Pattern management challenges

Lecture Notes in Computer Science, 2006

Lecture Notes in Computer Science, 1998

Knowledge discovery in databases (KDD) is a process that can include steps like forming the data set, data transformations, discovery of patterns, searching for exceptions to a pattern, zooming on a subset of the data, and postprocessing some patterns. We describe a comprehensive framework in which all these steps can be carried out by means of queries over an inductive database. An inductive database is a database that in addition to data also contains intensionally defned generalizations about the data. We formalize this concept: an inductive database consists of a normal database together with a subset of patterns from a class of patterns, and an evaluation function that tells how the patterns occur in the data. Then, looking for potential query languages built on top of SQL, we consider the research on the MINE RULE operator by Meo, Psaila and Ceri. It is a serious step towards an implementation framework for inductive databases, though it addresses only the association rule mining problem. Perspectives are then discussed.

2004

To represent and manage data mining patterns, several aspects have to be taken into account: (i) patterns are heterogeneous in nature; (ii) patterns can be extracted from raw data by using data mining tools (a-posteriori patterns) but also defined by the users and used for example to check how well they represent some input data source (a-priori patterns); (iii) since source data change frequently, issues concerning pattern validity and synchronization are very important; (iv) patterns have to be manipulated and queried according to specific languages. Several approaches have been proposed so far to deal with patterns, however all of them lack some of the previous characteristics. The aim of this paper is to present an overall framework to cope with all these features.

Knowledge Discovery in Databases: …, 2006

2015

This demo presents Pattern-Miner, an integrated environment for pattern management and mining that deals with the whole lifecycle of patterns, from their generation (using data mining techniques) to their storage and querying, putting also emphasis on the comparison between patterns and meta-mining operations over the extracted patterns. Pattern comparison (comparing results of the data mining process) and meta-mining are high level pattern operations that can be applied in a variety of applications, from database change management to image comparison and retrieval.

2003

Data intensive applications produce complex information that is posing requirements for novel Database Management Systems (DBMSs). Such information is characterized by its huge volume of data and by its diversity and complexity, since the data processing methods such as pattern recognition, data mining and knowledge extraction result in knowledge artifacts like clusters, association rules, decision trees and others. These artifacts that we call patterns need to be stored and retrieved efficiently. In order to accomplish this we have to express them within a formalism and a language.

Lecture Notes in Computer Science, 2006

In this paper we present the application of the inductive database approach to two practical analytical case studies: Web usage mining in Web logs and financial data. As far as concerns the Web domain, we have considered the enriched XML Web logs, that we call conceptual logs, produced by specific Web applications. These ones have been built by using a conceptual model, namely WebML, and its accompanying CASE tool, WebRatio. The Web conceptual logs integrate the usual information about user requests with meta-data concerning the Web site structure. As far as concerns the analysis of financial data, we have considered the trade stock exchange index Dow Jones and studied its component stocks from 1997 to 2002 using the so-called technical analysis. Technical analysis consists in the identification of the relevant (graphical) patterns that occur in the plot of evolution of a stock quote as time proceeds, often adopting different time granularities. On the plots the correlations between distinctive variables of the stocks quote are pointed out, such as the quote trend, the percentage variation and the volume of the stocks exchanged. In particular we adopted candle-sticks, a figurative pattern representing in a condensed diagram the evolution of the stock quotes in a daily stock exchange. In technical analysis, candle-sticks have been frequently used by practitioners to predict the trend of the stocks quotes in the market.

Lecture Notes in Computer Science, 2003

Nowadays, the vast volume of collected digital data obliges us to employ processing methods like pattern recognition and data mining in order to reduce the complexity of data management. In this paper, we present the architecture and the logical foundations for the management of the produced knowledge artifacts, which we call patterns. To this end, we first introduce the concept of Pattern-Base Management System; then, we provide the logical foundations of a general framework based on the notions of pattern types and pattern classes, which stand for the intensional and extensional description of pattern instances, respectively. The framework is general and extensible enough to cover a broad range of real-world patterns, each of which is characterized by its structure, the related underlying data, an expression that carries the semantics of the pattern, and measurements of how successful the representation of raw data is. Finally, some remarkable types of relationships between patterns are discussed.

2003

Data intensive applications produce complex information that is posing requirements for novel Database Management Systems (DBMSs). Such information is characterized by its huge volume of data and by its diversity and complexity, since the data processing methods such as pattern recognition, data mining and knowledge extraction result in knowledge artifacts like clusters, association rules, decision trees and others. These artifacts that we call patterns need to be stored and retrieved efficiently. In order to accomplish this we have to express them within a formalism and a language.

Proceedings of the 2007 ACM symposium on Applied computing - SAC '07, 2007

Data mining algorithms are now able to efficiently deal with huge amount of data. Various kinds of patterns may be discovered and may have some great impact on the general development of knowledge. In many domains, end users may want to have their data mined by data mining tools in order to extract patterns that could impact their business. Nevertheless, those users are often overwhelmed by the large quantity of patterns extracted in such a situation. Moreover, some privacy issues, or some commercial one may lead the users not to be able to mine the data by themselves. Thus, the users may not have the possibility to perform many experiments integrating various constraints in order to focus on specific patterns they would like to extract. Post processing of patterns may be an answer to that drawback. Thus, in this paper we present a framework that could allow end users to manage collections of patterns. We propose to use an efficient data structure on which some algebraic operators may be used in order to retrieve or access patterns in pattern bases.