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Many applications today need to manage data that is uncertain, such as information extraction (IE), data integration, sensor RFID networks, and scientific experiments. Top-k queries are often natural and useful in analyzing uncertain data in those applications. In this paper, we study the problem of answering top-k queries in a probabilistic framework from a state-of-the-art statistical IE model-semi-Conditional...
Information extraction (IE) is the problem of constructing a knowledge base from a corpus of text documents. In recent years, uncertain data applications have grown in importance in the large number of real-world applications, and IE as an uncertain data source. This paper investigated the uncertain data represent and presented a probabilistic framework from IE model that adapting principles of a...
To keep balance of global and local searching ability, and to overcome the slow convergence and prematurely of particle swarm optimization, a new algorithm that isolation niches embedded in particle swarm optimization (INPSO) is introduced. Solving the heavy traffic problem in existing condition is also worth considering. In this paper, the method of two-intersections traffic controlling is proposed...
Rough set theory is an efficient information processing tool used in the discovery of data dependencies. It evaluates the importance of attributes, discovers the patterns of data, reduces all redundant objects and attributes, and seeks the minimum subset of attributes. This paper presents a method for attribute reduction on combination of rough set and neighborhood systems. Neighborhood decision system...
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