2016 IEEE International Conference on Services Computing (SCC)最新文献_第10页

Service Assurance Sustaining Enterprise Task Crowdsourcing Service 服务保证维持企业任务众包服务

2016 IEEE International Conference on Services Computing (SCC) Pub Date : 2016-06-01 DOI: 10.1109/SCC.2016.100

Chithralekha Balamurugan, K. K. Budhraja

{"title":"Service Assurance Sustaining Enterprise Task Crowdsourcing Service","authors":"Chithralekha Balamurugan, K. K. Budhraja","doi":"10.1109/SCC.2016.100","DOIUrl":"https://doi.org/10.1109/SCC.2016.100","url":null,"abstract":"Service Assured task execution is an exemplary need for Enterprise Task Crowdsourcing, even when crowdsourcing tasks on open and public crowdosourcing platforms, which are characteristic of non-committed and discretionary task execution by crowd workers. Service Assurance (SA) has broader semantics than SLA fulfillment in the context of enterprise task crowdsourcing, as explained in our previous work on Service Assurance Framework for Enterprise task crowdsourcing. The framework is designed for open crowd platforms and enables to map a given requester SLA to worker SA requirements and select crowd workers who evince high probability of SA requirement fulfillment. Even with these select crowd workers, it might not be possible to implicitly assume the sustenance of workers' expected SA (which is computed based on workers' prior task executions) in the day to day task executions on the Enterprise Requester tasks, owing to the unbinding task execution facilitated by the open crowd platforms. This might result in defaulting in requester SLA fulfillment, rendering the worker selection (by the framework) futile. Hence, in addition to selecting the required SA fulfilling workers, it is essential to employ suitable SA sustaining strategies in place for sustaining the SA expected of workers. The SA Sustaining Strategies and the Service Assurance Framework together constitute for the Service Assurance Sustaining Enterprise Task Crowdsourcing Service (SAS Service)for Enterprise Tasks, which is deliberated in this paper. The SAS Service's performance is validated using appropriate crowd experiments.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"79 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":"125986534","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}

引用次数: 0

Case Studies in Managing Traffic in a Developing Country with Privacy-Preserving Simulation as a Service 在发展中国家使用隐私保护模拟服务管理流量的案例研究

2016 IEEE International Conference on Services Computing (SCC) Pub Date : 2016-06-01 DOI: 10.1109/SCC.2016.130

B. Srivastava, M. Pallan, M. Madhavan, Ravi Kokku

引用次数: 0

Global and Personal App Networks: Characterizing Social Relations among Mobile Apps 全球和个人应用网络:手机应用的社交关系特征

2016 IEEE International Conference on Services Computing (SCC) Pub Date : 2016-06-01 DOI: 10.1109/SCC.2016.37

Youqiang Hao, Zhongjie Wang, Xiaofei Xu

{"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}

引用次数: 10

Mitigating Performance Unpredictability in Heterogeneous Clouds 减轻异构云中的性能不可预测性

2016 IEEE International Conference on Services Computing (SCC) Pub Date : 2016-06-01 DOI: 10.1109/SCC.2016.83

Boris Teabe, A. Tchana, D. Hagimont

引用次数: 2

Recommending Analytic Services for Population Health Studies Based on Feature Significance 基于特征显著性推荐人群健康研究分析服务

2016 IEEE International Conference on Services Computing (SCC) Pub Date : 2016-06-01 DOI: 10.1109/SCC.2016.67

Jinhui Yao, M. Shepherd, Jing Zhou, Lina Fu, Dennis Quebe, J. Echols, Xuejin Wen

{"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}

引用次数: 0

Fads Phenomenon Operation through Cascade-Based Service Innovation Design 基于层叠式服务创新设计的时尚现象运营

2016 IEEE International Conference on Services Computing (SCC) Pub Date : 2016-06-01 DOI: 10.1109/SCC.2016.114

Pin-Rui Hwang

{"title":"Fads Phenomenon Operation through Cascade-Based Service Innovation Design","authors":"Pin-Rui Hwang","doi":"10.1109/SCC.2016.114","DOIUrl":"https://doi.org/10.1109/SCC.2016.114","url":null,"abstract":"People have a tendency to mimic the actions of the larger group. Herd behavior is one of the most common phenomena in our daily life. Previous studies criticized that homogeneity of herd behavior and blind conformity will cause the bubble to burst. Crowd psychology plays an important role in service marketing. However, is it possible for service operation to thrive in service industry by manipulating the herd behavior? In this study, we proposed and validated that the Cascade-based service innovation (CSI) design which utilizes the attraction effect that highlights the service value and meaning interpretation enable service innovation opportunities. The cascade-based innovation design uses service meaning interpretation as the decoy and applies the attraction effect to drive the cascade-based service innovations. The result indicates that the CSI design could facilitate service providers to manipulate and reshape their service design to achieve sustainable development of service marketing. This study applied hybrid research methodologies that combine empirical research to examine the CSI design for fads operation and qualitative case study, and explore the criteria that enables service providers to operate and manipulate fads phenomenon successfully.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"06 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":"130706749","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}

引用次数: 0

An Empirical Analysis of Diagnosis of Industrial Business Processes at Sub-process Levels 基于子过程层次的工业业务过程诊断实证分析

2016 IEEE International Conference on Services Computing (SCC) Pub Date : 2016-06-01 DOI: 10.1109/SCC.2016.33

Suman Roy, A. Sajeev, A. Gopichand, A. Bhattacharya

{"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}

引用次数: 0

A Service-Friendly Approach to Discover Traveling Companions Based on ANPR Data Stream 基于ANPR数据流的服务友好型同伴发现方法

2016 IEEE International Conference on Services Computing (SCC) Pub Date : 2016-06-01 DOI: 10.1109/SCC.2016.29

Meiling Zhu, Chen Liu, Jianwu Wang, Xiongbin Wang, Yanbo Han

引用次数: 8

Traceability Architecture: Extending EPCIS to Enhance Track and Trace with NoSQL Data Model 可追溯性架构:扩展EPCIS以增强NoSQL数据模型的跟踪和跟踪

2016 IEEE International Conference on Services Computing (SCC) Pub Date : 2016-06-01 DOI: 10.1109/SCC.2016.129

Yalew Kidane, Daeyoung Kim

{"title":"Traceability Architecture: Extending EPCIS to Enhance Track and Trace with NoSQL Data Model","authors":"Yalew Kidane, Daeyoung Kim","doi":"10.1109/SCC.2016.129","DOIUrl":"https://doi.org/10.1109/SCC.2016.129","url":null,"abstract":"Radio Frequency Identification (RFID), specifically the Electronic Product Code (EPC, the next generation of barcode), have improved the way the world works and lives. Basing EPC as an identification, GS1 developed standard framework for common visibility of supply chain data called EPCglobal. EPC information system(EPCIS) is one of the standards which encompasses interface for sharing and capturing data triggered by different events. Because the amount of data captured and stored in each event type is huge and the query interfaces are general, it is difficult to get traceability data easily in reasonable time. Even for some traceability queries like finding products at a location in certain time are too complex to fulfill with the current EPCIS standard implementation. In this paper, by extending EPCIS query interface we have proposed an efficient, well-organized, and scalable traceability architecture. In order to effectively collect and process events in real time using our stream processing, we first proposed an efficient pub/sub based EPCIS. Then, to retrieve information effectively we proposed an adequate column family based data modeling. Finally, we have implemented our system and developed a RESTfull service interface to query track and trace information. Experimental result shows that our traceability architecture with the column based data modeling process traceability-queries more efficiently and scales gracefully.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"56 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":"131617929","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}

引用次数: 2

A Framework for Passengers Demand Prediction and Recommendation 乘客需求预测与推荐框架

2016 IEEE International Conference on Services Computing (SCC) Pub Date : 2016-06-01 DOI: 10.1109/SCC.2016.51

Kai Zhang, Zhiyong Feng, Shizhan Chen, Keman Huang, Guiling Wang

引用次数: 62