Context-aware systems: A literature review and classification

Hong, Jong-Yi; Suh, Eui–Ho; Kim, Sungjin

doi:10.1016/j.eswa.2008.10.071

Cited by 489 publications

(302 citation statements)

References 245 publications

Supporting

Mentioning

292

Contrasting

Unclassified

Order By: Relevance

“…5 Despite the diversity in context information, the same information can be acquired using different techniques, which, in turn, may vary in terms of quality, accuracy, granularity, cost, etc. The quality of context (QoC) is a crucial factor in the effective operation of context-aware systems.…”

Section: Context Acquisitionmentioning

confidence: 99%

“…From the methodological perspective, a large body of literature has surveyed the state-of-the-art development of context-aware systems, primarily focusing on system conceptualization, classification framework, architectural stages, challenges during design processes and system integrations, supporting infrastructures, security and privacy issues, etc. [3][4][5][6][7] From an application perspective, Verbert et al 8 specifically presented a survey of context-aware recommendation systems that have been established for technology-enhanced learning. More comprehensively, Liu et al 9 surveyed the existing studies according to the entire spectrum of context-aware mobile recommendation systems and their specifications, seeking to predict and anticipate the needs of users and respond to their behavior.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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...

show abstract

Section: Context Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…For instance, (Physical) Mobility allows a user to move out of his proper context, traveling across different contexts and, to our purposes, the difference among contexts is determined in terms of available resources like connectivity, energy, software, etc. However other dimensions of contexts can exist relevant to the user, system and physical domains, which are the main context domains identified in the literature [38,9,26,7,20,40]. In the software development practice when building a system the context is determined and it is also part of the (non-functional) requirements (operational, social, organizational constraints).…”

Section: Setting the Contextmentioning

confidence: 99%

Run Time Models in Adaptive Service Infrastructure

Autili

Inverardi

Tivoli

2010

Run-Time Models for Self-Managing Systems and Applications

View full text Add to dashboard Cite

Abstract. Software in the near ubiquitous future will need to cope with variability, as software systems get deployed on an increasingly large diversity of computing platforms and operates in different execution environments. Heterogeneity of the underlying communication and computing infrastructure, mobility inducing changes to the execution environments and therefore changes to the availability of resources and continuously evolving requirements require software systems to be adaptable according to the context changes. Software systems should also be reliable and meet the user's requirements and needs. Moreover, due to its pervasiveness, software systems must be dependable. Supporting the validation of these self-adaptive systems to ensure dependability requires a complete rethinking of the software life cycle. The traditional division among static analysis and dynamic analysis is blurred by the need to validate dynamic systems adaptation. Models play a key role in the validation of dependable systems, dynamic adaptation calls for the use of such models at run time. In this paper we describe the approach we have undertaken in recent projects to address the challenge of assessing dependability for adaptive software systems.

show abstract

“…15 in which the system is essentially defined by the user through the selection and specification of appropriate components which then use context based information, supported by user feedback, to establish the desired outputs [38][39][40][41][42][43][44][45][46][47][48][49][50][51].…”

Section: System Design and Complexitymentioning

confidence: 99%