A comparative analysis of data warehouse data models

Bojicic, Ivan; Marjanovic, Zoran; Turajlić, Nina; Petrović, Marko D.; Vuckovic, Milica; Jovanovic, Vladan

doi:10.1109/icccc.2016.7496754

Cited by 4 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Very often, multidimensional databases have been implemented on relational data base systems, in a modality known as ROLAP. The adaptation to relational technology obviously has some negative consequences, one of the most obvious being the need to transform OLAP queries into relational queries [10] [1].…”

Section: Introductionmentioning

confidence: 99%

A Simpler and Semantic Multidimensional Database Query Language to Facilitate Access to Information in Decision-making

Palominos

Córdova

Durán

et al. 2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

OLAP and multidimensional database technology have contributed significantly to speed up and build confidence in the effectiveness of methodologies based on the use of management indicators in decision-making, industry, production, and services. Although there are a wide variety of tools related to the OLAP approach, many implementations are performed in relational database systems (R-OLAP). So, all interrogation actions are performed through queries that must be reinterpreted in the SQL language. This translation has several consequences because SQL language is based on a mixture of relational algebra and tuple relational calculus, which conceptually responds to the logic of the relational data model, very different from the needs of the multidimensional databases. This paper presents a multidimensional query language that allows expressing multidimensional queries directly over ROLAP databases. The implementation of the multidimensional query language will be done through a middleware that is responsible for mapping the queries, hiding the translation to a layer of software not noticeable to the end-user. Currently, progress has been made in the definition of a language where through a key statement, called aggregate, it is possible to execute the typical multidimensional operators which represent an important part of the most frequent operations in this type of database.

show abstract

Section: Introductionmentioning

confidence: 99%

A Simpler and Semantic Multidimensional Database Query Language to Facilitate Access to Information in Decision-making

Palominos

Córdova

Durán

et al. 2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

show abstract

“…Para designer dimensional model biasanya melakukan proses manual untuk menganalisis sumber data yang akan digunakan dalam pembuatan dimensional model dan selanjutnya membuat skema desain dimensional model secara manual dari sumber data yang telah dianalisis (Wamars & Randriatoamanana, 2016). Hal tersebut dilakukan karena tidak adanya model standar untuk mewakili struktur data warehouse (Bojičić, et al, 2016).…”

unclassified

Transformasi Ontologi ke Model Dimensional

Togatorop

Simanjuntak²,

Nababan³

et al. 2022

JTIIK

View full text Add to dashboard Cite

Data warehouse adalah salah satu komponen penting untuk analisis bisnis yang efektif. Salah satu model pada data warehouse adalah dimensional model yang banyak digunakan karena performa permrosesan yang lebih cepat dari model lain. Dua faktor utama pada perancangan dimensional model adalah adalah sumber data dan kebutuhan bisnis.Salah satu sumber data yang banyak digunakan adalah ontologi karena mampu merepresentasikan data menjadi informasi yang koheren yang dapat dimasukkan ke dalam Data Warehouse. Dalam penelitian ini dihasilkan sebuah tools berbasis ontologi yang digunakan untuk secara otomatis mendapatkan dimensional model untuk data warehouse dari sumber data dan kebutuhan bisnis. Tahapan yang dilakukan mengidentifikasi semua informasi pada file ontology yang dimasukkan pengguna, kemudian daftar dimensi dan fact dihasilkan berdasarkan aturan perancangan dimensional model. Pembuatan fact tabel dan dimensi tabel dirancang berdasarkan rule perancangan dimensional model yang diperkenalkan oleh Kimball. Setelah tabel fact dan dimensidiidentifikasi, maka tabel fact dan dimensi tersebut diubah ke dalam bentuk kueri yang dapat dieksekusi pada MySQL. Penelitian ini berhasil menghasilkan dimensional model dari sumber data ontology. AbstractThe data warehouse is one of the essential components for effective business analysis. One of the models in the data warehouse is the dimensional model that is widely used because the processing performance is faster than other models. The two main factors in designing a dimensional model are data sources and business requirements. One of the most widely used data sources is ontology because it is able to represent data into coherent information that can be entered into the Data Warehouse. In this research, an ontology-based tool is produced which is used to automatically obtain dimensional models for the data warehouse from data sources and business needs. The step taken identifies all the information in the ontology file entered by the user, then a list of dimensions and facts is generated based on the design rules for the dimensional model. The creation of fact tables and dimension tables are designed based on the dimensional model design rules introduced by Kimball. After the fact and dimension tables are identified, the fact and dimension tables are converted into queries that can be executed in MySQL. This study succeeded in producing dimensional models from the ontology data source.

show abstract

“…In this paper a comparative analysis of the four most prominent DW data models (namely the relational/normalized model, data vault model, anchor model and dimensional model) will be given. On the basis of the results of [1] a , the new DW data model (the Domain/Mapping model-DMM) which would more adequately fulfill the posed requirements is presented. …”

mentioning

confidence: 99%

Domain/Mapping Model: A Novel Data Warehouse Data Mode

Bojicic

Marjanovic

Turajlić

et al. 2017

INT J COMPUT COMMUN

Self Cite

View full text Add to dashboard Cite

Abstract:In order for a data warehouse to be able to adequately fulfill its integrative and historical purpose, its data model must enable the appropriate and consistent representation of the different states of a system. In effect, a DW data model, representing the physical structure of the DW, must be general enough, to be able to consume data from heterogeneous data sources and reconcile the semantic differences of the data source models, and, at the same time, be resilient to the constant changes in the structure of the data sources. One of the main problems related to DW development is the absence of a standardized DW data model. In this paper a comparative analysis of the four most prominent DW data models (namely the relational/normalized model, data vault model, anchor model and dimensional model) will be given. On the basis of the results of [1] a , the new DW data model (the Domain/Mapping model-DMM) which would more adequately fulfill the posed requirements is presented.

show abstract

A comparative analysis of data warehouse data models

Cited by 4 publications

References 7 publications

A Simpler and Semantic Multidimensional Database Query Language to Facilitate Access to Information in Decision-making

A Simpler and Semantic Multidimensional Database Query Language to Facilitate Access to Information in Decision-making

Transformasi Ontologi ke Model Dimensional

Domain/Mapping Model: A Novel Data Warehouse Data Mode

Contact Info

Product

Resources

About