Improving user interaction in mobile-cloud database query processing

Abstract : When running queries on a database, choosing an optimal query execution plan to minimize query costs is crucial for the query optimizer. This is especially true in mobile-cloud database systems, where there are multiple costs to execute a query plan such as money, time and energy. In order to fulfill different cost objectives for different users, some query optimizers allow users to select the query execution plan from a Pareto Set based on Skyline queries. The users must select from a potentially large quantity of options, and these options present the values of costs. It is not straightforward to the users how to compare these values in such a way to choose the option that suits their needs best. This increases the possibility for users to choose in-optimal options, and the amount of time spent to make that choice. However, the existing user interaction model during multi-objective query processing is unable to solve this issue. To fill this gap, this paper presents a new user interaction model in multi-objective query processing. This model introduces the administrators, or super users, to the user interaction process, allowing them to preset Weight Profiles and their logical descriptions. Weight Profiles contain objective preferences for the users before the query is executed. By using this model, the users can select a Weight Profile that will obtain their optimal query execution plan, and the process of choosing will be more accurate and efficient.
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Communication dans un congrès
Workshop on Scalable Cloud Data Management, Dec 2017, Boston, United States
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Contributeur : Laurent D'Orazio <>
Soumis le : lundi 20 novembre 2017 - 16:36:25
Dernière modification le : jeudi 7 février 2019 - 14:33:40
Document(s) archivé(s) le : mercredi 21 février 2018 - 14:53:57


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  • HAL Id : hal-01640072, version 1


Chenxiao Wang, Jason Arenson, Florian Helff, Le Gruenwald, Laurent D'Orazio. Improving user interaction in mobile-cloud database query processing. Workshop on Scalable Cloud Data Management, Dec 2017, Boston, United States. 〈hal-01640072〉



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