Comonent-based data mining frameworks

Encyclopedia of Data Warehousing …, 2005

Information by itself is no longer perceived as an asset. Billions of business transactions are recorded in enterprise scale data warehouses every day. The acquisition, storage and management of business information are commonplace and often automated. Recent advances in (remote or other) sensor technologies have led to the development of scientific data repositories. Database technologies, ranging from relational systems, to extensions like spatial, temporal, time series, text or media, as well as specialized tools like geographical information systems (GIS) or on-line analytical processing (OLAP), have transformed the design of enterprisescale business or large scientific applications. The question increasingly faced by the scientific or business decision-maker, is not how one can get more information or design better information systems, but what to make of the information and systems already in place. The challenge is to be able to utilize the available information, to gain a better understanding of the past, and predict or influence the future through better decision-making. Researchers in data mining technologies (DMT) and decision support systems (DSS) are responding to this challenge. Broadly defined, data mining (DM) relies on scalable statistics, artificial intelligence, machine learning or knowledge discovery in databases (KDD). DSS utilize available information and DMT to provide a decision-making tool usually relying on human-computer interaction. Together, DMT and DSS represent the spectrum of analytical information technologies (AIT), and provide a unifying platform for an optimal combination of data dictated and human driven analytics.

Sigmod Record, 2002

Mining information from data: A presentday gold rush. Data Mining is a multidisciplinary field which supports knowledge workers who try to extract information in our "data rich, information poor" environment. Its name stems from the idea of mining knowledge from large amounts of data. The tools it provides assist us in the discovery of relevant information through a wide range of data analysis techniques. Any method used to extract patterns from a given data source is considered to be a data mining technique.

2003

Data mining and decision support: integration and collaboration / edited by Dunja Mladenic ... [et al.]. p. cm.-(The Kluwer international series in engineering and computer science ; SECS 745) Includes bibliographical references and index.

Data mining is a concept which looks for valuable resources as exapmles from substantial measure of information i.e from a large amount of data. The thoery or the information in this paper examines few of the data mining procedures, calculations and a portion of the associations which have adjusted information mining innovation to enhance their organizations and discovered great outcomes.

Proceedings of SPIE, 2002

Models and algorithms for effective decision-making in a data-driven environment are discussed. To enhance the quality of the extracted knowledge and decision-making, the data sets are transformed, the knowledge is extracted with multiple algorithms, the impact of the decisions on the modeled process is simulated, and the parameters optimizing process performance are recommended. The applications discussed in this paper differ from most data mining tasks, where the extracted knowledge is used to assign decision values to new objects that have not been included in the training data. For example, in a typical data mining application the equipment fault is recognized based on the failure symptoms. In this paper, a subset of rules is selected from the extracted knowledge to meet the established decision-making criteria. The parameter values represented by the conditions of this set of rules are called a decision signature. A model and algorithms for the selection of the desired parameters (decision signatures) will be developed. The parameters of this model are updated using a framework provided by the learning classifier systems.

2011

In this paper we considered several frameworks for data mining. These frameworks are based on different approaches, including inductive databases approach, the reductionist statistical approaches, data compression approach, constructive induction approach and some others. We considered advantages and limitations of these frameworks. We presented the view on data mining research as continuous and never- ending development process of an adaptive DM system towards the efficient utilization of available DM techniques for solving a current problem impacted by the dynamically changing environment. We discussed one of the traditional information systems frameworks and, drawing the analogy to this framework, we considered a data mining system as the special kind of adaptive information system. We adapted the information systems development framework for the context of data-mining systems development.

Encyclopedia of Information Science and Technology, Third Edition

Information Systems, 2009

The number, variety and complexity of projects involving data mining or knowledge discovery in databases activities have increased just lately at such a pace that aspects related to their development process need to be standardized for results to be integrated, reused and interchanged in the future. Data mining projects are quickly becoming engineering projects, and current standard processes, like CRISP-DM, need to be revisited to incorporate this engineering viewpoint. This is the central motivation of this paper that makes the point that experience gained about the software development process over almost 40 years could be reused and integrated to improve data mining processes. Consequently, this paper proposes to reuse ideas and concepts underlying the IEEE Std 1074 and ISO 12207 software engineering model processes to redefine and add to the CRISP-DM process and make it a data mining engineering standard.