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

Research output: Chapter in Book or Conference Publication/Proceeding › Chapter › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	The Elements of Big Data Value
Subtitle of host publication	Foundations of the Research and Innovation Ecosystem
Publisher	Springer International Publishing
Pages	97-126
Number of pages	30
ISBN (Electronic)	9783030681760
ISBN (Print)	9783030681753
DOIs	https://doi.org/10.1007/978-3-030-68176-0_5
Publication status	Published - 1 Aug 2021

Keywords

Data analytics
Data ecosystem
Data management
Data processing
Data protection
Data standardisation
Data visualisation
Research challenges
User interactions

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Curry, E; Zillner, S; Metzger, A; Berre, AJ; Auer, S; Walshe, R; Despenic, M; Petkovic, M; Roman, D; Waterfeld, W; Seidl, R; Hasan, S; Ul Hassan, U; Ojo, A

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

Cite this

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 (pp. 97-126). Springer International Publishing. https://doi.org/10.1007/978-3-030-68176-0_5

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Curry, E, Zillner, S, Metzger, A, Berre, AJ, 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. Springer International Publishing, pp. 97-126. https://doi.org/10.1007/978-3-030-68176-0_5

TY - CHAP

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AU - Curry, Edward

AU - Zillner, Sonja

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

PY - 2021/8/1

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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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M3 - Chapter

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BT - The Elements of Big Data Value

PB - Springer International Publishing

ER -

Technical research priorities for big data

Abstract

Keywords

Authors (Note for portal: view the doc link for the full list of authors)

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