reasoning web reasoning and the web in the big data era

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Reasoning Web Reasoning And The Web In The Big Data Era

Author : Manolis Koubarakis
ISBN : 9783319105871
Genre : Computers
File Size : 40. 10 MB
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This volume contains the lecture notes of the 10th Reasoning Web Summer School 2014, held in Athens, Greece, in September 2014. In 2014, the lecture program of the Reasoning Web introduces students to recent advances in big data aspects of semantic web and linked data, and the fundamentals of reasoning techniques that can be used to tackle big data applications.

The Intelligent Web

Author : Gautam Shroff
ISBN : 9780191664625
Genre : Computers
File Size : 20. 17 MB
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As we use the Web for social networking, shopping, and news, we leave a personal trail. These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot of changing political opinion. These are just basic examples of the growth of "Web intelligence", as increasingly sophisticated algorithms operate on the vast and growing amount of data on the Web, sifting, selecting, comparing, aggregating, correcting; following simple but powerful rules to decide what matters. While original optimism for Artificial Intelligence declined, this new kind of machine intelligence is emerging as the Web grows ever larger and more interconnected. Gautam Shroff takes us on a journey through the computer science of search, natural language, text mining, machine learning, swarm computing, and semantic reasoning, from Watson to self-driving cars. This machine intelligence may even mimic at a basic level what happens in the brain.

Big Data And Computational Intelligence In Networking

Author : Yulei Wu
ISBN : 9781498784870
Genre : Computers
File Size : 80. 7 MB
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This book presents state-of-the-art solutions to the theoretical and practical challenges stemming from the leverage of big data and its computational intelligence in supporting smart network operation, management, and optimization. In particular, the technical focus covers the comprehensive understanding of network big data, efficient collection and management of network big data, distributed and scalable online analytics for network big data, and emerging applications of network big data for computational intelligence.

Big Data In Complex Systems

Author : Aboul-Ella Hassanien
ISBN : 9783319110561
Genre : Computers
File Size : 56. 94 MB
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This volume provides challenges and Opportunities with updated, in-depth material on the application of Big data to complex systems in order to find solutions for the challenges and problems facing big data sets applications. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Therefore transforming such content into a structured format for later analysis is a major challenge. Data analysis, organization, retrieval, and modeling are other foundational challenges treated in this book. The material of this book will be useful for researchers and practitioners in the field of big data as well as advanced undergraduate and graduate students. Each of the 17 chapters in the book opens with a chapter abstract and key terms list. The chapters are organized along the lines of problem description, related works, and analysis of the results and comparisons are provided whenever feasible.

A Semantic Web Primer

Author : Grigoris Antoniou
ISBN : 9780262304689
Genre : Computers
File Size : 49. 57 MB
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The development of the Semantic Web, with machine-readable content, has the potential to revolutionize the World Wide Web and its uses. A Semantic Web Primer provides an introduction and guide to this continuously evolving field, describing its key ideas, languages, and technologies. Suitable for use as a textbook or for independent study by professionals, it concentrates on undergraduate-level fundamental concepts and techniques that will enable readers to proceed with building applications on their own and includes exercises, project descriptions, and annotated references to relevant online materials.The third edition of this widely used text has been thoroughly updated, with significant new material that reflects a rapidly developing field. Treatment of the different languages (OWL2, rules) expands the coverage of RDF and OWL, defining the data model independently of XML and including coverage of N3/Turtle and RDFa. A chapter is devoted to OWL2, the new W3C standard. This edition also features additional coverage of the query language SPARQL, the rule language RIF and the possibility of interaction between rules and ontology languages and applications. The chapter on Semantic Web applications reflects the rapid developments of the past few years. A new chapter offers ideas for term projects. Additional material, including updates on the technological trends and research directions, can be found at

Supply Chain Management In The Big Data Era

Author : Chan, Hing Kai
ISBN : 9781522509578
Genre : Business & Economics
File Size : 49. 37 MB
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Technological advancements in recent years have led to significant developments within a variety of business applications. In particular, data-driven research provides ample opportunity for enterprise growth, if utilized efficiently. Supply Chain Management in the Big Data Era is an authoritative reference source for the latest scholarly material on the implementation of big data analytics for improved operations and supply chain processes. Highlighting emerging strategies from different industry perspectives, this book is ideally designed for managers, professionals, practitioners, and students interested in the most recent research on supply chain innovations.

The Internet Of Us Knowing More And Understanding Less In The Age Of Big Data

Author : Michael P. Lynch
ISBN : 9781631491863
Genre : Philosophy
File Size : 80. 94 MB
Format : PDF
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With far-reaching implications, this urgent treatise promises to revolutionize our understanding of what it means to be human in the digital age. We used to say "seeing is believing"; now googling is believing. With 24/7 access to nearly all of the world's information at our fingertips, we no longer trek to the library or the encyclopedia shelf in search of answers. We just open our browsers, type in a few keywords and wait for the information to come to us. Indeed, the Internet has revolutionized the way we learn and know, as well as how we interact with each other. And yet this explosion of technological innovation has also produced a curious paradox: even as we know more, we seem to understand less. While a wealth of literature has been devoted to life with the Internet, the deep philosophical implications of this seismic shift have not been properly explored until now. Demonstrating that knowledge based on reason plays an essential role in society and that there is much more to “knowing” than just acquiring information, leading philosopher Michael Patrick Lynch shows how our digital way of life makes us overvalue some ways of processing information over others, and thus risks distorting what it means to be human. With far-reaching implications, Lynch's argument charts a path from Plato's cave to Shannon's mathematical theory of information to Google Glass, illustrating that technology itself isn't the problem, nor is it the solution. Instead, it will be the way in which we adapt our minds to these new tools that will ultimately decide whether or not the "Internet of Things"—all those gadgets on our wrists, in our pockets and on our laps—will be a net gain for humanity. Along the way, Lynch uses a philosopher's lens to examine some of the most urgent issues facing digital life today, including how social media is revolutionizing the way we think about privacy; why a greater reliance on Wikipedia and Google doesn't necessarily make knowledge "more democratic"; and the perils of using "big data" alone to predict cultural trends. Promising to modernize our understanding of what it means to be human in the digital age, The Internet of Us builds on previous works by Nicholas Carr, James Gleick and Jaron Lanier to give us a necessary guide on how to navigate the philosophical quagmire that is the Information Age.

Ethical Reasoning In Big Data

Author : Jeff Collmann
ISBN : 9783319284224
Genre : Computers
File Size : 40. 22 MB
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This book springs from a multidisciplinary, multi-organizational, and multi-sector conversation about the privacy and ethical implications of research in human affairs using big data. The need to cultivate and enlist the public’s trust in the abilities of particular scientists and scientific institutions constitutes one of this book’s major themes. The advent of the Internet, the mass digitization of research information, and social media brought about, among many other things, the ability to harvest – sometimes implicitly – a wealth of human genomic, biological, behavioral, economic, political, and social data for the purposes of scientific research as well as commerce, government affairs, and social interaction. What type of ethical dilemmas did such changes generate? How should scientists collect, manipulate, and disseminate this information? The effects of this revolution and its ethical implications are wide-ranging. This book includes the opinions of myriad investigators, practitioners, and stakeholders in big data on human beings who also routinely reflect on the privacy and ethical issues of this phenomenon. Dedicated to the practice of ethical reasoning and reflection in action, the book offers a range of observations, lessons learned, reasoning tools, and suggestions for institutional practice to promote responsible big data research on human affairs. It caters to a broad audience of educators, researchers, and practitioners. Educators can use the volume in courses related to big data handling and processing. Researchers can use it for designing new methods of collecting, processing, and disseminating big data, whether in raw form or as analysis results. Lastly, practitioners can use it to steer future tools or procedures for handling big data. As this topic represents an area of great interest that still remains largely undeveloped, this book is sure to attract significant interest by filling an obvious gap in currently available literature.

Rdf Database Systems

Author : Olivier Curé
ISBN : 9780128004708
Genre : Computers
File Size : 20. 18 MB
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RDF Database Systems is a cutting-edge guide that distills everything you need to know to effectively use or design an RDF database. This book starts with the basics of linked open data and covers the most recent research, practice, and technologies to help you leverage semantic technology. With an approach that combines technical detail with theoretical background, this book shows how to design and develop semantic web applications, data models, indexing and query processing solutions. Understand the Semantic Web, RDF, RDFS, SPARQL, and OWL within the context of relational database management and NoSQL systems Learn about the prevailing RDF triples solutions for both relational and non-relational databases, including column family, document, graph, and NoSQL Implement systems using RDF data with helpful guidelines and various storage solutions for RDF Process SPARQL queries with detailed explanations of query optimization, query plans, caching, and more Evaluate which approaches and systems to use when developing Semantic Web applications with a helpful description of commercial and open-source systems

Lifelong Machine Learning

Author : Zhiyuan Chen
ISBN : 9781627058773
Genre : Computers
File Size : 67. 47 MB
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Lifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong learning aims to achieve this capability. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to new heights. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning.

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