Technical research priorities for big data

Edward Curry; Sonja Zillner; Andreas Metzger; Arne J. Berre; Sören Auer; Ray Walshe; Marija Despenic; Milan Petkovic; Dumitru Roman; Walter Waterfeld; Robert Seidl; Souleiman Hasan; Umair ul Hassan; Adegboyega Ojo

doi:10.1007/978-3-030-68176-0_5

Technical research priorities for big data

Edward Curry
, Sonja Zillner
, Andreas Metzger
, Arne J. Berre
, Sören Auer
, Ray Walshe
, Marija Despenic
, Milan Petkovic
, Dumitru Roman
, Walter Waterfeld
, Robert Seidl
, Souleiman Hasan
, Umair ul Hassan
, Adegboyega Ojo

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Hoofdstuk › Academic › peer review

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To drive innovation and competitiveness, organisations need to foster the development and broad adoption of data technologies, value-adding use cases and sustainable business models. Enabling an effective data ecosystem requires overcoming several technical challenges associated with the cost and complexity of management, processing, analysis and utilisation of data. This chapter details a community-driven initiative to identify and characterise the key technical research priorities for research and development in data technologies. The chapter examines the systemic and structured methodology used to gather inputs from over 200 stakeholder organisations. The result of the process identified five key technical research priorities in the areas of data management, data processing, data analytics, data visualisation and user interactions, and data protection, together with 28 sub-level challenges. The process also highlighted the important role of data standardisation, data engineering and DevOps for Big Data.

Originele taal-2	Engels
Titel	The Elements of Big Data Value
Subtitel	Foundations of the Research and Innovation Ecosystem
Plaats van productie	Cham
Uitgeverij	Springer
Hoofdstuk	5
Pagina's	97-126
Aantal pagina's	30
ISBN van elektronische versie	9783030681760
ISBN van geprinte versie	9783030681753, 9783030681784
DOI's	https://doi.org/10.1007/978-3-030-68176-0_5
Status	Gepubliceerd - 2021

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10.1007/978-3-030-68176-0_5

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Curry, E., Zillner, S., Metzger, A., Berre, A. J., Auer, S., Walshe, R., Despenic, M., Petkovic, M., Roman, D., Waterfeld, W., Seidl, R., Hasan, S., ul Hassan, U., & Ojo, A. (2021). Technical research priorities for big data. In The Elements of Big Data Value: Foundations of the Research and Innovation Ecosystem (blz. 97-126). Springer. https://doi.org/10.1007/978-3-030-68176-0_5

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AU - Metzger, Andreas

AU - Berre, Arne J.

AU - Auer, Sören

AU - Walshe, Ray

AU - Despenic, Marija

AU - Petkovic, Milan

AU - Roman, Dumitru

AU - Waterfeld, Walter

AU - Seidl, Robert

AU - Hasan, Souleiman

AU - ul Hassan, Umair

AU - Ojo, Adegboyega

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AB - To drive innovation and competitiveness, organisations need to foster the development and broad adoption of data technologies, value-adding use cases and sustainable business models. Enabling an effective data ecosystem requires overcoming several technical challenges associated with the cost and complexity of management, processing, analysis and utilisation of data. This chapter details a community-driven initiative to identify and characterise the key technical research priorities for research and development in data technologies. The chapter examines the systemic and structured methodology used to gather inputs from over 200 stakeholder organisations. The result of the process identified five key technical research priorities in the areas of data management, data processing, data analytics, data visualisation and user interactions, and data protection, together with 28 sub-level challenges. The process also highlighted the important role of data standardisation, data engineering and DevOps for Big Data.

KW - Data analytics

KW - Data ecosystem

KW - Data management

KW - Data processing

KW - Data protection

KW - Data standardisation

KW - Data visualisation

KW - Research challenges

KW - User interactions

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SN - 9783030681784

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Technical research priorities for big data

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