2018 IEEE 15th International Conference on e-Business Engineering (ICEBE)最新文献

{"title":"Service Dependency Based Dynamic Load Balancing Algorithm for Container Clusters","authors":"Jianxin Zhang, Rui Ren, Chengxi Huang, Xiang Fei, Wu Qun, Hongming Cai","doi":"10.1109/ICEBE.2018.00021","DOIUrl":"https://doi.org/10.1109/ICEBE.2018.00021","url":null,"abstract":"Recently e-business have grown explosively with the widespread of Internet of Things (IoT). To acheive better performance and higher resource utilization, many services providers design their services following the principle of micro service and deploy them on container clusters like kubernetes. However, the existing approaches fail to address service dependency problem. The service dependency is a challenge introduced when micro service is applied for e-business, the performance of a service is not only decided by the processing ability of its backend servers, but also by performance of other serivces it relies on to provide its functionality. To address this, this paper proposes a dynamic load-balancing solution for kubernetes which can combine service dependency to give a more accurate description for request resource consumption and backend server ability, and finally performs a better load balancing. Experimental results are given to validate the performance of proposed solution.","PeriodicalId":221376,"journal":{"name":"2018 IEEE 15th International Conference on e-Business Engineering (ICEBE)","volume":"18 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116865725","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}

{"title":"Publisher's Information","authors":"","doi":"10.1109/icebe.2018.00065","DOIUrl":"https://doi.org/10.1109/icebe.2018.00065","url":null,"abstract":"","PeriodicalId":221376,"journal":{"name":"2018 IEEE 15th International Conference on e-Business Engineering (ICEBE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134572900","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}

{"title":"Optimal Evidence Collection for Accountability in the Cloud","authors":"Fatma Masmoudi, M. Sellami, M. Loulou, A. Kacem","doi":"10.1109/ICEBE.2018.00022","DOIUrl":"https://doi.org/10.1109/ICEBE.2018.00022","url":null,"abstract":"In multi-tenant cloud services, accountability can be used to strengthen the trust of tenants in the cloud. It provides the reassurance that the processing of personal data hosted in the cloud is done according to tenants' requirements (a.k.a. accountability obligations). Ensuring accountability requires multiple measures ranging from preventive controls to violation detection and analysis, based on evidences so as to prove that a violation has occurred or to ensure violation judgment. In a complex cloud environment with multi-tenant services, judging violations encounters difficulties due to the plethora of evidences to be analyzed, which may burden the post-violation analysis in terms of latency and workloads. In this work, we offer a method ensuring the collection of the necessary and minimal (optimal) evidences and avoiding re-evaluating all of them for each violated obligation. Basically, we use a linear program -with an objective function under a set of constraints-and we perform actions in order to obtain optimal evidences elements for the judgment. Finally, our approach is implemented and the results of our experiments highlight its feasibility.","PeriodicalId":221376,"journal":{"name":"2018 IEEE 15th International Conference on e-Business Engineering (ICEBE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133094859","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}

{"title":"Design of Evaluation System for Digital Education Operational Skill Competition Based on Blockchain","authors":"Bin Wu, Yinsheng Li","doi":"10.1109/ICEBE.2018.00025","DOIUrl":"https://doi.org/10.1109/ICEBE.2018.00025","url":null,"abstract":"By letting students simulate operations and games on a digital education operation system, schools are able to inspect learning achievement and teaching quality. In digital education area, we are able to use blockchain technology to improve competition mode. It's helpful to simplify process, improve efficiency and avoid the problem of opaque and falsification messages. Besides, it can provide unchangeable digital certification of academic achievement. Based on existing research foundation, against features on related users and services, especially the standard and trustful problem in competitions and evaluation mode nowadays, I studied competition mode based on blockchain technology, designed blockchain's application mode and frame, analyzed evaluation criteria and algorithm, designed an operational skill evaluation model, developed an operational skill competition evaluation system based on e-business sandbox and experimented it.","PeriodicalId":221376,"journal":{"name":"2018 IEEE 15th International Conference on e-Business Engineering (ICEBE)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123166428","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}

{"title":"Research on Influence Factors of Micro-Blog Users' Social Influence","authors":"Y. Su, Wei Qi, J. Qin","doi":"10.1109/ICEBE.2018.00056","DOIUrl":"https://doi.org/10.1109/ICEBE.2018.00056","url":null,"abstract":"This study examines the factors that have an impact on users' social influence and the affecting mechanism as well in the context of Sina Micro-blog. A conceptual model of users' social influence factors with different effecting paths was established and empirically tested with a sample of 438 microblog users of Sina(from weibo.com). The research results show that Degree of Active, Sustained Duration and Account Maturity all have a direct positive impact on Micro-blog users' social influence, while Innovation Ability has a indirect positive impact on it.","PeriodicalId":221376,"journal":{"name":"2018 IEEE 15th International Conference on e-Business Engineering (ICEBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124285495","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}

{"title":"Electronic Human Resource Management Survey Enabled by Big Data Analysis","authors":"Hongwei Wang, Danmoyi Tan, W. Liu","doi":"10.1109/ICEBE.2018.00066","DOIUrl":"https://doi.org/10.1109/ICEBE.2018.00066","url":null,"abstract":"This paper studies on the application of big data in human resource management. However, the application of big data still stays at the starting stage. the most commonly mentioned concept still remains at the field of marketing. How to apply relevant principles and concepts of \"big data\" to the field of human resource by utilizing data visualization and raise the efficiency of enterprise human resource management is a very interesting topic to explore.[1] This paper first introduces the research background of the topic, the research status and relevant theory of human resource management and big data theory worldwide This paper also elaborates the relevant concepts and theory of big data and data visualization. Moreover, it also shows the reform and influence brought by data to human resource management. This paper also analyzes the feasibility, modules and analysis of big data and data visualization in human resource management and analyzes the effect of data visualization in human resource management by setting Diankou, China as an example. Model GM ( 1, 1) is established by using grey system theory on the basis of the present talent situation. The demand for talents in Diankou for 3 years is predicted and suggestions of talent cultivation and introduction can help with the manufacturing industry on the basis of the predicted data.","PeriodicalId":221376,"journal":{"name":"2018 IEEE 15th International Conference on e-Business Engineering (ICEBE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125420625","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}

{"title":"Shapes Similarity and Feature Reconstruction Comparison Based Active Contour Model","authors":"Ni Bo, Xiantao Cai, Jiaxin Chen","doi":"10.1109/ICEBE.2018.00026","DOIUrl":"https://doi.org/10.1109/ICEBE.2018.00026","url":null,"abstract":"The medical Internet of things is the foundation of smart medical. Medical image is the main resource for transmission on the medical Internet of things. Ultrasound image, as a primary medical image, is widely used in computer-aided therapy. The segmentation of lesion region in ultrasound image sequences plays a crucial role in computer-aided therapy. Active contour models are widely used in ultrasound image segmentation to extract the lesion boundary through the low level appearance cues of lesion region. However, due to diseases and imaging artifacts, the low level appearance cues might cause weak or misleading features which corrupts the performance of active contour. In this situation, the shape prior becomes a powerful tool to aid active contour to resist the interference with misleading features. However, the various ways to model the prior of shapes are usually learnt from a large set of annotated data, which is not always feasible in practice. It is doubted that the existing shapes in the training set will be sufficient to model the new instance in the testing image. In this paper, a novel active contour based on shape similarity and feature reconstruction comparison is proposed to segmenting ultrasonic image sequence. In our works, the similarity of object shapes in the image sequence is modeled as a shape prior in a active contour model, which can be interpreted as an unsupervised approach of shape prior modeling without a large number of annotated data. Furthermore, a novel sparse representation based object boundary searching strategy, named feature reconstruction comparison, is proposed by exploiting both the low level appearance cues comparison of the object and background to reduce the error of searching, which is also used to resist the defects of ultrasound image. In order to verify the performance of our method, the clinical image sequences were used as the training and test set to validate our method. The proposed method was compared with three well-known methods in the same test set. The results demonstrates that the proposed method can consistently improve the performance of active contour models and increase the robustness against image defects consequently, it improves the efficiency and effect of the computer assisted therapy","PeriodicalId":221376,"journal":{"name":"2018 IEEE 15th International Conference on e-Business Engineering (ICEBE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115938869","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}

{"title":"Host Overloading Detection Based on EWMA Algorithm in Cloud Computing Environment","authors":"Shin-Li Lu, Jen-Hsiang Chen","doi":"10.1109/ICEBE.2018.00052","DOIUrl":"https://doi.org/10.1109/ICEBE.2018.00052","url":null,"abstract":"Energy consumption and Service Level Agreement (SLA) in Cloud computing environment are important cloud management issues. Dynamic consolidation of the Virtual Machines (VMs) need effective and efficient distribution for VMs migration to hosts in data center. The process of VMs migration needs to evaluate host capability, VM placement and reallocation, which satisfy SLA criterions under a flexible service plan. Therefore, the plan is to select effective resource allocation to achieve cost minimization, reduce energy consumption and avoid SLA violation. We proposes Exponentially Weighted Moving Average (EWMA) algorithm to detect overloaded hosts, which deals with dynamic consolidation of VMs based on an analysis of historical data of the resource usage by VMs. It increases the accuracy in calculation of the upper threshold for host overloading and consequently increases accuracy in identification to deal with VMs migration issue.","PeriodicalId":221376,"journal":{"name":"2018 IEEE 15th International Conference on e-Business Engineering (ICEBE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127092130","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}

{"title":"A Traffic Prediction System Based on Traffic Investigation Data and Mobile Phone Signaling","authors":"Han Feng, Lihong Jiang, Hongming Cai","doi":"10.1109/ICEBE.2018.00035","DOIUrl":"https://doi.org/10.1109/ICEBE.2018.00035","url":null,"abstract":"Traffic prediction is becoming more important for users to avoid traffic congestion. This paper represents a traffic prediction method based on the modelling of road traffic investigation data and mobile phone signaling as ElasticNet Regression using traffic investigation data and Road Node Fitting Algorithm using User Track. A framework is proposed to combine the two kinds of data and a multi-functional traffic prediction system. A prototype system is developed. Discussion is demonstrated in the paper.","PeriodicalId":221376,"journal":{"name":"2018 IEEE 15th International Conference on e-Business Engineering (ICEBE)","volume":"24 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125891343","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}

{"title":"Capital Structure and Firm Performance: Empirical Research Based on Global E-Retailing Companies","authors":"Rui She, J. Guo","doi":"10.1109/ICEBE.2018.00048","DOIUrl":"https://doi.org/10.1109/ICEBE.2018.00048","url":null,"abstract":"This paper presents an empirical research study investigating the relationship between capital structure and firm performance in a sample of global e-retailing companies. We adopt Data Envelopment Analysis (DEA) to calculate the best practice frontier, then, use firm efficiency as a measure of the distance from best practice frontier. We also use debt ratio to represent capital structure. Through these measures, we first examine whether the effect of leverage on efficiency is positive or negative, based on the trade-off theory, the agency cost theory, and the pecking order theory. Next, we explore their reverse causality relationship following the efficiency-risk hypothesis and franchise-value hypothesis. After building the model and quantilen regressions, we find out the results are partial consistent with pecking order theory.","PeriodicalId":221376,"journal":{"name":"2018 IEEE 15th International Conference on e-Business Engineering (ICEBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129899024","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}