电子商务Web数据库不精确查询方法研究
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摘要
近年来,随着World Wide Web的迅速膨胀,电子商务也随之得到迅速发展,以Web站点形式展示公司产品信息已成为电子商务交易的一个重要环节,这些Web站点通常由一个后台在线数据库支持,这些数据库称为电子商务Web数据库,电子商务Web数据库中的内容只能通过基于Web表单形式的查询接口来访问。目前,随着Internet的普遍应用和电子商务Web数据库所蕴含信息量的快速增长,访问电子商务Web数据库已成为大量普通用户获取商品信息的重要手段。
现有的电子商务Web数据库查询处理模式通常假定用户明确自己的查询意图并且仅支持严格查询匹配,但随着查询电子商务Web数据库的用户群从熟悉领域知识的专业人员逐渐扩展到需要即时满足的普通用户,这种精确查询处理模式已经不再适用于普通用户的查询习惯。这是因为,大量普通用户对电子商务Web数据库的结构和内容并不很了解,并且他们的查询意图本身可能就是模糊或不精确的,因此查询条件仅是他们查询意图的部分或近似描述,相应地,除了与查询要求完全匹配的查询结果之外,一些与查询要求相近的查询结果也可能是他们所需要的。在现有的电子商务Web数据库查询处理模式下,为获得更多与查询要求相近的信息,用户将不得不多次修改查询条件,直到获得满意的查询结果或丧失耐心放弃尝试为止。由此可见,对于那些希望不用手工多次调整查询条件就能从大规模电子商务Web数据库中一次性获取更多满足查询要求的大量普通用户来说,电子商务Web数据库不精确查询方法的研究具有非常重要的意义。
本文针对当前电子商务Web数据库查询中亟待解决的不精确查询问题进行了研究,从满足普通用户不精确查询需求的角度出发,按照不精确查询、不精确查询下的查询结果排序和查询结果top-k检索的研究顺序,提出一套行之有效的电子商务Web数据库不精确查询解决方案并给出具体的实现技术。本文的创新性研究成果主要有:
(1)为了解决电子商务Web数据库不精确查询问题,提出了基于近似函数依赖的不精确查询方法。对于一个Web数据库关系表,基于一致集的概念导出最大集,生成最小平凡函数依赖集,从而找出属性之间的近似函数依赖关系,进而提出了属性权重评估方法,最不重要属性上的基本查询条件最先放松并且放松程度最大;基于关联规则思想,提出了文本型属性值之间的相似度评估方法;根据属性权重、属性值之间的相似度和松弛阈值,提出了查询松弛重写算法。实验结果表明,提出的属性权重评估和文本型值之间的相似度评估算法是合理、稳定的;用户调查结果表明,提出的查询松弛方法具有较高的召回率,能够有效地处理电子商务Web数据库查询中的不精确查询问题。
(2)为了解决由不精确查询导致的电子商务Web数据库多查询结果问题,提出了基于概率信息检索(Probability Information Retrieval, PIR)模型的不精确查询结果排序方法。该方法在原始数据和查询日志基础上,利用概率信息检索模型评估查询未指定的属性值与指定的属性值以及用户偏好之间关联关系,进而构建查询结果元组打分函数并以此对查询结果进行排序。实验结果表明,提出的排序方法能够较好地满足用户需求和偏好,从而提高了电子商务Web数据库不精确查询结果排序的有效性。
(3)针对查询结果排序算法执行效率的高效性要求,提出了基于阈值(Threshould Algorithm, TA)算法的top-k检索方法。该方法利用PIR模型构建对应于数据库中每个不同属性值的单调打分函数,在此基础上提出了基于TA算法的top-k检索解决方法,给出了相应的元组列表创建、聚类和top-k元组检索算法。实验结果表明,元组列表聚类算法能够准确发现聚类中心,top-k检索算法具有较高的准确性并且在很大程度上缩短了执行时间,从而提高了大规模数据环境下top-k查询结果的检索效率。
In recent years, with the rapid expansion of the World Wide Web, E-commerce has developed fastly as well. To exhibit the product information by using web site is becoming a key for e-business. The web site is usually supported by an underlying online database, and this type of databases is referred to E-commerce Web database that is accessible only via web form based interface. Recently, with the universal use of the Internet and fast grows of the size of E-commerce Web databases, accessing the E-commerce Web database has become an important way for people to obtain the product information.
The existing E-commerce Web database query processing models have usually assumed that users know what they want and they supported only a strict query matching model. But with the change of the E-commerce databases users from professional users that known application area to lay users that demaning“instant gradification”, this precise query processing model is difficult to suitable for these users’query style. The users have insufficient knowledge about the structure and content of the database, and their query intentions are often vague or imprecise, thus the query conditions can just describe the query intentions approximately. Consequently, the items that are relevant to the query conditions are also needed by the users besides the query results that match the query conditions exactly. In order to obtain the relevant answer items, the user has to reformulate query conditions until she/he gets the satisfactory answers or gives up. It can be seen that the study on technologies of anwering imprecise queries of E-commerce Web databases is very important for the large number of users that need obtain the more relevant information from the large size E-commerce Web database in once time.
In this dissertation, the problems of imprecise query, which occur in searching the Web databases and standing in need of solutions, are investigated. Also, from the perspective of satisfying the users’imprecise query needs, an efficient imprecise query solution and corresponding technologies for the E-commerce Web database, in accordance with the order of imprecise query, query results ranking and top-k retrieval, are proposed. The main contributions of this dissertation are summarized as follows:
(i) To deal with the problem of imprecise query of the E-commerce Web database, an imprecise query answering approach, which is based on approximate functional dependence relationship, is proposed. Based on the concept of the agree set, the maximum set is exported, and the minimum nontrivial functional dependence sets are generated consequently, which is used to find the approximate dependence relations. By using the approximate dependence relations, the attribute weight measuring approach is proposed. The first attribute to be relaxed must be the least important attribute and has the maximum relaxation degree. Next, based on the ideas of association rules, the semantic similarity measuring methods of categorical attribute values is proposed. According to the relaxation threshold, attribute weight and semantic similarities of attribute values, an adaptive query relaxation rewriting algorithm is proposed. Results of experiments demonstrate that the performance and results of attribute weight and attribute values similarity measuring methods proposed are stable and reasonable respectively, the query relaxation method proposed has higher recall and can resolve the problem of imprecise query of the E-commerce Web database effectively as well.
(ii) To deal with the problem of many answers returned from an E-commerce Web database in response to an imprecise query, a query results ranking approach which is based on probabilistic information retrieval model, is proposed. Firstly, based on the database and query history, this approach takes advantage of the probabilistic information retrieval model to capture the correlations between the unspecified and specified attribute values as well as the user preferences, and then constructs the scoring function and ranks the query results according to the ranking scores. Results of experiments demonstrate that ranking method proposed can meet the user’s needs and preferences effectively, which means that the ranking quality of imprecise query results of E-commerce Web database can be improved as well.
(iii) In order to improve the efficiency of the query results ranking algorithm, a top-k retrieval method based on threshold algorithm, is proposed. Based on the monotonous scoring function of different attribute values constructed by PIR model, a TA-based top-k retrieval solution is proposed. Next, the algorithms of tuples’orders creating, tuples’orders clustering and top-k tuples retrieval, are presented. Results of experiments demonstrate that the tuple’s order clustering algorithm can find the cluster center correctly; the top-k retrieval method has higher precision and better efficiency, which can improve the retrieval efficiency of the large da taset environment.
现有的电子商务Web数据库查询处理模式通常假定用户明确自己的查询意图并且仅支持严格查询匹配,但随着查询电子商务Web数据库的用户群从熟悉领域知识的专业人员逐渐扩展到需要即时满足的普通用户,这种精确查询处理模式已经不再适用于普通用户的查询习惯。这是因为,大量普通用户对电子商务Web数据库的结构和内容并不很了解,并且他们的查询意图本身可能就是模糊或不精确的,因此查询条件仅是他们查询意图的部分或近似描述,相应地,除了与查询要求完全匹配的查询结果之外,一些与查询要求相近的查询结果也可能是他们所需要的。在现有的电子商务Web数据库查询处理模式下,为获得更多与查询要求相近的信息,用户将不得不多次修改查询条件,直到获得满意的查询结果或丧失耐心放弃尝试为止。由此可见,对于那些希望不用手工多次调整查询条件就能从大规模电子商务Web数据库中一次性获取更多满足查询要求的大量普通用户来说,电子商务Web数据库不精确查询方法的研究具有非常重要的意义。
本文针对当前电子商务Web数据库查询中亟待解决的不精确查询问题进行了研究,从满足普通用户不精确查询需求的角度出发,按照不精确查询、不精确查询下的查询结果排序和查询结果top-k检索的研究顺序,提出一套行之有效的电子商务Web数据库不精确查询解决方案并给出具体的实现技术。本文的创新性研究成果主要有:
(1)为了解决电子商务Web数据库不精确查询问题,提出了基于近似函数依赖的不精确查询方法。对于一个Web数据库关系表,基于一致集的概念导出最大集,生成最小平凡函数依赖集,从而找出属性之间的近似函数依赖关系,进而提出了属性权重评估方法,最不重要属性上的基本查询条件最先放松并且放松程度最大;基于关联规则思想,提出了文本型属性值之间的相似度评估方法;根据属性权重、属性值之间的相似度和松弛阈值,提出了查询松弛重写算法。实验结果表明,提出的属性权重评估和文本型值之间的相似度评估算法是合理、稳定的;用户调查结果表明,提出的查询松弛方法具有较高的召回率,能够有效地处理电子商务Web数据库查询中的不精确查询问题。
(2)为了解决由不精确查询导致的电子商务Web数据库多查询结果问题,提出了基于概率信息检索(Probability Information Retrieval, PIR)模型的不精确查询结果排序方法。该方法在原始数据和查询日志基础上,利用概率信息检索模型评估查询未指定的属性值与指定的属性值以及用户偏好之间关联关系,进而构建查询结果元组打分函数并以此对查询结果进行排序。实验结果表明,提出的排序方法能够较好地满足用户需求和偏好,从而提高了电子商务Web数据库不精确查询结果排序的有效性。
(3)针对查询结果排序算法执行效率的高效性要求,提出了基于阈值(Threshould Algorithm, TA)算法的top-k检索方法。该方法利用PIR模型构建对应于数据库中每个不同属性值的单调打分函数,在此基础上提出了基于TA算法的top-k检索解决方法,给出了相应的元组列表创建、聚类和top-k元组检索算法。实验结果表明,元组列表聚类算法能够准确发现聚类中心,top-k检索算法具有较高的准确性并且在很大程度上缩短了执行时间,从而提高了大规模数据环境下top-k查询结果的检索效率。
In recent years, with the rapid expansion of the World Wide Web, E-commerce has developed fastly as well. To exhibit the product information by using web site is becoming a key for e-business. The web site is usually supported by an underlying online database, and this type of databases is referred to E-commerce Web database that is accessible only via web form based interface. Recently, with the universal use of the Internet and fast grows of the size of E-commerce Web databases, accessing the E-commerce Web database has become an important way for people to obtain the product information.
The existing E-commerce Web database query processing models have usually assumed that users know what they want and they supported only a strict query matching model. But with the change of the E-commerce databases users from professional users that known application area to lay users that demaning“instant gradification”, this precise query processing model is difficult to suitable for these users’query style. The users have insufficient knowledge about the structure and content of the database, and their query intentions are often vague or imprecise, thus the query conditions can just describe the query intentions approximately. Consequently, the items that are relevant to the query conditions are also needed by the users besides the query results that match the query conditions exactly. In order to obtain the relevant answer items, the user has to reformulate query conditions until she/he gets the satisfactory answers or gives up. It can be seen that the study on technologies of anwering imprecise queries of E-commerce Web databases is very important for the large number of users that need obtain the more relevant information from the large size E-commerce Web database in once time.
In this dissertation, the problems of imprecise query, which occur in searching the Web databases and standing in need of solutions, are investigated. Also, from the perspective of satisfying the users’imprecise query needs, an efficient imprecise query solution and corresponding technologies for the E-commerce Web database, in accordance with the order of imprecise query, query results ranking and top-k retrieval, are proposed. The main contributions of this dissertation are summarized as follows:
(i) To deal with the problem of imprecise query of the E-commerce Web database, an imprecise query answering approach, which is based on approximate functional dependence relationship, is proposed. Based on the concept of the agree set, the maximum set is exported, and the minimum nontrivial functional dependence sets are generated consequently, which is used to find the approximate dependence relations. By using the approximate dependence relations, the attribute weight measuring approach is proposed. The first attribute to be relaxed must be the least important attribute and has the maximum relaxation degree. Next, based on the ideas of association rules, the semantic similarity measuring methods of categorical attribute values is proposed. According to the relaxation threshold, attribute weight and semantic similarities of attribute values, an adaptive query relaxation rewriting algorithm is proposed. Results of experiments demonstrate that the performance and results of attribute weight and attribute values similarity measuring methods proposed are stable and reasonable respectively, the query relaxation method proposed has higher recall and can resolve the problem of imprecise query of the E-commerce Web database effectively as well.
(ii) To deal with the problem of many answers returned from an E-commerce Web database in response to an imprecise query, a query results ranking approach which is based on probabilistic information retrieval model, is proposed. Firstly, based on the database and query history, this approach takes advantage of the probabilistic information retrieval model to capture the correlations between the unspecified and specified attribute values as well as the user preferences, and then constructs the scoring function and ranks the query results according to the ranking scores. Results of experiments demonstrate that ranking method proposed can meet the user’s needs and preferences effectively, which means that the ranking quality of imprecise query results of E-commerce Web database can be improved as well.
(iii) In order to improve the efficiency of the query results ranking algorithm, a top-k retrieval method based on threshold algorithm, is proposed. Based on the monotonous scoring function of different attribute values constructed by PIR model, a TA-based top-k retrieval solution is proposed. Next, the algorithms of tuples’orders creating, tuples’orders clustering and top-k tuples retrieval, are presented. Results of experiments demonstrate that the tuple’s order clustering algorithm can find the cluster center correctly; the top-k retrieval method has higher precision and better efficiency, which can improve the retrieval efficiency of the large da taset environment.
引文
1. Meng, X F, Ma, Z M, and Yan L. Answering approximate queries over autonomous web databases [C], Proceedings of the 18th International World Wide Web Conference, 2009, 2021-1030.
2. Kaplan S. Cooperative aspects of database interactions [J], Artificial Intelligence, 1982, 19(2): 65-87.
3. Motro A. Flex: a tolerant and cooperative user interface databases [J], IEEE Transactions on Knowledge and Data Engineering, 1990, 2(2): 231-246.
4. Gaasterland T. Cooperative answering through controlled query relaxation [J], IEEE Expert, 1997, 12(5): 48-59.
5. Godfrey P. Minimization in cooperative response to failing database queries [J], International Journal of Cooperative Information Systems, 1997, 6(2): 95-149.
6. Muslea I. Machine learning for online query relaxation [C], Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining, 2004, 246-255.
7. Muslea I and Lee T J. Online query relaxation via Bayesian causal structures discovery [C], Proceedings of the 20th Artificial Intelligence Conference, 2005, 831-836.
8. Friedman N, Goldszmidt M, and Lee T J. Bayesian network classification with continuous attributes: getting the best of both discretization and parametric fitting [C], Proceedings of the 15th International Conference on Machine Learning, 1998, 179-187.
9. Zhou X, Gaugaz J, Balke W T, Nejdl W. Query relaxation using malleable schemas [C], Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, 2007, 815-818.
10. Heer J, Agrawala M, Willett W. Generalized selection via interactive query relaxation [C], Proceedings of the ACM Conference on Human Factors in Computing Systems, 2008, 959-968.
11. Cohen S and Shiloach M. Flexible XML querying using skyline semantics [C], Proceedings of the International Conference on Data Engineering, 2009, 553-564.
12. Tahani V. A comceptual framework for fuzzy querying processing: a step toward very intelligent databases systems [J], Information Processing Management, 1997, 13: 289-303.
13. Kacprzyk J and Prade H. Terminological difficulties in fuzzy set theory—the case of:
intuitionistic fuzzy sets [J], Fuzzy Sets and Systems, 1986, 8(2): 24-32.
14. Bosc P and Pivert O. SQLf: a relational database language for fuzzy querying [J], IEEE Transactions on Fuzzy Systems, 1995, 3(1): 1-17.
15. Bosc P, Hadjali A, and Pivert O. Relaxation paradigm in a flexible querying context [C], Proceedings of the 7th International Conference on Flexible Query Answering Systems, 2006, 4027, 39-50.
16. Bosc P, Hadjali A, and Pivert O. About overabundant answers to flexible queries [C], Proceedings of the 11st International Conference on Information and Processing and Management of Uncertainty in Knowledge-based System, 2006, 2221-2228.
17. Bosc P, Hadjali A, and Pivert O. Weakening of fuzzy relational queries: an absolute proximity relation-based approach [J], Journal of Mathware Soft Computing, 2007, 14(1): 35-55.
18. Chen S M and Jong W T. Fuzzy query translation for relational database systems [J], IEEE Transactions on Systems, Man Cyb-Part B: Cybernetics, 1997, 27(4): 714-721.
19. Ma Z M and Yan Li. Generalization of strategies for fuzzy query translation in classical relational databases [J], Information and Software Technology, 2007, 49(2):172-180.
20. Meng X F and Ma Z M. A knowledge-based approach for answering fuzzy queries over relational databases [C], Proceedings of the 12th International Conference on Knowledge-based and Intelligent Information and Engineering Systems, 2008, 5178, 623-630.
21. Polyzotis N, Garofalakis M, and Ioannidis Y. Approximate XML query answers [C], Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, 2004, 263-274.
22. Amer-Yahia S, Curtmola E, and Deutsch A. Flexible and efficient XML search with complex full-text predicates [C], Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, 2006, 575-586.
23. Nasser T, Alhajj R, Ridley M. Flexible approach for representing object oriented databases in XML format [C], Proceedings the iiWAS International Conference, 2008, 430-433.
24.衡星辰,覃征,邵利平,曹玉辉,高洪江.基于两阶段查询重写的XML近似查询算法[J],电子学报, 2007, 35 (7): 1271-1278.
25. Abiteboul S, Manolescu I, Polyzotis N, Preda N, and Sun C. XML processing in DHTnetworks [C], Proceedings of the 24th International Conference on Data Engineering, 2008, 606-615.
26. Koloniari G and Pitoura E. Distributed structural relaxation of XPath queries [C], Proceedings of the 25th International Conference on Data Engineering, 2009, 524-540.
27. Damiani E, Marrara S, and Pasi G. A flexible extension of Xpath to improve XML querying [C], Proceedings of the Proceedings of the 31st International ACM SIGIR conference on research and development in Information Retrieval, 2008, 849-850.
28. Huang H, Liu C F, and Zhou X F. Computing relaxed answers on RDF databases [C], Proceedings of the 9th International Conference on Web Information Systems Engineering, 2008, 163-175.
29. Hurtado1C A, Poulovassilis A, and Wood P T. Query relaxation in RDF [J], Journal on Data Semantics X, 2008, 4900: 31-61.
30. Grossman D and Frieder O. Information retrieval-algorithms and heuristics [M], Berlin: Springer, 2004.
31. Wu H C, Luk R W P and Wong K F. Probability ranking principle via optimal expected rank [C], Proceedings of the 30th International ACM SIGIR conference on research and development in Information Retrieval, 2007, 713-714.
32. Mechmache F Z, Boughanem M and Alimazighi Z. Possibility and necessity measures for relevance assessment [C], Proceedings of the 16th ACM Conference on Information and Knowledge Management, 2007, 155-162.
33. Metzler D. Automatic feature selection in the markov random field model for information retrieval [C], Proceedings of the 16th ACM Conference on Information and Knowledge Management, 2007, 253-262.
34. Motro A. Vague: A user interface to relational databases that permits vague queries [J] ACM Transactions on Information Systems, 1988, 6(3): 187–214.
35. Ortega-Binderberger M, Chakrabarti K, and Mehrotra S. An approach to integrating query refinement in SQL [C], Proceedings of the 8th International Conference on Extending Database Technology, 2002, 15-33.
36. Shen X H, Tan B and Zhai C X. Context-sensitive information retrieval using implicit feedback [C], Proceedings of the 14th ACM Conference on Information and Knowledge Management, 2005, 43-50.
37. Limbu D K, Connor A, Pears R and MacDonell S. Contextual relevance feedback in web information retrieval [C], Proceedings of the 15th ACM Conference on Information and Knowledge Management, 2006, 138–143.
38. Kieβling W. Foundations of preferences in database systems [C], Proceedings of the 28th International Conference on Very Large Data Bases, 2002, 311-322.
39. Chomicki J. Preference formulas in relational queries [J], ACM Transactions on Database Systems, 2003, 28(4): 427-466.
40. Agrawal R and Wimmers E L. A framework for expressing and combining preferences [C], Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 2000, 297-306.
41. Hristidis V, Koudas N, and Papakonstantinou Y. PREFER: A system for the efficient execution of multiparametric ranked queries [C], Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, 2001, 259-270.
42. Koutrika G and Ioannidis Y E. Personalization of queries in database systems [C], Proceedings of the 20th International Conference on Data Engineering, 2004, 597-608.
43. Koutrika G and Ioannidis Y. Constrained optimalities in query personalization [C], Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, 2005, 73-84.
44. Koutrika G and Ioannidis Y E. Personalized queries under a generalized preference model [C], Proceedings of the 21st International Conference on Data Engineering, 2005, 841-852.
45. Stefanidis K, Pitoura E, Vassiliadis P. Adding context to preferences [C], Proceedings of the 23rd International Conference on Data Engineering, 2007, 846-855.
46. Chen Z Y and Li T. Addressing diverse user preferences in SQL-Query-Result navigation [C], Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, 2007, 641-652.
47. Agrawal S, Chaudhuri S, Das G, and Gionis A. Automated ranking of database query results [J], ACM Transactions on Database Systems, 2003, 28(2): 140-174.
48. Su W, Wang J, Huang Q, and Lochovsky F. Query result ranking over e-commerce web databases [C], Proceedings of the 15th ACM Conference on Information and Knowledge Management, 2006, 575-584.
49. Ughetto L, Voglozin W A, Mouaddib N. Database querying with personalized vocabularyusing data summaries [J], Fuzzy Sets and Systems, 2008, 159: 2030-2046.
50. Fakas G. Automated generation of object summaries from relational databases: a novel keyword searching paradigm [C], Proceedings of the DBRank Workshop, 2008, 564-567.
51. Fakas G, Cai Z. Ranking of object summaries [C], Proceedings of the International Conference on Data Engineering, 2009, 1580-1583.
52. Fagin R. Combining fuzzy information from multiple systems [C], Proceedings of the 15th Symposium on Principles of Database Systems, 1996, 216-226.
53. Fagin R and Wimmers E. Incorporating user preferences in multimedia queries [C], Proceedings of 6th International Conference on Database Theory, 1997, 247-261.
54. Fagin R. Fuzzy queries in multimedia database systems [C], Proceedings of the 17th Symposium on Principles of Database Systems, 1998, 1-10.
55. Guntzer U. Optimizing multi-feature queries in image databases [C], Proceedings of the 29th International Conference on Very Large Data Bases, 2003, 87-98.
56. Xin D, Han J, Chang K C. Progressive and selective merge: computing top-k with ad-hoc ranking functions [C], Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, 2007, 103–114.
57. Sarawagi S, Deshpande V, Kasliwal S. Efficient top-k count queries over imprecise duplicates [C], Proceedings of the International Conference on Extending Database Technology, 2009, 450-461.
58. Marian L G and Bruno N. Evaluating top-k queries over web accessible sources [J], ACM Transactions on Database Systems, 2004, 29(2): 319-362.
59. Xu J, Li H. AdaRank: a boosting algorithm for information retrieval [C], Proceedings of the ACM SIGIR International Conference, 2007, 392-398.
60. Bruno L G N and Chaudhuri S. Top-k selection queries over relational databases: mapping strategies and performance evaluation [J], ACM Transactions on Database Systems, 2002, 27(2): 153-187.
61. Ilyas A E I and Aref W. Supporting top-k queries in relational databases [J], VLDB Journal, 2004, 13(3): 207-221.
62. Chang S K. Minimal probing: supporting expensive predicates for top-k queries [C], Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, 2002, 346-357.
63. Hua M, Pei J, Ada W, Fu C, Lin X M, and Leung H F. Top-k typicality queries and efficient query answering methods on large databases [J], VLDB Journal, 2009, 18: 809-835
64. Bruno N and Wang H. The threshold algorithm: from middleware systems to the relational engine [J], IEEE Transactions on Knowledge and Data Engineering, 2007, 19(4): 523-537.
65. Yi K, Li F F, Kollios G, and Srivastava D. Efficient processing of top-k queries in uncertain databases [C], Proceedings of the 24th International Conference on Data Engineering, 2008, 1406-1408.
66. Yi K, Li F F, Kollios G, and Srivastava D. Efficient processing of top-k queries in uncertain databases with x-relations [J], IEEE Transactions on Knowledge and Data Engineering, 2008, 20(12): 1669-1682.
67. Hua M, Pei J, Zhang W J, and Lin X M. Efficiently answering probabilistic threshold top-k queries on uncertain data [C], Proceedings of the 24th International Conference on Data Engineering, 2008, 1403-1405.
68. Hua M, Pei J, Zhang W J, and Lin X M. Ranking queries on uncertain data: a probabilistic threshold approach [C], Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, 2008, 673-686.
69. Soliman M A, Ilyas I F, and Chang C C. Top-k query processing in uncertain databases [C], Proceedings of the 23rd International Conference on Data Engineering, 2007, 896-905.
70. Angel A, Chaudhuri S, and Das G. Ranking objects based on relationships and fixed associations [C], Proceedings of the 12th International Conference on Extending Database Technology, 2009, 910-921.
71. Chakrabarti K, Ganti V, Han J, and Xin D. Ranking objects based on relationships [C], Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, 2006, 371-382.
72. Deutch D and Milo T. Evaluating top-k queries over business processes [C], Proceedings of the 25th International Conference on Data Engineering, 2009, 1195-1198.
73. Deutch D and Milo T. Top-k projection queries for probabilistic business processes [C], Proceedings of the 4th International Conference on Database Theory Conference, 2009, 239-251.
74. Marian A, Amer-Yahia S, Koudas N, and Srivastava D. Adaptive processing of top-k queries in XML [C], Proceedings of the 21st International Conference on Data Engineering, 2005,162-173.
75. Chang L J, Yu J X, Qin L. Query ranking in probabilistic XML data [C], Proceedings of the 12th International Conference on Extending Database Technology, 2009, 156-167.
76. Li L, Lee M L, Hsu W, and Zhen H. A prufer based approach to process top-k queries in XML [C], Proceedings of the 20th International Conference on Database and Expert Systems Applications, 2009, 348-355.
77. Li G L, Li C, Feng J H. SAIL: Structure-aware indexing for effective and progressive top-k keyword search over XML documents [J], Information Sciences, 2009, 179: 3745-3762.
78. Tsaparas P, Palpanas T, Koudas N, and Srivastava D. Ranked join indices [C], Proceedings of the International Conference on Data Engineering, 2003, 277-288.
79. Lian X, Chen L. Probabilistic group nearest neighbor queries in uncertain databases [J], IEEE Transactions on Knowledge and Data Engineering, 2008, 20(6): 809-824.
80.萨师煊,王珊.数据库系统概论[M],北京:高等教育出版社.
81. Ullman J. Principles of database systems [M], Rockville, Md.: Computer Science Press (the 2nd edition), 1982.
82. Calders T, Goethals B, Jaroszewicz S. Mining rank-correlated sets of numerical attributes [C], Proceedings of the International Conference on Knowledge and Data Discovery, 2006, 96-105.
83. Maier D. The theory of relational databases [M], Rockville, Md.: Computer Sicence Press, 1983.
84. Armstrong W W. Dependency structures of data base relationships [C], Proceedings of the International Federation for Information Processing, 1974, 108-136.
85. Huhtala Y, K?rkk?inen J, Porkka P, and Toivonen H. Tane: an efficient algorithm for discovering functional and approximate dependencies [J], The Computer Journal, 1999, 42(2): 100-111.
86. Zaniolo C. Analysis and design of relational schemata for database system [D], , Los Angeles: Ph.D. Thesis, University of California, 1976.
87. Agrawal R, Imielinski T, and Swami A N. Mining association rules between sets of items in large databases [C], Proceedings of the 12th ACM SIGMOD International Conference on Management of Data, 1993, 207-216.
88. Agrawal R and Srikant R. Fast algorithms for mining association rules [C], Proceedings ofthe 20th International Conference on Very Large Data Bases, 1994, 487-499.
89. Piatetsky-Shapiro G and Connell C. Accurate estimation of the number of tuples satisfying a condition [C], Proceedings of the 1984 ACM SIGMOD International Conference on Management of Data, 1984, 256-276.
90. http://autos.yahoo.com
91. http://realestate.msn.com
92. Hachani N, Ounelli H. A knowledge-based approach for database flexible querying [C]. Proceedings of the 17th International Conference on Database and Expert Systems Applications, 2006, 420-424.
93.张守志,施伯乐.一种发现函数依赖集的方法及应用[J],软件学报, 2003, 14 (10): 1692-1696.
94. Beeri, C., Dowd, M., Fagin, R. and Statman, R. On the structure of Armstrong relations for functional dependencies [J], Journal of the ACM, 1984, 31(1): 30-46.
95. Maier D. The theory of relational databases [M], Rockville, Md.: Computer Science Press, 1983.
96. Lopes S, Petit J M, and Lakhal L. Efficient discovery of functional dependencies and Armstrong relations [C], Proceedings of the International Conference on Extending Database Technology, 2000, 350-364.
97. Huhtala Y, Kakkainen J, Porkka P, and Toivonen H. Tane: An efficient algorithm for discovering functional and approximate dependencies [J], The Computer Journal, 1999, 42(2): 100-111.
98. Huhtala Y, Krkkinen J, Porkka P. Efficient discovery of functional and approximate dependencies using partitions [C], Proceedings of the International Conference on Data Engineering, 1998, 392-401.
99. Beeri, C., Dowd, M., Fagin, R. and Statman, R. On the structure of Armstrong relations for functional dependencies [J], Journal of the ACM, 1984, 31(1): 30-46.
100. Beeri C, Fagin R, Howard J H. A complete axiomatization for functional and multivalued dependencies [C], Proceeding s of the 1977 ACM SIGMOD International Conference on Management of Data, 1977, 82-93.
101. Watson G A. An algorithm for the single facility location problem using the Jaccard metric [J]. SIAM Journal on Scientific and Statistical Computing, 1983 4: 748–756.
102. Rui Y, Huang T, and Merhotra S. Content-based image retrieval with relevance feedback in MARS [C], Proceedings of IEEE International Conference on Image Processing, 1997, 815-818.
103. Wu L. Faloutsos C, Sycara K, and Payne T. FALCON: feedback adaptive loop for content-based retrieval [C], Proceedings of the International Conference on Very Large Databases, 2000, 297-306.
104. Kieβling W, Kostler G. Preference SQL-Design, implementation, experiences [C], Proceedings of the International Conference on Very Large Databases, 2002, 990-1001.
105. Agrawal R and Rantzau R. Context-sensitive ranking [C], Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, 2006, 383-394.
106. Sparck J, Walker K S, and Robertson S E. A probabilistic model of information retrieval: Development and comparative experiments—Part 1 [J]. Inf. Process. Man, 2000, 36(6): 779–808.
107. Sparck J, Walker K S, and Robertson S E. A probabilistic model of information retrieval: Development and comparative experiments—Part 2 [J]. Inf. Process. Man. 2000, 36(6): 809–840.
108. Yu C T and Meng W. Principles of database query processing for advanced applications [M], Morgan Kaufmann, San Francisco, CA, 1998.
109. Baeza-yates R and Riberiro-neto B. Modern information retrieval [M], Addison-Wesley, Reading, MA, 1999.
110. Grossman D and Frieder O. Information retrieval—algorithms and heuristics [M], Springer, Berlin, Germany, 2004.
111. Meng X F, Ma Z M, and Yan L. Providing flexible queries over web databases [C], Proceedings of the 12th International Conference on Knowledge-based and Intelligent Information & Engineering Systems, 2008, 5178, 601-606.
112. Chaudhuri S, Das G, and Hristidis V. Probabilistic ranking of database query results [C]. Proceedings of the International Conference on Very Large Data Bases, 2004, 888-899.
113. Ilyas I F, Beskales G, and Soliman M A. A survey of top-k query processing techniques in relational database systems [J], ACM Computing Surveys, 2008, 40(4): 1101-1158.
114. Gower J C. Properties of euclidean and non-euclidean distance matrices [J], Linear AlgebraAppl, 1985, 67: 81-97.
115. Chrobak M., Keynon C, and Young N. The reverse greedy algorithm for the metric k-median problem [J], Information Processing Letters, 2005, 97(2): 68-72.
116.潘锐.设施选址与K-中间点问题的复杂性与近似算法[D],山东大学博士学位论文, 2007.
6.李昕,刘建辉.基于PKI的电子商务信息安全性研究.中国管理信息化, 2007, 114: 68-70
7.李昕,刘建辉.一种电子商务信息安全保障机制.商场现代化, 2007, 494:174.
8.李昕,刘建辉.一种确保电子商务信息机密性的改进算法.中国管理信息化, 2007, 120: 80-81.
9.李昕,刘建辉.基于SHA-1的电子商务数字签名方法研究.中国管理信息化, 2007, 126: 66-67.
10.李昕,刘建辉. XML数字签名在电子商务中的应用研究.中国管理信息化, 2007, 124: 72-74.
11.李昕,刘建辉.基于RSA的XML不可否认签名方法研究.计算机应用研究, 2008, 1900-1903.
12.李昕,张军.基于模糊综合评判的电子商务系统安全评估方法研究.计算机工程与设计, 2008, 4002-4005.
13.李昕,孟祥福,刘玥.基于WLAN的酒店无线点菜系统的设计与实现.微计算机信息, 2006, 17-18.
14.李昕,佟绍成,张军.基于嵌入式的温湿度模糊控制系统的实现.微计算机信息, 2008, 35-37.
2. Kaplan S. Cooperative aspects of database interactions [J], Artificial Intelligence, 1982, 19(2): 65-87.
3. Motro A. Flex: a tolerant and cooperative user interface databases [J], IEEE Transactions on Knowledge and Data Engineering, 1990, 2(2): 231-246.
4. Gaasterland T. Cooperative answering through controlled query relaxation [J], IEEE Expert, 1997, 12(5): 48-59.
5. Godfrey P. Minimization in cooperative response to failing database queries [J], International Journal of Cooperative Information Systems, 1997, 6(2): 95-149.
6. Muslea I. Machine learning for online query relaxation [C], Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining, 2004, 246-255.
7. Muslea I and Lee T J. Online query relaxation via Bayesian causal structures discovery [C], Proceedings of the 20th Artificial Intelligence Conference, 2005, 831-836.
8. Friedman N, Goldszmidt M, and Lee T J. Bayesian network classification with continuous attributes: getting the best of both discretization and parametric fitting [C], Proceedings of the 15th International Conference on Machine Learning, 1998, 179-187.
9. Zhou X, Gaugaz J, Balke W T, Nejdl W. Query relaxation using malleable schemas [C], Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, 2007, 815-818.
10. Heer J, Agrawala M, Willett W. Generalized selection via interactive query relaxation [C], Proceedings of the ACM Conference on Human Factors in Computing Systems, 2008, 959-968.
11. Cohen S and Shiloach M. Flexible XML querying using skyline semantics [C], Proceedings of the International Conference on Data Engineering, 2009, 553-564.
12. Tahani V. A comceptual framework for fuzzy querying processing: a step toward very intelligent databases systems [J], Information Processing Management, 1997, 13: 289-303.
13. Kacprzyk J and Prade H. Terminological difficulties in fuzzy set theory—the case of:
intuitionistic fuzzy sets [J], Fuzzy Sets and Systems, 1986, 8(2): 24-32.
14. Bosc P and Pivert O. SQLf: a relational database language for fuzzy querying [J], IEEE Transactions on Fuzzy Systems, 1995, 3(1): 1-17.
15. Bosc P, Hadjali A, and Pivert O. Relaxation paradigm in a flexible querying context [C], Proceedings of the 7th International Conference on Flexible Query Answering Systems, 2006, 4027, 39-50.
16. Bosc P, Hadjali A, and Pivert O. About overabundant answers to flexible queries [C], Proceedings of the 11st International Conference on Information and Processing and Management of Uncertainty in Knowledge-based System, 2006, 2221-2228.
17. Bosc P, Hadjali A, and Pivert O. Weakening of fuzzy relational queries: an absolute proximity relation-based approach [J], Journal of Mathware Soft Computing, 2007, 14(1): 35-55.
18. Chen S M and Jong W T. Fuzzy query translation for relational database systems [J], IEEE Transactions on Systems, Man Cyb-Part B: Cybernetics, 1997, 27(4): 714-721.
19. Ma Z M and Yan Li. Generalization of strategies for fuzzy query translation in classical relational databases [J], Information and Software Technology, 2007, 49(2):172-180.
20. Meng X F and Ma Z M. A knowledge-based approach for answering fuzzy queries over relational databases [C], Proceedings of the 12th International Conference on Knowledge-based and Intelligent Information and Engineering Systems, 2008, 5178, 623-630.
21. Polyzotis N, Garofalakis M, and Ioannidis Y. Approximate XML query answers [C], Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, 2004, 263-274.
22. Amer-Yahia S, Curtmola E, and Deutsch A. Flexible and efficient XML search with complex full-text predicates [C], Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, 2006, 575-586.
23. Nasser T, Alhajj R, Ridley M. Flexible approach for representing object oriented databases in XML format [C], Proceedings the iiWAS International Conference, 2008, 430-433.
24.衡星辰,覃征,邵利平,曹玉辉,高洪江.基于两阶段查询重写的XML近似查询算法[J],电子学报, 2007, 35 (7): 1271-1278.
25. Abiteboul S, Manolescu I, Polyzotis N, Preda N, and Sun C. XML processing in DHTnetworks [C], Proceedings of the 24th International Conference on Data Engineering, 2008, 606-615.
26. Koloniari G and Pitoura E. Distributed structural relaxation of XPath queries [C], Proceedings of the 25th International Conference on Data Engineering, 2009, 524-540.
27. Damiani E, Marrara S, and Pasi G. A flexible extension of Xpath to improve XML querying [C], Proceedings of the Proceedings of the 31st International ACM SIGIR conference on research and development in Information Retrieval, 2008, 849-850.
28. Huang H, Liu C F, and Zhou X F. Computing relaxed answers on RDF databases [C], Proceedings of the 9th International Conference on Web Information Systems Engineering, 2008, 163-175.
29. Hurtado1C A, Poulovassilis A, and Wood P T. Query relaxation in RDF [J], Journal on Data Semantics X, 2008, 4900: 31-61.
30. Grossman D and Frieder O. Information retrieval-algorithms and heuristics [M], Berlin: Springer, 2004.
31. Wu H C, Luk R W P and Wong K F. Probability ranking principle via optimal expected rank [C], Proceedings of the 30th International ACM SIGIR conference on research and development in Information Retrieval, 2007, 713-714.
32. Mechmache F Z, Boughanem M and Alimazighi Z. Possibility and necessity measures for relevance assessment [C], Proceedings of the 16th ACM Conference on Information and Knowledge Management, 2007, 155-162.
33. Metzler D. Automatic feature selection in the markov random field model for information retrieval [C], Proceedings of the 16th ACM Conference on Information and Knowledge Management, 2007, 253-262.
34. Motro A. Vague: A user interface to relational databases that permits vague queries [J] ACM Transactions on Information Systems, 1988, 6(3): 187–214.
35. Ortega-Binderberger M, Chakrabarti K, and Mehrotra S. An approach to integrating query refinement in SQL [C], Proceedings of the 8th International Conference on Extending Database Technology, 2002, 15-33.
36. Shen X H, Tan B and Zhai C X. Context-sensitive information retrieval using implicit feedback [C], Proceedings of the 14th ACM Conference on Information and Knowledge Management, 2005, 43-50.
37. Limbu D K, Connor A, Pears R and MacDonell S. Contextual relevance feedback in web information retrieval [C], Proceedings of the 15th ACM Conference on Information and Knowledge Management, 2006, 138–143.
38. Kieβling W. Foundations of preferences in database systems [C], Proceedings of the 28th International Conference on Very Large Data Bases, 2002, 311-322.
39. Chomicki J. Preference formulas in relational queries [J], ACM Transactions on Database Systems, 2003, 28(4): 427-466.
40. Agrawal R and Wimmers E L. A framework for expressing and combining preferences [C], Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 2000, 297-306.
41. Hristidis V, Koudas N, and Papakonstantinou Y. PREFER: A system for the efficient execution of multiparametric ranked queries [C], Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, 2001, 259-270.
42. Koutrika G and Ioannidis Y E. Personalization of queries in database systems [C], Proceedings of the 20th International Conference on Data Engineering, 2004, 597-608.
43. Koutrika G and Ioannidis Y. Constrained optimalities in query personalization [C], Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, 2005, 73-84.
44. Koutrika G and Ioannidis Y E. Personalized queries under a generalized preference model [C], Proceedings of the 21st International Conference on Data Engineering, 2005, 841-852.
45. Stefanidis K, Pitoura E, Vassiliadis P. Adding context to preferences [C], Proceedings of the 23rd International Conference on Data Engineering, 2007, 846-855.
46. Chen Z Y and Li T. Addressing diverse user preferences in SQL-Query-Result navigation [C], Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, 2007, 641-652.
47. Agrawal S, Chaudhuri S, Das G, and Gionis A. Automated ranking of database query results [J], ACM Transactions on Database Systems, 2003, 28(2): 140-174.
48. Su W, Wang J, Huang Q, and Lochovsky F. Query result ranking over e-commerce web databases [C], Proceedings of the 15th ACM Conference on Information and Knowledge Management, 2006, 575-584.
49. Ughetto L, Voglozin W A, Mouaddib N. Database querying with personalized vocabularyusing data summaries [J], Fuzzy Sets and Systems, 2008, 159: 2030-2046.
50. Fakas G. Automated generation of object summaries from relational databases: a novel keyword searching paradigm [C], Proceedings of the DBRank Workshop, 2008, 564-567.
51. Fakas G, Cai Z. Ranking of object summaries [C], Proceedings of the International Conference on Data Engineering, 2009, 1580-1583.
52. Fagin R. Combining fuzzy information from multiple systems [C], Proceedings of the 15th Symposium on Principles of Database Systems, 1996, 216-226.
53. Fagin R and Wimmers E. Incorporating user preferences in multimedia queries [C], Proceedings of 6th International Conference on Database Theory, 1997, 247-261.
54. Fagin R. Fuzzy queries in multimedia database systems [C], Proceedings of the 17th Symposium on Principles of Database Systems, 1998, 1-10.
55. Guntzer U. Optimizing multi-feature queries in image databases [C], Proceedings of the 29th International Conference on Very Large Data Bases, 2003, 87-98.
56. Xin D, Han J, Chang K C. Progressive and selective merge: computing top-k with ad-hoc ranking functions [C], Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, 2007, 103–114.
57. Sarawagi S, Deshpande V, Kasliwal S. Efficient top-k count queries over imprecise duplicates [C], Proceedings of the International Conference on Extending Database Technology, 2009, 450-461.
58. Marian L G and Bruno N. Evaluating top-k queries over web accessible sources [J], ACM Transactions on Database Systems, 2004, 29(2): 319-362.
59. Xu J, Li H. AdaRank: a boosting algorithm for information retrieval [C], Proceedings of the ACM SIGIR International Conference, 2007, 392-398.
60. Bruno L G N and Chaudhuri S. Top-k selection queries over relational databases: mapping strategies and performance evaluation [J], ACM Transactions on Database Systems, 2002, 27(2): 153-187.
61. Ilyas A E I and Aref W. Supporting top-k queries in relational databases [J], VLDB Journal, 2004, 13(3): 207-221.
62. Chang S K. Minimal probing: supporting expensive predicates for top-k queries [C], Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, 2002, 346-357.
63. Hua M, Pei J, Ada W, Fu C, Lin X M, and Leung H F. Top-k typicality queries and efficient query answering methods on large databases [J], VLDB Journal, 2009, 18: 809-835
64. Bruno N and Wang H. The threshold algorithm: from middleware systems to the relational engine [J], IEEE Transactions on Knowledge and Data Engineering, 2007, 19(4): 523-537.
65. Yi K, Li F F, Kollios G, and Srivastava D. Efficient processing of top-k queries in uncertain databases [C], Proceedings of the 24th International Conference on Data Engineering, 2008, 1406-1408.
66. Yi K, Li F F, Kollios G, and Srivastava D. Efficient processing of top-k queries in uncertain databases with x-relations [J], IEEE Transactions on Knowledge and Data Engineering, 2008, 20(12): 1669-1682.
67. Hua M, Pei J, Zhang W J, and Lin X M. Efficiently answering probabilistic threshold top-k queries on uncertain data [C], Proceedings of the 24th International Conference on Data Engineering, 2008, 1403-1405.
68. Hua M, Pei J, Zhang W J, and Lin X M. Ranking queries on uncertain data: a probabilistic threshold approach [C], Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, 2008, 673-686.
69. Soliman M A, Ilyas I F, and Chang C C. Top-k query processing in uncertain databases [C], Proceedings of the 23rd International Conference on Data Engineering, 2007, 896-905.
70. Angel A, Chaudhuri S, and Das G. Ranking objects based on relationships and fixed associations [C], Proceedings of the 12th International Conference on Extending Database Technology, 2009, 910-921.
71. Chakrabarti K, Ganti V, Han J, and Xin D. Ranking objects based on relationships [C], Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, 2006, 371-382.
72. Deutch D and Milo T. Evaluating top-k queries over business processes [C], Proceedings of the 25th International Conference on Data Engineering, 2009, 1195-1198.
73. Deutch D and Milo T. Top-k projection queries for probabilistic business processes [C], Proceedings of the 4th International Conference on Database Theory Conference, 2009, 239-251.
74. Marian A, Amer-Yahia S, Koudas N, and Srivastava D. Adaptive processing of top-k queries in XML [C], Proceedings of the 21st International Conference on Data Engineering, 2005,162-173.
75. Chang L J, Yu J X, Qin L. Query ranking in probabilistic XML data [C], Proceedings of the 12th International Conference on Extending Database Technology, 2009, 156-167.
76. Li L, Lee M L, Hsu W, and Zhen H. A prufer based approach to process top-k queries in XML [C], Proceedings of the 20th International Conference on Database and Expert Systems Applications, 2009, 348-355.
77. Li G L, Li C, Feng J H. SAIL: Structure-aware indexing for effective and progressive top-k keyword search over XML documents [J], Information Sciences, 2009, 179: 3745-3762.
78. Tsaparas P, Palpanas T, Koudas N, and Srivastava D. Ranked join indices [C], Proceedings of the International Conference on Data Engineering, 2003, 277-288.
79. Lian X, Chen L. Probabilistic group nearest neighbor queries in uncertain databases [J], IEEE Transactions on Knowledge and Data Engineering, 2008, 20(6): 809-824.
80.萨师煊,王珊.数据库系统概论[M],北京:高等教育出版社.
81. Ullman J. Principles of database systems [M], Rockville, Md.: Computer Science Press (the 2nd edition), 1982.
82. Calders T, Goethals B, Jaroszewicz S. Mining rank-correlated sets of numerical attributes [C], Proceedings of the International Conference on Knowledge and Data Discovery, 2006, 96-105.
83. Maier D. The theory of relational databases [M], Rockville, Md.: Computer Sicence Press, 1983.
84. Armstrong W W. Dependency structures of data base relationships [C], Proceedings of the International Federation for Information Processing, 1974, 108-136.
85. Huhtala Y, K?rkk?inen J, Porkka P, and Toivonen H. Tane: an efficient algorithm for discovering functional and approximate dependencies [J], The Computer Journal, 1999, 42(2): 100-111.
86. Zaniolo C. Analysis and design of relational schemata for database system [D], , Los Angeles: Ph.D. Thesis, University of California, 1976.
87. Agrawal R, Imielinski T, and Swami A N. Mining association rules between sets of items in large databases [C], Proceedings of the 12th ACM SIGMOD International Conference on Management of Data, 1993, 207-216.
88. Agrawal R and Srikant R. Fast algorithms for mining association rules [C], Proceedings ofthe 20th International Conference on Very Large Data Bases, 1994, 487-499.
89. Piatetsky-Shapiro G and Connell C. Accurate estimation of the number of tuples satisfying a condition [C], Proceedings of the 1984 ACM SIGMOD International Conference on Management of Data, 1984, 256-276.
90. http://autos.yahoo.com
91. http://realestate.msn.com
92. Hachani N, Ounelli H. A knowledge-based approach for database flexible querying [C]. Proceedings of the 17th International Conference on Database and Expert Systems Applications, 2006, 420-424.
93.张守志,施伯乐.一种发现函数依赖集的方法及应用[J],软件学报, 2003, 14 (10): 1692-1696.
94. Beeri, C., Dowd, M., Fagin, R. and Statman, R. On the structure of Armstrong relations for functional dependencies [J], Journal of the ACM, 1984, 31(1): 30-46.
95. Maier D. The theory of relational databases [M], Rockville, Md.: Computer Science Press, 1983.
96. Lopes S, Petit J M, and Lakhal L. Efficient discovery of functional dependencies and Armstrong relations [C], Proceedings of the International Conference on Extending Database Technology, 2000, 350-364.
97. Huhtala Y, Kakkainen J, Porkka P, and Toivonen H. Tane: An efficient algorithm for discovering functional and approximate dependencies [J], The Computer Journal, 1999, 42(2): 100-111.
98. Huhtala Y, Krkkinen J, Porkka P. Efficient discovery of functional and approximate dependencies using partitions [C], Proceedings of the International Conference on Data Engineering, 1998, 392-401.
99. Beeri, C., Dowd, M., Fagin, R. and Statman, R. On the structure of Armstrong relations for functional dependencies [J], Journal of the ACM, 1984, 31(1): 30-46.
100. Beeri C, Fagin R, Howard J H. A complete axiomatization for functional and multivalued dependencies [C], Proceeding s of the 1977 ACM SIGMOD International Conference on Management of Data, 1977, 82-93.
101. Watson G A. An algorithm for the single facility location problem using the Jaccard metric [J]. SIAM Journal on Scientific and Statistical Computing, 1983 4: 748–756.
102. Rui Y, Huang T, and Merhotra S. Content-based image retrieval with relevance feedback in MARS [C], Proceedings of IEEE International Conference on Image Processing, 1997, 815-818.
103. Wu L. Faloutsos C, Sycara K, and Payne T. FALCON: feedback adaptive loop for content-based retrieval [C], Proceedings of the International Conference on Very Large Databases, 2000, 297-306.
104. Kieβling W, Kostler G. Preference SQL-Design, implementation, experiences [C], Proceedings of the International Conference on Very Large Databases, 2002, 990-1001.
105. Agrawal R and Rantzau R. Context-sensitive ranking [C], Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, 2006, 383-394.
106. Sparck J, Walker K S, and Robertson S E. A probabilistic model of information retrieval: Development and comparative experiments—Part 1 [J]. Inf. Process. Man, 2000, 36(6): 779–808.
107. Sparck J, Walker K S, and Robertson S E. A probabilistic model of information retrieval: Development and comparative experiments—Part 2 [J]. Inf. Process. Man. 2000, 36(6): 809–840.
108. Yu C T and Meng W. Principles of database query processing for advanced applications [M], Morgan Kaufmann, San Francisco, CA, 1998.
109. Baeza-yates R and Riberiro-neto B. Modern information retrieval [M], Addison-Wesley, Reading, MA, 1999.
110. Grossman D and Frieder O. Information retrieval—algorithms and heuristics [M], Springer, Berlin, Germany, 2004.
111. Meng X F, Ma Z M, and Yan L. Providing flexible queries over web databases [C], Proceedings of the 12th International Conference on Knowledge-based and Intelligent Information & Engineering Systems, 2008, 5178, 601-606.
112. Chaudhuri S, Das G, and Hristidis V. Probabilistic ranking of database query results [C]. Proceedings of the International Conference on Very Large Data Bases, 2004, 888-899.
113. Ilyas I F, Beskales G, and Soliman M A. A survey of top-k query processing techniques in relational database systems [J], ACM Computing Surveys, 2008, 40(4): 1101-1158.
114. Gower J C. Properties of euclidean and non-euclidean distance matrices [J], Linear AlgebraAppl, 1985, 67: 81-97.
115. Chrobak M., Keynon C, and Young N. The reverse greedy algorithm for the metric k-median problem [J], Information Processing Letters, 2005, 97(2): 68-72.
116.潘锐.设施选址与K-中间点问题的复杂性与近似算法[D],山东大学博士学位论文, 2007.
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