Modern social and technological trends result in an enormous increase in the amount of accessible data, with a significant portion of the resources being massive and having inherent imprecision and uncertainty. Such data, often referred to as “Big Data,” typically features substantial social and/or business value if become amenable to computing machinery. Towards that, Computer Science has made a significant progress over the years. The Artificial Intelligence (AI) community has established principled and well-practiced methodologies to model, explore, and learn realistic knowledge within imprecise and uncertain environments. The Database (DB) community has established fundamental concepts and machinery for the management of data, with focus on scaling to high volumes and intensive access. Albeit the complementary focuses, these two communities share key features and often tackle similar challenges. But until recently, research in them has progressed independently with limited collaboration. It is then evident that time has come to integrate the efforts of the two towards unified concepts and methodologies that share the benefits of both worlds.

This workshop aims to bring together researchers and practitioner from the AI community and the DB community, in order to highlight relevant state of the art research, and to capture opportunities of collaboration and mutual enhancement. The organizers believe that research achievements from both communities provide a significant ground to benefit from such an assembly. Examples include AI concepts such as Markov Logic, lifted inference, and learning/mining of relational data, and DB concepts such as probabilistic databases, query optimization and descriptive complexity. With the synergy between the two communities, the hope is for this workshop to serve as a stepping stone towards the realization of the value in Big Data.