SummaryThe goal of a query optimizer is to provide an optimal Query Execution Plan (QEP) by comparing alternative query plans. In a distributed database system over cloud environment, the relations required by a query plan may be stored at multiple sites. This leads to an exponential increase in the number of possible equivalent plan alternatives to find an optimal QEP. Although it is not computationally reasonable to explore exhaustively all possible plans in such large search space. Although query optimization mechanisms are important in the cloud environments, to the best of our knowledge, there exists no complete and systematic review on investigating these issues. Therefore, in this paper, four categories to study these mechanisms are considered which are search‐based, machine learning‐based, schema‐based, and security‐based mechanisms. Also, this paper represents the advantages and disadvantages of the selected query optimization techniques and investigates the metrics of their techniques. Finally, the important challenges of these techniques are reviewed to develop more efficient query optimization techniques in the future.

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Recent advances in sensor networks and the Internet of Things (IoT) technologies have led to the gathering of an enormous scale of data. The exploration of such huge quantities of data needs more efficient methods with high analysis accuracy. Artificial Intelligence (AI) techniques such as machine learning and evolutionary algorithms able to provide more precise, faster, and scalable outcomes in big data analytics. Despite this interest, as far as we are aware there is not any complete survey of various artificial intelligence techniques for big data analytics. The present survey aims to study the research done on big data analytics using artificial intelligence techniques. The authors select related research papers using the Systematic Literature Review (SLR) method. Four groups are considered to investigate these mechanisms which are machine learning, knowledge-based and reasoning methods, decision-making algorithms, and search methods and optimization theory. A number of articles are investigated within each category. Furthermore, this survey denotes the strengths and weaknesses of the selected AI-driven big data analytics techniques and discusses the related parameters, comparing them in terms of scalability, efficiency, precision, and privacy. Furthermore, a number of important areas are provided to enhance the big data analytics mechanisms in the future.

Query optimization is considered as one of the main challenges of query processing phases in the cloud environments. The query optimizer attempts to provide the most optimal execution plan by considering the possible query plans. Therefore, the execution cost of a query can be affected by some factors, including communication costs, unavailability of resources, and access to large distributed data sets. In addition, it is known as NP-hard problem and many researchers are focused on this problem in recent years. Some techniques are proposed for solving this problem. Deterministic and non-deterministic methods are two main categories to study these techniques. The deterministic and non-deterministic query optimization methods can be further divided into three subcategories, cost-based query plan enumeration, multiple query optimization, and adaptive query optimization methods. Moreover, this paper presents the advantages and disadvantages of the algorithms for solving the query optimization problems in the cloud environments. Moreover, these techniques are compared in terms of optimization, time, cost, efficiency, and scalability. Finally, some key areas are offered to improve the cloud query optimization mechanisms in the future. KEYWORDScloud computing, database, query optimization, review INTRODUCTIONThe data transfer operation and resource sharing are facilitated by rapid progress of the distributed IT-based systems. 1,2 Cloud computing supports several computers through a network. 3 The cloud computing has a large-scale distributed architecture and virtualized services to deliver the requests to users. 4,5 Moreover, the cloud computing provides important financial advantages and long level cooperation possibilities for organizations and institutions. 6 The cloud computing is defined as a distributed IT-based technology based on service business model. 7This paradigm provides many benefits for users, such as the provision of computing capabilities, heterogeneous network access, scalability, and elasticity with measured services. 8,9 The cloud computing gives shared access to a large pool of resources, including data storage, memory, processing, and virtual machines. 10 A cloud client, such as a web browser and mobile app can be helpful in accessing these services. 11 Enormous amounts of data are retrieved from geo-distributed data sources and cross-layer data-handling requirements to make a change in business model. 12 The cloud storage as one of the main services is provided by cloud computing, 13 which allows the users to store their data in virtual pools instead of their servers. 14 In addition, subscribers can access the data from any area of cloud. 15 Therefore, the reliability and availability are necessary to recover the information and query processing.The query processing involves three main steps, as shown in Figure 1. First, the query is translated into an expression of the relational algebra.Second, an optimal evaluation plan for the query plan is generated. The query optimization is the main part o...

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Since early 2020, Coronavirus Disease 2019 (COVID-19) has spread widely around the world. COVID-19 infects the lungs, leading to breathing difficulties. Early detection of COVID-19 is important for the prevention and treatment of pandemic. Numerous sources of medical images (e.g., Chest X-Rays (CXR), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI)) are regarded as a desirable technique for diagnosing COVID-19 cases. Medical images of coronavirus patients show that the lungs are filled with sticky mucus that prevents them from inhaling. Today, Artificial Intelligence (AI) based algorithms have made a significant shift in the computer aided diagnosis due to their effective feature extraction capabilities. In this survey, a complete and systematic review of the application of Machine Learning (ML) methods for the detection of COVID-19 is presented, focused on works that used medical images. We aimed to evaluate various ML-based techniques in detecting COVID-19 using medical imaging. A total of 26 papers were extracted from ACM, ScienceDirect, Springerlink, Tech Science Press, and IEEExplore. Five different ML categories to review these mechanisms are considered, which are supervised learning-based, deep learningbased, active learning-based, transfer learning-based, and evolutionary learningbased mechanisms. A number of articles are investigated in each group. Also, some directions for further research are discussed to improve the detection of COVID-19 using ML techniques in the future. In most articles, deep learning is used as the ML method. Also, most of the researchers used CXR images to diagnose COVID-19. Most articles reported accuracy of the models to evaluate model performance. The accuracy of the studied models ranged from 0.84 to 0.99. The studies demonstrated the current status of AI techniques in using AI potentials in the fight against COVID-19.

Query optimization is the process of identifying the best Query Execution Plan (QEP). The query optimizer produces a close to optimal QEP for the given queries based on the minimum resource usage. The problem is that for a given query, there are plenty of different equivalent execution plans, each with a corresponding execution cost. To produce an effective query plan thus requires examining a large number of alternative plans. Access plan recommendation is an alternative technique to database query optimization, which reuses the previously-generated QEPs to execute new queries. In this technique, the query optimizer uses clustering methods to identify groups of similar queries. However, clustering such large datasets is challenging for traditional clustering algorithms due to huge processing time. Numerous cloud-based platforms have been introduced that offer low-cost solutions for the processing of distributed queries such as Hadoop, Hive, Pig, etc. This paper has applied and tested a model for clustering variant sizes of large query datasets parallelly using MapReduce. The results demonstrate the effectiveness of the parallel implementation of query workloads clustering to achieve good scalability.

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