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Health information becomes importantly valuable for protecting public health in the current coronavirus situation. Knowledge-based information systems can play a crucial role in helping individuals to practice risk assessment and remote diagnosis. We introduce a novel approach that will develop causality-focused knowledge learning in a robust and transparent manner. Then, the machine gains the causality and probability knowledge for inference (thinking) and accurate prediction later. Besides, the hidden knowledge can be discovered beyond the existing understanding of the diseases. The whole approach is built on a Causal Probability Description Logic Framework that combines Natural Language Processing (NLP), Causality Analysis and extended Knowledge Graph (KG) technologies together. The experimental work has processed 801 diseases in total (from the UK NHS website linking with DBpedia datasets). As a result, the machine learnt comprehensive health causal knowledge and relations among the diseases, symptoms, and other facts efficiently.

With industry 4.0, data-based approaches are in vogue. However, extracting the essential features is not a trivial task and greatly influences the final result. There is also a need for specialized system knowledge to monitor the environment and diagnose faults. In this context, the diagnosis of faults is significant, for example, in a vehicle fleet monitoring system, since it is possible to diagnose faults even before the customer is aware of the fault, minimizing the maintenance costs of the modules. In this paper, several models using machine learning (ML) techniques were applied and analyzed during the fault diagnosis process in vehicle fleet tracking modules. Two approaches were proposed: ‘With Knowledge’ and ‘Without Knowledge’, to explore the dataset using ML techniques to generate classifiers that can assist in the fault diagnosis process. The approach ‘With Knowledge’ performs the feature extraction manually, using the ML techniques: random forest, naive Bayes, support vector machine and Multi Layer Perceptron; on the other hand, the approach ‘Without Knowledge’ performs an automatic feature extraction, through a convolutional neural network. The results showed that the proposed approaches are promising. The best models with manual feature extraction obtained a precision of 99.76% and 99.68% for detection and detection and isolation of faults, respectively, in the provided dataset. The best models performing an automatic feature extraction obtained, respectively, 88.43% and 54.98% for detection and detection and isolation of failures.

The fast development of the Internet of Things (IoT) changes our life in many areas, especially in the health domain. For example, remote disease diagnosis can be achieved more efficiently with advanced IoT-technologies which not only include hardware but also smart IoT data processing and learning algorithms, e.g. image-based disease classification. In this paper, we work in a specific area of skin condition classification. This research work aims to provide an implementable solution for IoT-led remote skin disease diagnosis applications. The research output can be concluded into three folders. The first folder is about dynamic AI model configuration supported IoT-Fog-Cloud remote diagnosis architecture with hardware examples. The second folder is the evaluation survey regarding the performances of machine learning models for skin disease detection. The evaluation contains a variety of data processing methods and their aggregations. The evaluation takes account of both training-testing and cross-testing validations on all seven conditions and individual condition. In addition, the HAM10000 dataset is picked for the evaluation process according to the suitability comparisons to other relevant datasets. In the evaluation, we discuss the earlier work of ANN, SVM and KNN models, but the evaluation process mainly focuses on six widely applied Deep Learning models of VGG16, Inception, Xception, MobileNet, ResNet50 and DenseNet161. The result shows that each of the top four models for the major seven skin conditions has better performance for the specific condition than others. Based on the evaluation discovery, the last folder proposes a novel classification approach of the Targeted Ensemble Machine Classify Model (TEMCM) to enable dynamically combining a suitable model in a two-phase detection process. The final evaluation result shows the proposed model can archive better performance.

In the field of process mining, it is worth noting that process mining techniques assume that the resulting event logs can not only continuously record the occurrence of events but also contain all event data. However, like in IoT systems, data transmission may fail due to weak signal or resource competition, which causes the company’s information system to be unable to keep a complete event log. Based on a incomplete event log, the process model obtained by using existing process mining technologies is deviated from actual business process to a certain degree. In this paper, we propose a method for repairing missing activities based on succession relation of activities from event logs. We use an activity relation matrix to represent the event log and cluster it. The number of traces in the cluster is used as a measure of similarity calculation between incomplete traces and cluster results. Parallel activities in selecting pre-occurrence and post-occurrence activities of missing activities from incomplete traces are considered. Experimental results on real-life event logs show that our approach performs better than previous method in repairing missing activities.

Conselho Nacional de Desenvolvimento Cientifico e Tecnologico. Grant Number: 165009/2015-2 Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (at CEMEAI). Grant Number: 2013/07375-0

Molecular disease subtype discovery from omics data is an important research problem in precision medicine. The biggest challenges are the skewed distribution and data variability in the measurements of omics data. These challenges complicate the efficient identification of molecular disease subtypes defined by clinical differences, such as survival. Existing approaches adopt kernels to construct patient similarity graphs from each view through pairwise matching. However, the distance functions used in kernels are unable to utilize the potentially critical information of extreme values and data variability which leads to the lack of robustness. In this paper, a novel robust distance metric (ROMDEX) is proposed to construct similarity graphs for molecular disease subtypes from omics data, which is able to address the data variability and extreme values challenges. The proposed approach is validated on multiple TCGA cancer datasets, and the results are compared with multiple baseline disease subtyping methods. The evaluation of results is based on Kaplan-Meier survival time analysis, which is validated using statistical tests e.g, Cox-proportional hazard (Cox p-value). We reject the null hypothesis that the cohorts have the same hazard, for the P-values less than 0.05. The proposed approach achieved best P-values of 0.00181, 0.00171, and 0.00758 for Gene Expression, DNA Methylation, and MicroRNA data respectively, which shows significant difference in survival between the cohorts. In the results, the proposed approach outperformed the existing state-of-the-art (MRGC, PINS, SNF, Consensus Clustering and Icluster+) disease subtyping approaches on various individual disease views of multiple TCGA datasets.

Cloud computing provides an extensible infrastructure for executing workflows that demand high processing and storage capacity. Tasks are distributed and resources selected during scheduling where choices have a significant impact on data protection. Some workflow scheduling algorithms apply security services such as authentication, integrity verification, and encryption for both sensitive and non-sensitive tasks. However, this approach requires long makespan and monetary cost for execution. In this paper, we introduce a scheduling approach that considers the user annotation of workflow tasks according to the sensitiveness. We also optimize the scheduling using a multi-population genetic algorithm for minimizing cost while meeting a deadline. Extensive experiments using three workflow applications with different ratios of sensitive tasks and data size were performed to evaluate in terms of cost, makespan, risk, and wastage. The results showed that our approach can protect sensitive tasks more appropriately while achieving a better cost compared to other approaches in the literature.

Recommender systems are filters that suggest products of interest to customers, which may positively impact sales. Nowadays, there is a multitude of algorithms for recommender systems, and their performance varies widely. So it is crucial to choose the most suitable option given a situation, but it is not a trivial task. In this context, we propose the Recommender Systems Evaluator (RSE): a framework aimed to accomplish an offline performance evaluation of recommender systems. We argue that the usage of a proper methodology is crucial when evaluating the available options. However, it is frequently overlooked, leading to inconsistent results. To help appraisers draw reliable conclusions, RSE is based on statistical concepts and displays results intuitively. A comparative study of classical recommendation algorithms is presented as an evaluation, highlighting RSE’s critical features.

The diversity and proliferation of Knowledge bases have made data integration one of the key challenges in the data science domain. The imperfect representations of entities, particularly in graphs, add additional challenges in data integration. Graph dependencies (GDs) were investigated in existing studies for the integration and maintenance of data quality on graphs. However, the majority of graphs contain plenty of duplicates with high diversity. Consequently, the existence of dependencies over these graphs becomes highly uncertain. In this paper, we proposed graph probabilistic dependencies (GPDs) to address the issue of uncertainty over these large-scale graphs with a novel class of dependencies for graphs. GPDs can provide a probabilistic explanation for dealing with uncertainty while discovering dependencies over graphs. Furthermore, a case study is provided to verify the correctness of the data integration process based on GPDs. Preliminary results demonstrated the effectiveness of GPDs in terms of reducing redundancies and inconsistencies over the benchmark datasets.

The microservice architecture is an architecture in which a single system is divided into small independently deployed services that are orchestrated together with the use of a lightweight mechanism. Each microservice does not rely much on other microservices (low coupling), but rather on its own resources to perform its task (high cohesion). This paper proposes a novel methodology which decomposes a monolith or other system into microservices in such a way that each microservice will function independently of other microservices while preserving some other key features, with the functionality distribution across each microservice being optimized with regards to usage of the functionality. This methodology makes use of dynamic analysis to identify resources that play a role in enabling the microservice to fulfill its functionality. We establish a set of rules which allows optimized distribution of the functionality. We evaluate the approach by applying it to real systems.

As research and development in Web Services and Services Computing has only existed for approximately a decade and half, it is remarkably that it has lead to a multi-billion dollar services industry. The first major research conference on the subject, the International Conference on Web Services, just started in 2003. Research in services computing is active and multifaceted. This special issue of IEEE Transactions on Services Computing considers two areas in services computing that have experienced “Recent Advances in Web Services Research”: Security, Privacy, and Trust; and New Approaches to Services Composition. It consists of the best extended papers from the two premier research conferences on services computing: the 2016 IEEE International Conference on Web Services and the 2016 IEEE Conference on Services Computing.

The cloud ecosystem provides transformative advantages that allow elastically offering on-demand services. However, it is not always possible to provide adequate services to all customers and thus to fulfill service level agreements (SLA). To enable compliance with these agreements, service providers leave the customer responsible for determining the service settings and expect that the client knows what to do. Some studies address SLA compliance, but the existing works do not adequately address the problem of resource allocation according to clients' needs since they consider a limited set of objectives to be analyzed and fulfilled. In previous work, we have already addressed the problem considering a single-objective approach. In that work, we identified that the problem has a multi-objective characteristic since several attributes simultaneously influence the SLA agreement, which can lead to conflicts. This paper proposes a multi-objective combinatorial optimization approach for computational resources provisioning, seeking to optimize the efficient use of the infrastructure and provide the client with greater flexibility in contract closure.

In this paper, four techniques of machine learning (ML) were applied and analyzed during the diagnosis of failures in vehicle fleet tracking modules. A comparison of the sampling methods was carried out considering the training and testing process using real data provided by DDMX, that acts in vehicle fleet tracking. A methodology was defined for pre-processing the collected data before the application of the ML techniques. Totally 16 models were created using the Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM) and Multi Layer Perceptron (MLP) techniques. We have obtained promising results, where the techniques achieved a precision of 99.76% and 99.68% for detection and isolation of faults, respectively, on the provided dataset. These models can serve as prototypes to diagnose faults remotely and states that traditional ML techniques with manual feature extraction are still able to achieve high metrics.

Nowadays the access to a cloud computing environment is provided on-demand offering transparent services to customers. Although the cloud allows an abstraction of the behavior of the service providers in the infrastructure (involving logical and physical resources), it remains a challenge to fully comply with the Service Level Agreements (SLAs), because, depending on the service demand and system configuration, the providers may not be able to meet the requirements of the customers. There is a need for mechanisms that take account of load balancing algorithms to provide an efficient load distribution with the available resources. However, the studies in the literature do not effectively address the problem of the availability of resources to meet customers' requirements with analysis restricted to a limited set of objectives. This paper proposes algorithms to address the need for optimization when handling computational resources during the execution time. The methods optimizes the efficient use of the resources available in the infrastructure aiming to comply with the service level agreements defined between client and provider.

The growth of real‐world objects with embedded and globally networked sensors allows to consolidate the Internet of things paradigm and increase the number of applications in the domains of ubiquitous and context‐aware computing. The merging between cloud computing and Internet of things named cloud of things will be the key to handle thousands of sensors and their data. One of the main challenges in the cloud of things is context‐aware sensor search and selection. Typically, sensors require to be searched using two or more conflicting context properties. Most of the existing work uses some kind of multi‐criteria decision analysis to perform the sensor search and selection, but does not show any concern for the quality of the selection presented by these methods. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi‐objective decision methods and their quality of selection comparing them with the Pareto‐optimality solutions. The gathered results allow to analyse and compare these algorithms regarding their behaviour, the number of optimal solutions and redundancy.

The Internet of Things aims to digitize everyday physical objects by connecting them to the internet. As a result, cyber-physical environments of multiple sizes emerge, imposing new requirements on applications and software systems in regards support to heterogeneity and volatility. A challenging stage in the engineering of these systems is the validation. Although, there have been significant efforts to offer shared real-world testbeds, the simulations platforms are required to make the validation process cost and time effective. Existing simulation approaches only offer partial coverage to the key IoT environment characteristics, focus on communication or are specific for particular use cases and domains. In this paper, we propose a novel agent-based model that enables the simulation of the IoT systems with the key characteristics of an IoT environment. This model is designed to be flexible and adaptable to different experiments. Our approach introduces events in IoT environments as stochastic processes, enabling the evaluation of IoT systems under different conditions that otherwise would be time consuming and costly. We present the results of our experiments for evaluation of our model. These show that our proposal is a practical solution for the validation of IoT software systems, complementary to the real-world tests.

Scheduling is an important topic to support data security for workflow execution in clouds. Some workflow scheduling algorithms use security services such as authentication, integrity verification, and encryption for all workflow tasks. However, applying security services to no sensitive data does not make sense as no benefit is gained, yet it increases the makespan and monetary costs. In this paper, we introduce five policies for selection of tasks that handle sensitive data. We also propose a workflow scheduling algorithm based on a multi-populational genetic algorithm for minimizing cost while meeting a deadline. Experiments using four workflow applications show that our proposal can minimize both the makespan and cost, while maintaining the security of sensitive data compared to another approach in the literature.

In this study, a concept is presented that extends the My Remote previous project to a smart home solution, or the My Smart Remote. The concept extends its management capabilities for children's lives in the home, while at the same time safely and securely sharing collected data about children's activities with external parties including educationalists, health care professionals, psychologists and marketers of products and services used by children. Sharing data not only benefits these external parties but also the children themselves as solutions can be tailored to their needs. Because the My Smart Remote is for children, security and privacy concerns are paramount. A contribution of this concept that is primarily derived from the need for safety and security, and also efficiency in processing, is a solution where much of the sensor data of the My Smart Remote is processed locally, in addition to the cloud. This solution is based on the idea that smart solutions, such as smart cities and smart homes, generate a large amount of sensor data which can be more efficiently processed locally rather than depending on the cloud. The My Smart Remote using sensor data from around the home to manage television viewing, video gaming, activity on PCs, mobile phones and tablets, all in terms of time spent and types of activity. Additionally, the My Smart Remote will monitor these activities which will also include other activities such as taking drinks from the fridge and homework activities and collect, and share the associated data with external parties.

The Internet of Things (IoT) is able to connect billions of devices and services at anytime in any place, with various applications. Recently, the IoT became an emerging technology. One of the most significant current research discussion topics on the IoT is about the smart car parking. A modern urban city has over a million of cars on its roads but it does not have enough parking space. Moreover, most of the contemporary researchers propose management of the data on cloud. However, this method may be considered as an issue since the raw data is sent promptly from distributed sensors to the parking area via cloud and then received back after it is processed. This is considered as an expensive technique in terms of the data transmission as well as the energy cost and consumption. While the majority of proposed solutions address the problem of finding unoccupied parking space and ignore some other critical issues such as information about the nearest car parking and the roads traffic congestion, this paper goes beyond and proposes the alternative method. The paper proposes a smart car parking system that will assist users to solve the issue of finding a parking space and to minimise the time spent in searching for the nearest available car park. In addition, it provides users with roads traffic congestion status. Moreover, the proposed system collects the raw data locally and extracts features by applying data filtering and fusion techniques to reduce the transmitted data over the network. After that, the transformed data is sent to the cloud for processing and evaluating by using machine learning algorithms.

In a cloud computing environment, companies have the ability to allocate resources according to demand. However, there is a delay that may take minutes between the request for a new resource and it being ready for using. This causes the reactive techniques, which request a new resource only when the system reaches a certain load threshold, to be not suitable for the resource allocation process. To address this problem, it is necessary to predict requests that arrive at the system in the next period of time to allocate the necessary resources, before the system becomes overloaded. There are several time series forecasting models to calculate the workload predictions based on history of monitoring data. However, it is difficult to know which is the best time series forecasting model to be used in each case. The work becomes even more complicated when the user does not have much historical data to be analyzed. Most related work considers only single methods to evaluate the results of the forecast. Other works propose an approach that selects suitable forecasting methods for a given context. But in this case, it is necessary to have a significant amount of data to train the classifier. Moreover, the best solution may not be a specific model, but rather a combination of models. In this paper we propose an adaptive prediction method using genetic algorithms to combine time series forecasting models. Our method does not require a previous phase of training, because it constantly adapts the extent to which the data are coming. To evaluate our proposal, we use three logs extracted from real Web servers. The results show that our proposal often brings the best result and is generic enough to adapt to various types of time series.

The deluge of intelligent objects that are providing continuous access to data and services on one hand and the demand of developers and consumers to handle these data on the other hand require us to think about new communication paradigms and middleware. In hyper-scale systems, such as in the Internet of Things, large scale sensor networks or even mobile networks, one emerging requirement is to process, procure, and provide information with almost zero latency. This work is introducing new concepts for a middleware to enable fast communication by limiting information flow with filtering concepts using policy obligations and combining data processing techniques adopted from complex event processing.

Every day more and more objects are connected to the Internet to sense or actuate in some environment, composing the Internet of Things. IoT platforms will play a key role, as they will be responsible for managing low-level devices and data acquisition processes, and also support the development of new applications. One of the main challenges in IoT platforms will be the search and discovery of resources in large-scale and heterogeneous environments for reuse by other applications to support their specific requirements. In this paper, we propose an elimination-selection algorithm for search and discovery of resources in IoT environments. Our case study considers a real agricultural problem to be solved by the ViSIoT tool. The results show that our approach improves the quality of the proposed solution adding a small time overhead when compared to the TOPSIS algorithm used by ViSIoT.

Science Gateways have been widely accepted as an important tool in academic research, due to their flexibility, simple use and extension. However, such systems may yield performance traps that delay work progress and cause waste of resources or generation of poor scientific results. This paper addresses an investigation on some of the failures in a Galaxy system and analyses of their impacts. The use case is based on protein structure prediction experiments performed. A novel science gateway component is proposed towards the definition of the relation between general parameters and capacity of machines. The machine-learning strategies used appoint the best machine setup in a heterogeneous environment and the results show a complete overview of Galaxy, a diverse platform organization, and the workload behavior. A Support Vector Regression (SVR) model generated and based on a historic data-set provided an excellent learning module and proved a varied platform configuration is valuable as infrastructure in a science gateway. The results revealed the advantages of investing in local cluster infrastructures as a base for scientific experiments.

Scientific Gateways are one of the most important tools for designing and running experiments. Despite the possibility of local operation they are mainly available via online interfaces based on cloud computing instances. Our studies show that cloud machines may not be the best solution to every situation and that the advantages of heterogeneous cluster machines should be considered in scheduling experiments, saving both financial and computational resources, avoiding network delays and managing the infrastructure as needed. We run a variety of scenarios of bioinformatic experiments in three different sets of machines, a workstation, a cluster and cloud platform. Then, using Support Vector Machines (SVM), a nonlinear regression technique over the results, we can define the best machine configuration in terms of processing time according to the input parameters. The results show an approximation with a small error, that can define with good confidence the proper infrastructure to host a instance of the framework Galaxy, used as study case. With a system based on diverse environments, the researchers can properly schedule each set of experiments.

There are many computational options nowadays, capable to serve properly a wide range of users. Web Services, Cloud Computing, Internet of Things among others are some of the ways technology can be offered. To scientific research this variety is fundamental. One of the major concerns in distributed environments such as cloud computing is the size of data and how to handle it's transport. In some cases, it is necessary to save information in hard drives and send it to other locations. The experiments in Protein Structure Predictions (PSP) usually results in very large amounts of data that are difficult to move from one place to another. Besides that, those results have different computational costs, processing time and price range. The current state-of-art shows the limitations the researchers have to face when working with local computers and most of their work is focused on improve the PSP algorithms. We understand that the historic runs in such systems are a reliable source to be studied and we propose a application of machine learning techniques that can be used as a base for a decision maker capable of defining machine configurations and a proper set-up, while keeping Quality of Service (QoS). The results of the kNN algorithm show a strategy capable of predicting with good accuracy the outcomes such as processing time and resulting file sizes. This defines the base for the decision maker mechanism.

Nowadays, it is obvious that technology has revolutionised our lives by supporting us to do complicated jobs. The Internet of Things (IoT) is one of the emerging technologies. One of the most significant current research topics in the IoT is smart city. The smart city includes several applications such as smart home, smart industry and smart mobility. The smart car parking system is an aspect of smart mobility and an important application in smart city projects, because of the rapidly increasing number of cars in urban areas. However, most of the current proposals in smart car parking systems manage the data on the cloud side which is a problem since the system needs to send the raw data from sensor to cloud and receive instructions back: this is expensive in terms of energy and data transmission cost. To tackle this issue we present a proposal to save energy and to reduce the amount of data that is transmitted over the network to cloud by processing closer to source in this paper. The architecture is demonstrated through a case study.

This paper presents a cloud approach for low cost capacity planning evaluations. To perform these evaluations we have to specify and measure the workload on the target system to discover issues and make the necessary adjustments. However, due to high costs, these evaluations are usually done using simulations, which does not consider stochastic effects. We propose to use a tool named PEESOS, a generic and flexible approach to apply real workloads and measure used resources on these real systems. As a proof of concept, our case study use a real ticket sales service to evaluate the influence of scalability in the resource provisioning to show how PEESOS can lower the cost of such real evaluations. The results show the efficiency and savings that we can obtain using PEESOS for large-scale capacity planning evaluations before the real services are deployed. This approach can avoid several problems that real services faces when they launch.

The vision of the Internet of Things is to allow currently unconnected physical objects to be connected to the internet. There will be an extremely large number of internet connected devices that will be much more than the number of human being in the world all producing data. These data will be collected and delivered to the cloud for processing, especially with a view of finding meaningful information to then take action. However, ideally the data needs to be analysed locally to increase privacy, give quick responses to people and to reduce use of network and storage resources. To tackle these problems, distributed data analytics can be proposed to collect and analyse the data either in the edge or fog devices. In this paper, we explore a hybrid approach which means that both innetwork level and cloud level processing should work together to build effective IoT data analytics in order to overcome their respective weaknesses and use their specific strengths. Specifically, we collected raw data locally and extracted features by applying data fusion techniques on the data on resource constrained devices to reduce the data and then send the extracted features to the cloud for processing. We evaluated the accuracy and data consumption over network and thus show that it is feasible to increase privacy and maintain accuracy while reducing data communication demands., Comment: Accepted to be published in the Proceedings of the 7th ACM International Conference on the Internet of Things (IoT 2017)

IoT promises a world where Smart Objects (SO) are able to autonomously communicate and work together to make the (human) user's life easier. Popular approaches for development of IoT applications take for granted that on-object resources are evenly constrained. As consequence, we observe a trend towards a data-feeder architecture in which "smart objects" are simple data gatherers and senders. Raw data is stored and processed in cloud platforms feeding web applications and services. The autonomy of SOs is then compromised as they are not able to operate without these platforms. We propose a framework and architecture for the development of IoT applications where smart objects exhibit autonomy in regards to platforms and human users. We completed the successful evaluation of our proposal with the implementation of a prototype and the execution of a use case for physical resources provisioning.

It is a challenging task to ensure quality in service-oriented systems deployed in cloud computing owing to the dynamicity of its environment. Many approaches have been adopted to identify and evaluate bottlenecks and problems in performance. The most common scenario consists of distributed systems that use a workload capable of enabling clients to exploit the target system in different operational conditions. However, one requirement that tends to be overlooked is to determine how the workload is executed, as software and hardware faults can lead to its mischaracterization. In this paper, a number of problems in the workload generation have been identified and summarized. A new architecture, called PEESOS-Cloud, is proposed which allows these services to be evaluated as well as to improve the ability of the workload so that it conforms with its described characteristics. Experiments in a cloud environment were conducted to show how PEESOS-Cloud works and validate its capabilities. Our experiment also showed that the mischaracterization of the workload leads to poor results, whereas an workload-aware implementation leads to a better performance evaluation.

The vast cloud computing environment holds out good prospects for researchers in the computing technology field. However, with several Cloud providers offering different pricing models, the evaluation and modeling of Cloud environments and applications are getting harder because there is a lack of tools for this task. We propose the CM Cloud Simulator to fill this gap since it provides a comprehensive and dynamic simulation of applications with various deployment configurations and incurs the cost it would require when implemented in a Cloud Provider, according to the cost model of any service provider. The CM Cloud Simulator also provides custom-built cost models through the XML file.

Traditional web services transaction processing mechanism handle exception by forward recovery and backward recovery. These compensation mechanisms often lead to waste of resources and time. In this paper, we propose a framework for predicting outcomes of service executions as part of service compositions which allows to choose service instances that are likely to lead to a successful result in the first instance and thus reduces the need for invoking costly recovery mechanisms. The framework makes use of watchdogs to maintain an awareness of service availability and a pre-coordinator which has oversight of the whole composite Web service and acts as a control center. An analysis of a scenario shows that we cannot only provide users with a more satisfactory result, but also can reduce the overhead costs of resources and waste.

Automatic Web services composition can be achieved using AI planning techniques. HTN planning has been adopted to handle the OWL-S Web service composition problem. However, existing composition methods based on HTN planning have not considered the choice of decompositions available to a problem, which can lead to a variety of valid solutions. In this paper, the authors propose a model of combining a Markov decision process model and HTN planning to address Web services composition. In the model, HTN planning is enhanced to decompose a task in multiple ways and find more than one plan, taking into account both functional and non-functional properties. Furthermore, an evaluation method to choose the optimal plan and experimental results illustrate that the proposed approach works effectively. The paper extends previous work by refining a number of aspects of the approach and applying it to a realistic case study.

Service Oriented Computing is a paradigm for developing software systems as the composition of a number of services. Services are loosely coupled entities, that can be dynamically published, discovered and invoked over a network. The engineering of such systems presents novel challenges, mostly due to the dynamicity and distributed nature of service-based applications. In this paper, we focus on the modelling of service orchestrations. We discuss the relationship between two languages developed under the Sensoria project: SRML as a high level modelling language for Service Oriented Architectures, and StPowla as a process-oriented orchestration approach that separates core business processes from system variability at the end-user’s level, where the focus is towards achieving business goals. A fundamental challenge of software engineering is to correctly align business goals with IT strategy, and as such we present an encoding of StPowla to SRML. This provides a formal framework for StPowla and also a separated view of policies representing system variability that is not present in SRML.

Over the last few years, the number of smart objects connected to the Internet has grown exponentially in comparison to the number of services and applications. The integration between Cloud Computing and Internet of Things, named as Cloud of Things, plays a key role in managing the connected things, their data and services. One of the main challenges in Cloud of Things is the resource discovery of the smart objects and their reuse in different contexts. Most of the existent work uses some kind of multi-criteria decision analysis algorithm to perform the resource discovery, but do not evaluate the impact that the user constraints has in the final solution. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision analyses algorithms and the impact of user constraints on them. We evaluated the quality of the proposed solutions using the Pareto-optimality concept., Comment: Proceedings of the 9th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2016) Shaghai, China, December, 2016

Industrial Control Systems (ICS) and SCADA (Supervisory Control and Data Acquisition) systems are typically used in industries such as electricity generation and supply, gas supply, logistics, manufacturing and hospitals and are considered critical national infrastructure. The evolution of these systems from isolated environments into internet connected ones, in combination with their long service life and real-time nature have raised severe security concerns in the event of a cyber-attack. In this paper, we review the current literature surrounding the threats, vulnerabilities, exploits and existing approaches to securing vulnerable SCADA systems. We then focus specifically on the development of a distributed online runtime monitor to detect violations of safety properties. We conclude with suggestions for further research needed to progress the state of the art in the area of distributed online runtime verification of SCADA systems.

Recently, the number of devices has grown increasingly and it is hoped that, between 2015 and 2016, 20 billion devices will be connected to the Internet and this market will move around 91.5 billion dollars. The Internet of Things (IoT) is composed of small sensors and actuators embedded in objects with Internet access and will play a key role in solving many challenges faced in today's society. However, the real capacity of IoT concepts is constrained as the current sensor networks usually do not exchange information with other sources. In this paper, we propose the Visual Search for Internet of Things (ViSIoT) platform to help technical and non-technical users to discover and use sensors as a service for different application purposes. As a proof of concept, a real case study is used to generate weather condition reports to support rheumatism patients. This case study was executed in a working prototype and a performance evaluation is presented., Comment: International Journal of Services Computing (ISSN 2330-4472) Vol. 4, No.1, January - March, 2016

Formation of collaborative settings (virtual organizations, collaborative network organizations, etcetera) is hindered by the lack of trust between unfamiliar but potential partners. Gamification is the application of game design principles to non- game environments. We claim that incorporating the concepts of gamification into Virtual Organisation Breeding Environments (VBEs) leads to building trust between VBE members, as well as motivating them to be proactive members of the VBEs that contribute towards the flourishing of VBEs. This paper explores a novel gamified approach, supported by a suitable methodology, to address the relatively neglected concept of ‘preparedness’ of VBEs.

Development of IoT applications relies heavily on middleware architectures. Multiple solutions have been proposed using service and agent paradigms, which consequently impose a specific application development model. We observe a trend towards a data-feeder approach in which smart objects are simple data gatherers and senders. This approach reduces the autonomy of smart objects and brings privacy concerns about the data and service manipulation by remote applications and services. We propose a new approach for middleware and application development for smart objects based on web agents and services. Specifically, we integrate services as part of activities included in roles of a web agent allowing us to keep control of the service and data generated on-object, in contrast to delegating to remote applications or services. In addition, our P2P-inspired method for service discovery gives autonomy to smart objects since a central directory is not required to interact with others. We completed an initial implementation of our architecture and demonstrated its feasibility in a case study in the smart home domain.