On Mining Movement Pattern from Mobile Users

Taniar, David; Goh, John

doi:10.1080/15501320601069499

Cited by 44 publications

(24 citation statements)

References 32 publications

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“…Our proposed research may also be applied to various domain applications, such as mobile systems 28,29,30,31,32,33,34 , RFID 35,36 , real-time systems 37 , business process 38 , web systems 39,40,41 , and fuzzy data 42 .…”

Section: Resultsmentioning

confidence: 99%

Mining Hierarchical Negative Association Rules

Taniar¹,

Rahayu²,

Daly³

et al. 2012

IJCIS

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The negative association between items in databases is as important and interesting as the positive one. But, it has not been studied as much. We consider negative association in a hierarchical setting, in which we are able to generate negative association rules at different hierarchy levels. It allows to impose restrictions when we proceed to the next level and discover only most interesting negative association rules among the vast number of possible negative association rules. In this paper, we propose two algorithms for mining negative association rules by considering that items are organized in a hierarchy, and this hierarchy is reflected on the association rules we produce. In this way, we can mine for both general and specialized rules of negative association between items.

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Section: Resultsmentioning

confidence: 99%

Mining Hierarchical Negative Association Rules

Taniar¹,

Rahayu²,

Daly³

et al. 2012

IJCIS

Self Cite

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show abstract

“…In our future research, we plan to extend our advanced range search approaches to continuous range search query processing as well as to the moving objects of interest [27][28][29] in mobile database [30][31][32][33] and parallel database environment [34,35]. And how these approaches can be applied in the intelligent traffic control system [36] and context-aware system [37] are also expected.…”

Section: Discussionmentioning

confidence: 99%

Constrained range search query processing on road networks

Xuan

Zhao

Taniar

et al. 2010

Concurrency and Computation

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SUMMARYRange search is one of the most common queries in the spatial databases and geographic information systems (GIS). Most range search processing depends on the length of the distance that expresses the relative position of the objects of interest in the Euclidean space or road networks. But, in reality, the expected result is normally constrained by other factors (e.g. number of spatial objects, pre-defined area, and so forth.) rather than the distance alone; hence, range search should be comprehensively discussed in various scenarios. In this paper, we propose two constrained range search approaches based on network Voronoi diagram, namely Region Constrained Range (RCR) and k nearest neighbor Constrained Range (kCR), which make the range search query processing more flexible to satisfy different requirements in a complex environment. The performance of these approaches is analyzed and evaluated to illustrate that both of them can process constrained range search queries very efficiently.

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“…There are mobile-based applications, or systems [4,7,8,13,15,16,18,20,21,25,28,30] which are able to predict users' behaviors using mobile phone sensors. These applications use adaptive algorithms that save the mobile's battery power and are sometimes used to infer or predict mobile users' future behavior or behavior patterns.…”

Section: Related Workmentioning

confidence: 99%

Personalized Behavior Pattern Recognition and Unusual Event Detection for Mobile Users

Ahn

Han

2013

Mobile Information Systems

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Abstract. Mobile phones have become widely used for obtaining help in emergencies, such as accidents, crimes, or health emergencies. The smartphone is an essential device that can record emergency situations, which can be used for clues or evidence, or as an alert system in such situations. In this paper, we focus on mobile-based identification of potentially unusual, or abnormal events, occurring in a mobile user's daily behavior patterns. For purposes of this research, we have classified events as "unusual" for a mobile user when an event is an infrequently occurring one from the user's normal behavior patterns -all of which are collected and recorded on a user's mobile phone. We build a general unusual event classification model to be automated on the smartphone for use by any mobile phone users. To classify both normal and unusual events, we analyzed the activity, location, and audio sensor data collected from 20 mobile phone users to identify these users' personalized normal daily behavior patterns and any unusual events occurring in their daily activity. We used binary fusion classification algorithms on the subjects' recorded experimental data and ultimately identified the most accurately performing fusion algorithm for unusual event detection.

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On Mining Movement Pattern from Mobile Users

Cited by 44 publications

References 32 publications

Mining Hierarchical Negative Association Rules

Mining Hierarchical Negative Association Rules

Constrained range search query processing on road networks

Personalized Behavior Pattern Recognition and Unusual Event Detection for Mobile Users

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