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Journal ArticleDOI

An overview of data warehousing and OLAP technology

01 Mar 1997-Vol. 26, Iss: 1, pp 65-74
TL;DR: An overview of data warehousing and OLAP technologies, with an emphasis on their new requirements, is provided, based on a tutorial presented at the VLDB Conference, 1996.
Abstract: 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 the VLDB Conference, 1996.

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Citations
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Book
08 Sep 2000
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

23,600 citations

Journal ArticleDOI
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Abstract: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

9,873 citations


Cites methods from "An overview of data warehousing and..."

  • ...aggregated recommendations is by utilizing the OLAP-based approach [ 19 ] to multidimensional...

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Posted Content
01 Jan 2001
TL;DR: This paper gives a lightning overview of data mining and its relation to statistics, with particular emphasis on tools for the detection of adverse drug reactions.
Abstract: The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

3,765 citations

01 Jan 2006
TL;DR: There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99].
Abstract: The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.

2,591 citations


Cites background from "An overview of data warehousing and..."

  • ...[CD97] S....

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  • ...Chaudhuri and Dayal [CD97] present a comprehensive overview of data warehouse technology....

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Journal Article
TL;DR: This work classifies data quality problems that are addressed by data cleaning and provides an overview of the main solution approaches and discusses current tool support for data cleaning.
Abstract: We classify data quality problems that are addressed by data cleaning and provide an overview of the main solution approaches. Data cleaning is especially required when integrating heterogeneous data sources and should be addressed together with schema-related data transformations. In data warehouses, data cleaning is a major part of the so-called ETL process. We also discuss current tool support for data cleaning.

1,675 citations


Cites background from "An overview of data warehousing and..."

  • ...Data warehouses [6][16] require and provide extensive support for data cleaning....

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References
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Book
01 Jan 1992
TL;DR: This Second Edition of Building the Data Warehouse is revised and expanded to include new techniques and applications of data warehouse technology and update existing topics to reflect the latest thinking.
Abstract: From the Publisher: The data warehouse solves the problem of getting information out of legacy systems quickly and efficiently. If designed and built right, data warehouses can provide significant freedom of access to data, thereby delivering enormous benefits to any organization. In this unique handbook, W. H. Inmon, "the father of the data warehouse," provides detailed discussion and analysis of all major issues related to the design and construction of the data warehouse, including granularity of data, partitioning data, metadata, lack of creditability of decision support systems (DSS) data, the system of record, migration and more. This Second Edition of Building the Data Warehouse is revised and expanded to include new techniques and applications of data warehouse technology and update existing topics to reflect the latest thinking. It includes a useful review checklist to help evaluate the effectiveness of the design.

2,820 citations

Journal ArticleDOI
26 Feb 1996
TL;DR: The data cube operator as discussed by the authors generalizes the histogram, cross-tabulation, roll-up, drill-down, and sub-total constructs found in most report writers.
Abstract: Data analysis applications typically aggregate data across many dimensions looking for unusual patterns. The SQL aggregate functions and the GROUP BY operator produce zero-dimensional or one-dimensional answers. Applications need the N-dimensional generalization of these operators. The paper defines that operator, called the data cube or simply cube. The cube operator generalizes the histogram, cross-tabulation, roll-up, drill-down, and sub-total constructs found in most report writers. The cube treats each of the N aggregation attributes as a dimension of N-space. The aggregate of a particular set of attribute values is a point in this space. The set of points forms an N-dimensionaI cube. Super-aggregates are computed by aggregating the N-cube to lower dimensional spaces. Aggregation points are represented by an "infinite value": ALL, so the point (ALL,ALL,...,ALL, sum(*)) represents the global sum of all items. Each ALL value actually represents the set of values contributing to that aggregation.

2,308 citations

Proceedings ArticleDOI
01 Jun 1996
TL;DR: In this article, a lattice framework is used to express dependencies among views and greedy algorithms are presented to determine a good set of views to materialize, with a small constant factor of optimal.
Abstract: Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total sales. The values of many of these cells are dependent on the values of other cells in the data cube. A common and powerful query optimization technique is to materialize some or all of these cells rather than compute them from raw data each time. Commercial systems differ mainly in their approach to materializing the data cube. In this paper, we investigate the issue of which cells (views) to materialize when it is too expensive to materialize all views. A lattice framework is used to express dependencies among views. We present greedy algorithms that work off this lattice and determine a good set of views to materialize. The greedy algorithm performs within a small constant factor of optimal under a variety of models. We then consider the most common case of the hypercube lattice and examine the choice of materialized views for hypercubes in detail, giving some good tradeoffs between the space used and the average time to answer a query.

1,499 citations

Proceedings ArticleDOI
02 Dec 1995
TL;DR: This paper motivates the concept of a data warehouse, outlines a general data warehousing architecture, and proposes a number of technical issues arising from the architecture that are suitable topics for exploratory research.
Abstract: The topic of data warehousing encompasses architectures, algorithms, and tools for bringing together selected data from multiple databases or other information sources into a single repository, called a data warehouse, suitable for direct querying or analysis. In recent years data warehousing has become a prominent buzzword in the database industry, but attention from the database research community has been limited. In this paper we motivate the concept of a data warehouse, we outline a general data warehousing architecture, and we propose a number of technical issues arising from the architecture that we believe are suitable topics for exploratory research.

772 citations