BigDataBench-S: An Open-Source Scientific Big Data Benchmark Suite

Tian, Xinhui; Dai, Shaopeng; Du, Z.; Gao, Wanling; Ren, Rui; Cheng, Yaodong; Zhang, Zhifei; Jia, Zhen; Peijian, Wang; Zhan, Jianfeng

doi:10.1109/ipdpsw.2017.111

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…For quality control, automatic DQ checking rules should be inserted into the appropriate IP-Map elements, such as data collecting, preserving, analysing and submitting. Moreover, with the urgent demand for a higher quality of scientific data sharing and open access, it is especially necessary to establish scientific data platforms to manage scientific data (Tian et al , 2017). According to the scientific data IP-Map, scientific data platforms should integrate the entire manufacturing processes from scientific data collecting to data sharing.…”

Section: Strategies For Improving Scientific Data Qualitymentioning

confidence: 99%

Process-driven quality improvement for scientific data based on information product map

Zong

Song-tao

Gao

et al. 2022

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Purpose This paper aims to provide a process-driven scientific data quality (DQ) monitoring framework by information product map (IP-Map) in identifying the root causes of poor DQ issues so as to assure the quality of scientific data. Design/methodology/approach First, a general scientific data life cycle model is constructed based on eight classical models and 37 researchers’ experience. Then, the IP-Map is constructed to visualize the scientific data manufacturing process. After that, the potential deficiencies that may arise and DQ issues are examined from the aspects of process and data stakeholders. Finally, the corresponding strategies for improving scientific DQ are put forward. Findings The scientific data manufacturing process and data stakeholders’ responsibilities could be clearly visualized by the IP-Map. The proposed process-driven framework is helpful in clarifying the root causes of DQ vulnerabilities in scientific data. Research limitations/implications As for the implications for researchers, the process-driven framework proposed in this paper provides a better understanding of scientific DQ issues during implementing a research project as well as providing a useful method to analyse those DQ issues based on IP-Map approach from the aspects of process and data stakeholders. Practical implications The process-driven framework is beneficial for the research institutions, scientific data management centres and researchers to better manage the scientific data manufacturing process and solve the scientific DQ issues. Originality/value This research proposes a general scientific data life cycle model and further provides a process-driven scientific DQ monitoring framework for identifying the root causes of poor data issues from the aspects of process and stakeholders which have been ignored by existing information technology-driven solutions. This study is likely to lead to an improved approach to assuring the scientific DQ and is applicable in different research fields.

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Section: Strategies For Improving Scientific Data Qualitymentioning

confidence: 99%

Process-driven quality improvement for scientific data based on information product map

Zong

Song-tao

Gao

et al. 2022

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“…For example, PARSEC benchmark suite [21] includes several applications to help the study of chip-multiprocessors (CMPs). BigDataBench-S [22] is an open source benchmark suite that focuses the evaluation of big data systems. The DaCapo benchmark suite [23] has been developed to evaluate the Java platforms.…”

Section: Benchmarkmentioning

confidence: 99%

MBBench: WCET Kıyaslama Kümesi

Kuzhan

Şahin

2020

Sakarya University Journal of Computer and Information Sciences

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One of the important features of any real-time software is the worst-case execution time (WCET). To get an understanding of the timing behavior of real-time systems and to prove that the real-time software meets its deadlines, WCET analysis is performed. Today, researchers actively develop new WCET analysis methods and tools. Therefore, they need benchmark programs to evaluate and compare their work. To meet this need, in this study we present a new benchmark suite, called MBBench. MBBench includes a collection of C programs for Linux operating system and RTEMS real-time operating system. Its main aim is to help the evaluation and comparison of measurement-based WCET analysis methods/tools. MBBench has been published as open source. It can be obtained freely over the Internet.

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“…For example, big data is a growing area which needs benchmarks. BigDataBench-S [12] is an open source benchmark suite developed to measure the performance of big data management systems. PARSEC benchmark suite [13] focuses on the evaluation of Chip-Multiprocessors (CMPs).…”

Section: Related Workmentioning

confidence: 99%

ABench2020: A WCET Benchmark Suite in Ada Programming Language

Gökçe¹,

Şahin²

2020

acperpro

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Validation is an important part of the development process of real-time systems. During validation, it should be proved that, the system meets its timing constraints. Therefore, worst-case execution time (WCET) analysis is performed. Currently, several WCET analysis tools are being developed. Researchers who develop new WCET analysis tools, need benchmarks to evaluate and compare their tools with alternatives. These benchmarks are called WCET benchmarks. In this paper a new WCET benchmark suite named ABench2020 is introduced. Its main focus is to provide benchmark programs in Ada programming language for WCET research. Therefore, ABench2020 includes several benchmark programs which were written in Ada programming language. The benchmark programs implement different program structures and properties to help researchers test their systems from different aspects. ABench2020 was published as open source. It is freely available over the Internet.

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BigDataBench-S: An Open-Source Scientific Big Data Benchmark Suite

Cited by 8 publications

References 9 publications

Process-driven quality improvement for scientific data based on information product map

Process-driven quality improvement for scientific data based on information product map

MBBench: WCET Kıyaslama Kümesi

ABench2020: A WCET Benchmark Suite in Ada Programming Language

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