Design a Data Warehouse Schema from Document-Oriented database

Bouaziz, Senda; Nabli, Ahlem; Gargouri, Faïez

doi:10.1016/j.procs.2019.09.177

Cited by 21 publications

(11 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process also specifies how to transfer the merged data [1]. Data warehouse (DW) can be defined as a subject-oriented, time-variant, integrated and non-volatile data used to support strategic decision making [2]. DW holds a collection of permanent historical data that assists in administrative decision making to help in accessing data for the purposes of time analysis, knowledge discovery and decision making [3].…”

Section: Introductionmentioning

confidence: 99%

Implementing data-driven decision support system based on independent educational data mart

Hamoud

Hussein

Alhilfi

et al. 2021

IJECE

View full text Add to dashboard Cite

<span>Decision makers in the educational field always seek new technologies and tools, which provide solid, fast answers that can support decision-making process. They need a platform that utilize the students’ academic data and turn them into knowledge to make the right strategic decisions. In this paper, a roadmap for implementing a data driven decision support system (DSS) is presented based on an educational data mart. The independent data mart is implemented on the students’ degrees in 8 subjects in a private school (Al-Iskandaria Primary School in Basrah province, Iraq). The DSS implementation roadmap is started from pre-processing paper-based data source and ended with providing three categories of online analytical processing (OLAP) queries (multidimensional OLAP, desktop OLAP and web OLAP). Key performance indicator (KPI) is implemented as an essential part of educational DSS to measure school performance. The static evaluation method shows that the proposed DSS follows the privacy, security and performance aspects with no errors after inspecting the DSS knowledge base. The evaluation shows that the data driven DSS based on independent data mart with KPI, OLAP is one of the best platforms to support short-to-long term academic decisions.</span>

show abstract

Section: Introductionmentioning

confidence: 99%

Implementing data-driven decision support system based on independent educational data mart

Hamoud

Hussein

Alhilfi

et al. 2021

IJECE

View full text Add to dashboard Cite

show abstract

“…HDFS running on a server, not on the cloud, (2). one needs to estimate in advance the requirement of storage and processor prior running the HDFS, (3). One needs to do the estimation precisely, otherwise it will be a wasted budget, (4) Map Reduce jobs in HDFS cannot be started until we started the Name Node exit safe mode; (5) Hadoop requires regular maintenance; (6) Hadoop requires a pretty complex configuration to use other services; (7) Data is stored in computer/server node clusters so that they are vulnerable to a single-point failure.…”

Section: Introductionmentioning

confidence: 99%

a Schema Extraction of Document-Oriented Database for Data Warehouse

Istiqamah

Wiharja

2021

ijoict

View full text Add to dashboard Cite

The data warehouse is a very famous solution for analyzing business data from heterogeneous sources. Unfortunately, a data warehouse only can analyze structured data. Whereas, nowadays, thanks to the popularity of social media and the ease of creating data on the web, we are experiencing a flood of unstructured data. Therefore, we need an approach that can "structure" the unstructured data into structured data that can be processed by the data warehouse. To do this, we propose a schema extraction approach using Google Cloud Platform that will create a schema from unstructured data. Based on our experiment, our approach successfully produces a schema from unstructured data. To the best of our knowledge, we are the first in using Google Cloud Platform for extracting a schema. We also prove that our approach helps the database developer to understand the unstructured data better.

show abstract

“…Basically, the OLAP paradigm establishes that in decision-making processes the data must be organized according to the analysis perspectives, for this reason the data is not structured according to the objects present in the problem, nor neither in lines or sequences of transactions, but according to the needs of analysis, which then translates into dimensions, which in turn are structured in hierarchies of attributes, to which indicators called measures are associated that are finally subject to analysis [9] [10] [2] [3].…”

Section: Introductionmentioning

confidence: 99%

“…Query languages in multidimensional databases, such as the case of MDX [3], are usually quite aligned with other more classic query languages. Generally, the most operational aspects have been privileged over the more formal aspects.…”

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

Design a Data Warehouse Schema from Document-Oriented database

Cited by 21 publications

References 1 publication

Implementing data-driven decision support system based on independent educational data mart

Implementing data-driven decision support system based on independent educational data mart

a Schema Extraction of Document-Oriented Database for Data Warehouse

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

Contact Info

Product

Resources

About