Welcome to the second ACM DATA ECONOMY WORKSHOP (DEC), co-located with ACM SIGCMOD 2023. Data-driven decision making through machine learning algorithms (ML) is transforming the way society and the economy work and is having a profound positive impact on our daily lives. With the exception of very large companies that have both the data and the capabilities to develop powerful ML-driven services, the vast majority of demonstrably possible ML services, from e-health to transportation to predictive maintenance, to name a few, still remain at the level of ideas or prototypes for the simple reason that data, the capabilities to manipulate it, and the business models to bring it to market rarely exist under one roof. Data must somehow meet the ML and business skills that can unleash its full power for society and the economy. This has given rise to an extremely dynamic sector around the Data Economy, involving Data Providers/Controllers, data Intermediaries, often-times in the form of Data Marketplaces or Personal Information Management Systems for end users to control and even monetize their personal data. Despite its enormous potential and observed initial growth, the Data Economy is still in its early stages and therefore faces a still uncertain future and a number of existential challenges. These challenges include a wide range of technical issues that affect multiple disciplines of computer science, including networks and distributed systems, security and privacy, machine learning, and human-computer interaction. The mission of the ACM DEC workshop will be to bring together all CS capabilities needed to support the Data Economy. We would like to thank the entire technical program committee for reviewing and selecting papers for the workshop. We hope you will find the papers interesting and stimulating.

Second Data Economy Workshop (DEC)

Trevisan, Martino
2023-01-01

Abstract

Welcome to the second ACM DATA ECONOMY WORKSHOP (DEC), co-located with ACM SIGCMOD 2023. Data-driven decision making through machine learning algorithms (ML) is transforming the way society and the economy work and is having a profound positive impact on our daily lives. With the exception of very large companies that have both the data and the capabilities to develop powerful ML-driven services, the vast majority of demonstrably possible ML services, from e-health to transportation to predictive maintenance, to name a few, still remain at the level of ideas or prototypes for the simple reason that data, the capabilities to manipulate it, and the business models to bring it to market rarely exist under one roof. Data must somehow meet the ML and business skills that can unleash its full power for society and the economy. This has given rise to an extremely dynamic sector around the Data Economy, involving Data Providers/Controllers, data Intermediaries, often-times in the form of Data Marketplaces or Personal Information Management Systems for end users to control and even monetize their personal data. Despite its enormous potential and observed initial growth, the Data Economy is still in its early stages and therefore faces a still uncertain future and a number of existential challenges. These challenges include a wide range of technical issues that affect multiple disciplines of computer science, including networks and distributed systems, security and privacy, machine learning, and human-computer interaction. The mission of the ACM DEC workshop will be to bring together all CS capabilities needed to support the Data Economy. We would like to thank the entire technical program committee for reviewing and selecting papers for the workshop. We hope you will find the papers interesting and stimulating.
2023
9781450395076
https://dl.acm.org/doi/10.1145/3555041.3590825
File in questo prodotto:
File Dimensione Formato  
3555041.3590825.pdf

Accesso chiuso

Tipologia: Documento in Versione Editoriale
Licenza: Copyright Editore
Dimensione 808.59 kB
Formato Adobe PDF
808.59 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
3555041.3590825-Post_print.pdf

accesso aperto

Tipologia: Bozza finale post-referaggio (post-print)
Licenza: Digital Rights Management non definito
Dimensione 881.26 kB
Formato Adobe PDF
881.26 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3048278
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact