ExNav: An Interactive Big Data Exploration Framework for Big Unstructured Data

Abstract

Driven by the increasing gap between the exponential growth of data and the limited human ability to comprehend them, recently, a novel interactive data exploration approach called Explore-by-Examples has generated a lot of attention for its capabilities to bridge this gap and to help the user obtain high-value content from the data that are often hidden using the traditional search methods. However, despite their effectiveness in extracting valuable information, existing Explore-by-Examples systems focus solely on structured data, which represents a small portion of the data available today. In this work, we present a novel data exploration framework, namely ExNav (Exploration Navigator), which enables the user to effortlessly explore the world of unstructured data for insights that are often unreachable from traditional search and exploration methods. In particular, we exploit the space of advanced machine learning, data embedding, and active learning algorithms to design effective exploration and space pruning approaches tailored for unstructured datasets. Our experimental evaluation using multiple real-world unstructured datasets (i.e., text, image, and graph) show that ExNav can reduce users’ effort by up to 9x while still achieving the same accuracy as the state-of-the-art alternative. Moreover, ExNav is also able to identify relevant data items that are often undetectable by current techniques, even when a large number of samples are explored.