New trends on exploratory methods for data analytics

Abstract

Data usually comes in a plethora of formats and dimensions, rendering the exploration and information extraction processes cumbersome. Thus, being able to cast exploratory queries in the data with the intent of having an immediate glimpse on some of the data properties is becoming crucial. An exploratory query should be simple enough to avoid complicate declarative languages (such as SQL) and mechanisms, and at the same time retain the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so called example-based methods, in which the user, or the analyst circumvent query languages by using examples as input. An example is a representative of the intended results, or in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind, but may not able to (easily) express. They can be useful both in cases where a user is looking for information in an unfamiliar dataset, or simply when she is exploring the data without knowing what to find in there. In this tutorial, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data.