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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An overview of data warehousing and OLAP technology

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.

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Data Mining: Concepts and Techniques (2nd edition)

Design science research (DSR) has staked its rightful ground as an important and legitimate Information Systems (IS) research paradigm We contend that DSR has yet to attain its full potential impact on the development and use of information systems due to gaps in the understanding and application of DSR concepts and methods This essay aims to help researchers (1) appreciate the levels of artifact abstractions that may be DSR contributions, (2) identify appropriate ways of consuming and producing knowledge when they are preparing journal articles or other scholarly works, (3) understand and position the knowledge contributions of their research projects, and (4) structure a DSR article so that it emphasizes significant contributions to the knowledge base Our focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study In addition, we propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section We evaluate the DSR contribution framework and the DSR communication schema via examinations of DSR exemplar publications

https://www.misq.org/skin/frontend/default/misq/pdf/appendices/2013/V37I2_Appendices/GregorHevnerAppendices.pdf

Positioning and presenting design science research for maximum impact

Since the early 1970s, decision support systems (DSS) technology and applications have evolved significantly. Many technological and organizational developments have exerted an impact on this evolution. DSS once utilized more limited database, modeling, and user interface functionality, but technological innovations have enabled far more powerful DSS functionality. DSS once supported individual decision-makers, but later DSS technologies were applied to workgroups or teams, especially virtual teams. The advent of the Web has enabled inter-organizational decision support systems, and has given rise to numerous new applications of existing technology as well as many new decision support technologies themselves. It seems likely that mobile tools, mobile e-services, and wireless Internet protocols will mark the next major set of developments in DSS. This paper discusses the evolution of DSS technologies and issues related to DSS definition, application, and impact. It then presents four powerful decision support tools, including data warehouses, OLAP, data mining, and Web-based DSS. Issues in the field of collaborative support systems and virtual teams are presented. This paper also describes the state of the art of optimization-based decision support and active decision support for the next millennium. Finally, some implications for the future of the field are discussed.

Past, present, and future of decision support technology

STRIDE (Stanford Translational Research Integrated Database Environment) is a research and development project at Stanford University to create a standards-based informatics platform supporting clinical and translational research STRIDE consists of three integrated components: a clinical data warehouse, based on the HL7 Reference Information Model (RIM), containing clinical information on over 13 million pediatric and adult patients cared for at Stanford University Medical Center since 1995; an application development framework for building research data management applications on the STRIDE platform and a biospecimen data management system STRIDE’s semantic model uses standardized terminologies, such as SNOMED, RxNorm, ICD and CPT, to represent important biomedical concepts and their relationships The system is in daily use at Stanford and is an important component of Stanford University’s CTSA (Clinical and Translational Science Award) Informatics Program

STRIDE--An integrated standards-based translational research informatics platform.

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.

Building the data warehouse

The Data Warehouse: A Definition. The Operational Data Store. The Data Warehouse and the ODS: Architecture for Information Systems. Using the Data Warehouse: The Manager's Perspective. Using the Data Warehouse: The End User's Perspective. Using the Data Warehouse: Creating the Analysis. Using Information for Competitive Advantage: Some Examples. Administering the Data Warehouse Environment. Migration to the Architected Environment. Connecting to the Data Warehouse. Index.

Using the Data Warehouse

Data Warehousing has been around for 20 years and has become part of the information technology infrastructure. Data warehousing originally grew in response to the corporate need for information--not data--and it supplies integrated, granular, and historical data to the corporation. There are many kinds of data warehouses, in large part due to evolution and different paths of software and hardware vendors. But DW 2.0, defined by this author in many talks, articles, and his b-eye-network newsletter that reaches 65,000 professionals monthly, is the well-identified and defined next generation data warehouse. The book carries that theme and describes the future of data warehousing that is technologically possible now, at both an architectural level and technology level. The perspective of the book is from the top down: looking at the overall architecture and then delving into the issues underlying the components. The benefit of this for people who are building or using a data warehouse can see what lies ahead, and can determine: what new technology to buy, how to plan extensions to the data warehouse, what can be salvaged from the current system, and how to justify the expense--at the most practical level. All of this gives the experienced data warehouse professional everything and exactly what is needed in order to implement the new generation DW 2.0.* First book on the new generation of data warehouse architecture, DW 2.0. * Written by the "father of the data warehouse", Bill Inmon, a columnist and newsletter editor of The Bill Inmon Channel on the Business Intelligence Network. * Long overdue comprehensive coverage of the implementation of technology and tools that enable the new generation of the DW: metadata, temporal data, ETL, unstructured data, and data quality control.

Dw 2.0: The Architecture for the Next Generation of Data Warehousing

From the Publisher:
From traditional data warehousing to data marts and operational data stores, a dizzying array of architectures and tools are now available to help enterprises strategically use and manage information. Each has its unique costs and benefits associated with delivering value to the business. But, despite all the hype, not all solutions are equally well suited to every company's needs. In Corporate Information Factory, Bill Inmon, Claudia Imhoff, and Ryan Sousa introduce a practical and proven framework that shows companies how to leverage these solutions to build a company-wide information ecosystem.

Corporate Information Factory

From the Publisher:
The operational data store, or ODS, is a dynamic architectural construct designed specifically for high-speed, integrated operational processing. It achieves at the operational level what the data warehouse does at the strategic/managerial level. Now, from a team of experts headed by W. H. Inmon, "father of the data warehouse," here is the first comprehensive guide to understanding, building, and using operational data stores.

William H. Inmon

Papers

Building the data warehouse

Using the Data Warehouse

Dw 2.0: The Architecture for the Next Generation of Data Warehousing

Corporate Information Factory

Building the Operational Data Store