Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | The Elements of Big Data Value |
| Untertitel | Foundations of the Research and Innovation Ecosystem |
| Herausgeber (Verlag) | Springer International Publishing AG |
| Seiten | 97-126 |
| Seitenumfang | 30 |
| ISBN (elektronisch) | 9783030681760 |
| ISBN (Print) | 9783030681753 |
| Publikationsstatus | Veröffentlicht - 2021 |
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.
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The Elements of Big Data Value: Foundations of the Research and Innovation Ecosystem. Springer International Publishing AG, 2021. S. 97-126.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review
}
TY - CHAP
T1 - Technical research priorities for big data
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
N1 - Acknowledgments: We greatly acknowledge the collective effort of the SRIA teams: Carlos A. Iglesias, Antonio Alfaro, Jesus Angel, Sören Auer, Paolo Bellavista, Arne Berre, Freek Bomhof, Stuart Campbell, Geraud Canet, Giuseppa Caruso, Edward Curry, Paul Czech, Davide Dalle Carbonare, Nuria de Lama, Stefano de Panfilis, Thomas Delavallade, Marija Despenic, Ana Garcia Robles, Wolfgang Gerteis, Aris Gkoulalas-Divanis, Nuria Gomez, Paolo Gonzales, Thomas Hahn, Souleiman Hasan, Jim Keneally, Bjarne Kjær Ersbøll, Bas Kotterink, Yannick Legré, Yves Mabiala, Julie Marguerite, Dirk Mayer, Ernestina Menasalves, Andreas Metzger, Elisa Molino, Thierry Nagellen, Dalit Naor, Maria Perez, Milan Petkovic, Roberta Piscitelli, Klaus-Dieter Platte, Pierre Pleven, Dumitru Roman, Titi Roman, Alexandra Rosén, Nikos Sarris, Stefano Scamuzzo, Simon Scerri, Corinna Schulze, Robert Seidl, Bjørn Skjellaug, Caj Södergård, Claire Tonna, Francois Troussier, Colin Upstill, Josef Urban, Meilof Veeningen, Tonny Velin, Ray Walshe, Walter Waterfeld, Stefan Wrobel, and Sonja Zillner.
PY - 2021
Y1 - 2021
N2 - 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.
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
UR - http://www.scopus.com/inward/record.url?scp=85143085292&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-68176-0_5
DO - 10.1007/978-3-030-68176-0_5
M3 - Contribution to book/anthology
AN - SCOPUS:85143085292
SN - 9783030681753
SP - 97
EP - 126
BT - The Elements of Big Data Value
PB - Springer International Publishing AG
ER -