, and design two effective search methods to find relevant objects for satisfying users requirements. Our experiments on synthetic and real datasets demonstrate that our proposed methods are scalable and efficient.
Starting Page 31
Ending Page 40
Page Count 10
File Format PDF
ISBN 9781450309288
DOI 10.1145/2025876.2025883
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2011-09-18
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Spatial keyword queries Mapping function Preference
Content Type Text
Resource Type Article
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