الفهرس 
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Abstract
Massive and various data from the Internet of Things (IoT) generate enormous storage challenges. The IoT applications caused an extensive development. In the past two decades, the expansion of computational asset had a significant effect on the flow of the data. The vast flow of data is identified as ”Big data,” which is the data that cannot be managed using current ordinary techniques or tools. If it is correctly handled, it generates interesting information, such as investigating the user’s behavior and business intelligence. In this thesis, the proposed system is implemented to store and retrieve massive data. The results and discussion show that the proposed system generates a solution for storing and retrieving big data IoT-based smart applications. In the data preprocessing stage, we used the K-nearest neighbors (KNN) technique to clean noisy data and a Singular Value Decomposition (SVD) to reduce data dimensionality. In the processing stage, we proposed a hybrid technique of a Fuzzy C-mean and Density-based spatial clustering (FCM-DBSCAN) to deal with the applications with noise. The clustering technique is implemented based on both MapReduce and Spark models.