Contextual location prediction using spatio-temporal clustering

Guessoum, Djamel; Miraoui, Moeiz; Tadj, Chakib

doi:10.1108/ijpcc-05-2016-0027

Cited by 7 publications

(7 citation statements)

References 34 publications

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“…Context-awareness has recently received attention in many movement research directions in the areas of geographic information science (GIScience), [12][13][14][15] geocomputation (i.e., a wide array of spatial analytical methods and tools 16 ), visual analytics 17,18 (i.e., analytical reasoning facilitated by interactive visual interfaces 19 ), remote sensing 20 and tracking, 21 spatial data mining and knowledge discovery, or a combination thereof. The majority of this research has thus far merely used context as ancillary information to better understand mobilities, such as event-based movement analysis, 22 similarity measurement of trajectories, 23,24 uncertainty reduction and ranking in road networks, 25 uncertainty modeling associated with moving objects, 26 modeling spatial relevancy in context-aware systems, 27 determining significant places from mobility data, 28 visual analysis of movement behavior, 29 simulation models for movement, 30 analysis of human mobility patterns 31 and predictions, 32 and location prediction, 33 among others.…”

Section: Introductionmentioning

confidence: 99%

“…The majority of this research has thus far merely used context as ancillary information to better understand mobilities, such as event-based movement analysis, 22 similarity measurement of trajectories, 23,24 uncertainty reduction and ranking in road networks, 25 uncertainty modeling associated with moving objects, 26 modeling spatial relevancy in context-aware systems, 27 determining significant places from mobility data, 28 visual analysis of movement behavior, 29 simulation models for movement, 30 analysis of human mobility patterns 31 and predictions, 32 and location prediction, 33 among others.…”

Section: Introductionmentioning

confidence: 99%

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Context‐aware movement analytics: implications, taxonomy, and design framework

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

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Movement of an entity is greatly affected by its internal and external contexts. Such consequential influence has created new paradigms for context-aware movement data mining and analysis. The significance of incorporating contextual information and movement data is becoming quite evident because of the growing interest in context-aware movement analysis. Despite such importance, there is limited consensus among researchers on the definition of context and context-aware system design in movement studies. Therefore, this paper comprehensively reviews current concepts of context and provides a definition and a taxonomy for context in movement analysis. The paper proceeds by providing a definition of context-aware systems in the movement area after a complete review and comparison of the present definitions present in the literature. Inspired by related works, the paper further suggests a holistic three-layer design framework tailored to context-aware systems in movement studies to examine in greater depth the techniques applied during the design stages. The paper outlines the challenges and emergent issues in future research directions in context-aware movement analysis. The present study is an attempt to bridge the gap between solely using context and developing context-aware systems, thus paving the way for further research in movement applications. © 2017 Wiley Periodicals, Inc. How to cite this article:WIREs Data Mining Knowl Discov 2018Discov , 8:e1233. doi: 10.1002Discov /widm.1233 INTRODUCTIONM ovement is intrinsically a continuous phenomenon and is normally recorded as discrete snapshots at different temporal resolutions. The three fundamental sets pertinent to movement are space S (i.e., set of locations/places), time T (i.e., set of instants/intervals), and object O (i.e., set of entities). 1 In this paper, by movement we mean the change in the spatial location of one or more entities (e.g., people, animals, and vehicles) over time, not a change in entities' geometries (e.g., an individual's body parts). Such changes can be represented as a function μ: T ! S, which maps any time to a location in space. The purpose of analyzing movement data in the majority of research fields (e.g., geography, ecology, sociology, and transportation) has focused on the development of insights into the behavior of moving entities. However, the movement behavior of entities is generally associated with their internal states and external factors. 2 In other words, understanding entities' movement greatly depends on recognizing the characteristics attributed to the entity itself (e.g., the entities' purposes and intentions for movement, the entities' perceptions of space) as well as the discovery of geographical circumstances during the movement (e.g., weather conditions and traffic). We, respectively, term these influential variables internal and external contexts in this paper. of 19Because of the progress in sensing, imaging, tracking, and navigation technologies and infrastructures in recent years, unprecedented amount...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Context‐aware movement analytics: implications, taxonomy, and design framework

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

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“…The same data set is used, in this paper, for the simulation. Compared to the result obtained in [33] as shown in Table 1, the results obtained are:…”

Section: Ontology Creationmentioning

confidence: 67%

“…The different algorithms were presented in the related work section. These algorithms were implemented in [33] and they were compare the results obtained by every algorithm using a real data set. The same data set is used, in this paper, for the simulation.…”

Section: Ontology Creationmentioning

confidence: 99%

Ontological Model to Predict user Mobility

Zaguia¹,

Alroobaea²

2019

ijacsa

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With the remarkable technological evolution of mobile devices, the use of computing resources has become possible at any time and independent of the geographical position of the user. This phenomenon has various names such as omnipresent diffuse computing, pervasive computing, or ubiquitous systems. This new form of computing allows users to have access to shared and ubiquitous services focused on their needs, and it is based on context prediction, especially the prediction of the user's location. This paper aims to present a new approach for predicting a user's next probable location, by presenting an ontological model which is based on the pattern technique. This is carried out by using an ontological model that comprises different user behaviors and presents details about the environment, where the user is located. The results after tested on real data show that the presented ontological model was able to achieve 85% future location-prediction accuracy (in the case of no similar patterns). Future work will focus on the integration of the Bayesian network to improve the results. This approach will be implemented in smart homes or smart cities to reduce energy consumption.

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“…A competitive result was obtained a comparing to the approach presented in [18]. In [23], they used the same data set to test different algorithms, comparing to their results (Table II), motivating results were attained for the prediction of a user's next location using our approach based of the combination of pattern technique and Bayesian networks.…”

Section: Use Case and Resultsmentioning

confidence: 99%

Location Prediction in a Smart Environment

Alosaimi¹,

Binmahfoudh²,

Alroobaea³

et al. 2019

ijacsa

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The context prediction and especially the location prediction is an important feature for improving the performance of smart systems. Predicting the next location or context of the user make the system proactive, so the system will be capable to offer the suitable services to the user without his involving. In this paper, a new approach will be presented based on the combination of pattern technique and Bayesian network to predict the next location of the user. This approach was tested on real data set, our model was able to achieve 89% of the next location prediction accuracy.

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Contextual location prediction using spatio-temporal clustering

Cited by 7 publications

References 34 publications

Context‐aware movement analytics: implications, taxonomy, and design framework

Context‐aware movement analytics: implications, taxonomy, and design framework

Ontological Model to Predict user Mobility

Location Prediction in a Smart Environment

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