High-dimensional state and action spaces com- bined with sparse reward structures in reinforcement learning (RL) environments typically require advanced control architec- tures. Hierarchical Reinforcement Learning (HRL) demonstrates superior performance compared to atomic RL approaches in these challenging scenarios. HRL can manage the complexity of commands to achieve task objectives through its hierarchical structure. One of the key challenges in HRL is efficiently training each level’s policy with optimal data collection from its experience. Off-policy correction is a critical technique for facilitating sample-efficient off-policy training in HRL, as it addresses the non-stationary issue of higher-level policy training. However, existing methods typically employ indirect probabilistic approaches that fail to accurately capture the current capability of the lower-level policy. This mismatch ultimately constrains the effectiveness of higher-level policy training. In this paper, we propose a novel HRL model that supports direct off-policy correction based on a Flow-based Deep Generative Model (FDGM). This approach leverages the inverse operation of FDGM to achieve goals aligned with the current knowledge of the lower-level policy. Additionally, our model addresses the limitations of FDGM to enable its effective use in HRL. Through comparative experiments on benchmark environments, our model demonstrates superior performance over existing models

The opaque nature of large language models (LLMs) poses challenges for transparency, particularly in high-stakes domains such as healthcare. Although advances in LLM architectures and explainable AI (XAI) techniques have improved performance, a critical gap remains in generating explanations that are both accessible and trustworthy for non-expert users. In this study, we present MEDEXA (Medical Explainable Diagnosis with Evidence, Xplanations, and Assurance), a modular framework that combines counterfactual explanations and uncertainty estimation with prompt engineering and a self-reflective retrieval-augmented generation mechanism inspired by the Self-RAG framework. MEDEXA is designed to enhance the clarity, faithfulness, and user-alignment of LLM-generated responses to medical queries. We evaluate the framework using two patient-doctor dialogue datasets (iCliniq and GenMedGPT-5k), and demonstrate consistent improvements over baseline RAG models across several automatic evaluation metrics, particularly in clarity, consistency and faithfulness. These results highlight the importance of structured explainability components in enhancing trust and strengthening reliability in LLM-driven medical applications.

• Innovative Fractional Ownership Framework: The Fractional Digital Asset Ownership (FDAO) model uses fractional NFTs (FNFTs) to facilitate the co-ownership of digital assets, focusing on software code. • Addressing Ownership Management Challenges: Building on FNFT and blockchain technology, this study proposes an intelligent solution for fractional digital asset ownership that ensures the accurate tracking of ownership rights through the integration of FNFTs with blockchain technology. • Practical Prototype Development: This study demonstrates the FDAO framework’s capability to securely and transparently manage handling digital asset transactions using FNFTs and smart contracts implemented through Remix and OpenZeppelin. • Empirical Evaluation of FNFT Application: This research examines the effectiveness of FNFT frameworks in supporting fractional ownership, highlighting their potential for real-world digital asset applications. • Market Accessibility and Inclusivity: By enabling fractional ownership, the FDAO model increases accessibility to digital assets and supports ownership democratisation. • Identification of Limitations and Future Directions: The study discusses the challenges related to regulatory compliance, scalability, and costs associated with FNFTs and other blockchain platforms and outlines compliance strategies that may support a broad range of applications. A new generation of digital assets is being managed using blockchain technology and non-fungible tokens (NFTs), which introduce novel opportunities for verifying ownership rights and establishing provenance. This paper presents an innovative framework called Fractional Digital Asset Ownership (FDAO), which aims to create NFTs for digital artifacts, such as software code, and extend their functionality through fractionalized NFTs (FNFT). Leveraging the Model-View-Controller (MVC) design pattern, FDAO enables effective co-ownership tracking across the lifecycle of digital assets, providing a structured and efficient mechanism for defining and managing co-ownership. A system prototype has been developed and tested in an integrated development environment (IDE) using decentralised applications (DApps) and smart contracts. Unlike existing NFT-based models, FDAO incorporates an intelligent, automated fractionalization and verification mechanism that combines the ERC-1155 and ERC-20 standards to enhance co-ownership management and scalability. This integration addresses the critical challenges related to transparency, security, and lifecycle management in digital asset co-ownership. The prototype, implemented using Remix and OpenZeppelin, demonstrates how FDAO enables secure, transparent, and efficient transfer and management of digital assets. By integrating FNFT functionality with smart contracts, the framework provides a robust, scalable, and intelligent method for managing digital assets. It also maintains transparency and trust throughout the asset lifecycle.

Chatbots are being widely used by businesses worldwide to offer customer service and facilitate interactions with their clients. They are becoming increasingly advanced due to their foundation in artificial intelligence and natural language processing, enabling them to address complicated requests and responses. However, chatbots face numerous issues and challenges that could potentially result in low-quality outcomes. One of these issues is chatbot dialogue breakdown, an issue which needs to be solved to enhance user satisfaction and elevate the chatbot quality of service. Although there are several reviews related to chatbots, this is the first review to focus on chatbot dialogue breakdown. In this study, we comprehensively discuss intelligent solutions for chatbot dialogue breakdown. We apply a systematic literature review approach to identify and analyze the methodologies and the datasets used with their evaluation metrics to answer our two key questions. Then, we comprehensively examine studies that offer intelligent solutions to dialogue breakdowns and compare them with our defined requirements to build a chatbot dialogue breakdown early warning system. Finally, based on the findings, we identify seven areas of open challenges that could guide future research.

Short-term water demand prediction is essential for optimizing residential and industrial water management. Several studies have demonstrated the usefulness of water usage demand forecasting in making smart cities sustainable. However, the real-world translation of such forecasting systems still needs to be exploited. In this research, we have developed +10 days short-term demand forecasting framework that utilizes the existing state-of-the-art statistical and deep learning models. The designed framework has been integrated into the utility company's legacy water usage demand forecasting process to promote digitization and sustainability. The research outcomes have demonstrated that the designed framework has improved the forecast accuracy upon successful integration with the legacy operational process. Index Terms-Short-term Forecasting, Time Series Modeling, Regression Modeling, Deep Learning Modeling, Water Demand, Water Supply, Weather-based Demand Prediction

Recently, there has been a surge in recommendations based on heterogeneous information networks (HINs), attributed to their ability to integrate complex and rich semantics. Despite this advancement, most HIN-based recommenders overlook two critical aspects. First, they often fail to consider HIN's heterophily nature, hindering the capture of non-local structures in HINs. Second, most methods lack the capability for logical reasoning. In this paper, we propose a novel structure- and logic-aware heterogeneous graph learning framework for recommender systems (SLHRec). Our SLHRec contains a structure-aware module and a logic-aware module. The former uses network geometry to construct non-local neighborhoods for nodes in HINs, and then introduces a graph neural network to integrate constructed neighbors for modeling the heterophily of HINs. The logic-aware module uses the Markov logic network (MLN) to infuse logic rules into heterogeneous graph learning, thereby boosting logic reasoning in recommendations. Furthermore, we utilize contrastive learning to model cooperative signals between modules, enabling them to complement each other. In the prediction stage, both modules contribute to generating recommendations. Compared with several strong recommender baselines, our SLHRec achieves superior performance on four real-world datasets.

Unsupervised pre-training has been on the lookout for the virtue of a value function representation referred to as successor features (SFs), which decouples the dynamics of the environment from the rewards. It has a significant impact on the process of task-specific fine-tuning due to the decomposition. However, existing approaches struggle with local optima due to the unified intrinsic reward of exploration and exploitation without considering the linear regression problem and the discriminator supporting a small skill sapce. We propose a novel unsupervised pre-training model with SFs based on a non-monolithic exploration methodology. Our approach pursues the decomposition of exploitation and exploration of an agent built on SFs, which requires separate agents for the respective purpose. The idea will leverage not only the inherent characteristics of SFs such as a quick adaptation to new tasks but also the exploratory and task-agnostic capabilities. Our suggested model is termed Non-Monolithic unsupervised Pretraining with Successor features (NMPS), which improves the performance of the original monolithic exploration method of pre-training with SFs. NMPS outperforms Active Pre-training with Successor Features (APS) in a comparative experiment.

Cross-lingual sentiment analysis has developed as a significant area of research in linguistics, especially for languages having diverse syntactic and morphological structures. The objective of this study emphasizes creating a sophisticated sentiment analysis model that connects English and Arabic datasets, two languages with distinct linguistic problems. Using cutting-edge transformer architectures, we utilize pre-trained models—BERT for English and AraBERT for Arabic—to address the challenges of morphologically rich but resource-limited languages such as Arabic. The foundation of this study is the IMDB movie review dataset, which is similarly structured and large for both languages. To find the best deep learning architecture, we conducted extensive experiments using Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and attention methods. While LSTM-based models produced competitive results, transformer-based models such as proposed BERT-LA that included bidirectional and attention layers outperformed them substantially, particularly on Arabic and English data. Furthermore, ablation research was conducted to evaluate the models' performance using important measures such as accuracy, precision, recall, and the F1-score. Our model got an impressive 97.04% accuracy on the English dataset and 98.02% on the Arabic dataset. This study contributes to understanding how language-specific embeddings and transformer models affect under-represented languages.

Proactive identification and the management of disruption risks play a crucial role in the achievement of a global supply chain’s aims. Given the velocity and volume by which such disruption events occur, it is impractical to expect supply chain managers to determine the occurrence of such events manually. Given the pressures facing global supply chains due to the COVID-19 crisis, it is important for supply chain managers to proactively identify disruption risks to their supply chains and manage them to either achieve the outcomes or develop plans by which resilience against them can be built. In this paper, we demonstrate how the integration of natural language processing and reinforcement learning, which are fundamental artificial intelligence methods, can be used to assist supply chain risk managers in the timely identification of such disruption events. We explain in detail our proposed approach, namely RL-SCRI and show its superiority over the current models in achieving its aim.

The volume of data exchanged over the network has increased exponentially over the past few years, driving the need for a new solution. To address this issue, a new layer for computation and storage between the cloud and the user has been introduced to reduce the load on the network. To facilitate this solution, small datacentres have emerged characterised by their limited capacity in terms of computation and storage resources. These datacentres are managed by small fog service providers (SFSPs) and are used to host virtual machines (VMs) with QoS requirements that vary based on application and user subscription. Being limited in capacity, these SFSPs are unable to accept all incoming VM requests, forcing them to reject some VM requests based on QoS requirements. This causes an issue for VMs with minimum QoS requirements as they are kept waiting for unlimited time causing them to starve. VM starvation in SFSPs is a critical issue that has not been addressed in the literature. To address this issue, in this paper, we propose to support the SFSP by horizontally scaling up its resources using volunteer nodes. We propose, EleVMate, a novel management framework to manage the allocation of VMs to the SFSP nodes. Our solution works to prioritize the placement of priority VMs in these datacentres. We validated the solution using a publicly available VM dataset and we concluded that priority influences VM placement in SFSP. Last, our framework has improved the performance of the SFSP by reducing rejection rate by at least 4% with existing solutions.

Abstract In the rapidly evolving digital era, the growing trend of conducting real estate e-business transactions through online platforms has led to escalated challenges in ensuring transactional security and trust. These challenges underscore the importance of balancing transparency with data privacy and enhancing accountability in this field. As an extension of our previously published work (Abualhamayl AJ, Almalki MA, Al-Doghman F, Alyoubi AA, Hussain FK (2023) Towards fractional NFTs for joint ownership and provenance in real estate. In: 2023 IEEE international conference on e-business engineering (ICEBE), p. 143–8. 10.1109/ICEBE59045.2023.00022.), this paper introduces the Global Real Estate Platform (GREP), a novel hybrid blockchain system that utilizes real estate provenance to establish a secure and trustworthy environment for real estate e-business, specifically focusing on two key challenges: ensuring data authenticity and effectively managing access rights. Integral to GREP's design is the involvement of government entities, which is essential for maintaining the required balance between transparency, privacy, and high levels of accountability. This proposed framework is explained conceptually and demonstrated practically, offering an innovative perspective on the integration of hybrid blockchain technology in the real estate system. Furthermore, our research encompasses a detailed implementation, using various tools, and an in-depth examination of three use cases. This combined analysis effectively demonstrates GREP's efficacy in addressing the targeted challenges in the field. While acknowledging the system's limitations, including challenges in user adoption and performance variability under different network conditions, our findings open new avenues for further exploration, such as landlords' payment histories and utility bills, and using blockchain as a secondary user identifier. These features collectively highlight the transformative potential of blockchain technology in real estate e-business.

Most exploration research on reinforcement learning (RL) has paid attention to ‘the way of exploration’, which is ‘how to explore’. The other exploration research, ‘when to explore’, has not been the main focus of RL exploration research. The issue of ‘when’ of a monolithic exploration in the usual RL exploration behaviour binds an exploratory action to an exploitational action of an agent. Recently, a non-monolithic exploration research has emerged to examine the mode-switching exploration behaviour of humans and animals. The ultimate purpose of our research is to enable an agent to decide when to explore or exploit autonomously. We describe the initial research of an autonomous multi-mode exploration of non-monolithic behaviour in an options framework. The higher performance of our method is shown against the existing non-monolithic exploration method through comparative experimental results.

This paper investigates how to leverage more readily acquired annotations, i.e., 3D bounding boxes instead of dense point-wise labels, for instance segmentation. We propose a Weakly-supervised point cloud Instance Segmentation framework with Geometric Priors (WISGP) that allows segmentation models to be trained with 3D bounding boxes of instances. Considering intersections among bounding boxes in a scene would result in ambiguous la- bels, we first group points into two sets, i.e., univocal and equivocal sets, indicating the certainty of a 3D point belonging to an instance, respectively. Specifically, 3D points with clear labels belong to the univocal set while the rest are grouped into the equivocal set. To assign reliable labels to points in the equivocal set, we design a Geometry-guided Label Propagation (GLP) scheme that progressively propagates labels to linked points based on geometric structure, e.g., polygon meshes and superpoints. Afterwards, we train an instance segmentation model with the univocal points and equivocal points labeled by GLP, and then employ it to assign pseudo labels for the remainder of the unlabeled points. Lastly, we retrain the model with all the labeled points to achieve better instance segmentation performance. Experiments on large-scale datasets ScanNet-v2 and S3DIS demonstrate that WISGP is superior to competing weakly-supervised algorithms and even on par with a few fully-supervised ones.

Blockchain technology has the potential to be applied widely in supply chain operations. One such area is proactive supply chain risk management (SCRM). In this area, existing researchers have highlighted the fraudulent behaviour of supply chain partners who do not disclose information on the risks that impact their operations. Blockchain can address this problem by encoding each partner’s commitment to SCRM and achieve consensus. However, before this can be achieved, a key challenge to address is the inability of existing consensus mechanisms such as Proof of Work (PoW), Proof of Authority (PoA) and Proof of Stake (PoS) to deal with information that does not have a digital footprint. In this paper, we address this gap by proposing the Proof by Earnestness (PoE) consensus mechanism which accounts for the authenticity, legitimacy and trustworthiness of information that does not have a digital footprint. We also propose the Subjective Information Authenticity Earnestness Framework (SIAEF) as the overarching framework that assists PoE in achieving its aim. We test the applicability of SIAEF and PoE in a real-world blockchain environment by deploying it as a decentralized application (Dapp) and applying it in BscScan Testnet which is an official test blockchain network.

The accuracy and reliability of XAI methods are important to establish their credibility and use in complex decision-making tasks. Existing XAI methods provide little information about the correctness and reliability of their outputs. Furthermore, post-hoc explanation approaches explain the outcomes after producing them, not in a step-by-step glass-box manner to explain how an output is reached. Our proposed approach addresses these drawbacks by designing a Belief-Rule-Based (BRB) framework that interprets in a glass-box manner why a particular decision has been reached. It does that by determining the chance of different output classes occurring for a specific time period by considering the different possible permutations of the inputs along with their influence. This also assists the user to determine if the given input dataset is incomplete, vague, imprecise or inconsistent before trusting the analysis emanating from it. We compare the performance of the proposed BRB approach against the different eXplainable artificial intelligence (XAI) methods, such as SHAP, LIME and LINDA-BN to ensure the users of the trustworthiness of its analysis. This also enables users to determine the extent to which each of the XAI techniques meets the requirements of XAI and the gaps that need to be addressed.

Advanced persistent threats (APTs) are one of today’s major threats to cyber security. Highly determined attackers along with novel and evasive exfiltration techniques mean APT attacks elude most intrusion detection and prevention systems. The result has been significant losses for governments, organizations, and commercial entities. Intriguingly, despite greater efforts to defend against APTs in recent times, frequent upgrades in defense strategies are not leading to increased security and protection. In this paper, we demonstrate this phenomenon in an appropriately designed APT rivalry game that captures the interactions between attackers and defenders. What is shown is that the defender’s strategy adjustments actually leave useful information for the attackers, and thus intelligent and rational attackers can improve themselves by analyzing this information. Hence, a critical part of one’s defense strategy must be finding a suitable time to adjust one’s strategy to ensure attackers learn the least possible information. Another challenge for defenders is determining how to make the best use of one’s resources to achieve a satisfactory defense level. In support of these efforts, we figured out the optimal timings of a player’s strategy adjustment in terms of information leakage, which form a family of Nash equilibria. Moreover, two learning mechanisms are proposed to help defenders find an appropriate defense level and allocate their resources reasonably. One is based on adversarial bandits, and the other is based on deep reinforcement learning. Experimental simulations show the rationales behind the game and the optimality of the equilibria. The results also demonstrate that players indeed have the ability to improve themselves by learning from past experiences, which shows the necessity of specifying optimal strategy adjustment timings when defending against APTs.

Recommender systems have seen wide adoption in different domains. The motive of such systems has evolved from providing generic recommendations in the past to providing customized and user-focused recommendations. To achieve this aim, the complexity and sophistication of the underlying techniques such systems use have evolved. Current recommender systems use advanced Artificial Intelligence techniques to provide intelligent recommendations and adapt their future workings to the user’s interest and requirements. One such technique currently being used in the literature to achieve this aim is Reinforcement Learning. However, a drawback of this technique is that it is data intensive and needs to be trained on data that represent different scenarios to ensure that the recommended output in a given scenario is accurate. In this article, we present an approach, namely Reinforcement Learning-based News Recommendation System (RL-NRS), to address this drawback in the domain of news recommendation. We explain the different stages of RL-NRS in detail and compare its performance with news articles recommended by Google for a particular search term.

Hypercomplex algebras are well-developed in the area of mathematics. Recently, several hypercomplex recommendation approaches have been proposed and yielded great success. However, two vital issues have not been well-considered in existing hypercomplex recommenders. First, these methods are only designed for specific and low-dimensional hypercomplex algebras (e.g., complex and quaternion algebras), ignoring the exploration and utilization of high-dimensional ones. Second, most recommenders treat every user-item interaction as an isolated data instance, without considering high-order collaborative relationships.

An increasing number of organizations and businesses around the world use cloud computing services to improve their performance in the competitive marketplace. However, one of the biggest challenges in using cloud computing services is performance measurement and the selection of the best cloud service providers (CSPs) based on quality of service (QoS) requirements[13]. To address this shortcoming in this article we propose a network data envelopment analysis (DEA) method in measuring the efficiency of CSPs. When network dimensions are taken into consideration, a more comprehensive analysis is enabled where divisional efficiency is reflected in overall efficiency estimates. This helps managers and decision makers in organizations to make accurate decisions in selecting cloud services. In the current study, the non-oriented network slacks-based measure (SBM) model and conventional SBM model with the assumptions of constant returns to scale (CRS) and variable returns to scale (VRS) are applied to measure the performance of 18 CSPs. The obtained results show the superiority of the network DEA model and they also demonstrate that the proposed model can evaluate and rank CSPs much better than compared to traditional DEA models.

Detecting breakdown is a common phenomenon in the conversational system, which is referred to when the system fails to provide appropriate responses to the user. Existing studiesare detect breakdown using different features such as word similarity, topic transition, and clustering. In this paper, we focus on the different important feature, which is human thinking and reasoning. We use this feature to model chatbot quality of services (CQoS) based on detecting the breakdown. Thus we introduce the fuzzy prediction rule-based framework to measure chatbot quality of service by detecting the breakdown utterance considering end-user and chatbot points of view. Inputs utilized in the proposed fuzzy logic-based model are multiple useful features extracted from utterances. The outputs are the degrees of relevance for each utterance to the quality of services. Several fuzzy rules are designed, and the defuzzification method is used in order to achieve desired CQoS results. Based on the outputs from the fuzzy model, the handover mechanism will activate. We evaluate the proposed formwork with other state-of-the-art models.

Cloud computing has become an exciting platform in modern IT era that provides on-demand services and resources such as compute power, memory, storage, networking, and various software services. A large number of small and medium size companies and big organizations have been exploiting cloud computing services for various applications in order to save time and cost as they do not have to maintain their own IT infrastructure. Cloud computing has various benefits such as flexibility, scalability, automatic maintenance of software, and anywhere and anytime service provisioning over the Internet.1 This special issue was organized to solicit papers via an open call as well as selected papers from the IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud 2016) and the 13th International Conference on Mobile Web and Intelligent Information Systems (MobiWis 2016), which were held in Vienna, Austria, 22-24 August 2016. The conferences were attended by a large number of participants from different countries across the world. The conferences featured a number of technical sessions of research papers, industry talks, and keynote talks. A number of workshops and symposia were also organized alongside the FiCloud and MobiWis conferences. This special issue focused on the emerging challenges and frontiers in cloud computing. The objective of this special issue is to showcase the most recent developments and research in the area of cloud computing such as cloud models and architectures, eg, Software as a Service (SaaS), Software-defined network (SDN), security in cloud computing, mobile cloud, cloud networking, and communication and cloud REST services. A number of papers were submitted to this special issue. Each of the paper was reviewed by multiple reviewers. Based on the reviews, eight papers were accepted for this special issue. The work presented in these papers is summarized as follows. Stavrinides and Karatza2 carry out investigations into the challenges involved in Software as a Service cloud computing. These challenges are mainly due to heterogeneity and multi-tenancy in the host environment. Authors argue that according to the Service Level Agreement (SLA) between the cloud providers and the end-users, the execution of applications must be time constrained. It has been claimed that the effective scheduling of multiple parallel applications is one of the most important aspects of SaaS in order to avoid any violation of SLA. Their work focuses on the enhancement of the most commonly used scheduling algorithms by utilizing approximate computation and evaluating the impact of the computational demand variability on the performance of the employed scheduling strategies. The simulated-based experimental results give a very interesting insight. One of the main objectives of Software-Defined Networking (SDN) is to enable cloud computing to be flexible and agile in order to support virtualized servers and data storage infrastructure of the data centers. Benkhelifa et al3 underpin a very interesting issue of combining mobile computing with cloud computing. The basic idea in this research is to create a cloud-computing environment by exploiting localized mobile devices. Through application of social models, they claim that the mobile devices can employ cooperative strategies for effective sharing of resources. They propose the design of a system that employs aggregated user and application resource profiling. This determines the optimal place for data processing and leads to finding a balance between optimum energy consumption and efficient resource utilization. Vinh et al4 investigate the security aspects of mobile cloud computing. One of the key challenges highlighted in this work is the assurance of trust on mobile devices, which play a key role in mobile cloud computing. Mobile device attestation has been recognized as a way to tackle this problem. The main contribution presented in this paper includes the property-based token attestation (PTA), which takes the advantage of existing techniques, such as Trusted Platform Module (TPM), Bring Your Own Device (BYOD), etc, to secure the mobile devices. A detailed analysis of the security threats has been included. This is followed by the proposed attestation schema for tackling these threats. Omezzine et al5 highlight the complexity of a cloud operational environment where the application providers want to maximize their profit and the end user demands high quality efficient application provisioning. The significance of negotiation has been addressed as a way forward to satisfy both service providers and service users. Two of the main characteristics of cloud application provisioning, which have been identified in this work, have been neglected in the current negotiation models. This work addresses both these properties and proposes a multi-layered negotiation framework that includes a generic negotiation model for cloud provisioning. The paper also presents an instantiation among cloud layers for efficient SaaS provisioning to demonstrate maximization of provider profit and increased satisfaction of users. Simulation-based experiments using multi-agent system technology demonstrate that negotiation improves the application provisioning in terms of profit for the SaaS provider and in terms of user satisfaction. Bull et al6 investigate the performance of periodic data flows on the real SDN hardware in a variety of situations. The work uses a Preemptive Flow Installation Mechanism (PFIM) to improve SDN performance for periodic traffic by limiting the number of controller look-ups through predicting when a flow is likely to need a particular rule. The experimental results demonstrate that the system can detect the majority of temporary periodic flows and preemptively install a flow rule, facilitating performance comparable to layer two switching speeds. Kim et al7 investigated the transmission of data in IoT devices using Low Power Wide Area Network (LPWAN), which is managed by a centralized server in the cloud. Their research highlights the vulnerability of the server in terms of response time and bandwidth utilization. To address this issue, the authors have proposed an interesting approach to move the control of LPWAN to the edge cloud. In the proposed approach, an LPWAN gateway is integrated with the edge cloud and LPWAN data is cached at the edge cloud in the gateway for LPWAN control. The proposed approach has demonstrated performance gains over the existing approaches and improves the data transmission response time while enhancing the bandwidth utilization. Khosrowshahi-Asl et al8 investigate a very complex issue of forming communities of identical or complementary cloud services for better visibility and efficiency. Contrary to most of the existing models, which require real-time global knowledge of services, this work proposes an efficient yet computationally cost-effective model based on partially available information to form communities. The proposed strategic Distributed Decision-making Mechanism (DDM) regulates the cloud services decision-making process. DDM generates an initial set of data based on information obtained from existing cloud services regarding their single and cooperative efficiency. This is then analyzed based on the distance function followed by the implementation of communities formation. The experimental results clearly indicate the performance gains over the existing solution. Mesfin et al9 work underpins an interesting framework of the REST services development for smartphones. The main objective of this work is to enhance the usability of composing services on smartphones. This work employed the design science research methodology to describe the new framework, REST4Mobile. The framework's usability evaluation is conducted using a systems usability scale. The evaluation results reveal that the guidelines in the REST4Mobile framework can enhance usability of REST services on smartphones. This encourages further studies on design approaches for enterprise level mobile end-users. The papers presented in this special issue are related to the emerging topics in cloud computing. The topics covered in this collection include scheduling, security, formation of community, service negotiation, and Data control, among others. We hope the readers will benefit from the research findings included in this special issue. Irfan Awan is a Professor of Computer Science at the University of Bradford. His research focuses on Network Security, Cyber Security, Performance Modeling of Cloud, and Communication Networks. His research work won Best Paper Awards from 24th IEEE AINA 2010, Australia and 8th BWCCA 2013, France. His research work has been published in IEEE Transactions, IEE Proceedings, IEEE Computer Magazine, among others. He is a member of editorial board of the Elsevier journal of Simulation, Modeling and Practice and served as a guest editor for various special issues. Professor Awan organized and chaired several international conference and workshops and edited proceedings. He has successfully supervised 34 Ph.D. students. Professor Awan is a fellow of HEA, fellow of BCS, and member IEEE. Muhammad Younas is a Reader in Computer Science at the School of Engineering, Computing and Mathematics, Oxford Brookes University, UK. His research interests include Web technologies, cloud and services computing, and Big data. He received a Ph.D. in Computer Science from the University of Sheffield, UK. He has published more than hundred papers in international journals and conferences. He is on the editorial and advisory boards of international journals and is also involved in the steering, organizing, and program committees of refereed international conferences and workshops. Farookh Khadeer is Associate Professor at the School of Computer Science, University of Technology, Sydney, Australia. He received the B.Tech. degree in Computer Science and Computer Engineering and the M.S. degree in Information Technology from the La Trobe University, Melbourne, Australia, and the Ph.D. degree in Information Systems from Curtin University of Technology, Perth, Australia, in 2006. His areas of active research are cloud computing, services computing, trust and reputation modeling, semantic web technologies, and industrial informatics. He works actively in the domain of cloud computing and business intelligence. In the area of business intelligence, the focus of his research is to develop smart technological measures such as trust and reputation technologies and semantic web for enhanced and accurate decision making. We would like to express our special thanks to all authors who contributed to this special issue. We are also very thankful to all reviewers for participating in a rigorous review process and providing useful feedback to authors to enhance their work. Finally, we would like to express our special gratitude to Professor Geoffrey Fox, the Editor-in-Chief, for providing us with this unique opportunity to present state-of-the-art research work in a special issue of the international journal of Concurrency and Computation: Practice and Experience.

Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved There is a growing interest in the use of Internet of Things (IoT) in information systems (IS). Data or information governance is a critical component of IoT enabled digital IS ecosystem. There is insufficient guidance available on how to effectively establish data governance for IoT enabled digital IS ecosystem. The introduction of new regulations related to privacy such as General Data Protection Regulation (GDPR) as well as existing regulations such as Health Insurance Portability and Accountability Act (HIPPA) has added complexity to this issue of data governance. This could possibly hinder the effective IoT adoption in healthcare digital IS ecosystem. This paper enhances the 4I framework, which is iteratively developed and updated using the design science research (DSR) method to address this pressing need for organizations to have a robust governance model to provide the coverage across the entire data lifecycle in IoT-enabled digital IS ecosystem. The 4I framework has four major phases: Identify, Insulate, Inspect and Improve. The application of this framework is demonstrated with the help of a Healthcare case study. It is anticipated that the proposed framework can help the practitioners to identify, insulate, inspect and improve governance of data in IoT enabled digital IS ecosystem.

The constant increase in the growth of the cloud market creates new challenges for cloud service providers. One such challenge is the need to avoid possible service level agreement (SLA) violations and their consequences through good SLA management. Researchers have proposed various frameworks and have made significant advances in managing SLAs from the perspective of both cloud users and providers. However, none of these approaches guides the service provider on the necessary steps to take for SLA violation abatement; that is, the prediction of possible SLA violations, the process to follow when the system identifies the threat of SLA violation, and the recommended action to take to avoid SLA violation. In this paper, we approach this process of SLA violation detection and abatement from a risk management perspective. We propose a Risk Management-based Framework for SLA violation abatement (RMF-SLA) following the formation of an SLA which comprises SLA monitoring, violation prediction and decision recommendation. Through experiments, we validate and demonstrate the suitability of the proposed framework for assisting cloud providers to minimize possible service violations and penalties.

Summary Business service advertisements are today published online to convey essential information about services to customers. However, current Web search engines are unable to search and combine online service advertisements. Semantic service annotation is important for its ability to enable machines to understand the meaning of services and support in effective service retrieval and service composition. Existing research in the area of semantic service annotation has focused on the annotation of Web services in a semi‐automated approach. It cannot be applied to business service information as it is not in the form of Web Services Description Language but in free text format. Moreover, semi‐automated approaches are inappropriate for annotating a large amount of online service information which changes dynamically and they are therefore not suitable for the timely dissemination of service information to customers. To solve these issues, we propose SERNOTATE, which is an automated approach for business service description annotation for efficient service retrieval and composition. We propose new semantic‐based linking approaches, namely, Extended Case‐based Reasoning, vector‐based, and classification‐based, that automatically annotate business services to relevant service concepts. Each approach assists in the single‐label and multi‐label annotation of service terms to concept terms to provide a better representation of services. The experimental results test and validate the applicability of the proposed approaches to the automatic annotation of business service descriptions to service concepts on a real‐world dataset.

We propose an interactive decision-making framework to assist a Customer Service Representative (CSR) in the efficient and effective recognition of customer records in a database with many ambiguous entries. Our proposed framework consists of three integrated modules. The first module focuses on the detection and resolution of duplicate records to improve effectiveness and efficiency in customer recognition. The second module determines the level of ambiguity in recognizing an individual customer when there are multiple records with the same name. The third module recommends the series of feature-related questions that the CSR should ask the customer to enable rapid recognition, based on that level of ambiguity. In the first module, the F-Swoosh approach for duplicate detection is used, and in the second module a dynamic programming-based technique is used to determine the level of ambiguity within the customer database for a given name. In the third module, Levenshtein edit distance is used for feature selection in combination with weights based on the Inverse Document Frequency (IDF) of terms. The algorithm that requires the minimum number of questions to be put to the customer to achieve recognition is the algorithm that is chosen. We evaluate the proposed framework on a synthetic dataset and demonstrate how it assists the CSR to rapidly recognize the correct customer.

There is increasing interest in sharing the experience of products and services on the web platform, and social media has opened a way for product and service providers to understand their consumers needs and expectations. This paper explores reviews by cloud consumers that reflect consumers experiences with cloud services. The reviews of around 6,000 cloud service users were analysed using sentiment analysis to identify the attitude of each review, and to determine whether the opinion expressed was positive, negative, or neutral. The analysis used two data mining tools, KNIME and RapidMiner, and the results were compared. We developed four prediction models in this study to predict the sentiment of users reviews. The proposed model is based on four supervised machine learning algorithms: K-Nearest Neighbour (k-NN), Nave Bayes, Random Tree, and Random Forest. The results show that the Random Forest predictions achieve 97.06% accuracy, which makes this model a better prediction model than the other three.

Cloud computing has been advancing at an impressive rate in recent years and is likely to increase more and more in the near future. New services are being developed constantly, such as cloud infrastructure, security and platform as a service, to name just a few. Due to the vast pool of available services, review websites have been created to help customers make decisions for their business. This leads to some reviewers taking advantage of these tools to promote the providers that hire them or to discredit competitors. These reviewers can either act individually or cooperate with each other. When reviewers collude to promote one product or defame another, they are called spammer groups. In this paper, we present an approach to identify spammer groups. First, a network-based method is used to identify individual spam reviewers. Then, a fuzzy k-means clustering algorithm is used to find the group that they belong to. A case study that suggests which group an incorrect review belongs to is provided to further understand the new method.

With the rapid growth of cloud services, there has been a significant increase in the number of online consumer reviews and opinions on these services on different social media platforms. These reviews are a source of valuable information in regard to cloud market position and cloud consumer satisfaction. This study explores cloud consumers' reviews that reflect the user's experience with Software as a Service (SaaS) applications. The reviews were collected from different web portals, and around 4000 online reviews were analysed using sentiment analysis to identify the polarity of each review, that is, whether the sentiment being expressed is positive, negative, or neutral. Also, this research develops a model for predicting the sentiment of Software as a Service consumers' reviews using a supervised learning machine called a support vector machine (SVM). The sentiment results show that 62% of the reviews are positive which indicates that consumers are most likely satisfied with SaaS services. The results show that the prediction accuracy of the SVM-based Binary Occurrence approach (3-fold crossvalidation testing) is 92.30%, indicating it performs better in determining sentiment compared with other approaches (Term Occurrences, TFIDF). This work also provides valuable insight into online SaaS reviews and offers the research community the first SaaS polarity dataset.

A cloud business environment comprises service providers and service consumers. Services are supplied through a Service Level Agreement (SLA) which defines all deliverables, commitments, obligations, QoS, violation penalties etc. that help a service provider and a service consumer to execute their business transactions. The primary aim of a service provider is to fulfill its commitment to a consumer by forming a viable SLA that wisely assigns the appropriate amount of resources to a requesting consumer. In this paper, we propose a viable SLA model that helps a service provider form a viable agreement with a consumer, based on its previous resource usage profile. The model uses a Fuzzy Inference System and takes the reliability and the contract duration of a consumer as input to calculate the suitability of this consumer, which is also used as input along with the risk propensity of a service provider to determine the amount of resources to offer to a consumer. We evaluate our approach and find that by using an optimized viable SLA model, providers are able to allocate an appropriate amount of resources to avoid an SLA violation.

There has been a dramatic increase in the sharing of opinions and information across different web platforms and social media, especially online product reviews. Cloud web portals, such as getApp.com, were designed to amalgamate cloud service information and to also examine how consumers evaluate their experience of using cloud computing products. The current literature shows the growing importance of online users' reviews, hence this study focuses on investigating consumers' feedback on Software-as-a- Service (SaaS) products by developing models to predict reviewers' attitudes. The goal of this paper is to develop prediction models to predict the sentiment of SaaS consumers' reviews (positive or negative). This research proposes five models that are based on five algorithms, the Support Vector Machine algorithm, Naive Bayes algorithm, Naive Bayes (Kernel) algorithm, k-nearest neighbors algorithm, and the decision tree algorithm to predict the attitude of SaaS reviews. The prediction accuracy of the space vector algorithm (5-fold cross-validation) is 92.37% which suggests that this algorithm is able to better determine the sentiment of online reviews compared with the other models. The results of this study provide valuable insight into online SaaS reviews and will assist in the design of SaaS review websites.

Online reviews nowadays are an important source of information for consumers to evaluate online services and products before deciding which product and which provider to choose. Therefore, online reviews have significant power to influence consumers' purchase decisions. Being aware of this, an increasing number of companies have organized spammer review campaigns, in order to promote their products and gain an advantage over their competitors by manipulating and misleading consumers. To make sure the Internet remains a reliable source of information, we propose a method to identify both individual and group spamming reviews by assigning a suspicion score to each user. The proposed method is a network-based approach combining clustering techniques. We demonstrate the efficiency and effectiveness of our approach on a real-world and manipulated dataset that contains over 8000 restaurants and 600,000 restaurant reviews from TripAdvisor website. We tested our method in three testing scenarios. The method was able to detect all spammers in two testing scenarios, however it did not detect all in the last scenario.

Service level agreement (SLA) is an essential agreement formed between a consumer and a provider in business activities. The SLA defines the business terms, objectives, obligations and commitment of both parties to a business activity, and in cloud computing it also defines a consumer's request for both fixed and variable resources, due to the elastic and dynamic nature of the cloud-computing environment. Providers need to thoroughly analyze such variability when forming SLAs to ensure they commit to the agreements with consumers and at the same time make the best use of available resources and obtain maximum returns. They can achieve this by entering into viable SLAs with consumers. A consumer's profile becomes a key element in determining the consumer's reliability, as a consumer who has previous service violation history is more likely to violate future service agreements; hence, a provider can avoid forming SLAs with such consumers. In this paper, we propose a novel optimal SLA formation architecture from the provider's perspective, enabling the provider to consider a consumer's reliability in committing to the SLA. We classify existing consumers into three categories based on their reliability or trustworthiness value and use that knowledge to ascertain whether to accept a consumer request for resource allocation, and then to determine the extent of the allocation. Our proposed architecture helps the service provider to monitor the behavior of service consumers in the post-interaction time phase and to use that information to form viable SLAs in the pre-interaction time phase to minimize service violations and penalties.

Software as a Service is the most adopted cloud service (46%) compared with Infrastructure as a Service (IaaS) (35%) and Platform as a Service (PaaS) (34%) [1]. Currently, the capability of discovering a SaaS of interest online across multiple cloud providers and reviews websites is a significant challenge, especially when using general search mechanisms (Google and Yahoo!) and search tools provided by existing reviews and directories. Discovering a SaaS is time-consuming, requiring consumers to browse several websites to select the appropriate service. This paper addresses the issues related to the efficient discovery of SaaS across review websites by developing the SaaS Nutch Hadoop-based Crawler Engine - SaaS Nhbased Crawler. The crawler is capable of crawling cloud reviews to find SaaSs of interest and enable the establishment of a central repository that could be used to discover SaaSs much more efficiently. The results show that the SaaS Nhbased crawler can effectively crawl review websites and provide a list of the latest SaaS being offered.

Due to the rapid increase in the use of social networking websites and applications, the need to stream video over wireless networks has increased. There are a number of considerations when transmitting streaming video between the nodes connected through wireless networks, such as throughput, the size of the multimedia file, response time, delay, scalability and loss of data. The scalability of ad-hoc networks needs to be analyzed by considering various aspects, such as self-organization, security, routing flexibility, availability of bandwidth, data distribution, Quality of Service, throughput, response time and efficiency. In this paper, we discuss the existing approaches to multimedia routing and transmission over wireless ad-hoc networks by considering scalability. The study draws several conclusions and makes recommendations for future directions.

In the recent past, semantic technologies have played an significant role in service retrieval and service querying. Annotating services semantically enables machines to understand the purpose of services and can further assist in intelligent and precise service retrieval, selection and composition. A key issue in semantically annotating services is the manual nature of service annotation. Manual service annotation requires a large amount of time and updating happens infrequently, hence annotations may get out-of-date due to service description changes. Although some researchers have studied semantic service annotation, they have only focused on web services not business service information. Moreover, their approaches are semi-automated, and still require service providers to select appropriate service annotations. In this paper, we propose a completely automated semantic annotation approach for e-services. The aim of this paper is to semantically annotate a service to relevant service concepts in domain-specific ontologies. Services and service concepts are represented by an extended VSM model, based on fuzzy rules. Then, we link a service to a concept, based on the similarity value of the representing vectors. We found during the experimentation process that the performances of the proposed approach and the VSM-based approach were quite similar and, as a result, developed a system to retrieve services that are annotated to relevant concepts. Experiments using a high service retrieval threshold demonstrated a retrieval approach based on extended VSM annotation performed much better than an approach based on VSM annotation.

A Service Level Agreement (SLA) is a contract between a service provider and a consumer which specifies in detail the level of service expected from the service provider, obligations, commitment and objectives. In the cloud computing environment, both the cloud provider and the cloud consumer want to know of a likely service violation before the actual violation occurs and to adjust the scaling of the cloud resources appropriately. A consumer's previous resource usage profile is a key element in determining the possibility of service violation in the cloud computing environment, which has not been an area of research focus so far. In this paper, we analyze and compare QoS prediction by considering the consumer's previous resource usage profile in various conditions. From comparative analysis, we observe that by combining a consumer's previous resource usage profile history along with the previous resource usage profile history of its nearest neighbors, we obtain an optimal result.

Class imbalanced data is a common problem for predictive modelling in domains such as bioinformatics. It occurs when the distribution of classes is not uniform among samples and results in a biased prediction of learning towards majority classes. In this study, we propose the ABC-Sampling algorithm based on a swarm optimization method called Artificial Bee Colony, which models the natural foraging behaviour of honeybees. Our algorithm lessens the effects of imbalanced classes by selecting the most informative majority samples using a forward search and storing them in a ranked subset. Then we construct a balanced dataset with a planned undersampling strategy to extract the most frequent majority samples from the top ranked subset and combine them with all minority samples. Our algorithm is superior to a state-of-the-art method on nine benchmark datasets with various levels of imbalance ratios.

Cloud computing is a very attractive option for service users and service providers for their businesses because of the benefits it provides. A major concern among service users regarding cloud adoption, however, is the unpredictability of performance in relation to the services provided. Even though guarantees in the form of service-level agreements are provided to users by service providers, real-time service-level degradability remains a critical concern; hence, there is a need for an approach that assists users to manage a service before it fails. The approaches proposed in the literature assess and evaluate the performance of the cloud infrastructure of providers, but this does not guarantee that a given service instance will meet the desired quality level because there may be factors other than the provider's infrastructure that will affect the level of quality of the service instance. In this paper, we present an approach that measures the quality of a service instance in real time and provides important analysis for service users as to whether they will achieve their desired objectives. This analysis also constitutes an important input for service users in the assessment and management of a service to avoid the failure to achieve objectives.

Argumentation is the practice of systematic conscious reasoning involving the construction and evaluation of arguments to justify or support a particular conclusion. This article discusses, compares, contrasts and categorizes existing argumentation-based frameworks and applications as either philosophical or logic-based, and provides critical analysis that emphasizes the structure of arguments and the interactions between them. This review compares and contrasts the frameworks and applications of argumentation-based approaches on Web 2.0 and the Semantic Web, and subsequently highlights the importance and challenges of attaining monological argumentation on the Semantic Web.

Summary The demand of software as a service (SaaS)‐based services delivering computing resources as on‐demand software is on the rise in the IT industry. However, one of the drawbacks of the existing SaaS services is that they offer limited or no personalization of the services provided according to the users profile. Personalization as mentioned in the literature has been a key driver in the adoption and usage of various applications and in providing better service experience to the users. However, overwhelming majority of such personalized services rely extensively on the server side, without embracing fast‐developing client‐side technologies. In SaaS‐based cloud services, utilizing this technology is necessary considering their limited processing specifications. Approaches have been proposed in the literature that focus on cloud‐based personalization using client‐side technologies but none of them actually address all the different components that are required for a scalable and holistic personalization framework for SaaS‐based cloud services. In this paper, we address this drawback by proposing a user‐focussed personalization framework. Our proposed framework takes advantage of powerful client side browsers to reduce server overheads, ameliorate performance, establish high intelligence and enrich data processing. To validate and demonstrate the applicability of our framework, we build a prototype model and compare its performance against existing approaches using different metrics. Copyright © 2014 John Wiley & Sons, Ltd.

The ubiquitous nature of the World Wide Web (WWW) coupled with the flexibility and agility associated with Software Services is one of the primary contributing factors in the development and deployment of intelligent (Software) e-services. The applications of these intelligent e-services span a number of domains such as manufacturing, transportation, and health. The aim of this special issue (SI) is to present some of the cutting-edge research done in this area. In response to the call-forpapers for this SI, more than 40 submissions were received. After thorough peer-review process, which spanned multiple rounds, six papers were selected.

Summary Recent growth and advancements in the Semantic Web have shifted the research focus from being knowledge‐centered to data‐centered. This has led to the increased use of ontologies to structurally represent the data, thereby generating huge amounts of RDF data, which we term Big RDF Data. Nevertheless, the literature lacks the tools to analyze Big RDF Data and make sense of it. Access to such tools would enable pragmatic inputs and insights for users in respect of such tasks as the usage and adoption of Ontologies, their uptake by different users in the community, and the identification of prevalent patterns. This analysis, which we term Ontology Usage, is important from the viewpoint of users who need informed inputs in the various stages of the ontology engineering lifecycle, such as ontology evolution, ontology population, and ontology deployment. In this paper, we propose the Ontology USage Analysis F̌ramework (OUSAF), which performs analysis of Ontology Usage on Big RDF Data and synthesizes the usage knowledge acquired. OUSAF provides a methodological approach to performing the various phases such as identifying, analyzing, representing, and utilizing the Ontology usage results from Big RDF Data. We describe in detail each of those phases and the metrics required to perform the analysis of each phase. The utilization of the OUSAF results obtained by users such as data publishers and ontology developers is demonstrated by a dataset collected in the e‐business domain. Copyright © 2014 John Wiley & Sons, Ltd.

Trust modeling of both the interacting parties in a virtual world, is a critical element of business intelligence. A key aspect in trust modeling is to be able to accurately predict the future trust value of an interacting party. In this paper, we propose an intelligent method for predicting the future trust value of a trusted entity. We propose the use of Z-number to represent both the trust value and its corresponding reliability. Subsequently, we apply Artificial Neural Network (ANN) to predict future trust values. We generate a large number of synthetic time series, with a view to model real-world trust values of trusted entity. We validate the working of our methodology using the generated time series.

In today's highly dynamic economy and society, the performance evaluation of Decision Making Units (DMUs) is of high importance. This study presents an efficient model for analyzing the outputs of performance measurement methodologies by means of trust, which provides explicit qualitative scales instead of representing pure numerical data. The efficiency rate of the current, previous and coming years, as well as the average efficiency and standard deviation, are the five inputs for this model. These efficiency rates are calculated using Data Envelopment Analysis (DEA). The approach uses time series forecasting to predict the future efficiency rate. Furthermore, the implemented Auto Regressive (AR) model includes an Auto Correlation Function (ACF) for input selection. The model utilizes T-norms and S-norms as the final modeling tools. To illustrate the applicability of the proposed model, we apply it to a data set of DMUs. Ultimately, modified trust values for these DMUs are determined using the proposed approach.

With the advent of Cloud computing and subsequent big data, online decision makers usually find it difficult to make informed decisions because of the great amount of irrelevant, uncertain, or inaccurate information. In this paper, we explore the application of multicriteria decision-making (MCDM) techniques in the area of Cloud computing and big data, to find an efficient way of dealing with criteria relations and fuzzy knowledge based on a great deal of information. We propose a MCDM framework, which combines the ISM-based and ANP-based techniques, to model the interactive relations between evaluation criteria, and to handle data uncertainties. We present an application of Cloud service selection to prove the efficiency of the proposed framework, in which a user-oriented sigmoid utility function is designed to evaluate the performance of each criterion.

QoS-based service rating has made positive contributions to the area of service selection. Especially for Cloud service users, the right decision when choosing suitable Cloud services can help them improve user satisfaction and trading revenues. This work aims to address the issue of uncertainty in service requests, service descriptions, user and expert preferences, as well as evaluation criteria in a MCDM-based service selection procedure. A hybrid fuzzy framework for Cloud service selection is proposed, addressing the challenge using three approaches: a fuzzy-ontology-based approach for function matching and service filtering, a fuzzy AHP technique for informed criterion weighting, and, a fuzzy TOPSIS approach for service ranking.

SUMMARY In the recent past, there has been an exponential growth in Resource Description Framework data on the web known as web of data. The emergence of the web of data is transforming the existing web from a document‐sharing medium to a decentralized knowledge platform for publishing and sharing information between humans and computers. To enable common understanding between different users, domain ontologies are being developed and deployed to annotate information on the web. This semantically annotated information is then accessed by machines to extract and aggregate information, on the basis of the underlying ontologies used. To effectively and efficiently access data on the web, insight into the usage of ontology is pivotal, because this assists users in experiencing the benefits offered by the Semantic Web. However, such an approach has not been proposed in the literature. In this paper, we present a pragmatic approach to the analysis of domain ontology usage on the web. We propose metrics to measure the use of domain ontology constructs on the web from different aspects. To comprehensively understand the usage patterns of conceptual knowledge, instance data, and ontology co‐usability, we considered GoodRelations ontology as the domain ontology and built a dataset by collecting structured data from 211 web‐based data sources that have published information using the domain ontology. The dataset is analyzed by using the proposed metrics and observations along with their usability and applicability to the different users of the Semantic Web. Copyright © 2013 John Wiley & Sons, Ltd.

It is well recognized that the Internet has become the largest marketplace in the world, and online advertising is very popular with numerous industries, including the traditional mining service industry where mining service advertisements are effective carriers of mining service information. However, service users may encounter three major issues – heterogeneity, ubiquity, and ambiguity, when searching for mining service information over the Internet. In this paper, we present the framework of a novel self-adaptive semantic focused crawler – SASF crawler, with the purpose of precisely and efficiently discovering, formatting, and indexing mining service information over the Internet, by taking into account the three major issues. This framework incorporates the technologies of semantic focused crawling and ontology learning, in order to maintain the performance of this crawler, regardless of the variety in the Web environment. The innovations of this research lie in the design of an unsupervised framework for vocabulary-based ontology learning, and a hybrid algorithm for matching semantically relevant concepts and metadata. A series of experiments are conducted in order to evaluate the performance of this crawler. The conclusion and the direction of future work are given in the final section.

The growing number of cloud services have made service selection a challenging decision-making problem by providing wide ranging choices for cloud service consumers. This necessitates the use of formal decision making methodologies to assist a decision maker in selecting the service that best fulfils the user's requirements. In this paper, we present a cloud service selection methodology that utilizes QoS history over different time periods, performs Multi-Criteria Decision Analysis to rank all cloud services in each time period in accordance with user preferences before aggregating the results to determine the overall service rank of all the available services for cloud service selection.

A decade-long conscious effort by the Semantic Web community has resulted in the formation of a decentralized knowledge platform which enables data interoperability at a syntactic and semantic level. For information interoperability, at a syntactic level, RDF provides the standard format for publishing data and RDFS gives structure to the information. For semantic-level interoperability, ontologies are used which allow information dissemination and assimilation among diverse applications and systems; where information is equally accessible and useful to humans and machines. The success of the linked open data project, recognition of explicit semantics (annotated through web ontologies) by search engines and the realized potential advantages of semantic data for publishers have resulted in tremendous growth in the use of web ontologies on the web. In order to promote the adoption of ontologies (to new users), reusability of adopted ontologies, effective and efficient utilization on ontological knowledge and evolving the ontological model, erudite insight on the usage of ontologies is imperative. While ontology evaluation attempts to evaluate a developed ontology to assess its fitness and quality, it does not provide any insight into how ontologies are being used and what is the state of prevalent knowledge patterns. Realizing the importance of measuring and analysing ontology usage to advance the adoption, reusability and exploitation of ontologies, we present a semantic framework for measuring and analysing ontology usage on the Web on empirical grounding. Our methodological approach is discussed to highlight the detail and role of each step. A framework is presented along with the set of metrics developed to measure ontology usage from different aspects such as ontology richness, usage and incentives to provide a holistic view on the state of ontology usage. The framework is then evaluated using an important use-case scenario to identify the prevalent knowledge patterns in order to rank the terminological knowledge for annotating the information.

SUMMARY The rapid increase in the volume of data available on the Internet makes it increasingly impractical for a crawler to index the whole Web. Instead, many intelligent crawlers, known as ontology‐based semantic focused crawlers, have been designed by making use of Semantic Web technologies for topic‐centered Web information crawling. Ontologies, however, have constraints of validity and time, which may influence the performance of the crawlers. Ontology‐learning‐based focused crawlers are therefore designed to automatically evolve ontologies by integrating ontology learning technologies. Nevertheless, surveys indicate that the existing ontology‐learning‐based focused crawlers do not have the capability to automatically enrich the content of ontologies, which makes these crawlers unreliable in the open and heterogeneous Web environment. Hence, in this paper, we propose a framework for a novel semi‐supervised ontology‐learning‐based focused (SOF) crawler, the SOF crawler, which embodies a series of schemas for ontology generation and Web information formatting, a semi‐supervised ontology learning framework, and a hybrid Web page classification approach aggregated by a group of support vector machine models. A series of tests are implemented to evaluate the technical feasibility of this proposed framework. The conclusion and the future work are summarized in the final section. Copyright © 2012 John Wiley & Sons, Ltd.

SUMMARY Web services provide a standard means for the interoperable operations between electronic devices in a network. The mission of Web service discovery is to seek an appropriate Web service for a service requester on the basis of the service descriptions in Web service advertisements and the service requester's requirements. Nevertheless, the standard language used for encoding service descriptions does not have the capacity to specify the capabilities of a Web service, leading to the problem of ambiguity in the service discovery process. This brings up the vision of Semantic Web Services and Semantic Web Service discovery, which make use of the Semantic Web technologies to enrich the semantics of service descriptions for service discovery. Semantic Web Service matchmakers are the programs or frameworks designed to implement the task of Semantic Web Service discovery and have drawn a significant amount of attention from both academia and industry from the start of this century. In this paper, we conduct a survey of the contemporary Semantic Web Service matchmakers in order to obtain an overview of the state of the art in this research area. We summarize six technical dimensions from the past literature and analyze the typical Semantic Web Service matchmakers mostly developed during the past 4 or 5 years in terms of the six dimensions. By means of this analysis, we gain an understanding of the current research and summarize a series of potential issues to that would provide the foundation for future research in this area.Copyright © 2012 John Wiley & Sons, Ltd.

Cognitive Radio (CR) is considered to be a necessary mechanism to detect whether a particular segment of the radio spectrum is currently in use, and to rapidly occupy the temporarily unused spectrum without interfering with the transmissions of other users. As Cognitive Radio has dynamic properties, so a member of Cognitive Radio Networks may join or leave the network at any time. These properties mean that the issue of secure communication in CRNs becomes more critical than for other conventional wireless networks. This work thus proposes a trust-based security system for community-based CRNs. A CR node's trust value is analyzed according to its previous behavior in the network and, depending on this trust value, it is decided whether this member node can take part in the communication of CRNs. For security purposes, we have designed our model to ensure that the proposed approach is secure in different contexts.

The increasing popularity of the cloud computing paradigm and the emerging concept of federated cloud computing have motivated research efforts towards intelligent cloud service selection aimed at developing techniques for enabling the cloud users to gain maximum benefit from cloud computing by selecting services which provide optimal performance at lowest possible cost. Given the intricate and heterogeneous nature of current clouds, the cloud service selection process is, in effect, a multi criteria optimization or decision-making problem. The possible criteria for this process are related to both functional and nonfunctional attributes of cloud services. In this context, the two major issues are: (1) choice of a criteria-set and (2) mechanisms for the assessment of cloud services against each criterion for thorough continuous cloud service monitoring. In this paper, we focus on the issue of cloud service monitoring wherein the existing monitoring and assessment mechanisms are entirely dependent on various benchmark tests which, however, are unable to accurately determine or reliably predict the performance of actual cloud applications under a real workload. We discuss the recent research aimed at achieving this objective and propose a novel user-feedback-based approach which can monitor cloud performance more reliably and accurately as compared with the existing mechanisms.

The main purpose of this paper is to propose a content analysis approach in order to develop an ontology of university social responsibility (USR). The proposed approach comprises four main phases in which two content analyses software have been utilized to extract the main USR components and to identify the domain of this concept. To achieve the goal, the existing body of knowledge of USR definitions and specifications - using a variety of terms - has been considered to identify the main notions of USR and their relationships. The developed ontology can be applied to define a formal, explicit description of the USR concept and to construct a more reliable basis for measurement purposes.

Due to the rapid growth of wireless applications, Cognitive Radio (CR) has been considered as a technology to solve spectrum source deficiency by improving effective utilization of limited radio spectrum resources for future wireless communications and mobile computing. However, the unique characteristics of CRNs make security a greater challenge. Moreover, due to the dynamic characteristics of CRNs, a member of CRNs, may join or leave the network at any time. The issue of supporting secure communication in CRNs therefore becomes more serious than for conventional wireless networks. Public key encryption and signature techniques have been adopted here to ensure the security of message transmission in CRNs. This work proposes a digital signature-based trust-oriented scheme to ensure communication by identifying efficient trustworthy users in CRNs. The security analysis is analyzed to guarantee that the proposed approach achieves security proof.

Recently, there has been much research focus on trust and reputation modelling as one of the key strategies for the formation of successful business intelligence strategies, particularly for service in mobile applications. One of the key trust modelling activities is trust prediction. During this process, the accuracy and reliability of the predicted trust values play an important role in the making of informed business decisions. Key factors to be considered at this stage are the variability and the high levels of distortion in the input series that have to be captured when predicting the trust values at a point in time in the future. In this paper, we propose a Multi-layer Feed Forward Artificial Neural Network to predict the future trust values of entities (services, agents, products etc.) for a future point in time based on data series input. We use four different non-uniform’ data input series and measure the accuracy of the predicted values under different experimental scenarios for benchmarking and comparison with existing approaches. Results indicate that the model is reliable in predicting trust values even in scenarios where there are only limited data available on training the neural network and a high level of distortion is present in the input series.

Cognitive Radio is considered as a promising and demanding technology to examine whether a particular radio spectrum band is currently in use or not and to switch into the temporarily unoccupied spectrum band in order to improve the usage of the radio electromagnetic spectrum without creating interference to the transmissions of other users. Because of the dynamic properties of CRNs, the issue of supporting secure communication in CRNs becomes more critical than that of other conventional wireless networks. In this paper, we propose a combination of certificate-based trust with a behavior-based trust which will benefit both by representing the trust as certificates in the the predeployment trust relation and by providing a continuous behaviour-based evaluation of trust.

Abstract While many researchers have contributed to the field of semantic similarity models so far, we find that most of the models are designed for the semantic network environment. When applying the semantic similarity model within the semantic‐rich ontology environment, two issues are observed: (1) most of the models ignore the context of ontology concepts and (2) most of the models ignore the context of relations. Therefore, in this paper, we present a solution for the two issues, including a novel ontology conversion process and a context‐aware semantic similarity model, by considering the factors of both the context of concepts and relations, and the ontology structure. Furthermore, in order to evaluate this model, we compare its performance with that of several existing models' performance in a large‐scale knowledge base, and the evaluation result preliminarily proves the technical advantage of our model in ontology environments. Conclusions and future works are described in the final section. Copyright © 2010 John Wiley & Sons, Ltd.

Cognitive Radio (CR) has been considered as a promising concept for improving the utilization of limited radio spectrum resources for future wireless communications and mobile computing. As cognitive radio network (CRN) is a general wireless heterogeneous network, it is very essential for detecting the misbehaving or false nodes in the network. So in this paper we propose a trust aware model which provides a reliable approach to establish trust for CRN. This approach combines all kinds of trust values together, including the direct trust and indirect trust value for the secondary users. Depending on this trust value, it is decided that whether the secondary user can user the primary user's spectrum band or not. The mathematical results show that our trust model can efficiently take decision for assigning spectrums to the users.

Knowledge would be considered as important element in knowledge-based economy and it makes a strong competitive advantage in dynamic business environment. In knowledge management, knowledge sharing is the most critical elements of effective knowledge processing. Several studies have been done to explain why people share knowledge and some of them have been mentioned in this paper. The next issue is how knowledge sharing can be improved and how it can be measured. Different models from different view points such as social and psychological aspect or economic benefit aspect have been proposed to analyse and measure knowledge sharing effectiveness. In this paper, we will review some of the main models in knowledge sharing effectiveness and will explain a new method to measure knowledge sharing effectiveness among individuals.

Digital Ecosystem is an innovative high-tech environment with the purpose of supporting the activities among species within the business ecosystem. In this paper, we concern about the research issue of service retrieval within such an environment. Due to the fact that species are heterogeneous and geographically dispersed, to precisely and quickly locate a service provider becomes an issue. In addition, the Digital Ecosystem environment urgently requires the structualization of service information and a set of unified QoS measurement for service ranking and evaluation. In order to unfold the issues in detail, we use the means of case study and literature survey. Eventually we formulate the research issues in this domain and provide a possible solution.

The nature of trust in business relationships is dynamic rather than static. Trust has evolutionary phases or a life cycle. This pattern of evolution can be described as building, maintaining and destroying. Building trust comes at high cost and hard effort. Therefore, once trust has been established in a business relationship, every effort must be made to maintain it. Maintaining trust can be defined as an effort to maximize the benefits of a relationship and to prevent the level of trust from decreasing to the destroying phase. Grounded in state-of-the-art literature, this paper presents current insights for the research into trust maintenance and suggests directions for future research in this field.

The Digital Business Ecosystem (DBE) is the result of the co-evolution of the Business Ecosystem and the Digital Ecosystem. There are numerous approaches and enabling technologies which are used in modeling open business marketplaces and, due to the similarities between the Digital Business environments, they can also help to enable the Digital Business Ecosystem but with some limitations. The complete lifecycle of the DBE can be decomposed into the following phases: formation, evolution and dissipation. In this work, our main focus is on the importance of negotiation in the DBE formation phase and especially on the structure of Contract Net Protocol. We will present an extension to the primitive Contract Net Protocol and name it Contract Net with Quality Protocol (CNQP or Q-Contract Net) to facilitate the negotiation process by adding the quality evaluation steps during the negotiation phase of the DBE formation.

Over the past few years, Cognitive Radio (CR) has been considered as a demanding concept for improving the utilization of limited radio spectrum resources for future wireless communications and mobile computing. Since a member of Cognitive Radio Networks may join or leave the network at any time, the issue of supporting secure communication in CRNs becomes more critical than for the other conventional wireless networks. This work thus proposes a secure trust-based authentication approach for CRNs. A CR node's trust value is determined from its previous trust behavior in the network and depending on this trust value, it is decided whether or not this CR node will obtain access to the Primary User's free spectrum. The security analysis is performed to guarantee that the proposed approach achieves security proof.

There is much interest in using the virtual community as a business medium to establish a relationship between customer and stakeholders. While studies on virtual communities have widely discussed ways to sustain this community, there is the need for a complete framework or methodology to regulate members' interactions so as to produce sustainability. In order to achieve this sustainability, it is important to consider the existing trust relationship between community members and ways to identify an untrustworthy agent in a community. In this paper, we propose a framework for creating a sustainable community in Virtual Environments. The role of a third party agent and the effectiveness of continuous performance monitoring are the main keys to creating a sustainable virtual community. We also present the results of an experimental study. The study shows that the framework will help the administrator to identify all non-compliant agents after a transaction or interactions.

With the rapid increase of wireless applications, Cognitive Radio (CR) has been considered as a promising concept to improve the utilization of limited radio spectrum resources for future wireless communications and mobile computing. Because of the dynamic behavior of Cognitive Radio Networks (CRNs), secure communication in CRNs becomes more critical than for other conventional Wireless networks. So, in this paper we propose a trust-based security solution for CRNs. Trust is calculated from the requesting node depending on various communication attributes and the evaluated trust is compared with the trust threshold value. Depending on the resultant decision, the requested service is available to the requesting user. We prove the security of our proposed scheme in terms of security analysis.

In a business interaction, transactional risk highlights the uncertainty associated in not achieving the desired outcomes. The assessment of transactional risk gives the interacting user the different levels of failure in achieving its desired outcomes and the consequences that it can experience. In a business interaction, the consequences that can be experienced pertain to the financial resources invested to achieve the desired outcomes. The level of financial loss that could be experienced plays a very important role in the interacting userpsilas decision to form a business interaction. The level of financial loss is dependent on the different types of uncertain events associated with the business interaction. In this paper, we will propose a methodology by which the interacting user in an e-business interaction can capture the different types of uncertainties and ascertain the financial risk that could be experienced from it.

Self-healing key distribution mechanism can be utilized for distributing session keys over an unreliable network. A self-healing key distribution scheme using bilinear pairings is proposed in this paper. As far as we know, it is the first pairing-based authenticated self-healing key distribution scheme. The scheme achieves a number of excellent properties. Firstly, the users can check the integrity and correctness of the ciphertext before carrying out more complex key recovery operations thus fruitless work can be avoided. Secondly, the scheme is collusion-free for any coalition of non-authorized users. Thirdly, the private key has nothing to do with the number of revoked users and can be reused as long as it is not disclosed. Finally, the storage overhead for each user is a constant.

Trust is widely acknowledged as being important for the efficient and effective operation of business in virtual environments. This is because trust functions like a glue that holds and links virtual agents together as they relate and collaborate remotely. In virtual environments, trust needs to be established swiftly as there is little time to build it gradually in the absence of face-to-face meetings. However, the manner in which trust develops and is maintained is a critical factor in relationships, both physically and virtually. In virtual environments, trust needs to be managed including network connection as well as social aspects of interaction. Maintaining trust in virtual environments is defined as an effort to maximize the benefits of such virtual relationship and to prevent the level of trust from decreasing. In this paper, we undertake a general survey of the current situation of trust maintenance in virtual environments. We review several researches from the perspective of terminology, strategies presented, and types of strategies. We describe the benefits and shortcomings of each strategy, conduct an integrative review of these strategic approaches, and make suggestions for future research.

There is a lack of well-maintained ontologies thus ontology evolution now becomes an important filed of ontology research. The evolution may reflect new categories of systems being evaluated on broader and different understandings of certain concepts and relations. Alternatively ontologies evolve because the conceptualization improves. For ontology evolution, we focus in this paper a social network based approach in which the user community has direct control over the evolution of the ontologies. Ontologies can be enriched, learnt, and obtained from social network users using various empirical techniques. In this paper, we ground the social network based approach on the philosophy of wikis so called ontology Evolution Wiki.

The concept of mashup is gaining tremendous popularity and its application can be seen in a large number of domains. Enterprises using and relying upon mashup have improved their mass collaboration and personalization. In order for mashup technology to be widely accepted and widely used, we need a methodology by which can make use of the quality of the input to the mashup process as a governing principle to carry out mashup. This paper reviews the concept of mashup in different domains and proposes a conceptual solution framework for providing quality based mashup process.

It is observed that there are few service evaluation and ranking methodologies currently available in the SOE. In this paper, we propose an innovative service evaluation and ranking strategy, based on the measurement of trustworthiness and reputation of services (or service providers'). CCCI Metrics originally proposed and developed by Chang et al [1] is used to measure the trustworthiness and reputation of e-services. Here we extend the application of CCCI Metrics to the field of service retrieval. A java-based search engine prototype is designed, with the purpose of implementing the trustworthiness and reputation-based service search, evaluation and ranking. Conclusions and future works are drawn in the final section.

Digital Ecosystems is a neoteric terminology and there are two major definitions about it ◻ respectively from Soluta.Net and from Digital Ecosystem and Business Intelligence Institute. In this paper, to solve the ambiguous problem in Digital Ecosystem's definitions and to help researchers better understand what it is, by means of ontology, we propose a conceptual model to completely illustrate the concepts in Digital Ecosystem. By introducing a new ontology notation system, we deliver the Digital Ecosystem Ontology, to well define the components and explain the relationships between these components. Finally we realize the ontology in Protégé-owl and conclude our future works in the field.

To ascertain the possible level of risk in a digital business ecosystem interaction, the initiating agent has to determine beforehand the probability of failure, the possible consequences of failure, and the loss of investment probability to its resources while interacting with the other agent. Out of these three constituents, the initiating agent can determine beforehand the probability of failure in interacting with an agent either by considering its past interaction history with it or by soliciting recommendations from other agents. In both cases, it is imperative for the agent who is either considering its past interaction history, or who is communicating a recommendation about the other agent, to know the accurate level of failure in interacting with the other agent. To achieve this, in this paper we propose a methodology by which the initiating agent of the interaction ascertains the level of failure in the interaction, after interacting with an agent.

In this paper we present an overview of the definitions of the terms of trust and reputation from the literature. Trust and reputation have been defined in different ways by the various researchers. As a result of these various definitions of trust and reputation there is a lot of confusion regarding what these terms actually mean. Additionally in the literature there is no work towards collecting all the definitions of trust and reputation. In this paper we discuss and present an overview of the terms of trust and reputation from the literature.

Biomedical knowledge of proteomics domain is represented in the protein ontology, whose instantiations, which are undergoing evolution, need a good management and maintenance system. Protein ontology instantiations signify information about proteins that is shared and has evolved to reflect development in protein ontology project and proteomics domain itself. In this paper we explore the development of a conceptual framework for protein ontology instantiations management by using the concepts of trust and reputation in biomedical domain. The developed and engineered ontology approach is trustworthy and facilitates reliable additions and updates to the protein ontology.

It is well-known that ontology is utilized as an effective methodology to share domain-specific knowledge in multidisciplinary fields. In the field of project management, due to the characteristic of project organizations in which project members are geographically dispersed and from different cultural background, senior management would feel difficulty when they attempt to know about the detailed project completion status from dispersed project groups. Thus, the objective of this paper is to propose an automated project track and trace methodology through the use of ontology technology, to challenge the knowledge sharing issues in project organizations. By means of extending CCCI metrics into the field of project management and introducing a new ontological notation system, we deliver the project track and trace ontology.

Due to technological advancement of the Internet, conducting e-commerce transactions have become a part of our daily lives. In a financial interaction to be carried over the digital business ecosystem domain, it is rational for an agent instigating the interaction to analyse beforehand the possible risk in interacting with any other agent. Doing so would give the instigating agent an idea of direction in which its interaction might head and also help it to make an informed decision of its future course of action with that particular agent. For risk analysis, the instigating agent has to determine beforehand the probability of failure and the possible consequences of failure in interacting with an agent. In this paper, we propose such a methodology by which the instigating agent quantifies the probability of failure beforehand in interacting with an agent according to the demand of its future interaction with it.

In this article, we introduce trust ontologies. An ontology represents a set of concepts that are commonly shared and agreed to by all parties in a particular domain. Here, we introduce generic and specific trust ontologies. These ontologies include the following: an agent trust ontology and trustworthiness; agents include sellers, service providers, Web sites, brokers, shops, suppliers, buyers, or reviewers. A services trust ontology and trustworthiness assists in measuring the quality of service that agents provide in the service-oriented environment such as sales, orders, track and trace, warehousing, logistics, education, governance, advertising, entertainment, trading, online databases, virtual community services, security, information services, opinions, and e-reviews. A goods or products trust ontology and trustworthiness is useful for measuring the quality of products such as commercial products, information products, entertainment products, or second-hand products. We present a trust ontology that is suitable for all types of agents that exist in the service-oriented environment. As agent trust is measured through the quality of goods and services, we introduce two additional distinct concepts of service trust ontology and product trust ontology. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 519–545, 2007.

Trust and Reputation are vital components for trusted e-business. In the literature however there has been no effort in proposing ontology for trust. The trusted agent in service oriented environment may trust a software agent or human agent or a service or product. Based on this distinction, trust ontology could be proposed for different domains. The trust ontology for the individual domains is proposed and discussed.

Trust and Reputation are vital components for trusted e-business. In this paper, we propose a definition of trust relationship. Additionally we discuss in depth about the concept of trust relationship. A definition for reputation relationship is proposed in this paper. Three inner relationships with in the reputation relationship are detailed and discussed. © 2006 IEEE.

Trust and Reputation are vital components for trusted e-business. In this paper, we propose a definition of trust relationship. Additionally we discuss in depth the concept of trust relationship. A definition for reputation relationship is proposed in this paper. From the analysis of reputation relationship we find that the reputation relationship is a composite relationship and is composed of three inner relationships. We propose the three inner relationships and define them. We then propose the characteristics of the inner relationships within the reputation relationship.

The trusting peer in order to determine the likelihood of the loss in its resources might analyze the Risk before engaging in an interaction with any trusted peer. This likelihood of the loss in the resources is termed as Risk in the interaction. Risk analysis is important in e-commerce transactions because of the vast literature that argues that the decision to buy is based on the Risk-adjusted cost-benefit analysis. If the trusting peer can determine the future Riskiness value or reputation of a trusted peer for the time period of its interaction, before engaging in an activity with it, then it can ease its decision making process of whether to interact with the trusted peer or not. In this paper we present such a novel method which predicts the dynamic nature of Risk and determines the future Riskiness value of the trusted peer, before the interaction starts, thus helping the trusting peer considerably in making its decision. © 2006 IEEE.

As there is a lack of central management in an e-commerce interaction carried out based on peer-to-peer architecture, it is obvious for the trusting peer to analyze the risk beforehand that could be involved in dealing with a trusted peer in these types of interactions. Another characteristic of peer-to-peer architecture interactions is that the trusting peer might have to choose a peer to interact with, from a set of possible trusted peers. It can ease its decision making process of choosing a peer to interact with by analyzing the risk that could be involved in dealing with each of the possible trusted peers. In this paper we highlight and propose a solution to this problem by which the trusting peer can decide with which peer to interact with after analyzing the risk that could be associated in dealing with each of them

Biomedical Ontologies are huge. It is not possible for any one person to manage and engineer a complete ontology. They would need the help of Research Assistants and other people to develop and maintain the ontology. In the process of developing and maintaining the ontology the Research Assistants may enter incorrect data, resulting in low quality of the ontology. In this paper we will propose a conceptual framework to solve these ontology management and ontology development issues. There can be N assistants entering data into the ontology. All the data entered initially is stored in an intermediate ontology. The administrator of the ontology has a set of rules, which makes a checklist that checks and validates the data in intermediate ontology for correctness according to the ontology schema. We use the Case Study of Protein Ontology for this proposed approach to develop interfaces for assistants and administrators. The proposed approach can easily be extended to other biomedical ontologies just by tweaking the administrator rule set according to the ontology.

Recent progress in proteomics, computational biology, and ontology development has presented an opportunity to investigate protein data sources from a unique perspective that is, examining protein data sources through structure and hierarchy of Protein Ontology (PO). Various data mining algorithms and mathematical models provide methods for analyzing protein data sources; however, there are two issues that need to be addressed: (1) the need for standards for defining protein data description and exchange and (2) eliminating errors which arise with the data integration methodologies for complex queries. Protein Ontology is designed to meet these needs by providing a structured protein data specification for Protein Data Representation. Protein Ontology is a standard for representing protein data in a way that helps in defining data integration and data mining models for Protein Structure and Function. We report here our development of PO; a semantic heterogeneity framework based on relationships between PO concepts; and analysis of resultant PO Data of Human Proteins. We also talk in this paper briefly about our ongoing work of designing a trustworthy framework around PO.

The growing development of Web based trust and reputation systems in the 21st century will have powerful social and economic impact on all business entities, and will make transparent the quality assessment and customer assurance realities in the distributed Web based service oriented environments. The Web based trust and reputation systems will be the foundation for Web intelligence in the future. Trust and reputation systems help capture business intelligence through establishing customer relationships, learning consumer behavior, capturing market reaction on products and services, disseminating customer feedback, buyers' opinions and end-user recommendations, and revealing dishonest services, unfair trading, biased assessment, discriminatory actions, fraudulent behaviors, and untrue advertising. The continuing development of these technologies will help in the improvement of professional business behavior, sales, reputation of sellers, providers, products and services. However there has been no effort in defining ontology for reputation. In this paper we attempt to define ontology for reputation.