Supporting context-based query in personal DataSpace

Abstract

Many users need to refer to content in existing files (pictures, tables, emails, web pages and etc.) when they write documents (programs, presentations, proposals and etc.), and often need to revisit these referenced files for review, revision or reconfirmation. Therefore it is meaningful to discover an approach to help users revisit these references effectively. Traditional approaches(file explorer, desktop search, and etc.) fail to work in this case. In this paper, we propose an efficient solution for this problem. We firstly define a new personal data relationship: Context-based Reference(CR), which is generated by user behaviors. We also propose efficient methods to identify CR relationship and present a new type of query based on it: Context-based Query(CQuery), which helps users efficiently revisit personal documents based on CR relationship. Our experiments validate the effectiveness and efficiency of our methods.

A Real-time Linked Dataspace for the Internet of Things: Enabling “Pay-As-You-Go” Data Management in Smart Environments

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A visual XML dataspace approach for satisfying ad hoc information needs

“…They assume either that the available data sources have been organized according to the underlying data model or that the data sources have been converted to obey it. For example, Quarry (Howe et al., ) and Semex (Cai, Dong, Halevy, Liu, & Madhavan, ) use resource description framework (RDF) as their data model, whereas OrientSpace (Li & Meng, , ) and iMemex (Dittrich & Salles, ; Salles, Dittrich, Karakashian, Girard, & Blunschi, ) use their own data model specifically designed for their respective purposes.…”

Unstructured queries based on mobile user context

“…It enhances traditional single-box search with facet and association graph navigation, as well as a personalized result ranking scheme. Li and Meng proposed a query processing framework based on detecting context-based reference relationship between contents in a user's access history (Li and Meng, 2009). Deng et al (2011) proposed a context-based memory model and a content re-finding framework based on clustering associative context instances related to the contents.…”