Netværkskomponenter

This book constitutes revised selected papers from the proceedings of the 26th International Conference on Financial Cryptography and Data Security, FC 2022, which was held in Grenada during May 2022.The 32 full papers and 4 short papers included in this book were carefully reviewed andselected from 159 submissions. They were organized in topical sections as follows: tokenomics; MPC (mostly); privacy; ZKP; old-school consensus; mostly payment networks; incentives; not proof of work; performance; measurements.

This book investigates compressive sensing techniques to provide a robust and general framework for network data analytics. The goal is to introduce a compressive sensing framework for missing data interpolation, anomaly detection, data segmentation and activity recognition, and to demonstrate its benefits. Chapter 1 introduces compressive sensing, including its definition, limitation, and how it supports different network analysis applications. Chapter 2 demonstrates the feasibility of compressive sensing in network analytics, the authors we apply it to detect anomalies in the customer care call dataset from a Tier 1 ISP in the United States. A regression-based model is applied to find the relationship between calls and events. The authors illustrate that compressive sensing is effective in identifying important factors and can leverage the low-rank structure and temporal stability to improve the detection accuracy. Chapter 3  discusses that there are several challenges in applying compressive sensing to real-world data. Understanding the reasons behind the challenges is important for designing methods and mitigating their impact. The authors analyze a wide range of real-world traces. The analysis demonstrates that there are different factors that contribute to the violation of the low-rank property in real data. In particular, the authors find that (1) noise, errors, and anomalies, and (2) asynchrony in the time and frequency domains lead to network-induced ambiguity and can easily cause low-rank matrices to become higher-ranked. To address the problem of noise, errors and anomalies in Chap. 4, the authors propose a robust compressive sensing technique. It explicitly accounts for anomalies by decomposing real-world data represented in matrix form into a low-rank matrix, a sparse anomaly matrix, an error term and a small noise matrix. Chapter 5 addresses the problem of lack of synchronization, and the authors propose a data-driven synchronization algorithm. It can eliminate misalignment while taking into account the heterogeneity of real-world data in both time and frequency domains. The data-driven synchronization can be applied to any compressive sensing technique and is general to any real-world data. The authors illustrates that the combination of the two techniques can reduce the ranks of real-world data, improve the effectiveness of compressive sensing and have a wide range of applications. The networks are constantly generating a wealth of rich and diverse information. This information creates exciting opportunities for network analysis and provides insight into the complex interactions between network entities. However, network analysis often faces the problems of (1) under-constrained, where there is too little data due to feasibility and cost issues in collecting data, or (2) over-constrained, where there is too much data, so the analysis becomes unscalable. Compressive sensing is an effective technique to solve both problems. It utilizes the underlying data structure for analysis. Specifically, to solve the under-constrained problem, compressive sensing technologies can be applied to reconstruct the missing elements or predict the future data.  Also, to solve the over-constraint problem, compressive sensing technologies can be applied to identify significant elementsTo support compressive sensing in network data analysis, a robust and general framework is needed to support diverse applications. Yet this can be challenging for real-world data where noise, anomalies and lack of synchronization are common. First, the number of unknowns for network analysis can be much larger than the number of measurements. For example, traffic engineering requires knowing the complete traffic matrix between all source and destination pairs, in order to properly configure traffic and avoid congestion. However, measuring the flow between all source and destination pairs is very expensive or even infeasible. Reconstructing data from a small number of measurements is an underconstrained problem. In addition, real-world data is complex and heterogeneous, and often violate the low-level assumptions required by existing compressive sensing techniques. These violations significantly reduce the applicability and effectiveness of existing compressive sensing methods. Third, synchronization of network data reduces the data ranks and increases spatial locality. However, periodic time series exhibit not only misalignment but also different frequencies, which makes it difficult to synchronize data in the time and frequency domains.The primary audience for this book is data engineers, analysts and researchers, who need to deal with big data with missing anomalous and synchronization problems. Advanced level students focused on compressive sensing techniques will also benefit from this book as a reference.

This book constitutes the proceedings of the 15th Latin American Symposium on Theoretical Informatics, LATIN 2022, which took place in Guanajuato, Mexico, in November 2022. The 46 papers presented in this volume were carefully reviewed and selected from 114 submissions. They were organized in topical sections as follows: Algorithms and Data Structures; Approximation Algorithms; Cryptography; Social Choice Theory; Theoretical Machine Learning; Automata Theory and Formal Languages; Combinatorics and Graph Theory; Complexity Theory; Computational Geometry.Chapter "e;Klee's Measure Problem Made Oblivious"e; is available open access under a CC BY 4.0 license.

This volume constitutes the proceedings of the 28th International Conference on Collaboration Technologies and Social Computing, CollabTech 2022, held in Santiago, Chile during November 8-11, 2022. The 18 full and 4 work-in-process papers presented in this volume were selected from 37 submissions and underwent careful double-blind peer review. The papers focus on innovative technical, human and organizational approaches to expand collaboration support including computer science, management science, design science, cognitive and social science.

This book presents applications of cognitive management and cognitive computing in the fields of risk management, cognitive fraud detection, and in business decision making. The book provides insights on how cognitive management and cognitive computing enable businesses to quickly augment human intelligence and help humans perform tasks better. For example, the authors describe how by analyzing patterns in big data, small data, and "e;dark data,"e; cognitive technologies can detect human behavior and suggest options for personalizing of products and services. The book studies companies in industries such as automotive, airline, health care, retail, wealth management, and litigation who have adopted these approaches.Presents applications of cognitive computing and cognitive management used in augmenting and empowering business decisions;Shows how to employ the Internet of Things in businesses using a cognitive management framework;Discusses technical aspects and alternatives to traditional tools, algorithms, and methodologies in cognitive computing.

This book constitutes the proceedings of the 20th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2022, held in L'Aquila, Italy in July 2022.The 37 full papers in this book were reviewed and selected from 67 submissions. Another 10 demonstrations papers were selected from 11 submissions are presented here as short papers. The papers deal with the application and validation of agent-based models, methods, and technologies in a number of key applications areas, including: advanced models and learning, agent-based programming, decision-making, education and social interactions, formal and theoretic models, health and safety, mobility and the city, swarms and task allocation.

The Cisco Unified Computing System (UCS) can be found in the majority of data centers across the world. However, getting hands-on practice to learn this infrastructure can be difficult, as many companies will push to have it production-ready as soon as possible. Home-labs are also cost-prohibitive, cumbersome, electricity-hungry, and noisy. So, how do you get hands-on experience? With Unified Computing System Platform Emulator (UCSPE) and this book. UCSPE is free and can run on a laptop. Using it along with this book, you will learn how to set up, manage and troubleshoot a UCS, including the fabric interconnects, chassis and IOMs, and servers through the GUI and the CLI. All from the comfort of your own home. Introducing Cisco Unified Computing System will show you how to set up a UCS (comparing the UCSPE to a real-world deployment), customize the hardware, configure the UCS system, and secure it. You'll start by creating an organization and then the policies to control storage, networking, boot options, maintenance policies, and server pools. Once you have the required policies you'll use them to create service profiles (using the policies) and templates and assign these to the blade and rack-mount servers in the virtual environment. You'll also be looking at real-life scenarios such as upgrades (and downgrades), northbound networking, and Storage Area Networking (SAN) connectivity. Using the GUI and the CLI you'll look at real-world examples that  data center engineers may encounter. What You'll Learn Set up the Cisco UCSPE on VMWareCreate UCS service profiles Secure the UCS system Troubleshoot the UCSWho This Book Is For Datacenter and network engineers and individuals studying for the CCNA and CCNP Cisco data center qualification.

Emerging machine learning techniques bring new opportunities to flexible network control and management. This book focuses on using state-of-the-art machine learning-based approaches to improve the performance of Software-Defined Networking (SDN). It will apply several innovative machine learning methods (e.g., Deep Reinforcement Learning, Multi-Agent Reinforcement Learning, and Graph Neural Network) to traffic engineering and controller load balancing in software-defined wide area networks, as well as flow scheduling, coflow scheduling, and flow migration for network function virtualization in software-defined data center networks. It helps readers reflect on several practical problems of deploying SDN and learn how to solve the problems by taking advantage of existing machine learning techniques. The book elaborates on the formulation of each problem, explains design details for each scheme, and provides solutions by running mathematical optimization processes, conducting simulated experiments, and analyzing the experimental results.

This book constitutes the refereed proceedings of the 41st International Conference on Conceptual Modeling, ER 2022, held in Hyderabad, India, in October 2022. The 19 full and 11 short papers were carefully reviewed and selected from 82 submissions. The papers are organzed in the following topical sections: foundations of conceptual modeling; ontologies and their applications; applications of conceptual modeling; data modeling and analysis; business process; quality and performance; security, privacy and risk management; goals and requirements.

This book constitutes selected papers presented during the First International Conference on Bangabandhu and Digital Bangladesh, ICBBDB 2021, held in Dhaka, Bangladesh, in December 2021. Due to the COVID-19 pandemic the conference was partly held online. The 16 papers presented were thoroughly reviewed and selected from the 90 submissions. They present research in the areas of artificial intelligence, machine learning, image processing, blockchain technology, human-computer interaction, etc. 

This book constitutes the revised selected papers of the 10th International Conference on Networked Systems, NETYS 2022, held as virtual event, in May 17-19, 2022. The conference was held virtually due to the COVID-19 crisis.The 18 full papers and 2 short papers presented were carefully reviewed and selected from 100 submissions. The scope of the conference covers all aspects related to the design and the development of these systems, including multi-core architectures, Concurrent and distributed algorithms, parallel/concurrent/distributed programming, distributed databases, big data applications and systems, cloud systems, networks, security, and formal verification.They were organized in topical sections as follows: Distributed System; Networking; Verification; Security.

Mit dem Phanomen Big Data"e; als Teil einer datengetriebenen Zukunft verbinden sich seit Jahren enorme Hoffnungen und groe Angste. Immer mehr Akteure aus dem privaten und offentlichen Sektor sammeln und nutzen solche Datenmassen zu vielfaltigen Zwecken. Dabei stellt sich aus datenschutzrechtlicher Perspektive die Frage: Ist es moglich, Big-Data-Verfahren im Einklang mit der Datenschutz-Grundverordnung durchzufuhren oder bedeutet Big Data zwangslaufig Small Privacy"e;? Am Beispiel der Betrugsbekampfung mit Big Data in der Kraftfahrzeughaftpflichtversicherung analysiert Constantin Herfurth die datenschutzrechtlichen Rahmenbedingungen und entwickelt neue Modelle, um bewahrte Datenschutzgrundsatze innovativ anwenden zu konnen und eine "e;Big Accountability"e; zu schaffen. Dabei zeichnet er nicht nur ein differenzierteres Bild von Big Data, sondern zeigt auch Wege fur eine datenschutzkonforme Gestaltung auf und regt die Weiterentwicklung bestehender Mechanismen und Instrumente der Datenschutz-Grundverordnung an.

This book provides an overview of the emerging smart connected world, and discusses the roles and the usage of underlying semantic computing and Internet-of-Things (IoT) technologies. The book comprises ten chapters overall, grouped in two parts. Part I "e;Smart Connected World: Overview and Technologies"e; consists of seven chapters and provides a holistic overview of the smart connected world and its supporting tools and technologies. Part II "e;Applications and Case Studies"e; consists of three chapters that describe applications and case studies in manufacturing, smart cities, health, and more. Each chapter is self-contained and can be read independently; taken together, readers get a bigger picture of the technological and application landscape of the smart connected world.This book is of interest for researchers, lecturers, and practitioners in Semantic Web, IoT and related fields. It can serve as a reference for instructors and students taking courses in hybrid computing getting abreast of cutting edge and future directions of a connected ecosystem. It will also benefit industry professionals like software engineers or data scientists, by providing a synergy between Web technologies and applications.This book covers the most important topics on the emerging field of the smart connected world. The contributions from leading active researchers and practitioners in the field are thought provoking and can help in learning and further research. The book is a valuable resource that will benefit academics and industry. It will lead to further research and advancement of the field. Bharat K. Bhargava, Professor of Computer Science, Purdue University, United States

This book provides a review of advanced topics relating to the theory, research, analysis and implementation in the context of big data platforms and their applications, with a focus on methods, techniques, and performance evaluation. The explosive growth in the volume, speed, and variety of data being produced every day requires a continuous increase in the processing speeds of servers and of entire network infrastructures, as well as new resource management models. This poses significant challenges (and provides striking development opportunities) for data intensive and high-performance computing, i.e., how to efficiently turn extremely large datasets into valuable information and meaningful knowledge.The task of context data management is further complicated by the variety of sources such data derives from, resulting in different data formats, with varying storage, transformation, delivery, and archiving requirements. At the same time rapid responses are needed for real-time applications. With the emergence of cloud infrastructures, achieving highly scalable data management in such contexts is a critical problem, as the overall application performance is highly dependent on the properties of the data management service.