Microservice architecture (MSA) has been widely adopted to develop various large-scale distributed systems. Microservice-based systems (MBSs) comprise a number of independently deployed microservices fulfilling the specific functionalities. Unique characteristics of microservices, such as independent and parallel development, rapid iteration, and distributed deployment, result in low observability and reliability of MBSs. A typical solution is to regulate system behavior in specifications of MBSs, and then develop and test MBSs based on these specifications. However, current microservice specifications focus on describing the APIs of microservices without describing the behavior expectation for an MBS. In this article, we propose an annotation-assisted and scenario-oriented approach, called MSA_Sighter, to detect behavior inconsistencies in MBSs. In MSA_Sighter, the details of an MBS are captured in a description model (MSDM), which can be extracted automatically from the functional services through annotation-assisted runtime component instance analysis and static program analysis. Given a specific business scenario, inconsistency detection is conducted by analyzing the actual behavior's conformance to the expected behavior, where the former is collected through distributed tracing while the latter is derived from the MSDM. We have developed a supporting tool called ConsChecker and evaluated MSA_Sighter's effectiveness on three open-source MBSs in GitHub. The experimental results have shown that MSA_Sighter can effectively detect inconsistencies in MBSs during system development and evolution.

The rapid development of web services has made it increasingly challenging for developers to find desired web services. To address this issue, researchers have developed various powerful models for service recommender systems. Recently, graph neural networks have shown promising performance in various deep learning tasks including service recommendation. This paper proposes a novel graph neural network for web service recommendation using a hierarchical attention mechanism that combines a node-level and a motif-level attention mechanisms. The node-level attention mechanism is responsible for aggregating information by the importance of different neighbors, while the motif-level attention mechanism performs a weighted combination of the node embeddings generated from different motif adjacency matrices. Finally, the generated node embeddings are optimized by the multi-layer perceptron (MLP), which in turn provide recommendations. Experimental results on real-world datasets demonstrate that our proposed model outperforms state-of-the-art approaches. Additionally, we conduct a model analysis to investigate the importance of different motifs. Overall, our proposed method shows promising performance for web service recommendation and highlights the potential of using graph neural networks in this domain.

Deep Neural Networks (DNN) based collaborative filtering has been successful in recommending services by effectively generalizing graph-structured data. However, most existing approaches focus on first-order interactions. Although recent approaches have utilized high-order connectivity, they still limit themselves to simple interactions and ignore the pattern of structural sub-graphs/motifs. In this study, we first explore the commonly used motifs in the Mashup-API interaction bipartite graph and propose a dedicated algorithm to generate the motif adjacency matrix. We then propose a Motif-based Graph Attention Network for service recommendation (MGSR) that utilizes a motif-based attention mechanism to capture the high-order information of various motifs, and a Collaborative Filtering model to generate the recommendation prediction. We have conducted extensive experiments on ProgrammableWeb dataset and our results demonstrate the superior performance of our proposed framework over some state-of-the-art approaches.

By leveraging IoT Big Data, BPM can gain real-time physical world information to make faster and more accurate decisions, but there is a technical gap between IoT sensors and businesses. To bridge the gap, an event penetration mechanism from IoT sensors to business processes is proposed along a practical case study. This paper presents a concrete IoT-BPM application case dealing with seaborne safety ensurance in transporting liquefied natural gas (LNG), analyzes its technical challenges, and examines the feasibility and supposed effects of the BRIBOT approach.

A microservice-based system is composed of numerous independently deployed and executed microservices, among which normally exist the complex dependencies. Traditional service composition approaches usually expect the business process predefined at design time. As a result, it is difficult for the microservice-based system to quickly adapt to the frequently changing operation environments and business requirements. To address the above limitations, we propose a variability-enabling and model-driven approach to developing adaptive microservice-based systems. Our approach first models the business process with variability using VxBPMN4MS, an extension of Business Process Model and Notation (BPMN) with support for variability, then transforms the business process model to variability supported microservice composition frameworks, and finally derives business process instances at run-time according to the predefined process configuration. We have developed a platform to automate the proposed approach as much as possible, and conducted a case study to evaluate the effectiveness of the proposed approach and platform.

The complex interventions among sensor streams bring new challenges for IoT applications to derive meaningful information from large amounts of sensor streams. This paper aims to provide a data-driven service creation method for effectively capturing events based on our previous service abstraction – proactive data service. For improving the effectiveness of proactive data service, we consider the potential correlations among sensor streams besides user's pre-definitions when creating service. Based on the assumption that events frequently co-occurred in history have high probability to co-occur again, we regard frequent event sets as one kind of correlations among sensor streams, and propose an algorithm called FP-MFIM to efficiently find the maximum frequent event sets co-occurred in multiple sensor streams. For providing more effective information, we create PD-services with frequent co-occurred event types besides user-defined event types. This paper reports the tryout use of the method in China power grid for power quality event detection and location. Through a series of experiments based on real sensor data from power grid, we verified the efficiency of FP-MFIM algorithm and the effectiveness of our PD-services in real-world scenario.

In an IoT (Internet of Things) environment, event correlations may be dynamically interwoven because events usually span over many interrelated sensors. Our previous works used frequent sequence to measure the event correlations and mined them from a statistical perspective. We also proposed a service hyperlink model to encapsulate the event correlations. With the service hyperlink model, we made a preliminary attempt to reuse valuable event correlations to facilitate IoT applications. To consummate our previous method, this paper refines it in reusing the correlations, and focuses on which event would most probably appear after a previous event has occurred. To effectively mine the event correlations, we extend the traditional motif mining algorithms by introducing time constraint. Moreover, we connect services by service hyperlinks (i.e., abstraction of event correlations) to form a directed graph. An event can be routed on this graph. We have applied our approach in anomaly warning in a coal power plant and made extensive experiments to verify the effectiveness of the approach.

Edge computing may improve the processing quality of big IoT stream data and reduce network operational cost by moving computation onto the edge. However, there are two challenges in integrating cloud and edge computing for big stream data. Firstly, edge equipment usually has very limited computing power as well as storage ability, and apparently cannot support all the processing of big and real-time stream data. A flexible division of such services between edge and cloud is needed. Secondly, edge-end collaboration continuously changes due to some intrinsic interaction of data stream. In this paper, we propose a service-based approach to seamlessly integrating cloud and edge equipment. Based on our service model, we split a cloud service into two parts running on cloud and edge respectively. Also, we propose a dynamic service scheduling mechanism based on the improved bipartite graphs. We can deploy a cloud service to the edge at the right time when a key node emerges. The effectiveness of the proposed approach is demonstrated by examining real cases of China's State Power Grid. Experimental results verify the effectiveness and efficiency of our approach.

Predictive industrial maintenance promotes proactive scheduling of maintenance to minimize unexpected device faults. A fault is not always isolated and may be formed by a propagation of trivial anomalies, which are regarded as service events herein. In this paper, we firstly propose an algorithm for generating service event correlation. Such correlations can show us lots of clues to the anomaly/fault propagation. The correlations are encapsulated into service hyperlinks as our previous works did, and thus we depict the anomaly/fault propagation as service event routing among services via the refined service hyperlinks. Our scenario illustrates that a trivial anomaly may propagate into different faults under different service event correlations. It indicates that the destination of a service event is often uncertain. Therefore, this paper further proposes a heuristic approach to handle the uncertainty problem. Extensive experiments have been made to verify the effectiveness of the approach.

A surge in sensor data volume has exposed the shortcomings of cloud computing, particularly the limitation of network transmission capability and centralized computing resources. The dynamic intervention among sensor streams also brings challenges for IoT applications to derive meaningful information from multiple sensor streams. To handle these issues, this paper proposes a service‐based method with fog computing paradigm based on our previous service abstraction, which can capture meaningful events from multiple sensor streams. In our service abstraction, we utilize correlation analysis method to capture events as variations of correlation among sensor streams. Facing inconsistent frequency and shift of correlation, we propose a Dynamic Time Warping‐ (DTW‐) based algorithm to obtain sensor streams’ lag‐correlation. For adaptively aggregating related events from different services, we also propose an event routing algorithm to assist the composition of cascaded events through service collaboration. This paper reports the tryout use of our method in Chinese power grid for detecting abnormal situations of power quality. Through a series of experiments based on real sensor data in power grid, we verified that our method can reduce the network transmission and computing resource with high accuracy.

Abnormality Detection in power plant is a typical IoT application which aims to identify anomalies in these routinely collected monitoring sensor data; intend to help detect possible faults in the equipment. However, on the development of abnormality detection, we find that there are three challenges. The first one is the lack of cooperation between sensors. It means that the physical sensors cannot share and interact with each other. Secondly, the rapid increase in volume of sensor data and dynamic situation of production result in challenges to predefine all possible associations between sensors. Thirdly, it is difficult to build IoT application for developers who have little or no professional knowledge about production process. In this paper, we proposed a proactive data service model to encapsulate stream sensor data into services. We spread events among the proactive data services. By analysis of event correlations, we have realized service hyperlinks which help to offer the proactive real-time interaction with services. Real application and experiments verified that our proactive data service based method is more effective compare with traditional rule-based methods to detect abnormalities in power plant.

In this demo paper, we present a new data service composition sequence generation approach to solve the ad-hoc data query problem in EDMIS. Our approach allows end users to input some keywords, and then the data services related are found and the Top-K data services composition sequences are generated as output.

IoT service and service composition provide an effective means to develop IoT applications based on correlating multiple sensor data. The change of specific sensor data can cause others' changes under uncertain situations. It makes difficult for defining service composition plan in advance to build IoT application. This paper proposes a data-driven service composition method based on our previous proactive data service model. We regard service events frequently happen together with given service event as its situation, and the service events happen next as reacted actions under the situation. We analyze two kinds of correlation among service events via an improved FP-tree algorithm, and realize the service composition at runtime based on the real-time service events. Based on the real sensor data set in a coal-fired power plant, a series of experiments demonstrate that our method can effectively detect new service events based on current service events.

In an IoT environment, process analysis becomes more difficult as a process usually spans over a set of autonomous and distributed sensors. This paper consummates our previous service hyperlink model, to encapsulate dependencies among events generated from services. To effectively discover service hyperlinks, we transform the service hyperlink discovery problem into a frequent sequence mining problem. Existing frequent sequence mining algorithms cannot be directly used because they do not take the temporal constraints in event dependencies into consideration. Based on the dataset from a real power plant as well as several synthetic datasets, we do lots of experiments to verify the effectiveness and efficiency of our algorithm.

In Big Data era, continuous data with low latency and high throughput makes high-availability essential for stream computing. Traditional availability guarantee is tightly-coupled and inefficient for customization and reuse. In this paper, a framework is proposed to improve the availability of stream computing, in which basic functions are provided as general services like reliable point-to-point communication and distributed status management. With its help, high-level patterns can be achieved effectively. Comprehensive experiments have been designed and evaluated to show the availability improvement with acceptable extra overheads.

With the increasing abundance of traffic data from sensors and devices, the integration and analysis of such streaming data are gaining importance in many application scenarios. This paper proposes a service-based approach for integrating and analyzing the traffic sensor data to support green commute in China by automatically discovering carpooling companions within a community. Our focuses are on modeling and design of the carpooling discovery services, algorithms for implementing the services, and performance enhancement when the data volume scales up. The proposed approach is verified with experiments using real-world data.

In recent years, artifact-centric business process modeling is gaining momentum with its improved flexibility and extensibility. In order to support the rapid translation of the traditional activity-centric processes into this new type of processes, this paper proposes a novel approach to automatically transforming an activity-centric process model into a group of lifecycles of artifacts and their interactions, which represent the behavior of its corresponding artifact-centric process model. Algorithms on translating an activity-centric process model into a tree model, finding the dependencies between two object/artifact states based on the tree model, and synthesizing the lifecycles of artifacts have been proposed. Throughout the paper, we illustrate our approach with an order processing running scenario.

Though the existing data service mashup tools are gaining acceptance, it is still challenging for developers with no or little programming skills to develop data service mashups for dealing with situational and ad-hoc business problems. The paper focuses on interactive recommendation in which assistance is provided in a context-sensitive manner when the mashup plan can't be determined in advance. The paper analyzes the problem with a motivating scenario of mashup building for criminal investigation. Inspired by the observation that there exist dataflow patterns for certain integration functionalities, a dataflow-pattern-based recommendation framework is proposed to solve the problems. The framework can not only recommend data services by discovering similar situations, but also recommend mashup patterns and target data services. We propose a method to analyze the relationships between data services and dataflow patterns through both mining history logs and matching the input/output parameters. Further, to recommend target data services, we propose a method to transform the data mashup plans into mixed graphs and apply the graph-based substructure pattern mining (gSpan) algorithm on them. Experiments show that the dataflow-pattern-based recommendation approach for data service mashup is effective and efficient.

With the continuous expansion of the scope of traffic sensor networks, traffic sensory data becomes widely available and is continuously being produced. Traffic sensory data gathered by large amounts of sensors show the massive, continuous, streaming and spatio-temporal characteristics compared to traditional traffic data. In order to satisfy the requirements of different applications with these data, we need to have the capability of processing both real-time traffic sensory data in streaming way and historical traffic sensory data in large amount. In this paper, we present an approach and corresponding system for traffic sensory data processing, which is designed to combine spatio-temporal data partition, parallel pipeline processing and stream computing to support traffic sensory data processing in a scalable architecture with real-time guarantee. Three types of applications in real project are also described in detail to show the significant effect gains of the proposed approach and system. Numerical evaluations according to experiment results also show that the system can gain high performance in terms of the processing time of traffic sensory data stream.

The Second International Workshop on Service and Cloud Based Data Integration (SCDI 2014) intends to bring researchers, practitioners and vendors together to discuss and share ideas and experiences. It fosters novel models, methodologies, and solution patterns that address the data integration issue and fit in the service and cloud based settings. This workshop will focus on the use of service and/or cloud based technologies to meet the new data integration challenges that are not well served by the current approaches.

SUMMARY Under distributed Cloud environment, the real‐time and continuous data stream makes the availability during processing essential but expensive. For aggregation tasks of data stream processing systems, traditional replica‐based high‐availability mechanisms require large overheads at run‐time and long recovery latency at fail‐time, because of specific nature of aggregations. In this paper, we focus on the typical quantile tasks and propose a feature‐based high‐availability mechanism to reduce related overhead and the latency. With the help of monitor module, quantile feature is maintained incrementally through histogram synopsis over time‐based sliding window, and the failed quantile tasks can be recovered precisely with high probability in an efficient way. The effectiveness has been analyzed theoretically, and meanwhile, the acceptable tradeoff between overheads and performance has been demonstrated by comprehensive experiments on both synthetic and real data. Copyright © 2013 John Wiley & Sons, Ltd.

Data process in Cloud or IoT (Internet of Things) sometimes implies continuous real-time queries as data streams. In order to acquire extreme value of data stream over time-based sliding window, traditional approaches computed the exact solution through vast space especially under ultra circumstances like high-rate or high-concurrency. In this paper, we design space-bounded synopsis data structure and extreme aggregation algorithm to get approximate solution by finite extreme candidates over time sliding window, whose validity can be theoretically guaranteed. Comprehensive experiments over synthetic and real data set are designed to analyze the tradeoff between accuracy and overhead, which also illustrate the efficiency.

Cloud computing provides virtualized, dynamically-scalable computing power. At the same time, reduction of cost is also considered as an important advantage of cloud computing. Data integration can notably benefit from cloud computing because integrating data is usually an expensive task. However, existing optimization techniques pay less attention on the fact that different execution plans of the same data integration application generate different usage costs while cloud computing provides good enough performance, so this paper introduces the cost optimization of cloud-based data integration system. The data integration system's data service layer facilitates accessing and composing information from a range of enterprise data sources through data service composition. In addition, two task scheduling algorithms for parallel part and non-parallel part are proposed to minimize the usage cost required to complete the execution of composite data service when computational capability provided by cloud computing is charged. Both of the two can obtain optimal plans in polynomial time. Experiments with the system indicate that our algorithms can lead to significant cost saving over more straightforward techniques.

Today, when the Internet is concerned, one seldom thinks about the network itself and the TCP/IP suite only. With the boom of Web applications, Internet services, new business models and innovative computing paradigms (e.g. software-as-a-service model and cloud computing), the Internet has evolved into an indispensable social infrastructure and the largest computing platform of the world as well. It remains a challenging issue herein how end users can "program" and share their own dependable internet-based applications with cyber services and within virtual communities in cyberspace. In the first part of this tutorial, we discuss the above-stated trends, identify the bottlenecks of the contemporary service computing approaches and Web service technologies, in particular when data and discrete events are concerned, and raise some considerations for further developments.

This paper proposes a novel approach to service management and monitoring on the basis of service collaboration relations that can be extracted from service composition history. An undirected and weighted service collaboration network is constructed therefore. On the network, two abstractions are defined: service centrality, which measures a service's collaboration capability comparing to other services for a long period, and service activeness, which measures the frequency of a service's collaboration with others for a short period. The capability of service management among Open APIs is investigated and demonstrated. Experimental data are collected from Programmable Web with the period from 2005-9-14 to 2009-9-21. Experiments show that the defined metrics can properly reflect the service properties.

The ability to deliver different performance levels based on tenant-specific service level agreements (SLAs) is a key requirement for multi-tenant Internet applications. However, workload variations and extensive resource sharing among tenants make this goal hard to achieve. We address the issue with a performance regulator based on feedback-control. The regulator has a hierarchical structure, with which a high-level controller manages request admission rates to prevent overloading and a low-level controller manages resource allocation for admitted requests to track a specified level of service differentiation between the cohosted tenants. A prototype implementation of the performance regulator based on Tomcat and MySQL is provided and a multi-tenant version of RUBBoS benchmark is used for evaluation. Experimental results indicate that the regulator effectively bounds the response time for each tenant while maintaining high resource utilization levels.

Process enactment plays a pivotal role in BPM systems. In order to enhance the scalability and robustness of BPM systems, a straightforward solution is to provide a redundant system with multi-engine architecture. However, without an effective scheduler, the multi-engine BPM systems cannot play out its advantages. This paper focuses on the design of an adaptive scheduler which can handle both process-level and activity-level scheduling based on dynamic weighted scheduling algorithm. The experiments show that, with the scheduler, the load capacity of the multi-engine BPM system can be improved and the average response time of process requests can be reduced, especially when each engine node has some differences in configurations.

This paper presents an end-user-oriented programming environment called Mashroom. Major contributions herein include an end-user programming model with an expressive data structure as well as a set of formally-defined mashup operators. The data structure takes advantage of nested table, and maintains the intuitiveness while allowing users to express complex data objects. The mashup operators are visualized with contextual menu and formula bar and can be directly applied on the data. Experiments and case studies reveal that end users have little difficulty in effectively and efficiently using Mashroom to build mashup applications.

In this paper, an agent-mediated service framework is proposed to ease the construction of virtual organizations of standalone applications. Using this framework, on one hand, service providers can control the usage of their own services, and on the other hand, an agent representing certain stakeholders can dynamically search for capable partners and negotiate with their agents to dynamically form a virtual organization. Incorporating business level service composition language - VINCA into the framework further promotes the participation of business level users in constructing virtual organizations

No abstract available.

Issues of workflow systems adaptability and evolution capability are discussed from two perspectives, namely design and evolution of workflow systems from a continuous software engineering perspective as well as redesign and runtime on-the-fly modifications of workflow models from an application perspective. Special attention is paid to a suitable infrastructure support for designing more configurable and adaptive workflow systems. A new workflow language, called Higher-Order Object Nets (HOON), is introduced. The intended uses of HOON are, on one side, to directly describe real-world business processes, and on the other side, to serve as a build-time composition model and a run-time overall control skeleton of the underlying software components that support individual business activities. On the basis of HOON and CORBA, a layered, framework-based and generic system architecture is presented.