INDUSTRIAL SIGNIFICANCE OF DATA WAREHOUSING. (original) (raw)
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2013
This paper provides an overview of Data warehousing, Data Mining, OLAP, OLTP technologies, exploring the features, applications and the architecture of Data Warehousing. The data warehouse supports on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the on-line transaction processing (OLTP) applications traditionally supported by the operational databases. Data warehouses provide on-line analytical processing (OLAP) tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data mining. Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. OLTP is customer-oriented and is used for transaction and query processing by clerks, clients and information technology professionals. An OLAP system is market-oriented and is used for data analysis by knowledge...
IJERT-Data warehousing and olap technologies for decision making process IJERTV1IS
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/data-warehousing-and-olap-technologies-for-decision-making-process https://www.ijert.org/research/data-warehousing-and-olap-technologies-for-decision-making-process-IJERTV1IS6253.pdf Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. Many commercial products and services are now available, and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications. This paper provides an overview of data warehousing and OLAP technologies. Data warehousesprovide on-line analytical processing (OLAP) tools for theinteractive analysis of multidimensional data of varied granularities, which facilitates effective data mining. Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. s. An OLAP system is market-oriented and is used for data analysis by knowledge workers, including managers, executives and analysts. Data warehousing and OLAP have emerged as leading technologies that facilitate data storage, organization and then, significant retrieval. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications.
An overview of data warehousing and OLAP technology
ACM Sigmod record, 1997
Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. Many commercial products and services are now available, and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications. This paper provides an overview of data warehousing and OLAP technologies, with an emphasis on their new requirements. We describe back end tools for extracting, cleaning and loading data into a data warehouse; multidimensional data models typical of OLAP; front end client tools for querying and data analysis; server extensions for efficient query processing; and tools for metadata management and for managing the warehouse. In addition to surveying the state of the art, this paper also identifies some promising research issues, some of which are related to problems that the database research community has worked on for years, but others are only just beginning to be addressed. This overview is based on a tutorial that the authors presented at
In recent years, it has been imperative for organizations to make fast and accurate decisions in order to make them much more competitive and profitable. Data warehouses appear as key technological elements for the exploration and analysis of data, and subsequent decision making in a business environment. This book deals with the fundamental concepts of data warehouses and explores the concepts associated with data warehousing and analytical information analysis using OLAP. The reader is guided by the theoretical description of each of the concepts and by the presentation of numerous practical examples that allow assimilating the acquisition of skills in the field.
Data Mining and Data Warehousing
Today in organizations, the developments in the transaction processing technology requires that, amount and rate of data capture should match the speed of processing of the data into information which can be utilized for decision making. A data warehouse is a subject- oriented, integrated, time-variant and non-volatile collection of data that is required for decision making process. Data mining involves the use of various data analysis tools to discover new facts, valid patterns and relationships in large data sets. The data warehouse supports on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the on-line transaction processing (OLTP) applications traditionally supported by the operational databases. Data warehouses provide on-line analytical processing (OLAP) tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data mining. Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. OLTP is customer-oriented and is used for transaction and query processing by clerks, clients and information technology professionals. An OLAP system is market-oriented and is used for data analysis by knowledge workers, including managers, executives and analysts. Data warehousing and OLAP have emerged as leading technologies that facilitate data storage, organization and then, significant retrieval. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications.
Review on Data Warehouse: As Prerequisite Aspect of Business Decision Making Activity
— This paper describes the technology of data warehouse in decision making. Data warehousing and on-line analytical processing (OLAP) are prerequisite aspects of decision support, which has increasingly become a focus of the database industry. The construction of data warehouses involves data cleaning, data integration, data transformation and as important pre-processing step for data mining. The data warehouse supports on-line analytical processing (OLAP) which has the functional and performance requirements. Data warehousing have evolved as one of primary technologies that facilitate data storage, organization and, denoting retrieval. Different requirements on database technology compared to traditional on-line transaction processing applications.
The Survey On: Data Mining Data Warehousing & OLAP
International Journal on Recent and Innovation Trends in Computing and Communication, 2016
This paper gives a review of the Data mining handle. After the investigation of the way of data mining and its significance in information warehousing is included. It depicts the CRISP-DM standard now being utilized as a part of industry as the standard for an innovation impartial data mining prepare display. The paper finishes up with a noteworthy delineation of the data mining handle system and the unsolved issues that offer open doors for research. The approach is both reasonable and theoretically stable to be valuable to both scholastics and experts.
Role of OLAP Technology in Data Warehousing for Knowledge Discovery
There are a set of noteworthy newfangled concepts and tools developed into a innovative technology that makes it conceivable to occurrence the problem of providing all the key people in the innovativeness with admittance to whatever level of information needed for the inventiveness to endure and flourish in an progressively modest world. The term that has come to characterize this new technology is "Data Warehousing" The problem of getting combined and generalized information fast from an active enterprise database becomes actual having its data been accumulated for some years. The classical reports even if optimized for particular purposes do not let one obtain fast the enterprise information with differently data-dependent views. The problem is proper to absolutely all the systems that accumulate large data volumes of information for further processing. To solve the problem is the destiny of the OLAP (On-Line Analytical Processing) technology. The technology nowadays acquiring more and more popularity is assigned to be active and operative handle for multidimensional data and Knowledge D.iscovery is defined as "the non-trivial extraction of implicit, unknown, and potentially useful information from data''. This paper shows the role of OLAP Technology in Data Warehousing for Knowledge Discovery.
Advances and Research Directions in Data-Warehousing Technology
Australasian Journal of Information Systems, 1999
Information is one of the most valuable assets of an organisation and when used properly can assist in intelligent decision making that can significantly improve the functioning of an organisation. Data Warehousing is a recent technology that allows information to be easily and efficiently accessed for decision-making activities by collecting data from many operational, legacy and possibly heterogeneous data sources. On-Line Analytical Processing (OLAP) tools are well-suited for complex data analysis, such as multi-dimensional data analysis, and to assist in decision support activities while data mining tools take the process one step further and actively search the data for patterns and hidden knowledge in the data held in the warehouse. Many organisations are building, or are planning to develop, a data warehouse for their operational and decision support needs. In this paper, we present an overview of data warehousing, multi-dimensional databases, OLAP and data mining technology and discuss the directions of current research in the area. We also discuss recent developments in data warehouse modelling, view selection and maintenance, indexing schemes, parallel query processing and data mining issues. A number of technical issues for exploratory research are presented and possible solutions are also discussed.