Granular best match algorithm for context-aware computing systems

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Abstract

In order to be context-aware, a system or application should adapt its behaviour according to current context, acquired by various context provision mechanisms. After acquiring current context, this information should be matched against the previously defined context sets. In this paper, a granular best match algorithm dealing with the subjective, fuzzy, multi-granular and multi-dimensional characteristics of contextual information is introduced. The CAPRA – Context-Aware Personal Reminder Agent tool is used to show the applicability of the new context matching algorithm. The obtained outputs showed that proposed algorithm produces the results which are more sensitive to the user’s intention, and more adaptive to the aforementioned characteristics of the contextual information than the traditional exact match method.

Introduction

Context-aware computing research is a subset of ubiquitous computing. The aim of ubiquitous computing is to realize unconscious use of computing capabilities and continues availability of information resources (Weiser, 1991). Context-aware research plays a very critical role in this scenario.

Active Badge (Want et al., 1992) and ParcTab (Want et al., 1995) known as the first context-aware applications were emerged in early 1990s. After that research on this field both context-aware computing and ubiquitous computing have increased tremendously. When we consider the past decade, research and solutions were mostly application specific and technologically dependent (Mitchell, 1999).

Since it was a relatively new field of research, there are many rooms for development. Generally, the aim is to realize effective and efficient provision and usage of contextual information. There are some general research directions in this field. First of all, to develop context-aware computing applications, it is required to have tools that are based on clearly defined models of context and of system software architecture (Dey et al., 2001). Another essential need is the common formal and reusable context representation format and ontology (Henricksen et al., 2002). Moreover, sensor technology perceiving the most of the physical context data should be improved (Schmidt, 2002). Context fusion is one of areas in which most of the research effort is done.

Researches on this sub-field deals with the abstraction and classification of low level context information to the high level ones (Van Laerhoven and Cakmakci, 2000; Wu et al., 2002). Finally, sophisticated and configurable context matching mechanisms are required for the better coupling of provided and desired context information and thus more adaptive servicing.

While dealing with contextual information, its multi-dimensionality, multi-granularity, subjectivity, and fuzziness characteristics should be taken into account. This brings complexity to matching of contextual information. The exact match method has been widely used due to its simplicity so far. However, in order to deal with this complexity properly, we need more powerful matching mechanisms. In this study, we introduce a more elaborate matching mechanism to address these issues.

The rest of the paper is structured in the following way. In Section 2, context matching operation will be described and some of the special characteristics of contextual information will be discussed. Various approaches to the context matching problem will be introduced in Section 3. After giving the details of Granular Best Match Algorithm in Section 4, CAPRA – Context-Aware Personal Reminder Agent will be described in Section 5. Finally, in the last Section, conclusion and future work will be explained.

Section snippets

Context matchıng

Context matching is the matching process taking place in context-aware computing systems. It matches two context data: provided context and desired context. Provided context information is coming from the sensors, other applications, and generally from context providers. On the other hand, desired context information is the query of the context consumers in active or passive format. Context matching is needed when an explicit query is made by the user (active) or a previously recorded query

Related work

The most elaborative approaches to context matching in context-aware computing issue have been done so far by Brown and Jones, 2002, Jones and Brown, 2002. Brown claims that current search engines take no account of the individual user and their personal interests and their current context. The development of personal networked mobile computing devices and environmental sensors mean that personal and context information is potentially available for the retrieval process. He refers to this

Granular best match algorithm

As stated before the focus of this research is on the matching of contextual information at the same level of abstraction. Granular best match algorithm to be introduced deals with the multi-dimensional, fuzzy, subjective, and multi-granular structure of the contextual information.

For the multi-dimensionality, the introduced algorithm uses four dimensional context; location, time, activity and identity. Although, there are no limits of number of contextual dimensions to be matched for granular

CAPRA

The objectives of the CAPRA – Context-Aware Personal Reminder Agent case is to show the usage of more contextual elements to tag reminders; to enhance the capabilities of current reminder tools and to show the applicability of new best granular context matching algorithm in a context-aware computing system.

CAPRA has the abilities to search previous context with related information and abilities to make future appointments and reminders based on the contextual elements Location, Time, Activity

Conclusion and future work

In this paper, context matching operation in context-aware computing systems was investigated and a new best granular context matching algorithm was introduced. The problem is the inadequate capabilities of exact match method in context-aware systems. Since context information has some unique characteristics like high dimensionality, high subjectivity and multi-granularity; it is needed to have more advanced matching algorithms to satisfy the requirements for context matching.

Granular best

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