A Vision of a Decisional Model for Re-optimizing Query Execution Plans Based on Machine Learning Techniques

International audienceMany of the existing cloud database query optimization algorithms target reducing the monetary cost paid to cloud service providers in addition to query response time. These query optimization algorithms rely on an accurate cost estimation so that the optimal query execution plan (QEP) is selected. The cloud environment is dynamic, meaning the hardware configuration, data usage, and workload allocations are continuously changing. These dynamic changes make an accurate query cost estimation difficult to obtain. Concurrently, the query execution plan must be adjusted automatically to address these changes. In order to optimize the QEP with a more accurate cost estimation, the query needs to be optimized multiple times during execution. On top of this, the most updated estimation should be used for each optimization. However, issues arise when deciding to pause the execution for minimum overhead. In this paper, we present our vision of a method that uses machine learning techniques to predict the best timings for optimization during execution

Improving user interaction in mobile-cloud database query processing

International audienceWhen 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