Abstract---Data Integration is the process of transferring the data in source format into the destination format. Many

Recent years have witnessed an enormous development in the area of cloud computing and big data, which brings up challenges in decision making process. As the size of the dataset becomes extremely big, the process of extracting useful information by analyzing these data has also become tedious. To overcome this problem of extracting information, parallel programming models can be used. Parallel Programming model achieves this by partitioning these huge data. MapReduce is the one of the parallel programming model which can be used with Hadoop Distributed File Sytems(HDFS), to partition the data in a more efficient and effective way. Once the data is partitioned, Holt-winters of time series algorithm is used with MapReduce programming model in order to improve the utilization of large volume of data and to reduce the time complexity of handling huge volume of data. In this paper, distributed implementation of Holt-winters algorithm is proposed with MapReduce computing model.

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Today the volume of healthcare data generated increased rapidly because of the number of patients in each hospital increasing.  These data are most important for decision making and delivering the best care for patients. Healthcare providers are now faced with collecting, managing, storing and securing huge amounts of sensitive protected health information. As a result, an increasing number of healthcare organizations are turning to cloud based services. Cloud computing offers a viable, secure alternative to premise based healthcare solutions. The infrastructure of Cloud is characterized by a high volume storage and a high throughput. The privacy and security are the two most important concerns in cloud-based healthcare services. Healthcare organization should have electronic medical records in order to use the cloud infrastructure. This paper surveys the challenges of cloud in healthcare and benefits of cloud techniques in health care industries.

Recent years have witnessed the growth of Big Data, particularly Time Series data which initiates major research interest in Time Series analysis and forecasting future values. It finds interest in many applications such as business, stock market and exchange, weather forecasting, electricity demand, cost and usage of products and in any kind of place that has specific seasonal or trendy changes over time. The forecasting of Time Series data provides the organization with useful information that is necessary for making important decisions. In this paper, a detailed study is performed to find the total number of bike users with respect to the season and weather on Capital Bike Sharing System (CBS) dataset. The study covers the Auto Regressive Integrated Moving Average (ARIMA), Holt-Winters Additive and Multiplicative forecasting models to analyse the seasonal and trendy fluctuations of the given dataset to improve performance and accuracy.

Prediction plays an important role everywhere particularly in business, technology and many others. It helps organizations to take timely decisions, to improve profits and to reduce lost sales. Recent years have witnessed an enormous development in the area of cloud computing and big data, which brings up challenges in decision making process. As the size of the dataset becomes extremely big, the process of extracting useful information by analysing these data has also become tedious. Today data are generated in an unprecedented manner, prediction plays major role in utilizing these data. Time Series based prediction models take great part in handling Big Data such as online sales data, weather data etc. In this paper a methodology for prediction is introduced and the model is evaluated by applying various time series models with time series data which is seasonal and non-stationary. From the analysis it is proved that Holt-Winter's model performs better in seasonal and non-stationary time series data. The Holt-Winters (HW) methods estimate three smoothing parameters, associated with level, trend and seasonal factors. The seasonal variation can be of either an additive or multiplicative form. Also in this paper, Performance Improved Holt-Winters (PIHW) prediction algorithm is proposed and the results demonstrate that a considerable reduction in forecast error (Mean Square Error) can be achieved in the proposed model compared to Holt-Winters (HW) model.