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The rapid proliferation of mobile context aware applications has resulted in an increased research interest towards developing specialized context data management strategies for mobile entities. The purpose of this paper is to aim to develop a new way to model mobile entities and manage their contexts accordingly.

This paper proposes the concept of “Mobile Space” to model mobile entities and presents strategies to manage the various contexts associated therein. To handle availability related issues, two system services are designed: the “Availability Updating Service” which is an identifier based mechanism and is designed to keep track of mobile objects and handle availability related issues, and the “Application Callback Service” which is a publish/subscribe based mechanism to handle application disruptions and interruptions arising due to mobility.

The paper presents a detailed study of the proposed framework and a description of the underlying services and the components therein to validate the framework. Experimental results carried out in WiFi and 3G environments indicate that the proposed techniques can support mobile applications and minimize application disruptions with minimal overhead.

The proposed context management framework is generic in nature and is not designed for a specific class of applications. Any mobile context aware application can leverage on the framework and utilize the provided functionalities to manage application disruptions. Also, the decoupling of mobile application layer and the underlying context data management layer renders context data management layer transparent to the application design.

Many mobile devices today are equipped with diversified sensors that enable the acquisition of rich user context (e.g. GPS location, phone activity) for application utilization. With the growing usage of mobile devices in daily life, the problem of conveniently and promptly searching a piece of content that a user has viewed on his/her device before becomes more and more crucial. This paper aims to propose a context‐based query processing framework called UCQP that supports unstructured queries for content search in a user's access history.

Beyond the keywords related to the content properties, a context query in the framework is specified with freeform phrases that describe high‐level mobile contexts of the user at a previous time when the user viewed the searched content.

Experimental results on a prototype system of the framework illustrate its good accuracy and small response time.

To tolerate the incompleteness and inaccuracy in user query texts caused by fading human memory, the authors develop several semantic query parsers that are tailored for different types of contexts using natural language processing and information retrieval techniques. The authors further propose a similarity model to rank the multiple result contents of a query by comparing context entities specified in the query and historical context values associated with each result.