Service-Oriented Computing – ICSOC 2022 Workshops

The spelling correction problem, the task of automatically correcting misspellings in a text, is critical in natural language processing (NLP). Although it can be considered a standalone task, in most cases, it is an integral component of various NLP tasks as a preprocessing step since a dataset with typos can lead to erroneous results. Many previous automatic spelling correctors use a dictionary, independently search the word in a predefined list of words, and recommend the most similar one without considering the context. Even though these models’ output may be a correctly spelled word, it could be semantically incorrect. Therefore, some correctors consider the context when correcting typos based on language models. However, only employing the language model is insufficient, and the corrected word should be similar to the misspelled word. In our approach, we select a candidate for the typo based on masked language model output, character-level similarities, and edit distance. Exploiting the combination of the masked language model, character-level similarities, and edit distance assists us in recommending similar context-related candidates. We have used recall (correction rate) as our evaluation metric, and the results demonstrate a considerable improvement compared with previous studies.

Deep neural networks (DNNs) perform exceptionally well in many vision tasks, including image classification, annotation, and object recognition. However, these networks are like a black box, and high-quality training datasets are required for deep learning models to achieve high performance. Due to the high cost of collecting a vast number of data samples, data augmentation techniques have been employed in many vision applications. Data augmentation aims to increase the dataset size without collecting new data while introducing variability. One of the means of augmenting the image data is by employing image transformations such as flipping, clipping, or rotation. Activation maps, also known as feature maps, illustrate how the filters are applied to the input image. The objective of visualizing a feature map for an input image is to comprehend what input features are captured in the feature maps. In this paper, we apply various transformations on images and investigate their effect on the multiple convolutional layers (at low, middle, and high levels) by employing intermediate feature map visualizations. We use the famous deep learning-based pre-trained network, VGG-16. Finally, we compare the visualization results of the image transformations at multiple levels and analyze their differences to evaluate the validity of these networks.

Predictive process monitoring approaches aim to make predictions about the future behavior for running instances of business processes, such as next activity or remaining time. Most of these approaches use single object type event logs as if the business process is operating in isolation. Whereas, in an organization, several instances of different processes related to a set of objects can be executed at the same time and may interact with each other. This paper investigate the use of object-centric event logs as they offer information about events and their related objects, allowing access to a global view about the running processes in an organization. We propose an object-centric predictive approach considering interactions between different object types. The proposed approach is evaluated on a publicly available object-centric log. The analysis of the results shows that using additional features (i.e., several object types’ information) can generally help increase prediction performances.

Process discovery aims to learn process models from observed behaviors, i.e., event logs, in the information systems. The discovered models serve as the starting point for process mining techniques that are used to address performance and compliance problems. Compared to the state-of-the-art Inductive Miner, the algorithm applying synthesis rules from the free-choice net theory discovers process models with more flexible (non-block) structures while ensuring the same desirable soundness and free-choiceness properties. Moreover, recent development in this line of work shows that the discovered models have compatible quality. Following the synthesis rules, the algorithm incrementally modifies an existing process model by adding the activities in the event log one at a time. As the applications of rules are highly dependent on the existing model structure, the model quality and computation time are significantly influenced by the order of adding activities. In this paper, we investigate the effect of different ordering strategies on the discovered models (w.r.t. fitness and precision) and the computation time using real-life event data. The results show that the proposed ordering strategy can improve the quality of the resulting process models while requiring less time compared to the ordering strategy solely based on the frequency of activities.

Supply Chain Risk Management (SCRM) is necessary for economic development and the well-being of society. Therefore, many researchers and practitioners focus on developing new methods to identify, assess, mitigate and monitor supply chain risks. This paper developed the Risk Management by Counterfactual Explanation (RMCE) framework to manage risks in Supply Chain Networks (SCNs). The RMCE framework focuses on monitoring SCN, and in case of any risks eventuating, it explains them to the user and recommends mitigation strategies to avoid them proactively. RMCE uses optimisation models to design the SCN and Counterfactual Explanation (CE) to generate mitigation recommendations. The developed approach is applied to an actual case study related to a global SCN to test and validate the proposed framework. The final results show that the RMCE framework can correctly predict risks and give understandable explanations and solutions to mitigate the impact of the monitored risks on the case study.

Several decision points exist in business processes (e.g., whether a purchase order needs a manager’s approval or not), and different decisions are made for different process instances based on their characteristics (e.g., a purchase order higher than €500 needs a manager approval). Decision mining in process mining aims to describe/predict the routing of a process instance at a decision point of the process. By predicting the decision, one can take proactive actions to improve the process. For instance, when a bottleneck is developing in one of the possible decisions, one can predict the decision and bypass the bottleneck. However, despite its huge potential for such operational support, existing techniques for decision mining have focused largely on describing decisions but not on predicting them, deploying decision trees to produce logical expressions to explain the decision. In this work, we aim to enhance the predictive capability of decision mining to enable proactive operational support by deploying more advanced machine learning algorithms. Our proposed approach provides explanations of the predicted decisions using SHAP values to support the elicitation of proactive actions. We have implemented a Web application to support the proposed approach and evaluated the approach using the implementation.

Blockchain, a type of distributed ledger technology, has revolutionized the digital economy such as cryptocurrencies and supply chain management with its transparency, immutability, and decentralization properties. In addition, smart contracts are introduced to the blockchain to provide programmability removing third parties for administration. Although promising, blockchains and smart contracts are closed technologies meaning they have no interaction with the external world where real-world data and events exist, i.e., off-chain data. It becomes more challenging when the off-chain data is unstorable onto the blockchain due to data volume and privately maintained by third parties for security and confidentiality. In this paper, we address the problem of enabling a private blockchain platform to access privately owned sensitive off-chain data (i.e., DNA fingerprinting). This off-chain data is used for the traceability of products (i.e., products’ origin) along the supply chain with a real-world livestock use case. To this end, we present a livestock blockchain oracle (LBO) as a service to mitigate the accessibility issue and automate the process of verifying purchasable products for livestock DNA fingerprinting verification. We have conducted an evaluation study using real-world livestock data from third-party service providers. Results based on the livestock product information and registered DNA service providers show that LBO is a reliable and responsive decentralized oracle blockchain for verification.

Context: In today’s health care, multi-modal image registration increasingly important role in medical analysis and diagnostics. Multi-modal image registration is a challenging task because of the different imaging conditions that changes from one imaging modality to another.Objective: The purpose of this work is to determine the current state of the art in the field of medical image registration shedding light on techniques that have been used to register medical image combinations from different modalities and the importance of combining different modalities in automatic way in the medical domain.Method: To fulfill this objective we chose a Systematic Literature Review (SLR) as method to follow. Which allows to collect and structure the information that exists in the field of multi-modal image registration.Results: Several automatic solutions based on different registration techniques were proposed according to each specific modality combination.Conclusion: The results provide the following conclusions: First, the machine learning in the recent years plays an important role in the automatic registration process. An important number of research propose a learning-based registration solution. Second, There few solutions in literature that tackle the automatic registration of histology - CT modality combination. Finally, the existing research work propose registration solutions for only combination of two modalities. A very few number of work suggest a tri-modality combining.

From antiquity until nowadays, educational systems have evolved in parallel with major social and economic changes, integrating pedagogical and technological innovations into the educational ecosystem and leading to the advent of a new educational revolution, called Education 4.0. The Education 4.0 paradigm allows both, to support educational organizations in the adoption of pedagogical and digital transformations, and at the same time to meet the needs of new generations of learners and teachers. The increasing use of ICT and the power of the Internet have fostered the emergence of a new generation of learners for whom technology is an integral part of their lives and therefore of their learning style. To respond effectively to the specific needs of these new learner profiles, educational organizations must adopt significant and ongoing transformations in the way we teach and learn. Enabling student-centered teaching, personalized learning paths, and access to a variety of heterogeneous educational resources have made educational processes too complex for the educator to effectively monitor the individual progress of each student. It becomes necessary to automate the management of learning processes to provide better answers to the evolving and specific needs of the different actors (students, educators, training program managers, companies, etc.). This research project aims at proposing an autonomous architecture of Cyber-Physical systems for Education 4.0. This architecture responds to the needs of educational systems, integrating digital technologies for the development of heterogeneous learning environments, capable of meeting the needs of a plurality of learners with different profiles and supporting teachers and training managers in the management of learning processes. This architecture offers the possibility of autonomous management of learning processes to analyze the progress of students and to prescribe the necessary recommendations to increase the chances of success, facilitating the mission of teachers.

The creation of adaptive and reconfigurable Service Oriented Architectures (SOA) must take into account the unpredictability of the Internet and of potentially buggy software, and thus requires monitoring subsystems for detecting degradations and failures as soon as possible. In this paper we propose EXOGEM, a novel and lightweight monitoring framework for REpresentational State Transfer (REST) Application Programming Interfaces (APIs). EXOGEM is an extension to the mainstream code generator OpenAPI Generator, and it allows to create a monitoring subsystem for generated APIs with limited changes to the usual API development workflow. We showcase the approach on a smart grid testbed, where EXOGEM monitors the interaction of a heatpump with a system that optimizes its operations. Our measurements estimate EXOGEM’s comparable to the usage of HTTPS when the server is not flooded with requests. Moreover, in one experiment EXOGEM was used to identify high load, and to activate computational elasticity. Together, this suggests that EXOGEM can be a useful monitoring framework for real-life systems and services.

Self-adaptive approaches aim to address the complexity of modern computing generated by the runtime variabilities and uncertainties. In this context, MAPE-K loop is considered today a major approach for the design and implementation of self-adaptive solutions because it captures in a systematic way the main steps of the adaptation process: (1) Monitor the execution context, (2) Analyze the monitored context, (3) Plan the appropriate adaptation strategy, (4) Execute the adaptation strategy, all these steps using a common Knowledge about the context. Implementations of MAPE-K loops may be particularly complex, domain specific, as well as case study dependent. In this paper, we provide a preliminary analysis of MAPE-K loops in various artifacts in different application domains (i.e., cloud - Hogna and TMA, cyber-physical systems - TRAPP and AMELIA, Internet of Things - DeltaIoT). Our main objective is to outline the similarities and differences among the available implementations of MAPE-K control feedback loops in self-adaptive systems. Additionally, the application domains of the considered examples are highly related, so that solutions in one domain may trigger developments in others. We also provide an insight into MAPE-K loops to enable researchers and practitioners to use, re-use, improve the available solutions.

With the large scale and user demands on modern cloud systems there is a need for autonomous approaches to self-healing. When there is no operator in the loop for self-healing actions, it is crucial to ensure that the actions taken are safe and effective. In this paper we propose SASH: Safe Autonomous Self-Healing, which uses surrogate models to estimate the safety and effectiveness of self-healing actions. SASH uses system metrics, configuration parameters, domain information and available actions to decide on the best fault remediation action or combination of actions. The performance of the action(s) are then verified through a validation block that updates the knowledge base with how the actions performed for that fault. This data is then used to update the safety and effectiveness estimation algorithm. The results show the framework is able to successfully remediate faults with a low number of actions and with protection against unsafe actions.

Water scarcity has become a global issue affecting many countries, particularly in rural and desert areas. In this research, a fuzzy computational analysis is proposed for IoT smart irrigation systems. With the increase in the number of connected devices, security and data privacy are becoming an important challenge in today’s IoT applications, especially as most of these tools are increasingly vulnerable. We present a combination of fuzzy logic for decision analysis and secure real-time data collection. Various sensors are distributed in the fields for data collection: temperature as well as humidity. The transfer of these data is ensured following a secured distributed architecture via Blockchain. A decision concerning the position of the valve is taken following the analysis through the Mamdani fuzzy logic model. For the design of the fuzzy system, a rule base as well as a modelling of the different input and output parameters are proposed. The Blockchain technology allows access to trusted devices only. The results obtained are promising in terms of water consumption, which has been reduced by more than 60% compared to manual irrigation. On the security side, our solution ensures the transparent identification of the different trusted nodes.

Software systems need to be maintained and frequently updated to provide the best possible service to the end-users. However, updates sometimes, cause the system or part of it to restart and disconnect, causing downtime and potentially reducing the quality of service.In this work we studied and analyzed the case of a large Nordic company running a service-oriented system running on edge nodes, and providing services to 270K IoT devices. To update the system while minimizing downtime, we develop a smart edge service update scheduler for a service-oriented architecture, which suggests the best possible update schedule that minimizes the loss of connections for IoT devices.Our approach was validated by applying the scheduling algorithm to the whole system counting 270k edge nodes distributed among 800 locations.By taking into account the topology of the software system and its real-time utilization, it is possible to optimize the updates in a way that substantially minimizes downtime.

The virtualization of network functions aims to replace traditional network functions running on proprietary middleboxes with software instances running on general-purpose virtualization solutions, looking for more flexible, scalable and sustainable networks. However, despite the availability of platforms and technologies that enable its realisation, there are still technical challenges that have to be addressed to obtain those benefits. One of the challenges is the efficient placement of network functions, considering, for instance, the energy footprint among other constraints and available resources. This paper proposes an energy-aware virtual network function placement and resource-allocation solution for heterogeneous edge infrastructures that considers the computation and communication delays according to the virtual network functions’ location in the infrastructure. The solution has been integrated with the ETSI-sponsored project Open Source Management and Orchestration (OSM) as an extension that allows the configuration of virtual network functions and their subsequent resource allocation and deployment at the edge, minimizing energy consumption and ensuring the quality of service. Applied to the deployment of augmented reality services in different scenarios, the results show up to a 51% reduction in energy consumption compared to the default OSM placement and quality of service compliance in all scenarios considered.

Estimating the number of people within a public building with multiple entrances is an interesting problem, especially when limitations on building occupancy hold as during the Covid-19 pandemic. In this article, we illustrate the design, prototyping and assessment of an open-source distributed Cloud-IoT service that performs such a task and detects crowd formation via EdgeAI, also accounting for privacy and security concerns. The service is deployed and thoroughly assessed over a low-cost Fog infrastructure, showing an average accuracy of 94%.

The number of computing devices, mostly smartphones is tremendous. The potential for distributed computing on them is no less huge. But developing applications for such networks is challenging especially as most middleware solutions for distributed computing are tailored to managed grids and clusters, so they lacks the elasticity needed to deal with the difficult conditions brought by multi-hops, mobility, heterogeneity, untrustability, etc. To solve this, several middleware were released, but none of them feature workable deployment solutions. This paper presents the deployment service of the Idawi middleware, which implements a fully decentralized and automatised deployment strategy into a Open Source middleware tailored to enabling distributed computing in difficult networking conditions like in the IoT/fog/edge.

As Artificial Intelligence (AI) planning is utilised in industry to address complex real-world problems, the need for the construction of advanced and deployable AI planning systems emerges. This task, however, proves to be difficult due to a general lack of established mechanisms in AI planning for system design, interoperability, and deployment. In this context, we provide an overview of key engineering challenges that one is faced with when developing and using systems that incorporate AI planning functionalities. To help address the challenges, we propose to leverage service-orientation together with architectural patterns. In particular, we identify a set of planning functionalities, and we present an initial concept for engineering the planning functionalities as services. Having planning services would enable not only to quickly compose and deploy service-oriented planning systems but also expand them with more features as required by application domains.

Decentralised data exchanges are promising alternatives to monolithic data lakes and warehouses which are typically emerging around complex service solutions. In theory, this removes some of the bottlenecks of traditional data management solutions. In practice, the road towards achieving such goal is a long way ahead. In this work, we provide an industry perspective on the implications for such work, with a focus on metadata management; the work in question draws from an in-vivo action research approach we enacted at a major German automotive company that is transitioning to an internal decentral data market. Our results provide insight into an industry perspective on the requirements for metadata management. Additionally, we propose and validate a solution design for metadata management in decentralised data exchanges based on semantic web service technology.

Function as a Service (FaaS) development has numerous benefits for application deployment, management, and maintenance. However, the lack of control over the infrastructure and, often, over the FaaS platform itself, makes it necessary to look for external solutions that allow the operation of the application to be adapted to different requirements or changing execution conditions. In a FaaS application, the quality of service (QoS) is determined by the characteristics of the functions executed to perform each workflow operation. Deciding the most suitable functions providing a QoS is a complex process due to the high variability of possible function implementations, each giving different qualities. Leaving this task in the hands of the developer is not a good solution and makes it difficult to program the application. We present FUSPAQ, a framework for working with serverless architectures, which can automatically select the best functions executed at runtime to satisfy specific QoS requirements. With this objective, a Software Product Line approach is used, modeling the application’s tasks and operations using Feature Models that specify the variability of functions that can perform the same operation as a family of functions. We use Z3, a cross-platform satisfiability modulo theories (SMT) solver, to generate optimal configurations. As requirements can change over time, the system automatically adapts to these changes to continue maintaining the desired QoS. We test our approach with different QoS parameters, and analyse the value added to serverless frameworks.

The generation of code templates from web API specifications is a common practice in the software industry. However, existing tools neglect the dependencies among input parameters (so called inter-parameter dependencies), extremely common in practice and usually described in natural language. As a result, developers are responsible for implementing the corresponding validation logic manually, a tedious and error-prone process. In this paper, we present an approach for the automated generation of code for inter-parameter dependencies in web APIs. Specifically, we exploit the IDL4OAS extension for specifying inter-parameter dependencies as a part of OpenAPI Specification (OAS) files. To make our approach applicable in practice, we present an extension of the popular OpenAPI Generator tool ecosystem, automating the generation of Java and Python code for the management of inter-parameter dependencies in both servers and clients. Evaluation results show the effectiveness of the approach in accelerating the development of APIs, generating up to 9.4 times more code than current generators, while making APIs potentially more reliable.

The DevOps (Development Operations) approach to software development has been progressively adopted in the last decade, to support the development, testing, continuous integration and deployment of software in an integrated manner. More recently, several extensions have been proposed, one being the BizDevOps (Business DevOps) approach, where business people are also integrated into the development effort, in order to further help in closing what is known as business-systems gap. This gap makes new software developments and changes to existing ones to require important efforts, in general without fulfilling the business area expectations about development and production deployment times. Support for the organization’s business processes has been increasingly provided by Business Process Management Systems (BPMS), often integrating internal and/or external services/microservices. Microservices are key allies for the BizDevOps approach realizing business processes providing the application’s flexibility and timely results needed. In this paper we present a BizDevOps proposal to support business process microservices-based application. We extended a previous proposal based on services, shifting the focus to microservices development, testing, continuous integration and deployment (DevOps) within the already business-IT alignment (BizDev) view we provided.

Blockchain is a secure and distributed technology which is growing in popularity since it enables the traceability, immutability and transparency of data. However, monitoring blockchain networks requires experts who have vast experience in this technology. To address this challenge, in this paper we present a low-code tool, which allows inexperienced blockchain developers to define graphical flows that specify inputs, outputs and the logic necessary to monitor in real time the elements of a blockchain network. This tool has been successfully applied to a vaccine delivery scenario, facilitating the monitoring of a smart contract that stores temperature measurements of a certain vaccine. As a result, when a new transaction is mined in the blockchain network, it will be promptly notified and sent to the different data sinks specified in the flow modeled by a non-expert in blockchain.

With the widespread adoption of the Internet of Things (IoT), billions of devices are now connected to the Internet and can reach computing facilities along the Cloud-IoT continuum to process the data they produce. This has led to a dramatic increase in the amount of deployed IoT-based applications as well as the data they need to crunch. Those applications often have Quality of Service (QoS) requirements to be met by determining suitable placements for all services they are made of and all data they manage, as well as software-defined routings across the IoT and all different application components. In this context, we aim at supporting the QoS- and data-aware placement as well as management of multi-service applications onto Cloud-IoT resources. We describe our main objectives and discuss some preliminary results of our research.

New applications and new needs to be dealt with arise every day in nowadays society of the Internet of Things (IoT) and smart everything. Even though there are many applications for these domains, most of them do not facilitate context integration in their data processing. There are applications that present solutions to this problem, however, they are either too domain-specific or too general, and therefore cannot be easily reused in other software products. To fill this gap, we propose a context-aware framework that makes use of an ontology reusable for multiple IoT and smart domains. The framework will also provide an integration mechanism and an event pre-processing system, which will facilitate the work of developers. The latter will make use of the ontology by facilitating the definition of contextual events and their integration into decision-making systems. All this, together with our complex-event processing decision-making system will finally make possible to offer intelligent context-aware services and applications.

In the context of IoT systems, the use of services is a key element in managing system complexity. Concepts such as service-oriented computing/architecture or quality of service (QoS) are present in many IoT systems and are the aim of several studies. However, the analysis and assessment of the behaviour of these concepts requires the deployment of the IoT system, implying high investments in hardware and software. Thus, in order to decrease these costs, the system can be simulated. In this regard, IoT simulations have been tackled focusing on low level aspects such as networks, motes, etc. rather than on high-level concepts, such as services or computing layers. In this proposal, a model-driven development approach named SimulateIoT is proposed to model, generate code and deploy IoT systems simulations from a high abstraction level (from models). Besides of modeling the IoT environment call generation, the IoT system could be simulated. From these simulations it is possible to assess QoS-related aspects such as the delay or jitter between two nodes, the variation of delay or jitter over time, the use of bandwidth, the packet loss, the variation of these parameters as the system changes (e.g. increase of sensors), check whether Service level agreements (SLA) are met, etc. In order to show the proposal, a case study, focused on an Internet of Vehicles (IoV) system is presented.

We live in an Internet-connected world, where tens of billions of smart entities are constantly sending information, the Internet of Things (IoT). In some cases, the communication of data messages is straightforward. Devices are connected in the same ecosystem, where it is pretty easy to agree on the communication data format. However, when devices have to communicate information to outside environments, each manufacturer defines its own data communication format. In the IoT domain, these cases are not covered extensively, and information homogenization must be done. In this thesis, we propose an ontology, an abstract interpretation of domain concepts and data, aiming to provide a solution for data heterogeneity in the domain. For this regard, we also propose a working environment where operators can manipulate all the system’s information, developed using a microservices architecture.

When handling long-tailed changes (LTC) in business processes, there is a tension between flexibility and compliance. The tension has been addressed in our previous work, while compliance needs further discussion. Compliance is often expressed in terms of functional and non-functional constraints. This paper studies the modeling and verification of functional constraints.

Process-aware information systems are valuable for automating business tasks leading to cost reduction and efficiency. This research aims to advance the state of the art in process management towards autonomic process performance improvement by contributing control-flow change recommendations for process instances that is supporting automatic change enactment as a response to predicted KPI violations. Towards that goal, the related literature has been investigated in two literature review studies and research gaps have been identified. The proposed generic architecture provides a feedback loop that enables evaluation of the resulting recommendations for future process instances. We also present the current state of the research and future plans.

Board-Based Collaborative Work Management Tools (BBTs) like Trello and Microsoft Planner are commonly used as low-code tools to manage the data and processes that support the services an organization provides. Their main advantage over other process execution platforms like traditional business process management systems is that they can be adapted by the users themselves. This is particularly useful in agile and changing environments. However, this flexibility carries some risks, such as badly designed boards, or inefficient or improper board uses (even with quality board designs). Board Miner is a library that leverages board event logs, which capture all the activity that has taken place within the boards, to analyze how boards are used and evolve over time. Therefore, the user will be able to understand the behavior that is hidden behind the structure of the board, which facilitates the detection of inefficient uses or errors that reduce the quality of the board.

Recorded event data of processes inside organizations is a valuable source for providing insights and information using process mining. Most techniques analyze process executions at detailed levels, e.g., process instances, which may result in missing insights. Techniques at detailed levels using detailed event data should be complemented by techniques at aggregated levels. We designed and developed a standalone tool for diagnostics in event data of business processes based on both detailed and aggregated data and techniques. The data-driven framework first analyzes the event data of processes for possible compliance and performance problems, e.g., bottlenecks in processes. The results are used for aggregating the event data per window of time, i.e., extracting features in the time series format. The tool is able to uncover hidden insights in an explainable manner using time series analysis. The focus of the tool is to provide a data-driven business process analysis at different levels while reducing the dependencies on the user’s domain knowledge for interpretation and feature engineering steps. The tool is applied to both real-world and synthetic event data.

Blockchain is a distributed, secure and leading technology that enables the immutability, traceability and transparency of data. Nevertheless, integrating blockchain network monitoring with other systems is a difficult task that requires a vast knowledge of the technology. To deal with this challenge, in this demo we present Node4Chain, a novel extension of the Node-RED low-code tool that allows for defining graphical flows that specify data inputs, outputs and processing logic needed to monitor in real time blockchain networks, such as Ethereum, Binance or Polygon, as well as integrating it with other systems and technologies.

Recent developments in the automotive industry show a rising demand for in-car gaming and entertainment. Series-produced vehicles offer high-performance hardware, displays, sensors, and actors, which can be used for gaming. This trend was recently confirmed in particular by the Mercedes-Benz Group AG, which integrated the racing game SuperTuxKart in its series-produced Mercedes-Benz CLA Coupé. However, integrating and interacting with C++ games, such as SuperTuxKart, is cumbersome as developers need deep technical knowledge about the internal structure of the game and its APIs. This makes development processes time-consuming, knowledge-intensive, and error-prone. To overcome this issue, we developed (i) a domain model for SuperTuxKart and (ii) a REST API supporting this model. The developed API serves as an abstraction layer that enables developers as well as researchers to integrate SuperTuxKart in a service-oriented manner into other applications.

Quantum Computing has started to demonstrate its first practical applications. As the technology develops to a point of maturity that allows quantum computers to expand commercially, large companies such as Google, Microsoft, IBM and Amazon are making a considerably effort to make them accessible through the cloud so that research and industry initiatives can test their capabilities. The characteristics of this paradigm and the lack of mature tools still make the process of defining, implementing, and running quantum, or hybrid classical-quantum software systems difficult compared with the procedures used for pure classical ones. To address this lack, we present a demonstration of a method for defining quantum services and the automatic generation of the corresponding source code through an extension of the Open API Specification. In this demo we present an extension that enables developers to define quantum services with a high abstraction level, link them with quantum circuits, and generate the source code of the service to be deployed in a quantum computer in the same way they do for classical services.

Hybrid quantum-classical applications are often implemented as monolithic applications that comprise various tightly-coupled classical and quantum tasks. However, the lifecycle of such applications can benefit from using service-oriented architectures, as they simplify scalable and resilient deployments and improve development processes by decoupling complex processes into more comprehensible work packages. In this demonstration, we (i) introduce Quokka, a service ecosystem that facilitates workflow-based development and execution of quantum applications by providing dedicated services for implementing each task in variational quantum algorithms. Further, (ii) we show how it can be used to orchestrate an example quantum application using workflows.

Over the last years, we faced an exponential growth of illegal online market services in the Dark Web, making it easier than ever before of acquiring illicit goods online via a simple service interaction. To study and understand this emerging illegal services economy, we developed a trend analysis and (dark-)web services monitoring tool: SENSEI, which stands for ‘Scraper for ENhanced analySis to Evaluate Illicit trends’. SENSEI extracts specific service transaction trends and analyses the human behaviours behind, to produce symmetric insights on specific service transaction habits from both customers and vendors on the Dark Web. Moreover, a trend analysis tool is provided to discover and typify relationships among different criminal activities and hence provide evidence and support investigation activities and Law Enforcement Agencies (LEAs) detecting criminal operations.

One of the most challenging tasks that emerged in the last few years is linking records from distinct organizations, while maintaining privacy. Private Record Linkage is by definition a resource demanding task. Considering the continuously increasing volumes of data that have to be linked is a fact leading us to develop solutions that will conclude the process in a timely manner. To this end, we demonstrate PRES $$^3$$ 3 , a system for performing private record linkage based on a service-oriented architecture, harvesting the power of Apache Spark.

Crowdsourcing wireless energy services is a novel convenient alternative to charge IoT devices. We demonstratepeer-to-peer wireless energy services sharing between smartphones over a distance. Our demo leverages (1) a service-based technique to share energy services, (2) state-of-the-art power transfer technology over a distance, and (3) a mobile application to enable communication between energy providers and consumers. In addition, our application monitors the charging process between IoT devices to collect a dataset for further analysis. Moreover, in this demo, we compare the peer-to-peer energy transfer between two smartphones using different charging technologies, i.e., cable charging, reverse charging, and wireless charging over a distance. A set of preliminary experiments have been conducted on a real collected dataset to analyze and demonstrate the behavior of the current wireless and traditional charging technologies.

Biohazards and noise risks in wastewater treatment plants are a real concern. These stations generate risks of gas inhalation due to contaminants carried by the wastewater and exposure to dangerous high noise generated by the work equipment. The stations are equipped with sensors that are capable of monitoring ambient gas levels and noise levels. This is not sufficient and geolocation of the operators is necessary. However, indoor geolocation is still a problem due to limited GPS accuracy. There are alternatives such as Bluetooth, which allow more accurate geolocation to be obtained. In this work, we present a IoT system that allows to geolocate the operators indoor through Bluetooth beacons and cross-reference it with the information from gas and noise sensors to prevent occupational risks.

As cities expand and the pace of life accelerates, modern logistics services need to explore new air routes. While air logistics is fast and convenient, it also faces many problems, such as time-sensitive dynamic order requirements, the limited battery power of unmanned aerial vehicle (UAV) and so on. Besides, UAVs need to cooperate and make decisions in real time to meet city-wide logistics needs. However, limited by the computing power, it is difficult to process massive logistics demands in real time. In this paper, we propose an Air Logistics Service-Oriented Digital Twin Network based on collaborative decision model, called AlsoDTN. Firstly, in order allocation task, we establish an information fusion mechanism based on Transformer architecture to obtain the optimal order. Secondly, to adapt to the long-term route planning task, we use multi-agent deep reinforcement learning technology to make UAVs cooperate with each other. Experimental results show that our algorithm has an improvement of 18% relative to baseline algorithm.

Internet distributed systems are subjected to a new transformation thanks to the success of Cloud Computing.

More and more enterprises are increasingly gaining technical and economic benefits from the global cloud marketplace.

RESTful web APIs nowadays may be considered the de facto standard for web integration, since they enable interoperability between heterogeneous software systems in a standard way, and their usage is widespread in industry. Testing these systems thoroughly is therefore of utmost importance: a single bug in an API could compromise hundreds of services using it, potentially affecting millions of end users. In recent years, there has been an explosion in the number of tools and approaches to test RESTful web APIs, making it difficult for researchers and practitioners to select the right solution for the problem at hand.In this tutorial, we overview some of the main industrial and research tools for testing RESTful APIs, with a primarily practical approach. We analyze different testing tools and frameworks from three different perspectives: a) manual vs automated testing; b) black-box vs white-box testing; and c) online vs offline testing. First, we show the capabilities of industrial tools and libraries for manual testing of web APIs, including REST Assured [3] and Postman [1]. Then, we delve into some of the main research tools for automatically generating test cases for RESTful APIs such as RESTler [6], EvoMaster [5], and RESTest [7]. Finally, we overview existing industrial Testing as a Service (TaaS) platforms such as RapidAPI [2] and Sauce Labs [4], and we show the latest research advances on the provision of continuous online testing of RESTful APIs (including automated test generation and execution) with the RESTest testing ecosystem [8]. We finish the tutorial outlining some of the most pressing research challenges in the domain of web API testing automation, which will hopefully open a range of opportunities for future researchers working on the topic.