A Model for Contextual Cooperative Query Answering in E-Commerce Applications.
ABSTRACT In computer based internet services, queries are usually submitted in a context. Either the contexts are created, or are assumed
- e.g., a purchase order, or an airline reservation. Unfortunately, there is little theoretical foundation for contexts, and
systems usually do not use them formally. In this paper, we propose a model for context representation in the direction of
aspect oriented programming and object-oriented systems, and show that contexts can be used to process queries better. We
outline a brief model that we are pursuing based on the idea of constraint inheritance with exceptions in a query tree.
SourceAvailable from: Md. Saiful Islam
Conference Paper: On Modeling Query Refinement by Capturing User Intent through Feedback[Show abstract] [Hide abstract]
ABSTRACT: SQL queries in relational data model implement the binary satisfaction of tuples. Tuples are generally filtered out from the result set if they miss the constraints expressed in the predicates of the given query. For naive or inexperienced users posing precise queries in the first place is very difficult as they lack of knowledge of the underlying dataset. Therefore, imprecise queries are commonplace for them. In connection with it, users are interested to have explanation of the missing answers. Even for unexpected tuples present in the result set advanced users may also want to know why a particular piece of information is present in the result set. This paper presents a simple model for generating explanations for both unexpected and missing answers. Further, we show how these explanations can be used to capture the user intent via feedback specifically for refining initial imprecise queries. The presented framework can also be thought as a natural extension for the existing SQL queries where support of explanation of expected and unexpected results are required to enhance the usability of relational database management systems. Finally, we summarize future research directions and challenges that need to be addressed in this endeavour.ADC; 01/2012
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ABSTRACT: SQL queries in the existing relational data model implement the binary satisfaction of tuples. That is, a data tuple is filtered out from the result set if it does not satisfy the constraints expressed in the predicates of the user submitted query. Posing appropriate queries for ordinary users is very difficult in the first place if they lack knowledge of the underlying dataset. Therefore, imprecise queries are commonplace for many users. In connection with this, this paper presents a framework for capturing user intent through feedback for refining the initial imprecise queries that can fulfill the users’ information needs. The feedback in our framework consists of both unexpected tuples currently present in the query output and expected tuples that are missing from the query output. We show that our framework does not require users to provide the complete set of feedback tuples because only a subset of this feedback can suffice. We provide the point domination theory to complement the other members of feedback. We also provide algorithms to handle both soft and hard requirements for the refinement of initial imprecise queries. Experimental results suggest that our approach is promising compared to the decision tree based query refinement approach.Journal of Systems and Software 06/2013; 86(6-6):1580-1595. DOI:10.1016/j.jss.2013.01.069 · 1.25 Impact Factor