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Cloud computing is an internet based computing. This computing paradigm has enhanced the use of network where the capability of one node can be utilized by other node. Cloud service provides access on demand to distributive resources such as database, servers, software, infrastructure etc. in pay as you go basis. Load balancing is one of the vexing issues in distributed environment. Resources of service provider need to balance the load of client request. Load balancing is adapted in order to increase the resource consumption in Data centers that leads to enhance the overall performance of system achieving client satisfaction.

Now-a-days Most of the industries are having large volumes of data. Data has range of Tera bytes to Peta byte. Organizations are looking to handle the growth of data. Enterprises are using cloud deployments to address the big data and analytics with respect to the interaction between cloud and big data. This paper presents big data issues and research directions towards the ongoing work of processing of big data in the distributed environments.

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In today's world, most of the data (real world) is present in imbalanced form by nature. This is because of not having efficient algorithms to put this data (i.e., generated data by billion of internetconnected devices (IoTs)) in respective format. Imbalanced data poses a great challenge to (both) data mining and machine learning algorithms. The imbalanced dataset consists of a majority class and a minority class, where the majority class takes the lead over the minority class. Generally, several standard learning algorithms assume the balanced class distribution or equal misclassification costs. If prediction is performed by these learning algorithms on imbalanced data, the accuracy will be high for majority classes, i.e., resulting in poor performance. To overcome this problem (or improving accuracy of deision/prediction-making process), data mining and machine learning researchers have addressed the problem of imbalanced data using datalevel, algorithmic level and ensemble or hybrid methods. This article presents a systematic literature review and analyze the results of more than 400 research papers published between 2002-2017 (till June 2017), resulting in a broader and elaborate investigation of the literature in this area of research. Note that extension of this article/work will contain till December 2018 research articles, which will be published in June 2019 (now these more papers/articles did not include due to no. of pages/space issues). The systematic analysis of the research literature has focus on the key role of Data Intrinsic Problems in classification, handling the imbalanced data and the techniques used to overcome the skewed distribution. Furthermore, this article reveals patterns, trends and gaps in the existing literature and discusses briefly the next generation research directions in this area.

The malicious insider can be an employees, user and/or third party business partner. In cloud environment, clients may store sensitive data about their organization in cloud data centers. The cloud service provider should ensure integrity, security, access control and confidentiality about the stored data at cloud data centers. The malicious insiders can perform stealing on sensitive data at cloud storage and at organizations. Most of the organizations ignoring the insider attack because it is harder to detect and mitigate. This is a major emerging problem at the cloud data centers as well as in organizations. In this paper, we proposed a method that ensures security, integrity, access control and confidentiality on sensitive data of cloud clients by employing multi cloud service providers. The organization should encrypt the sensitive data with their security policy and procedures and store the encrypted data in trusted cloud. The keys which are used during encryption process are again encrypted and stored in another cloud area. So that organization contains only keys for keys of encrypted data. The Administrator of organization also does not know what data kept in cloud area and if he accesses the data, easily caught during the auditing. Hence, the only authorized used can access the data and use it and we can mitigate insider attacks by providing restricted privileges.

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