User Feedback Based Query Refinement by Exploiting Skyline Operator

Islam, Md. Saiful; Liu, Chengfei; Zhou, Rui

doi:10.1007/978-3-642-34002-4_33

Cited by 5 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can also easily be observed from Fig. 15 [16] propose a user feedback based query refinement framework for encountering both why and why-not questions in SPJ query output by exploiting the skyline operator. The authors consider minimizing the unexpected (why) tuples as well as maximizing the expected (why-not) tuples in the refined query output.…”

Section: Quality Of Results In Cardb Datasetsmentioning

confidence: 90%

On answering why-not questions in reverse skyline queries

Islam

Zhou

Liu

2013

2013 IEEE 29th International Conference on Data Engineering (ICDE)

View full text Add to dashboard Cite

This paper aims at answering the so called whynot questions in reverse skyline queries. A reverse skyline query retrieves all data points whose dynamic skylines contain the query point. We outline the benefit and the semantics of answering why-not questions in reverse skyline queries. In connection with this, we show how to modify the why-not point and the query point to include the why-not point in the reverse skyline of the query point. We then show, how a query point can be positioned safely anywhere within a region (i.e., called safe region) without losing any of the existing reverse skyline points. We also show how to answer why-not questions considering the safe region of the query point. Our approach efficiently combines both query point and data point modification techniques to produce meaningful answers. Experimental results also demonstrate that our approach can produce high quality explanations for why-not questions in reverse skyline queries.

show abstract

Section: Quality Of Results In Cardb Datasetsmentioning

confidence: 90%

On answering why-not questions in reverse skyline queries

Islam

Zhou

Liu

2013

2013 IEEE 29th International Conference on Data Engineering (ICDE)

View full text Add to dashboard Cite

show abstract

“…2) Query Refinement by Exploiting Skyline Operator: In [14], we propose a SPJ query refinement framework, called FlexIQ, by exploiting the skyline operator. In FlexIQ, we use feedback to discover the query intent of the user.…”

Section: Research Problem and Terminologymentioning

confidence: 99%

On answering why and why-not questions in databases

Islam

2013

2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW)

View full text Add to dashboard Cite

There is a growing interest in allowing users to ask questions on received results in the hope of improving the usability of database systems. This research aims at answering the so called why and why-not questions on received results w.r.t. different query settings in databases. The main goals of this research are: (i) studying the problem of answering the why and the why-not questions in databases; (ii) finding efficient strategies for answering these questions in terms of different query settings and (iii) finally, developing a framework that can take advantage of the existing data indexing and query evaluation techniques available to answer such questions in databases. We believe that the research undertaken by us can contribute towards improving the usability of traditional database systems. I. INTRODUCTIONAfter decades of efforts made by the database community, today's systems have become highly efficient in terms of both query execution time and resource usage. However, these systems are not usable for the end users to the same degree as they are proficient in underlying data management and query evaluation [16]. These days users expect systems to be more interactive and cooperative. That is, the users are not satisfied only with receiving the result from the system, but also they want to know why the system returns only the current set of objects in the result set (i.e., the current result set does not match the user's expectation). In particular, users may want to know why a certain data object (which is unexpected) appears in the result set and similarly, why a certain data object (which is expected) does not appear in the result set. As a next step, users may also seek appropriate explanations for these questions. Any system that can provide good explanations for the above questions can be very helpful for users to understand their information needs and also make the system more interactive and transparent to the users [27], [10], [13]. At present, our traditional database systems do not provide any kind of exploratory data analysis facilities to support the above why and why not questions.There are different aspects of answering why and why-not questions in databases. These are: (i) computing provenance (also called "lineage" or "pedigree") information to describe the origins of data output and the process by which it was arrived at [4], [6] ; (ii) modifying the original database so that missing objects become part of the query output [12], [11] ; (iii) identifying the causes (e.g., operator(s) in the user submitted query) that filters the desired query output [5]; and (iv) modifying the query so that missing objects appear in

show abstract

“…The second group, query rewriting methods, modifies a query in such a way that it delivers items of interest (Tran & Chan, 2010;Islam, Liu, & Zhou, 2012). A response to a modified query includes original query results and items of interest (Tran & Chan, 2010) or unexpected results are removed from the result set (Islam, Liu, & Zhou, 2012). The second approach involves a user in a query refinement process who has to provide an exemplar subset of tuples to be delivered.…”

Section: "Why Not?" Queriesmentioning

confidence: 99%

Considering User Intention in Differential Graph Queries

Thiele

Vasilyeva²,

Bornhövd³

et al. 2015

Journal of Database Management

View full text Add to dashboard Cite

Empty answers are a major problem by processing pattern matching queries in graph databases. Especially, there can be multiple reasons why a query failed. To support users in such situations, differential queries can be used that deliver missing parts of a graph query. Multiple heuristics are proposed for differential queries, which reduce the search space. Although they are successful in increasing the performance, they can discard query subgraphs relevant to a user. To address this issue, the authors extend the concept of differential queries and introduce top-k differential queries that calculate the ranking based on users' preferences and significantly support the users' understanding of query database management systems. A user assigns relevance weights to elements of a graph query that steer the search and are used for the ranking. In this paper the authors propose different strategies for selection of relevance weights and their propagation. As a result, the search is modelled along the most relevant paths. The authors evaluate their solution and both strategies on the DBpedia data graph.

show abstract

User Feedback Based Query Refinement by Exploiting Skyline Operator

Cited by 5 publications

References 12 publications

On answering why-not questions in reverse skyline queries

On answering why-not questions in reverse skyline queries

On answering why and why-not questions in databases

Considering User Intention in Differential Graph Queries

Contact Info

Product

Resources

About