Extracting Region of Interest (ROI) Details Using LBS Infrastructure and Web-Databases

Tiwari, Shivendra; Kaushik, Saroj

doi:10.1109/mdm.2012.29

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…The concept presented in [30] is to use existing Location-Based Services (LBS) infrastructure to track the user and achieve other system navigation goals. The information source is the freely available and up-to-date internet infrastructure.…”

Section: Related Workmentioning

confidence: 99%

Location Based Indoor and Outdoor Lightweight Activity Recognition System

et al. 2022

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In intelligent environments one of the most relevant information that can be gathered about users is their location. Their position can be easily captured without the need for a large infrastructure through devices such as smartphones or smartwatches that we easily carry around in our daily life, providing new opportunities and services in the field of pervasive computing and sensing. Location data can be very useful to infer additional information in some cases such as elderly or sick care, where inferring additional information such as the activities or types of activities they perform can provide daily indicators about their behavior and habits. To do so, we present a system able to infer user activities in indoor and outdoor environments using Global Positioning System (GPS) data together with open data sources such as OpenStreetMaps (OSM) to analyse the user’s daily activities, requiring a minimal infrastructure.

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Section: Related Workmentioning

confidence: 99%

Location Based Indoor and Outdoor Lightweight Activity Recognition System

et al. 2022

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“…It is also useful in spatial data 978-1-4799-7964-6/15/$31.00 © 2015 IEEE ICDE Conference 2015 mining to find the nearest neighborhood. Many works in spatial data mining involve a large amount of historical location data, and aim to identify regions of interest that are usually represented as spatial clusters [2], [3], [4]. In such works, it is not always necessary to partition the all the location data into meaningful clusters, but to identify one or a few clusters in the proximity of a location of interest such as school, city hall, and museum.…”

Section: Introductionmentioning

confidence: 99%

“…With a toy dataset including only 1000 points, its preprocessed structure was built in about 6 days 3. http://www.chorochronos.org…”

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confidence: 99%

Nearest neighborhood search in spatial databases

Choi

Chung

2015

2015 IEEE 31st International Conference on Data Engineering

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This paper proposes a group version of the nearest neighbor (NN) query, called the nearest neighborhood (NNH) query, which aims to find the nearest group of points, instead of one nearest point. Given a set O of points, a query point q, and a ρ-radius circle C, the NNH query returns the nearest placement of C to q such that there are at least k points enclosed by C. We present a fast algorithm for processing the NNH query based on the incremental retrieval of nearest neighbors using the Rtree structure on O. Our solution includes several techniques, to efficiently maintain sets of retrieved nearest points and identify their validities in terms of the closeness constraint of their points. These techniques are devised from the unique characteristics of the NNH search problem. As a side product, we solve a new geometric problem, called the nearest enclosing circle (NEC) problem, which is of independent interest. We present a linear expected-time algorithm solving the NEC problem using the properties of the NEC similar to those of the smallest enclosing circle. We provide extensive experimental results, which show that our techniques can significantly improve the query performance.

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General Purpose Index-Based Method for Efficient MaxRS Query

Zhou

Wang

2016

Lecture Notes in Computer Science

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Extracting Region of Interest (ROI) Details Using LBS Infrastructure and Web-Databases

Cited by 7 publications

References 7 publications

Location Based Indoor and Outdoor Lightweight Activity Recognition System

Location Based Indoor and Outdoor Lightweight Activity Recognition System

Nearest neighborhood search in spatial databases

General Purpose Index-Based Method for Efficient MaxRS Query

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