A Survey on Pattern Application Domains and Pattern Management Approaches (original) (raw)

A Survey on Pattern Application Domains and Pattern

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.

PAtterns for Next-generation DAtabase systems: preliminary results of the PANDA project

2003

Abstract. 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. The output of these techniques are knowledge artifacts, heterogeneous in both structure and semantics. We claim that the concept of pattern is a good candidate for generic representation of these novel information types. The PANDA project is aimed at studying the main issues related to pattern handling.

Recent Advances on Pattern Representation and Management

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, called patterns, need to be stored and retrieved effectively and efficiently. In this paper, we review the concept of patterns and their applicability in several research domains and we define the knowledge domain related to the PANDA project. We examine the different types of patterns that are extracted from a data set, in order to gather the necessary requirements for the definition of a pattern model. This model will constitute the heart of the Pattern Base Management System that will be designed.

A Framework for Data Mining Pattern Management

Patterns are concise, but rich in semantic, representation of data. The approaches proposed in the literature to cope with pattern management problems usually deal with a single type of knowledge artifact and mainly concern pattern extraction issues. Little emphasis has been posed in defining an overall environment to represent and efficiently manage different types of patterns. The first general approach to deal with patterns has been proposed in the context of the PANDA project . In this paper, we discuss some basic requirements for pattern manipulation and retrieval, represented according to the PANDA model. The proposed languages extend previous proposals and represent the basis for the development of an efficient pattern query processor.

Modeling and Language Support for the Pattern Management

Advances in computational intelligence and robotics book series, 2017

Patterns are mentioned usually in the extraction context. Little stress is posed in their representation and management. This chapter is focused on the representation of the patterns, manipulation with patterns and query patterns. Crucial issue can be seen in systematic approach to pattern management and specific pattern query language which takes into consideration semantics of patterns. In the background we discuss two different approaches to the pattern store and manipulation (based on inductive database and PANDA project). General pattern model is illustrated using abstract data type implemented in Oracle. In the following chapters the introduction to querying patterns and simple scheme of the architecture PBMS is shown.

A model for managing collections of patterns

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.

A framework for data mining pattern management

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.

Towards a language for pattern manipulation and querying

2004

Abstract. Patterns are concise, but rich in semantic, representation of data. The approaches proposed in the literature to cope with pattern management problems usually deal with a single type of knowledge artifact and mainly concern pattern extraction issues. Little emphasis has been posed in defining an overall environment to represent and efficiently manage different types of patterns. The first general approach to deal with patterns has been proposed in the context of the PANDA project [1].

Pattern-Miner: Integrated Management and Mining over Data Mining Models

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.