Kirjahaku

Big Data has created radical shifts, in less than a decade, with implications that are more subtle than they appear. While the technology is taking far major leaps ahead creating an unprecedented amount of data there is an urgent need to address Big Data' subtle implications and challenges related for instance to the establishment of mathematical theoretical frameworks to scale inferences and machine learning algorithms. Big Data has shown its tremendous potential to transform industries, such as healthcare and insurance industries, and to empower artificial intelligence and machine learning at an unequivocal scale, today. However, there are concerns that Big Data may lose much of its usefulness, potentially generating new unintended consequences if epistemological (knowledge generation) challenges are not addressed. Big Data has grown tremendously rapidly leading to data to outpace concepts. Conceptual investigations and mathematical frameworks are to theory formulation what methodology is to Big Data gathering and Big Data analytics. A lack of conceptual frameworks to address epistemological challenges of Big Data may slow progress in innovations and delay the development of Big Data's prospective applications according to recent reports and publications on Big Data. There is an urgent need to address Big Data's epistemological challenges along with technological challenges, in both public and private sectors, and to catch up with both shortages in skills and concepts to better leverage Big Data for our increasingly data-driven society.

Edge and fog analytics are on the rise and expected to continue growing in coming years. The global edge analytics market is expected to see a significant growth rate with a substantial increase in investment aiming to improve the overall edge analytics. Today, edge and fog analytics are not only powered by machine learning (ML) and artificial intelligence (AI) but also by the expansion of cloud-to-edge range of ML/AI chips that are built-in to accommodate high-performance computing (HPC) requirements, in the vicinity of where data is produced.The Internet of Things (IoT) devices with their embedded processing capabilities using new tiny chips designed to run complex models at the edge, are more appealing for ML and AI applications. These chip-enhanced devices are directly connected to a wide range of sensors, such as iPhone sensors, meteorological and hydrological sensors, which are capturing large quantity of input data as streaming data. Edge and fog analytics, at the edges, reduce the latency between data capture and decision-making by acting immediately on streaming data, which may be required in critical remote operations.Both cloud and edge analytics will be supplementing each other in handling large-scale workloads and delivering data insights. The edge analytics handles a subset of data, which can be both processed and analysed at the edge with the results transmitted back to the local area network level, the fog, which in turn transmits the data into the cloud. This period, of the emerging cloud-to-edge ecosystem, is referred to as the

This new book focuses on the practical aspects of addressing Big Data Challenges of scaling, spanning data integration, data preparation and data analytics including the emerging edge analytics. The dramatic increase in processing power, storage, and the rapid growth of cloud coupled with the availability of Big Data offer unprecedented opportunities for scaling analytics and discovery.Big Data has definitely created radical shifts with new opportunities, leading organisations and companies to shift their activities towards more data-driven decisions with the help of machine learning (ML) techniques and artificial intelligence (AI). A shift from stand-alone traditional desktop computing to embrace a more comprehensive strategy such as Mesh App and Service Architecture (MASA) strategy banking on Big Data to allow more dynamic connection of people, processes, things and services that are supporting today's increasingly intelligent (AI) digital ecosystems, according to Garner (2017).The book refers to the different techniques and tools used to address the ambiguity and uncertainty as well as scaling challenges that helped to transform, analyse, and reveal hidden patterns. The identified patterns were used, in turn, successfully in the discovery process. The tools are presented within the perspective that these tools lend themselves to also scale as the technologies and methodologies evolve.

Machine Learning (ML), which is a subset of Artificial intelligence (AI), enhances the ability of a computer to learn, from data, without being explicitly programmed end-to-end. As ML and AI learn they acquire the ability to carry out cognitive functions, such as perceiving, learning, reasoning and automatically digging deeper to identify important insights or new novel discovery. With the advance in machine learning, in particular its Deep Learning (DL) subset, ML is rapidly spreading across sectors and will continue to do so at an even higher rate with the ever increasing growth of Big Data. Gartner predicts that companies will combine Big Data and Machine Learning to carry out some or most of their service processes by 40% in 2022, up from 5% in 2017.ML is used to accelerate data-driven discovery in research and development. Recently, it has enabled scientists to discover largely unknown diversity of viruses, amounting to thousands of previously unknown viruses. The book refers to previous as well recent research work, with colleagues, where ML was used to capture subtle variation and to discover rare items, such as rare genes which researchers have so long sought for in vain. Such processes to identify genes or medicine can be daunting, as it may take years and can be expensive and the outcome can be uncertain. ML is used today to shorten the time and even help to identify medicine that can be more effective for people with a particular gene, which will help in turn in personalized medicine. ML is a critical ingredient for intelligent applications and provides the opportunity to further accelerate discovery processes as well as enhancing decision making processes. These trends promise that every sector will be data-driven and will be using machine learning in the cloud to incorporate artificial intelligence applications and to ultimately supplement existing analytical and decision making tools. The book introduces ML and its potential along with some ML applications using Spark and R platforms combined. While Spark has the possibility to scale and speed up analytics, it harness R language's machine learning capabilities beyond what is available on Spark or any other Big Data system. R and Spark can share codes and different types of data and carry out powerful large scale machine learning capabilities. Machine learning with Spark and R language combined can not only speed up but also light up Big Data Discovery.The book contains 10 chapters, the first chapter highlights ML quests, chapter 2 provides a detailed historical perspective, chapter 3 shows how ML works by introducing conceptual frameworks of ML, chapter 4 lists some of the metrics used to assess the performance of ML types. Chapter 5, 6 and 7 focus on different types of ML including supervised, unsupervised and reinforced learning. Chapter 8 and chapter 9 introduce the ML implementation platforms of R and Spark with their different libraries including Spark MLlib. Chapter 10 provides different walk-through ML examples using both R and Spark ML techniques.

Les enjeux et d fis du Big Data, appel aussi donn es massives, ne sont pas seulement conomiques, politiques ou thiques mais aussi pist mologiques in dits et in vitables pour pouvoir bien valoriser ces donn es prometteuses. La contribution de ces donn es massives ou m gadonn es repr sente 5% 6% de la productivit enregistr e pour les entreprises qui adoptent une prise de d cision bas e sur les m gadonn es, d'apr s le rapport de 2014 par le Massachusetts Institute of Technology (MIT). Le rapport de 2011 par McKinsey Global Institute (MGI) pr voit des am liorations encore plus substantielles et plus importantes venir dans le futur proche. En termes d' thique, en Europe un r glement g n ral sur la protection des donn es (RGPD) a t d j labor et il est appliqu dans l'Union Europ enne partir du 25 mai 2018. Le RGPD s'applique toute organisation qui traite des donn es personnelles des citoyens, aussi bien que des r sidents partout en Europe. Il permet aux citoyens et r sidents un contr le sur leurs donn es caract re personnel et en m me temps aux entreprises de b n ficier de conditions de concurrence quitables. Cependant, mesure que ces m gadonn es augmentent massivement et rapidement elles cr ent galement des enjeux et des d fis pist mologiques avec de nouvelles controverses telles que la question de savoir si ces m gadonn es peuvent rendre la science obsol te? Ce livre se consacre aux enjeux et d fis pist mologiques des m gadonn es, depuis leur acquisition jusqu' leur analyse et prise de d cisions. Il se r f re au travail pist mologique de Gaston Bachelard pour aider dans les processus d'acquisition et d'analyse de ces donn es massives. Gaston Bachelard nous rappelle la vigilance pour bien valoriser les donn es et g n rer des connaissances pour appuyer des applications pratiques, y inclus les solutions d'Intelligence Artificielle et les solutions durables.On trouve aussi dans le livre plusieurs cas pratiques du Big Data en relation avec l'Intelligence Artificielle et les solutions durables. Je cite, comme cas pratique que je conduisais avec les coll gues, l'exemple qui voque l'approche de Gaston Bachelard et de Pierre-Simon de Laplace pour identifier des g nes. Le livre souligne galement l'importance de faire face la p nurie des talents sans pr c dent, perturbant le march du travail presque dans tous les pays.