Logical Schema for Data Warehouse on Column-Oriented NoSQL Databases

2017 ◽  
pp. 247-256 ◽  
Author(s):  
Mohamed Boussahoua ◽  
Omar Boussaid ◽  
Fadila Bentayeb
Keyword(s):  
Data Warehouse ◽  
Nosql Databases

Formalizing the Mapping of UML Conceptual Schemas to Column-Oriented Databases

2018 ◽  
Vol 14 (3) ◽  
pp. 44-68 ◽  
Author(s):  
Fatma Abdelhedi ◽  
Amal Ait Brahim ◽  
Gilles Zurfluh
Keyword(s):  
Big Data ◽  
Data Warehouse ◽  
Relational Databases ◽  
Traditional Approach ◽  
Physical Models ◽  
Decision Making Process ◽  
Nosql Databases ◽  
Care Field ◽  
Sufficient Degree

Nowadays, most organizations need to improve their decision-making process using Big Data. To achieve this, they have to store Big Data, perform an analysis, and transform the results into useful and valuable information. To perform this, it's necessary to deal with new challenges in designing and creating data warehouse. Traditionally, creating a data warehouse followed well-governed process based on relational databases. The influence of Big Data challenged this traditional approach primarily due to the changing nature of data. As a result, using NoSQL databases has become a necessity to handle Big Data challenges. In this article, the authors show how to create a data warehouse on NoSQL systems. They propose the Object2NoSQL process that generates column-oriented physical models starting from a UML conceptual model. To ensure efficient automatic transformation, they propose a logical model that exhibits a sufficient degree of independence so as to enable its mapping to one or more column-oriented platforms. The authors provide experiments of their approach using a case study in the health care field.

Towards Convergence in Information Systems Design

2020 ◽  
pp. 247-263
Author(s):  
Deepika Prakash
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Information Systems ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Systems Design ◽  
Information Model ◽  
Information Systems Design ◽  
Nosql Databases ◽  
Nosql Database

Three technologies—business intelligence, big data, and machine learning—developed independently and address different types of problems. Data warehouses have been used as systems for business intelligence, and NoSQL databases are used for big data. In this chapter, the authors explore the convergence of business intelligence and big data. Traditionally, a data warehouse is implemented on a ROLAP or MOLAP platform. Whereas MOLAP suffers from having propriety architecture, ROLAP suffers from the inherent disadvantages of RDBMS. In order to mitigate the drawbacks of ROLAP, the authors propose implementing a data warehouse on a NoSQL database. They choose Cassandra as their database. For this they start by identifying a generic information model that captures the requirements of the system to-be. They propose mapping rules that map the components of the information model to the Cassandra data model. They finally show a small implementation using an example.

NOSQL DATABASES AS A DATA WAREHOUSE FOR DECISION SUPPORT SYSTEMS

2017 ◽  
Vol 185 (3) ◽  
pp. 124-131
Author(s):  
Jarosław KURPANIK
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Data Warehouse ◽  
Data Storage ◽  
Relational Databases ◽  
Support Systems ◽  
Large Data ◽  
Daily Basis ◽  
Nosql Databases ◽  
Big Data Storage

Nowadays, to some extent decision support systems are forced to base their operation on large data warehouses whose analysis is difficult and time consuming. This is why where data are stored becomes vital. The use of an efficient and productive data warehouse for this purpose can significantly improve application/system operation. Currently one of the most common solutions used in Big Data storage and quick processing are non-relational databases NoSQL. They are a relatively new solution, however, their development is dy-namic and their market share is increased on a daily basis, which means that it worth in-vestigating what they offer.

RANCANGAN DATA WAREHOUSE PADA SEKOLAH TINGGI TEKNIK POLIPROFESI MEDAN

KURVATEK ◽  
2018 ◽  
Vol 3 (1) ◽  
pp. 63-69
Author(s):  
Siti Jamilah Tarigan ◽  
Wing Wahyu Winarno ◽  
Henderi Safei
Keyword(s):  
Data Warehouse

Pengambilan keputusan dan perencanaan bidang akademik sering kali tidak berdasarkan pada informasi yang lengkap. Jajaran pengambil keputusan (rektorat atau tingkat eksekutif) hanya bisa melihat sebuah data dalam satu dimensi. Pengambil keputusan akan lebih baik jika informasi dapat disajikan dari berbagai dimensi. Perguruan tinggi telah memiliki data operasional yang lengkap dari kegiatan akademik, kepegawaian, dan penerimaan mahasiswa yang telah dikumpulkan lebih dari 4 tahun. Data warehouse adalah suatu koleksi optimasi database untuk mendukung keputusan. Konsep ini mengintegrasikan antara sistem lama dan sistem baru sehingga tidak terjadi duplikasi data. Data yang telah diintegrasikan dapat diolah dalam berbagai bentuk laporan sesuai dengan kebutuhan.Tujuan dari penelitian ini adalah bagaimana data yang ada bisa menghasilkan informasi yang akurat dan multidimensi sehingga pengambilan keputusan lebih cepat dan akurat. Penelitian ini menggunakan analisis data OLAP, dan skema bintang. Kesimpulan dari penelitian ini adalah rancangan yang dihasilkan bisa membantu pihak akademik dalam membuat keputusan berdasarkan data dan informasi yang mulitidimensi.

Sign in / Sign up

Export Citation Format

Share Document