Multidimensional Databases and Data Warehousing

Jensen, Christian S.; Pedersen, Torben Bach; Thomsen, Christian

doi:10.2200/s00299ed1v01y201009dtm009

Cited by 37 publications

(31 citation statements)

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“…The MD model organizes data in terms of facts, i.e., data being analyzed, and dimensions, i.e., analytical perspectives [14]. Dimensions consist of levels representing different data granularities that are hierarchically organized into dimension hierarchies.…”

Section: Multidimensional Model and Olap Operationsmentioning

confidence: 99%

“…On-Line Analytical Processing (OLAP) is a well-established approach for data analysis to support decision-making [14]. Due to its wide acceptance and successful use by non-technical users, novel tendencies endorse broadening of its use from solutions working with in-house data sources to analysis considering external and non-controlled data.…”

Section: Introductionmentioning

confidence: 99%

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SM4MQ: A Semantic Model for Multidimensional Queries

et al. 2017

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Abstract. On-Line Analytical Processing (OLAP) is a data analysis approach to support decision-making. On top of that, Exploratory OLAP is a novel initiative for the convergence of OLAP and the Semantic Web (SW) that enables the use of OLAP techniques on SW data. Moreover, OLAP approaches exploit different metadata artifacts (e.g., queries) to assist users with the analysis. However, modeling and sharing of most of these artifacts are typically overlooked. Thus, in this paper we focus on the query metadata artifact in the Exploratory OLAP context and propose an RDF-based vocabulary for its representation, sharing, and reuse on the SW. As OLAP is based on the underlying multidimensional (MD) data model we denote such queries as MD queries and define SM4MQ: A Semantic Model for Multidimensional Queries. Furthermore, we propose a method to automate the exploitation of queries by means of SPARQL. We apply the method to a use case of transforming queries from SM4MQ to a vector representation. For the use case, we developed the prototype and performed an evaluation that shows how our approach can significantly ease and support user assistance such as query recommendation.

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Section: Multidimensional Model and Olap Operationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SM4MQ: A Semantic Model for Multidimensional Queries

et al. 2017

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“…Some of the major shortcomings that are removed during the data cleaning stage include inconsistencies between values recorded in different attributes that have the same meaning, data duplications, missing data and existence of inadmissible values [7]. Other common operations inside the transformation phase includes the conversion of character sets (e.g., from EBCDIC to Unicode), normalization/denormalization of the data into the desired dimensional DW schema, the assignment of surrogate keys to the data and maintenance of slowly changing dimensions by detecting changes and taking the actions necessary to contain with these [8].…”

Section: Data Capture/acquisitionmentioning

confidence: 99%

Building an Effective Data Warehousing for Financial Sector

Ferreira¹,

Almeida²,

Monteiro³

2017

ACIS

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This article presents the implementation process of a Data Warehouse and a multidimensional analysis of business data for a holding company in the financial sector. The goal is to create a business intelligence system that, in a simple, quick but also versatile way, allows the access to updated, aggregated, real and/or projected information, regarding bank account balances. The established system extracts and processes the operational database information which supports cash management information by using Integration Services and Analysis Services tools from Microsoft SQL Server. The end-user interface is a pivot table, properly arranged to explore the information available by the produced cube. The results have shown that the adoption of online analytical processing cubes offers better performance and provides a more automated and robust process to analyze current and provisional aggregated financial data balances compared to the current process based on static reports built from transactional databases.

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“…The data warehouse also included the data in the sources, but in multidimensional format. This format is very powerful for data analysis, and it is widely used for analyzing large amount of data [4].…”

Section: Development and Implementation Of A Clinical And Business Inmentioning

confidence: 99%

Development and Implementation of a Clinical and Business Intelligence System for the Florida Health Data Warehouse

AlHazme

Rana

Lucca

2014

OJPHI

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Objective: To develop and implement a Clinical and Business Intelligence (CBI) system for the Florida Health Data Warehouse (FHDW) in order to bridge the gap between Florida's healthcare stakeholders and the health data archived in FHWD. Materials and Methods: A gap analysis study has been conducted to evaluate the technological divide between the relevant users and FHWD health data, which is maintained by the Broward Regional Health Planning Council (BRHPC). The study revealed a gap between the health care data and the decision makers that utilize the FHDW data. To bridge the gap, a CBI system was proposed, developed and implemented by BRHPC as a viable solution to address this issue, using the System Development Life Cycle methodology. Results: The CBI system was successfully implemented and yielded a number of positive outcomes. In addition to significantly shortening the time required to analyze the health data for decision-making processes, the solution also provided end-users with the ability to automatically track public health parameters. Discussion: A large amount of data is collected and stored by various health care organizations at the local, state, and national levels. If utilized properly, such data can go a long way in optimizing health care services. CBI systems provide health care organizations with valuable insights for improving patient care, tracking trends for medical research, and for controlling costs. Conclusion:The CBI system has been found quite effective in bridging the gap between Florida's healthcare stake holders and FHDW health data. Consequently, the solution has improved in the planning and coordination of health care services for the state of Florida.

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Multidimensional Databases and Data Warehousing

Cited by 37 publications

References 50 publications

SM4MQ: A Semantic Model for Multidimensional Queries

SM4MQ: A Semantic Model for Multidimensional Queries

Building an Effective Data Warehousing for Financial Sector

Development and Implementation of a Clinical and Business Intelligence System for the Florida Health Data Warehouse

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