Data Mining Practical Machine Learning Tools and Techniques

https://andreashellander.se/wp-content/uploads/2016/01/1-s2.0-S0306437914001288-main.pdf

The rise of big data on cloud computing

https://bura.brunel.ac.uk/bitstream/2438/13152/4/Fulltext.pdf

Critical analysis of Big Data challenges and analytical methods

Voluminous amounts of data have been produced, since the past decade as the miniaturization of Internet of things (IoT) devices increases. However, such data are not useful without analytic power. Numerous big data, IoT, and analytics solutions have enabled people to obtain valuable insight into large data generated by IoT devices. However, these solutions are still in their infancy, and the domain lacks a comprehensive survey. This paper investigates the state-of-the-art research efforts directed toward big IoT data analytics. The relationship between big data analytics and IoT is explained. Moreover, this paper adds value by proposing a new architecture for big IoT data analytics. Furthermore, big IoT data analytic types, methods, and technologies for big data mining are discussed. Numerous notable use cases are also presented. Several opportunities brought by data analytics in IoT paradigm are then discussed. Finally, open research challenges, such as privacy, big data mining, visualization, and integration, are presented as future research directions.

Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges

Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.

Big Data Analytics in Intelligent Transportation Systems: A Survey

Big Data computing and clouds

This paper discusses approaches and environments for carrying out analytics on Clouds for Big Data applications. It revolves around four important areas of analytics and Big Data, namely (i) data management and supporting architectures; (ii) model development and scoring; (iii) visualisation and user interaction; and (iv) business models. Through a detailed survey, we identify possible gaps in technology and provide recommendations for the research community on future directions on Cloud-supported Big Data computing and analytics solutions.

Big Data Computing and Clouds: Challenges, Solutions, and Future Directions

The more instrumented society is demanding smarter services to help coordinate daily activities and exceptional situations. Applications become sophisticated and context-aware as the pervasiveness of technology increases. In order to cope with resource limitations of mobile-based environments, it is a common practice to delegate processing intensive components to a Cloud Computing infrastructure. In this scenario, executions of server-based jobs are still dependent on the local variations of the end-user context. We claim that there is a need for an advanced model for smarter services that combines techniques of context awareness and adaptive job scheduling. This model aims at rationalising the resource utilisation in a Cloud Computing environment, while leading to significant improvement of quality of service. In this paper, we introduce such a model and describe its performance benefits through a combination of social and service simulations. We analyse the results by demonstrating gains in performance, quality of service, reduction of wasted jobs, and improvement of overall end-user experience.

/pdf/context-aware-job-scheduling-for-cloud-computing-c6acy5aaou.pdf

Context-Aware Job Scheduling for Cloud Computing Environments

A method (and structure) includes receiving an input from a sensor on a mobile device. Based on the sensor input, a processor determines whether a user of the mobile device is engaged in a specific physical activity. A control setting on the mobile device is set for delivering content during a period the specific physical activity is detected.

Method and system for creating and refining rules for personalized content delivery based on users physical activities

A method for processing an attempted payment made using a mobile device includes receiving information about the attempted payment, receiving data indicative of a behavior of a user of the mobile device at the time of the attempted payment, computing a likelihood that the attempted payment is fraudulent, based on a comparison of the behavior of the user to an historical behavior pattern of the user, and sending an instruction indicating how to proceed with the attempted payment, based on the likelihood.

Silvia Cristina Sardela Bianchi

Papers

Big Data computing and clouds

Big Data Computing and Clouds: Challenges, Solutions, and Future Directions

Context-Aware Job Scheduling for Cloud Computing Environments

Method and system for creating and refining rules for personalized content delivery based on users physical activities

Detecting fraudulent mobile payments