海量數(shù)據(jù)存儲與全文檢索.pdf

1、江蘇科技大學碩士學位論文海量數(shù)據(jù)存儲與全文檢索姓名：苗帥申請學位級別：碩士專業(yè)：模式識別與智能系統(tǒng)指導教師：王衛(wèi)東2010-03-13Abstract III Abstract With the rapid development of China's naval vessels, the development of weapon systems and logistics support generated a lot of

2、 technical information and project management documents. Because of poor management of these documents, resulting in duplication of information about equipment and a huge waste of human and material resources. Establish

3、a secure, high availability, integrated management system of technical publications, which is to establish a sound mechanism for logistics support. In order to meet the actual needs for technical staff of naval vessels,

4、the thesis made more in-depth and systematic study on integrated management system of technical publications, so as to provide various, more accurate information. This thesis presents mass data storage environment, anal

5、yzes the current storage mode, and puts focus on object-oriented storage technology. Storage mode of the object has good scalability, high performance, cross-platform and secures data sharing capabilities, which makes it

6、 an ideal mass data storage choice. Second, according to software engineering development processes and user needs, the thesis described in detail feasibility analysis and demand analysis of integrated management system

7、 for technical publications. On the basis, the thesis design outline of the logic architecture and physical architecture of the system, meanwhile, detailed design the logical structure of function modules. Finally, each

8、module of the system was realized. In this thesis, based on the realization of the basic functions of the system, also full-text search of the system was optimized. These include: ⑴ For the full-text search technology, t

9、he thesis found the lack of existing technology about Chinese segmentation and made better the maximum matching algorithm; ⑵ With the base of inverted index, a full-text index model which is concerning incremental B+-Lis

10、ts is adopted. ⑶ In order to improve both precision and recall rate in information retrieva, This thesis proposes a new query optimization method based on analysis of local classification and genetic algorithm. The thesi

11、s uses analysis of local classification which is query expansion method to expand the query, and then uses genetic algorithms to reweight the query vector which is expanded and does experimental verification on the effec