Kugamoorthy Gajananan, Aly Megahed, M. Abe, Taiga Nakamura, Mark A. Smith
Information technology (IT) service providers competing for high valued contracts need to produce a compelling proposal with competitive price. The traditional approach to pricing IT service deals, which builds up the bottom-up costs from the hierarchy of services, is often time consuming, resource intensive, and only available late as it requires granular information of a solution. Recent work on top-down pricing approach enables efficient and early estimates of cost and prices using high level services to overcome and complement these problems. In this paper, we describe an extended pricing method for top-down pricing using the secondary service level. The method makes use of data lower level services to calculate improved estimates, yet still requires minimal input. We compare the previous and new approaches based on industrial data on historical and market deals, and demonstrate that the new approach can generate more accurate estimates. In addition, we also show that mining historical data would yield more accurate estimation than using market data for services, experimental results are in consistent with our findings in previous work.
{"title":"A Top-Down Pricing Algorithm for IT Service Contracts Using Lower Level Service Data","authors":"Kugamoorthy Gajananan, Aly Megahed, M. Abe, Taiga Nakamura, Mark A. Smith","doi":"10.1109/SCC.2016.99","DOIUrl":"https://doi.org/10.1109/SCC.2016.99","url":null,"abstract":"Information technology (IT) service providers competing for high valued contracts need to produce a compelling proposal with competitive price. The traditional approach to pricing IT service deals, which builds up the bottom-up costs from the hierarchy of services, is often time consuming, resource intensive, and only available late as it requires granular information of a solution. Recent work on top-down pricing approach enables efficient and early estimates of cost and prices using high level services to overcome and complement these problems. In this paper, we describe an extended pricing method for top-down pricing using the secondary service level. The method makes use of data lower level services to calculate improved estimates, yet still requires minimal input. We compare the previous and new approaches based on industrial data on historical and market deals, and demonstrate that the new approach can generate more accurate estimates. In addition, we also show that mining historical data would yield more accurate estimation than using market data for services, experimental results are in consistent with our findings in previous work.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128428184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Enterprises today are keen to unlock new business values of their legacy services towards new trends (e.g., cloud and mobile). To accelerate such process, automatic feature location techniques can enable developers to rapidly locate/understand implementations of certain services (e.g., services to expose, transform or improve). Existing feature location techniques [1-3, 5-10, 32] provide a good foundation but have several key limitations: limited leverage of description sources, less considerations of internal behaviors, and ineffectiveness for the identification of service-relevant code entries. To address these limitations, we propose a behavior model based feature location approach and implement a tool named BMLocator. In the offline phase, BMLocator applies Natural Language Processing (NLP) techniques and static code analysis to extract “behavior models” of code units via considering multiple information sources. While in the online phase, given a service description, BMLocator first extracts its behavior model and then recommends service-relevant code units/entries by matching its behavior model with code units under analysis. Through evaluations with public service requests of open-source projects (e.g., Tomcat and Hadoop), we show that the approach is more effective in recommending service-relevant code entries (e.g., most of entries are prioritized as the first ones) than existing techniques (i.e., TopicXP[37], CVSSearch[6]).
{"title":"What Code Implements Such Service? A Behavior Model Based Feature Location Approach","authors":"Guangtai Liang, Yabin Dang, Hao Chen, Lijun Mei, Shaochun Li, Yi-Min Chee","doi":"10.1109/SCC.2016.23","DOIUrl":"https://doi.org/10.1109/SCC.2016.23","url":null,"abstract":"Enterprises today are keen to unlock new business values of their legacy services towards new trends (e.g., cloud and mobile). To accelerate such process, automatic feature location techniques can enable developers to rapidly locate/understand implementations of certain services (e.g., services to expose, transform or improve). Existing feature location techniques [1-3, 5-10, 32] provide a good foundation but have several key limitations: limited leverage of description sources, less considerations of internal behaviors, and ineffectiveness for the identification of service-relevant code entries. To address these limitations, we propose a behavior model based feature location approach and implement a tool named BMLocator. In the offline phase, BMLocator applies Natural Language Processing (NLP) techniques and static code analysis to extract “behavior models” of code units via considering multiple information sources. While in the online phase, given a service description, BMLocator first extracts its behavior model and then recommends service-relevant code units/entries by matching its behavior model with code units under analysis. Through evaluations with public service requests of open-source projects (e.g., Tomcat and Hadoop), we show that the approach is more effective in recommending service-relevant code entries (e.g., most of entries are prioritized as the first ones) than existing techniques (i.e., TopicXP[37], CVSSearch[6]).","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125318850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hafida Naim, Mustapha Aznag, M. Quafafou, Nicolas Durand
The number of community detection algorithms is growing continuously adopting a topological based approach to discover optimal subgraphs or communities. In this paper, we propose a new method combining both topology and semantic to evaluate and rank community detection algorithms. To achieve this goal we consider a probabilistic topic based approach to define a new measure called semantic divergence of communities. Combining this measure with others related to prior knowledge, we compute a score for each algorithm to evaluate the effectiveness of its communities and propose a ranking method. We have evaluated our approach considering communities of real web services.
{"title":"Semantic Divergence Based Evaluation of Web Service Communities","authors":"Hafida Naim, Mustapha Aznag, M. Quafafou, Nicolas Durand","doi":"10.1109/SCC.2016.101","DOIUrl":"https://doi.org/10.1109/SCC.2016.101","url":null,"abstract":"The number of community detection algorithms is growing continuously adopting a topological based approach to discover optimal subgraphs or communities. In this paper, we propose a new method combining both topology and semantic to evaluate and rank community detection algorithms. To achieve this goal we consider a probabilistic topic based approach to define a new measure called semantic divergence of communities. Combining this measure with others related to prior knowledge, we compute a score for each algorithm to evaluate the effectiveness of its communities and propose a ranking method. We have evaluated our approach considering communities of real web services.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114774893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Takao Nakaguchi, Yohei Murakami, Donghui Lin, T. Ishida
Service composition is the technique of creating new services by combining several existing services. Composite services can be also combined with other composite services to form nested or hierarchical services. Given that service composition depends on the interoperability created by using common network protocols and invocation interfaces, a composite service can have an impractically large number of variations depending of the number of available services and the composite's structure. It is hard to enumerate and maintain all variations possible. To solve this problem, we introduce a higher-order function that can take functions as parameters to allow function invocation. In concrete terms, we propose the following methods: (1) a hierarchical service composition description by introducing higher-order functions and (2) a method to implement (1) in an existing composite service execution system. As a test, we apply the proposals to Language Grid, and evaluate the results. They show that our methods can reduce the number of variations that need to be registered and managed even though their overheads are quite practical.
{"title":"Higher-Order Functions for Modeling Hierarchical Service Bindings","authors":"Takao Nakaguchi, Yohei Murakami, Donghui Lin, T. Ishida","doi":"10.1109/SCC.2016.110","DOIUrl":"https://doi.org/10.1109/SCC.2016.110","url":null,"abstract":"Service composition is the technique of creating new services by combining several existing services. Composite services can be also combined with other composite services to form nested or hierarchical services. Given that service composition depends on the interoperability created by using common network protocols and invocation interfaces, a composite service can have an impractically large number of variations depending of the number of available services and the composite's structure. It is hard to enumerate and maintain all variations possible. To solve this problem, we introduce a higher-order function that can take functions as parameters to allow function invocation. In concrete terms, we propose the following methods: (1) a hierarchical service composition description by introducing higher-order functions and (2) a method to implement (1) in an existing composite service execution system. As a test, we apply the proposals to Language Grid, and evaluate the results. They show that our methods can reduce the number of variations that need to be registered and managed even though their overheads are quite practical.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122113469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Ahvar, S. Ahvar, Z. Mann, N. Crespi, Joaquín García, R. Glitho
Distributed clouds have recently attracted many cloud providers and researchers as a topic of intensive interest. High energy costs and carbon emissions are two significant problems in distributed clouds. Due to the geographic distribution of data centers (DCs), there are a variety of resources, energy prices and carbon emission rates to consider in a distributed cloud, which makes the placement of virtual machines (VMs) for cost and carbon efficiency even more critical than in centralized clouds. Most previous work in this field investigated either optimizing cost without considering the amount of produced carbon or vice versa. This paper presents a cost and carbon emission-efficient VM placement method (CACEV) in distributed clouds. CACEV considers geographically varying energy prices and carbon emission rates as well as optimizing both network and server resources at the same time. By combining prediction-based A* algorithm with Fuzzy Sets technique, CACEV makes an intelligent decision to optimize cost and carbon emission for providers. Simulation results show the applicability and performance of CACEV.
{"title":"CACEV: A Cost and Carbon Emission-Efficient Virtual Machine Placement Method for Green Distributed Clouds","authors":"E. Ahvar, S. Ahvar, Z. Mann, N. Crespi, Joaquín García, R. Glitho","doi":"10.1109/SCC.2016.43","DOIUrl":"https://doi.org/10.1109/SCC.2016.43","url":null,"abstract":"Distributed clouds have recently attracted many cloud providers and researchers as a topic of intensive interest. High energy costs and carbon emissions are two significant problems in distributed clouds. Due to the geographic distribution of data centers (DCs), there are a variety of resources, energy prices and carbon emission rates to consider in a distributed cloud, which makes the placement of virtual machines (VMs) for cost and carbon efficiency even more critical than in centralized clouds. Most previous work in this field investigated either optimizing cost without considering the amount of produced carbon or vice versa. This paper presents a cost and carbon emission-efficient VM placement method (CACEV) in distributed clouds. CACEV considers geographically varying energy prices and carbon emission rates as well as optimizing both network and server resources at the same time. By combining prediction-based A* algorithm with Fuzzy Sets technique, CACEV makes an intelligent decision to optimize cost and carbon emission for providers. Simulation results show the applicability and performance of CACEV.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"337 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115806051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohamed Mohamed, Obinna Anya, T. Sakairi, S. Tata, N. Mandagere, Heiko Ludwig
Managing service quality in heterogeneous Cloud environments is complex: different Cloud providers expose different management interfaces. To manage Service Level Agreements (SLAs) in this context, we have developed the rSLA framework that enables fast setup of SLA monitoring in dynamic and heterogeneous Cloud environments. The rSLA framework is made up of three main components: the rSLA language to formally represent SLAs, the rSLA Service, which interprets the SLAs and implements the behavior specified in them, and a set of Xlets - lightweight, dynamically bound adapters to monitoring and controlling interfaces. In this paper, we present the rSLA framework, and describe how it enables the monitoring and enforcement of service level agreements for heterogeneous Cloud services.
{"title":"The rSLA Framework: Monitoring and Enforcement of Service Level Agreements for Cloud Services","authors":"Mohamed Mohamed, Obinna Anya, T. Sakairi, S. Tata, N. Mandagere, Heiko Ludwig","doi":"10.1109/SCC.2016.87","DOIUrl":"https://doi.org/10.1109/SCC.2016.87","url":null,"abstract":"Managing service quality in heterogeneous Cloud environments is complex: different Cloud providers expose different management interfaces. To manage Service Level Agreements (SLAs) in this context, we have developed the rSLA framework that enables fast setup of SLA monitoring in dynamic and heterogeneous Cloud environments. The rSLA framework is made up of three main components: the rSLA language to formally represent SLAs, the rSLA Service, which interprets the SLAs and implements the behavior specified in them, and a set of Xlets - lightweight, dynamically bound adapters to monitoring and controlling interfaces. In this paper, we present the rSLA framework, and describe how it enables the monitoring and enforcement of service level agreements for heterogeneous Cloud services.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131684913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jinhui Yao, M. Shepherd, Jing Zhou, Lina Fu, Dennis Quebe, J. Echols, Xuejin Wen
Service-oriented thinking is one of the fastest growing paradigms in information technology, with relevance to many other disciplines. Service-oriented analytic workflows can bring together various analytic computing tools and compute resources offered as services to answer complex research questions. The current healthcare system in United States is experiencing fundamental transformation as it moves from a volume-based business to a value-based business. One strategy that healthcare organizations start to deploy is leveraging their healthcare data to gain insights for optimizing their operation. Therefore it is perfectly logical to extend the application of service-oriented analytic workflows to population health studies, as these rely on both medical expertise and processing of large data sets to serve end users of various backgrounds and skill sets. However, in the practical application of such service oriented approach, the user often finds it difficult to choose the right services or workflows that can help them to find the answers to their questions. To tackle this problem, we propose a heuristic recommendation method based on the feature significance. The user submits an enquiry, then based on which, the system will recommend the services and compositions that are likely to produce meaningful answers. In this paper, we will elaborate the interactions between different roles in a service oriented analytic system, develop the modeling to illustrate the relations among enquiry, features, services and workflows, propose the algorithm for service recommendation, architect the system and show a reference implementation of a prototype.
{"title":"Recommending Analytic Services for Population Health Studies Based on Feature Significance","authors":"Jinhui Yao, M. Shepherd, Jing Zhou, Lina Fu, Dennis Quebe, J. Echols, Xuejin Wen","doi":"10.1109/SCC.2016.67","DOIUrl":"https://doi.org/10.1109/SCC.2016.67","url":null,"abstract":"Service-oriented thinking is one of the fastest growing paradigms in information technology, with relevance to many other disciplines. Service-oriented analytic workflows can bring together various analytic computing tools and compute resources offered as services to answer complex research questions. The current healthcare system in United States is experiencing fundamental transformation as it moves from a volume-based business to a value-based business. One strategy that healthcare organizations start to deploy is leveraging their healthcare data to gain insights for optimizing their operation. Therefore it is perfectly logical to extend the application of service-oriented analytic workflows to population health studies, as these rely on both medical expertise and processing of large data sets to serve end users of various backgrounds and skill sets. However, in the practical application of such service oriented approach, the user often finds it difficult to choose the right services or workflows that can help them to find the answers to their questions. To tackle this problem, we propose a heuristic recommendation method based on the feature significance. The user submits an enquiry, then based on which, the system will recommend the services and compositions that are likely to produce meaningful answers. In this paper, we will elaborate the interactions between different roles in a service oriented analytic system, develop the modeling to illustrate the relations among enquiry, features, services and workflows, propose the algorithm for service recommendation, architect the system and show a reference implementation of a prototype.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122351497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid proliferation of wireless networks and mobile devices, Mobile Peer-to-Peer (MP2P) networks have attracted a huge amount of users for resource sharing. However, in an MP2P network, peers frequently join and leave the network, which makes the network topology dynamically change. Thus, it is difficult to establish long term and effective trust relationship among peers, making trust management become a challenging task. In this paper, we propose a dynamic grouping-based trust model DGTM, to classify peers. A group is formed according to the peers' interests. The experiments illustrate that our proposed dynamic grouping-based trust model DGTM always achieves the highest successful transaction rate under different circumstances.
{"title":"A Dynamic Grouping-Based Trust Model for Mobile P2P Networks","authors":"Meijuan Jia, Huiqiang Wang, Bin Ye, Yan Wang","doi":"10.1109/SCC.2016.121","DOIUrl":"https://doi.org/10.1109/SCC.2016.121","url":null,"abstract":"With the rapid proliferation of wireless networks and mobile devices, Mobile Peer-to-Peer (MP2P) networks have attracted a huge amount of users for resource sharing. However, in an MP2P network, peers frequently join and leave the network, which makes the network topology dynamically change. Thus, it is difficult to establish long term and effective trust relationship among peers, making trust management become a challenging task. In this paper, we propose a dynamic grouping-based trust model DGTM, to classify peers. A group is formed according to the peers' interests. The experiments illustrate that our proposed dynamic grouping-based trust model DGTM always achieves the highest successful transaction rate under different circumstances.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115663908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the flourish of mobile computing, mobile Apps dominate the daily lives of users. Focusing on the social relations among Apps, this paper makes an empirical study on constructing the Global App Network (GAN) in terms of three types of inter-App relations (i.e., intent-based, semantics correlation based, and similarity-based ones), recovering Personal App Network (PAN) in terms of App usage log of each user, and exploring the characteristics of GAN and PAN. The study is based on two real-world datasets: the first one includes thousands of Apps collected from a real-world Android App store, and the second one contains 2-month App usage logs of 40 volunteers. Several interesting phenomena are observed from the study, such as (1) a large portion of implicit inter-App relations that are welcome by massive users are actually ignored by App developers, (2) some explicit relations proactively designed by App developers are actually not frequently used by users, (3) although there is a certain commonness among PANs of different users, each PAN shows a significant personalized pattern which delineates the individualized behaviors of a user. These conclusions are of significance to bi-directional App recommendations, i.e., to recommend neglected inter-App relations to App developers, and, to recommend common and popular inter-App relations to users.
{"title":"Global and Personal App Networks: Characterizing Social Relations among Mobile Apps","authors":"Youqiang Hao, Zhongjie Wang, Xiaofei Xu","doi":"10.1109/SCC.2016.37","DOIUrl":"https://doi.org/10.1109/SCC.2016.37","url":null,"abstract":"With the flourish of mobile computing, mobile Apps dominate the daily lives of users. Focusing on the social relations among Apps, this paper makes an empirical study on constructing the Global App Network (GAN) in terms of three types of inter-App relations (i.e., intent-based, semantics correlation based, and similarity-based ones), recovering Personal App Network (PAN) in terms of App usage log of each user, and exploring the characteristics of GAN and PAN. The study is based on two real-world datasets: the first one includes thousands of Apps collected from a real-world Android App store, and the second one contains 2-month App usage logs of 40 volunteers. Several interesting phenomena are observed from the study, such as (1) a large portion of implicit inter-App relations that are welcome by massive users are actually ignored by App developers, (2) some explicit relations proactively designed by App developers are actually not frequently used by users, (3) although there is a certain commonness among PANs of different users, each PAN shows a significant personalized pattern which delineates the individualized behaviors of a user. These conclusions are of significance to bi-directional App recommendations, i.e., to recommend neglected inter-App relations to App developers, and, to recommend common and popular inter-App relations to users.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129835006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Suman Roy, A. Sajeev, A. Gopichand, A. Bhattacharya
Business process models expressed in languages such as BPMN (Business Process Model and Notation) play a critical role in implementing the workflows in modern organizations. However, control flow errors such as deadlock and lack of synchronization as well as syntactic errors arising out of poor modeling practices often occur in industrial process models. In this paper, we provide an empirical diagnostic analysis of such errors for real-life industrial process models. The investigation involved models from different application domains. It turns out that error frequency has non-linear relation with error depth (the maximum depth at which an error occurred) across models from all domains. Error occurrence has statistically significant correlations (p <; 0.0001) with the size of sub-processes as well as with the swim-lane interactions, however only the former correlation is strong (Spearman's ρ = 0.579). We also develop a logistic regression model to estimate error probability in terms of the following metrics: sub-process size, coefficient of connectivity, sequentiality and structuredness; the predictive model fits well with the data (χ2(4, N = 1261) = 720.68, p <; 0.001).
用BPMN(业务流程模型和符号)等语言表示的业务流程模型在实现现代组织中的工作流方面发挥着关键作用。然而，控制流错误(如死锁和缺乏同步)以及由不良建模实践引起的语法错误经常发生在工业流程模型中。在本文中，我们为现实工业过程模型提供了这种误差的实证诊断分析。调查涉及来自不同应用领域的模型。结果表明，在所有领域的模型中，错误频率与错误深度(错误发生的最大深度)呈非线性关系。错误发生率与统计学显著相关(p <;0.0001)与子过程的大小以及泳道相互作用有关，但只有前者的相关性很强(斯皮尔曼ρ = 0.579)。我们还开发了一个逻辑回归模型，根据以下指标来估计错误概率:子过程大小、连通性系数、顺序性和结构性;预测模型与数据拟合良好(χ2(4, N = 1261) = 720.68, p <;0.001)。
{"title":"An Empirical Analysis of Diagnosis of Industrial Business Processes at Sub-process Levels","authors":"Suman Roy, A. Sajeev, A. Gopichand, A. Bhattacharya","doi":"10.1109/SCC.2016.33","DOIUrl":"https://doi.org/10.1109/SCC.2016.33","url":null,"abstract":"Business process models expressed in languages such as BPMN (Business Process Model and Notation) play a critical role in implementing the workflows in modern organizations. However, control flow errors such as deadlock and lack of synchronization as well as syntactic errors arising out of poor modeling practices often occur in industrial process models. In this paper, we provide an empirical diagnostic analysis of such errors for real-life industrial process models. The investigation involved models from different application domains. It turns out that error frequency has non-linear relation with error depth (the maximum depth at which an error occurred) across models from all domains. Error occurrence has statistically significant correlations (p <; 0.0001) with the size of sub-processes as well as with the swim-lane interactions, however only the former correlation is strong (Spearman's ρ = 0.579). We also develop a logistic regression model to estimate error probability in terms of the following metrics: sub-process size, coefficient of connectivity, sequentiality and structuredness; the predictive model fits well with the data (χ2(4, N = 1261) = 720.68, p <; 0.001).","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128406837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：