Open Source Big Data Platforms and Tools: An Analysis

Big data is attracting an excessive amount of interest in the IT and academic sectors. On a regular basis, computer and digital industries generate more data than they have space to store. In the current situation, five billion people have their own mobile phone, and over two billion people are linked globally to exchange various types of data. By 2020, it is estimated that about fifty billion people will be connected to the internet. During2020, data generation, use, and sharing would be forty-four times higher than in previous years. A variety of sectors and organizations are using big data to manage various operations. As a result, a thorough examination of big data's benefits, drawbacks, meaning, and characteristics is needed. The primary goal of this research is to gather information on the various open-source big data tools and platforms that are used by various organizations. In this paper we use a three perspective methodology to identify the strength and weaknesses of the workflow in a open source big data arena. This helps to establish a pipeline of workflow events for both researcher and entrepreneur decision making

A Pattern Approach to Examine the Design Space of Spatiotemporal Visualization

Pattern language has been widely used in the development of visualization systems. This dissertation applies a pattern language approach to explore the design space of spatiotemporal visualization. The study provides a framework for both designers and novices to communicate, develop, evaluate, and share spatiotemporal visualization design on an abstract level. The touchstone of the work is a pattern language consisting of fifteen design patterns and four categories. In order to validate the design patterns, the researcher created two visualization systems with this framework in mind. The first system displayed the daily routine of human beings via a polygon-based visualization. The second system showed the spatiotemporal patterns of co-occurring hashtags with a spiral map, sunburst diagram, and small multiples. The evaluation results demonstrated the effectiveness of the proposed design patterns to guide design thinking and create novel visualization practices

A visual analytics approach for visualisation and knowledge discovery from time-varying personal life data

A thesis submitted to the University of Bedfordshire, in ful filment of the requirements for the degree of Doctor of PhilosophyToday, the importance of big data from lifestyles and work activities has been the focus of much research. At the same time, advances in modern sensor technologies have enabled self-logging of a signi cant number of daily activities and movements. Lifestyle logging produces a wide variety of personal data along the lifespan of individuals, including locations, movements, travel distance, step counts and the like, and can be useful in many areas such as healthcare, personal life management, memory recall, and socialisation. However, the amount of obtainable personal life logging data has enormously increased and stands in need of effective processing, analysis, and visualisation to provide hidden insights owing to the lack of semantic information (particularly in spatiotemporal data), complexity, large volume of trivial records, and absence of effective information visualisation on a large scale. Meanwhile, new technologies such as visual analytics have emerged with great potential in data mining and visualisation to overcome the challenges in handling such data and to support individuals in many aspects of their life. Thus, this thesis contemplates the importance of scalability and conducts a comprehensive investigation into visual analytics and its impact on the process of knowledge discovery from the European Commission project MyHealthAvatar at the Centre for Visualisation and Data Analytics by actively involving individuals in order to establish a credible reasoning and effectual interactive visualisation of such multivariate data with particular focus on lifestyle and personal events. To this end, this work widely reviews the foremost existing work on data mining (with the particular focus on semantic enrichment and ranking), data visualisation (of time-oriented, personal, and spatiotemporal data), and methodical evaluations of such approaches. Subsequently, a novel automated place annotation is introduced with multilevel probabilistic latent semantic analysis to automatically attach relevant information to the collected personal spatiotemporal data with low or no semantic information in order to address the inadequate information, which is essential for the process of knowledge discovery. Correspondingly, a multi-signi ficance event ranking model is introduced by involving a number of factors as well as individuals' preferences, which can influence the result within the process of analysis towards credible and high-quality knowledge discovery. The data mining models are assessed in terms of accurateness and performance. The results showed that both models are highly capable of enriching the raw data and providing significant events based on user preferences. An interactive visualisation is also designed and implemented including a set of novel visual components signifi cantly based upon human perception and attentiveness to visualise the extracted knowledge. Each visual component is evaluated iteratively based on usability and perceptibility in order to enhance the visualisation towards reaching the goal of this thesis. Lastly, three integrated visual analytics tools (platforms) are designed and implemented in order to demonstrate how the data mining models and interactive visualisation can be exploited to support different aspects of personal life, such as lifestyle, life pattern, and memory recall (reminiscence). The result of the evaluation for the three integrated visual analytics tools showed that this visual analytics approach can deliver a remarkable experience in gaining knowledge and supporting the users' life in certain aspects

Integrating Big Data Into the Monitoring and Evaluation of Development Programmes

This report provides guidelines for evaluators, evaluation and programme managers, policy makers and funding agencies on how to take advantage of the rapidly emerging field of big data in the design and implementation of systems for monitoring and evaluating development programmes. The report is organized into two parts. Part I: Development evaluation in the age of big data reviews the data revolution and discusses the promise, and challenges this offers for strengthening development monitoring and evaluation. Part II: Guidelines for integrating big data into the monitoring and evaluation frameworks of development programmes focuses on what a big data inclusive M&E system would look like. The report also includes guidelines for integrating big data into programme monitoring and evaluation

A survey of online data-driven proactive 5G network optimisation using machine learning

In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capitaland operational expenditure. Proactive network optimisation is widely acknowledged as on e of the most promising ways to transform the 5G network based on big data analysis and cloud-fog-edge computing, but there are many challenges. Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees. Context in Cyber-Physical-Social Systems (CPSS) is often challenging to uncover, unfolds over time, and even more difficult to quantify and integrate into decision making. The first part of the review focuses on mining and inferring CPSS context from heterogeneous data sources, such as online user-generated-content. It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive algorithms. The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching, and interference management. In both parts, appropriate state-of-the-art machine learning techniques (including probabilistic uncertainty cascades in proactive optimisation), complexity-performance trade-offs, and demonstrative examples are presented to inspire readers. This survey couples the potential of online big data analytics, cloud-edge computing, statistical machine learning, and proactive network optimisation in a common cross-layer wireless framework. The wider impact of this survey includes better cross-fertilising the academic fields of data analytics, mobile edge computing, AI, CPSS, and wireless communications, as well as informing the industry of the promising potentials in this area