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131.
132.
分布式数据库中多元连接查询优化的研究 总被引:1,自引:0,他引:1
论文对分布式数据库中多元连接查询操作次序的确定问题提出了优化,通过引入收益代价比的概念,提出了一基于贪心算法的选择模型。通过该模型,可以得到理想的连接次序的选取方案。 相似文献
133.
Shengsheng Wang Dayou Liu Jie Liu 《通讯和计算机》2005,2(5):1-5
This paper focuses on spatial query optimization in distributed GIS. A new qualitative spatial relation model and its consistency problem solution which is composed of topology, direction, distance and size, are proposed. Research integrating the four aspects has not appeared before. A new method to deduce the constraints of spatial query is given, so it saves the query process time in distributed GIS. Finally, the methods and theories are applied to a distributed GIS project, and the experiment result is satisfactory. 相似文献
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135.
For efficient image retrieval, the image database should be processed to extract a representing feature vector for each member image in the database. A reliable and robust statistical image indexing technique based on a stochastic model of an image color content has been developed. Based on the developed stochastic model, a compact 12-dimensional feature vector was defined to tag images in the database system. The entries of the defined feature vector are the mean, variance, and skewness of the image color histogram distributions as well as correlation factors between color components of the RGB color space. It was shown using statistical analysis that the feature vector provides sufficient knowledge about the histogram distribution. The reliability and robustness of the proposed technique against common intensity artifacts and noise was validated through several experiments conducted for that purpose. The proposed technique outperforms traditional and other histogram based techniques in terms of feature vector size and properties, as well as performance. 相似文献
136.
The Web is a source of valuable information, but the process of collecting, organizing, and effectively utilizing the resources it contains is difficult. We describe CorpusBuilder, an approach for automatically generating Web search queries for collecting documents matching a minority concept. The concept used for this paper is that of text documents belonging to a minority natural language on the Web. Individual documents are automatically labeled as relevant or nonrelevant using a language filter, and the feedback is used to learn what query lengths and inclusion/exclusion term-selection methods are helpful for finding previously unseen documents in the target language. Our system learns to select good query terms using a variety of term scoring methods. Using odds ratio scores calculated over the documents acquired was one of the most consistently accurate query-generation methods. To reduce the number of estimated parameters, we parameterize the query length using a Gamma distribution and present empirical results with learning methods that vary the time horizon used when learning from the results of past queries. We find that our system performs well whether we initialize it with a whole document or with a handful of words elicited from a user. Experiments applying the same approach to multiple languages are also presented showing that our approach generalizes well across several languages regardless of the initial conditions. 相似文献
137.
Many continual range queries can be issued against data streams. To efficiently evaluate continual queries against a stream,
a main memory-based query index with a small storage cost and a fast search time is needed, especially if the stream is rapid.
In this paper, we study a CEI-based query index that meets both criteria for efficient processing of continual interval queries.
This new query index is an indirect indexing approach. It centres around a set of predefined virtual containment-encoded intervals, or CEIs. The CEIs are used to first decompose query intervals and then perform efficient search operations. The CEIs are
defined and labeled such that containment relationships among them are encoded in their IDs. The containment encoding makes
decomposition and search operations efficient; from the encoding of the smallest CEI containing a data point, the encodings
of other containing CEIs can be easily derived. Closed-form formulae for the bounds of the average index storage cost are
derived. Simulations are conducted to evaluate the effectiveness of the CEI-based query index and to compare it with alternative
approaches. The results show that the CEI-based query index significantly outperforms existing approaches in terms of both
storage cost and search time.
Kun-Lung Wu received the B.S. degree in electrical engineering from the National Taiwan University, Taipei, Taiwan, the M.S. and Ph.D.
degrees in computer science from the University of Illinois at Urbana–Champaign. He is with the IBM Thomas J. Watson Research
Center, currently a member of the Software Tools and Techniques Group. His current research interests include data streams,
continual queries, mobile computing, Internet technologies and applications, database systems and distributed and parallel
computing. He has published extensively and holds various patents in these areas.
Dr. Wu is a Senior Member of the IEEE Computer Society and a member of the ACM. He was an Associate Editor for the IEEE Transactions
on Knowledge and Data Engineering, 2000–2004. He was the general chair for the 3rd International Workshop on e-Commerce and
Web-Based Information Systems (WECWIS 2001). He has served as an organising and program committee member on various conferences.
He has received various IBM awards, including IBM Corporate Environmental Affair Excellence Award, Research Division Award
and Invention Achievement Awards. He received a best paper award from IEEE EEE 2004. He is an IBM Master Inventor.
Shyh-Kwei Chen received the B.S. degree in computer science and information engineering from National Taiwan University, Taipei, Taiwan,
in 1983, the M.S. degree in computer science from the University of Minnesota, Minneapolis, in 1987, and the Ph.D. degree
in computer science from University of Illinois at Urbana–Champaign, in 1994.
Dr. Chen has been with the IBM Thomas J. Watson Research Center, Yorktown Heights, New York since October 1994, where he is
currently a research staff member. His current research interests include XML, electronic commerce, business performance management,
data engineering and compilers. He is a member of the ACM, the IEEE and the IEEE Computer Society.
Philip S. Yu received the B.S. degree in electrical engineering from National Taiwan University, the M.S. and Ph.D. degrees in electrical
engineering from Stanford University, and the M.B.A. degree from New York University. He is with the IBM Thomas J. Watson
Research Center and is currently manager of the Software Tools and Techniques group. His research interests include data mining,
Internet applications and technologies, database systems, multimedia systems, parallel and distributed processing and performance
modelling. Dr. Yu has published more than 400 papers in refereed journals and conferences. He holds or has applied for more
than 250 US patents.
Dr. Yu is a Fellow of the ACM and a Fellow of the IEEE. He is an associate editor of ACM Transactions on Internet Technology.
He is a member of the IEEE Data Engineering steering committee and is also on the steering committee of IEEE Conference on
Data Mining. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001–2004), an editor and
advisory board member of IEEE Transactions on Knowledge and Data Engineering and also a guest coeditor of the special issue
on mining of databases. He had also served as an associate editor of Knowledge and Information Systems. In addition to serving
as program committee member on various conferences, he was the program cochair of the 11th International Conference on Data
Engineering, the 6th Pacific Area Conference on Knowledge Discovery and Data Mining, and the 9th ACM SIGMOD Workshop on Research
Issues in Data Mining and Knowledge Discovery, and the program chair of the 2nd International Workshop on Research Issues
on Data Engineering: Transaction and Query Processing, the PAKDD Workshop on Knowledge Discovery from Advanced Databases and
the 2nd International Workshop on Advanced Issues of E-Commerce and Web-based Information Systems. He served as the general
chair of the 14th International Conference on Data Engineering and the general cochair of the 2nd IEEE International Conference
on Data Mining. He has received several IBM honours, including two IBM Outstanding Innovation Awards, an Outstanding Technical
Achievement Award, two Research Division Awards and the 81st Plateau of Invention Achievement Awards. He received an Outstanding
Contributions Award from IEEE International Conference on Data Mining in 2003 and also an IEEE Region 1 Award for “promoting
and perpetuating numerous new electrical engineering concepts” in 1999. Dr. Yu is an IBM Master Inventor and was recognised
as one of the IBM's 10 top leading inventors in 1999. 相似文献
138.
139.
Information sources in the World Wide Web usually offer two different schemes to their users, an Interface Schema which the user can query and a Result Schema which the user can browse. Often the Interface Schema is more restricted than the Result Schema, moreover many sources offer keyword-search interfaces only. Thus query capabilities of such sources are very small and a useful integration into a mediator-based information system using query capabilities is almost impossible. We propose the Query Tunnelling architecture for the wrapping of these restricted web sources. Wrapping of sources by Query Tunneling hides restrictive query interfaces and makes such sources fully queryable based on their result schema. The process of Query Tunneling is divided into two main steps, Query Relaxation to make a higher order query suitable to a restricted interface and Result Restriction in order to filter the results using the original query. 相似文献
140.