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Model-Driven Development and Operation of Multi-Cloud Applications : The MODAClouds Approach
In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I "The Big Data Opportunity" explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission's BIG project.
Integrated Risk and Vulnerability Management Assisted by Decision Support Systems : Relevance and Impact on Governance
This book includes terms of reference and offers an augmented volume of relevant work initiated within the comprehensive concept of “Knowledge Management and Risk Governance”.
Implementing machine learning for finance : A systematic approach to predictive risk and performance analysis for investment portfolios
Introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios. You will: Understand the fundamentals of the financial market and algorithmic trading, as well as supervised and unsupervised learning models that are appropriate for systematic investment portfolio management / Know the concepts of feature engineering, data visualization, and hyperparameter optimization / Design, build, and test supervised and unsupervised ML and DL models / Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices / Structure and optimize an investment portfolio with preeminent asset classes and measure the / underlying risk
Embedded Java Security : Security for Mobile Devices
Whereas Java brings functionality and versatility to the world of mobile devices, at the same time it also introduces new security threats. The rapid growth of the number of mobile devices that support Java makes this a pressing issue. Embedded Java Security carefully examines the security aspects of Java and offers a security evaluation for the Java platform. After explaining background material on the architecture of embedded platforms and relating to its role in security, the book deconstructs the security model into its main components: It explains each component and relates it to the aim of securing the applications and the device. Toward this end, several implementations of the Java platform are examined and tested to relate the model to its actual implementation on devices. The security holes found are further used to clarify security issues and point out common errors. Finally, the book provides an evaluation of embedded Java security that includes security models and security tests performed on real-life implementations.
Developments in Risk-based Approaches to Safety ; Proceedings of the Fourteenth Safety-citical Systems Symposium, Bristol, UK, 7-9 February 2006
The papers included in this volume address the most critical topics in the field of safety-critical systems. The focus this year, considered from various perspectives, is on recent developments in risk-based approaches. Subjects discussed include innovation in risk analysis, management risk, the safety case, software safety, language development and the creation of systems for complex control functions.
Data mining and knowledge management ; Chinese academy of sciences symposium CASDMKD 2004, Beijing, China, July 12-14, 2004, Revised Paper
Knowledge management for enterprise: These papers address various issues related to the application of knowledge management in corporations using various techniques. A particular emphasis here is on coordination and cooperation. • Risk management: Better knowledge management also requires more advanced techniques for risk management, to identify, control, and minimize the impact of uncertain events, as shown in these papers, using fuzzy set theory and other approaches for better risk management. • Integration of data mining and knowledge management: As indicated earlier, the integration of these two research fields is still in the early stage. Nevertheless, as shown in the papers selected in this volume, researchers have endearored to integrate data mining methods such as neural networks with various aspects related to knowledge management,
Certification and security in inter-organizational E-services ; IFIP 18th World Computer Congress, August 22-27, 2004, Toulouse, France
This collection of papers offers real-life application experiences, research results and methodological proposals of direct interest to systems experts and users in governmental, industrial and academic communities. This book also documents several important developments. The uptake of distributed computational infrastructure oriented to service provision, like Web-Services and Grid, is making C&S even more important. E-services based on legacy systems managed by autonomous and independent organizations, a common situation in the public administration sector, increase overall complexity. The increased presence and use of e-service IT-infrastructures depends on the critical ability required for all security issues, from the basic (availability, authentication, integrity, confidentiality) to the more complex (e.g. authorization, non-repudiation).
Argumentation in multi-agent systems ; Third International Workshop, ArgMAS 2006, Hakodate, Japan, May 8, 2006, revised selected and invited papers
Argumentation provides tools for designing, implementing and analyzing sophisticated forms of interaction among rational agents. It has made a solid contribution to the practice of multiagent dialogues. Application domains include: legal disputes, business negotiation, labor disputes, team formation, scientific inquiry, deliberative democracy, ontology reconciliation, risk analysis, scheduling, and logistics.
Anti-fragile ICT Systems
Introduces a novel approach to the design and operation of large ICT systems. It views the technical solutions and their stakeholders as complex adaptive systems and argues that traditional risk analyses cannot predict all future incidents with major impacts. To avoid unacceptable events, it is necessary to establish and operate anti-fragile ICT systems that limit the impact of all incidents, and which learn from small-impact incidents how to function increasingly well in changing environments. The book applies four design principles and one operational principle to achieve anti-fragility for different classes of incidents. It discusses how systems can achieve high availability, prevent malware epidemics, and detect anomalies. Analyses of Netflix’s media streaming solution, Norwegian telecom infrastructures, e-government platforms, and Numenta’s anomaly detection software show that cloud computing is essential to achieving anti-fragility for classes of events with negative impacts.
Agent-oriented software engineering VII ; 7th International Workshop, AOSE 2006, Hakodate, Japan, May 8, 2006, Revised and Invited Papers
Software architectures that contain many dynamically interacting components, each with their own thread of control, and engaging in complex coordination protocols, are difficult to correctly and efficiently engineer. Agent-oriented modelling techniques are important for supporting the design and development of such applications.The book is organized in topical sections on modelling and design of agent systems, modelling open agent systems, formal reasoning about designs, as well as testing, debugging and evolvability.
Advanced Techniques in Knowledge Discovery and Data Mining
This explosion is a result of the growing use of electronic media. But what is data mining (DM)? A Web search using the Google search engine retrieves many (really many) definitions of data mining. We include here a few interesting ones. One of the simpler definitions is: “As the term suggests, data mining is the analysis of data to establish relationships and identify patterns” [1]. It focuses on identifying relations in data. Our next example is more elaborate: An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis .
