Second Data Economy Workshop (DEC)

G. Koutrika, Nikolaos Laoutaris, Martino Trevisan
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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.
第二届数据经济工作坊(DEC)
欢迎参加第二届ACM数据经济研讨会(DEC),与ACM SIGCMOD 2023同址。通过机器学习算法(ML)进行数据驱动的决策正在改变社会和经济的运作方式,并对我们的日常生活产生深远的积极影响。除了那些既拥有数据又有能力开发强大的机器学习驱动服务的大公司之外,从电子医疗到交通运输到预测性维护等,绝大多数明显可行的机器学习服务仍然停留在想法或原型的层面,原因很简单,数据、操纵数据的能力以及将数据推向市场的商业模式很少存在于一个屋檐下。数据必须以某种方式满足机器学习和商业技能,才能释放其对社会和经济的全部力量。这就产生了一个围绕数据经济的极具活力的行业,涉及数据提供者/控制者、数据中介,通常以数据市场或个人信息管理系统的形式,供最终用户控制甚至货币化他们的个人数据。尽管数据经济具有巨大的潜力和初步的增长,但它仍处于早期阶段,因此仍面临着不确定的未来和一些存在的挑战。这些挑战包括影响计算机科学多个学科的广泛技术问题,包括网络和分布式系统、安全和隐私、机器学习和人机交互。ACM DEC研讨会的任务是汇集支持数据经济所需的所有CS能力。我们要感谢整个技术计划委员会对研讨会论文的审查和选择。我们希望你会觉得这些论文有趣又刺激。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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