In an era of escalating cyber threats, the imperative for robust and comprehensive cybersecurity measures has never been more pressing. To address this challenge, SYNAPSE presents a pioneering approach by conceptualising, designing, and delivering an Integrated cybersecurity Risk & Resilience Management Platform. The innovation of this platform lies in the integration of key elements, such as situational awareness, incident response, and preparedness (i.e., cyber range), augmented by advanced AI capabilities. Through its holistic approach, SYNAPSE aims to elevate cyber resilience by not only mitigating threats but also fostering a culture of proactive defence, informed decision-making, and collaborative response within organisations and across industries.

With digital technologies now being part of the fabric of our societies, identifying and managing cybersecurity threats becomes imperative. Within the European Union, several initiatives are underway, aiming to motivate, regulate and eventually orchestrate the establishment of capacity and enhancement of situational awareness, incident response, and preparedness capabilities, with an expected emphasis on operators of essential services and state actors entrusted with cybersecurity. In this context, the institution of cooperation and information exchange channels to allow for coordinated cross-border responses to large-scale incidents is particularly prioritized. Motivated by the above, this work presents a conceptual blueprint in support of architecting and establishing interoperable Cyber Security Operations Centres that combine capacity for situational awareness, incident response, and preparedness, also benefiting from the interplay between them, ultimately enhancing national cybersecurity capabilities, cross-border collaboration, and national supervision of their critical sectors, in line with current and upcoming regulatory requirements and the ever-increasing need for national and international cooperation.

This paper describes a cloud-based platform that offers evidence-based, personalised interventions powered by Artificial Intelligence to help support efficient remote monitoring and clinician-driven guidance to people over 65 who suffer or are at risk of hearing loss, cardiovascular diseases, cognitive impairments, balance disorders, and mental health issues. This platform has been developed within the SMART-BEAR integrated project to power its large-scale clinical pilots and comprises a standards-based data harmonisation and management layer, a security component, a Big Data Analytics system, a Clinical Decision Support tool, and a dashboard component for efficient data collection across the pilot sites.

Healthcare ecosystems form a critical type of infrastructures that provide valuable services in today societies. However, the underlying sensitive information is also of interest of malicious entities around the globe, with the attack volume being continuously increasing. Safeguarding this complex computerized setting constitutes a major challenge for the involved organizations. This paper presents an incident handling system for healthcare organizations and their supply-chain. The proposed approach utilizes swarm intelligence in order to assess the current security posture in a continuous basis and respond to attacks in real-time. The overall solution is based on the related NIST 800.61 standard and implements the operations of i) preparation, ii) detection and analysis, iii) containment, eradication, and recovery, and iv) post-incident activity. The system is developed under the EU funded project AI4HEALTHSEC and is applied in the relevant healthcare pilots.

It is widely recognised that the process of public health policy making (i.e., the analysis, action plan design, execution, monitoring and evaluation of public health policies) should be evidenced based, and supported by data analytics and decision-making tools tailored to it. This is because the management of health conditions and their consequences at a public health policy making level can benefit from such type of analysis of heterogeneous data, including health care devices usage, physiological, cognitive, clinical and medication, personal, behavioural, lifestyle data, occupational and environmental data. In this paper we present a novel approach to public health policy making in a form of an ontology, and an integrated platform for realising this approach. Our solution is model-driven and makes use of big data analytics technology. More specifically, it is based on public health policy decision making (PHPDM) models that steer the public health policy decision making process by defining the data that need to be collected, the ways in which they should be analysed in order to produce the evidence useful for public health policymaking, how this evidence may support or contradict various policy interventions (actions), and the stakeholders involved in the decision-making process. The resulted web-based platform has been implemented using Hadoop, Spark and HBASE, developed in the context of a research programme on public health policy making for the management of hearing loss called EVOTION, funded by the Horizon 2020.

Botnets constitute nowadays one of the most dangerous security threats worldwide. High volumes of infected machines are controlled by a malicious entity and perform coordinated cyber-attacks. The problem will become even worse in the era of the Internet of Things (IoT) as the number of insecure devices is going to be exponentially increased. This paper presents WARDOG - an awareness and digital forensic system that informs the end-user of the botnet's infection, exposes the botnet infrastructure, and captures verifiable data that can be utilized in a court of law. The responsible authority gathers all information and automatically generates a unitary documentation for the case. The document contains undisputed forensic information, tracking all involved parties and their role in the attack. The deployed security mechanisms and the overall administration setting ensures non-repudiation of performed actions and enforces accountability. The provided properties are verified through theoretic analysis. In simulated environment, the effectiveness of the proposed solution, in mitigating the botnet operations, is also tested against real attack strategies that have been captured by the FORTHcert honeypots, overcoming state-of-the-art solutions. Moreover, a preliminary version is implemented in real computers and IoT devices, highlighting the low computational/communicational overheads of WARDOG in the field.

Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust—a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy.

Ensuring the preservation of security is a key requirement and challenge for Service-Based Systems (SBS) due to the use of third party software services not operating under different security perimeters. In this paper, we present an approach for verifying the security properties of SBS workflows and adapting them if such properties are not preserved. Our approach uses secure service composition patterns. These patterns encode proven dependencies between service level and workflow level security properties. These dependencies are used in reasoning processes supporting the verification of SBS workflows with respect to workflow security properties and their adaptation in ways that guarantee the properties if necessary. Our approach has been implemented by extending the Eclipse BPEL Designer and validated experimentally. The experimental evaluation has produced positive results, indicating that even for complex workflows and large sets of secure service composition patterns, verification can be performed efficiently.

The advent of the Internet of Things opens a plethora of possibilities, provided the research and industry communities are able to overcome a number of challenges such as the dynamicity, scalability, heterogeneity and end-to-end security and privacy requirements of such environments. Motivated by these challenges, this paper proposes leveraging architectural patterns to provide, in an integrated manner, security, dependability, privacy, and interoperability guarantees, across horizontal and vertical compositional structures of IoT applications. The pattern language design process and definition is presented, along with an implementation enabling the automated, pattern- driven property verification and adaptation of IoT orchestrations.

Interoperability stands for the capacity of a system to interact with the units of another entity. Although it is quite easy to accomplish this within the products of the same brand, it is not facile to provide compatibility for the whole spectrum of the Internet-of-Things (IoT) and the Linked Data (LD) world. Currently, the different applications and devices operate in their own cloud/platform, without supporting sufficient interaction with different vendor-products. As it concerns the meaning of data, which is the main focus of this paper, semantics can settle commonly agreed information models and ontologies for the used terms. However, as there are several ontologies for describing each distinct 'Thing', we need Semantic Mediators (SMs) in order to perform common data mapping across the various utilized formats (i.e. XML or JSON) and ontology alignment (e.g. resolve conflicts). Our goal is to enable end-to-end vertical compatibility and horizontal cooperation at all levels (field/network/backend). Moreover, the implication of security must be taken into consideration as the unsafe adoption of semantic technologies exposes the linking data and the user's privacy, issues that are neglected by the majority of the semantic-web studies. A motivating example of smart sensing is described along with a preliminary implementation on real heterogeneous devices. Two different IoT platforms are integrating in the case study, detailing the main SM features. The proposed setting is secure, scalable, and the overall overhead is sufficient for runtime operation, while providing significant advances over state-of-the-art solutions.

Enabled by advances in Cloud technologies, Big Data Analytics Services (BDAS) can improve many processes and identify extra information from previously untapped data sources. As our experience with BDAS and its benefits grows and technology for obtaining even more data improves, BDAS becomes ever more important for many different domains and for our daily lives. Most efforts in improving BDAS technologies have focused on scaling and efficiency issues. However, an equally important property is that of security, especially as we increasingly use public Cloud infrastructures instead of private ones. In this paper we present our approach for strengthening BDAS security by modifying the popular Spark infrastructure so as to monitor at run-time the integrity of data manipulated. In this way, we can ensure that the results obtained by the complex and resource-intensive computations performed on the Cloud are based on correct data and not data that have been tampered with or modified through faults in one of the many and complex subsystems of the overall system.

The holistic management of hearing loss (HL) requires appropriate public health policies for HL prevention, early diagnosis, long-term treatment and rehabilitation; detection and prevention of cognitive decline; protection from noise; and socioeconomic inclusion of HL patients. However, currently the evidential basis for forming such policies is limited. Holistic HL management policies require the analysis of heterogeneous data, including Hearing Aid (HA) usage, noise episodes, audiological, physiological, cognitive, clinical and medication, personal, behavioural, life style, occupational and environmental data. To utilise these data in forming holistic HL management policies, EVOTION, a new European research and innovation project, aims to develop an integrated platform supporting: (a) the analysis of related datasets to enable the identification of causal and other effects amongst them using various forms of big data analytics, (b) policy decision making focusing on the selection of effective interventions related to the holistic management of HL, based on the outcomes of (a) and the formulation of related public health policies, and (c) the specification and monitoring of such policies in a sustainable manner. In this paper, we describe the EVOTION approach.

Software Defined Networking (SDN) and Network Function Virtualization (NFV) are a promising combination for programmable connectivity, rapid service provisioning and service chaining as they offer the necessary end-to-end optimizations. However, with the actual exponential growth of connected devices, future networks such as SDN/NFV require an open-solutions architecture, facilitated by standards and a strong ecosystem. Such networks need to support communication services that offers guarantees about fault tolerance, redundancy, resilience and security. The construction of complex networks preserving Security and Dependability (S&D) properties is necessary to avoid system vulnerabilities, which may occur in the various layers of SDN architectures. In this work, we propose a pattern framework build in an SDN controller able to import design patterns in a rule-based language in order to provide fault tolerance in SDN networks. To evaluate the importance and the functionality of this framework, fault tolerance patterns are proposed to guarantee network connectivity, detection and restoration of network traffic in SDN network infrastructures.

The innovative application of 5G core technologies, namely Software Defined Networking (SDN) and Network Function Virtualization (NFV), can help reduce capital and operational expenditures in industrial networks. Nevertheless, SDN expands the attack surface of the communication infrastructure, thus necessitating the introduction of additional security mechanisms. A wind park is a good example of an industrial application relying on a network with strict performance, security, and reliability requirements, and was chosen as a representative example of industrial systems. This work highlights the benefit of leveraging the flexibility of SDN/NFV-enabled networks to deploy enhanced, reactive security mechanisms for the protection of the industrial network, via the use of Service Function Chaining. Moreover, a proof of concept implementation of the reactive security framework for an industrial-grade wind park network is presented. The framework is equipped with SDN and Supervisory Control and Data Acquisition (SCADA) honeypots, modelled on (and deployable to) an actual, operating wind park, allowing continuous monitoring of the industrial network and detailed analysis of potential attacks, thus isolating attackers and enabling the assessment of their level of sophistication.

The effective management of various health conditions depends on and requires appropriate public health policies (PHP).Such policies are important for several aspects of healthcare provision, including: (a) screening for prevention of disease; (b) early diagnosis and treatment; (c) long-term management of chronic diseases and disabilities; and (d) setting-up standards.Although it is widely recognised that the PHP life cycle (i.e., the analysis, action plan design, execution, monitoring and evaluation of public health policies) should be evidenced based, current support for it is mainly in the form of guidelines, and is not supported by data analytics and decision making tools tailored to it.In this paper, we present a novel model driven approach to PHP life cycle management and an integrated platform for realising this life cycle.Our approach is based on PHP decision making models.Such models steer the PHP decision making process by defining the data that need to be collected and the ways in which these data should be analysed in order to produce the evidence required for PHP making.Our work is part of a new research programme on public health policy making for the management of hearing loss, called EVOTION, that is funded by the European Union.

Dizziness is a common symptom for both benign and life-threatening disorders with subtle distinguishing features. This poses a clinical challenge for physicians dealing with patients suffering from dizziness and vertigo and managing them within primary care. The objective of the EMBalance project is to present a decision support system to assist general practitioners in the diagnosis and management of vestibular disorders. In this work we review the modeling techniques integrated with clinical data to produce a multi-scale, patient-specific balance model that is incorporated in the DSS based on data mining techniques. To understand this we have outlined both technical and clinical aspects to the project. Further we discuss how we intend to test this product in a multicentred, double blind, parallel group randomized controlled trial and the impact we expect the DSS to have both clinically and technologically.

In this work we present the decision support of the EMBalance platform. EMBalance is a platform for the management of balance disorders in terms of diagnosis, treatment and evolution. The EMBalance platform aims to extend existing but generic and currently uncoupled balance modelling activities leading to a multi-scale and patient-specific balance model, which will be incorporated in a Decision Support System (DSS), towards the early diagnosis, prediction and the efficient treatment planning of balance disorders. The diagnosis part of the decision support system uses various data ranging from demographic characteristics to clinical examinations, auditory and vestibular tests. Currently we present some initial technical choices and indicative results of the decision support system for diagnosing balance disorders, based on data mining techniques and clinical guidelines.

Cyber-Physical Systems (CPS) appear to be of paramount importance due to their increasing use on critical infrastructure. New challenges have occurred because of the nature and the complexity of such systems in supporting heterogeneous physical and cyber components simultaneously. Failures or attacks on system components decrease system reliability creating severe consequences to CPS and the attached applications. The construction of complex CPS with respect to security and dependability (S&D) properties is necessary to avoid system vulnerabilities at design level. Design patterns are solutions for reusable designs and interactions of objects. In this work we present a pattern-based language for designing CPS able to guarantee S&D properties. The first set of S&D patterns includes the Reliability Component Composition (RCC) Patterns for designing reliable CPS. RCC patterns are encoded in Drools, which is a rule-based reasoning system. To evaluate our approach, we use RCC patterns as a methodology for designing a reliable wireless sensor network attached to a physical architecture to send monitored data to a central controller through relay nodes and paths.

Service level agreements (SLAs) are typically used to specify rules regarding the consumption of services that are agreed between the providers of the service-based applications (SBAs) and their consumers. An SLA includes a list of terms that contain the guarantees that must be fulfilled during the provisioning and consumption of the services. Since the violation of such guarantees may lead to the application of potential penalties, it is important to assure that the SBA behaves as expected. In this paper, we propose a proactive approach to test SLA-aware SBAs by means of identifying test requirements, which represent situations that are relevant to be tested. To address this issue, we define a four-valued logic that allows evaluating both the individual guarantee terms and their logical relationships. Grounded in this logic, we devise a test criterion based on the modified condition decision coverage (MCDC) in order to obtain a cost-effective set of test requirements from the structure of the SLA. Furthermore by analyzing the syntax and semantics of the agreement, we define specific rules to avoid non-feasible test requirements. The whole approach has been automated and applied over an eHealth case study.

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Data with high security requirements is being processed and stored with increasing frequency in the Cloud. To guarantee that the data is being dealt in a secure manner we investigate the applicability of Assurance methodologies. In a typical Cloud environment the setup of multiple layers and different stakeholders determines security properties of individual components that are used to compose Cloud applications. We present a methodology adapted from Common Criteria for aggregating information reflecting the security properties of individual constituent components of Cloud applications. This aggregated information is used to categorise overall application security in terms of Assurance Levels and to provide a continuous assurance level evaluation. It gives the service owner an overview of the security of his service, without requiring detailed manual analyses of log files.

In cloud computing, measurable services such as packet loss and memory are quantized into different levels to provide different level of services to users. Initially, there will be a service level agreement (SLA) between users and service providers (SPs) and/or SPs and infrastructure providers (IPs). However, the most crucial service required by the users and SPs in cloud computing is security and privacy. Security parameters can be used to prevent attacks and to protect data and systems. In literature, there is no comprehensive solution which quantify all the security parameters associated with the cloud computing paradigm. In this paper, for the first time, we attempt to generalize and quantify the security parameters.

This paper presents a certification model for Non-repudiation (NR) of cloud storage services. NR, i.e., The possession of proofs that certain exchanges have taken place amongst interacting parties, is a significant security property for cloud data storage services. Our model for certifying NR is based on continuous monitoring and has been defined and realised according to the CUMULUS approach. It also corresponds to certification of level 3 maturity in the reference certification framework of Cloud Security Alliance.

Recovery software system operations from a state of extensive damage without human intervention is a challenging problem as it may need to be based on a different infrastructure from the one that the system was originally designed for and deployed on (i.e., computational and communication devices) and significant reorganization of system functionalities. In this paper, we introduce a bio-inspired approach for reconstructing nearly extinct complex software systems. Our approach is based on encoding a computational DNA (co-DNA) of a system and computational analogues of biological processes to enable the transmission of co-DNA over computational devices and, through it, the transformation of these devices into system cells that can realise chunks of the system functionality, and spread further its reconstruction process.

The identification of services during the execution of service-based applications to replace services in them that are no longer available and/or fail to satisfy certain requirements is an important issue. In this paper, we present a framework to support runtime service discovery. This framework can execute service discovery queries in pull and push mode. In pull mode, it executes queries when a need for finding a replacement service arises. In push mode, queries are subscribed to the framework to be executed proactively and, in parallel with the operation of the application, to identify adequate services that could be used if the need for replacing a service arises. Hence, the proactive (push) mode of query execution makes it more likely to avoid interruptions in the operation of service-based applications when a service in them needs to be replaced at runtime. In both modes of query execution, the identification of services relies on distance-based matching of structural, behavioral, quality, and contextual characteristics of services and applications. A prototype implementation of the framework has been developed and an evaluation was carried out to assess the performance of the framework. This evaluation has shown positive results, which are discussed in the paper.

Service Level Agreements (SLAs) are used to specify the negotiated conditions between the provider and the consumer of services. In this paper we present a stepwise method to identify and categorize a set of test requirements that represent the potential situations that can be exercised regarding the specification of each isolated guarantee term of an SLA. This identification is addressed by means of devising a set of coverage levels that allow grading the thoroughness of the tests. The utilization of these test requirements would focus on twofold objectives: (1) the generation of a test suite that allows exercising the situations described in the test requirements and (2) the support for the derivation of a monitoring plan that checks the compliance of these requirements at runtime. The approach is illustrated over an eHealth case study.

In service based applications, it is often necessary to construct compositions of services in order to provide required functionality in cases where this is not possible through the use of a single service. Whilst creating service compositions, it is necessary to ensure not only that the functionality required of the composition is achieved but also that certain security properties are preserved. In this paper, we describe an approach to constructing secure service compositions. Our approach is based on the use of composition patterns and rules that determine the security properties that should be preserved by the individual services that constitute a composition in order to ensure that security properties of the overall composition are also satisfied. Our approach extends a framework developed to support the runtime service discovery.

This paper presents a prediction model for software services availability measured by the mean-time-to-repair (MTTR) and mean-time-to-failure (MTTF) of a service. The prediction model is based on the experimental identification of probabilistic prediction for variables that affect MTTR/MTTF, based on monitoring service data collected at runtime.

In this paper we propose a framework for proactive SLA negotiation that integrates this process with dynamic service discovery and, hence, can provide integrated runtime support for both these key activities which are necessary in order to achieve the runtime operation of service based systems with minimised interruptions. More specifically, our framework discovers candidate constituent services for a composite service, establishes an agreed but not enforced SLA and a period during which this pre-agreement can be activated should this become necessary.

Recently, there has been a proliferation of service-based systems, i.e., software systems that are composed of autonomous services but can also use software code. In order to support the development of these systems, it is necessary to have new methods, processes, and tools. In this paper, we describe a UML-based framework to assist with the development of service-based systems. The framework adopts an iterative process in which software services that can provide functional and nonfunctional characteristics of a system being developed are discovered, and the identified services are used to reformulate the design models of the system. The framework uses a query language to represent structural, behavioral, and quality characteristics of services to be identified, and a query processor to match the queries against service registries. The matching process is based on distance measurements between the queries and service specifications. A prototype tool has been implemented. The work has been evaluated in terms of recall, precision, and performance measurements.

Trust in software services is a key prerequisite for the success and wide adoption of services-oriented computing (SOC) in an open Internet world. However, trust is poorly assessed by existing methods and technologies, especially in dynamically composed and deployed SOC systems. In this paper, we discuss current methods for assessing trust in service-oriented computing and identify gaps of current platforms, in particular with regards to runtime trust assessment. To address these gaps, we propose a model of runtime trust assessment of software services and introduce a framework for realizing the model. A key characteristic of our approach is the support that it offers for customizable assessment of trust based on evidence collected during the operation of software services and its ability to combine this evidence with subjective assessments coming from service clients.

Runtime monitoring of Service Based Systems (SBSs) usually relies on information derived from I/O messages exchanged within business processes implementing services. When service provisioning is regulated by complex Service Level Agreements (SLAs) between service requesters, (composed) services, and infrastructure providers, monitoring may require additional features, such as (i) coordination among events captured at different sources involved in service provisioning and (ii) delegation of properties monitoring to local sites. This paper discusses an architecture and engagement protocol supporting the two aforementioned requirements for monitoring complex SLA-driven service provisioning.

In modern service economies, service provisioning needs to be regulated by complex SLA hierarchies among providers of heterogeneous services, defined at the business, software, and infrastructure layers. Starting from the SLA Management framework defined in the SLA@SOI EU FP7 Integrated Project, we focus on the relationship between establishment and monitoring of such SLAs, showing how the two processes become tightly interleaved in order to provide meaningful mechanisms for SLA management. We first describe the process for SLA establishment adopted within the framework; then,we propose an architecture for monitoring SLAs, which satisfies the two main requirements introduced by SLA establishment: the availability of historical data for evaluating SLA offers and the assessment of the capability to monitor the terms in a SLA offer.

We present a static verification tool (SVT), a system that performs static verification on UML models composed of UML class and state machine diagrams. Additionally, the SVT allows the user to add extra behavior specification in the form of guards and effects by defining a small action language. UML models are checked against properties written in a special-purpose property language that allows the user to specify linear temporal logic formulas that explicitly reason about UML components. Thus, the SVT provides a strong foundation for the design of reliable systems and a step towards model-driven security.

Service discovery has been recognised as an important activity for service-based systems. In this paper we describe a framework for dynamic service discovery that supports the identification of service during the execution time of service-based systems. In the framework, services are identified based on structural, behavioural, quality, and contextual characteristics of a system represented in query languages. The framework supports both pull and push modes of query execution. In the approach, a service is identified based on the computation of distances between a query and a candidate service. A prototype tool has been implemented in order to illustrate and evaluate the framework. The paper also describes the results of a set of experiments that we have conducted to evaluate the work.

Ensuring dependability and security of peer-to-peer (P2P) systems is an intricate task due to the autonomous and volatile nature of peers and the decentralization that characterizes such systems. Dynamic verification provides the means of monitoring aspects of peer behaviour at runtime and the capacity to react to identified violations with the aim of preserving the system in the desired state. Thus, it can provide an extra layer of checking properties like security and dependability and lead to enhanced system resilience in this respect. In this paper, we introduce a framework that supports the dynamic verification of P2P systems.

In this paper we describe a platform that supports context aware runtime service discovery. The platform supports service discovery based on structural and behavioural service models as well as complex context related service discovery conditions which are specified in a newly introduced query language. During discovery, context information is obtained through a uniform scheme of calling "context operations" and is subsequently used in the evaluation of service discovery queries.

As a result of IT outsourcing and offshoring, IT professionals and educators are faced with the following question: What SE & KE skill sets will make a software engineer or a knowledge engineer immune to the impact of outsourcing and offshoring? This article summarizes the position papers from a panel held during the 2006 International Conference on Software Engineering and Knowledge Engineering from July 5 to 7 at the Hotel Sofitel, Redwood City in California, USA. Bringing software and knowledge engineers closer to the needs of their prospective customers and providing more value than simply pure software development and maintenance, is an open challenge at least for traditional computer science and software engineering curricula.

The development of service centric systems has been recognised as an important approach for software system development. In this paper, we present a UML-based tool to identify services that can provide the functionality and satisfy properties and constraints of service centric systems specified during the design phase of the development of these systems and allows for the (re-) formulation of the design models based on the discovered services

This paper presents a framework for monitoring the compliance of systems composed of Web-services with requirements set for them at runtime. This framework assumes systems composed of Web-services which are co-coordinated by a service composition process expressed in BPEL and uses event calculus to specify the requirements to be monitored. These requirements may include behavioral properties of a system which are automatically extracted from the specification of its composition process in BPEL and/or assumptions that system providers can specify in terms of events extracted from this specification.

This paper describes a framework supporting the runtime monitoring of requirements for systems implemented as compositions of Web-services specified in BPEL. The requirements that can be monitored are specified in event calculus. The paper presents an overview of the framework and describes the architecture and implementation of a tool that we have developed to operationalise it. It also presents the results of a preliminary experimental evaluation of the framework.

This paper proposes a framework for monitoring the compliance of systems composed of web-services with requirements set for th. This framework assumes systems composed of web-services that are co-ordinated by a service composition process expressed in BPEL4WS and uses event calculus to specify the properties to be monitored. The monitorable properties may include behavioural properties of a syst which are automatically extracted from the specification of its composition process in BPEL4WS and/or assumptions that syst providers can specify in terms of events extracted from this specification.

This paper presents a diagnostic framework for assessing the significance of inconsistencies (i.e., violations of consistency rules) in software design models expressed in the Unified Modeling Language (UML). The assessment is based on significance criteria that software designers can specify and associate with specific consistency rules. These criteria define characteristics that the model elements involved in the violation of a rule should have for the inconsistency to be significant, and they are specified in a formal language derived from the Object Constraint Language (OCL). The satisfiability of the criteria by individual model elements is measured by belief functions defined by the framework. The measures generated by these functions are used to rank the inconsistencies caused by different model elements. The presented framework has been evaluated through a set of experiments. The results of these experiments show that criteria definable in the framework can be used to produce relatively elaborate significance-rankings of inconsistencies.

This paper presents an extension of a traceability system which automates the generation of traceability relations between textual requirement artefacts and object models using heuristic traceability rules. These rules match syntactically related terms in the textual parts of the requirements artefacts with related elements in an object model (e.g. classes, attributes, operations) and create traceability relations of different types when a match is found. The extension described in this paper measures beliefs in: (1) the ability of specific traceability rules to generate correct traceability relations, (2) the satisfiability of traceability rules by particular types of artefacts, and (3) the correctness of individual traceability relations. It also provides a mechanism with well-founded semantics for revising these beliefs on the basis of partial (and even conflicting) assessments of the relations that these rules generate provided by different users.

This article defines and analyzes a computational model of similarity which detects analogies between objects based on conceptual descriptions of them, constructed from classification, generalization relations, and attributes. Analogies are detected (elaborated) by functions which measure conceptual distances between objects with respect to these semantic modeling abstractions. The model is domain independent and operational upon objects described in nonuniform ways. It does not require any special forms of knowledge for identifying analogies and distinguishes the importance of distinct object elements. Also, it has a polynomial complexity. Due to these characteristics, it may be used in complex tasks involving intra- or interdomain analogical reasoning. So far the similarity model has been applied in the domain of software engineering. First, to support the specification of software requirements by analogical reuse and second, to enable the integration of requirements specifications, generated by the multiple agents involved in information system development. Details of these applications can be found in cited references. Also, we have conducted an empirical evaluation of: (i) the consistency of the estimates generated by the model against human intuition about similarity and (ii) its recall performance in tasks of analogical retrieval, the results of which are presented in this article. © 1996 John Wiley & Sons, Inc.