Human-in-the-loop data exploration is seeing a renewed interest in our big data science and data management community. With the rise of big data analytics, this area is growing to encompass not only approaches and algorithms to find the next best data items to explore but also the aspect of interactivity, i.e. accounting for feedback from the human users during exploration. Interactivity is essential to account for evolving needs during the exploration and also customize the discovery process. In this tutorial, we focus on exploration of Composite Items (CIs) that requires repeated interaction with human users. CIs address complex information needs that arise in a wide variety of emerging applications.
The tutorial will have the following parts: We will first review CI applications and shapes (15mn). We then discuss three big research questions (60 mn): (i) existing algorithms for CI formation, (ii) human-in-the-loop CIs, and (iii) optimization opportunities. We will conclude with research directions (15mn).
The proposed tutorial is timely. It brings together several related efforts and addresses unsolved questions in the emerging area of human-in-the-loop exploration of complex information needs. The tutorial is relevant to the general area of data science and more specifically to Scalable Analytics, Data Mining, Clustering and Knowledge Discovery, Indexing, Query Processing and Optimization, and Crowdsourcing. The technical topics covered are constrained optimization, ranking semantics, clustering, algorithms, and empirical evaluations.
Senjuti Basu Roy
Senjuti Basu Roy
Assistant Professor, Department of Computer Science at the New Jersey Institute of Technology