Topic
Data mart
About: Data mart is a research topic. Over the lifetime, 559 publications have been published within this topic receiving 8550 citations.
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01 Jan 2000
TL;DR: This chapter looks at more or less the same issues again, focusing, however, on problems rather than solutions, and introduces the DWQ conceptual framework which takes the business perspective of data warehousing into account as well as the so far dominant technical aspects.
Abstract: In the previous chapter, we have given a broad-brush state of the practice in data warehousing. In this chapter, we look at more or less the same issues again, focusing, however, on problems rather than solutions. Each of the topics we address is covered in the following chapters. In Section 2.6, we briefly review some larger research projects which address more than one of the issues and will therefore be cited in several places throughout the book. Finally, Section 2.7 takes a critical overall look at this work and introduces the DWQ conceptual framework which takes the business perspective of data warehousing into account as well as the so far dominant technical aspects.
2 citations
TL;DR: This paper proposes an algorithm to optimize the number of clusters and it also uses novel way to construct the data mart using the concept of multiprocessing Pri-tri algorithm.
Abstract: the process of data mining to extract knowledge from large data set needs great potential to extract the hidden nuggets. To cluster the numerical data there are enormous clustering technique. Data mining for categorical data(qualitative and quantitative) the most frequently used algorithms are k-means, k-mediods and fuzzy rule all these methods needs a threshold value to overcome this problem. This paper propose an algorithm to optimize the number of clusters and it also uses novel way to construct the data mart using the concept of multiprocessing Pri-tri algorithm. Keywordsmining, clustering, k-means, Multiprocessing.
2 citations
Proceedings Article•
01 Jan 2003
TL;DR: The author concludes that the web-like features of the Network Model are most conducive to the promotion of knowledge management principles, even though this model does have liabilities that require careful monitoring.
Abstract: This paper addresses the expectations, organizational implications, and information processing requirements, of the emerging knowledge management paradigm. A brief discussion of the enablement of the individual through the wide-spread availability of computer and communication facilities, is followed by a description of the structural evolution of organizations, and the architecture of a computer-based knowledge management system. The author discusses two trends that are driven by the treatment of information and knowledge as a commodity: increased concern for the management and exploitation of knowledge within organizations; and, the creation of an organizational environment that facilitates the acquisition, sharing and application of knowledge. Tracing the evolution of the structure of organizations, the author concludes that the web-like features of the Network Model are most conducive to the promotion of knowledge management principles, even though this model does have liabilities that require careful monitoring. The paper further discusses in some detail the architecture of a knowledge management system that consists of a lower integrated data layer and an upper information layer. Attention is drawn to the need of the data layer to include not only archived summary data as found in Data Warehouses and Data Marts, but also near real-time operational data with convenient access provided by Data Portals. An important distinction is drawn between data-centric and information-centric software environments in terms of software with an internal information model capable of supporting agents with automatic reasoning capabilities. The paper concludes with a brief description of the mechanisms through which a Web-Services environment provides access to distributed data sources, as well as heterogeneous data-centric and information-centric software applications.
2 citations
TL;DR: This study aims to develop the comprehensive GIS-based traffic accident database system through the integration of hospital-based data, police data and the road inventory data through the use of data taxonomy.
Abstract: This study aims to develop the comprehensive GIS-based traffic accident database system through the integration of hospital-based data, police data and the road inventory data. To determine how to integrate the data from three data sources, data taxonomy is utilized. Available data are hierarchically classified based on their share characteristic. Grouping data in this way is useful for understanding, designing, and building integrated data system. Data warehouse, a common data storage approach to integration, is utilized for the data integration. GIS is an enabling technology for the integration as well. The scope of data integration is established by identifying the model of target data (or integrated data) and identifying the disparate data that would be mapped to the target data. During the physical data integration process, data from the three data sources are extracted, transformed, cleaned and finally loaded into an integrated data source, a data mart or data warehouse.
2 citations