• Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia

      Cavallaro, Lucia; Ficara, Annamaria; De Meo, Pasquale; Fiumara, Giacomo; Catanese, Salvatore; Bagdasar, Ovidiu; Song, Wei; Liotta, Antonio; University of Derby; niversity of Palermo, Palermo, Italy; et al. (Public Library of Science (PLoS), 2020-08-05)
      Compared to other types of social networks, criminal networks present particularly hard challenges, due to their strong resilience to disruption, which poses severe hurdles to Law-Enforcement Agencies (LEAs). Herein, we borrow methods and tools from Social Network Analysis (SNA) to (i) unveil the structure and organization of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently reduce the Largest Connected Component (LCC) of two networks derived from them. Mafia networks have peculiar features in terms of the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts also face difficulties in collecting reliable datasets that accurately describe the gangs’ internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two real-world datasets, based on raw data extracted from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). In both the sequential, and the node block removal intervention procedures, the Betweenness centrality was the most effective strategy in prioritizing the nodes to be removed. For instance, when targeting the top 5% nodes with the largest Betweenness centrality, our simulations suggest a reduction of up to 70% in the size of the LCC. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions’ frequency), no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for perturbing the operations of criminal and terrorist networks.
    • CRT-BIoV: A cognitive radio technique for blockchain-enabled internet of vehicles

      Rathee, Geetanjali; Farhan, Ahmad; Kurugollu, Fatih; Azad, Muhammad; Iqbal, Razi; Imran, Muhammad; Jaypee University of Information Technology, India; University of Derby; University of Engineering and Technology, Lahore, Pakistan; King Saud University, Kingdom of Saudi Arabia (IEEE, 2020-07-23)
      Cognitive Radio Network (CRN) is considered as a viable solution on Internet of Vehicle (IoV) where objects equipped with cognition make decisions intelligently through the understanding of both social and physical worlds. However, the spectrum availability and data sharing/transferring among vehicles are critical improving services and driving safety metrics where the presence of Malicious Devices (MD) further degrade the network performance. Recently, a blockchain technique in CRN-based IoV has been introduced to prevent data alteration from these MD and allowing the vehicles to track both legal and illegal activities in the network. In this paper, we provide the security to IoV during spectrum sensing and information transmission using CRN by sensing the channels through a decision-making technique known as \textit{Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS)}, a technique that evokes the trust of its Cognitive Users (CU) by analyzing certain predefined attributes. Further, blockchain is maintained in the network to trace every activity of stored information. The proposed mechanism is validated rigorously against several security metrics using various spectrum sensing and security parameters against a baseline solution in IoV. Extensive simulations suggest that our proposed mechanism is approximately 70% more efficient in terms of malicious nodes identification and DoS threat against the baseline mechanism.
    • A multi-objective optimized service level agreement approach applied on a cloud computing ecosystem

      Azevedo, Leonildo Jose de Melo de; Estrella, Julio C.; Toledo, Claudia F. Motta; Reiff-Marganiec, Stephan; University of São Paulo (USP), São Carlos, SP, Brazil; University of Derby (IEEE, 2020-06-30)
      The cloud ecosystem provides transformative advantages that allow elastically offering ondemand services. However, it is not always possible to provide adequate services to all customers and thus to fulfill service level agreements (SLA). To enable compliance with these agreements, service providers leave the customer responsible for determining the service settings and expect that the client knows what to do. Some studies address SLA compliance, but the existing works do not adequately address the problem of resource allocation according to clients’ needs since they consider a limited set of objectives to be analyzed and fulfilled. In previous work, we have already addressed the problem considering a single-objective approach. In that work, we identified that the problem has a multi-objective characteristic since several attributes simultaneously influence the SLA agreement, which can lead to conflicts. This paper proposes a multi-objective combinatorial optimization approach for computational resources provisioning, seeking to optimize the efficient use of the infrastructure and provide the client with greater flexibility in contract closure.
    • Privacy-preserving crowd-sensed trust aggregation in the user-centeric internet of people networks

      Azad, Muhammad; Perera, Charith; Bag, Samiran; Barhamgi, Mahmoud; Hao, Feng; University of Derby; Cardiff University; University of Warwick; Universite Claude Bernard Lyon (ACM, 2020)
      Today we are relying on the Internet technologies for various types of services ranging from personal communication to the entertainment. The online social networks (Facebook, twitter, youtube) has seen an increase in subscribers in recent years developing a social network among people termed as the Internet of People. In such a network, subscribers use the content disseminated by other subscribers. The malicious users can also utilize such platforms for spreading the malicious and fake content that would bring catastrophic consequences to a social network if not identified on time. People crowd-sensing on the Internet of people system has seen a prospective solution for the large scale data collection by leveraging the feedback collections from the people of the internet that would not only help in identifying malicious subscribers of the network but would also help in defining better services. However, the human involvement in crowd-sensing would have challenges of privacy-preservation, intentional spread of false high score about certain user/content undermining the services, and assigning different trust scores to the peoples of the network without disclosing their trust weights. Therefore, having a privacy-preserving system for computing trust of people and their content in the network would play a crucial role in collecting high-quality data from the people. In this paper, a novel trust model is proposed for evaluating the trust of the people in the social network without compromising the privacy of the participating people. The proposed systems have inherent properties of the trust weight assignment to a different class of user i.e. it can assign different weights to different users of the network, has decentralized setup, and ensures privacy properties under the malicious and honest but curious adversarial model. We evaluated the performance of the system by developing a prototype and applying it to different online social network dataset.
    • Designing privacy-aware internet of things applications

      Perera, Charith; Barhamgi, Mahmoud; Bandara, Arosha K.; Ajmal, Muhammad; Price, Blaine; Nuseibeh, Bashar; Cardiff University; Universite Claude Bernard Lyon; Open University, United Kingdom; University of Derby (Elsevier BV, 2019-09-28)
      Internet of Things (IoT) applications typically collect and analyse personal data that can be used to derive sensitive information about individuals. However, thus far, privacy concerns have not been explicitly considered in software engineering processes when designing IoT applications. With the advent of behaviour driven security mechanisms, failing to address privacy concerns in the design of IoT applications can also have security implications. In this paper, we explore how a Privacy-by-Design (PbD) framework, formulated as a set of guidelines, can help software engineers integrate data privacy considerations into the design of IoT applications. We studied the utility of this PbD framework by studying how software engineers use it to design IoT applications. We also explore the challenges in using the set of guidelines to influence the IoT applications design process. In addition to highlighting the benefits of having a PbD framework to make privacy features explicit during the design of IoT applications, our studies also surfaced a number of challenges associated with the approach. A key finding of our research is that the PbD framework significantly increases both novice and expert software engineers’ ability to design privacy into IoT applications.
    • IoT forensics: A state-of-the-art review, callenges and future directions

      Alenezi, Ahmed; Atlam, Hany; Alsagri, Reem; Alassafi, Madini; Wills, Gary; University of Southampton (SCITEPRESS - Science and Technology Publications, 2019-05-10)
      The IoT is capable of communicating and connecting billions of things at the same time. The concept offers numerous benefits for consumers that alters how users interact with the technology. With this said, however, such monumental growth within IoT development also gives rise to a number of legal and technical challenges in the field of IoT forensics. Indeed, there exist many issues that must be overcome if effective IoT investigations are to be carried out. This paper presents a review of the IoT concept, digital forensics and the state-of-the-art on IoT forensics. Furthermore, an exploration of the possible solutions proposed in recent research and IoT forensics challenges that are identified in the current research literature are examined. Picks apart the challenges facing IoT forensics which have been established in recent literature. Overall, this paper draws attention to the obvious problems – open problems which require further efforts to be addressed properly.
    • Experts reviews of a cloud forensic readiness framework for organizations

      Alenezi, Ahmed; Atlam, Hany F.; Wills, Gary B.; University of Southampton (Springer Science and Business Media LLC, 2019-08-14)
      Cloud computing has drastically altered the ways in which it is possible to deliver information technologies (ITs) to consumers as a service. In addition, the concept has given rise to multiple benefits for consumers and organizations. However, such a fast surge in the adoption of cloud computing has led to the emergence of the cloud as a new cybercrime environment, thus giving rise to fresh legal, technical and organizational challenges. In addition to the vast number of attacks that have had an impact on cloud computing and the fact that cloud-based data processing is carried out in a decentralized manner, many other concerns have been noted. Among these concerns are how to conduct a thorough digital investigation in cloud environments and how to be prepared to gather data ahead of time before the occurrence of an incident; indeed, this kind of preparation would reduce the amount of money, time and effort that is expended. As a number of cloud forensics challenges have not received enough attention, this study is motivated by a particular gap in research on the technical, legal and organizational factors that facilitate forensic readiness in organizations that utilize an Infrastructure as a Service (IaaS) model. This paper presents a framework with which to investigate the factors that facilitate the forensic readiness of organizations. This framework was identified by critically reviewing previous studies in the literature and by performing an in-depth examination of the relevant industrial standards. The factors were comprehensively studied and extracted from the literature; then, the factors were analysed, duplicates were removed, and the factors were categorized and synthesized to produce the framework. To obtain reliable results, the research method involved two steps: a literature review, followed by expert reviews. These techniques help us paint a comprehensive picture of the research topic and validate and confirm the results.
    • Security, cybercrime and digital forensics for IoT

      Atlam, Hany F.; Alenezi, Ahmed; Alassafi, Madini O.; Alshdadi, Abdulrahman A.; Wills, Gary B.; University of Southampton; Menoufia University, Menouf, Egypt; Northern Border University, Rafha, Saudi Arabia; King Abdulaziz University, Jeddah, Saudi Arabia; University of Jeddah, Jeddah, Saudi Arabia (Springer International Publishing, 2019-11-14)
      The Internet of Things (IoT) connects almost all the environment objects whether physical or virtual over the Internet to produce new digitized services that improve people’s lifestyle. Currently, several IoT applications have a direct impact on our daily life activities including smart agriculture, wearables, connected healthcare, connected vehicles, and others. Despite the countless benefits provided by the IoT system, it introduces several security challenges. Resolving these challenges should be one of the highest priorities for IoT manufacturers to continue the successful deployment of IoT applications. The owners of IoT devices should guarantee that effective security measures are built in their devices. With the developments of the Internet, the number of security attacks and cybercrimes has increased significantly. In addition, with poor security measures implemented in IoT devices, the IoT system creates more opportunities for cybercrimes to attack various application and services of the IoT system resulting in a direct impact on users. One of the approaches that tackle the increasing number of cybercrimes is digital forensics. Cybercrimes with the power of the IoT technology can cross the virtual space to threaten human life, therefore, IoT forensics is required to investigate and mitigate against such attacks. This chapter presents a review of IoT security and forensics. It started with reviewing the IoT system by discussing building blocks of an IoT device, essential characteristic, communication technologies and challenges of the IoT. Then, IoT security by highlighting threats and solutions regarding IoT architecture layers are discussed. Digital forensics is also discussed by presenting the main steps of the investigation process. In the end, IoT forensics is discussed by reviewing related IoT forensics frameworks, discussing the need for adopting real-time approaches and showing various IoT forensics.
    • A famework for data sharing between healthcare providers using blockchain

      Alzahrani, Ahmed G.; Alenezi, Ahmed; Atlam, Hany F.; Wills, Gary; University of Southampton (SCITEPRESS - Science and Technology Publications, 2020-05)
      The healthcare data are considered as a highly valuable source of information that can improve healthcare systems to be more intelligent and improve the quality of the provided services. However, due to security and privacy issues, sharing data between healthcare organisations is challenging. This has led to data shortage in the healthcare sector which is considered as a significant issue not only in the Kingdom of Saudi Arabia (KSA) but also worldwide. The primary objective of conducting this paper is to investigate the various factors that enable secure sharing and exchange of healthcare information between different healthcare providers in the KSA. It starts by discussing the current literature and frameworks for managing healthcare data information and the challenges that health providers encounter, particularly when it comes to issues such as data security, patient privacy, and healthcare information exchange. These challenges in managing healthcare data have necessitated the nee d for implementing a solution that can allow medical providers to have access to updated healthcare information. Attention in the healthcare sector has been drawn to blockchain technology as a part of the solution, especially after the technology was successfully applied in the financial sector to improve the security of financial transactions, particularly involving digital currencies such as Bitcoin. Therefore, a framework based on the blockchain technology has been proposed to achieve the goals of the present research.
    • Intersections between IoT and distributed ledger

      Atlam, Hany F.; Wills, Gary B.; University of Southampton; Menoufia University, Shebeen El-Kom, Egypt (Elsevier, 2019-01-14)
      The Internet of Things (IoT) is growing exponentially. It allows not only humans but also all various devices and objects in the environment to be connected over the Internet to share their data to create new applications and services which result in a more convenient and connected lifestyle. However, the current centralized IoT architecture faces several issues. For instance, all computing operations of all nodes in the network are carried out using a single server. This creates a single point of failure in which if the server goes down, the entire system will be unavailable. Also, the IoT centralized architecture is an easy target of various types of security and privacy attacks, since all IoT data collected from different devices is under the full authority of a single server. Therefore, adopting one of the Distributed Ledger Technologies (DLTs) for the IoT may be the right decision. One of the popular types of DLTs is the blockchain. It provides an immutable ledger with the capability of maintaining the integrity of transactions by decentralizing the ledger among participating nodes in the blockchain network which eliminates the need for a central authority. Integrating the IoT system with the blockchain technology can provide several benefits which can resolve the issues associated with the IoT centralized architecture. Therefore, this chapter provides a discussion of the intersection between IoT and DLTs. It started by providing an overview of the DLT by highlighting its main components, benefits and challenges. The centralized IoT system is also discussed with highlighting its essential limitations. Then, the integration of blockchain with IoT is presented by highlighting the integration benefits. Various application and challenges of integrating blockchain with IoT are also discussed.
    • IoT security, privacy, safety and ethics

      Atlam, Hany F.; Wills, Gary B.; University of Southampton; Menoufia University, Menoufia, Egypt (Springer International Publishing, 2019-07-23)
      The Internet of Things (IoT) represents a revolution of the Internet which can connect nearly all environment devices over the Internet to share their data to create novel services and applications for improving our quality of life. Using cheap sensors, the IoT enables various devices and objects around us to be addressable, recognizable and locatable. Although the IoT brought infinite benefits, it creates several challenges, especially in security and privacy. Handling these issues and ensuring security and privacy for IoT products and services must be a fundamental priority. Users need to trust IoT devices and related services are secure. Moreover, the IoT safety must be considered to prevent the IoT system and its components from causing an unacceptable risk of injury or physical damage and at the same time considering social behaviour and ethical use of IoT technologies to enable effective security and safety. This chapter provides a discussion of IoT security, privacy, safety and ethics. It starts by providing an overview of the IoT system, its architecture and essential characteristics. This is followed by discussing IoT security challenges, requirements and best practices to protect IoT devices. The IoT privacy is also discussed by highlighting various IoT privacy threats and solutions to preserve the privacy of IoT devices. The IoT safety, ethics, the need for the ethical design and challenges encountered are also discussed. In the end, smart cities are introduced as a case study to investigate various security threats and suggested solutions to maintain a good security level in a smart city.
    • Understanding and managing sound exposure and noise pollution at outdoor events

      Hill, Adam J.; University of Derby (Audio Engineering Society, 2020-05-22)
      This report is intended to present the current state of affairs surrounding the issue of outdoor event-related sound and noise. The two principal areas of investigation are sound exposure on-site and noise pollution off-site. These issues are different in nature and require distinct approaches to mitigate the associated negative short-term and long-term effects. The key message that is presented throughout this report is that the problems/ambiguities with current regulations are due to a lack of unbiased, scientifically-based research. It is possible to deliver acceptably high sound levels to audience members in a safe manner (minimizing risk of hearing damage) while also minimizing annoyance in local communities, where solutions to the on-site and off-site problems should begin with a well-informed sound system design. Only with a properly designed sound system can sound/noise regulations be realistically applied.
    • Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and Markov random field

      Bi, Haixia; Xu, Lin; Cao, Xiangyong; Xue, Yong; Xu, Zongben; University of Derby; University of Bristol; Shanghai Em-Data Technology Co., Ltd.; Xi’an Jiaotong University, Xi’an, China; University of Derby (IEEE, 2020-06-02)
      Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging task due to two main reasons. Firstly, the label information is difficult to acquire due to high annotation costs. Secondly, the speckle effect embedded in the PolSAR imaging process remarkably degrades the segmentation performance. To address these two issues, we present a contextual PolSAR image semantic segmentation method in this paper.With a newly defined channelwise consistent feature set as input, the three-dimensional discrete wavelet transform (3D-DWT) technique is employed to extract discriminative multi-scale features that are robust to speckle noise. Then Markov random field (MRF) is further applied to enforce label smoothness spatially during segmentation. By simultaneously utilizing 3D-DWT features and MRF priors for the first time, contextual information is fully integrated during the segmentation to ensure accurate and smooth segmentation. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on three real benchmark PolSAR image data sets. Experimental results indicate that the proposed method achieves promising segmentation accuracy and preferable spatial consistency using a minimal number of labeled pixels.
    • Fuzzy logic with expert judgment to implement an adaptive risk-based access control model for IoT

      Atlam, Hany F.; Walters, Robert J.; Wills, Gary B.; Daniel, Joshua; University of Southampton; Menoufia University, Menoufia, Egypt; Security Futures Practice, BT Research & Innovation, Ipswich, UK (Springer Science and Business Media LLC, 2019-01-28)
      The Internet of Things (IoT) is becoming the future of the Internet with a large number of connected devices that are predicted to reach about 50 billion by 2020. With proliferation of IoT devices and need to increase information sharing in IoT applications, risk-based access control model has become the best candidate for both academic and commercial organizations to address access control issues. This model carries out a security risk analysis on the access request by using IoT contextual information to provide access decisions dynamically. This model solves challenges related to flexibility and scalability of the IoT system. Therefore, we propose an adaptive risk-based access control model for the IoT. This model uses real-time contextual information associated with the requesting user to calculate the security risk regarding each access request. It uses user attributes while making the access request, action severity, resource sensitivity and user risk history as inputs to analyze and calculate the risk value to determine the access decision. To detect abnormal and malicious actions, smart contracts are used to track and monitor user activities during the access session to detect and prevent potential security violations. In addition, as the risk estimation process is the essential stage to build a risk-based model, this paper provides a discussion of common risk estimation methods and then proposes the fuzzy inference system with expert judgment as to be the optimal approach to handle risk estimation process of the proposed risk-based model in the IoT system.
    • Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax

      Hussain, Maqbool; Afzal, Muhammad; Ali, Taqdir; Ali, Rahman; Khan, Wajahat Ali; Jamshed, Arif; Lee, Sungyoung; Kang, Byeong Ho; Latif, Khalid; Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si 446-701, Gyeonggi-do, Republic of Korea; et al. (Elsevier BV, 2015-10-28)
      The objective of this study is to help a team of physicians and knowledge engineers acquire clinical knowledge from existing practices datasets for treatment of head and neck cancer, to validate the knowledge against published guidelines, to create refined rules, and to incorporate these rules into clinical workflow for clinical decision support. A team of physicians (clinical domain experts) and knowledge engineers adapt an approach for modeling existing treatment practices into final executable clinical models. For initial work, the oral cavity is selected as the candidate target area for the creation of rules covering a treatment plan for cancer. The final executable model is presented in HL7 Arden Syntax, which helps the clinical knowledge be shared among organizations. We use a data-driven knowledge acquisition approach based on analysis of real patient datasets to generate a predictive model (PM). The PM is converted into a refined-clinical knowledge model (R-CKM), which follows a rigorous validation process. The validation process uses a clinical knowledge model (CKM), which provides the basis for defining underlying validation criteria. The R-CKM is converted into a set of medical logic modules (MLMs) and is evaluated using real patient data from a hospital information system. We selected the oral cavity as the intended site for derivation of all related clinical rules for possible associated treatment plans. A team of physicians analyzed the National Comprehensive Cancer Network (NCCN) guidelines for the oral cavity and created a common CKM. Among the decision tree algorithms, chi-squared automatic interaction detection (CHAID) was applied to a refined dataset of 1229 patients to generate the PM. The PM was tested on a disjoint dataset of 739 patients, which gives 59.0% accuracy. Using a rigorous validation process, the R-CKM was created from the PM as the final model, after conforming to the CKM. The R-CKM was converted into four candidate MLMs, and was used to evaluate real data from 739 patients, yielding efficient performance with 53.0% accuracy. Data-driven knowledge acquisition and validation against published guidelines were used to help a team of physicians and knowledge engineers create executable clinical knowledge. The advantages of the R-CKM are twofold: it reflects real practices and conforms to standard guidelines, while providing optimal accuracy comparable to that of a PM. The proposed approach yields better insight into the steps of knowledge acquisition and enhances collaboration efforts of the team of physicians and knowledge engineers.
    • The mining minds digital health and wellness framework

      Banos, Oresti; Bilal Amin, Muhammad; Khan, Wajahat Ali; Afzal, Muhammad; Hussain, Maqbool; Kang, Byeong Ho; Lee, Sungyong; Kyung Hee University, 1732 Deokyoungdae-ro, Giheung-ug, Yongin-si, 446-701, Korea; University of Tasmania (Springer Science and Business Media LLC, 2016-07-15)
      The provision of health and wellness care is undergoing an enormous transformation. A key element of this revolution consists in prioritizing prevention and proactivity based on the analysis of people’s conducts and the empowerment of individuals in their self-management. Digital technologies are unquestionably destined to be the main engine of this change, with an increasing number of domain-specific applications and devices commercialized every year; however, there is an apparent lack of frameworks capable of orchestrating and intelligently leveraging, all the data, information and knowledge generated through these systems. This work presents Mining Minds, a novel framework that builds on the core ideas of the digital health and wellness paradigms to enable the provision of personalized support. Mining Minds embraces some of the most prominent digital technologies, ranging from Big Data and Cloud Computing to Wearables and Internet of Things, as well as modern concepts and methods, such as context-awareness, knowledge bases or analytics, to holistically and continuously investigate on people’s lifestyles and provide a variety of smart coaching and support services. This paper comprehensively describes the efficient and rational combination and interoperation of these technologies and methods through Mining Minds, while meeting the essential requirements posed by a framework for personalized health and wellness support. Moreover, this work presents a realization of the key architectural components of Mining Minds, as well as various exemplary user applications and expert tools to illustrate some of the potential services supported by the proposed framework. Mining Minds constitutes an innovative holistic means to inspect human behavior and provide personalized health and wellness support. The principles behind this framework uncover new research ideas and may serve as a reference for similar initiatives.
    • An adaptive semantic based mediation system for data interoperability among health information systems

      Khan, Wajahat Ali; Khattak, Asad Masood; Hussain, Maqbool; Amin, Muhammad Bilal; Afzal, Muhammad; Nugent, Christopher; Lee, Sungyoung; Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do, 446-701, Republic of Korea; University of Ulster Newtownabbey, Co. Antrim, Northern Ireland (Springer Science and Business Media LLC, 2014-06-26)
      Heterogeneity in the management of the complex medical data, obstructs the attainment of data level interoperability among Health Information Systems (HIS). This diversity is dependent on the compliance of HISs with different healthcare standards. Its solution demands a mediation system for the accurate interpretation of data in different heterogeneous formats for achieving data interoperability. We propose an adaptive AdapteR Interoperability ENgine mediation system called ARIEN, that arbitrates between HISs compliant to different healthcare standards for accurate and seamless information exchange to achieve data interoperability. ARIEN stores the semantic mapping information between different standards in the Mediation Bridge Ontology (MBO) using ontology matching techniques. These mappings are provided by our System for Parallel Heterogeneity (SPHeRe) matching system and Personalized-Detailed Clinical Model (P-DCM) approach to guarantee accuracy of mappings. The realization of the effectiveness of the mappings stored in the MBO is evaluation of the accuracy in transformation process among different standard formats. We evaluated our proposed system with the transformation process of medical records between Clinical Document Architecture (CDA) and Virtual Medical Record (vMR) standards. The transformation process achieved over 90 % of accuracy level in conversion process between CDA and vMR standards using pattern oriented approach from the MBO. The proposed mediation system improves the overall communication process between HISs. It provides an accurate and seamless medical information exchange to ensure data interoperability and timely healthcare services to patients.
    • Multi-model-based interactive authoring environment for creating shareable medical knowledge

      Ali, Taqdir; Hussain, Maqbool; Khan, Wajahat Ali; Afzal, Muhammad; Hussain, Jamil; Ali, Rahman; Hassan, Waseem; Jamshed, Arif; Kang, Byeong Ho; Lee, Sungyoung; et al. (Elsevier BV, 2017-07-22)
      Technologically integrated healthcare environments can be realized if physicians are encouraged to use smart systems for the creation and sharing of knowledge used in clinical decision support systems (CDSS). While CDSSs are heading toward smart environments, they lack support for abstraction of technology-oriented knowledge from physicians. Therefore, abstraction in the form of a user-friendly and flexible authoring environment is required in order for physicians to create shareable and interoperable knowledge for CDSS workflows. Our proposed system provides a user-friendly authoring environment to create Arden Syntax MLM (Medical Logic Module) as shareable knowledge rules for intelligent decision-making by CDSS. Existing systems are not physician friendly and lack interoperability and shareability of knowledge. In this paper, we proposed Intelligent-Knowledge Authoring Tool (I-KAT), a knowledge authoring environment that overcomes the above mentioned limitations. Shareability is achieved by creating a knowledge base from MLMs using Arden Syntax. Interoperability is enhanced using standard data models and terminologies. However, creation of shareable and interoperable knowledge using Arden Syntax without abstraction increases complexity, which ultimately makes it difficult for physicians to use the authoring environment. Therefore, physician friendliness is provided by abstraction at the application layer to reduce complexity. This abstraction is regulated by mappings created between legacy system concepts, which are modeled as domain clinical model (DCM) and decision support standards such as virtual medical record (vMR) and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT). We represent these mappings with a semantic reconciliation model (SRM). The objective of the study is the creation of shareable and interoperable knowledge using a user-friendly and flexible I-KAT. Therefore we evaluated our system using completeness and user satisfaction criteria, which we assessed through the system- and user-centric evaluation processes. For system-centric evaluation, we compared the implementation of clinical information modelling system requirements in our proposed system and in existing systems. The results suggested that 82.05% of the requirements were fully supported, 7.69% were partially supported, and 10.25% were not supported by our system. In the existing systems, 35.89% of requirements were fully supported, 28.20% were partially supported, and 35.89% were not supported. For user-centric evaluation, the assessment criterion was ‘ease of use’. Our proposed system showed 15 times better results with respect to MLM creation time than the existing systems. Moreover, on average, the participants made only one error in MLM creation using our proposed system, but 13 errors per MLM using the existing systems. We provide a user-friendly authoring environment for creation of shareable and interoperable knowledge for CDSS to overcome knowledge acquisition complexity. The authoring environment uses state-of-the-art decision support-related clinical standards with increased ease of use.
    • Mapping evolution of dynamic web ontologies

      Khattak, A.M.; Pervez, Z.; Khan, Wajahat Ali; Khan, A.M.; Latif, K.; Lee, S.Y.; Zayed University, United Arab Emirates; University of the West of Scotland; Kyung Hee University, Republic of Korea; Innopolis University, Russia; et al. (Elsevier BV, 2015-01-03)
      Information on the web and web services that are revised by stakeholders is growing incredibly. The presentation of this information has shifted from a representational model of web information with loosely clustered terminology to semi-formal terminology and even to formal ontology. Mediation (i.e., mapping) is required for systems and services to share information. Mappings are established between ontologies in order to resolve terminological and conceptual incompatibilities. Due to new discoveries in the field of information sharing, the body of knowledge has become more structured and refined. The domain ontologies that represent bodies of knowledge need to be able to accommodate new information. This allows for the ontology to evolve from one consistent state to another. Changes in resources cause existing mappings between ontologies to be unreliable and stale. This highlights the need for mapping evolution (regeneration) as it would eliminate the discrepancies from the existing mappings. In order to re-establish the mappings between dynamic ontologies, the existing systems require a complete mapping process to be restructured, and this process is time consuming. This paper proposes a mapping reconciliation approach between the updated ontologies that has been found to take less time to process compared to the time of existing systems when only the changed resources are considered and also eliminates the staleness of the existing mappings. The proposed approach employs the change history of ontology in order to store the ontology change information, which helps to drastically reduce the reconciliation time of the mappings between dynamic ontologies. A comprehensive evaluation of the performance of the proposed system on standard data sets has been conducted. The experimental results of the proposed system in comparison with six existing mapping systems are provided in this paper using 13 different data sets, which support our claims.
    • Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

      Martins de Oliveira, Edvard; Estrella, Júlio Cézar; Botazzo Delbem, Alexandre Claudio; Souza Pardo, Mário Henrique; Guzzo da Costa, Fausto; Defelicibus, Alexandre; Reiff‐Marganiec, Stephan; Federal University of Itajubá, Brazil; University of Sao Paulo, Brazil; A.C. Camargo Cancer Center, Sao Paulo, Brazil; et al. (Wiley, 2020-02-26)
      Science Gateways provide portals for experiments execution, regardless of the users' computational background. Nowadays its construction and performance need enhancement in terms of resource provision and task scheduling. We present the Modular Distributed Architecture to support the Protein Structure Prediction (MDAPSP), a Service‐Oriented Architecture for management and construction of Science Gateways, with resource provisioning on a heterogeneous environment. The Decision Maker, central module of MDAPSP, defines the best computational environment according to experiment parameters. The proof of concept for MDAPSP is presented in WorkflowSim, with two novel schedulers. Our results demonstrate good Quality of Service (QoS), capable of correctly distributing the workload, fair response times, providing load balance, and overall system improvement. The study case relies on PSP algorithms and the Galaxy framework, with monitoring experiments to show the bottlenecks and critical aspects.