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本文提出了一种模糊查询控制的模型,这个模型吸收了两种思想,即有穷自动机思想和聚类分组算法,基础是采用了分组算法。模型分为两个部分,第一部分是对原始数据的分析,为后面的查询控制做准备,第二部分则是具体的模糊查询控制。 相似文献
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基于关系数据库的模糊查询技术 总被引:2,自引:0,他引:2
樊新华 《计算机与数字工程》2009,37(10):149-152,156
在关系数据库中,SQL只能处理精确查询,而现实中存在许多模糊查询的问题。在模糊逻辑理论的基础上,提出了关系数据库的模糊查询思路,并详细地讨论了在数值和字符属性上的实现方法。实现方法不仅对数据库的查询进行了扩展,而且对实际系统的开发也有一定的借鉴作用。 相似文献
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模糊关系数据库查询在传统的关系型数据库语言本身在模糊范围内进行精确化查找,其要优于传统的关系数据库查询。本文主要对"模糊基础理论"、"模糊查询实现方法"进行了分析。 相似文献
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模糊关系数据库查询语言FSQL 总被引:1,自引:0,他引:1
模糊数据库是模糊信息处理系统的重要组成部分。本文以SQL语言为基础,设计了模糊关系数据库查询语言FSQL。FSQL语言采用了模糊值模糊关系数据模型,提供了相应的模糊数据定义与模糊数据操纵功能。为了便于模糊信息的表示和管理,FSQL语言增加了模糊数据类型,如简单标量型、模糊标量型、简单数集、模糊数集等。另外,为了便于模糊查询,扩充了模糊比较库函数及自定义隶属函数。 相似文献
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关系数据库上的关键词查找技术使得用户像使用搜索引擎一样获取数据库中的相关数据.然而,这种技术只实现了精确查询,还不能很好地实现模糊查询.本文通过引进分类学习中的Rocchio算法并对其做小部分修改,用于数据库的关键词查询中,结合不同类型对象之间相异度和相关度的量化计算,每次返回的结果集按照相关度降序排列,实现精确到模糊的查询.如果用户不满意初始查询结果集,利用Rocchio算法经过几次交互,便可不断满足需求.对权值优化的Rocchio算法反馈过程进行了实验测试,结果证明是比较令用户满意的,而且返回的结果集中少量的不相关集合可以提高查询的性能. 相似文献
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SQL中分组查询的设计与应用 总被引:2,自引:1,他引:1
肖海蓉 《计算机与数字工程》2009,37(5):85-87
针对SQL的数据查询,重点探讨了分组查询在实际应用中的设计,以及查询涉及到单表和多表的情况下,分组查询的具体应用,并在SQLserver 2000的环境下通过了验证。 相似文献
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In this paper, we develop a new method to measure the quality of each tuple as an answer with respect to Select‐Project‐Join (SPJ) queries so that we can determine which answers are better answers to the given query in a fuzzy relational database. The quality of an answer is viewed as how much sure information is provided, and how much extra information is needed so that it will be a sure answer to the query. The less extra information that is required and the more sure information that is provided by an answer, the higher the quality of that answer is, and in consequence, it will be more reliable. © 2001 John Wiley & Sons, Inc. 相似文献
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Nan‐Chen Hsieh 《国际智能系统杂志》2005,20(6):647-668
A model of an extended fuzzy relational database was proposed to accommodate uncertain and imprecise information. We use two supplementary measurements, satisfactory degree and extra degree, for determining the quality of answers to Select‐Project‐Join (SPJ) queries. The method of measurement determines how much satisfactory information is provided and how much truth information is required for a query. The answers to the query thus contain sure answers and maybe answers. The core of this study is the detailed discussion on the quality of answers in an extended fuzzy relation to query processing. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 647–668, 2005. 相似文献
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关联是数据挖掘领域的一个重要研究课题。对模糊关联规则挖掘进行了研究,针对普通关联规则不能精确表达数据库中模糊信息关联性的问题,提出了一种新的模糊关联规则挖掘算法FARM_New,结果表明算法是有效的,提高了模糊挖掘的速度。 相似文献
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借助模糊概念和模糊运算,对时间区间的描述很容易实现。对于指定的日历模式,不同的时间区间可根据它们的隶属度具有不同的权重。在模糊日历代数基础上,结合增量挖掘和累进计数的思想,提出了一种基于模糊日历的模糊时序关联规则挖掘方法。理论分析和实验结果均表明,该算法是高效可行的。 相似文献
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Reliability of answers to queries in relational databases 总被引:1,自引:0,他引:1
The author studies the problem of determining the reliability of answers to queries in a relational database system, where the information in the database comes from various sources with varying degrees of reliability. An extended relational model is proposed in which each tuple in a relation is associated with an information source vector which identifies the information source(s) that contributed to that tuple. The author shows how relational algebra operations can be extended, and implemented using information source vectors, to calculate the vector corresponding to each tuple in the answer to a query, and hence, to identify information source(s) contributing to each tuple in the answer. This also enables the database system to calculate the reliability of each tuple in the answer to a query as a function of the reliability of information sources 相似文献
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Floriana Esposito Donato Malerba Vincenza Ripa Giovanni Semeraro 《Applied Artificial Intelligence》2013,27(1):71-84
This article explores the combined application of inductive learning algorithms and causal inference techniques to the problem of discovering causal rules among the attributes of a relational database. Given some relational data each field can be considered as a random variable and a hybrid graph can be built by detecting conditional independencies among variables. The induced graph represents genuine and potential causal relations as well as spurious associations. When the variables are discrete or have been discretized to test condi tional independencies supervised induction algorithms can be used to learn causal rules that is conditional statements in which causes appear as antecedents and effects as consequences. The approach is illustrated by means of some experiments conducted on different data sets. 相似文献
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Due to the pervasive data uncertainty in many real applications, efficient and effective query answering on uncertain data has recently gained much attention from the database community. In this paper, we propose a novel and important query in the context of uncertain databases, namely probabilistic group subspace skyline (PGSS) query, which is useful in applications like sensor data analysis. Specifically, a PGSS query retrieves those uncertain objects that are, with high confidence, not dynamically dominated by other objects, with respect to a group of query points in ad-hoc subspaces. In order to enable fast PGSS query answering, we propose effective pruning methods to reduce the PGSS search space, which are seamlessly integrated into an efficient PGSS query procedure. Furthermore, to achieve low query cost, we provide a cost model, in light of which uncertain data are pre-processed and indexed. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our proposed approaches. 相似文献
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With the proliferation of mobile devices and wireless technologies, location based services (LBSs) are becoming popular in smart cities. Two important classes of LBSs are Nearest Neighbor (NN) queries and range queries that provide user information about the locations of point of interests (POIs) such as hospitals or restaurants. Answers of these queries are more reliable and satisfiable if they come from trustworthy crowd instead of traditional location service providers (LSPs). We introduce an approach to evaluate NN and range queries with crowdsourced data and computation that eliminates the role of an LSP. In our crowdsourced approach, a user evaluates LBSs in a group. It may happen that group members do not have knowledge of all POIs in a certain area. We present efficient algorithms to evaluate queries with accuracy guarantee in incomplete databases. Experiments show that our approach is scalable and incurs less computational overhead. 相似文献
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Rewriting rules to permeate complex similarity and fuzzy queries within a relational database system
In recent years, the availability of complex data repositories (e.g., multimedia, genomic, semistructured databases) has paved the way to new potentials as to data querying. In this scenario, similarity and fuzzy techniques have proven to be successful principles for effective data retrieval. However, most proposals are domain specific and lack of a general and integrated approach to deal with generalized complex queries, i.e., queries where multiple conditions are expressed, possibly on complex as well as on traditional data. To overcome such limitations, much work has been devoted to the development of middleware systems to support query processing on multiple repositories. On a similar line, We present a formal framework to permeate complex similarity and fuzzy queries within a relational database system. As an example, we focus on multimedia data, which is represented in an integrated view with common database data. We have designed an application layer that relies on an algebraic query language, extended with MM-tailored operators, and that maps complex similarity and fuzzy queries to standard SQL statements that can be processed by a relational database system, exploiting standard facilities of modern extensible RDBMS. To show the applicability of our proposal, we implemented a prototype that provides the user with rich query capabilities, ranging from traditional database queries to complex queries gathering a mixture of Boolean, similarity, and fuzzy predicates on the data. 相似文献
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Efficient fuzzy ranking queries in uncertain databases 总被引:1,自引:1,他引:0
Recently, uncertain data have received dramatic attention along with technical advances on geographical tracking, sensor network and RFID etc. Also, ranking queries over uncertain data has become a research focus of uncertain data management. With dramatically growing applications of fuzzy set theory, lots of queries involving fuzzy conditions appear nowadays. These fuzzy conditions are widely applied for querying over uncertain data. For instance, in the weather monitoring system, weather data are inherent uncertainty due to some measurement errors. Weather data depicting heavy rain are desired, where ??heavy?? is ambiguous in the fuzzy query. However, fuzzy queries cannot ensure returning expected results from uncertain databases. In this paper, we study a novel kind of ranking queries, Fuzzy Ranking queries (FRanking queries) which extend the traditional notion of ranking queries. FRanking queries are able to handle fuzzy queries submitted by users and return k results which are the most likely to satisfy fuzzy queries in uncertain databases. Due to fuzzy query conditions, the ranks of tuples cannot be evaluated by existing ranking functions. We propose Fuzzy Ranking Function to calculate tuples?? ranks in uncertain databases for both attribute-level and tuple-level uncertainty models. Our ranking function take both the uncertainty and fuzzy semantics into account. FRanking queries are formally defined based on Fuzzy Ranking Function. In the processing of answering FRanking queries, we present a pruning method which safely prunes unnecessary tuples to reduce the search space. To further improve the efficiency, we design an efficient algorithm, namely Incremental Membership Algorithm (IMA) which efficiently answers FRanking queries by evaluating the ranks of incremental tuples under each threshold for the fuzzy set. We demonstrate the effectiveness and efficiency of our methods through the theoretical analysis and experiments with synthetic and real datasets. 相似文献