R. Ranchal, A. Mohindra, N. Zhou, Shubir Kapoor, B. Bhargava
With the wide availability of products and services through popular e-commerce platforms and dozens of similar offerings to choose from, there is a need to accurately assess and evaluate the quality of offerings. Several studies have shown that consumer feedback is an important source of information. This paper presents: (a) consumer Rating as a Service (RaaS) -- a building block service that can be used to add the consumer feedback lifecycle feature in the development of e-commerce platforms, (b) an approach to evaluate the quality of composite offerings based on the aggregation of consumer ratings using the composition structure and component relationships. Benefits of the proposed service include reduced development effort, shorter delivery time and a fine-grained aggregation of consumer ratings for composite offerings even with limited ratings.
{"title":"Hierarchical Aggregation of Consumer Ratings for Service Ecosystem","authors":"R. Ranchal, A. Mohindra, N. Zhou, Shubir Kapoor, B. Bhargava","doi":"10.1109/ICWS.2015.82","DOIUrl":"https://doi.org/10.1109/ICWS.2015.82","url":null,"abstract":"With the wide availability of products and services through popular e-commerce platforms and dozens of similar offerings to choose from, there is a need to accurately assess and evaluate the quality of offerings. Several studies have shown that consumer feedback is an important source of information. This paper presents: (a) consumer Rating as a Service (RaaS) -- a building block service that can be used to add the consumer feedback lifecycle feature in the development of e-commerce platforms, (b) an approach to evaluate the quality of composite offerings based on the aggregation of consumer ratings using the composition structure and component relationships. Benefits of the proposed service include reduced development effort, shorter delivery time and a fine-grained aggregation of consumer ratings for composite offerings even with limited ratings.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133236739","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}
Due to the large and increasing number of web services, it is very helpful to provide a proactive feed on what is available to users, i.e., Recommending web services. As collaborative filtering (CF) is an effective recommendation method by capturing latent factors, it has been used for service recommendation as well. However, the majority of current CF-based service recommendation approaches predict users' interests through the historical usage data, but not the service description. This makes them suitable for making QoS-based recommendation, but not for functionality-based recommendation. In this paper, we propose to use machine learning approaches to recommend web services to users from both historical usage data and service descriptions. Considering the great popularity of Restful services, our approach is applicable to both structured and unstructured service description, i.e., Free text descriptions. We exploit the idea of collaborative topic regression, which combines both probabilistic matrix factorization and probabilistic topic modeling, to form user-related, service-related, and topic related latent factor models and use them to predict user interests. We extracted public web service data and developer invocation history from Programmable Web and conducted a comprehensive experiment study. The result indicates that this approach is effective and outperforms other representative recommendation methods.
{"title":"Incorporating User, Topic, and Service Related Latent Factors into Web Service Recommendation","authors":"Xumin Liu, Isankumar Fulia","doi":"10.1109/ICWS.2015.34","DOIUrl":"https://doi.org/10.1109/ICWS.2015.34","url":null,"abstract":"Due to the large and increasing number of web services, it is very helpful to provide a proactive feed on what is available to users, i.e., Recommending web services. As collaborative filtering (CF) is an effective recommendation method by capturing latent factors, it has been used for service recommendation as well. However, the majority of current CF-based service recommendation approaches predict users' interests through the historical usage data, but not the service description. This makes them suitable for making QoS-based recommendation, but not for functionality-based recommendation. In this paper, we propose to use machine learning approaches to recommend web services to users from both historical usage data and service descriptions. Considering the great popularity of Restful services, our approach is applicable to both structured and unstructured service description, i.e., Free text descriptions. We exploit the idea of collaborative topic regression, which combines both probabilistic matrix factorization and probabilistic topic modeling, to form user-related, service-related, and topic related latent factor models and use them to predict user interests. We extracted public web service data and developer invocation history from Programmable Web and conducted a comprehensive experiment study. The result indicates that this approach is effective and outperforms other representative recommendation methods.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125723638","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}
Y. Mao, Yang Zhang, Qiang Hua, Hong-Yang Dai, Xing Wang
For realizing non-intrusive protection of open SCADA systems, a non-intrusive solution for distributed open SCADA systems is proposed. The solution consists of three functionality parts: Abstract Execution, Refine State, and Behavior Checking. The approach provides a runtime verification of the system by combining cyclic semantic reconstruction of VM and abstract execution of SCADA services. First, all Internet packets through virtual network bridges are extracted and symbolically linked to specific service model to get simulated traces. Then, cyclic semantic reconstruction is performed to acquire the current service runtime state. According to the service instance state of semantic reconstruction, the simulated traces are refined. When a trace is identified, behavior checking is adopted to verify whether the runtime state is compliant to the system specification that is defined based on milestone events for meeting SCADA real-time requirements.
{"title":"A Non-intrusive Solution to Guarantee Runtime Behavior of Open SCADA Systems","authors":"Y. Mao, Yang Zhang, Qiang Hua, Hong-Yang Dai, Xing Wang","doi":"10.1109/ICWS.2015.105","DOIUrl":"https://doi.org/10.1109/ICWS.2015.105","url":null,"abstract":"For realizing non-intrusive protection of open SCADA systems, a non-intrusive solution for distributed open SCADA systems is proposed. The solution consists of three functionality parts: Abstract Execution, Refine State, and Behavior Checking. The approach provides a runtime verification of the system by combining cyclic semantic reconstruction of VM and abstract execution of SCADA services. First, all Internet packets through virtual network bridges are extracted and symbolically linked to specific service model to get simulated traces. Then, cyclic semantic reconstruction is performed to acquire the current service runtime state. According to the service instance state of semantic reconstruction, the simulated traces are refined. When a trace is identified, behavior checking is adopted to verify whether the runtime state is compliant to the system specification that is defined based on milestone events for meeting SCADA real-time requirements.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123454860","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}
Jia Zhang, Wei Wang, Xing Wei, Chris Lee, Seungwon Lee, L. Pan, Tsengdar J. Lee
Existing scientific workflow tools, created by computer scientists, require that domain scientists meticulously design their multi-step experiments before analyzing data. However, this is oftentimes contradictory to a domain scientist's routine of conducting research and exploration. This paper presents a novel way to resolve this dispute, in the context of service-oriented science. After scrutinizing how Earth scientists conduct data analytics research in their daily work, a provenance model is developed to record their activities. Reverse-engineering the provenance, a technology is developed to automatically generate workflows for scientists to review and revise, supported by a Petri nets-based workflow verification instrument. In addition, dataset is proposed to be treated as first-class citizen to drive the knowledge sharing and recommendation. A data-centric repository infrastructure is established to catch richer provenance to further facilitate collaboration in the science community. In this way, we aim to revolutionize computer-supported Earth science.
{"title":"Climate Analytics Workflow Recommendation as a Service - Provenance-Driven Automatic Workflow Mashup","authors":"Jia Zhang, Wei Wang, Xing Wei, Chris Lee, Seungwon Lee, L. Pan, Tsengdar J. Lee","doi":"10.1109/ICWS.2015.22","DOIUrl":"https://doi.org/10.1109/ICWS.2015.22","url":null,"abstract":"Existing scientific workflow tools, created by computer scientists, require that domain scientists meticulously design their multi-step experiments before analyzing data. However, this is oftentimes contradictory to a domain scientist's routine of conducting research and exploration. This paper presents a novel way to resolve this dispute, in the context of service-oriented science. After scrutinizing how Earth scientists conduct data analytics research in their daily work, a provenance model is developed to record their activities. Reverse-engineering the provenance, a technology is developed to automatically generate workflows for scientists to review and revise, supported by a Petri nets-based workflow verification instrument. In addition, dataset is proposed to be treated as first-class citizen to drive the knowledge sharing and recommendation. A data-centric repository infrastructure is established to catch richer provenance to further facilitate collaboration in the science community. In this way, we aim to revolutionize computer-supported Earth science.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122335768","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}
The objective of this paper is to demonstrate that sharing the vocabulary for service description enhances the service discovery mechanism. The proposed solution is a distributed architecture for enhanced context-aware web services. The starting point is a motivation scenario in which university students are trying to share a solution about a specific problem in a campus environment. The proposed solution includes an ontology-based context model for describing service vocabulary. This model is shared among users to facilitate the description of their petitions. The Devices Profile for Web Services (DPWS) was integrated in the architecture as a framework for sending, describing and discovering web services. The adopted validation methodology consisted in comparing scenarios with the context ontology as vocabulary source and others that use synonyms from Word net. A series of discrete-event simulations were set up by specifying performance metrics related to the discovery mechanism, control parameters and user behavior models. The results have shown that using the context ontology enhances the discovery ratio as well as the mean discovered services per request.
{"title":"Enhanced Service Discovery via Shared Context in a Distributed Architecture","authors":"M. Khouja, C. Juiz","doi":"10.1109/ICWS.2015.45","DOIUrl":"https://doi.org/10.1109/ICWS.2015.45","url":null,"abstract":"The objective of this paper is to demonstrate that sharing the vocabulary for service description enhances the service discovery mechanism. The proposed solution is a distributed architecture for enhanced context-aware web services. The starting point is a motivation scenario in which university students are trying to share a solution about a specific problem in a campus environment. The proposed solution includes an ontology-based context model for describing service vocabulary. This model is shared among users to facilitate the description of their petitions. The Devices Profile for Web Services (DPWS) was integrated in the architecture as a framework for sending, describing and discovering web services. The adopted validation methodology consisted in comparing scenarios with the context ontology as vocabulary source and others that use synonyms from Word net. A series of discrete-event simulations were set up by specifying performance metrics related to the discovery mechanism, control parameters and user behavior models. The results have shown that using the context ontology enhances the discovery ratio as well as the mean discovered services per request.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125107886","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}
As a result of recent trends in enhancing Service-Oriented Requirement Engineering (SORE) activities, a number of requirement specification methods have been proposed for fitting the reuse infrastructure in a Service-Oriented Architecture (SOA). The availability of different Requirement Engineering methods offers developers a range of options to choose from. However, most of existing research effort uses traditional Requirement Engineering methods in service-based application developments. During requirements specification, a reusable infrastructure of available web services is not considered at all. The risk is that atomic requirements do not always fit reusable services. As a result, the service composition is time-consuming and needs costly adaption. This paper therefore proposes a novel method by introducing service discovery in the early Requirement Engineering stages so as to guide the requirement decomposition process. Although several researchers have already recommended to involve service discovery in SORE, they do not focus on how to guide requirement decomposition. Our approach is implemented on top of the widely used goal-oriented approach. To this end, we leverage a semantic service discovery method as a means to act as a guide and sentinel in requirement elaboration. We demonstrate the requirement decomposition process by implementing a case study from the Business Traveling domain.
{"title":"Discovering Web Services to Improve Requirements Decomposition","authors":"Hongbing Wang, Suxiang Zhou, Qi Yu","doi":"10.1109/ICWS.2015.52","DOIUrl":"https://doi.org/10.1109/ICWS.2015.52","url":null,"abstract":"As a result of recent trends in enhancing Service-Oriented Requirement Engineering (SORE) activities, a number of requirement specification methods have been proposed for fitting the reuse infrastructure in a Service-Oriented Architecture (SOA). The availability of different Requirement Engineering methods offers developers a range of options to choose from. However, most of existing research effort uses traditional Requirement Engineering methods in service-based application developments. During requirements specification, a reusable infrastructure of available web services is not considered at all. The risk is that atomic requirements do not always fit reusable services. As a result, the service composition is time-consuming and needs costly adaption. This paper therefore proposes a novel method by introducing service discovery in the early Requirement Engineering stages so as to guide the requirement decomposition process. Although several researchers have already recommended to involve service discovery in SORE, they do not focus on how to guide requirement decomposition. Our approach is implemented on top of the widely used goal-oriented approach. To this end, we leverage a semantic service discovery method as a means to act as a guide and sentinel in requirement elaboration. We demonstrate the requirement decomposition process by implementing a case study from the Business Traveling domain.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130092743","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 Zhu, Guanfeng Liu, Yan Wang, An Liu, Zhixu Li, Pengpeng Zhao, Lei Li
Online Social Networks (OSNs) have been used as the means for a variety of applications, like employment system, e-Commerce and CRM system. In these applications, social influence acts as a significant role, affecting people's decision-making. However, the existing social influence evaluation methods do not fully consider the social contexts, like the social relationships and the social trust between participants, and the preferences of participants, which have significant impact on social influence evaluation in OSNs. Thus, these existing methods cannot deliver accurate social influence evaluation results. In our paper, we propose a Context-Aware Trust-Oriented Influencers Finding method, called CT-Influence, with social contexts taken into account. We conduct experiments onto two real social network datasets, i.e., Epinions and DBLP. The experimental results illustrate that our CT-Influence method greatly outperforms the state-of-the-art method So Cap in terms of effectiveness and efficiency.
在线社交网络(Online Social Networks, OSNs)已被用作各种应用的手段，如就业系统、电子商务和客户关系管理系统。在这些应用中，社会影响扮演着重要的角色，影响着人们的决策。然而，现有的社会影响力评价方法并没有充分考虑社会情境，如参与者之间的社会关系和社会信任，以及参与者的偏好，这些因素对社交网络的社会影响力评价有重要影响。因此，这些现有的方法无法提供准确的社会影响力评价结果。在我们的论文中，我们提出了一种考虑社会背景的上下文感知信任导向的影响者发现方法，称为CT-Influence。我们在两个真实的社交网络数据集，即Epinions和DBLP上进行实验。实验结果表明，我们的CT-Influence方法在有效性和效率方面大大优于最先进的So Cap方法。
{"title":"A Context-Aware Trust-Oriented Influencers Finding in Online Social Networks","authors":"Feng Zhu, Guanfeng Liu, Yan Wang, An Liu, Zhixu Li, Pengpeng Zhao, Lei Li","doi":"10.1109/ICWS.2015.67","DOIUrl":"https://doi.org/10.1109/ICWS.2015.67","url":null,"abstract":"Online Social Networks (OSNs) have been used as the means for a variety of applications, like employment system, e-Commerce and CRM system. In these applications, social influence acts as a significant role, affecting people's decision-making. However, the existing social influence evaluation methods do not fully consider the social contexts, like the social relationships and the social trust between participants, and the preferences of participants, which have significant impact on social influence evaluation in OSNs. Thus, these existing methods cannot deliver accurate social influence evaluation results. In our paper, we propose a Context-Aware Trust-Oriented Influencers Finding method, called CT-Influence, with social contexts taken into account. We conduct experiments onto two real social network datasets, i.e., Epinions and DBLP. The experimental results illustrate that our CT-Influence method greatly outperforms the state-of-the-art method So Cap in terms of effectiveness and efficiency.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127621418","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}
Bin Cao, Jiaxing Wang, Jing Fan, Tianyang Dong, Jianwei Yin
Efficient query processing over a large amount of business process models is important for managing the business process model repository. The structural similarity between two process models is considered as the main measurement for ranking the process models for a given search model. Current business process query methods are inefficient since too many expensive computations of the graph edit distance are involved for constructing the elements mapping as well as deriving the structural similarity. To address this, using Petri-net as the modelling method, this paper presents the Hungarian algorithm based query method, where we firstly define the context similarity for a pair of place nodes that are from different process models by taking into account both the common paths and common transitions, then transform the elements (e.g., The transitions and the places) mapping to classical assignment problem that can be solved by Hungarian algorithm efficiently. In this way, we can save a lot of time for searching the best combination of elements mapping. Finally, we use the common method of the graph edit distance to measure the structural similarity based on the found best combination of elements mapping.
对大量业务流程模型进行高效查询处理对于管理业务流程模型库非常重要。两个流程模型之间的结构相似性被认为是对给定搜索模型的流程模型进行排序的主要衡量标准。目前的业务流程查询方法效率低下，因为在构建元素映射和推导结构相似性时，需要进行大量昂贵的图编辑距离计算。为了解决这个问题，本文使用 Petri 网作为建模方法，提出了基于匈牙利算法的查询方法。在这种方法中，我们首先通过考虑共同路径和共同转换来定义来自不同流程模型的一对地点节点的上下文相似性，然后将元素（如转换和地点）映射转换为可由匈牙利算法高效解决的经典赋值问题。这样，我们就可以节省大量时间来搜索元素映射的最佳组合。最后，我们根据找到的最佳元素映射组合，使用常用的图编辑距离方法来测量结构相似性。
{"title":"Mapping Elements with the Hungarian Algorithm: An Efficient Method for Querying Business Process Models","authors":"Bin Cao, Jiaxing Wang, Jing Fan, Tianyang Dong, Jianwei Yin","doi":"10.1109/ICWS.2015.27","DOIUrl":"https://doi.org/10.1109/ICWS.2015.27","url":null,"abstract":"Efficient query processing over a large amount of business process models is important for managing the business process model repository. The structural similarity between two process models is considered as the main measurement for ranking the process models for a given search model. Current business process query methods are inefficient since too many expensive computations of the graph edit distance are involved for constructing the elements mapping as well as deriving the structural similarity. To address this, using Petri-net as the modelling method, this paper presents the Hungarian algorithm based query method, where we firstly define the context similarity for a pair of place nodes that are from different process models by taking into account both the common paths and common transitions, then transform the elements (e.g., The transitions and the places) mapping to classical assignment problem that can be solved by Hungarian algorithm efficiently. In this way, we can save a lot of time for searching the best combination of elements mapping. Finally, we use the common method of the graph edit distance to measure the structural similarity based on the found best combination of elements mapping.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127906663","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 wide adoption of Service-Oriented Architecture (SOA), the number of web accessible services and their compositions is increasing rapidly. Among huge number of services, how to recommend appropriate ones for automatic composition satisfying users' need is challenging. We investigate services and their compositions in Programmable Web which characterize services as APIs and their compositions as mashups. We study the problem of recommending suitable APIs satisfying users' need for mash up creation. To this end, we propose a manifold ranking framework for API recommendation. First, we categorize existing mashups into functionally similar clusters. Then we recommend APIs for each mash up cluster using manifold ranking algorithm which incorporate the relationships between mashups, between APIs and between mashups and APIs. Intuitively, we take three factors into consideration: (1) We recommend APIs that are in functionally similar mashups. (2) We recommend APIs that are popular in the mashups. (3) We recommend APIs that are similar to each other. Finally, we map a user's requirement for mash up creation to a mash up cluster and recommend APIs generated by the algorithm to user. Experiments based on real dataset crawled from Programmble Web demonstrate the effectiveness of the proposed approach in terms of precision, recall, and NDCG.
{"title":"Manifold-Learning Based API Recommendation for Mashup Creation","authors":"Wei Gao, Liang Chen, Jian Wu, Honghao Gao","doi":"10.1109/ICWS.2015.64","DOIUrl":"https://doi.org/10.1109/ICWS.2015.64","url":null,"abstract":"With the wide adoption of Service-Oriented Architecture (SOA), the number of web accessible services and their compositions is increasing rapidly. Among huge number of services, how to recommend appropriate ones for automatic composition satisfying users' need is challenging. We investigate services and their compositions in Programmable Web which characterize services as APIs and their compositions as mashups. We study the problem of recommending suitable APIs satisfying users' need for mash up creation. To this end, we propose a manifold ranking framework for API recommendation. First, we categorize existing mashups into functionally similar clusters. Then we recommend APIs for each mash up cluster using manifold ranking algorithm which incorporate the relationships between mashups, between APIs and between mashups and APIs. Intuitively, we take three factors into consideration: (1) We recommend APIs that are in functionally similar mashups. (2) We recommend APIs that are popular in the mashups. (3) We recommend APIs that are similar to each other. Finally, we map a user's requirement for mash up creation to a mash up cluster and recommend APIs generated by the algorithm to user. Experiments based on real dataset crawled from Programmble Web demonstrate the effectiveness of the proposed approach in terms of precision, recall, and NDCG.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122762936","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}
Yaoliang Chen, Jingjing Wang, Hongwei Wang, Sheng Huang, Chen Lin
Publish/subscribe (pub/sub) systems are widely used in numerous Internet-Of-Things (IoT) applications such as environment monitoring, supply chain tracing, healthcare, and vehicle networks. In these applications, publishers (e.g. Smart devices, sensors) are continuously generating large volume of data with an extremely high throughput, whereas subscribers are only interested in a small portion of the data. Recently, content-based subscription systems have raised more and more attentions by the researchers where subscribers can specify rules on the content of messages that are composed of many attributes. For example, in traffic monitoring, an operator is only interested in the data within a specified area defined by constraints on latitude and longitude instead of the whole map. In this paper, we present COSS, the first Content-based Subscription Service for IoT with natural multi-tenant support and easy-to-use REST APIs. Moreover, we investigate in the problem of Balanced Rule Engine Partitioning for content-based subscription under the Tenant-Message-Rule (TMR) model. We show the NP-hardness of the problem and design a heuristics to enable COSS to adaptively adjust the message distribution according to the workload history, and to scale on both the high data throughput of IoT workloads and multi-tenant. Extensive experiments show that COSS offers high performance and scalability for content-based subscription in terms of the number of tenants, and the data throughput of the messages.
{"title":"COSS: Content-Based Subscription as an IoT Service","authors":"Yaoliang Chen, Jingjing Wang, Hongwei Wang, Sheng Huang, Chen Lin","doi":"10.1109/ICWS.2015.56","DOIUrl":"https://doi.org/10.1109/ICWS.2015.56","url":null,"abstract":"Publish/subscribe (pub/sub) systems are widely used in numerous Internet-Of-Things (IoT) applications such as environment monitoring, supply chain tracing, healthcare, and vehicle networks. In these applications, publishers (e.g. Smart devices, sensors) are continuously generating large volume of data with an extremely high throughput, whereas subscribers are only interested in a small portion of the data. Recently, content-based subscription systems have raised more and more attentions by the researchers where subscribers can specify rules on the content of messages that are composed of many attributes. For example, in traffic monitoring, an operator is only interested in the data within a specified area defined by constraints on latitude and longitude instead of the whole map. In this paper, we present COSS, the first Content-based Subscription Service for IoT with natural multi-tenant support and easy-to-use REST APIs. Moreover, we investigate in the problem of Balanced Rule Engine Partitioning for content-based subscription under the Tenant-Message-Rule (TMR) model. We show the NP-hardness of the problem and design a heuristics to enable COSS to adaptively adjust the message distribution according to the workload history, and to scale on both the high data throughput of IoT workloads and multi-tenant. Extensive experiments show that COSS offers high performance and scalability for content-based subscription in terms of the number of tenants, and the data throughput of the messages.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117087342","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：