Automatic identification of location semantics based on environmental entities

Naoharu Yamada; Yoshinori Isoda; Kenji Sakamoto; Satoshi Tanaka; Goro Kunito; Kenichi Yamazaki

doi:10.1201/b13081-9

Automatic identification of location semantics based on environmental entities

Naoharu Yamada, Yoshinori Isoda, Kenji Sakamoto, Satoshi Tanaka, Goro Kunito, Kenichi Yamazaki

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Introduction Acquiring context information is essential in achieving context-aware systems and applications. Since location is the most important context, many works have tried to identify the user’s location [11,21,22,36,37]. By acquiring a user’s location periodically, the system can provideman navigation and specify user’s significant location and “hub of activity” [13,32]. Furthermore, by acquiring the location of multiple users, the system can identify friends in the neighborhood or how crowded a specific location is [1].

Original language	English
Title of host publication	Handbook on Mobile and Ubiquitous Computing
Subtitle of host publication	Status and Perspective
Publisher	CRC Press
Pages	3-21
Number of pages	19
ISBN (Electronic)	9781439848128
ISBN (Print)	9781466588974
DOIs	https://doi.org/10.1201/b13081-9
Publication status	Published - 2016 Jan 1
Externally published	Yes

ASJC Scopus subject areas

Computer Science(all)
Engineering(all)

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10.1201/b13081-9

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AU - Yamada, Naoharu

AU - Isoda, Yoshinori

AU - Sakamoto, Kenji

AU - Tanaka, Satoshi

AU - Kunito, Goro

AU - Yamazaki, Kenichi

PY - 2016/1/1

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AB - Introduction Acquiring context information is essential in achieving context-aware systems and applications. Since location is the most important context, many works have tried to identify the user’s location [11,21,22,36,37]. By acquiring a user’s location periodically, the system can provideman navigation and specify user’s significant location and “hub of activity” [13,32]. Furthermore, by acquiring the location of multiple users, the system can identify friends in the neighborhood or how crowded a specific location is [1].

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Automatic identification of location semantics based on environmental entities

Abstract

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