The goal of DSIA is to foster innovative research at the intersection of databases, machine learning, and interactive visualization.

Database researchers have developed techniques for storing and querying massive amounts of data, including methods for distributed, streaming and approximate computation. Machine learning techniques provide ways to discover unexpected patterns and to automate and scale well-defined analysis procedures. Recent systems research has looked at how to develop novel database systems architectures to support the iterative, optimization-oriented workloads of machine learning algorithms.

Of course, both the inputs and outputs of these systems are ultimately driven by people, in support of analysis tasks. The life-cycle of data involves an iterative, interactive process of determining which questions to ask, the data to analyze, appropriate features and models, and interpreting results. In order to achieve better analysis outcomes, data processing systems require improved interfaces that account for the strengths and limitations of human perception and cognition. Meanwhile, to keep up with the rising tide of data, interactive visualization tools need to integrate more techniques from databases and machine learning.

In this workshop, we will explore the idea that the next generation of database, machine learning, and interactive visualization systems should not be designed in isolation. For example, machine learning techniques might recommend improved data transformation and visual encoding decisions. Or, database query optimizers might take advantage of perceptual constraints, while prefetching methods reduce latency by modeling likely interactions.

This workshop seeks to jump start cross-pollination between these fields. The program will be split between invited talks from researchers in these communities, and speculative, ongoing work that straddles the areas.