Big Data Management Performance Evaluation in Hadoop Ecosystem

2017 ◽  
Author(s):  
Qing Liu ◽  
Yinjin Fu ◽  
Guiqiang Ni ◽  
Jianmin Mei
Keyword(s):  
Big Data ◽  
Performance Evaluation ◽  
Data Management ◽  
Management Performance ◽  
Hadoop Ecosystem

scholarly journals Data-Centric Benchmarking

2019 ◽  
pp. 342-353
Author(s):  
Jérôme Darmont
Keyword(s):  
Big Data ◽  
Performance Evaluation ◽  
Decision Support ◽  
Data Management ◽  
Present State ◽  
Transaction Processing ◽  
Real System ◽  
Future Trends ◽  
On Line ◽  
Line Analysis

In data management, both system designers and users casually resort to performance evaluation. Performance evaluation by experimentation on a real system is generally referred to as benchmarking. The aim of this chapter is to present an overview of the major past and present state-of-the-art data-centric benchmarks. This review includes the TPC standard benchmarks, but also alternative or more specialized benchmarks. Surveyed benchmarks are categorized into three families: transaction benchmarks aimed at on-line transaction processing (OLTP), decision-support benchmarks aimed at on-line analysis processing (OLAP), and big data benchmarks. Issues, tradeoffs, and future trends in data-centric benchmarking are also discussed.

scholarly journals Data-Centric Benchmarking

2018 ◽  
pp. 1772-1782 ◽  
Author(s):  
Jérôme Darmont
Keyword(s):  
Big Data ◽  
Performance Evaluation ◽  
Decision Support ◽  
Data Management ◽  
Present State ◽  
Transaction Processing ◽  
Real System ◽  
Future Trends ◽  
On Line ◽  
Line Analysis

In data management, both system designers and users casually resort to performance evaluation. Performance evaluation by experimentation on a real system is generally referred to as benchmarking. The aim of this chapter is to present an overview of the major past and present state-of-the-art data-centric benchmarks. This review includes the TPC standard benchmarks, but also alternative or more specialized benchmarks. Surveyed benchmarks are categorized into three families: transaction benchmarks aimed at On-Line Transaction Processing (OLTP), decision-support benchmarks aimed at On-Line Analysis Processing (OLAP) and big data benchmarks. Issues, tradeoffs and future trends in data-centric benchmarking are also discussed.

scholarly journals DaLiF: a data lifecycle framework for data-driven governments

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Iftikhar Hussain Shah ◽  
Vassilios Peristeras ◽  
Ioannis Magnisalis
Keyword(s):  
Big Data ◽  
Data Management ◽  
Good Governance ◽  
Business Community ◽  
Data Driven ◽  
Governmental Organizations ◽  
Data Lifecycle ◽  
The Government ◽  
Data Ecosystem ◽  
Governance Transparency

AbstractThe public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.

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