Data has evolved into a large-scale data as big data in the recent era. The analysis of big data involves determined attempts on previous data. As new era of data has spatiotemporal facts that involve the time and space factors, which make them distinct from traditional data. The big data with spatiotemporal aspects helps achieve more efficient results and, therefore, many different types of frameworks have been introduced in cooperate world. In the present research, a qualitative approach is used to present the framework classification in two categories: architecture and features. Frameworks have been compared on the basis of architectural characteristics and feature attributes as well. These two categories project a significant effect on the execution of spatiotemporal data in big data. Frameworks are able to solve the real-time problems in less time of cycle. This study presents spatiotemporal aspects in big data with reference to several dissimilar environments and frameworks.

This paper investigates the changes that are required in networking technology for 'everything over IP' to become a reality. Initially the changes that are taking place in the telecommunications industry are reviewed. This review ranges from a discussion about the companies installing massive global IP networks to the emergence of novel routeing technologies, e.g. multi-protocol label switching (MPLS) and terabit router technologies. The role of existing telecommunications operators is then discussed, along with the reasons why they are developing interworking and intelligence layers based on distributed computing principles to support all their networks -mobile, fixed, broadband and IP.

Today, the era of smart devices evolving the human behavior interaction to a changing environment where the learning of activities is monitored to predict the next step of human behavior. The smart devices have these sensors built-in (accelerometer and gyroscope), which are continuously generating a large amount of data. The data used to identify the novel patterns of human behavior, together with machine learning and data mining techniques. Classification of human motions with motion sensor data is among the current topics of study. The classification is an important part of data mining techniques and used in this work to find the accuracy of instances in the given dataset. Thus, it is possible to follow the activities of a user carrying only a smartwatch. The smartwatches consisting of four different models from two manufacturers are used. Furthermore, the experiment contains nine users and seven activities performed by them. After the classification was determined, the data set to which the principal component analysis has been applied was classified by decision stump, j48, Bayes net, naive Bayes, naive Bayes multinomial text, random forest, and logit boost methods, and their performances were compared. The most successful result was obtained from the random forest method. The accuracy of the Random Forest classification algorithm on nominal datasets is 99.99% on both accelerometer and gyroscope sensors.

<p>The purpose of this paper is to analyze the climate changes in Pakistan, identify issues related to weather disasters and to revisit weather prediction approaches. The proposed approach is based on different algorithms and their comparisons with reference to past 5years (2010 - 2015) data on 12 attributes. A flow diagram is given that identifies steps included in the process. Results are obtained using WEKA 3.7.13 (latest version 2015). The KNN algorithm and memory-based reasoning algorithm shows the accuracy of predicting weather forecasts. The BPANN algorithm is used to analyze the data set along with KNN and memory-based reasoning algorithms. Decision tree shows the accuracy of predicting weather forecasts. The KNN is used with Bayesian approach in this research. Attributes used in this research shows significant relationship while many of those work as independent variables. Since, for weather prediction these attributes are very important, we used variant factors based on time and date. The KNN algorithm using Bayesian classifier provides accurate results compare with memory-based reasoning of Decision Tree and BPANN trainlm and trainbr.</p>

Cloud computing is a distributed environment for multiple organizations to use remotely and get high scalability, reliability on anytime, anywhere, and pay-as-you-go concepts. An organization has to create data centres to store, manage, and process the information to achieve benefits from data and make decisions. Cloud gives organizations a successful approach that leads to profit without maintaining the cost of data centres and technical staff to manage the services. Cloud has different types of architectures, types of clouds, and cost packages for using the cloud. These services can be scaled up or down when required by an organization. Cloud has unbeatable future because IT world is acquiring it and giving a boost to their businesses. Many cloud providers are using it and the remaining are moving to cloud. Cloud computing also gives birth to edge computing, fog computing, and many more zero downtime solutions.