دانلود مقاله ISI انگلیسی شماره 22061
عنوان فارسی مقاله

داده کاوی در پایگاه داده استقرایی با استفاده از دسته های پرس و جو

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
22061 2005 13 صفحه PDF سفارش دهید 7140 کلمه
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عنوان انگلیسی
Data mining in deductive databases using query flocks
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 28, Issue 3, April 2005, Pages 395–407

کلمات کلیدی
داده کاوی - داده کاوی قواعد اتحادیه - دسته پرس و جو - پایگاه های داده قیاسی - ارزشیابی پرس و جو بازگشتی
پیش نمایش مقاله
پیش نمایش مقاله داده کاوی در پایگاه داده استقرایی با استفاده از دسته های پرس و جو

چکیده انگلیسی

Data mining can be defined as a process for finding trends and patterns in large data. An important technique for extracting useful information, such as regularities, from usually historical data, is called as association rule mining. Most research on data mining is concentrated on traditional relational data model. On the other hand, the query flocks technique, which extends the concept of association rule mining with a ‘generate-and-test’ model for different kind of patterns, can also be applied to deductive databases. In this paper, query flocks technique is extended with view definitions including recursive views. Although in our system query flock technique can be applied to a data base schema including both the intensional data base (IDB) or rules and the extensible data base (EDB) or tabled relations, we have designed an architecture to compile query flocks from datalog into SQL in order to be able to use commercially available data base management systems (DBMS) as an underlying engine of our system. However, since recursive datalog views (IDB's) cannot be converted directly into SQL statements, they are materialized before the final compilation operation. On this architecture, optimizations suitable for the extended query flocks are also introduced. Using the prototype system, which is developed on a commercial database environment, advantages of the new architecture together with the optimizations, are also presented.

مقدمه انگلیسی

1.1. Association rule mining Since data mining is a process of finding trends and patterns in large data, it is usually applied on historical data, like organizational operations or customer transactions, in order to discover hidden regularities which can be used to improve the decision making process. In recent years, many organizations have begun to routinely capture huge volumes of data related to all levels of their operations. Nowadays, this huge historical data is used in decision making in many different ways, such as analyzing medical outcomes, detecting credit card fraud, predicting the personal interests of web users, and optimizing manufacturing processes. One of the most popular data mining application is known as market basket analysis ( Agrawal, 1994 and Agrawal et al., 1993). In this simple application, customers' purchase behaviors are tried to be predicted from customer transactions data. A market basket data is a set of items purchased by a customer in a single transaction. By using the basket data, one can make decisions, like how to place goods on the shelves or how to make sales promotions, in order to increase the profit of a supermarket. Therefore, the sets of items that are related must be determined from the market basket data. Association rules describe these kind of relations among the item sets ( Agrawal, 1994, Agrawal et al., 1993, Fayyad et al., 1996, Hand et al., 2001, Ramakrishnan, 1997, Srikant and Agrawal, 1995 and Tsur et al., 1998). Association rules are a class of simple but powerful regularities in large data. An association rule is in the form of A⇒B, where, in the case of market basket analysis, both A and B are sets of items. Such a rule means ‘if every item in A is purchased then it is likely that items in B will also be purchased’. There are two important measures related to association rules: • Support shows what percent of all the transactions should include (A∪B). If support for a rule is low, the rule may have arisen purely by chance. For example, if support for rule pencil⇒soap is low, only a small percentage of the transactions involve both pencil and soap, which means we do not have enough evidence to draw conclusions about the correlations between pencil and soap purchases. • Confidence is the probability of items in B to be in the basket, given that the ones in A are in the basket. It indicates the degree of correlation between purchases of these sets of items. For example, if the rule pencil⇒soap has a low confidence, then purchase of a pencil is not highly correlated with the purchase of soap in the given database. In order to search the sets with high support a-priory property is used, which can be defined as follows: A-priory Property. Every subset of a frequent item-set must also be a frequent item-set. A-priory property uses the fact that if a set of items S appears in c baskets then any subset of S appears in at least c baskets. For example, let us consider a database containing information about market baskets. Each time a customer appears at the cash register, the set of items bought is entered at the database. The database is a relation baskets(BID,Item), giving pairs consisting of a basket ID and an item that appeared in the basket. If we are looking for pairs of items that appear in at least c baskets then we can start by finding those items that by themselves appear in at least c baskets. If c is large enough most of the tuples in the baskets relation can be eliminated before joining baskets with itself to count occurrences of pairs of items. 1.2. Query flocks Query flock as presented in Tsur et al. (1998), is a generate-and-test system, in which a family of queries that are identical, except for the values of one or more parameters, are asked simultaneously. The answers to these queries are filtered and those that pass the filter test enable their parameters to become part of the answer to the query flock. Briefly, query flocks generalize the association rule mining for larger class of problems. The idea of flocks is to perform complex data analysis on relational database schemas including a number of relations. However, automatic generation of rules by pushing the constraints down into rule generation in ordinary association rule mining technique is lost in the query flock. Instead, the user has to specify the pattern of the rule and the system tests it, and determines if this association has enough support and confidence. On the other hand, in a query flock, complex rules including several relations can be generated. In order to define query flocks a language to express parameterized queries, and, a language to express filter conditions about the results of the queries are needed. Datalog, ( Ramakrishnan, 1997 and Tsur et al., 1998), is used for specifying the query part. In the query part, a-priory trick to arbitrary flocks can also be expressed easily in datalog. For the filter language, SQL aggregates can be used as in HAVING clauses in order to define the support and the confidence of the rule searched by the query flock. A query flock can be defined by specifying: • One or more predicates representing the relations, • A set of parameters appearing in these relations whose names beginning with $, • A query expressed in a datalog which might include ordinary variables, parameters and constants, • A filter condition that the results of the query must satisfy in order to specify the support and the confidence. The query flock means a set of tuples that represent acceptable assignments of values for the parameters. The acceptable parameter assignments are determined by trying all such assignments in the query, evaluating the query, and checking whether the result passes the filter test. For example (from Tsur et al., 1998) on the market basket analysis for pairs of items $I1 and $I2 that satisfy the filter condition is given as: QUERY: ans1(B):-baskets(B,$I1), baskets(B,$I2), $I1<$I2, ans2(B):-baskets(B,$I1), FILTER1: COUNT(ans1.B)>N FILTER2: COUNT(ans1.B)>COUNT(ans2.B)*c. The first filter condition represents the support by specifying that the pairs of items should appear in at least N basket. Even though in most applications the support is specified in terms of the percentage of the total number of transactions, as in this example it is also possible to specify it as the count of the transactions as well. The second filter condition represents the confidence of the rule ‘$I1 implies $I2’ by using a coefficient c. In this example, confidence is specified as the percentage of transactions including both items $I1 and $I2 to the number of transactions including only item $I1. Since query flock is a query about its parameters, the parameter values are the ones that satisfy minimum support and confidence conditions and they represent the rules about the related items. Query flocks have been successfully applied to text mining applications. In the TopCat project Clifton, to appear and Clifton and Cooley, 1999, documents have been viewed as market baskets of named entities and query flocks have been used in identifying topics that recur in documents of a text corpus. 1.3. Query flock architecture In this paper, we define a query flockcompiler, using tightly coupled approach with the database which was introduced in Nestorov, 2000 and Tsur and Nestorov, 1999, to process the flocks. The main advantage of choosing a tightly coupled approach is to enable to use the full capabilities of the existing database technology. The data is stored in the database, and, at the end, all query processing is done by using the query language of DBMSs. On the other hand, the disadvantage of this approach is the limitations of the current query optimizers ( Sarawagi, Thomas, & Agrawal, 1998). Therefore some optimizations could be done before sending the queries to the database. Most of the previous works on data mining is defined for relational data model. Prior to our work there have been only a few works related to deductive database mining, and all these works were in their very early stages (Furukawa et al., 1997, Russell, 1998 and Wang et al., 1997). One of the further research directions suggested in Tsur et al. (1998) for query flocks is extending query flocks for datalog. In this paper, we show how parameterized query definitions of Tsur et al. (1998) can be extended with views, including recursive views.1 That is, our work can be described as a data mining on deductive databases. In order to be able to define views in query flocks, a view evaluator is designed that sits on the top of external optimizer. To increase the performance, the external optimizer breaks a complex data-mining query into a sequence of smaller queries that can be executed by DBMS. Since our compiler is tightly coupled with relational DBMS (RDBMS), datalog rules have to be translated into SQL statements. However, recursive views cannot be translated into SQL statements. Therefore, more complicated view evaluator is needed. The main contribution of our work is the view evaluator, that converts any query flock even including recursive views, into query flocks including only EDB relations, so that in the final step, they can be translated into SQL statements. Between the view evaluator and the translator, there is another phase, called external optimization, which is used to improve the efficiency of execution of the queries. The translator takes the rules created by the external optimizer and generates corresponding SQL code. Our architecture for query flock compiler is shown in Fig. 1.The following sections give the design details of the architecture. The phases of the architecture, namely view evaluation, external optimization and translation are described in 2, 3 and 4, respectively. Section 5 includes implementation details and performance results of sample runs. Finally, Section 6 is the conclusion.

نتیجه گیری انگلیسی

The following sections give the design details of the architecture. The phases of the architecture, namely view evaluation, external optimization and translation are described in 2, 3 and 4, respectively. Section 5 includes implementation details and performance results of sample runs. Finally, Section 6 is the conclusion.

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