Effective business process execution requires the integration of both process logic and business data. While recent approaches explore the potential of Large Language Models (LLMs) in automating process modeling, their applicability is limited in real-world scenarios where textual descriptions — often authored by non-experts — are complex or incomplete. Moreover, these works primarily focus on the control-flow perspective and overlook the critical role of data modeling and execution. In this paper, we propose a hybrid and decomposed approach to automatically generate executable process and data models from text using LLMs. Our method modularizes the task: the LLM clarifies and enriches the description, then extracts both process and data elements, which are combined into a unified model. Structured algorithms ensure robust and executable outputs. Evaluation results demonstrate that our approach improves model completeness, clarity, and efficiency compared to existing methods.

Edge service monitoring is essential for ensuring the robustness and efficiency of service executions, where predictive monitoring enables proactive detection of potential service violations. Current approaches for predictive monitoring, which mostly adoptSignalTemporalLogic (STL) specifications for requirements representation and evaluation, primarily focus on deterministic signals, and thus, may lack probabilistic guarantees for uncertainty interpretation. To address these challenges, this paper proposesBayesianSTL(BSTL), an extension ofSTLthat enables probabilistic reasoning over stochastic signals. Specifically,BayesianNeuralNetworks (BNNs) are employed to generate sequences of posterior probability distributions, offering more comprehensive predictive insights compared to traditional point- or interval-based methods with deterministic sequential predictions. Uncertainty interpretation over these distribution predictions is achieved by a novel expected robustness metric that jointly quantifies both the degree and probability of service satisfaction. Thereafter, aBSTL-based predictive monitoring framework is developed, where a service constraint is formally specified by aBSTLformula and interpreted with both qualitative and quantitative semantics. Besides, confidence levels and constraint thresholds ensuring robust satisfaction of aBSTLformula are rigorously estimated. Extensive experiments on publicly available datasets demonstrate thatBSTLoutperforms baseline techniques in terms of expressiveness, robustness, and applicability.

Incident prediction is critical in industrial settings to prevent disruptions and optimize operations by anticipating failures. To this end, equipment logs are commonly utilized and converted into an XES-like log format, wherein each event is associated with a single case object (i.e., equipment) reporting its status. However, the resulting event log overlooks other data sources, such as logs of pre- and post-incident processes. These logs report activities applied to the equipment's related objects (e.g., hardware, software) in non-structred way, presenting challenges when using the XES-like log format. This paper introduces a meta-model to integrate and represent these diverse data sources in formalized format. To this end, we propose a domain-specific object log tailored to the incident-monitoring domain to represent multiple equipment-related objects. This meta-model was validated with a real-life dataset, showing how it provides an effective and structured representation of incident-related data.

Zsolt T. Kardkovács, Lynda Djennane, Anna Field, Boualem Benatallah, Yacine Gaci, Fabio Casati, Walid Gaaloul. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Battery lifetime remains a central constraint in scaling LoRaWAN deployments across diverse IoT applications. We propose a lightweight dilated causal Convolutional Neural Network (CNN) designed to forecast per-node energy consumption with high temporal fidelity. Unlike recurrent models, our approach captures both transient spikes and long-range patterns without sequential overhead, enabling efficient edge deployment. Trained on a 12-month NS-3 simulation dataset encompassing smart lighting, environmental monitoring, waste management, and agriculture, the model achieves 96.5% forecasting accuracy with mean absolute error below 0.3, improving over SARIMA and LSTM baselines by 55% and 32% respectively. We integrate this predictor into an end-to-end energy optimization pipeline where on-device inference executes every 15 minutes with under 5 ms overhead. Forecasts drive adaptive duty-cycling, transmission slotting, and data rate control, extending device lifetime by 20%, halving collision rates, and improving fairness by 15%. Real-world validation on ten STM32F407VG microcontrollers and a commercial RAK7258 gateway confirms practical feasibility: inference completes within 0.8 ms with 4.4 mW peak power draw and 95.6% packet delivery ratio. These results demonstrate the CNN's suitability for real-time, edge-centric forecasting and its potential for enabling sustainable, intelligent LoRaWAN networks.

The Web 3.0 and metaverse can empower intelligent application of Connected Autonomous Vehicles (CAVs). The adoption of edge computing can contribute to the low latency interaction between CAVs and the metaverse. Microservices are widely deployed on edge networks and the cloud nowadays. User’s requests from CAVs are typically fulfilled through the composition of microservices, which may be hosted by contiguous edge nodes. Requests may differ on their required resources at runtime. Consequently, when requests are continuously injected into edge networks, the usage of heterogenous resources, including CPU, memory, and network bandwidth, may not be the same, or differ significantly, on certain edge nodes. This happens especially when burst requests are injected into the network to be satisfied concurrently. Therefore, the usage of heterogenous resources provided by edge nodes should be co-optimized through re-scheduling microservices. To address this challenge, this article proposes a Web 3.0-enabled M icroservice R e- S cheduling approach (called MRS ), which is a migration-based mechanism integrating a placement strategy. Specifically, we formulate the MRS task as a multi-objective and multi-constraint optimization problem, which can be solved through a penalty signal-integrated framework and an improved pointer network. Extensive experiments are conducted on two real-world datasets. Evaluation results show that our MRS performs better than the counterparts with improvements of at least 7.7%, 2.4%, and 2.2% in terms of network throughput, latency, and energy consumption, respectively.

Anomaly detection has emerged as one of the core research topics to support workflow applications across various domains. To differentiate anomalies from underlying normal patterns of workflows, Graph Neural Networks (GNNs) models have been introduced. These models leverage time series data to construct graph structures, in order to explicitly capture task dependencies among industrial Internet of Things (IoT) devices, and thus, to identify deviations from predicted behaviours as anomalies. However, existing forecasting-based anomaly detection methods may not accurately detect certain anomalies, since they have seldom considered valuable information uncovered by historical sensory data, but presented as domain knowledge. To address this limitation, this paper proposes a Knowledge-enhanced graph attention-based Anomaly Detection (KeAD) method. Specifically, a knowledge-enhanced graph structure is constructed by incorporating domain-specific knowledge to represent spatio-temporal dependencies between IoT devices. Based on which, a knowledge-enhanced graph attention-based forecasting network is developed to predict the future behaviours of IoT devices. Anomalies, such as those caused by cyber-attacks in workflows, are detected by analyzing deviations from these predicted behaviours in conjunction with domain-specific knowledge. A case study is presented, along with extensive experiments conducted on publicly available datasets. Evaluation results demonstrate that KeAD outperforms the state-of-the-art techniques in terms of anomaly detection accuracy.

Anomaly detection is a long-standing research topic to support the prompt remedy of potential risks for dependency-aware tasks, where Graph Neural Networks (GNNs) models have been adopted to differentiate anomalies from normal patterns. Generally, GNN models utilize time series data to construct graph structures for capturing task dependencies between Internet of Things (IoT) devices, such that deviations from predicted behaviours are assumed as anomalies. Current forecasting-based anomaly detection methods can hardly detect anomalies, which are uncovered by historical sensory data, but are explicitly specified by domain knowledge. To solve this issue, this paper proposes a Knowledge-enhanced graph attention-based Anomaly Detection (KeAD) method. Specifically, a knowledge-enhanced graph structure is constructed by incorporating domain-specific knowledge to represent spatio-temporal dependencies between IoT devices. Thereafter, a knowledge-enhanced graph attention-based forecasting network is developed to predict future behaviours of IoT devices. Anomalies are detected by analyzing deviations from these predicted behaviours, taking domain-specific knowledge into account. Extensive experiments are conducted based on publicly-available datasets, and evaluation results demonstrate that our KeAD outperform the state-of-the-art techniques in terms of the accuracy of anomaly detection.

Empowered by edge computing, resources and computation capabilities provided by edge devices can be encapsulated as containerized services. When burst requests are coming, there may have edge devices which are overloaded, since most requests are spatially and temporally constrained, and edge devices are resource-scarceness and capacity-limited. Overloaded devices should be relieved through optimally migrating one or more activated services to contiguous edge devices. Besides, sensory data gathered by original edge devices should be periodically transmitted to migrated devices for data analysis purpose. To mitigate this issue, this paper proposes an Energy-efficient Online Service Migration (EOSM) mechanism to conduct the migration of multiple services simultaneously. Extensive experimental results show that our EOSM mechanism outperforms the state of arts techniques in mitigating overloaded services in terms of access latency, energy consumption, and request success rate.

Smart contracts, which are self-executing agreements, have a huge range of possible uses from finance to supply chain management. To avoid costly errors and vulnerabilities, it is crucial to guarantee the accuracy and reliability of these contracts. This paper explores the convergence of Blockchain technology, particularly Ethereum’s smart contracts, and Business Process Modeling (BPM), capitalizing on the synergies between these domains. We propose that viewing smart contracts as akin to business processes can significantly enhance the verification of Blockchain-based applications, addressing critical challenges in smart contract correctness and security. In this work we employ a formal verification approach based on Coloured Petri Nets and Linear Temporal Logic to detect potential vulnerabilities in Solidity smart contracts while considering their behavioral context as a business process model.

Empowered by edge computing, resources and computation capabilities provided by edge devices can be encapsulated as containerized services, and domain applications can be achieved through service compositions. When burst requests are coming to be satisfied, there may exist edge devices which are overloaded, since requests are mostly spatially and temporally constrained, and edge devices are resource-scarceness and capacity-limited. In this setting, overloaded devices should be relieved through optimally migrating one or more activated services to contiguous edge devices. Besides, sensory data gathered by original edge devices should be periodically transmitted to migrated devices for data analysis purpose. To mitigate this issue, this paper proposes an Energy-efficient Online Service Migration (EOSM) mechanism to conduct the migration of multiple services simultaneously. Specifically, a light service sharing strategy is developed to only transmit the top container layer, and a modified NSGA-II algorithm is adopted to generate one or multiple paths for the container layer and time-series sensory data migration of each migrated service. Extensive experimental results show that our EOSM strategy outperforms the state of arts techniques in mitigating overloading devices in terms of access latency, energy consumption, and request success rate.

Blockchain technologies have emerged to serve as a trust basis for the monitoring and execution of business processes, particularly business process choreographies. However, dealing with changes in smart contract-enabled business processes remains an open issue. For any required modification to an existing smart contract (SC), a new version of the SC with a new address is deployed on the blockchain and stored in a contract registry. Moreover, in a choreography, a change in a partner process might affect the processes of other partners, and thus, must be propagated to partners affected by the change. In this paper, we propose an approach overcoming the limitations of SCs and allowing for the change management of blockchain-enabled declarative business process choreographies modeled as DCR graphs. Our approach allows a partner in a running blockchain-based DCR choreography instance to change its private process. A change impacting other partners is propagated to their processes in a decentralized manner using a SC. The change propagation mechanism ensures the compatibility checks between public processes of the partners and the consistency between the private and public processes of one partner. We demonstrate the approach’s feasibility through an implemented prototype and its effectiveness via a set of evaluation tests.

Edge service monitoring is essential to guarantee the healthy of service compositions at runtime. Current techniques focus mostly on the monitoring of atomic edge services, but they are inadequate for that of inter- and composite services. Besides, constraints to be monitored are usually pre-specified, although certain parameters may have to be adapted online according to the execution context. To address these challenges, this paper formulates the problem of edge service monitoring as the interpretation of temporal constraints and time-dependent QoS constraints upon intra-, inter-, and composite services. Leveraging our proposed Compositional Signal Temporal Logic (CSTL) with extended compositional modalities and online parameter settings, an adaptive monitoring mechanism is developed, where constraints are converted to CSTL formulae, and QoS variations and temporal violations are interpreted qualitatively and quantitatively at runtime. Extensive experiments are conducted upon publicly-available datasets, and evaluation results show that CSTL performs better than baseline techniques in terms of expressiveness, applicability, and robustness.

Cloud consumers’ requirements possess an inherent dynamic nature, characterized by fluctuating needs in reliability and high-availability relative to their workload. To satisfy these requirements, service reconfiguration strategies are put in place ensuring first, adaptable service provisioning and second, compliance with the agreed-upon Service Level Agreements (SLAs) between consumers and providers. However, deviations between SLAs and “real” observed behaviours could occur even after triggering reconfiguration strategies. Additionally, as organizations increasingly embrace multi-cloud environments, careful consideration must be given to the inherent challenges that arise in this requirements satisfaction. In this paper, we represent these strategies as state machines used to report their conformance to collected logs which track what really happened at run-time. The collected logs are processed to construct state machines suitable for conformance checking. Experiments demonstrating the technical doability of using conformance checking to detect deviations between SLAs and logs, along with verifying the suitability of reconfiguration strategies, are also discussed in the paper.

With the wide-adoption of edge computing, the functionalities of Internet of Things (IoT) devices can be encapsulated as edge services, to facilitate domain applications through edge service compositions. Considering the capacity-fluctuating and resource-varying of IoT devices, edge service monitoring is essential to guarantee the healthy of their compositions at runtime. Current techniques focus mostly on the monitoring of atomic edge services, which, however, are inadequate for that of inter-and composite services. Besides, constraints to be monitored are usually pre-specified, although certain parameters may have to be adapted online according to execution context. To address these challenges, this paper proposes a novel logic-based adaptive monitoring mechanism, to achieve the interpretation of temporal constraints and time-dependent QoS constraints upon intra-, inter-, and composite services. Leveraging our proposed Compositional Signal Temporal Logic (CSTL) with extended compositional modalities and online parameter settings, constraints can be converted to CSTL formulae, and QoS variations and temporal violations are interpreted qualitatively and quantitatively at runtime. Extensive experiments are conducted upon publicly-available datasets, and evaluation results demonstrate that our CSTL performs better than baseline techniques in terms of expressiveness, applicability, and robustness.

Summary At the stage of identifying and modeling business processes (BP), traditional methods for collecting business expertise are time consuming since they mainly rely on physical communication. Moreover, there are some BP with no formal documentation and their execution relies on implicit knowledge of workers. Therefore, analyzing the logs data generated by messaging systems allowing workers to share such implicit knowledge has been a research area of growing interest. BP knowledge introduced by workers in emails could actually cover multiple BP perspectives that would vary from activity, actors, and data perspectives. This article gives an overview of the current state‐of‐the‐art in multi‐perspective BP discovery from messaging systems as well as the main existing research questions. Different approaches are presented, discussed, and compared against each other on a qualitative level deduced from the defined set of research questions. The resulting overview covers both advantages and disadvantages of current techniques and the research questions that have not been addressed to date. This should provide a solid basis for further research.

With the explosive growth of the Internet of Things (IoT) devices deployed in edge networks, the functionalities of IoT devices are typically encapsulated as IoT services, and user requests can be achieved through the composition of data and/or computation-intensive IoT services. Considering the prediction-uncertainty of forthcoming requests, certain IoT services may (i) not be hosted currently by appropriate IoT devices, or (ii) such an IoT service exists, but its non-functional properties may hardly be satisfied with respect to certain constraints prescribed by requests. To address this challenge, this paper proposes an efficiency-aware service Migration Scheduling (denoted eMS) mechanism in edge networks, in order to migrate IoT services on-demand, and thus, to optimally settle non-satisfiable constraints. Specifically, IoT services are re-scheduled, such that certain IoT services are migrated from their hosting IoT devices to neighboring ones, while minimizing the energy consumption and average delay caused by this service re-scheduling operation. We formulate this service re-scheduling as a multi-objective and multi-constraint optimization problem, which is solved through integrating the greedy algorithm into the fast non-dominated sorting and crowded-comparison operators as the hybrid genetic algorithm (G-NSGA-II). Based on real-life datasets provided by an oil pipeline monitoring project, extensive experiments are conducted, and evaluation results show that our eMS is promising in reducing the energy consumption and average delay of service re-scheduling in comparison with the state-of-art’s techniques.

With the recent adoption of edge computing,Internet ofThings (IoT) devices collaborate at the network edge to facilitate edge-native applications. In this setting,IoTdevices are typically encapsulated asIoTservices to encode their functionalities, and their collaboration is achieved throughIoTservice composition. Due to the continuous resource occupancy, release, and consumption ofIoTdevices at runtime, a composition, which is functionally compatible and non-functionally optimal at this moment, may not hold in the forthcoming time durations, when certainIoTservices may significantly downgrade in theirQuality-of-Services (QoS). To guarantee the compatibility of compositions withQoSvariations, this article proposes an adaptive composition mechanism leveragingComputationTreeLogic (CTL) specifications. Specifically, we formalize the composition as a temporal task, and convert it toCTLformulae with the abstractions of required functionalities and composite structures. Functional compatibility is formally interpreted byCTLsemantics during the execution of compositions. Besides, we construct aQoSDependencyGraph (QoSDG) to captureQoSvariations, and achieve adaptive composition with dynamicQoSsatisfactions. Extensive experiments are conducted upon publicly-available datasets, and comparison results demonstrate that our technique outperforms the state-of-the-art counterparts in heterogenous scenarios with higherQoSdependencies ranging from 0.3$\%$to 27.8$\%$.

Network as a Service (NaaS) enables tenants and cloud users to connect their distributed services across multiple clouds without relying on their own networking services and resources. Cloud service providers (CSPs) offer networking services as bundles delivered on a pay-per-use basis. Although service bundling is a common practice in the cloud providing multiple components as a single service, forcing the packaging of network resources in a pure bundle can severely limit visibility and control over network services. We argue that current descriptions of NaaS services lack transparency making cloud users unable to efficiently discover and consume these services from different providers. To address this issue, we propose to describe network services based on their functionality and according to the OSI model. Our approach explicitly describes bundling between network services that promotes discovery and enables users to create their own network services bundles from different CSPs.

Abstract Deploying a cloud configuration in a real cloud platform is mostly cost- and time- consuming, as large number of cloud resources have to be rented for the time needed to run the configuration. Thereafter, cloud simulation tools are used as a cheap alternative to test cloud configuration. However, most of the existing cloud simulation tools require extensive technical skills and do not support simulation of any kind of cloud resources. In this context, using a model-driven approach can be helpful as it allows developers to efficiently describe their needs at a high level of abstraction. To do, we propose, in this article, a model-driven engineering approach based on the Open Cloud Computing Interface(OCCI) standard metamodel and CloudSim toolkit. We firstly extend OCCI metamodel for the supporting simulation of any kind of cloud resources. Afterward, to illustrate the extensibility of our approach, we enrich the proposed metamodel by new simulation capabilities. As proof of concept, we study the elasticity and pricing strategies of Amazon Web Services (AWS). This article benefits from OCCIware Studio to design an OCCI simulation extension and to provide a simulation designer for designing cloud configurations to be simulated. We detail the approach process from defining an OCCI simulation extension until the generation and the simulation of the OCCI cloud configurations. Finally, we validate the proposed approach by providing a realistic experimentation to study its usability, the resources coverage rate and the cost. The results are compared with the ones computed from AWS.

Edge computing promises to facilitate the collaboration of smart sensors at the network edge, in order to satisfy the delay constraints of certain requests, and decrease the transmission of large-volume sensory data from the edge to the cloud. Generally, the functionalities provided by smart sensors are encapsulated as services, and the satisfaction of certain requests is reduced to the composition of services configured upon smart sensors in edge networks. Considering the dynamics and nonpredictability of incoming requests, an adaptive and online service configuration mechanism is essential, especially when various temporal constraints are prescribed by requests and satisfied by configured services. In this article, we formulate this problem in terms of a continuous-time Markov decision process model based on the state–action–reward mechanism. A temporal-difference learning approach is developed to optimize the service configuration while taking long-term delay sensitivity and energy efficiency into consideration. Extensive experiments are conducted, and evaluation results show that our approach outperforms the state-of-art's techniques for achieving close-to-optimal service configuration, and improving the temporal satisfaction of user requests.

Process mining techniques allow organizations to discover, monitor and improve their as-is processes by analyzing the process execution data, aka event data, recorded by their information systems. A recurrent task in process mining is querying. Querying allows users to get insights into specific executions of their processes and to retrieve relevant data. Existing process querying techniques require end users to be knowledgeable of the query language and the database schema. However, a key success factor for process analysis is to make querying accessible to business experts who may be inexperienced in database querying. This paper addresses this challenge by proposing a natural language interface (NLI) for querying event data. The interface allows users to formulate their questions in natural language and to automatically translate the questions into a structured query that can be executed over a database. We use graph based storage techniques, namely labeled property graphs, which allow to explicitly model event data relationships. As an executable query language, we use the Cypher language which is widely used for querying property graphs. The approach has been implemented and evaluated using a publicly available event log.

The collaboration of contiguous Internet of Things (IoT) devices is envisioned to satisfy complex applications which are beyond the capacity of single devices. The functionalities of IoT devices are encapsulated as IoT services, and their collaboration is implemented in terms of IoT service composition. Considering the capacity occupancy, release, and consumption caused by the implementation of IoT services, their composition is challenging in capacity-dynamically fluctuating IoT networks. This paper proposes a dynamic IoT service composition mechanism with inter-service dependencies adopted to capture the dynamic changes of IoT devices, and this change is specified by various Quality-of-Service factors. IoT service composition is formalized under Computation Tree Logic specification with certain composite structures and dynamic dependencies, and this composition is formally achieved by an optimized model checking method. Extensive experiments are conducted on publicly available datasets, and evaluation results show that our technique outperforms the state-of-the-art's approaches in relevant performance metrics.

While Blockchains can open intriguing opportunities of research in many application contexts, they come with the risk of bringing new unconventional problems. In fact, because of the monetary value they hold, Blockchains have been subject to many attacks. Smart contracts, which are at the core of second-generation Blockchains, have been proven to be the origin of such attacks due to the exploitable vulnerabilities their code may hold. It is therefore an essential requirement to prove the correctness of the smart contracts to be deployed on a Blockchain to ensure its protection. The existing approaches have been focusing on targeting generic vulnerabilities like reentrancy, without offering the possibility to check temporal-based contract-specific properties. In this paper, we aim to address smart contracts verification while supporting such properties. We propose and implement a transformation of Solidity smart contracts into Coloured Petri nets and investigate the capability of existing model checking tools to check specific temporal properties of the formally modeled contract.

Summary Despite the elasticity and pay‐per‐use benefits of cloud computing (aka fifth utility computing), organizations adopting clouds could be locked into single cloud providers, which is not always a “pleasant” experience when these providers stop operations. This is a serious concern for those organizations that who would like to deploy (core) business processes on the cloud along with tapping into these two benefits. To address the lock‐into concern, this paper proposes an approach for decomposing business processes into fragments that would run over multiple clouds and hence multiple providers. To develop fragments, the approach considers both restrictions over owners of business processes and potential competition among cloud providers. On the one hand, restrictions apply to each task in a business process and are specialized into budget to allocate, deadline to meet, and exclusivity to request. On the other hand, competition leads cloud providers to offer flexible pricing policies that would cater to the needs and requirements of each process owner. A policy handles certain clouds' properties referred to as limitedness, non‐renewability, and non‐shareability that impact the availability of cloud resources and hence the whole fragmentation. For instance, a non‐shareable resource could delay other processes should the current process do not release this resource on time. During fragmentation, interactions between owners of processes and providers of clouds happen according to two strategies referred to as global and partial. The former collects offers about cloud resources from all providers, while the latter collects such details from particular providers. To evaluate these strategies' pros and cons, a system implementing them, as well as demonstrating the technical feasibility of the fragmentation approach using credit‐application case study, is also presented in the paper. The system extends BPMN2‐modeler Eclipse plugin and supports interactions of processes' owners with clouds' providers that result to identifying the necessary fragments with focus on cost optimization.

International audience

Smart Contracts (SC for short) are gaining momentum as a suitable technology for ensuring trusted execution of Business Processes (BP for short) in open environment. Nevertheless, the transactional semantics of SC which follow ACID transactions are not appropriate for BP characteristics. Indeed, it is admitted that ACID transactions are limited to cope with complex control structure and long running execution of BP. Transactional Business Processes (TBP for short) have emerged as an extension to ACID models to overcome these limits. A TBP ensures transactional reliability of advanced transactions having a control structure as complex as for BP. In this paper, we propose an approach that builds on SC and extends them to implement TBP. We extend Caterpillar, an existing BP execution engine developed on top of Ethereum, to implement our approach and support the execution of TBP.

This paper proposes to derive temporal constraints and granularities corresponding to individual activities, collaborative activities and their connecting edges from event logs. Specifically, a timed hierarchical business process model is constructed. Temporal anomalies are measured with time-constrained and granularity-aware bounds according to user's acceptance of deviant executions. Temporal interestingness, as the complement to anomaly detection, is evaluated as the most probable execution times that are partitioned into user-defined granules and ranked by probability. Experimental evaluations upon public event logs demonstrate the effectiveness and applicability of our proposed model for temporal anomaly and interestingness detection in terms of accuracy and recall, in comparison with the state-of-art`s techniques.

The deployment descriptors in Network Function Virtualization (NFV) are usually designed and configured through static automation and manual edition by service providers without any formal strategy except best practices. Thus, leading to an error prone and time consuming approach. We propose in this paper 1) a configurable deployment descriptor model and 2) a learning approach based on machine learning to construct the configurable model automatically. Firstly, the configurable deployment descriptor model captures the relation and also the variability between the VNF elements of different deployment descriptors. It enables service providers to configure and generate customized deployment descriptors instead of designing them each time from scratch. Secondly, we define a learning approach to learn configurable deployment descriptor models by finding and federating similar VNF elements of different deployment descriptors. With our machine learning approach we construct automatically a configurable model from a set of deployment descriptors. The results of our experiments highlight the effectiveness of our approach into learning configurable deployment descriptor models.

At the stage of identifying and modelling business processes (BP), traditional methods for collecting business expertise are time consuming since they mainly rely on physical communication. Moreover, there are some BP with no formal documentation and their execution relies on implicit knowledge of workers. Therefore, analyzing the log data generated by messaging systems allowing workers to share such implicit knowledge has been a research area of growing interest. This paper gives an overview of the current state-of-the-art in BP and activity discovery from messaging systems as well as the main existing research questions. Different approaches are presented, discussed and compared against each other on a theoretical level deduced from the defined set of research questions. The resulting overview covers both advantages and disadvantages of current techniques and the research questions that have not been addressed to date. This should provide a solid basis for further research.

Summary Service Level Agreement (SLA) has recently garnered increasing attention in cloud computing due to its capability in the definition and the monitoring of the Quality of Service (QoS). SLA is generally generated following a process of negotiation in which the client chooses a subset of clauses among choices predefined by the provider on a “take it or leave it” basis. However, the heterogeneity between client requests and provider offers may result in inappropriate SLA. In addition, the cloud service context may change over the time, which causes a client requirement adjustment, and therefore, the SLA becomes unsatisfactory. In this paper, we propose a cloud SLA negotiation and re‐negotiation approach. Our aim is to ameliorate the SLA negotiation phase by adding the ability to integrate a semantic mapping between client requests and provider offers to generate a suitable SLA document. Moreover, our approach includes a context‐aware system to adapt the SLA throughout reasoning techniques and automatically ensure the re‐negotiation. We develop a prototype, and we test its performance. The results of a case study also show the efficiency of our approach.

Several DevOps tools have emerged to orchestrate cloud resources. However, inherent heterogeneity and complex implementation within these tools make it hard for DevOps users to design required resource-related artifacts. Currently, the defacto standard for cloud resource modeling and orchestration is TOSCA. Nonetheless, TOSCA is usually bound to TOSCA-compliant orchestration tools. Moreover, the actual integration between TOSCA and DevOps tools is still performed using costly coding and in ad-hoc manner. To resolve this, we believe that mapping and translation mechanisms between TOSCA and DevOps tools should be provided. In this paper, we propose a new model-driven approach for cloud resource orchestration. Our approach (i) adopts TOSCA to design resource-related artifacts regardless of a specific DevOps tool; (ii) enables a new model-driven translation technique that serves to translate the designed artifacts using TOSCA into DevOps specific artifacts and (iii) provides Connectors that intend to establish the bridge between DevOps-specific artifacts and the DevOps tools. Our approach provides a powerful enhancement to DevOps productivity and reusability by assisting toward a seamless integration between TOSCA and DevOps tools.

Future software networks promises to fully automate the network management, configuration, deployment and operations of devices. Currently the deployment automation is enabled by orchestrators using descriptor files associated with Virtual Network Functions (VNFs) and Network Services (NSs), called VNF Descriptors (VNFDs) and NS Descriptors (NSDs). We focus our attention in this demo paper on VNFDs and we propose a framework for VNFD-Mining with Word Embeddings and Deep Neural Networks. The aim of the framework is to augment orchestrators with an ability to select, recommend and complete NFV descriptors given an initial description. The framework is trained initially with a catalogue of predefined VNFDs.

Blockchain trends cover more and more tech domains making it one of the most used technologies in the last few years. This is due to two essential aspects. First, it is a distributed peer-to-peer network where there is no need for third part to execute operations between peers. Second, blockchain implements mechanisms to make the most data sensitive operations executed in a trusted way. Regarding these attractive aspects, we intend in this work to use the blockchain technology for the implementation of touristic itineraries. We consider the latter as process choreographies involving different participants. We intend to model and execute the touristic itineraries generated from a personalized trip planner called CART in raw XML format in a way that respects this collaborative aspect and resolves the problem of trust. We will propose a pattern and its transformation rules to reconstruct the itineraries presented in declarative annotations such as XML to smart contracts written in some smart contract-specific programming language called Solidity. Experimental results show promising perspectives of the deployment of the proposed solution to execute touristic plans.

To fulfill its complex business demands, an organization may require the integration of many SaaS applications. A main challenge in this regard is to select the suitable SaaS offerings that satisfy all the business requirements. Due to the diversity of QoS needs and the large number of SaaS providers, selecting the suitable set of SaaS applications to be integrated may result in QoS degradation and higher costs. In this paper, we propose to use a System-of-Systems (SoS)-based approach for the selection and integration of SaaS. We believe that the use of SoS and its dynamic properties could improve the service selection at SaaS level. To do so, we propose three Mixed Integer Programming models that optimize the global SaaS behavior by minimizing the overall delivery costs, and maximizing the global functional and QoS utilities. The implementation of the approach and the experimental results highlight the efficiency of our proposed solution.

Cloud environments offer an interesting infrastructure for any modern enterprise information systems due to their high performance and low operating cost. Cloud resources are offered in various pricing strategies based on temporal properties. In general, enterprises looking towards minimizing their spending on IT infrastructure find such pricing strategies very attractive to deploy and run their business processes. Nevertheless, due to the lack of explicit and formal description of (Cloud) resources in existing business processes modeling languages, such as BPMN, Cloud resources can not be correctly allocated. Therefore, we elaborate an extension to BPMN 2.0 to fully integrate (Cloud) resource perspective, especially the temporal properties of Cloud pricing strategies in business process model. In order to help the designer to allocate correctly the required Cloud resources, we propose an automatic generation of timed automata from this BPMN extensions to check the matching between both temporal constraints: activities and Cloud resources. To show its feasibility, our approach has been implemented and tested using a real use case from an industrial partner.

This paper proposes a multi-request cooperative-integrating mechanism to optimize concurrent multi-applications in service-oriented wireless sensor networks (WSNs). Specifically, a sensor node is encapsulated as one or multiple WSN services, which can be categorized into service classes. A service network is constructed by considering the invocation relationship between service classes. Candidate service class chains are recommended. These service classes chains will be instantiated by available WSN services, which can be reduced to a multi-objective and multi-constraint optimization problem, where the spatial-and temporal-constraints, and energy efficiency of the network, are taken into consideration. This combinational optimization problem is solved by adopting heuristic algorithms. Experimental results show that this technique improves the shareability and energy efficiency for supporting concurrent applications.

Configurable processes are increasingly being adopted by enterprises that seek experience sharing and best practice adoption. A configurable process is a customizable model that specifies how different enterprises perform similar processes. At the modeling level, a configurable process model provides for flexible business process (BP) reuse by (de)selecting the (ir)relevant parts to derive a particular process variant. At the exploitation level, it offers flexibility and agility to an enterprise looking to outsource its BP to different providers cooperating in a cloud federation. More specifically, an enterprise can use a configurable process model to derive particular process variants that it outsources depending on its objectives. In particular, it may opt for outsourcing the variant that results in the optimal deployment, e.g., having the minimal cost of allocated cloud services that fulfill the user quality of service (QoS) requirements. However, identifying the optimal deployment variants is a complex problem because of the heterogeneity of services within a cloud federation and the number of possible variants that can be derived from a configurable process model. In addition, the complexity of this problem increases for variable user QoS requirements. In this paper, we propose an approach to derive, from a configurable process model, the variant that has the optimal deployment in a cloud federation. We propose a linear programming approach that accounts for the variability of both the BP model and the user QoS requirements, and that ensures an optimal time-aware cloud service allocation. We experimentally show the effectiveness and flexibility of our approach on a generated testbed.

This article proposes an approach for identifying and recommending scientific workflows for reuse and repurposing. Specifically, a scientific workflow is represented as a layer hierarchy, which specifies hierarchical relations between this workflow, its sub-workflows, and activities. Semantic similarity is calculated between layer hierarchies of workflows. A graph-skeleton based clustering technique is adopted for grouping layer hierarchies into clusters. Barycenters in each cluster are identified, which refer to core workflows in this cluster, for facilitating cluster identification and workflow ranking and recommendation. Experimental evaluation shows that our technique is efficient and accurate on ranking and recommending appropriate clusters and scientific workflows with respect to specific requirements of scientific experiments.

Organizations are nowadays more and more adopting Cloud computing to execute their multi-tenant business processes in order to quickly adapt to changes of requirements at lower costs. In such environment, using configurable process models allows for various tenants to share a reference process which can be customized depending on their needs. However, there is a lack of support for cloud-specific resource configuration with considering quality of service requirements. In this paper, we cope with this gap by proposing a genetic-based approach that enables to optimally extract cloud resource configuration allocation w.r.t Cloud resource features (i.e., elasticity and shareability), and process non functional properties (i.e. QoS properties).

Poster based on accepted short paper

Cloud computing represents a new computing approach that allows creating robust and on-demand services controlled by means of Service Level Agreements (SLA). These documents are generated following a process of negotiation during which the client chooses a subset of clauses among choices predefined by the provider on a "take it or leave it" basis. However, the heterogeneity between client requests and provider offers may result in inappropriate SLA. In addition, the cloud service context may change over the time which causes a client requirements adjustment and therefore, the SLA remains unsatisfactory. In this paper, we propose a cloud SLA negotiation and re-negotiation approach that integrate a semantic mapping between client requests and provider offers with the aim of generating a suitable SLA document. Moreover, our approach includes a context-aware system to adapt the SLA throughout reasoning techniques and automatically ensure the re-negotiation. A prototype implementation demonstrates the feasibility and the efficiency of our approach.

The proliferation of connected devices, wherein Sensors, Actuators and Tags (such as Radio-Frequency Identification (RFID)) are able to seamlessly communicate to their environment and to each other for sharing information or to perform some actions has created the Internet of Things (IoT) ecosystem. These devices expose their functionality via standard services and application programming interfaces (APIs). They are considered to be one of the key technology enablers to foster the vision of a smart world, comprising of smart objects, smart supply chain management, smart manufacturing (Industry 4.0), smart buildings, to name a few. In fact, today these IoT devices continuously take part in various business processes that are being executing within the boundaries of the same enterprise or in different enterprises. Thus, there is an evident need to model these processes that are associated with IoT resources in a formal and unambiguous manner. However, in context of business processes, these is a lack of formalized and explicit description for IoT resources, thus hampering their efficient modeling and management. To bridge this gap, we propose a semantic framework for developing IoT-aware business processes as follows, (i) formalizing IoT resource description w.r.t Internet of Things Architecture (IoT-A) reference model in context of business processes, (ii) formalizing IoT properties and allocation rules for optimal resource management and (iii) resolving resource conflicts based on strategies. To illustrate the feasibility of our framework, we evaluated our semantic model for coverage of concepts in IoT-A reference model along with development of a proof of concept tool for integrating the IoT resources and our semantic model during the process modeling phase.

Cloud environments are being increasingly used for deploying and executing business processes to provide a high level of performance with low operating cost. Nevertheless, due to the lack of an explicit and formal description of the resource perspective in the existing business processes, the correctness of Cloud resources management can not be verified. The aim of the present work is to offer a formal definition of the resource perspective in business processes as a step towards ensuring a correct and consistent Cloud resource allocation in business process modeling. Concretely, we propose a formalism based on the Event-B language for specifying Cloud resource allocation policies in business process models. This formal specification is used to formally validate the consistency of Cloud resource allocation for process modeling at design time, and to analyze and check its correctness according to user requirements and resource capabilities. In order to show its feasibility, our approach has been tested using a real use case study from an industrial partner.

With the advent of Cloud Computing, organizations are increasingly migrating their information and communication technology (ICT) resources to the cloud. This has recently gained a great attention because of cloud pay-as-you-go model which drastically reduces actual IT infrastructure with high performance and continuous availability. In order to effectively utilize existing cloud resources, organizations may look for available cloud resources from different cloud providers. However, cloud resources are heterogeneous and described by different standards which prevents an easy and dynamic interoperability between organizations. In this paper, we propose a semantic framework tackling this heterogeneity issue. We develop a set of ontologies to semantically represent cloud resources by looking at three cloud resource description standards: Topology and Orchestration Specification for Cloud Applications (TOSCA), Open Cloud Computing Interface (OCCI), and Cloud Infrastructure Management Interface (CIMI). Hence, our framework promotes the creation of a common semantic knowledge base of cloud resources described using these different standards. This knowledge base allows a seamless translation of cloud providers' resource descriptions. We developed an application to validate our approach as a proof of concept. We also evaluated the feasibility and completeness of our semantic framework on use cases obtained from standard specifications.

Nowadays, a growing number of companies are using Cloud Computing to optimize their business processes by using dynamically scalable and often virtualized resources on demand. Nevertheless, due to the lack of explicit and formal description of the resource perspective in existing business processes, Cloud resource allocation behavior cannot be efficiently and correctly managed. In this paper, we aim at formally verifying resource allocation in business processes using Event-B. More precisely, our aim is to specify the resource allocation behavior both at design time and at runtime, and to check its correctness according to users' needs. Our model also takes into account different cloud properties such as elasticity and shareability. In order to show its feasibility, our approach has been tested using a use case study from an industrial partner.

Fog computing brings cloud closer to end-users and data sources by enabling computation at the edge of the network. Low latency is the main benefit. IoT applications are often latency-sensitive. Such applications may be provisioned as component-based in a hybrid cloud/fog environment with components spanning cloud and fog. This will enable placing some of its components in the fog domain closer to the IoT devices, and consequently reduce the latency. However, provisioning applications in hybrid cloud/fog environment is still manual today. Existing PaaS do not support interacting with fog nodes, at the edge, for applications' components provisioning. This demo shows the key features of the hybrid Platform as-a-Service (PaaS) we have designed for IoT applications provisioning in cloud and fog environments. Three goals are assigned to the demo: (1) How applications can be designed and developed in such environments, (2) how the hybrid PaaS deploys the applications' components across cloud and fog nodes, and (3) how it executes and manages them using appropriate orchestration techniques.

A configurable process model captures a family of similar processes. Such models can be configured to obtain a process variant according to specific requirements. With this aim, several approaches have been proposed for the configuration of process models. Nevertheless, an increasing attention is being paid to achieve this in a sound manner due to the complex inter-dependencies between the configuration decisions. In this work, we aim to guide the process analyst to easily configure process models while preserving soundness. To do so, we propose a formal approach for ensuring correctness of business process configurations while considering structural constraints they have to obey. Specifically, using the Event-B language, we formally define a configurable process model, its correctness-preserving conditions and its configuration constraints.

This demo will show the key features of a Platform as-a-Service (PaaS) we have proposed in a research paper accepted for presentation at IEEE LANMAN 2016 conference. The proposed PaaS enables IoT applications provisioning in hybrid cloud/fog environments. Two goals are assigned to the demo. On the one hand, we will highlight how IoT applications can be provisioned in such environments. On the other hand, we will show concretely the advantages these hybrid environments have over traditional cloud environments. The provisioning in these hybrid environments enables latency reduction and processing performance enhancement. Indeed, in order to reduce latency, the applications can have some of its components running in a distant cloud and interacting with the other components running in the fog, closer to IoT devices.

In recent years, cloud environments have emerged as a new paradigm for the execution and the deployment of cost-reduced and optimized business processes. Nevertheless, due to the lack of explicit and formal description of the resource perspective in existing business processes, cloud resources cannot be efficiently and optimally managed. With the aim of formalizing the consumed cloud resources using a shared knowledge base, we propose in this paper a semantic framework for resource-aware business process development in the cloud. Basically, our proposed semantic framework allows for (1) resource description in a formal and unique way, (2) resource allocation management and (3) resource constraints verification. In order to show its feasibility, our approach has been implemented as an extension of Signavio process editor and tested using a real use case study from an industrial partner.

Cyber Physical Society (CPS) feature a tight integration between computation, communication, and control in their operation and interactions with the environment in which they are deployed. The concept of capability, which is the semantics of an action or specific functionality, is therefore a cornerstone in modern Cyber Physical Society, especially those service or process aware. In this track, we present dedicated techniques for capability management and engineering, which can be applied and reused in a large number of areas such as SOA, BPM, Cloud Computing and Internet of Things. These techniques will certainly boost several related research efforts in Cyber Physical Society. This track aims at shedding the light on the importance of capability engineering as well as at bringing together researchers and practitioners working in capability engineering to achieve the vision of cyber physical society by means of current techniques.

Recently, Sensor/Actuator Networks have become an emergent technology for various application areas such as security and surveillance applications, traffic control, logistics, energy control in public and private buildings, etc. Designing and constructing new applications using these technologies remain, however, a challenging task. Indeed, finding the relevant sensors and actuators, and combining them in a proper way in order to achieve a specific goal is not an easy task and requires several skills from different stakeholders. Moreover, sensor environments are inherently highly dynamic. Furthermore, current applications are in general tightly coupled to the underlying infrastructure which hampers their reuse and flexibility to changes. In this paper, we present a process-oriented and service-based approach for supporting the development of adaptive sensor-based applications. Our approach decouples application logic from its implementation. A design-time model is first specified, as a flow of activities, which is then deployed in a particular environment. Decoupling the application logic from its implementation enables, on one hand, to foster the reuse at the application level and, on the other hand, to adapt the same application to different environments and situations. We propose also an activity recommendation technique to provide assistance to application designers by recommending to them activities that have been used in a similar compositional context. Our approach has been prototyped using service standards such as BPMN within the context of the VITRO European project and validated by several use cases.

Summary The last few years have seen an increase in the use of e‐services as a natural consequence of the service‐based economy growth. However, the distributed nature of services can raise several heterogeneity problems that can hamper the widespread adoption of service‐oriented architectures (SOA). In this paper, we are interested in data heterogeneity problems that can occur when using composite services. We propose a service‐based approach for automatically inserting appropriate mediation services in service compositions to resolve structural heterogeneities in their data flow. This mediation is ensured through a special kind of Web services called data mapping Web services. In addition, in order to be in agreement with the distributed nature of the Web, we propose a decentralized solution to publish/discover these services. We demonstrate how our approach applies to the data structural heterogeneity problem in data providing service compositions as a particular data heterogeneity case. Provided experimental results show that our approach is efficient in realistic situations. Copyright © 2013 John Wiley & Sons, Ltd.

With the intention of design by reuse, configurable process models provide a way to model variability in reference models that need to be configured according to specific needs. The design of configurable process models is a well known complex and error-prone task. Thus, many approaches have been proposed to automate their design by merging existing process models into configurable reference models. However, the complexity introduced by such approaches remains an open issue. The designer ends up with one model that integrates a family of process variants making the process design and update a complex task. In this work, we propose to assist the design of configurable process models with configurable process fragments. Concretely, we present an algorithm for extracting, clustering and merging process fragments around a particular activity to construct a configurable fragment. The approach has been implemented as an extension of the Signavio Process Editor and evaluated against a large collection of process models. Experimental results show that our approach is efficient and produces comprehensible configurable fragments.

The increasing complexity, the highly dynamic environment and the changing customers' needs in the Telecommunication sector present considerable challenges to achieve satisfactory service outcomes. Therefore, seeking reuse, automation and flexibility in Telco's processes turn into a pressing need. However, due to the lack of formal semantic information, dynamic and adaptive processes cannot be set up. Semantic business process management (SBPM), which aims at achieving more automation and flexibility in the BPM lifecyle by using semantic technologies, has recently gained a huge interest in both research and industry market. In this paper, we show how the theoretical benefits of SBPM can be made a reality in a real industry case study. Within a collaboration with Orange labs, we define a semantic framework for advanced Telco's workflow management. The framework has been implemented on the top of existing web-based platforms for business process design and execution and validated using standard service supervision processes from Orange labs. As a result, the benefits and limitations of using SBPM in a real Telco's setting are reported.

Composite applications leveraging the functionalities offered by Web services are today the underpinnings of enterprise computing. However, current Web services composition systems make only use of functional requirements in the selection process of component Web services, while the transactional consistency and quality of service (QoS) are crucial parameters of most business applications. In this paper, we propose a novel approach for Web services selection and composition based on transactional requirements and QoS constraints. The transactional requirements are defined by the engineers using the acceptable termination states concept. We give a formalization of our approach in Event-B. We incrementally combine the modeling and verification activities related to formal development process supported by Event-B method. The verification activity is based on the proof and model-checking.

Cyber Physical Society (CPS) feature a tight integration between computation, communication, and control in their operation and interactions with the environment in which they are deployed. The concept of capability, which is the semantics of an action or specific functionality, is therefore a cornerstone in modern Cyber Physical Society, especially those service or process aware. In this track, we present dedicated techniques for capability management and engineering, which can be applied and reused in a large number of areas such as SOA, BPM, Cloud Computing and Internet of Things. These techniques will certainly boost several related research efforts in Cyber Physical Society. This track aims at shedding the light on the importance of capability engineering as well as at bringing together researchers and practitioners working in capability engineering to achieve the vision of cyber physical society by means of current techniques.

Finding suitable services to be composed in a service-based business process is always a big challenge. In this paper, we present a query language that addresses this challenge by exploiting relations between services in process models. Concretely, we define the composition context of a service as a process fragment around it. We match composition contexts to compute the similarity between services. Finally, we conduct the query based on this computed similarity and flexibly added constraints. Our proposed query helps to retrieve services that have similar composition context with the queried service. It can be applied in process design or execution phase. We perform experiments on a large dataset of real process models. Experimental results show that our approach is feasible and efficient.

Verifying Web service composition in a dynamic environment remains one of the most difficult tasks despite the efforts and the previous proposed research works because new services can be composed during the execution step and others can automatically appear, disappear, or be updated. To achieve the Web service composition specification and verification, we introduce a new concept, called dynamic pattern. A dynamic pattern is an extension of a static one. Then, we propose to formalize dynamic Web Service composition in Event-B using dynamic patterns. The resulting model is progressively verified using proofs. We use animator of model (ProB) to detect a variety of problems, such as deadlocks or other unexpected behavior of a model.

In service-oriented computing, a user usually needs to locate a desired service for: (i) fulfilling her requirements or (ii) replacing a service, which disappears or is unavailable for some reasons, to perform an interaction. With the increasing number of services available within an enterprise and over the Internet, locating a service online may not be appropriate from the performance perspective, especially in large Internet-based service repositories. Instead, services usually need to be clustered according to their similarity. Thereafter, services in one or several clusters are necessary to be examined online during dynamic service discovery. In this paper, we propose to cluster data providing (DP) services using a refined fuzzy C-means algorithm. We consider the composite relation between DP service elements (i.e., input, output, and semantic relation between them) when representing DP services in terms of vectors. A DP service vector is assigned to one or multiple clusters with certain degrees. In addition, we introduce some operations for managing service clusters, when new services emerge or existing services disappear or become unavailable. When grouping similar services into one cluster, while partitioning different services into different clusters, the capability of service search engine is improved significantly. We have prototyped our approach and the source code is freely available on the web. We have evaluated our clustering approach in different settings and the results are very promising.

In recent years, we witness the increasing trend that more applications are developed by composing Web services. Services interact with each other in ways not necessarily foreseen during their development phase. In this setting, mismatches usually exist between services, and adapters are typically synthesized to reconcile mismatches occurring in certain interactions. The technique that identifies the most suitable provider service from a set of functionally equivalent candidates with respect to certain requirements specified by the requester is essential. To address this challenge, we propose a technique called adaptability assessment , which 1) provides a set of conditions that determines when service interactions can be conducted and 2) computes an adaptation degree that specifies to what extent a service protocol is adaptable to another service protocol. Adaptability assessment complements the techniques that synthesize adapters. Specifically, when adaptability assessment suggests that two service protocols can conduct some interactions according to the adaptation mechanisms of a certain adapter and these interactions can fulfill the requester's requirements, then the effort of synthesizing an adapter is beneficial to potential service interactions. As such, the requester can acknowledge whether his/her expected interactions can be conducted or not and under which conditions. This is important before conducting an interaction, particularly when this interaction is critical, long running, and nonrepeatable.

The tremendous growth in the amount of available web services (WS) impulses many researchers on proposing recommender systems to help users discover services. Most of the proposed solutions analyzed query strings and web service descriptions to generate recommendations. However, text based recommendations approaches depend mainly on user's perspective, languages and notations which easily decrease recommendation's efficiency. In this paper, we propose to take into account user's behaviors instead of text based analysis. We apply collaborative filtering technique on user's interactions. We propose and implement two algorithms based on Vector Space Model and Latent Semantic Indexing to validate our approach. We also provide evaluation methods with different datasets in order to compare and assert the efficiency of our two algorithms.

In this paper, we propose to use communities as a mean to organize Web services registries in a multi-registry environment. First, we propose a semantic model for Web services registry description (WSRD). A WSRD description depicts the functionalities offered by services advertised by a given registry. Thereafter, we propose an implicit approach for building communities based on the WSRD descriptions using a fuzzy clustering technique. Eventually, this clustering will be helpful for selecting an adequate registry for service requesters. Provided experiments in this paper show the feasibility of our approach.

Service Component Architecture specification (SCA) is an emerging and promising technology for the development, deployment and integration of Internet applications. This technology supports the management of dynamic availability and treats the heterogeneity between the components of distributed applications. However, this technology is not able to solve all problems. Currently, software systems are evolving. This factor makes development, verification and maintenance of systems more complex than before. One solution to remedy this was the use of the Model Driven Engineering (MDE) approach in the development and verification process. The purpose of this paper is to apply an approach MDE to obtain SCA models and to verify the properties of these models. To reach our purpose, we applied two transformations: The first one to obtain SCA models using UML 2.0 metamodel and the second transformation to ensure the verification of the properties of these models using event-B metamodel. To achieve this, we study the UML 2.0 component metamodel, the SCA metamodel and the event-B metamodel. We have defined transformation rules in ATL language.

In this paper, the work developed aims at contributing to the research related to the Quality of Service (QoS) for Web services. The aim of this research is twofold, first, it helps the designers and developers to provide better web services and second, and it helps ensure consistent software architecture as a reference model for many applications. To achieve this, we model, first, the meta-QoS model of the Web services. Then, we formalize the QoS of the Web services by referring to ARMANI. We also, handle the mediation of the composite Web services with the ACME using an automatic MDE approach and implementing a tool for this aim: Web services compositions are transformed onto ACME specifications.

In a service oriented B2B context, companies aiming to cooperate and interact with others have to make their services available through private service registries. Since the number of registries can be as large as the number of companies, the resulting registry network has to be organized to assist and improve the service discovery process. In this paper, we propose to use communities as a means to organize Web services registries in such a context. First, we propose a semantic model for Web services registry description (WSRD). A WSRD description is a semantic model depicting the functionalities offered by services advertised by a given registry. Thereafter, we propose an implicit approach for building communities based on the registries WSRD descriptions using a fuzzy clustering technique. Eventually, this clustering will be helpful for selecting an adequate registry for service requesters. Provided experimental evaluations in this paper show that our approach is efficient in realistic situations.

Web services are defined independently of any execution context. Due to their inherent autonomy and heterogeneity, it is difficult to examine the behaviour of composite services, especially in case of failures. This paper is interested in ensuring composite services reliability. Reliable composition is defined as a composition where all instance executions are correct from a transactional and business point of view. In this paper, the authors propose a transactional approach for ensuring reliable Web service compositions. The approach integrates the expressivity power of workflow models and the reliability of Advanced Transactional Models (ATM). This method offers flexibility for designers to specify their requirements in terms of control structure, using workflow patterns, and execution correctness. Contrary to ATM, the authors start from the designers’ specifications to define the appropriate transactional mechanisms that ensure correct executions according to their requirements.

The discovery of suitable Web services for a given task is one of the central operations in Service-Oriented Architectures, and research on Web services (WS)s aims at automating this step. For the large amount of available WSs that can be expected in real-world settings, the efficiency of automated discovery becomes important. For this, we propose to exploit previous discovery results to reduce the search space in distributed registry environment. The architecture we adopt consists in structuring registries of WSs into communities. Then, we propose to adapt recommendation techniques to ease the services discovery process. This consists of (1) the selection of communities of registries using semantic matching and (2) the recommendation of registries within these communities using user-characterization based technique. To put our proposed distributed registry architecture in practise and to test the efficiency of recommendation-based discovery, we propose to implement a Peer-to Peer (P2P) registries environment on top of JXTA platform.

SOA (Service Oriented Architecture) defines a new Web services cooperation paradigm in order to develop distributed applications using reusable services. The handling of such collaboration has different problems that lead to many research efforts. In this paper, we address the problem of Web service composition. Indeed, various heterogeneities can arise during the composition. The resolution of these heterogeneities, called mediation, is needed to achieve a service composition. In this paper, we propose a sound approach to formalize Web services composition mediation with the ADL (Architecture Description Language) ACME. To do so, we first model the meta model of composite service manager and mediation. Then we specify semi formal properties associated with this meta model using OCL (Object Constraint Language). Afterwards, we formalize the mediation protocol using Armani, which provides a powerful predicate language in order to ensure service execution reliability.

Different from process components, Web services are defined independently from any execution context. A key challenge of (Web) service compositions is how to ensure reliable execution. Due to their inherent autonomy and heterogeneity, it is difficult to reason about the behavior of service compositions especially in case of failures. Therefore, there is a growing interest for verification techniques which help to prevent service composition execution failures. In this paper, we propose an event-driven approach to validate the transactional behavior of service compositions. The transactional behavior verification is done either at design time to validate recovery mechanisms consistency, or after runtime to report execution deviations and repair design errors, and therefore, formally ensure service execution reliability. By using the Event Calculus formalism to specify and check the transactional behavior consistency of service composition, our approach provides a logical foundation to ensure service execution reliability.

Workflow models have been used and refined for years to execute processes within organisations. To deal with collaborative processes (choreographies) these internal workflow models have to be aligned with the external behaviour advertised through Web service interfaces. However, traditional workflow management systems (WfMS) do not offer this functionality. Simply sharing and merging process models is often not possible, because workflow management lacks a widely accepted standard theory for workflow models.Multiple research and standardisation efforts to integrate different workflow theories have been proposed over the years. XPDL is the most widely used standard for process model interchange and supported by over 80 systems.However, XPDL also lacks the possibility to relate a workflow model to its possible choreography interface abstractions.To remedy this situation, we propose to abstract the XPDL model to a higher-level model, perform the integration and the compaction algorithms at that level and then ground it back to the desired choreography models. We develop and use an integrated ontology which is based on the XPDL standard for this purpose. To facilitate the abstraction and grounding, we present a mapping procedure to automatically translate XPDL and BPMN workflow models into this ontology. After translation, these models are annotated with a parameterised role model and other collaborative properties. We present a compaction procedure that automatically maps the annotated models into external choreography interfaces that expose only the relevant information for a particular partner collaboration. Our procedure is agnostic with respect to the target choreography model. We demonstrate our approach using WSMO choreographies which enables us to automatically generate interface models from any WfMSs that supports XPDL export.

Web service interactions lie in the core of SOA. Due to the autonomy, heterogeneity and continuous evolution of Web services, mediators are usually needed to support service interactions to overcome possible mismatches that may exist among business processes. In this paper, we introduce a space-based architecture for process mediator which considers both control-flow and data-flow, present possible mismatch patterns, and suggest how they can be automatically mediated. Our work can be used to perform runtime mediation and thus to facilitate service interactions.

Due to the inherent autonomy and heterogeneity of Web services, ensuring composite services reliability remains a challenging problem. Extending the classical control flow with a transactional flow (encapsulating a set of recovery mechanisms) is widely accepted for ensuring composite services reliability. However, current approaches define recovery mechanisms in and ad-hoc way while they have to respect consistency rules regarding the control flow. In this paper, we propose a formal model based on event calculus for capturing both control and transactional flow of composite services. Following a pattern based modelling approach, we depict a set of rules characterising consistent transactional flow that can be defined w.r.t a given control flow. In addition, we propose two complementary approaches for enhancing composite services reliability.

One of the main challenges that encounter Web services is how to ensure reliable compositions. In this paper we present an approach that starts from a composite service effective executions to improve its reliability. Basically, we propose a set of mining techniques to discover its model and its transactional behavior from an event based log. Then, based on this mining step, we use a set of rules to improve its recovery mechanisms