FlexIQ: A flexible interactive Querying Framework by Exploiting the Skyline Operator

Abstract

Skyline operator has gained much attention in the last decade and is proved to be valuable for multi-criteria decision making. This paper presents a novel Flexible Interactive Querying (FlexIQ) framework for user feedback-based Select-Project-Join (SPJ) query refinement in databases. In FlexIQ, the user feedback is used to discover the query intent. In addition, we have used the skyline operator to confine the search space of the proposed query refinement algorithms. The user feedback consists of both unexpected information currently present in the query output and expected information that is missing from the query output. Once the feedback is given by the user, our framework refines the initial query by exploiting the skyline operator to minimize the unexpected information as well as maximize the expected information in the refined query output. In our framework, the user can also control different quality metric such as quality of results (e.g., false positive rates, false negative rates and accuracy) and complexity (i.e., quantified as the number of subqueries) in the refined query. We have validated our framework both theoretically and experimentally. In particular, we have demonstrated the effectiveness of our proposed framework by comparing its performance with the naïve decision tree based query refinement.

XPloreRank: exploring XML data via you may also like queries

“…There is a wide range of research conducted on the user query refinement to assist the users when their original queries do not retrieve expected or quality results. When the original query retrieves insufficient or empty results, query relaxation techniques modify or remove some constraints in the original query to produce additional or non-empty results [7], [12]. Conversely, when the original query retrieves many results, query reformulation techniques restrict the results by tightening or introducing new constraints on the original query [20], [15].…”

“…Therefore, query refinement methods add some constraints to the original query to limit the number of results [20]. Conversely, when the user query retrieves no result or insufficient results, the query refinement methods relax the original query constraints to generate more results for the user [17,3,12]. Some research works propose the query refinement techniques to discover and solve the mismatch problem when the original query retrieves erroneous results [5].…”

Efficient Answering of Why-Not Questions in Similar Graph Matching

“…In [5], Islam et al propose an approach for answering why-not questions in reverse skyline queries to start an automatic negotiation between customer and product of the company. In [15], Islam et al presents an approach of modifying the initial SPJ query by exploiting the skyline operator if both why and why-not tuples are provided by the user. Here, we address the problem of answering why-not questions for graphs via modifying the initial query into a new query so that the new query includes the missing graphs in the answer set as well as minimizes the distance with the set.…”

“…Though, these molecules are very similar to "Thymidine", only "AZEU", and "Azidothymidine" are active. In [3], [34], [15], these additional resultant objects are termed as false positives (FP). Also, if any of the initial Queries TOP5+6 TOP5+7 TOP5+10 TOP10+11 TOP10+15 TOP10+17 TOP10+20 λ(q, q ) FPR PPV λ(q, q ) FPR PPV λ(q, q ) FPR PPV λ(q, q ) FPR PPV λ(q, q ) FPR PPV λ(q, q ) FPR PPV λ(q, q ) FPR PPV [34], [15].…”

Similaridade em big data

“…Dentre os critérios de comparação para busca por similaridade que vêm sendo estudados, pode-se citar o critério baseado em k-vizinhos reversos mais próximos (YAO; LI; KUMAR, 2009;CHEEMA et al, 2010;CONG, 2011;SURATANEE;PLAIMAS, 2014;EMRICH et al, 2015) e critérios de diversidade (VIEIRA et al, 2011a;OUNIS, 2015;CATALLO et al, 2013;SHARAF, 2013;DOU et al, 2011;TAGLIASACCHI, 2012;PITOURA, 2015;AMAGATA;HARA, 2016). Dentre os operadores de busca por similaridade que vêm sendo estudados, pode-se citar operadores de junção por similaridade REED, 2012;SILVA et al, 2013a;CHEN et al, 2017;ZHANG et al, 2014;CARVALHO et al, 2016), operadores skyline (ISLAM;ZHOU, 2014;ZHANG et al, 2013;SAFAR;EL-AMIN;TANIAR, 2011;YAN;LEMIRE, 2016;DING et al, 2012), e operadores de buscas envolvendo mais de um centro de busca (SILVA; AREF; ALI, 2009;DENG et al, 2009;HUANG, 2017;TEODORO et al, 2011;LI et al, 2011;RAZENTE et al, 2008). Para cada nova técnica, também são desenvolvidos novos algoritmos que permitem executá-los de maneira mais eficiente, apoiando assim o desenvolvimento de ferramentas de análise e de novos recursos para os domínios de aplicações que precisam recuperar dados complexos.…”