1. Presented by Archana Kumari (31503103) | Supervised By Mr Vikram Singh
Guiding the User: Feedback-driven Result Ranking and Query Refinement for Data Exploration
Presented by Archana Kumari ( ) | Supervised By Mr Vikram Singh

2. Overview of Research Area
Data Exploration is efficiently extracting knowledge from data even if we do not know exactly what we are looking for.
This notion of Data Exploration gave birth to Exploratory Search where the user iteratively and if possible interactively try to gain insights from data.
Exploratory search can be used to describe an information-seeking problem context that is open-ended, persistent, and multi-faceted; and to describe information-seeking processes that are opportunistic, iterative, and multi-tactical [1].
[2] identified three facets of data exploration as shown.
We will explore the User Interaction facet that includes
guiding the user through the Exploratory Search Process.
Data Exploration
User Interaction
Middleware
Underlying Database

3. Current Status of Research Area
To meet this ever growing thirst of information several tools and techniques to support Information Seeking and Searching have been implemented (like Google etc.).
Much research has been conducted to carry out traditional look-up based search, navigational requests and closed informational requests. However, none of the existing systems provides the explicit functionality to support exploration.
The high involvement of user and the urgent need of Understanding rather than mere Finding the information lead to the development of Exploratory Search Systems (ESS).
Although a lot of theoretical research is going on ESS, the solutions are yet not efficient enough for commercialization.

4. Research Issues
Inability of recognising precise information needs mandate the need of user assistance in formulating the queries.
Understanding, Refining and Organizing search results on-the-fly as information needs evolve using feedback through out the exploratory search session since traditional keyword search systems mostly fetch relevance ranked list.
Rapid, Incremental, and Reversible control of the Query and Result Sets through highly interactive Interfaces as short queries typed into search boxes are not robust enough to meet all the demands.

5. Existing Techniques
An exploration session will include several queries where the results of each query trigger the formulation of the next one. In this setting the user does not have to be aware of the underlying schema of the database.
Several advanced visualization tools and exploration interfaces have been proposed recently to aid the user in overall search process.
Existing Search Systems can be broadly classified into three categories –
1. Systems assist in Query Formulation with the help of techniques
like Query Recommendations and labelling relevant objects to
construct exact query predicates.
Data Exploration
Systems Assisting Query Formulation
Systems Automating Search Process
Novel Query Interfaces

6. Existing Techniques
Systems automate search process by discovering data objects that are relevant to the user using techniques such as classification based on relevance-feedback facet search techniques.
Many novel interfaces have been proposed recently such as Looking Ahead [4] and Rank as you Go [3] where the aim is to provide strong visualization capabilities and more control over query predicates.
The aim of these techniques is to help people understand the relationship between the documents a query will retrieve and documents already found within in a search session. This better understanding will bridge the gap between information and the information needs of the searcher.

7. Proposal
We propose a highly interactive and user friendly Exploratory Search System visual analytic approach. Queries can be a poor representation of the information need. Short queries are often used in search engines due to the limitation of search engines few keywords per query on average. Hence limited results are obtained.
Our proposed approach will help user in following ways –
Helping user formulate precise queries using Query Refinement with feedback from user.
Feedback driven result ranking results as earlier approach of relevance ranked listing is not flexible enough to evolve with the user interests through the timeline of search session.
More control over Query and Result set with higher degree of user interaction. Features like “Keep in” , “Keep out”, “Like”, “Dislike” , selecting facets and ease of selecting desired Keywords from drop down menu will help to reduce cognitive load from the user.
Strong Visualization features in the shape of bars and pie charts to measure the overlap between previous and the current iteration of the search after modifications in query., and the relative distribution of selected keywords in first 100 retrieved results
Query refinement is a process of transforming a query into a new query that reflect the user information need in a higher accuracy.

8. Proposal
GUI Mock-ups

9. Tools / Data Needed MySQL Database
Libraries like jQuery, d3, Underscore.js
IDE for PHP, HTML, JavaScript, .

10. Current Status of work Initial Literature Survey
Problem Statement Formulation
Submitted a paper on Understanding User Search Intention in Interactive Data Exploration
Laying out the groundwork and preparing system for the Development of the proposed ESS

11. Significant References
[1] Idreos S, Papaemmanouil O, Chaudhuri S. “Overview of data exploration techniques”. InProceedings of ACM SIGMOD International Conference on Management of Data pp ACM.
[2] Marchionini G. Exploratory search: from finding to understanding. Communications of the ACM Apr 1;49(4):41-6.
[3] di Sciascio C, Sabol V, Veas EE. Rank As You Go: User-Driven Exploration of Search Results. InProceedings of the 21st International Conference on Intelligent User Interfaces Mar 7 (pp ). ACM.
[4] Qvarfordt P, Golovchinsky G, Dunnigan T, Agapie E. Looking ahead: query preview in exploratory search. InProceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval 2013 Jul 28 (pp ). ACM.

12. Thank You
Questions?