Improving Maintenance Processes with Data Science
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Analysis of big data requires an investment in computing architecture to store, manage, analyze and visualize an enormous amount of data. This chapter discusses the challenges that big data analytics is dealing with. It also discusses the benefits of the data analysis from Twitter, Google, Facebook and any other space. The analysis of a larger amount of data in real time is likely to improve and accelerate decisions in multiple sectors, from finance to health, both including research. Hospitals and healthcare practitioners are collecting information about patients as they can predict epidemics and design new treatments that can reduce waste and improve service delivery. Prediction approaches and analytics methods have strong roles in the creation of social and economic opportunity. The chapter provides the examples of big data analytics application. Big data analysis is essential when organizations want to engage in predictive analysis, natural language processing, image analysis or advanced statistical techniques.
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Modern systems like the Internet of Things, cloud computing, and sensor networks generate a huge data archive. The knowledge extraction from these huge archived data requires modified approaches in algorithm design techniques. The field of study in which analysis of such huge data is carried out is called big data analytics, which helps to optimize the performance with reduced cost and retrieves the information efficiently. The enhancement of traditional data analytics needs to modify to suit big data analytics because it may not manage huge amounts of data. The real thought is how to design the data mining algorithms suitable to handle big data analysis. This paper discusses data analytics at the initial level, to begin with, the insights about the analysis process for big data. Big data analytics have a current research edge in the knowledge extraction field. This paper highlights the challenges and problems associated with big data analysis and provide inner insights into several techniques and methods used.
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This chapter introduces different components of data science and its phases: from organisational questions to value-added information, to data-based conclusions. It also introduces the commonly used graphical and statistical tools for different data type constellations. The chapter discusses the necessary competencies a data scientist should acquire in order to keep up with the fast-moving development in analytics disciplines and related fields. Data analytics is an overarching discipline that encompasses the complete management of data including collecting, validating, cleaning, organising, storing, and analysing data. The chapter helps the readers to relate Big Data and data science and to understand how to bridge to data analytics. Artificial intelligence is based on many disciplines that are part of data science, and as such, data analytics. Data analysts or data scientists who care whether their work is implemented, care about the issue and devote a great deal of time and effort to it.
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Abstract With more sources of monitoring coming on‐line, manual analysis of the raw data becomes increasingly infeasible. Data analytics can provide the toolset for automated decision support for utility engineers, helping to unlock the potential of network data. This chapter will focus on when and how data analytics can support transmission and distribution engineers in various job functions. Some particular applications are considered first, highlighting what role analytics can play and how this benefits the utility. Then, the enabling technologies for data analytics are discussed, exploring the links with fields such as data science and Big Data. Finally, the chapter concludes with various case studies of transmission and distribution data analytics in practice, and draws out some key design and deployment challenges overcome in each case.
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Big data is a new driver of the world economic and societal changes. The world’s data collection is reaching a tipping point for major technological changes that can bring new ways in decision making, managing our health, cities, finance and education. While the data complexities are increasing including data’s volume, variety, velocity and veracity, the real impact hinges on our ability to uncover the value in the data through Data Analytics technologies. Data Analytics poses a grand challenge on the design of highly scalable algorithms and systems to integrate the data and uncover large hidden values from datasets that are diverse, complex, and of a massive scale. Potential breakthroughs include new algorithms, methodologies, systems and applications in Data Analytics that discover useful and hidden knowledge from the Data efficiently and effectively. I am trying to make the Data and Data Analysis simple to understand since the field is already impacting the civilisation. As the technology grows, steps linked to Data and Data Analysis are taking part for example Artificial Intelligence and Machine Learning. In this paper, we will mainly focus on Data and Data Analysis.
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Healthcare data can be collected from various sources, including sensors, and conventional electronic records, photographs, data from clinical notes/biological literature, among others. The variation in data representation and gathering gives rise to issues in both data interpretation and processing. The methodologies required to analyze these diverse sources of data exhibit considerable variation. The presence of heterogeneity within the data gives rise to a distinct set of challenges when it comes to the processes of integration and analysis. This article presents a detailed review of healthcare data analytics and the respective data sources. Secondly, it discusses advanced data analytics for the healthcare sector, and its practical systems as well as applications of healthcare data analytics.
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Recently big data have become a buzzword, which forced the researchers to expand the existing data mining techniques to cope with the evolved nature of data and to develop new analytic techniques. Big data analytic techniques are serving many domains. In this paper, we provide a detailed comprehensive analysis and discussion of the data mining techniques, studying the changes that have been introduced to some of them that have been successfully developed into big data analytic techniques. The analysis also investigates the reasons behind the rest of data mining techniques that could not be evolved to big data analytics. A detailed study is also presented to discuss the application of big data analytics in the field of renewable energy studies.
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Big data is currently a buzzword in both academia and industry, with the term being used to describe a broad domain of concepts, ranging from extracting data from outside sources, storing and managing it, to processing such data with analytical techniques and tools. Nowadays the increase of data variety considered a very dispute problem for analysis. So innovative methods are mandatory for analytics especially in big data where the data in characteristic very complex and unstructured. The analytics is the process of analysis to predict concealed patterns and associations among data. The main objective of this survey paper is to provide an exhaustive view of different predictive analytics applications and approaches. Analytics methods focused on dissimilar perspectives based on applications and data variety. Some of the application discussed is big data in hotel governance, higher education, health care, data e-governance, consumer orientations. This paper presents different predictive approach processes and their applications.
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The prediction of consumer behavior is largely based on the analysis of consumer data using statistics as a tool for prediction. Thanks to online social networks, large quantities of heterogeneous consumer data are now available at competitive costs. Though they have much in common with conventional data, online social network datasets display several different properties. The exploration of these unique properties is indispensable to insuring the accuracy of predictions and data analytics. This chapter presents online social data, discusses seven properties of online social network data, suggests some analysis tools, and draws implications regarding the use of social data analytics.
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