Data Extraction And Semantic Association Construction In Personal Dataspace Management System

In recent years, with the increase of user demand and development of information technology, users are facing heterogeneous data from unstructured to semi-structured and structured. In addition, the growing demands also bring about very large data. Because traditional database management system can't satisfy the rising demand for very large and heterogeneous data, dataspace is put forward, which doesn't rely on strict data model, and put stepwise-integration approach into use. In this paper, we study information extraction and semantic association construction in personal dataspace, our main works are as follows:1. Data extraction from heterogeneous data sources is realized in cloud computing environment. Related extraction technology from data sources such as local files, relational database, e-mail, browsers and so on is explored in this paper, and resulting information will be represented with a unified extended iDM model, which can weak the logical deviations between different file formats.2. Semantic associations are constructed in pre-defined and user-defined ways. To improve the efficiency of queries in personal dataspace, semantic associations are used to expanse the query expressions. Semantic associations sre constructed by two ways, which means that system can create some associations beforehand, furthermore users can create new associations according to his demand to improve the query efficiency.The experimental results show that if the amount of data comes to a certain threshold, data extraction efficiency in the cloud is much higher, and more data more efficienct. At the same time, also it is proved that construction of semantic associations will greatly improve the effectiveness of information search in personal dataspace.