Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


dissertation.pdf (4.42 MB)

ETD Abstract Container

Abstract Header

Interactive Data Exploration using Gestures

Jiang, Lilong
http://rave.ohiolink.edu/etdc/view?acc_num=osu1500553162723622

Abstract Details

2017, Doctor of Philosophy, Ohio State University, Computer Science and Engineering.
Ad-hoc data exploration is an important paradigm for users to analyze data and gain instant insights. In terms of interaction devices, there has been a revolution in the availability of new devices that go beyond the traditional keyboard modality. Tablets and smartphones use capacitive touch as their only mode of interaction. Furthermore, there has been a proliferation of motion capture-based devices in the recent past, including Google Glass, Kinect, HoloLens, and Leap Motion. Given these contexts, several key questions arise. The first question is can we explore the dataset with new devices (Question 1)? With new devices, the user specifies queries using gestures, which makes query specification more difficult. Then the next question is how do we guide users to their intended queries (Question 2)? The gestural querying behavior presents unique characteristics from traditional querying, generating individual query workloads. The last question is how do we evaluate data interaction using gestures (Question 3)? Following these three questions, we present our on-going efforts towards helping humans interactively explore data using gestures. To answer the first question, we propose a novel gestural query specification system – GestureDB, that allows the user to query databases using a series of gestures. We present a gesture recognition system that uses both the interaction and the state of the database to classify gestural input into relational database queries. We conduct exhaustive systems performance tests and user studies to demonstrate that our system is not only performant and capable of interactive latencies, but it is also more usable, faster to use and more intuitive than existing systems. To answer the second question, we propose an interactive feedback framework – SnapToQuery, that guides users through the query space by providing interactive feedback during the query specification process by “snapping” to the user’s likely intended queries. These intended queries can be derived from prior query logs, or from the data itself. In order to provide interactive response times over large datasets, we propose two data reduction techniques. Performance experiments demonstrate that our algorithms help maintain an interactive experience while allowing for accurate guidance. User studies over three kinds of devices (mouse, touch, and motion capture) show that SnapToQuery can help users specify queries quicker and more accurately; resulting in a query specification time speedup of 1.4x for mouse and touch-based devices and 2.2x for motion capture-based devices. To answer the third question, we propose three representative workloads which span various devices, query interfaces, interaction techniques, and query types. We survey metrics for data interaction in current literature and propose new metrics to evaluate data interaction. For each workload, we collect traces from the user study and generate the query workload from the trace. Then we perform behavior analyses and performance experiments. The former is used to understand the user’s interaction behavior while the latter is used to investigate possible behavior-driven optimizations or provide guidance for current behavior-driven optimizations.
Arnab Nandi (Advisor)
Srinivasan Parthasarathy (Committee Member)
Spyros Blanas (Committee Member)
175 p.
Computer Science
computer science

Recommended Citations

Citations

  • Jiang, L. (2017). Interactive Data Exploration using Gestures [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500553162723622

    APA Style (7th edition)

  • Jiang, Lilong. Interactive Data Exploration using Gestures. 2017. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1500553162723622.

    MLA Style (8th edition)

  • Jiang, Lilong. "Interactive Data Exploration using Gestures." Doctoral dissertation, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500553162723622

    Chicago Manual of Style (17th edition)

osu1500553162723622
908
© 2017, some rights reserved.Creative Commons License Interactive Data Exploration using Gestures by Lilong Jiang is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.