Big Data Analytics for Sustainable Computing

Abstract

Big Data analytics is the process of collecting, organizing and analyzing large sets of data. To analyze such a large volume of data, Big Data analytics is typically performed using specialized software tools and applications for predictive analytics, data mining, text mining, forecasting and data optimization. Big Data analytics can help organizations to better understand the information contained within the data and will also help identify the data that is most important to the business and future business decisions. Collectively these processes are separate but highly integrated functions of high-performance analytics. Using Big Data tools and software enables an organization to process extremely large volumes of data that a business has collected to determine which data is relevant and can be analyzed to drive better business decisions in the future. This issue focused high quality research papers that address significant and new big data application and related system development issues in the emerging sustainable application domains. We anticipate this issue will open new entrance for further research and technology improvements in this important area. This issue features fourteen selected papers with high quality. The first article, “An Attempt to Design Improved and Fool Proof Safe Distribution of Personal Healthcare Records for Cloud Computing”, presents the hitches of storing healthcare related data on cloud and safeguarding in an astounding way. Diverse technology applications of cloud can be reinforced with wrapping the information of the user’s unsigned verification based on the element based encoding (EBE) is employed and fine grained data access control based on the advanced encryption scheme are tailored. The author researched that the key generation based on various set of elements plays a vital role in hiding the information of the users while gaining access to the data present over the network. Moreover for reducing the setbacks in safeguarding the key of the data containers the personal health records are classified into several associated fields. Thereby, proposed research work focus on effectiveness in terms of safety and secrecy of data over cloud. The second article, “Prediction of Individual’s Character in Social Media Using Contextual Semantic Sentiment Analysis”, proposed Sentiment Analysis Prediction for text mining as well as natural language processing. Paper discussed about people’s opinion towards product, service, tourism, movies, political issues, education systems opinion through social media like Twitter, Facebook etc., The key focus of this paper is to introduce Opinion COW (Opinion Co-Occurrence Word) method for Opinion Circle Method using contextual semantic sentiment analysis and Hybrid method to categorize the twitter users based onMaslow theory and identify the sentiment of each tweet. The third article, “Ensembled Population Rescaled Differential Evolution with Weighted Boosting for Early Breast Cancer Detection”, proposed early detection of breast cancer assists in increase of survival rate. Here an ensemble classifier method called Population Rescaled Differential Evolution with Weighted Boosting (PRDE-WB) is presented for early detection. At first, the given input breast images are subjected to pre-processing using Logarithmic Cube-root Shift technique. Where the ROI is extracted according to the cube root of the given breast input images. Then Population Rescaled Differential Evolution Optimization (PRDE) is implemented on the extracted ROI for obtaining breast cancer regions. PRDE process undergoes initialization, mutation, crossover and selection, optimally sketching the contour of the detected tumor using Population Rescaling Factor. * H . Anandakumar anandakumar.psgtech@gmail.com