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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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Book ChapterDOI
01 Jan 2012
TL;DR: The problem of Data Mart integration is reviewed, introducing the major types of conflicts considered in the literature and proposing strategies for their reconciliation based on formula manipulation.
Abstract: In the present literature Data Mart integration is typically considered from a dimension point of view. Approaches elaborate upon the hierarchical structure of dimensions to find the minimum common hierarchy where original dimensions can be mapped to. Although the problem of the conformance of measures has been recognized in the literature (see e.g. Kimball R, Ross M (2002) The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling (2nd Ed.), John Wiley & Sons, p. 87) as a condition for effective Data Mart integration, it is considered as a pre-requisite, so that integration strategies borrowed from the Database domain can be used. Considering the functional structure of a measure, that is the formula used to compute it, method and tools can be developed to support conformance checking and reconciliation. In this paper we review the problem of Data Mart integration, introducing the major types of conflicts considered in the literature. Next, we define novel types of conflicts hindering the conformance of measures and propose strategies for their reconciliation based on formula manipulation.

4 citations

01 Jan 2009
TL;DR: The main concepts and terminology of temporal databases are introduced and the open research issues also in connection with their implementation on commercial tools are discussed.
Abstract: Data warehouses are information repositories specialized in supporting decision making. Since the decisional process typically requires an analysis of historical trends, time and its management acquire a huge importance. In this paper we consider the variety of issues, often grouped under term temporal data warehousing, implied by the need for accurately describing how information changes over time in data warehousing systems. We recognize that, with reference to a three-levels architecture, these issues can be classified into some topics, namely: handling data/schema changes in the data warehouse, handling data/schema changes in the data mart, querying temporal data, and designing temporal data warehouses. After introducing the main concepts and terminology of temporal databases, we separately survey these topics. Finally, we discuss the open research issues also in connection with their implementation on commercial tools.

4 citations

Book ChapterDOI
01 Jan 2011
TL;DR: A data warehouse is kind of database whose architecture (and underlying supporting technology) has been optimized for highly efficient query, at the cost of sacrificing features that support robust interactive inserts, updates and delete actions.
Abstract: A data warehouse is kind of database whose architecture (and underlying supporting technology) has been optimized for highly efficient query, at the cost of sacrificing features that support robust interactive inserts, updates and delete actions. The difference between a data warehouse and a data mart (which is also optimized for the same purpose) is partly one of scope. While warehouses are supposed to encompass data across an entire organization, data marts are typically smaller scale (e.g., departmental in scope) though in an ideal situation they would receive data from a warehouse, effectively serving as front-ends to the latter.

4 citations

Book ChapterDOI
03 Sep 2018
TL;DR: This work examines more lightweight data marts in an infrastructure which can support on-demand queries, and presents an evaluation which verifies the transformation process from source to data mart.
Abstract: The Agri sector has shown an exponential growth in both the requirement for and the production and availability of data. In parallel with this growth, Agri organisations often have a need to integrate their in-house data with international, web-based datasets. Generally, data is freely available from official government sources but there is very little unity between sources, often leading to significant manual overhead in the development of data integration systems and the preparation of reports. While this has led to an increased use of data warehousing technology in the Agri sector, the issues of cost in terms of both time to access data and the financial costs of generating the Extract-Transform-Load layers remain high. In this work, we examine more lightweight data marts in an infrastructure which can support on-demand queries. We focus on the construction of data marts which combine both enterprise and web data, and present an evaluation which verifies the transformation process from source to data mart.

4 citations

Proceedings Article
21 May 2000
TL;DR: This paper deals with the management of organizational risks in large scale data warehouse projects and presents a business case strategy for both the initial project and subsequent data mart projects.
Abstract: Managers of large data warehousing projects often put project failure down to organizational resistance. Technical requirements are usually not considered to be crucial for project success. However, the majority of the scientific work on data warehousing concentrates on technical aspects. As a consequence, a comprehensive framework or method for the introduction of a data warehouse is still missing. This paper takes this contradiction into account and deals with the management of organizational risks in large scale data warehouse projects. We base our research on information gathered from large organizations which develop and/or run data warehouses.This paper is structured as follows: After a short introduction the planning process of data warehousing is outlined. The first step is the strategic decision for data warehousing which is followed by the definition and evaluation of the initial project which is also called first increment. In the third section we present a business case strategy for both the initial project and subsequent data mart projects. Furthermore, the interdependencies between the initial phase and subsequent projects are discussed.

4 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202113
202020
201926
201823
201726
201627