A service-oriented context-awareness reasoning framework and its implementation

Pages1114-1134
Date10 December 2018
DOIhttps://doi.org/10.1108/EL-04-2017-0096
Published date10 December 2018
AuthorXiufeng Cheng,Jinqing Yang,Lixin Xia
Subject MatterInformation & knowledge management,Information & communications technology,Internet
A service-oriented
context-awareness reasoning
framework and its implementation
Xiufeng Cheng,Jinqing Yang and Lixin Xia
Department of Information Management, Huazhong Normal University,
Hubei, China
Abstract
Purpose This paper aims to propose an extensible, service-oriented framework for context-aware data
acquisition, description, interpretation and reasoning, which facilitates the development of mobile
applicationsthat provide a context-awareness service.
Design/methodology/approach First, the authors propose the context data reasoning framework
(CDRFM) for generating service-oriented contextual information. Then they used this framework to composite
mobile sensor data into low-level contextual information. Finally, the authors exploited some high-level contextual
information that can be inferredfrom the formatted low-level contextual information using particular inference rules.
Findings The authors take user behaviorpatternsas an exemplary context information generation schema in
their experimental study. The results reveal that the optimization of service can beguided by the implicit, high-level
context information inside user behavior logs. They also prove the validity of the authorsframework.
Research limitations/implications Further research will add more variety of sensor data. Furthermore,
to validate the effectiveness of our framework, more reasoning rules need to be performed. Therefore, the authors
may implement more algorithms in the framework to acquire more comprehensive context information.
Practical implications CDRFM expands the context-awareness framework of previous research and
unies the procedures of acquiring, describing, modeling, reasoning and discovering implicit context
informationfor mobile service providers.
Social implications Support the service-orientedcontext-awareness function in application design and
related developmentin commercial mobile software industry.
Originality/value Extant researches on context awarenessrarely considered the generation contextual
information for service providers.The CDRFM can be used to generate valuable contextual information by
implementingmore reasoning rules.
Keywords Android, Application framework, Context-awareness, Data acquisition, Service-oriented
Paper type Research paper
Introduction
With the ever-growing number of smart mobile devices, multitudinous applications are
pushed to mobile users. However, the massive amounts of valuable sensor data stored on
local cell phones are neither utilized nor managed. On the one hand, these data pose the
problem of information overloadfor end users (Allen and Wilson, 2003). On the other
hand, there are potential opportunities for researchers to investigatethe behaviors of mobile
users by acquiring and analyzing the data on each application of each mobile terminal.
These data created by mobile sensors are often stored temporarily on a local database,
This study is funded by grants from the National Natural Science Foundation of China (Grant No.
71503097) and the National Social Science Foundation of China (No. 13 & ZD183).
EL
36,6
1114
Received21 May 2017
Revised29 August 2017
4 October2017
10November 2017
19January 2018
12March 2018
Accepted29 March 2018
TheElectronic Library
Vol.36 No. 6, 2018
pp. 1114-1134
© Emerald Publishing Limited
0264-0473
DOI 10.1108/EL-04-2017-0096
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0264-0473.htm
which contains the situation-relevant information corresponding to a user or user set, or
what is known as the mobile context (Dey and Abowd, 2000;Abowdet al., 1999). Therefore,
more advanced systems or applications, such as applications with recommended plug-ins,
need to be aware of userspast and current contexts and to automatically adapt to changes
in these contexts in short,they need to have context-awareness (Gu et al., 2005).
The computing of context was rst introduced to computer science in 1994 as method sets
that adapt according to location of use, the collection of nearby people and objects, and changes to
those objects over time (Schilit et al.,1994). In terms of contextual computing, context awareness
comprises two phases: data acquisition and formalization (Sachenko, 2002). The former refers to a
series of data operations, which include data accessing, capturing, preprocessing and
standardization; these operations ensure the quality, accuracy and tness of generated context for
the later formalization process. Formalization refers to a series of creative works, which include
semantic model selecting, dening the inference rules and the development of middleware (or
plug-ins) to implement those rules; these works concern the quality of generated context
information and the corresponding service. A reasonable way to enhance usersexperience is to
detect efcient high-level context information for service feedback. However, recent research
shows that building context-aware systems is still a complex and time-consuming task because
of the lack of adequate infrastructural support (Guo et al.,2011). Therefore, a complete set of
methods for context acquisition, discovery and interpretation is necessary for context-based
systems in different usage scenarios. For this purpose, we have designed a data processing
framework (context data reasoning framework [CDRFM]) which can be used for scaling and
unifying the overall process of extracting, acquiring, formatting and generating both low-level
and high-level service-oriented context information.
The rst step of CDRFM is to collect cell phone logs from hardware and software sensors,
and to formulate these sensor data into raw data. The second step is to reorganize the raw data
into low-level and high-level contextual information. Then we need to synthesize these different
levels of contexts into service-oriented context information that will improve the situational
awareness ability of the mobile device and adapt the way it behaves according to the context.
To detect the high-level context from the composite context, researchers usually focus on
particular tasks according to their study purpose; large tasks can be decomposed into small
activities to adjust the contextual computing methods. For this reason, in our experimental
study, we rst implemented CDRFM by performing a daily rhythm usage frequency analysis
to demonstrate the objectsapplication usage routine as an example of low-level context
generation. Then we used a clustering algorithm in CDRFM to analyze the association of
university studentsusage patterns as a sample of high-level context generation, which may
validate the effectiveness of CDRFM. Researchers are increasingly using data-mining
algorithms and visualization technology to generate higher-level contextual information (Rook,
2014). Thus, we proposed several data-mining algorithms in a context-reasoning model to
acquire practical context information and to implement one of the inference methods in the
experimental study to visualize the results of our test.
In summary, this paper elaborateson the issues involved in the design and development
of the unied workow for context-service utilization and optimization. Particularly, the
results and analysis on context information generated in our experimental study can be
considered as positive suggestions for mobile application service providers when they
provide servicesto university students.
Literature review
Mobile usersbehaviorpatterns have received much attention from researchersover the past
decade, and in recent years, there has been a continuous increase in the amount of research
Reasoning
framework and
its
implementation
1115

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