AOP (aspect oriented programming) is a programming paradigm for enhancing the degree of modularity in a system and it helps developers to maintain and manage the system easier. A bad smell means that a bad design that may lead to negative effects while developing a software system. Bad smells may also appear in the system that is developed using AOP paradigm. Therefore, it is important that bad smells can be detected in an AOP-implemented system. In this paper, various types of AOP bad smells are described with its definition and discovering patterns. A two-stage analysis method is proposed for identifying these AOP bad smells in a software system. Furthermore, we provided flow charts that aim to identify these AOP bad smells for helping developers to understand how to extract AOP bad smells.
{"title":"A Study of the Definition and Identification of Bad Smells in Aspect Oriented Programming","authors":"Li-Qing Guo, Kuo-Hsun Hsu, Chang-Yen Tsai","doi":"10.1109/ICEBE.2015.59","DOIUrl":"https://doi.org/10.1109/ICEBE.2015.59","url":null,"abstract":"AOP (aspect oriented programming) is a programming paradigm for enhancing the degree of modularity in a system and it helps developers to maintain and manage the system easier. A bad smell means that a bad design that may lead to negative effects while developing a software system. Bad smells may also appear in the system that is developed using AOP paradigm. Therefore, it is important that bad smells can be detected in an AOP-implemented system. In this paper, various types of AOP bad smells are described with its definition and discovering patterns. A two-stage analysis method is proposed for identifying these AOP bad smells in a software system. Furthermore, we provided flow charts that aim to identify these AOP bad smells for helping developers to understand how to extract AOP bad smells.","PeriodicalId":153535,"journal":{"name":"2015 IEEE 12th International Conference on e-Business Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129745155","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}
Aiming at the problem that the NAIVE algorithm which is taken to handle the similarity query based on LCSS over data stream window (SQLSW) cannot get query results until calculations on all elements in the full dynamic programming matrix are finished, the SQLSW query processing algorithm based on Possible Solution domain optimization strategy (SQLSW-PS) is proposed. It defines possible solution (PS) domain of the dynamic programming matrix about every window. Based on characters of matrix members in the PS domain and the similarity query, it can get query result on the condition that the LCSS similarity function value has not been obtained yet, and reduce lots of computations related to matrix members. It is revealed by extensive experiments that the SQLSW-PS outperforms current algorithms in time, and is effective in handling the SQLSW query.
{"title":"Study on the Similarity Query Based on LCSS over Data Stream Window","authors":"Shaopeng Wang, Yingyou Wen, Hong Zhao","doi":"10.1109/ICEBE.2015.21","DOIUrl":"https://doi.org/10.1109/ICEBE.2015.21","url":null,"abstract":"Aiming at the problem that the NAIVE algorithm which is taken to handle the similarity query based on LCSS over data stream window (SQLSW) cannot get query results until calculations on all elements in the full dynamic programming matrix are finished, the SQLSW query processing algorithm based on Possible Solution domain optimization strategy (SQLSW-PS) is proposed. It defines possible solution (PS) domain of the dynamic programming matrix about every window. Based on characters of matrix members in the PS domain and the similarity query, it can get query result on the condition that the LCSS similarity function value has not been obtained yet, and reduce lots of computations related to matrix members. It is revealed by extensive experiments that the SQLSW-PS outperforms current algorithms in time, and is effective in handling the SQLSW query.","PeriodicalId":153535,"journal":{"name":"2015 IEEE 12th International Conference on e-Business Engineering","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128720172","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}
Performance prediction is one of the most important research topics of service-oriented systems. To investigate the performance of composite services in queuing condition, this paper introduces a queuing-network-based model. Analytical methods are introduced to evaluate the queue-length, wait-time and completion-duration. The case study (especially the case of airline ticket booking application) shows that the proposed model captures real-world composite services effectively. Through Monte-carlo simulations in the case study, we show analytical models are verified by simulative results.
{"title":"A Queuing-Network-Based Approach to Performance Evaluation of Service Compositions","authors":"Gang Zhou, Yunni Xia, K. Yu, Qiang Chen, Ping Gu","doi":"10.1109/ICEBE.2015.31","DOIUrl":"https://doi.org/10.1109/ICEBE.2015.31","url":null,"abstract":"Performance prediction is one of the most important research topics of service-oriented systems. To investigate the performance of composite services in queuing condition, this paper introduces a queuing-network-based model. Analytical methods are introduced to evaluate the queue-length, wait-time and completion-duration. The case study (especially the case of airline ticket booking application) shows that the proposed model captures real-world composite services effectively. Through Monte-carlo simulations in the case study, we show analytical models are verified by simulative results.","PeriodicalId":153535,"journal":{"name":"2015 IEEE 12th International Conference on e-Business Engineering","volume":"854 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126965715","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}
In recent years, recommendation systems have developed greatly, and is so widely used in online systems such as book, movie or friend recommendation. Current recommendation systems have problems with cold start and new entry. Cyber-Anima is a cyber-image of a user's anima, and we can derive one's cyber-anima by its user input and other observations of the user. With Cyber-Anima, we can build a recommendation system. The system take the concepts in cyber-anima into consideration and build relations between these concepts and user behavior to make further recommendations. We do some experiment with the dataset from travelhub.cn, which is a platform for travel services and is sponsored by the national technology plan. From the experiment, our implementation is better than FISM and Item-based collaborative and the time cost is reasonable.
{"title":"A Recommendation System for Travel Services Based on Cyber-Anima","authors":"Mi Zhang, Yinsheng Li, Aiqin Zhou, Zhou Fang","doi":"10.1109/ICEBE.2015.28","DOIUrl":"https://doi.org/10.1109/ICEBE.2015.28","url":null,"abstract":"In recent years, recommendation systems have developed greatly, and is so widely used in online systems such as book, movie or friend recommendation. Current recommendation systems have problems with cold start and new entry. Cyber-Anima is a cyber-image of a user's anima, and we can derive one's cyber-anima by its user input and other observations of the user. With Cyber-Anima, we can build a recommendation system. The system take the concepts in cyber-anima into consideration and build relations between these concepts and user behavior to make further recommendations. We do some experiment with the dataset from travelhub.cn, which is a platform for travel services and is sponsored by the national technology plan. From the experiment, our implementation is better than FISM and Item-based collaborative and the time cost is reasonable.","PeriodicalId":153535,"journal":{"name":"2015 IEEE 12th International Conference on e-Business Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133033639","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}
Feng Tian, Yan Chen, Xiaoqian Wang, Tian Lan, Q. Zheng, K. Chao
In general, the dataset of volunteer recommendation systems shows the sparsity, while a volunteer recommendation system required performing the function of recommending voluntary activities interesting to a specific volunteer. To our knowledge, there exists no such kind of recommendation systems. To begin with, this paper firstly presents an analysis of a dataset collected from a real volunteering application website and discovered two features: the locations between the volunteers and the voluntary activities are in close proximity, and the resulting graph which describes the participation relationship between volunteers and voluntary activities is a kind of bipartite, showing many small communities inside it. We call the first discovery 'geographically closely participating', and the second discovery 'participating together'. Based on these findings, a rating matrix, featuring a matching method for the recommendation algorithm has been constructed. Secondly, we propose a weighted Personal Rank algorithm to implement the required functions of a volunteer recommendation system by employing the registration information of volunteers and voluntary activities. This includes the volunteers' preferences, activities and location etc. The comparison of proposed method with the rating matrix-based collaborative filter algorithm and the Personal Rank algorithms shows that our proposed method outperforms them.
{"title":"Common Features Based Volunteer and Voluntary Activity Recommendation Algorithm","authors":"Feng Tian, Yan Chen, Xiaoqian Wang, Tian Lan, Q. Zheng, K. Chao","doi":"10.1109/ICEBE.2015.17","DOIUrl":"https://doi.org/10.1109/ICEBE.2015.17","url":null,"abstract":"In general, the dataset of volunteer recommendation systems shows the sparsity, while a volunteer recommendation system required performing the function of recommending voluntary activities interesting to a specific volunteer. To our knowledge, there exists no such kind of recommendation systems. To begin with, this paper firstly presents an analysis of a dataset collected from a real volunteering application website and discovered two features: the locations between the volunteers and the voluntary activities are in close proximity, and the resulting graph which describes the participation relationship between volunteers and voluntary activities is a kind of bipartite, showing many small communities inside it. We call the first discovery 'geographically closely participating', and the second discovery 'participating together'. Based on these findings, a rating matrix, featuring a matching method for the recommendation algorithm has been constructed. Secondly, we propose a weighted Personal Rank algorithm to implement the required functions of a volunteer recommendation system by employing the registration information of volunteers and voluntary activities. This includes the volunteers' preferences, activities and location etc. The comparison of proposed method with the rating matrix-based collaborative filter algorithm and the Personal Rank algorithms shows that our proposed method outperforms them.","PeriodicalId":153535,"journal":{"name":"2015 IEEE 12th International Conference on e-Business Engineering","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115223025","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}
Abderrahim Ait Wakrime, S. Benbernou, Saïd Jabbour
Cloud Computing based Software as a Service (SaaS) combines multiple Web Services to satisfy a SaaS request, therefore SaaS should be able to dynamically seek replacements for faulty or underperforming services, thus performing self-healing. However, it may be the case of available services that do not match all user's request, leading the system to grind to a halt. It is better to have an alternative candidate in the cloud while not fullfilling all the constraints. In this paper, we provide a Relaxation SaaS solution to repair the failed user's query by rewriting it with an approximation. It is based on an incremental approach that exploits Quantified Satisfiability (QSAT) problem to repair the query and provide an alternative SaaS that leads to a successful request closed to the original one with maximized Quality of Service (QoS).
{"title":"Relaxation Based SaaS for Repairing Failed Queries over the Cloud Computing","authors":"Abderrahim Ait Wakrime, S. Benbernou, Saïd Jabbour","doi":"10.1109/ICEBE.2015.49","DOIUrl":"https://doi.org/10.1109/ICEBE.2015.49","url":null,"abstract":"Cloud Computing based Software as a Service (SaaS) combines multiple Web Services to satisfy a SaaS request, therefore SaaS should be able to dynamically seek replacements for faulty or underperforming services, thus performing self-healing. However, it may be the case of available services that do not match all user's request, leading the system to grind to a halt. It is better to have an alternative candidate in the cloud while not fullfilling all the constraints. In this paper, we provide a Relaxation SaaS solution to repair the failed user's query by rewriting it with an approximation. It is based on an incremental approach that exploits Quantified Satisfiability (QSAT) problem to repair the query and provide an alternative SaaS that leads to a successful request closed to the original one with maximized Quality of Service (QoS).","PeriodicalId":153535,"journal":{"name":"2015 IEEE 12th International Conference on e-Business Engineering","volume":"280 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116190074","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}
Jingnan Tang, Liang Luo, Kai-Ming Wei, Xun Guo, Xiao-yu Ji
Cloud computing provides utility-oriented IT services for users worldwide, and it enables offering various kinds of applications to consumer in scientific or business field based on a pay-as-you-go model. Although cloud computing is still in its infancy, the scale of cloud infrastructure is expanding fast, which result in huge energy consumption and operating costs. Due to the complex architecture of cloud infrastructure, it is hard to evaluate and optimize energy consumption of cloud infrastructure in a non-intrusive manner under varying application, user configurations and requirements. In this paper, we present Bin-Balancing Algorithm (BBA), an innovative resource scheduling algorithm for private clouds that integrating the advantages of both bin packing solutions and polygons correlation calculations. BBA is designed to optimize energy consumption, while considering the task deadline, host PE (processing element), memory and bandwidth. Polygons correlation calculation integrated in BBA is used to meet the elastic characteristics of cloud computing services. BBA is validated and well compared with existing resource scheduling algorithms in Cloud Sim toolkit. The results demonstrate that BBA can save energy in cloud infrastructure while balancing the loss of performance and SLA of cloud users.
{"title":"A Heuristic Resource Scheduling Algorithm of Cloud Computing Based on Polygons Correlation Calculation","authors":"Jingnan Tang, Liang Luo, Kai-Ming Wei, Xun Guo, Xiao-yu Ji","doi":"10.1109/ICEBE.2015.68","DOIUrl":"https://doi.org/10.1109/ICEBE.2015.68","url":null,"abstract":"Cloud computing provides utility-oriented IT services for users worldwide, and it enables offering various kinds of applications to consumer in scientific or business field based on a pay-as-you-go model. Although cloud computing is still in its infancy, the scale of cloud infrastructure is expanding fast, which result in huge energy consumption and operating costs. Due to the complex architecture of cloud infrastructure, it is hard to evaluate and optimize energy consumption of cloud infrastructure in a non-intrusive manner under varying application, user configurations and requirements. In this paper, we present Bin-Balancing Algorithm (BBA), an innovative resource scheduling algorithm for private clouds that integrating the advantages of both bin packing solutions and polygons correlation calculations. BBA is designed to optimize energy consumption, while considering the task deadline, host PE (processing element), memory and bandwidth. Polygons correlation calculation integrated in BBA is used to meet the elastic characteristics of cloud computing services. BBA is validated and well compared with existing resource scheduling algorithms in Cloud Sim toolkit. The results demonstrate that BBA can save energy in cloud infrastructure while balancing the loss of performance and SLA of cloud users.","PeriodicalId":153535,"journal":{"name":"2015 IEEE 12th International Conference on e-Business Engineering","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127557459","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}
Within the retail sector, a broad range of sensing devices are used to capture data to be interpreted into retail intelligence. The sensors many capture simplified data sets, such as the number of customers who have walked through a doorway or down an aisle, to more complex data, such as demographic or behavioural data. For a retailer this provides an opportunity of analyzing a rich source of information to optimize the customer experience and thereby improve sales. However, the sensors that are deployed are typically manufactured by different vendors, and may be installed over an extended period of time. This leads to difficulties when integrating and triangulating the data in an automated system as each retailer may have a bespoke collection of capture devices. This paper reports upon a project to overcome these challenges through the adoption of approaches taken in Field Device Integration (FDI), commonly used to integrate sensors and actuators in a manufacturing environment. The paper proposes an architectural model based on investigative work, and also discusses a related issue that has arisen in the implementation of the framework, that of multitenancy.
{"title":"Integration of Sensors to Improve Customer Experience: Implementing Device Integration for the Retail Sector","authors":"Mark Anderson, Joseph Bolton","doi":"10.1109/ICEBE.2015.71","DOIUrl":"https://doi.org/10.1109/ICEBE.2015.71","url":null,"abstract":"Within the retail sector, a broad range of sensing devices are used to capture data to be interpreted into retail intelligence. The sensors many capture simplified data sets, such as the number of customers who have walked through a doorway or down an aisle, to more complex data, such as demographic or behavioural data. For a retailer this provides an opportunity of analyzing a rich source of information to optimize the customer experience and thereby improve sales. However, the sensors that are deployed are typically manufactured by different vendors, and may be installed over an extended period of time. This leads to difficulties when integrating and triangulating the data in an automated system as each retailer may have a bespoke collection of capture devices. This paper reports upon a project to overcome these challenges through the adoption of approaches taken in Field Device Integration (FDI), commonly used to integrate sensors and actuators in a manufacturing environment. The paper proposes an architectural model based on investigative work, and also discusses a related issue that has arisen in the implementation of the framework, that of multitenancy.","PeriodicalId":153535,"journal":{"name":"2015 IEEE 12th International Conference on e-Business Engineering","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129487717","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}
Matrix factorisation (MF), an effective recommendation algorithm, predicts user preferences on items based on the historical preferences of other like-minded users. Classical MF methods do not explicitly distinguish the significances across the underlying factors that determine a users' preference on an item. The identical contribution of latent factors during learning results unnecessary updates on unimportant variables that leads to slower and suboptimal convergence. In this paper, we propose a new matrix factorisation method that not only seeks the intrinsic and outstanding factors that determine the users' preferences but also systematically reinforces the contribution generated by these factors. Based on boosting, a factor selection mechanism is developed to account the variable importance of latent factors to generate an ensemble recommender on the selected subspace of the latent factors by the principle of model uncertainty reduction. The proposed method is evaluated against a variety of the state-of-the-art methods of recommender systems on three publicly available benchmark datasets. The results confirm the effectiveness and efficiency of the proposed method.
{"title":"Learning Factor Selection for Boosted Matrix Factorisation in Recommender Systems","authors":"N. Chowdhury, Xiongcai Cai, Cheng Luo","doi":"10.1109/ICEBE.2015.18","DOIUrl":"https://doi.org/10.1109/ICEBE.2015.18","url":null,"abstract":"Matrix factorisation (MF), an effective recommendation algorithm, predicts user preferences on items based on the historical preferences of other like-minded users. Classical MF methods do not explicitly distinguish the significances across the underlying factors that determine a users' preference on an item. The identical contribution of latent factors during learning results unnecessary updates on unimportant variables that leads to slower and suboptimal convergence. In this paper, we propose a new matrix factorisation method that not only seeks the intrinsic and outstanding factors that determine the users' preferences but also systematically reinforces the contribution generated by these factors. Based on boosting, a factor selection mechanism is developed to account the variable importance of latent factors to generate an ensemble recommender on the selected subspace of the latent factors by the principle of model uncertainty reduction. The proposed method is evaluated against a variety of the state-of-the-art methods of recommender systems on three publicly available benchmark datasets. The results confirm the effectiveness and efficiency of the proposed method.","PeriodicalId":153535,"journal":{"name":"2015 IEEE 12th International Conference on e-Business Engineering","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134475266","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}
Readers' continuance use is the basis for the development of mobile reading. This paper conducted an empirical research on mobile readers' continuance intention. The mobile readers are categorized into four groups: information readers, culture readers, recreation readers and research readers. Structural equation modeling is employed to empirically identify the factors that influence the main three types of mobile readers' continuance intention. The results show that, the factors and their significance are different in the three types of reader groups.
{"title":"Research on mobile readers' continuance intention: A reading type perspective","authors":"Xiao Jiang","doi":"10.1109/ICEBE.2015.56","DOIUrl":"https://doi.org/10.1109/ICEBE.2015.56","url":null,"abstract":"Readers' continuance use is the basis for the development of mobile reading. This paper conducted an empirical research on mobile readers' continuance intention. The mobile readers are categorized into four groups: information readers, culture readers, recreation readers and research readers. Structural equation modeling is employed to empirically identify the factors that influence the main three types of mobile readers' continuance intention. The results show that, the factors and their significance are different in the three types of reader groups.","PeriodicalId":153535,"journal":{"name":"2015 IEEE 12th International Conference on e-Business Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122165089","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}
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