This paper proposes a novel method to analyze and classify the cardiovascular ultrasound echocardiographic images using Naïve-Bayesian model via database OLAP-SQL. Efficient data mining algorithms based on tightly-coupled model is used to extract features. Three algorithms are proposed for classification namely Naïve-Bayesian Classifier for Discrete variables (NBCD) with SQL, NBCD with OLAP-SQL, and Naïve-Bayesian Classifier for Continuous variables (NBCC) using OLAP-SQL. The proposed model is trained with 207 patient images containing normal and abnormal categories. Out of the three proposed algorithms, a high classification accuracy of 96.59% was achieved from NBCC which is better than the earlier methods.

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In today’s world, the shopping is the largest fashionable trend where the transaction processing is meticulous to fetch the items from the shopping transaction history by using traditional Apriori algorithm. An Apriori algorithm is the one which is used for finding frequent pattern from the given dataset. The problem of Apriori is to find useful itemsets for business purpose was time consuming. To overcome this problem, we have proposed Map Reduce based Apriori algorithm which generates frequent itemset and association rules by using parallel computations to reduce computations. The Spark distributed systems along with data bricks technology have been used. The experimental result shows that have been reduced the time taken fetch the data from the database.

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The advent of Online Social Networks (OSNs) and the Internet-of-Things (IoT) has catalyzed an unprecedented surge in data generation at smart device endpoints. This phenomenon necessitates robust strategies for efficient data distribution and processing on data servers. Furthermore, the burgeoning volume of data intensifies challenges associated with data placement, replication, and migration in edge-cloud computing paradigms. Considerations such as access delay, cost implications, workload balance, and data security become critical parameters in the storage and processing of data from OSNs and IoT devices. Researchers have proposed various strategies to optimize data placement costs, access latency, migration costs, and load balancing constraints. This paper presents an extensive survey on the existing strategies for data placement, data replication, and data migration. The future research directions in edge-cloud computing informed by this survey are also delineated herein.