Classification of user interfaces for graph-based online analytical processing

2016 ◽  
Author(s):  
James R. Michaelis
Keyword(s):  
User Interfaces ◽  
Online Analytical Processing ◽  
Analytical Processing

Schema Evolution in Multiversion Data Warehouses

2021 ◽  
Vol 17 (4) ◽  
pp. 1-28
Author(s):  
Waqas Ahmed ◽  
Esteban Zimányi ◽  
Alejandro A. Vaisman ◽  
Robert Wrembel
Keyword(s):  
Efficient Solution ◽  
Business Processes ◽  
Online Analytical Processing ◽  
User Requirements ◽  
Schema Evolution ◽  
Data Warehouses ◽  
Relational Schema ◽  
External Sources ◽  
Analytical Processing

Data warehouses (DWs) evolve in both their content and schema due to changes of user requirements, business processes, or external sources to name a few. Although multiple approaches using temporal and/or multiversion DWs have been proposed to handle these changes, an efficient solution for this problem is still lacking. The authors' approach is to separate concerns and use temporal DWs to deal with content changes, and multiversion DWs to deal with schema changes. To address the former, previously, they have proposed a temporal multidimensional (MD) model. In this paper, they propose a multiversion MD model for schema evolution to tackle the latter problem. The two models complement each other and allow managing both content and schema evolution. In this paper, the semantics of schema modification operators (SMOs) to derive various schema versions are given. It is also shown how online analytical processing (OLAP) operations like roll-up work on the model. Finally, the mapping from the multiversion MD model to a relational schema is given along with OLAP operations in standard SQL.

An Online Analytical Processing Database for Environmental Water Quality Analytics

2018 ◽  
Author(s):  
Dallas Snider ◽  
John Derek Morgan ◽  
Matthew Schwartz ◽  
Austin Adkison ◽  
Delikarl Jean Baptiste
Keyword(s):  
Water Quality ◽  
Environmental Water ◽  
Online Analytical Processing ◽  
Analytical Processing

scholarly journals Perancangan Data Warehouse Rumah Sakit Berbasis Online Analytical Processing (OLAP)

2021 ◽  
Vol 8 (5) ◽  
pp. 1077
Author(s):  
Joko Purwanto ◽  
Renny Renny
Keyword(s):  
Information Technology ◽  
Health Services ◽  
Data Warehouse ◽  
Data Extraction ◽  
Online Analytical Processing ◽  
Data Source ◽  
Analytical Processing ◽  
Quality Of Health Services ◽  
Warehouse Design

<p class="BodyCxSpFirst">Pemanfaatan teknologi informasi sangat penting bagi rumah sakit, karena berpengaruh pula terhadap kualitas pelayanan kesehatan yang secara manual diubah menjadi digital dengan menggunakan teknologi informasi.Dalam penelitian ini penulis menggunakan metodologi <em>Nine step</em> sebagai acuan dalam merancang suatu <em>data warehouse</em><em>,</em> untuk pemodelan menggunakan skema konstelasi fakta dengan 3 tabel fakta dan 11 tabel dimensi. Perbedaan penelitian ini dengan penelitian sebelumnya terletak pada sumber data yang diekstrak langsung dari <em>database</em> SIMRS yang digunakan rumah sakit, sehingga tidak ada ekstraksi data secara manual.Penelitian ini bertujuan untuk menghasilkan desain data warehouse berbasis Online Analytical Processing (OLAP) sebagai sarana penunjang kualitas pelayanan kesehatan rumah sakit. OLAP yang dihasilkan akan berupa desain data warehouse dengan berbagai dimensi yang akan menghasilkan tampilan informasi berupa Chart maupun Grafik sehingga informasinya mudah dibaca dan dipahami oleh berbagai pihak.</p><p class="BodyCxSpFirst"> </p><p class="BodyCxSpFirst"><em><strong>Abtract</strong></em></p><p class="BodyCxSpFirst"><em>The use of information technology is very important for hospitals, because it also affects the quality of health services, which manualy changed to digital using information technology. In this study, the authors used the Nine step methodology as a reference in designing a data warehouse for modeling using a fact constellation schema with 3 fact tables and 11 dimension tables. the different in this study from previous research is that the data source was taken directly from the SIMRS database used by the hospital, so there is no manual data extraction.</em><em>The aim of this research is to be able to produce a Data Warehouse design based on Online Analytical Processing (OLAP) as a means of supporting the quality of hospital health services. The resulting OLAP will be a data warehouse design with various dimensions will produce the displays information in the form of a graph or chart so that the information is easy to read and understand by various parties.</em></p><p class="BodyCxSpLast"><em> </em></p><p class="BodyCxSpFirst"><em><strong><br /></strong></em></p>

Data Mining in Franchise Organizations

2008 ◽  
pp. 2722-2733 ◽  
Author(s):  
Ye-Sho Chen ◽  
Robert Justis ◽  
P. Pete Chong
Keyword(s):  
Data Mining ◽  
Growth Strategy ◽  
Online Analytical Processing ◽  
Pertinent Data ◽  
Analytical Processing ◽  
Customer Services ◽  
Internal Operations ◽  
Comprehensive Framework ◽  
Franchise Organizations

Franchising has been used by businesses as a growth strategy. Based on the authors’ cumulative research and experience in the industry, this paper describes a comprehensive framework that describes both the franchise environment — from customer services to internal operations — and the pertinent data items in the system. The authors identify the most important aspects of a franchising business, the role of online analytical processing (OLAP) and data mining play and the data items that data mining should focus on to ensure its success.

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