Now, we are experiencing the information explosion from web pages, emails, files, contacts, blogs, wikis, mobile SMS. . . , but we have limited time and energy to manage them. How to manage versatile, heterogeneous, personalized personal information efficiently so as to find the right information in time becomes a challenging research field. For example, It always costs much time for a person to locate a specific data items kept before. Personal data items have the following characters: large volume, distribute storage and evolving with time, which make it hard for a person to manage personal dataspace efficiently.
Corespace Framework on personal dataspace
The explosion of digital information makes Personal Dataspace Management (PDSMS) an important research area, and the characters of personal data, such as distribution, heterogeneous, and so on, bring it great challenges. Although there are some works attacking the problem, most of them ignore the importance of owner of PDS. The relation between owner and other objects is the root characteristic of PDS, and may play an important role in data operation of PDS. Based on the assumption, we propose a new concept Corespace . CoreSpace is a subspace of PDS which is composed of the objects with close relation to the owner during a period. CoreSpace framework makes it more efficiently for users to locate a certain object or backup specific files from PDS. The framework also explores many promising research topics on Personal DataSpace Management.
Pay-As-You-Go is a major feature that distinguishes Pay-As-You-Go from other data management systems. The ability of evolution measures how well the system improves its service quality as users invest more attention and experience into it. Capability of evolution labels the quality of the dataspace system to some extent.
As one of the first groups following dataspace research, we have been focusing on the topic of evolution. Our approach is to improve the system by automatic approach combined with user attention and feedback. This is an incremental process that may involve several kinds of user interaction.
Text data is ubiquitous. Management of string data in databases and information systems has taken on particular importance recently. Approximate search and matching is especially important as different data sources probably have different data quality and we can not make sure that the string data always keep the same when they refer to the same object in reality.
Approximate matching problem arises in several important applications such as extracting named entities ( e.g., people, location, product names) from web pages, identifying biological concepts from biomedical literature, implementing data cleaning on databases and answering user query on web (such as google ), etc. In these scenes, exact matching will not catch all answers we need because there may exist some errors in web pages, database records and user input as well. Besides, these applications require a high real-time performance for each query to be answered, especially for those applications adopting a Web-based service model. So its important to study efficient approximate matching problem.
Recently, we study the problem of approximate dictionary lookup: Given an input text string (documents) consisting of a sequence of tokens, identify all sub-strings that match with some string from a potentially large dictionary.
Users are free to create, modify schemas to their like, e.g., Contact, Event.Theyre also free to create and modify instances of each schema. Schemas and instances can be modified any time users want. Use RDF to support storage, we can make modification to schemas lightweight.
Content-based Association Construction and Utilization:
Association information is indispensable in dataspace systems, therefore the construction and utilization of association information is a crucial problem. In OrientSpace, we produce several kinds of associations by analyzing contents of resources (including text content and meta-data).We leverage the association information to support graph-based browse and query.
Selective Text Indexing:
Instead of using full-text indexing, we use the selective indexing technique to index contents. We use selective indexing approach based on the following reasons:
- Full-text takes up too much space, like Desktop Search Tools.
- Users normally want close related ones rather than everything that is related, which is the case for typical Web search.
- Actually, selective approach may be even faster.
Our approach is to improve the system by automatic approach combined with user attention and feedback. This is an incremental process that may involve several kinds of user interaction. We use a query-bridge based approach to realize the Pay-As-You-Go evolution.
- 2007.7-2009.12 Techniques on Model, Query and Index of Dataspace
Supported by the National High-Tech Research and Development Plan of China under grant number 2007AA01Z155
- 2003 Semantic Grid Project
Supported by the National Basic Research Program of China under grant number 2003CB317000
- Efficient Merging and Filtering Algorithms for Approximate String
United States Patent with patent no. 61/043,325
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