Cross Modal Data Discovery over Structured and Unstructured Data Lakes

Organizations are collecting increasingly large amounts of data for data driven decision making. These data are often dumped into a centralized repository, e.g., a data lake, consisting of thousands of structured and unstructured datasets. Perversely, such mixture of datasets makes the problem of discovering elements (e.g., tables or documents) that are relevant to a user's query or an analytical task very challenging. Despite the recent efforts in data discovery, the problem remains widely open especially in the two fronts of (1) discovering relationships and relatedness across structured and unstructured datasets where existing techniques suffer from either scalability, being customized for a specific problem type (e.g., entity matching or data integration), or demolishing the structural properties on its way, and (2) developing a holistic system for integrating various similarity measurements and sketches in an effective way to boost the discovery accuracy. In this paper, we propose a new data discovery system, named CMDL, for addressing these two limitations. CMDL supports the data discovery process over both structured and unstructured data while retaining the structural properties of tables.

Offline Reinforcement Learning for Road Traffic Control

Traffic signal control is an important problem in urban mobility with a significant potential of economic and environmental impact. While there is a growing interest in Reinforcement Learning (RL) for traffic control, the work so far has focussed on learning through interactions which, in practice, is costly. Instead, real experience data on traffic is available and could be exploited at minimal costs. Recent progress in offline or batch RL has enabled just that. Model-based offline RL methods, in particular, have been shown to generalize to the experience data much better than others. We build a model-based learning framework, A-DAC, which infers a Markov Decision Process (MDP) from dataset with pessimistic costs built in to deal with data uncertainties. The costs are modeled through an adaptive shaping of rewards in the MDP which provides better regularization of data compared to the prior related work. A-DAC is evaluated on a complex signalized roundabout using multiple datasets varying in size and in batch collection policy. The evaluation results show that it is possible to build high performance control policies in a data efficient manner using simplistic batch collection policies.