Exploring Personal CoreSpace for DataSpace Management

摘要

With rapid increment of personal data amount, how to efficiently search Personal DataSpace(PDS) becomes an interesting and promising research topic. Popular methods include folder explorer, desktop search tools, and etc. Because these methods ignore user features, they fail to work well in some cases. For example, sometimes users expect to relocate a personal document based on some fuzzy memory clues, such as its type, access time, and so on. These queries can't be supported well by current personal data management tools. The aim of this paper is to discover effective methods to help users search Personal DataSpace. We take Semantic Link Network(SLN) to describe PDS, and divide the semantic links of PDS into two classes: Objective Semantic Link(OSL) and Memory-based Semantic Link(MSL). Base on MSL, we propose a concept Personal CoreSpace(PCS), which is a classification view of personal resources and is specified as a n-dimensional space based on Resource Space Model(RSM). Furthermore we design an ontology of PCS based on user behavior features, and propose a method to design facet search interfaces for users to explore PCS efficiently. We validate the effectiveness of our methods by implementing a prototype system for PCS exploring.