Workflow framework to support data analytics in cloud computing

Chaisiri, Sivadon; Bong, Zoebir; Lee, Chonho; Lee, Bu-Sung; Sessomboon, Punyapat; Saisillapee, Tanakrit; Achalakul, Tiranee

doi:10.1109/cloudcom.2012.6427489

Cited by 7 publications

(3 citation statements)

References 6 publications

(7 reference statements)

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“…Chaisiri et al proposed a framework called CODA (Cloud-Oriented Data Analytics) [66]. This framework includes functions for composing, managing, and processing workflows for big data analytics in cloud computing.…”

Section: Analyticsmentioning

confidence: 99%

Design science research contribution to business intelligence in the cloud — A systematic literature review

Mwilu

Comyn-Wattiau

Prat

2016

Future Generation Computer Systems

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Business intelligence (BI) helps managers make informed decisions. In the age of big data, BI technology provides essential support for decision making. Cloud computing also attracts many organizations because of its potential: ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications, and services). This paper focuses on the deployment of BI in the cloud, from the vantage point of design science research (DSR). We produce a state of the art of research pertaining to BI in the cloud, following the methodology of systematic literature review. This literature review especially exhibits the different artifacts proposed by design science researchers regarding BI in the cloud. To structure the literature review, we propose a framework composed of two dimensions: artifact type and BI step. In particular, we propose a typology of artifact types, refining the coarse-grained typology commonly used in DSR. We use the two-dimensional framework both to map the current state of DSR regarding BI in the cloud, and to elicit future research avenues in terms of design science artifacts for BI in the cloud. The contribution is threefold: the literature review may help DSR researchers get an overview of this active research domain; the two-dimensional framework facilitates the understanding of different research streams; finally, the proposed future topics may guide researchers in identifying promising research avenues.

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Section: Analyticsmentioning

confidence: 99%

Design science research contribution to business intelligence in the cloud — A systematic literature review

Mwilu

Comyn-Wattiau

Prat

2016

Future Generation Computer Systems

View full text Add to dashboard Cite

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“…The CODA framework [23] was designed and implemented to support big data analytics in Cloud computing. Important functions, such as workflow scheduling, data locality, resource provisioning, and monitoring functions, had been integrated into the framework.…”

Section: Related Workmentioning

confidence: 99%

Devising a Cloud Scientific Workflow Platform for Big Data

Zhao

et al. 2014

2014 IEEE World Congress on Services

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Abstract-Scientificworkflow management systems (SWFMSs) are facing unprecedented challenges from big data deluge. As revising all the existing workflow applications to fit into Cloud computing paradigm is impractical, thus migrating SWFMSs into the Cloud to leverage the functionalities of both Cloud computing and SWFMSs may provide a viable approach to big data processing. In this paper, we first discuss the challenges for scientific workflow applications and the available solutions in details, and analyze the essential requirements for a scientific computing Cloud platform. Then we propose a service framework to normalize the integration of SWFMS with Cloud computing. Meanwhile, we also present our implementation experience based on the service Framework. At last, we set up a series of experiments to demonstrate the capability of our implementation and use a Montage Image Mosaic Workflow as a showcase of the implementation.

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“…The CODA framework [3] was designed and implemented to support big data analytics in cloud computing. Important functions, such as workflow scheduling, data locality, resource provisioning, and monitoring functions, has been integrated into the framework.…”

Section: Related Workmentioning

confidence: 99%

Architecting Cloud Workflow: Theory and Practice

Zhao

Raicu

et al. 2014

2014 IEEE International Conference on Computer and Information Technology

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Abstract-The data scale, science analysis and processing complexity in scientific community are growing exponentially in the "big data" era. Cloud computing paradigm has been widely adopted to provide unprecedented scalability and resources on demand, while scientific workflow management systems (SWFMSs) have been proven essential to scientific computing and services computing. Uniting the advantages of both cloud computing and SWFMSs can bring a valuable solution to the scientific "big data" problem to researchers. Although a series of work have concentrated on integrating SWFMSs with Cloud platforms that provide much experience for future research and development, a study from an architectural perspective is still missing. The main contributions of this paper are: 1) based on a comprehensive survey of the available integration options, we propose a service framework for integrating SWFMSs with Cloud computing; 2) we implement the service framework based on various Cloud platforms to validate the feasibility of the proposed framework; and 3) we conduct a set of experiments to demonstrate the capability and use a NASA MODIS image processing workflow as a showcase of the implementation.

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Workflow framework to support data analytics in cloud computing

Cited by 7 publications

References 6 publications

Design science research contribution to business intelligence in the cloud — A systematic literature review

Design science research contribution to business intelligence in the cloud — A systematic literature review

Devising a Cloud Scientific Workflow Platform for Big Data

Architecting Cloud Workflow: Theory and Practice

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