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Supercomputing Frontiers ; 4th Asian Conference, SCFA 2018, Singapore, March 26-29, 2018, Proceedings
Constitutes the refereed proceedings of the 4th Asian Supercomputing Conference, SCFA 2018, held in Singapore in March 2018. Supercomputing Frontiers will be rebranded as Supercomputing Frontiers Asia (SCFA), which serves as the technical programme for SCA18. The technical programme for SCA18 consists of four tracks: Application, Algorithms & Libraries Programming System Software Architecture, Network/Communications & Management Data, Storage & Visualisation
Sublinear computation paradigm : Algorithmic revolution in the big data era
Gives an overview of cutting-edge work on a new paradigm called the “sublinear computation paradigm,” which was proposed in the large multiyear academic research project “Foundations of Innovative Algorithms for Big Data.” That project ran from October 2014 to March 2020, in Japan. To handle the unprecedented explosion of big data sets in research, industry, and other areas of society, there is an urgent need to develop novel methods and approaches for big data analysis. To meet this need, innovative changes in algorithm theory for big data are being pursued. For example, polynomial-time algorithms have thus far been regarded as “fast,” but if a quadratic-time algorithm is applied to a petabyte-scale or larger big data set, problems are encountered in terms of computational resources or running time. To deal with this critical computational and algorithmic bottleneck, linear, sublinear, and constant time algorithms are required.
Statistical quantitative methods in finance : From theory to quantitative portfolio management
Explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to the generalized method of moments (GMM) used in econometrics. additionally, the book delves into non-linear methods and bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. the book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. these enhancements are illustrated through real-world examples from finance and econometrics, accompanied by python code.
Software engineering and data science
Data-driven software solutions are different from “traditional” software development projects, as the focus of the main development core is on managing the data (e.g., data store and data quality) and designing behavioral models with the aid of artificial intelligence and machine learning techniques. To this end, new life cycles, algorithms, methods, processes, and tools are required. This reprint is centered on the recent trends and advancements in the field of engineering data-intensive software solutions to address the challenges in developing, testing, and maintaining such data-driven systems, with a focus on the application of data-driven solutions to real-life problems and techniques and algorithms addressing the different challenges of data-driven software engineering.
Soft Computing and Signal Processing ; Proceedings of 3rd ICSCSP 2020 ; Vol.2
Presents selected research papers on current developments in the fields of soft computing and signal processing from the Third International Conference on Soft Computing and Signal Processing (ICSCSP 2020). The book covers topics such as soft sets, rough sets, fuzzy logic, neural networks, genetic algorithms and machine learning and discusses various aspects of these topics, e.g., technological considerations, product implementation and application issues.
Snowflake Essentials : Getting Started with Big Data in the Cloud
Understand the essentials of the Snowflake Database and the overall Snowflake Data Cloud. This book covers how Snowflake’s architecture is different from prior on-premises and cloud databases. The authors also discuss, from an insider perspective, how Snowflake grew so fast to become the largest software IPO of all time. You will learn : Run analytics in the Snowflake Data Cloud / Create users and roles in Snowflake / Set up security in Snowflake / Set up resource monitors in Snowflake / Set up and optimize Snowflake Compute / Load, unload, and query structured and unstructured data (JSON, XML) within Snowflake / Use Snowflake Data Sharing to share data / Set up a Snowflake Data Exchange / Use the Snowflake Data Marketplace
Smittestopp : A Case Study on Digital Contact Tracing
Describes Smittestopp, the first Norwegian system for digital contact tracing of Covid-19 infections, which was developed in March and early April 2020. The system was deployed after five weeks of development and was active for a little more than two months, when a drop in infection levels in Norway and privacy concerns led to shutting it down. The intention of this book is twofold. First, it reports on the design choices made in the development phase. Second, as one of the only systems in the world that collected population data into a central database and which was used for an entire population, we can share experience on how the design choices impacted the system's operation. By sharing lessons learned and the challenges faced during the development and deployment of the technology, we hope that this book can be a valuable guide for experts from different domains, such as big data collection and analysis, application development, and deployment in a national population, as well as digital tracing.
Smart and Sustainable Planning for Cities and Regions ; Results of SSPCR 2019—Open Access Contributions
Offers a selection of research papers and case studies presented at the 3rd international conference “Smart and Sustainable Planning for Cities and Regions”, held in December 2019 in Bolzano, Italy, and explores the concept of smart and sustainable planning, including top contributions from academics, policy makers, consultants and other professionals.
Simplifying data engineering and analytics with delta : Create analytics-ready data that fuels artificial intelligence and business intelligence
Data engineers, data scientists, ML practitioners, BI analysts, or anyone in the data domain working with big data will be able to put their knowledge to work with this practical guide to executing pipelines and supporting diverse use cases using the Delta protocol. Basic knowledge of SQL, Python programming, and Spark is required to get the most out of this book.
Services marketing : Integrating customer focus across the firm ; 7th ed.
Maintains a managerial focus by incorporating company examples and strategies for addressing issues in every chapter, emphasizing the knowledge needed to implement service strategies for competitive advantage across industries. New research references and examples in every chapter include increased coverage of new business model examples such as Airbnb, Uber, OpenTable, Mint/Intuit, and others, alongside greater emphasis on technology, digital and social marketing, Big Data, and data analytics as a service.
Sensors data processing using machine learning
Collects research focusing on data processing using machine learning and deep learning. We invited investigators to contribute both original and review articles, covering the research and development in the areas of data processing using machine learning (ML) and deep learning (DL). These areas include solutions that are designed for smart devices. In this reprint, leading experts in the field share their insights, research findings, and visions for the future. Together, we embark on a journey to unlock the potential of effective data processing that involves transforming data from a given format into a more usable and desirable form, rendering them more meaningful and informative. Machine learning (ML), deep learning (DL), and artificial intelligence (AI) have proven to be effective methods for this purpose. Through the utilization of machine learning algorithms, mathematical modeling, or various statistical techniques, the entire process can be automated.
Seeing ourselves through technology : How we use selfies, blogs and wearable devices to see and shape ourselves
This book is open access under a CC BY license. Selfies, blogs and lifelogging devices help us understand ourselves, building on long histories of written, visual and quantitative modes of self-representations. This book uses examples to explore the balance between using technology to see ourselves and allowing our machines to tell us who we are.
Security Infrastructure Technology for Integrated Utilization of Big Data : Applied to the Living Safety and Medical Fields
This book describes the technologies needed to construct a secure big data infrastructure that connects data owners, analytical institutions, and user institutions in a circle of trust. It begins by discussing the most relevant technical issues involved in creating safe and privacy-preserving big data distribution platforms, and especially focuses on cryptographic primitives and privacy-preserving techniques, which are essential prerequisites.
Security and privacy in social networks and big data ; 6th International symposium, SocialSec 2020, Tianjin, China, September 26–27, 2020, Proceedings
This book constitutes revised and selected papers from the 6th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2020, held in Tianjin, China, in September 2020. The 38 full papers presented in this volume were carefully reviewed and selected from a total of 111 submissions. The papers are organized according to the topical sections on big data security; social networks; privacy-preserving and security.
Security And Privacy For Big Data, Cloud Computing And Applications
Examines various topics and approaches related to the security and privacy in big data and cloud computing, where authors share their expertise in their respective chapters on a broad range of security and privacy challenges and state of the art solutions. As big data becomes increasingly pervasive and cloud computing utilization becomes the norm, the security and privacy of our systems and data becomes more critical with emerging security and privacy threats and challenges. This book presents a comprehensive view on how to advance security and privacy in big data, cloud computing, and their applications. Topics include cryptographic tools, SDN security, big data security in IoT, privacy preserving in big data, security architecture based on cyber kill chain, privacy-aware digital forensics, trustworthy computing, privacy verification based on machine learning, and chaos-based communication systems. This book is an essential reading for networking, computing, and communications professionals, researchers, students and engineers, working with big data and cloud computing.
Secure Data Science : Integrating Cyber Security and Data Science
Data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science.
Satellite Earth Observations and Their Impact on Society and Policy
The result of a workshop bringing together an international advisory board of experts in science, satellite technologies, industry innovations, and public policy, this book addresses the current and future roles of satellite Earth observations in solving large-scale environmental problems. The book showcases the results of engaging distinct communities to enhance our ability to identify emerging problems and to administer international regimes created to solve them. It also reviews the work of the Policy and Earth Observation Innovation Cycle (PEOIC) project, an effort aimed at assessing the impact of satellite observations on environmental policy and to propose a mission going forward that would launch an “innovation cycle”
Retailing management
Use of big data and analytical methods for decision making ; Social media and mobile channels for communicating with customers and enhancing their shopping experience ; Issues involved in providing a seamless multichannel experience for customers ; Engagement in the overarching emphasis on conscious marketing and corporate social responsibility when making business decisions ; E94Impact of globalization on the retail industry
Research methodologies and ethical challenges in digital migration studies : Caring for (big) data?
This book investigates the methodological and ethical dilemmas involved when working with digital technologies and large-scale datasets in relation to ethnographic studies of digital migration practices and trajectories. Digital technologies reshape not only every phase of the migration process itself (by providing new ways to access, to share and preserve relevant information) but also the activities of other actors, from solidarity networks to border control agencies. In doing so, digital technologies create a whole new set of ethical and methodological challenges for migration studies: from data access to data interpretation, privacy protection, and research ethics more generally.
ReRAM-Based Machine Learning
The transition towards exascale computing has resulted in major transformations in computing paradigms. The need to analyze and respond to such large amounts of data sets has led to the adoption of machine learning (ML) and deep learning (DL) methods in a wide range of applications. One of the major challenges is the fetching of data from computing memory and writing it back without experiencing a memory-wall bottleneck. To address such concerns, in-memory computing (IMC) and supporting frameworks have been introduced. In-memory computing methods have ultra-low power and high-density embedded storage. Resistive Random-Access Memory (ReRAM) technology seems the most promising IMC solution due to its minimized leakage power, reduced power consumption and smaller hardware footprint, as well as its compatibility with CMOS technology, which is widely used in industry. Introduce ReRAM techniques for performing distributed computing using IMC accelerators, present ReRAM-based IMC architectures that can perform computations of ML and data-intensive applications, as well as strategies to map ML designs onto hardware accelerators.
