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Knowledge in its pure state is tacit in nature—difficult to formalize and communicate—but can be converted into codified form and shared through both social interactions and the use of IT-based applications and systems. Even though there seems to be considerable synergies between the resulting huge data and the convertible knowledge, there is still a debate on how the increasing amount of data captured by corporations could improve decision making and foster innovation through effective knowledge-sharing practices. Big Data and Knowledge Sharing in Virtual Organizations provides innovative insights into the influence of big data analytics and artificial intelligence and the tools, methods, and techniques for knowledge-sharing processes in virtual organizations. The content within this publication examines cloud computing, machine learning, and knowledge sharing. It is designed for government officials and organizations, policymakers, academicians, researchers, technology developers, and students.
This book was prepared as the Final Publication of COST Action IC0703 "Data Traffic Monitoring and Analysis: theory, techniques, tools and applications for the future networks". It contains 14 chapters which demonstrate the results, quality,and the impact of European research in the field of TMA in line with the scientific objective of the Action. The book is structured into three parts: network and topology measurement and modelling, traffic classification and anomaly detection, quality of experience.
Network-wide traffic analysis and monitoring in large-scale networks is a challenging and expensive task. In this thesis work we have proposed to analyze the traffic of a large-scale IP network from aggregated traffic measurements, reducing measurement overheads and simplifying implementation issues. We have provided contributions in three different networking fields related to network-wide traffic analysis and monitoring in large-scale IP networks. The first contribution regards Traffic Matrix (TM) modeling and estimation, where we have proposed new statistical models and new estimation methods to analyze the Origin-Destination (OD) flows of a large-scale TM from easily available link traffic measurements. The second contribution regards the detection and localization of volume anomalies in the TM, where we have introduced novel methods with solid optimality properties that outperform current well-known techniques for network-wide anomaly detection proposed so far in the literature. The last contribution regards the optimization of the routing configuration in large-scale IP networks, particularly when the traffic is highly variable and difficult to predict. Using the notions of Robust Routing Optimization we have proposed new approaches for Quality of Service provisioning under highly variable and uncertain traffic scenarios. In order to provide strong evidence on the relevance of our contributions, all the methods proposed in this thesis work were validated using real traffic data from different operational networks. Additionally, their performance was compared against well-known works in each field, showing outperforming results in most cases. Taking together the ensemble of developed TM models, the optimal network-wide anomaly detection and localization methods, and the routing optimization algorithms, this thesis work offers a complete solution for network operators to efficiently monitor large-scale IP networks from aggregated traffic measurements and to provide accurate QoS-based performance, even in the event of volume traffic anomalies.
Traffic planners are facing a big challenge with an increasing demand for mobility and a need to drastically reduce the environmental impacts of the transportation system at the same time. The transportation system therefore needs to become more efficient, which requires a good understanding about the actual travel patterns. Data from travel surveys and traffic counts is expensive to collect and gives only limited insights on travel patterns. Cellular network data collected in the mobile operators infrastructure is a promising data source which can provide new ways of obtaining information relevant for traffic analysis. It can provide large-scale observations of travel patterns independent of the travel mode used and can be updated easier than other data sources. In order to use cellular network data for traffic analysis it needs to be filtered and processed in a way that preserves privacy of individuals and takes the low resolution of the data in space and time into account. The research of finding appropriate algorithms is ongoing and while substantial progress has been achieved, there is a still a large potential for better algorithms and ways to evaluate them. The aim of this thesis is to analyse the potential and limitations of using cellular network data for traffic analysis. In the three papers included in the thesis, contributions are made to the trip extraction, travel demand and route inference steps part of a data-driven traffic analysis processing chain. To analyse the performance of the proposed algorithms, a number of datasets from different cellular network operators are used. The results obtained using different algorithms are compared to each other as well as to other available data sources. A main finding presented in this thesis is that large-scale cellular network data can be used in particular to infer travel demand. In a study of data for the municipality of Norrköping, the results from cellular network data resemble the travel demand model currently used by the municipality, while adding more details such as time profiles which are currently not available to traffic planners. However, it is found that all later traffic analysis results from cellular network data can differ to a large extend based on the choice of algorithm used for the first steps of data filtering and trip extraction. Particular difficulties occur with the detection of short trips (less than 2km) with a possible under-representation of these trips affecting the subsequent traffic analysis.
This book constitutes the proceedings of the First International Workshop on Traffic Monitoring and Analysis, TMA 2008, held in Aachen, Germany, on May 11, 2008 in conjunction with the IFIP Networking 2008 conference. The workshop is an initiative from the COST Action IC0703 "Data Traffic Monitoring and Analysis: Theory, Techniques, Tools and Applications for the Future Networks". The 15 papers contained in this volume were carefully reviewed and selected from 34 submissions. They encompass research areas related to traffic analysis and classification, measurements, topology, discovery, detection of specific applications and events, packet inspection, and traffic inference. The papers are organized in topical sections on QoS measurement, rupture detection, traffic classification, as well as traffic analysis and topology measurements.
This book constitutes the proceedings of the 4th International Workshop on Traffic Monitoring and Analysis, TMA 2012, held in Vienna, Austria, in March 2012. The thoroughly refereed 10 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 31 submissions. The contributions are organized in topical sections on traffic analysis and characterization: new results and improved measurement techniques; measurement for QoS, security and service level agreements; and tools for network measurement and experimentation.
This book constitutes the proceedings of the Third International Workshop on Traffic Monitoring and Analysis, TMA 2011, held in Vienna, Austria, on April 27, 2011 - co-located with EW 2011, the 17th European Wireless Conference. The workshop is an initiative from the COST Action IC0703 "Data Traffic Monitoring and Analysis: Theory, Techniques, Tools and Applications for the Future Networks". The 10 revised full papers and 6 poster papers presented together with 4 short papers were carefully reviewed and selected from 29 submissions. The papers are organized in topical sections on traffic analysis, applications and privacy, traffic classification, and a poster session.
This book constitutes the proceedings of the Third International Workshop on Traffic Monitoring and Analysis, TMA 2011, held in Vienna, Austria, on April 27, 2011 - co-located with EW 2011, the 17th European Wireless Conference. The workshop is an initiative from the COST Action IC0703 "Data Traffic Monitoring and Analysis: Theory, Techniques, Tools and Applications for the Future Networks". The 10 revised full papers and 6 poster papers presented together with 4 short papers were carefully reviewed and selected from 29 submissions. The papers are organized in topical sections on traffic analysis, applications and privacy, traffic classification, and a poster session.
This book constitutes the proceedings of the Second International Workshop on Traffic Monitoring and Analysis, TMA 2010, held in Zurich, Switzerland, on April 7, 2010 - colocated with PAM 2010, the 11th Passive and Active Measurement conference. The workshop is an initiative from the COST Action IC0703 "Data Traffic Monitoring and Analysis: Theory, Techniques, Tools and Applications for the Future Networks". The 14 papers contained in this volume were carefully reviewed and selected from 34 submissions. They encompass research areas related to traffic analysis and classification, measurements, topology, discovery, detection of specific applications and events, packet inspection, and traffic inference. The papers are organized in topical sections on analysis of internet datasets, tools for traffic analysis and monitoring, traffic classification, and performance measurements.