Towards a Data Warehouse Architecture for Managing Big Data Evolution

2018 ◽

Vol 14 (3) ◽

pp. 44-68 ◽

Cited By ~ 1

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.

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Uncovering Data Warehouse Issues and Challenges in Big Data Management

Communications in Computer and Information Science - Big Data, Machine Learning, and Applications ◽

10.1007/978-3-030-62625-9_5 ◽

2020 ◽

pp. 48-59

Author(s):

Rohit Kr Batwada ◽

Namita Mittal ◽

Emmanuel S. Pilli

Keyword(s):

Big Data ◽

Data Management ◽

Data Warehouse

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Biomedical Cyber-Physical Systems in the Light of Database as a Service (DBaaS) Paradigm

Medical Technologies Journal ◽

10.26415/2572-004x-vol4iss3p577-577 ◽

2020 ◽

Vol 4 (3) ◽

pp. 577-577

Author(s):

Vania V Estrela

Keyword(s):

Big Data ◽

Data Warehouse ◽

Query Language ◽

Treatment Decisions ◽

Cyber Physical Systems ◽

Detailed Knowledge ◽

Data Handling ◽

Search Query ◽

Physical Systems ◽

Database As A Service

Background: A database (DB) to store indexed information about drug delivery, test, and their temporal behavior is paramount in new Biomedical Cyber-Physical Systems (BCPSs). The term Database as a Service (DBaaS) means that a corporation delivers the hardware, software, and other infrastructure required by companies to operate their databases according to their demands instead of keeping an internal data warehouse. Methods: BCPSs attributes are presented and discussed. One needs to retrieve detailed knowledge reliably to make adequate healthcare treatment decisions. Furthermore, these DBs store, organize, manipulate, and retrieve the necessary data from an ocean of Big Data (BD) associated processes. There are Search Query Language (SQL), and NoSQL DBs. Results: This work investigates how to retrieve biomedical-related knowledge reliably to make adequate healthcare treatment decisions. Furthermore, Biomedical DBaaSs store, organize, manipulate, and retrieve the necessary data from an ocean of Big Data (BD) associated processes. Conclusion: A NoSQL DB allows more flexibility with changes while the BCPSs are running, which allows for queries and data handling according to the context and situation. A DBaaS must be adaptive and permit the DB management within an extensive variety of distinctive sources, modalities, dimensionalities, and data handling according to conventional ways.

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