Business Intelligence and Big Data in the Cloud: Opportunities for Design-Science Researchers

Sangupamba, Odette Mwilu; Prat, Nicolas; Comyn-Wattiau, Isabelle

doi:10.1007/978-3-319-12256-4_8

Cited by 8 publications

(9 citation statements)

References 19 publications

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“…The second paper, entitled "Business Intelligence and Big Data in the Cloud: Opportunities for Design-Science Researchers" by Odette Sangupamba Mwilu, Nicolas Prat and Isabelle Comyn-Wattiau [25] deals with the new opportunities for business intelligence (BI) and analytics offered by Cloud computing and big data. They propose a typology of artifacts potentially produced by researchers in design science.…”

Section: The Aim Of the International Workhop On Modeling And Managementioning

confidence: 99%

Modeling and Management of Big Data: Challenges and opportunities

Gil

Song

2016

Future Generation Computer Systems

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Section: The Aim Of the International Workhop On Modeling And Managementioning

confidence: 99%

Modeling and Management of Big Data: Challenges and opportunities

Gil

Song

2016

Future Generation Computer Systems

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“…Our aim is to study which artifacts have been proposed regarding BI in the cloud. Building on these previous papers, we proposed a typology of artifacts that allows us to structure the literature review on BI in the cloud and detect open research questions [2].…”

Section: Research Gap and Research Questionmentioning

confidence: 99%

“…In a previous publication [2], we proposed a first answer to this question. We conducted a preliminary literature review to evaluate the state of the art of DSR for BI in the cloud.…”

Section: Research Gap and Research Questionmentioning

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

Self Cite

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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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“…Search engines have advanced in the last two decades in efforts to deliver the most relevant content to their users. However, issues of content and semantics have been, and continue to be, major challenges [11,51]. Business intelligence and data analytics initiatives have been developing web analytics techniques to mine usergenerated content in the form of unstructured data to perform sentiment analysis and other types of intelligence analysis on consumer opinions and needs, in an attempt to create new business opportunities [4,12].…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

“…[57]). It has also been applied to a wide range of topics dealing with the development and implementation of information systems, including the Internet of Things [7], modeling languages [19,36], social network analysis [15,37], semantic understanding [8,34], provenance [49], enterprise modeling [21], biology [47,50], mobile devices [20], cloud computing [2,51], and modeling for user-generated content [38,39].…”

Section: Introductionmentioning

confidence: 99%

Research on conceptual modeling: Themes, topics, and introduction to the special issue

Trujillo

Liddle

2015

Data & Knowledge Engineering

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Conceptual modeling continues to evolve as both practitioners and researchers reflect on the challenges of modeling and implementing data-intensive problems that continue to appear in business and in science. These challenges of data modeling and representation are well-recognized in contemporary applications of big data, ontologies, and semantics, along with traditional efforts associated with methodologies, tools, and theory development. This introduction contains a review of some current research in conceptual modeling and identifies emerging themes. It also introduces the articles that comprise this special issue of papers from the 32 nd International Conference on Conceptual Modeling.

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Business Intelligence and Big Data in the Cloud: Opportunities for Design-Science Researchers

Cited by 8 publications

References 19 publications

Modeling and Management of Big Data: Challenges and opportunities

Modeling and Management of Big Data: Challenges and opportunities

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

Research on conceptual modeling: Themes, topics, and introduction to the special issue

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