In visual exploration and analysis of data, determining how to select and transform the data for visualization is a challenge for data-unfamiliar or inexperienced users. Our main hypothesis is that for many data sets and common analysis tasks, there are relatively few "data slices" that result in effective visualizations. By focusing human users on appropriate and suitably transformed parts of the underlying data sets, these data slices can help the users carry their task to correct completion. To verify this hypothesis, we develop a framework that permits us to capture exemplary data slices for a user task, and to explore and parse visual-exploration sequences into a format that makes them distinct and easy to compare. We develop a recommendation system, DataSlicer, that matches a "currently viewed" data slice with the most promising "next effective" data slices for the given exploration task. We report the results of controlled experiments with an implementation of the DataSlicer system, using four common analytical task types. The experiments demonstrate statistically significant improvements in accuracy and exploration speed versus users without access to our system.
Significant advances have been made lately in developing various facets of visual solutions for data exploration and analysis. In this space, we focus mainly on projects that concentrate on the problem of finding the right visualization, e.g., @cite_13 @cite_16 @cite_2 @cite_15 . We refer the reader to the survey @cite_14 for a more general discussion of data-exploration techniques.
@cite_14 @cite_2 @cite_15 @cite_16 @cite_13
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Data exploration is about efficiently extracting knowledge from data even if we do not know exactly what we are looking for. In this tutorial, we survey recent developments in the emerging area of database systems tailored for data exploration. We discuss new ideas on how to store and access data as well as new ideas on how to interact with a data system to enable users and applications to quickly figure out which data parts are of interest. In addition, we discuss how to exploit lessons-learned from past research, the new challenges data exploration crafts, emerging applications and future research directions. Data analysts operating on large volumes of data often rely on visualizations to interpret the results of queries. However, finding the right visualization for a query is a laborious and time-consuming task. We demonstrate SeeDB, a system that partially automates this task: given a query, SeeDB explores the space of all possible visualizations, and automatically identifies and recommends to the analyst those visualizations it finds to be most "interesting" or "useful". In our demonstration, conference attendees will see SeeDB in action for a variety of queries on multiple real-world datasets. Curiosity, a fundamental drive amongst higher living organisms, is what enables exploration, learning and creativity. In our increasingly data-driven world, data exploration, i.e., Making sense of mounting haystacks of data, is akin to intelligence for science, business and individuals. However, modern data systems -- designed for data retrieval rather than exploration -- only let us retrieve data and ask if it is interesting. This makes knowledge discovery a game of hit-and-trial which can only be orchestrated by expert data scientists. We present the vision toward Queriosity, an automated and personalized data exploration system. Designed on the principles of autonomy, learning and usability, Queriosity envisions a paradigm shift in data exploration and aims to become a a personalized "data robot" that provides a direct answer to what is interesting in a user's data set, instead of just retrieving data. Queriosity autonomously and continuously navigates toward interesting findings based on trends, statistical properties and interactive user feedback. ± ABSTRACT Traditional DBSMs are suited for applications in which the structure, meaning and contents of the database, as well as the questions to be asked are already well understood. There is, however, a class of applications that we will collectively refer to as Interactive Data Exploration (IDE) applications, in which this is not the case. IDE is a key ingredient of a diverse set of discovery-oriented applications we are dealing with, including ones from scientific computing, financial analysis, evidence-based medicine, and genomics. The need for effective IDE will only increase as data are being collected at an unprecedented rate. IDE is fundamentally a multi-step, non-linear process with imprecise end-goals. For example, data-driven scientific discovery through IDE often requires non-expert users to iteratively interact with the system to make sense of and to identify interesting patterns and relationships in large, amorphous data sets. To make the most of the increasingly available complex and big data sets, users would need an "expert assistant" who would be able to effectively and efficiently guide them through the data space. Having a human assistant is not only expensive but also unrealistic. Thus, it is essential that we automate this task. We propose database systems be augmented with an automated "database navigator" (DBNav) service that assists as a "tour guide" to facilitate IDE. Just like a car navigation system that offers advice on the routes to be taken and display points of interest, DBNav would similarly steer the user towards interesting "trajectories" through the data, while highlighting relevant features. Like any good tour guide, DBNav should consider many kinds of information; in particular, it should be sensitive to a user's goals and interests, as well as common navigation patterns that applications exhibit. We sketch a general data navigation framework and discuss some specific components and approaches that we believe belong to any such system.