Finding dense locations in symbolic indoor tracking data: modeling, indexing, and processing

Research output: Contribution to journalJournal articleResearchpeer-review

2 Citations (Scopus)

Abstract

Finding the dense locations in large indoor spaces is very useful for many applications such as overloaded area detection, security control, crowd management, indoor navigation, and so on. Indoor tracking data can be enormous and are not immediately ready for finding dense locations. This paper presents two graph-based models for constrained and semi-constrained indoor movement, respectively, and then uses the models to map raw tracking records into mapping records that represent object entry and exit times in particular locations. Subsequently, an efficient indexing structure called Hierarchical Dense Location Time Index (HDLT-Index) is proposed for indexing the time intervals of the mapping table, along with index construction, query processing, and pruning techniques. The HDLT-Index supports very efficient aggregate point, interval, and duration queries as well as dense location queries. A comprehensive experimental study with both real and synthetic data shows that the proposed techniques are efficient and scalable and outperforms RDBMSs significantly.
Original languageEnglish
JournalGeoinformatica
Volume21
Issue number1
Pages (from-to)119-150
Number of pages32
ISSN1384-6175
DOIs
Publication statusPublished - Jan 2017

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indexing
Data structures
Processing
modeling
pruning
index construction
navigation
experimental study
Query processing
Navigation
index
time
management

Keywords

  • Dense Locations
  • indoor tracking
  • aggregate query
  • point query
  • interval query
  • range query
  • spatio-temporal indexing
  • query processing
  • duration query
  • count query
  • query optimization
  • indexing
  • indoor modelling
  • graph-based modelling

Cite this

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title = "Finding dense locations in symbolic indoor tracking data: modeling, indexing, and processing",
abstract = "Finding the dense locations in large indoor spaces is very useful for many applications such as overloaded area detection, security control, crowd management, indoor navigation, and so on. Indoor tracking data can be enormous and are not immediately ready for finding dense locations. This paper presents two graph-based models for constrained and semi-constrained indoor movement, respectively, and then uses the models to map raw tracking records into mapping records that represent object entry and exit times in particular locations. Subsequently, an efficient indexing structure called Hierarchical Dense Location Time Index (HDLT-Index) is proposed for indexing the time intervals of the mapping table, along with index construction, query processing, and pruning techniques. The HDLT-Index supports very efficient aggregate point, interval, and duration queries as well as dense location queries. A comprehensive experimental study with both real and synthetic data shows that the proposed techniques are efficient and scalable and outperforms RDBMSs significantly.",
keywords = "Dense Locations, indoor tracking, aggregate query, point query, interval query, range query, spatio-temporal indexing, query processing, duration query, count query, query optimization, indexing, indoor modelling, graph-based modelling",
author = "Tanvir Ahmed and Pedersen, {Torben Bach} and Hua Lu",
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language = "English",
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Finding dense locations in symbolic indoor tracking data : modeling, indexing, and processing. / Ahmed, Tanvir; Pedersen, Torben Bach; Lu, Hua.

In: Geoinformatica, Vol. 21, No. 1, 01.2017, p. 119-150.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Finding dense locations in symbolic indoor tracking data

T2 - modeling, indexing, and processing

AU - Ahmed, Tanvir

AU - Pedersen, Torben Bach

AU - Lu, Hua

PY - 2017/1

Y1 - 2017/1

N2 - Finding the dense locations in large indoor spaces is very useful for many applications such as overloaded area detection, security control, crowd management, indoor navigation, and so on. Indoor tracking data can be enormous and are not immediately ready for finding dense locations. This paper presents two graph-based models for constrained and semi-constrained indoor movement, respectively, and then uses the models to map raw tracking records into mapping records that represent object entry and exit times in particular locations. Subsequently, an efficient indexing structure called Hierarchical Dense Location Time Index (HDLT-Index) is proposed for indexing the time intervals of the mapping table, along with index construction, query processing, and pruning techniques. The HDLT-Index supports very efficient aggregate point, interval, and duration queries as well as dense location queries. A comprehensive experimental study with both real and synthetic data shows that the proposed techniques are efficient and scalable and outperforms RDBMSs significantly.

AB - Finding the dense locations in large indoor spaces is very useful for many applications such as overloaded area detection, security control, crowd management, indoor navigation, and so on. Indoor tracking data can be enormous and are not immediately ready for finding dense locations. This paper presents two graph-based models for constrained and semi-constrained indoor movement, respectively, and then uses the models to map raw tracking records into mapping records that represent object entry and exit times in particular locations. Subsequently, an efficient indexing structure called Hierarchical Dense Location Time Index (HDLT-Index) is proposed for indexing the time intervals of the mapping table, along with index construction, query processing, and pruning techniques. The HDLT-Index supports very efficient aggregate point, interval, and duration queries as well as dense location queries. A comprehensive experimental study with both real and synthetic data shows that the proposed techniques are efficient and scalable and outperforms RDBMSs significantly.

KW - Dense Locations

KW - indoor tracking

KW - aggregate query

KW - point query

KW - interval query

KW - range query

KW - spatio-temporal indexing

KW - query processing

KW - duration query

KW - count query

KW - query optimization

KW - indexing

KW - indoor modelling

KW - graph-based modelling

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