Handling KDD process changes by incremental replanning

The very fast technological progress provokes the creation of a big volume of the astronomical data, which is fed in the different forms. There are various directions in science that use high dimensional data to be analyzed. In this paper we presented the few aspects of the "knowledge discovery in databases" (KDD) process in astronomy, analyzed and reviewed some data mining approaches. We presented the examples of astronomical instruments, data types, astronomical sources of Big Data, processing algorithms, which are used for the knowledge discovery in databases process in astronomy. The paper deals with applying the CoLiTec (Collection Light Technology) software for the online processing of the different types of astronomical data using the knowledge discovery in databases tasks. This is achieved by using of the developed OnLine Data Analysis System (OLDAS), which helps with solving of the knowledge discovery in databases tasks, like selection, transformation, data mining, interpretation, and evaluation.

System for managing and refining structural characteristics discovered from databases

The terms Data Mining (DM) and Knowledge Discovery in Databases (KDD) have been used interchangeably in practice. Strictly speaking, KDD is the umbrella of the mining process and DM is only a step in KDD. We will follow this distinction in this chapter and present a simple introduction to the Knowledge Discovery in Databases process from an optimization perspective.

A number of KDD (Knowledge Discovery in Databases) systems, developed to meet the requirements of many different application domains, has been proposed in the literature. As a result, one can identify several different KDD tasks, depending mainly on the application domain and on the interest of the user. In general, each KDD task extracts a different kind of knowledge from a database, so that each task requires a different kind of KDD algorithm.

With the increase of data, data mining has been introducing to solve the overloading problem and to discover valid, novel, potentially useful patterns in existing data. However, to discover quantitative data, we face a sharp boundary problem that the traditional data mining techniques can not overcome. To pinpoint this problem, a lot of researches have been applied fuzzy sets to discover patterns, especially in sequential patterns . As we know, U. Fayyad et al proposed a unifying process-centric framework for Knowledge Discovery in Database (KDD) which can explain the process from top to toe for the traditional data mining problems. Unfortunately, it fails to be workable in part to describe the process for discovering sequential patterns based on fuzzy concept. Therefore, to have more general viewpoint for discovering of that sort patterns, we devote to proposing a general framework modified from the KDD process. An application is proposed to demonstrate that the framework can be a generalization of the realm problem.

A Knowledge discovery in databases (KDD) system with probability deduction capability is expected to provide more information for decision making. Based on Bacchus probability logic and formal concept analysis, we propose a logic model for KDD with probability deduction. We use formal concept analysis within the semantics of probability logic to import the notion of concept into modeling of KDD. One of the most important features of a KDD system is its ability to discover previously unknown and potentially useful patterns. We formalize the definitions of previously unknown and potentially useful patterns.

In this paper we deal with association rule mining in the context of a complex, interactive and iterative knowledge discovery process. After a general introduction covering the basics of association rule mining and of the knowledge discovery process in databases we draw the attention to the problematic aspects concerning the integration of both. Actually, we come to the conclusion that with regard to human involvement and interactivity the current situation is far from being satisfying. In our paper we tackle this problem on three sides: First of all there is the algorithmic complexity. Although today’s algorithms efficiently prune the immense search space the achieved run times do not allow true interactivity. Nevertheless we present a rule caching schema that significantly reduces the number of mining runs. This schema helps to gain interactivity even in the presence of extreme run times of the mining algorithms. Second, today the mining data is typically stored in a relational database management system. We present an efficient integration with modern database systems which is one of the key factors in practical mining applications. Third, interesting rules must be picked from the set of generated rules. This might be quite costly because the generated rule sets normally are quite large whereas the percentage of useful rules is typically only a very small fraction. We enhance the traditional association rule mining framework in order to cope with this situation.KeywordsAssociation RuleMining AlgorithmFrequent ItemsetsAssociation Rule MiningGeneralize Association RuleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

SMART software for decision makers KDD experience

Knowledge discovery in databases (KDD) is an iterative multi-stage process for extracting useful, non-trivial information from large databases. Each stage of the process presents numerous choices to the user that can significantly change the outcome of the project. This methodology, presented in the form of a roadmap, emphasises the importance of the early stages of the KDD process and shows how careful planning can lead to a successful and well-managed project. The content is the result of expertise acquired through research and a wide range of practical experiences; the work is of value to KDD experts and novices alike. Each stage, from specification to exploitation, is described in detail with suggested approaches, resources and questions that should be considered. The final section describes how the methodology has been successfully used in the design of a commercial KDD toolkit.

Knowledge discovery in databases is a process that aims at the discovery of associations within data sets. The analysis of geo-referenced data demands a particular approach in this process. This chapter presents a new approach to the process of knowledge discovery, in which qualitative geographic identifiers give the positional aspects of geographic data. Those identifiers are manipulated using qualitative reasoning principles, which allows for the inference of new spatial relations required for the data mining step of the knowledge discovery process. The efficacy and usefulness of the implemented system — Padrão — has been tested with a bank dataset. The results support that traditional knowledge discovery systems, developed for relational databases and not having semantic knowledge linked to spatial data, can be used in the process of knowledge discovery in geo-referenced databases, since some of this semantic knowledge and the principles of qualitative spatial reasoning are available as spatial domain knowledge.

Purpose – Much research on knowledge discovery in database (KDD) merely pays attention to data mining, one of many interacting steps in the process of discovering previously unknown and potentially interesting patterns in large databases, but little to the whole process. However, such approaches cannot satisfy the need of real applications of KDD. The purpose of this work is to extend a process model of KDD in practice at large.Design/methodology/approach – A new model based on research experiences of the knowledge discovery process is formalized as an extension of the model by Fayyad et al. A case study by a reduct method from rough set theory is to illustrate why the process model is proposed and in what situation it can be used in practice.Findings – This model incorporates data collection in the KDD process to supply a sound framework to better support KDD applications.Research limitations/implications – This model reflects the native of KDD in some tested cases. It may need further research to be use...

Data mining, or Knowledge Discovery in Databases (KDD), is of little benefit to commercial enterprises unless it can be carried out efficiently on realistic volumes of data. Operational factors also dictate that KDD should be performed within the context of standard DBMS. Fortunately, relational DBMS have a declarative query interface (SQL) that has allowed designers of parallel hardware to exploit data parallelism efficiently. Thus, an effective approach to the problem of efficient KDD consists of arranging that KDD tasks execute on a parallel SQL server. In this paper we devise generic KDD primitives, map these to SQL and present some results of running these primitives on a commercially-available parallel SQL server.

We introduce an integrated framework for Knowledge Discovery in Databases (KDD) and Knowledge Management and show how Knowledge Management can complement KDD. Specifically, we examine methods how to improve the knowledge intensive and weak structured process of KDD through the use of an experience factory using the method of experience packaging and case based reasoning (CBR).This paper investigates how knowledge contained in the textual components of experience packages can be used to improve the retrieval of lessons learned in KDD. We add textual CBR techniques to our CBR approach in order to improve the case retrieval mechanism of the experience factory. Our technique exploits domain-specific knowledge contained in the textual parts of the packages to find better reuse candidates of lessons learned in KDD.KeywordsKnowledge discovery in databasescase base reasoningexperience reuseinformation retrievalknowledge management

Knowledge discovery in databases, or data mining, is an important objective in the development of data- and knowledge-base systems. An attribute-oriented rough set method is developed for knowledge discovery in databases. The method integrates learning from example techniques with rough set theory. An attribute-oriented concept tree ascension technique is first applied in generalization, which substantially reduces the computational complexity of the database learning processes. Then the rough set techniques are applied to the generalized relation to derive different knowledge rules. Moreover, the approach can find all the maximal generalized rules in the data. Based on these principles, a prototype database learning system, DBROUGH, has been constructed. Our study shows that attribute-oriented induction combined with rough set techniques provide an efficient and effective mechanism for knowledge discovery in database systems.

This paper describes an implementation of a Knowledge Discovery in Databases (KDD) process for extracting the causes of iterations in Engineering Change Orders (ECOs). A data set of approximately 53,000 historical Engineering Change Orders (ECOs) was used for this purpose. Initially, the impact of iterations in ECO lead time and uncertainty is assessed. Subsequently, a semi-automatic text-mining process is employed to classify the causes of iterations. As a result, cost and technical categories of causes were identified as the main reasons for the occurrence of iterations. The study concludes that applying KDD in historic ECO data can help in identifying the causes of iterations of ECO which subsequently can provide a framework for companies to reduce these iterations. In addition, the case represents an example of benefits that can be achieved with the application of KDD in engineering change management.