data warehousing best papers
Data modeling techniques for data warehousing
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This redbook gives detail coverage to the topic of data modeling techniques for data warehousing , within the context of the overall data warehouse development process. The process of data warehouse modeling, including the steps required before and after the
Algorithms for materialized view design in data warehousing environment
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Data warehouses contain multiple views accessed by queries. When these views are related to each other and de ned over overlapping portions of the base data, it be more e cient not to materialize all the views, but rather to materialize certain\shared portions of
Data warehousing battle of the giants
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We begin our discussion by defining the data warehouse. We will introduce the Inmon- Kimball debate, and provide a brief history of the evolution of the two models. We also provide a brief explanation of the nature of the data warehouse, and conclude with a
Modern data warehousing , mining, and visualization: core concepts
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Another is the need to store unstructured data such as multimedia, maps and sound. The growth of the Internet allows integration of external data into a DW, but its unstable quality is likely to lead to the evolution of third-party intermediaries whose purpose is to rate data
A descriptive classification of causes of data quality problems in data warehousing
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Data warehousing is gaining in eminence as organizations become awake of the benefits of decision oriented and business intelligence oriented data bases. However, there is one key stumbling block to the rapid development and implementation of quality data warehouses
A Principled Approach to Data Integration and Reconciliation in Data Warehousing .
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Integration is one of the most important aspects of a Data Warehouse. When data passes from the sources of the application-oriented operational environment to the Data Warehouse, possible inconsistencies and redundancies should be resolved, so that the
The Netezza data appliance architecture: A platform for high performance data warehousing and analytics
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Success in any enterprise depends on having the best available information in time to make sound decisions. Anything less wastes opportunities, costs time and resources, and can even put the organization at risk. But finding crucial information to guide the best possible
Managing and measuring data quality in data warehousing
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High level data quality and the management of ensuring data quality is one of the key success factors for Data Warehousing projects. The following article describes an approach for Data Quality Management, which is based on theories as well as practical experiences
Data warehousing special report: Data quality and the bottom line
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During the past 50 years, the developed world has moved from an industrial economy to an information economy. Companies now compete on their ability to absorb and respond to information, not just manufacture and distribute products. Intellectual capital and know-how
The politics of data warehousing
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Data warehousing projects are frequently side-tracked or derailed completely by non- technical factors, in particular the political treaty lines within the firm, and the politicized nature of data itself. Because data warehouses are infrastructure for sociotechnical systems
Gulliver in the land of data warehousing : practical experiences and observations of a researcher.
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The gap between researchers and practitioners is widely discussed in the IT community. The purpose of this paper is towards showing the issues which occupy both research and practice, and the extent to which these issues have any overlap, in the field of data
Data warehousing : Beyond disaggregation
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Lawrence M. Rudner and Carol Boston ducators have long disaggregated test scores by race, sex, and socioeconomic class. The new No Child Left Behind legislation requires breaking down results further to consider disabilities and English-language status. The
Zero-latency data warehousing for heterogeneous data sources and continuous data streams
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In this paper, a framework for building an overall Zero-Latency Data Warehouse system (ZLDWH) is provided. Such a ZLDWH requires tasks such as data changes detection, continuous loading and updating of new data, autonomous analysis and the execution of
An integrative and uniform model for metadata management in data warehousing environments
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Due to the increasing complexity of data warehouses, a centralized and declarative management of metadata is essential for data warehouse administration, maintenance and usage. Metadata are usually divided into technical and semantic metadata. Typically, current
Simulated Annealing for Materialized View Selection in Data Warehousing Environment.
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In order to facilitate query processing, the information contained in data warehouses is typically stored as a set of materialized views. Deciding which views to materialize presents a considerable challenge. The task is to select from a very large search space a set of views
Data warehousing in disease management programs
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Disease management programs offer the benefits of lower disease occurrence, improved patient care, and lower healthcare costs. In such programs, the key mechanism used to identify individuals at risk for targeted diseases is the data warehouse. This article surveys
ETL evolution for real-time data warehousing
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Informed decision-making is required for competitive success in the new global marketplace, which is fraught with uncertainty and rapid technology changes. Decision makers must adjust operational processes, corporate strategies, and business models at lightning speed
Data Integration and Warehousing Using H2O.
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systems. Among a wide range of techniques addressing this problem, data warehousing , ie, materializing integrated information in a persistent store, is gaining increasing importance WHW90, KAAK9 IK9 ZGHW95]. This
The role of metadata for data warehousing
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Metadata has been identified as a key success factor in data warehouse projects. It captures all kinds of information necessary to extract, transform and load data from source systems into the data warehouse, and afterwards to use and interpret the data warehouse contents
Warehousing workflow data: Challenges and opportunities
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In this paper we present a packaged data warehousing solution, coupled with HP Process Manager, for collecting and analyzing workflow execution data. We first present the main challenges involved in this effort, and then detail the proposed approach CSE PROJECTS