الفهرس 
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Abstract
Many recent applications in several domains such as sensor networks and financial applications
generate continuous, rapid, and time varying datasets which are called data streams. Data streams
require real time processing. In most database systems, the query optimizers select a single plan to
process all streams tuples which is not efficient with the streams changeable nature. Also there is a
little research effort has been made towards the multiple data streams queries’ simultaneous
execution. In addition applying data streams’ multi-directional optimization over an optimized and
elastic environment has not been much considered. Thus in this thesis we proposed combined
frameworks and different optimization algorithms to solve these problems.
First, we proposed the optimized query mesh for data stream (OQMDS) framework. In which data
streams are processed over multiple query plans. Each plan is used to process a cluster of data that
have nearest properties. We proposed the Optimized Iterative Improvement Query Mesh (OII-QM) and
Non-Search based Query Mesh (NS-QM) algorithms, to efficiently generate the multiple plans. The
proposed algorithms improves the optimization time by 70.3%, the execution time by 21.8%, the
execution overheads by 80% and the memory usage by 96% over the II-QM algorithm.
Then in this thesis the Continuous Query Optimization based on Multiple Plans framework for data
streams over the cloud environment (CQOMP) was proposed. CQOMP provides an optimized streams
processing over the cloud. The Optimized Multiple plans (OMP) and the Auto Scaling Cloud Query Mesh
(AS-CQM) algorithms were proposed for streams processing over multiple query plans on cloud
computing. The proposed OMP improves the performance in terms of the execution time by 83.5%,
47.7%, and the throughput by 69.7%, 40% over the operator-set-cloud methodology and the NS-QM
algorithm. The elastic configurations of the proposed AS-CQM increases utilizing cloud processing
resources by 33.8% and reduces the costs by 50% over the static configuration.
Finally in this thesis the multiple queries optimization based on partitioning (MQOP) framework was
proposed to efficiently execute multiple queries simultaneously on the cloud environment. The
optimized global plan (OGP) and the optimized global plan based on partitioning (OGPP) algorithms
were proposed for jointly executing multiple continuous queries over an optimized global plan to each
cluster of data on the cloud. The proposed OGP improves the execution time by 80% and the
throughput by 76.8% over the operator tree technique. The proposed OGPP algorithm improves the
performance in terms of execution time by 61.1%, 72.6%, and the throughput by 55.5%, 66.5% over
the compile time optimization method and the operator tree technique.