With the current availability of massive datasets and scalability requirements, different systems are required to provide their users with the best performance possible in terms of speed. On the physical level, performance can be translated into queries' execution time in database management systems. Queries have to execute efficiently (i.e. in minimum time) to meet users' needs, which puts an excessive burden on the database management system (DBMS). In this paper, we mainly focus on enhancing the query optimizer, which is one of the main components in DBMS that is responsible for choosing the optimal query execution plan and consequently determines the query execution time. Inspired by recent research in reinforcement learning in different domains, this paper proposes A Deep Reinforcement Learning Based Query Optimizer (RL_QOptimizer), a new approach to find the best policy for join order in the query plan which depends solely on the reward system of reinforcement learning. The experimental results show that a notable advantage of the proposed approach against the existing query optimization model of PostgreSQL DBMS.INDEX TERMS Join ordering problem, query execution plan and query optimization.

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Smart homes have been recently important sources for providing Activity of Daily Living (ADL) data about their residents. ADL data can be a great asset while analyzing residents' behavior to provide residents with better and optimized services. A popular example is to analyze residents' behavior to predict their future activities and optimize smart homes performance accordingly. This paper proposes a forecasting framework that utilizes ADL data to predict residents' next activities in a smart home environment. Forecasting is performed via the conjunction of embedding algorithm to encode the data and Bidirectional Long Short-Term Memory (BiLSTM) deep neural networks to process the data. The proposed framework is evaluated over five real ADL datasets where the experiments show the outperformance of the proposed framework with accuracy scores ranging from 98.7% to 93.8%.