Conjoint utilization of structured and unstructured information for planning interleaving deliberation in supply chains

Effective business planning requires seamless access and intelligent analysis of information in its totality to allow the business planner to gain enhanced critical business insights for decision support. Current business planning tools provide insights from structured business data (i.e. sales forecasts, customers and products data, inventory details) only and fail to take into account unstructured complementary information residing in contracts, reports, user\u27s comments, emails etc. In this article, a planning support system is designed and developed that empower business planners to develop and revise business plans utilizing both structured data and unstructured information conjointly. This planning system activity model comprises of two steps. Firstly, a business planner develops a candidate plan using planning template. Secondly, the candidate plan is put forward to collaborating partners for its revision interleaving deliberation. Planning interleaving deliberation activity in the proposed framework enables collaborating planners to challenge both a decision and the thinking that underpins the decision in the candidate plan. The planning system is modeled using situation calculus and is validated through a prototype development

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Web@IDSS – Argumentation-enabled Web-based IDSS for reasoning over incomplete and conflicting information

Over the past few decades, there has been a resurgence of interest in using high-level software intelligence for business intelligence (BI). The objective is to produce actionable information that is delivered at the right time, easily comprehendible and exportable to other software to assist business decisionmaking processes. Although the design and development of decision support systems (DSS) has been carried out for over 40 years, DSS still suffer from many limitations such as poor maintainability, poor flexibility and less reusability. The development of the Internet and WWW has helped information systems to overcome those limitations and Web DSS is now an active area of research in business intelligence,impacting significantly on the way information is exchanged and businesses are conducted. However, to remain competitive, companies rely on business intelligence (BI) to continually monitor and analyze the operating environment (both internal and external), to identify potential risks, and to devise competitive business strategies. However, the current Web DSS applications are not able to reason over information present across organizational boundaries which could be incomplete and conflicting. The use of an argumentation-based mechanism has not been explored to address such shortcomings in Web DSS. Argumentationis a kind of commonsense reasoning used by human beings to reach a justifiable conclusionwhen available information is incomplete and/or inconsistent among participants.In this paper, we propose and elaborate in detail a conceptual framework and formal argumentation-based semantics for Web enabled Intelligent DSS (Web@IDSS). We evaluate the use of argumentative reasoning in Web DSS with the help of a case study, prototype development and future directions. Applications built according to the proposed framework will provide more practical, understandable results to decision makers

A Multi-Agent Approach to Advanced Persistent Threat Detection in Networked Systems

Advanced cyber threats that are well planned, funded and stealthy are an increasing issue facing secure networked systems. As our reliance on protected networked systems continues to grow, the motivation for developing new malicious techniques that cannot be easily detected by traditional signature-based systems, and that make use of previously unseen zero-day vulnerabilities, continues to grow. Lack of adaptivity, extended data-collection and generalised algorithms to detect stealthy attacks is contributing to the insecurity of modern networked systems. To protect these networks, new approaches that can monitor and respond to indicators of compromise in a reflective way that considers all of the available evidence rather than individual points of data is required. This thesis presents a novel approach to intrusion detection and specifically focuses on detecting advanced persistent threats which are characteristically stealthy and evasive attacks. This approach offers a multi-agent model for automatically collecting, analysing and classifying data in a distributed way that considers the context in which the data was found. Using a context-based classification that considers the likelihood of a data-point being a false alarm or legitimate is used to decrease the prevalence of erroneous classifications and regulate continuation of the data collection process. Using this architecture, a detection rate increase of up to 20% is achieved in false alarm environments and an efficiency increase of up to 50% made over traditional monolithic intrusion detection systems. Additionally, the shortcomings of algorithms to detect stealthy attacks are addressed by providing a generalised anomaly detection algorithm for detecting the initial traces of an attack and deploying the proposed multi-agent model to investigate the attack further. The generalised algorithms can detect a wide variety of network-based attacks at an average detection rate of 85% providing an accurate and scalable way to detect the initial traces of compromise. The main novelty of this thesis is providing systems for detecting attacks where the threat model is increasingly stealthy and assumed capable of bypassing traditional signature-based approaches. The multi-agent architecture is unique in its ability, and the generalised anomaly detection algorithm is novel in detecting a variety of different cyber attacks from the network-flow layer. The evidence from this research suggests that context-based evidence gathering can provide a more efficient approach to analysing data and the generalised anomaly detection algorithm can be applied widely to detect attack indicators

Cybersecurity of Digital Service Chains

This open access book presents the main scientific results from the H2020 GUARD project. The GUARD project aims at filling the current technological gap between software management paradigms and cybersecurity models, the latter still lacking orchestration and agility to effectively address the dynamicity of the former. This book provides a comprehensive review of the main concepts, architectures, algorithms, and non-technical aspects developed during three years of investigation; the description of the Smart Mobility use case developed at the end of the project gives a practical example of how the GUARD platform and related technologies can be deployed in practical scenarios. We expect the book to be interesting for the broad group of researchers, engineers, and professionals daily experiencing the inadequacy of outdated cybersecurity models for modern computing environments and cyber-physical systems

Cybersecurity of Digital Service Chains

This open access book presents the main scientific results from the H2020 GUARD project. The GUARD project aims at filling the current technological gap between software management paradigms and cybersecurity models, the latter still lacking orchestration and agility to effectively address the dynamicity of the former. This book provides a comprehensive review of the main concepts, architectures, algorithms, and non-technical aspects developed during three years of investigation; the description of the Smart Mobility use case developed at the end of the project gives a practical example of how the GUARD platform and related technologies can be deployed in practical scenarios. We expect the book to be interesting for the broad group of researchers, engineers, and professionals daily experiencing the inadequacy of outdated cybersecurity models for modern computing environments and cyber-physical systems

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions