الصفحة 2
الصفحة 2
Enabling things to talk : Designing IoT solutions with the IoT architectural reference model
The Internet of Things (IoT) is an emerging network superstructure that will connect physical resources and actual users. It will support an ecosystem of smart applications and services bringing hyper-connectivity to our society by using augmented and rich interfaces. Whereas in the beginning IoT referred to the advent of barcodes and Radio Frequency Identification (RFID), which helped to automate inventory, tracking and basic identification, today IoT is characterized by a dynamic trend toward connecting smart sensors, objects, devices, data and applications. The next step will be “cognitive IoT,” facilitating object and data re-use across application domains and leveraging hyper-connectivity, interoperability solutions and semantically enriched information distribution.
Emerging Technologies and Information Systems for the Knowledge Society ; 1st World Summit on the Knowledge Society, WSKS 2008, Athens, Greece, September 24-26, 2008. Proceedings
This book, in conjunction with the volume CCIS 19, constitutes the refereed proceedings of theFirst World Summit, WSKS 2008, held in Athens, Greece, in September 2008.
Electronic Government ; 7th International Conference, EGOV 2008, Turin, Italy, August 31 - September 5, 2008. Proceedings
This book constitutes the refereed proceedings of the 7th International Conference on Electronic Government, EGOV 2008, held in Torino, Italy, in August/September 2008 within the DEXA 2008 conference cluster.
E-Commerce and Web Technologies ; 9th International Conference, EC-Web 2008 Turin, Italy, September 3-4, 2008 Proceedings
This book constitutes the refereed proceedings of the 9th International Conference on Electronic Commerce and Web Technologies, EC-Web 2008, held in Turin, Italy, in September, 2008 in conjunction with Dexa 2008.
Distributed systems : Concurrency and consistency
Explores the gray area of distributed systems and draws a map of weak consistency criteria, identifying several families and demonstrating how these may be implemented into a programming language. Unlike their sequential counterparts, distributed systems are much more difficult to design, and are therefore prone to problems. On a large scale, usability reminiscent of sequential consistency, which would provide the same global view to all users, is very expensive or impossible to achieve.
Distributed systems : Concepts and design
Aims to provide an understanding of the principles on which the Internet and other distributed systems are based; their architecture, algorithms and design; and how they meet the demands of contemporary distributed applications.
Distributed computing and internet technology Vol. 4317 ; 3rd International conference, ICDCIT 2006, Bhubaneswar, India, December 20-23, 2006
Constitutes the refereed proceedings of the Third International Conference on Distributed Computing and Internet Technology, ICDCIT 2006, held in Bhubaneswar, India in December 2006. This book features the papers addressing and covering the areas distributed computing, internet technology, system security, data mining, and software engineering
Distributed computing and internet Technology ; Vol. 3816 ; 2nd International conference, ICDCIT 2005, Bhubaneswar, India, December 22-24, 2005, Proceedings
The opening ceremony and pre-conference tutorials on various related topics were held on December 21. The technical program started on December 22 and continued for three days. The program was arranged in single track so as to enable participants to attend sessions of di?erent tracks. Papers from the DM, IT, SE, and SS tracks were divided into two sessions, whereas DC track sessions were held on the ?rst two days of the conference. The program also included two plenary talks. The ?rst talk was delivered by S. S. Iyengar from Louisiana State University, USA. The second talk was delivered by He Jifeng from the International Institute for Software Technology (IIST) Macau. Prof. Iyenger’s talk on “The Distributed Sensor Networks — An Emerging Technology” was focused on new ideas about the use of distributed systems for emerging technology, while Prof. Jifeng’s talk on “Linking Theories of Concurrency by Retraction” dealt with semantics of concurrency.
Discovery Science ; 11th International Conference, DS 2008, Budapest, Hungary, October 13-16, 2008. Proceedings
This book constitutes the refereed proceedings of the 11th International Conference on Discovery Science, DS 2008, held in Budapest, Hungary, in October 2008, co-located with the 19th International Conference on Algorithmic Learning Theory, ALT 2008.
Digitization of healthcare data using blockchain
Gives a detailed description of the integration of blockchain technology for Electronic Health Records and provides the research challenges to consider in various disciplines such as supply chain, drug discovery, and data management. he aim of the book is to investigate the concepts of blockchain technology and its association with the recent development and advancements in the medical field. Moreover, it focuses on the integration of workflow strategies like NLP, and AI which could be adopted for boosting the clinical documentation and electronic healthcare records (EHR) usage by bringing down the physician EHR data entry. Also, the book covers the usage of smart contracts for securing patient records. Digitization of Healthcare Data Using Blockchain presents the practical implementations that deal with developing a web framework for building highly usable healthcare applications, a simple blockchain-powered EHR system.
Designing big data platforms : How to use, deploy, and maintain big data systems
Provides expert guidance and valuable insights on getting the most out of Big Data systems. Helps readers understand how to process large amounts of data with well-known Linux tools and database solutions, use effective techniques to collect and manage data from multiple sources, transform data into meaningful business insights, and much more. Author Yusuf Aytas, a software engineer with a vast amount of big data experience, discusses the design of the ideal Big Data platform: one that meets the needs of data analysts, data engineers, data scientists, software engineers, and a spectrum of other stakeholders across an organization. Detailed yet accessible chapters cover key topics such as stream data processing, data analytics, data science, data discovery, and data security. This real-world manual for Big Data technologies: Provides up-to-date coverage of the tools currently used in Big Data processing and management / Offers step-by-step guidance on building a data pipeline, from basic scripting to distributed systems / Highlights and explains how data is processed at scale / Includes an introduction to the foundation of a modern data platform
Database systems for advanced applications ; Vol. 3453 ; 10th international conference, DASFAA 2005, Beijing, China, April 17-20, 2005, Proceedings
Data Stream Mining and Resource Adaptive Computation.- Purpose Based Access Control for Privacy Protection in Database Systems.- Complex Networks and Network Data Mining.- Bioinformatics.- Indexing DNA Sequences Using q-Grams.- PADS: Protein Structure Alignment Using Directional Shape Signatures.- LinkageTracker: A Discriminative Pattern Tracking Approach to Linkage Disequilibrium Mapping.- Watermarking and Encryption.- Query Optimization in Encrypted Database Systems.- Watermarking Spatial Trajectory Database.- Effective Approaches for Watermarking XML Data.- XML Query Processing.- A Unifying Framework for Merging and Evaluating XML Information.- Efficient Evaluation of Partial Match Queries for XML Documents Using Information Retrieval Techniques.- PathStack: A Holistic Path Join Algorithm for Path Query with Not-Predicates on XML Data.- XML Coding and Metadata Management.- An Improved Prefix Labeling Scheme: A Binary String Approach for Dynamic Ordered XML.- Efficiently Coding and Indexing XML Document.- XQuery-Based TV-Anytime Metadata Management.- Data Mining.- Effective Database Transformation and Efficient Support Computation for Mining Sequential Patterns.
Data science, AI, and machine learning in drug development
The confluence of big data, AI, and machine learning has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R&D, emerging applications of big data, AI and machine learning in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations
Data science on the Google cloud platform : Implementing end-to-end real-time data pipelines : From ingest to machine learning
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. You'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines
Data science for economics and finance : Methodologies and applications
The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis.
Data mining : Concepts, models, methods, and algorithms ; 3rd ed.
Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces. Explores big data and cloud computing Examines deep learning Includes information on convolutional neural networks (CNN) Offers reinforcement learning Contains semi-supervised learning and S3VM Reviews model evaluation for unbalanced data
Data Management. Data, Data Everywhere ; 24th British National Conference on Databases, BNCOD 24, Glasgow, UK, July 3-5, 2007, Proceedings
One of the most pressing challenges is to ?nd ways of evolving database technology to cope with its new role in underpinning the massively distributed and heterogeneous applications built on top of the Internet. This has afiected both the ways in which data has been accessed and the ways in which it is represented, with XML data management becoming an important issue and, as such, heavily represented at this conference. It has also brought back issues of performance that might have been considered largely solved by the improvements in hardware, since data now has to be managed on devices of low power and small memory as well as on standard client and powerful server machines. We therefore invited papers on all aspects of data management, particularly related to how dataisused in the ubiquitous environment of the modern Internet by complex distributed and scientific applications.
Data Center Handbook : Plan, Design, Build, and Operations of a Smart Data Center ; 2nd ed.
Explains the fundamentals, advanced technologies, and best practices used in planning, designing, building and operating a mission-critical, energy-efficient, sustainable data center. This handbook, in its second edition, covers anatomy, ecosystem and taxonomy of data centers that enable the Internet of Things and artificial intelligent ecosystems and encompass the following: Data center overview and strategic planning Data center technologies Data center design and construction Data center operations technologies
Contextual Process Digitalization: Changing Perspectives – Design Thinking – Value-Led Design
This book presents an overview and step-by-step explanation of process management. It starts with the individual participants’ perspectives on their work in a process and its structuring and harmonization, and then moves on to its specification in a model and how it is embedded in the organizational and IT environment of the company.
Computer organization and design, enhanced : The hardware / software interface ; 5th ed.
Contains new examples and material highlighting the emergence of mobile computing and the cloud. It explores this generational change with updated content featuring tablet computers, cloud infrastructure, and the ARM (mobile computing devices) and x86 (cloud computing) architectures. The book uses a MIPS processor core to present the fundamentals of hardware technologies, assembly language, computer arithmetic, pipelining, memory hierarchies and I/O.Because an understanding of modern hardware is essential to achieving good performance and energy efficiency, this edition adds a new concrete example, Going Faster, used throughout the text to demonstrate extremely effective optimization techniques. There is also a new discussion of the Eight Great Ideas of computer architecture.
