On Modeling Query Refinement by Capturing User Intent through Feedback
Author(s)
Islam, MS
Liu, C
Zhou, R
Griffith University Author(s)
Year published
2012
Metadata
Show full item recordAbstract
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 naïve 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 ...
View more >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 naïve 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.
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View more >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 naïve 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.
View less >
Conference Title
Conferences in Research and Practice in Information Technology Series
Volume
124
Publisher URI
Subject
Data management and data science not elsewhere classified
Data mining and knowledge discovery
Query processing and optimisation