Mobile Edge Computing (MEC) integrates computing, storage and other resources on the edge of the network and constructs a unified user service platform. Then, according to the principle of nearest service, MEC responds to the task requests of the edge nodes in time and effectively processes them. In MEC, edge servers are virtualized into several slots so that resources can be shared among different mobile users. However, there are many unpredictable risks in MEC, these risks can cause edge servers to fail, the virtual machine deployed in the server slot fails and the task cannot be executed normally. The introduction of primary-backup virtual machines solves this problem well. However, when the primary virtual machine is working normally, its backup virtual machine is idle, this will result in a waste of resources. In order to improve the resource utilization of the system, this paper firstly overbooks the backup virtual machine reasonably, and then formulates the virtual machine deployment problem as a combinatorial optimization problem. Finally, Virtual Machine Deployment Algorithm (VMDA) is proposed based on genetic algorithm. With the increase of the number of algorithm iterations and the population size of the virtual machine deployment scheme, there may be more optimal virtual machine deployment scheme individuals in the population. Therefore, the algorithm can obtain the approximate optimal value of resource utilization within the risk range allowed by the system, and the algorithm is compared with two other typical bin packing algorithms. The results confirm that VMDA outperforms the other two algorithms.
{"title":"Overbooking-enabled Virtual Machine Deployment Approach in Mobile Edge Computing","authors":"Bingyi Hu, Jixun Gao, Quanzhen Huang, Huaichen Wang, Yanxin Hu, Jialei Liu, Yanmin Ge","doi":"10.1109/ICSS55994.2022.00041","DOIUrl":"https://doi.org/10.1109/ICSS55994.2022.00041","url":null,"abstract":"Mobile Edge Computing (MEC) integrates computing, storage and other resources on the edge of the network and constructs a unified user service platform. Then, according to the principle of nearest service, MEC responds to the task requests of the edge nodes in time and effectively processes them. In MEC, edge servers are virtualized into several slots so that resources can be shared among different mobile users. However, there are many unpredictable risks in MEC, these risks can cause edge servers to fail, the virtual machine deployed in the server slot fails and the task cannot be executed normally. The introduction of primary-backup virtual machines solves this problem well. However, when the primary virtual machine is working normally, its backup virtual machine is idle, this will result in a waste of resources. In order to improve the resource utilization of the system, this paper firstly overbooks the backup virtual machine reasonably, and then formulates the virtual machine deployment problem as a combinatorial optimization problem. Finally, Virtual Machine Deployment Algorithm (VMDA) is proposed based on genetic algorithm. With the increase of the number of algorithm iterations and the population size of the virtual machine deployment scheme, there may be more optimal virtual machine deployment scheme individuals in the population. Therefore, the algorithm can obtain the approximate optimal value of resource utilization within the risk range allowed by the system, and the algorithm is compared with two other typical bin packing algorithms. The results confirm that VMDA outperforms the other two algorithms.","PeriodicalId":327964,"journal":{"name":"2022 International Conference on Service Science (ICSS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116448935","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}
Pub Date : 2022-05-01DOI: 10.1109/ICSS55994.2022.00024
Jun Peng, Jingwei Zhu, L. Zhang
Due to its ease of use, the notion of Simple Temporal Networks with Uncertainty (STNU) has been successfully used in verifying temporal constraints of business processes. Considering the universality of non-functional attributes, it is significant to generalize STNU in characterizing these non-functional constraints, resulting in a better expressiveness to support process modeling and business applications. In this paper, we leverage STNU to such a level by using abstract algebra on STNU. It results in a general non-functional constraint modeling and verification method in business process management (BPM), from the original temporal constraints to broader qualitative and quantitative ones, which is not yet supported with STNU in the BPM. Based on the proposed method, we demonstrate the capability of verifying dynamic controllability (DC) for these non-functional attributes, such as satisfaction, reputation grade, etc.
{"title":"Generalizing STNU to Model Non-functional Constraints for Business Processes","authors":"Jun Peng, Jingwei Zhu, L. Zhang","doi":"10.1109/ICSS55994.2022.00024","DOIUrl":"https://doi.org/10.1109/ICSS55994.2022.00024","url":null,"abstract":"Due to its ease of use, the notion of Simple Temporal Networks with Uncertainty (STNU) has been successfully used in verifying temporal constraints of business processes. Considering the universality of non-functional attributes, it is significant to generalize STNU in characterizing these non-functional constraints, resulting in a better expressiveness to support process modeling and business applications. In this paper, we leverage STNU to such a level by using abstract algebra on STNU. It results in a general non-functional constraint modeling and verification method in business process management (BPM), from the original temporal constraints to broader qualitative and quantitative ones, which is not yet supported with STNU in the BPM. Based on the proposed method, we demonstrate the capability of verifying dynamic controllability (DC) for these non-functional attributes, such as satisfaction, reputation grade, etc.","PeriodicalId":327964,"journal":{"name":"2022 International Conference on Service Science (ICSS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124712160","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}
Pub Date : 2022-05-01DOI: 10.1109/ICSS55994.2022.00010
Tianshi Wang, Hongwei Kan, Qibo Sun, Shan Xiao, Shangguang Wang
Researchers and practitioners are exploiting Remote Direct Memory Access (RDMA) technology to improve the efficiency of distributed machine learning and meet the demands of data-center applications. RDMA requires lossless network link to fully unleash its power. RDMA Over Converged Ethernet (RoCE) v2 focuses on congestion control, but fails to achieve efficient packet loss recovery; Improved RoCE NIC (IRN) addresses this issue based on RoCEv2, but does not use the Priority-based Flow Control (PFC) to maintain the advantage of RoCEv2 in detecting congestion. This paper proposes a method of congestion detection and link control via feedback in RDMA transmission, namely Feedback Data Flow Control (FDFC), that does not rely on PFC. FDFC detects and controls the link condition in real time to achieve the goals of precise detection, congestion control, and efficient packet loss recovering.
研究人员和实践者正在利用远程直接内存访问(RDMA)技术来提高分布式机器学习的效率，满足数据中心应用的需求。RDMA需要网络链路无损，才能充分发挥其威力。RDMA Over Converged Ethernet (RoCE) v2侧重于拥塞控制，但无法实现高效的丢包恢复;改进的RoCE NIC (IRN)在RoCEv2的基础上解决了这个问题，但没有使用基于优先级的流量控制(PFC)来保持RoCEv2在检测拥塞方面的优势。本文提出了一种在RDMA传输中通过反馈进行拥塞检测和链路控制的方法，即反馈数据流控制(feedback Data Flow control, FDFC)，它不依赖于pfc，实时检测和控制链路状态，以达到精确检测、拥塞控制和高效丢包恢复的目的。
{"title":"Congestion Detection and Link Control via Feedback in RDMA Transmission","authors":"Tianshi Wang, Hongwei Kan, Qibo Sun, Shan Xiao, Shangguang Wang","doi":"10.1109/ICSS55994.2022.00010","DOIUrl":"https://doi.org/10.1109/ICSS55994.2022.00010","url":null,"abstract":"Researchers and practitioners are exploiting Remote Direct Memory Access (RDMA) technology to improve the efficiency of distributed machine learning and meet the demands of data-center applications. RDMA requires lossless network link to fully unleash its power. RDMA Over Converged Ethernet (RoCE) v2 focuses on congestion control, but fails to achieve efficient packet loss recovery; Improved RoCE NIC (IRN) addresses this issue based on RoCEv2, but does not use the Priority-based Flow Control (PFC) to maintain the advantage of RoCEv2 in detecting congestion. This paper proposes a method of congestion detection and link control via feedback in RDMA transmission, namely Feedback Data Flow Control (FDFC), that does not rely on PFC. FDFC detects and controls the link condition in real time to achieve the goals of precise detection, congestion control, and efficient packet loss recovering.","PeriodicalId":327964,"journal":{"name":"2022 International Conference on Service Science (ICSS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128087272","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, the demand for computing resources of AI industry is urgent because of the data explosion, which promoted the construction of computing power networks in the new era for operators. From the cloud network era to today's computing power network, stricter requirements are proposed to ensure the efficiency and security of computing services. Despite computing power scheduling technologies such as on-demand edge computing and efficient compute first network, knowledge graph techniques for graphs are less explored. As a new technology that can express the relationship between nodes in the graph extremely easily, knowledge graph has a natural advantage in expressing feature information of computing nodes in computing power network. Therefore, a novel knowledge graph representation for the architecture of computing power networks is proposed. And the knowledge graph of the computing power network is constructed by using the knowledge representation method. The scheduling tasks of computing power network is automatically executed by the proposed knowledge driven method based on the constructed knowledge graph. Different with the current scheduling technology of computing power network, the model will theoretically become more and more efficient and accurate with continuously addition of knowledge.
{"title":"Automatic Scheduling Technology of Computing Power Network Driven by Knowledge Graph","authors":"Yanheng Bi, Yingchi Long, Yanzheng Jin, Shengwen Zheng, Huaiyuan Liu, Hongzhi Wang","doi":"10.1109/ICSS55994.2022.00032","DOIUrl":"https://doi.org/10.1109/ICSS55994.2022.00032","url":null,"abstract":"In recent years, the demand for computing resources of AI industry is urgent because of the data explosion, which promoted the construction of computing power networks in the new era for operators. From the cloud network era to today's computing power network, stricter requirements are proposed to ensure the efficiency and security of computing services. Despite computing power scheduling technologies such as on-demand edge computing and efficient compute first network, knowledge graph techniques for graphs are less explored. As a new technology that can express the relationship between nodes in the graph extremely easily, knowledge graph has a natural advantage in expressing feature information of computing nodes in computing power network. Therefore, a novel knowledge graph representation for the architecture of computing power networks is proposed. And the knowledge graph of the computing power network is constructed by using the knowledge representation method. The scheduling tasks of computing power network is automatically executed by the proposed knowledge driven method based on the constructed knowledge graph. Different with the current scheduling technology of computing power network, the model will theoretically become more and more efficient and accurate with continuously addition of knowledge.","PeriodicalId":327964,"journal":{"name":"2022 International Conference on Service Science (ICSS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130479958","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}
Pub Date : 2022-05-01DOI: 10.1109/ICSS55994.2022.00039
Yongyan Yang, Lihong Yang, Mengda Xing
In recent years, as the business of the smart grid grows, the requirements for intelligent maintenance have become significant in the domain. One such typical application is fault prediction service for power equipment. However, traditional solutions to fault prediction have inherent limitations, because they cannot simultaneously employ patterns from global or partial segments and exclude irrelevant features from time series data. In this paper for power equipment, we propose a novel fault prediction service on multi-dimensional time series by a deep-learning model called MRNN-SA. Extensive experiments and a case study show our service can distinctly improve prediction performance on real-world sensory data from power transformers and database servers.
{"title":"MRNN-SA: A Multi-dimensional Time Series Fault Prediction Service for Power Equipment through Self-attention Network","authors":"Yongyan Yang, Lihong Yang, Mengda Xing","doi":"10.1109/ICSS55994.2022.00039","DOIUrl":"https://doi.org/10.1109/ICSS55994.2022.00039","url":null,"abstract":"In recent years, as the business of the smart grid grows, the requirements for intelligent maintenance have become significant in the domain. One such typical application is fault prediction service for power equipment. However, traditional solutions to fault prediction have inherent limitations, because they cannot simultaneously employ patterns from global or partial segments and exclude irrelevant features from time series data. In this paper for power equipment, we propose a novel fault prediction service on multi-dimensional time series by a deep-learning model called MRNN-SA. Extensive experiments and a case study show our service can distinctly improve prediction performance on real-world sensory data from power transformers and database servers.","PeriodicalId":327964,"journal":{"name":"2022 International Conference on Service Science (ICSS)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132175946","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}
Pub Date : 2022-05-01DOI: 10.1109/ICSS55994.2022.00044
Yi Yang, Mingkang Song, Jianming Zhou, Peng Dai, Tenghui Ke, Weidong Li, Zhengguan Wu, Xiayan Zheng, Xijin Li
In the field of machine learning, a machine learning system with multiple nodes is usually used, and each node is used to perform a machine learning distributed training process for a part of the data that is allocated to it and provide a server by performing the machine learning distributed training process. The obtained training result, its machine learning data needs to be transmitted through the network. This paper proposes a link allocation method for distributed machine learning. For machine learning computing nodes distributed across domains, due to inconsistencies in link distance, node performance, and link load, the traffic distribution between computing nodes is unbalanced. Aiming at the complex computing requirements of distributed machine learning, a link pre-allocation method is proposed, which establishes a central server-link-node topology map, integrates link resources, and determines the logical distance of nodes. For the synchronously distributed machine learning training set, preallocate transmission link resources and initiate transmission according to the remaining storage capacity of nodes. In order to improve the network utilization efficiency in the process of machine learning, it can break through the influence of large network transmission delay on the efficiency of distributed machine learning.
{"title":"Distributed machine learning based link allocation strategy *","authors":"Yi Yang, Mingkang Song, Jianming Zhou, Peng Dai, Tenghui Ke, Weidong Li, Zhengguan Wu, Xiayan Zheng, Xijin Li","doi":"10.1109/ICSS55994.2022.00044","DOIUrl":"https://doi.org/10.1109/ICSS55994.2022.00044","url":null,"abstract":"In the field of machine learning, a machine learning system with multiple nodes is usually used, and each node is used to perform a machine learning distributed training process for a part of the data that is allocated to it and provide a server by performing the machine learning distributed training process. The obtained training result, its machine learning data needs to be transmitted through the network. This paper proposes a link allocation method for distributed machine learning. For machine learning computing nodes distributed across domains, due to inconsistencies in link distance, node performance, and link load, the traffic distribution between computing nodes is unbalanced. Aiming at the complex computing requirements of distributed machine learning, a link pre-allocation method is proposed, which establishes a central server-link-node topology map, integrates link resources, and determines the logical distance of nodes. For the synchronously distributed machine learning training set, preallocate transmission link resources and initiate transmission according to the remaining storage capacity of nodes. In order to improve the network utilization efficiency in the process of machine learning, it can break through the influence of large network transmission delay on the efficiency of distributed machine learning.","PeriodicalId":327964,"journal":{"name":"2022 International Conference on Service Science (ICSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129717510","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}
Pub Date : 2022-05-01DOI: 10.1109/ICSS55994.2022.00053
Yuanjun Ding, Weili Chen
The birth of Bitcoin has created the cryptocurrency exchange, the average daily trading volume of cryptocurrency exchanges is now more than 100 billion. Cryptocurrency exchanges serve as a place for users to exchange cryptocurrencies, acting as a bridge between the blockchain ecosystem and the real world. Based on the transaction mechanism, cryptocurrency exchanges can be divided into centralized exchanges(CEXs) and decentralized exchanges(DEXs). CEXs still hold the dominant position, and we focus on Mt.Gox with the leaked dataset. By preprocessing the data, a usable internal dataset was obtained. To better study CEX, we further provide a comprehensive analysis of Mt.Gox based on three types of records and conclude its characteristics. Finally, we propose a matching method for on-chain and off-chain data, which restores the complete transaction path of the transaction account and some strange transaction phenomena are discovered. The results of this experiment showed that our algorithm can find addresses on blockchain and de-anonymize to a certain extent.
{"title":"Probing the Mystery of Cryptocurrency Exchange: The Case Study Based on Mt.Gox","authors":"Yuanjun Ding, Weili Chen","doi":"10.1109/ICSS55994.2022.00053","DOIUrl":"https://doi.org/10.1109/ICSS55994.2022.00053","url":null,"abstract":"The birth of Bitcoin has created the cryptocurrency exchange, the average daily trading volume of cryptocurrency exchanges is now more than 100 billion. Cryptocurrency exchanges serve as a place for users to exchange cryptocurrencies, acting as a bridge between the blockchain ecosystem and the real world. Based on the transaction mechanism, cryptocurrency exchanges can be divided into centralized exchanges(CEXs) and decentralized exchanges(DEXs). CEXs still hold the dominant position, and we focus on Mt.Gox with the leaked dataset. By preprocessing the data, a usable internal dataset was obtained. To better study CEX, we further provide a comprehensive analysis of Mt.Gox based on three types of records and conclude its characteristics. Finally, we propose a matching method for on-chain and off-chain data, which restores the complete transaction path of the transaction account and some strange transaction phenomena are discovered. The results of this experiment showed that our algorithm can find addresses on blockchain and de-anonymize to a certain extent.","PeriodicalId":327964,"journal":{"name":"2022 International Conference on Service Science (ICSS)","volume":"04 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129354181","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}
Pub Date : 2022-05-01DOI: 10.1109/ICSS55994.2022.00022
Yusuf Kirikkayis, Florian Gallik, M. Reichert
The relevance of the Internet of Things (IoT) for Business Process Management (BPM) support is increasing. IoT devices enable the collection and exchange of data over the Internet, whereby each physical device is uniquely identifiable through its embedded computing system. BPM, in turn, is concerned with analyzing, discovering, modeling, executing, and monitoring (digitized) business processes. By enhancing BPM systems with IoT capabilities, real-world data can be gathered and considered during process execution to enhance process monitoring as well as IoT-driven decision making. In this context, the aggregation of low-level IoT data into high-level process-relevant data constitutes a fundamental step towards IoT-driven decisions in business processes. This paper presents IoT Decision Making for Business Process Model and Notation (IoTDM4BPMN) a web-based framework for modeling, executing, and monitoring IoT-driven decisions in real-time. We give insights into the design and implementation of IoTDM4BPMN and provide a case study as a first validation that applies IoTDM4BPMN to the modeling, executing, and monitoring of a real-world IoT-driven decision process.
{"title":"IoTDM4BPMN: An IoT-Enhanced Decision Making Framework for BPMN 2.0","authors":"Yusuf Kirikkayis, Florian Gallik, M. Reichert","doi":"10.1109/ICSS55994.2022.00022","DOIUrl":"https://doi.org/10.1109/ICSS55994.2022.00022","url":null,"abstract":"The relevance of the Internet of Things (IoT) for Business Process Management (BPM) support is increasing. IoT devices enable the collection and exchange of data over the Internet, whereby each physical device is uniquely identifiable through its embedded computing system. BPM, in turn, is concerned with analyzing, discovering, modeling, executing, and monitoring (digitized) business processes. By enhancing BPM systems with IoT capabilities, real-world data can be gathered and considered during process execution to enhance process monitoring as well as IoT-driven decision making. In this context, the aggregation of low-level IoT data into high-level process-relevant data constitutes a fundamental step towards IoT-driven decisions in business processes. This paper presents IoT Decision Making for Business Process Model and Notation (IoTDM4BPMN) a web-based framework for modeling, executing, and monitoring IoT-driven decisions in real-time. We give insights into the design and implementation of IoTDM4BPMN and provide a case study as a first validation that applies IoTDM4BPMN to the modeling, executing, and monitoring of a real-world IoT-driven decision process.","PeriodicalId":327964,"journal":{"name":"2022 International Conference on Service Science (ICSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130639301","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}
Pub Date : 2022-05-01DOI: 10.1109/ICSS55994.2022.00038
Liwen Zhang, Weiping Li, Tong Mo, Weijie Chu
With the help of the sufficiency of heterogeneous information, heterogeneous information network(HIN) has been treated as the most advanced method to extract complex semantic data in recommender system. But it is still an empty field for some traditional industries such as testing and inspection industry, which mainly adopt the similarity-based collaborative filtering(CF) method. But it will make a huge waste of the rich heterogeneous auxiliary data, which could be fully utilized by HIN based method. Especially for testing and inspection industry, the profession will help the model to find a more accurate match between the user and business. In this work, we succeeded in building up a HIN embedding approach for recommendation, and design a unique network structure for testing and inspection industry, which both utilize the rich underlying information and properly solve the specialty problem in a professional industry, different from normal recommender scenario. An intensive experiment on the real world data set shows the performance of the model.
{"title":"HRET: Heterogeneous Information Network for Recommendation in testing and inspection","authors":"Liwen Zhang, Weiping Li, Tong Mo, Weijie Chu","doi":"10.1109/ICSS55994.2022.00038","DOIUrl":"https://doi.org/10.1109/ICSS55994.2022.00038","url":null,"abstract":"With the help of the sufficiency of heterogeneous information, heterogeneous information network(HIN) has been treated as the most advanced method to extract complex semantic data in recommender system. But it is still an empty field for some traditional industries such as testing and inspection industry, which mainly adopt the similarity-based collaborative filtering(CF) method. But it will make a huge waste of the rich heterogeneous auxiliary data, which could be fully utilized by HIN based method. Especially for testing and inspection industry, the profession will help the model to find a more accurate match between the user and business. In this work, we succeeded in building up a HIN embedding approach for recommendation, and design a unique network structure for testing and inspection industry, which both utilize the rich underlying information and properly solve the specialty problem in a professional industry, different from normal recommender scenario. An intensive experiment on the real world data set shows the performance of the model.","PeriodicalId":327964,"journal":{"name":"2022 International Conference on Service Science (ICSS)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123090004","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}
Pub Date : 2022-05-01DOI: 10.1109/ICSS55994.2022.00048
Wenjie Teng, Hanchuan Xu, Zhe Huang, Yunwen Bai, Zhongjie Wang
Crowd funding and crowd innovation can boost creativity of creators at a low cost. However, how to protect rights and benefits of relative stakeholders during the process in a credible way remains a problem. By introducing fungible tokens, non-fungible tokens and on-chain governance based on blockchain, we propose a set of smart contracts supporting crowd funding and crowd innovation to better reward participants and govern the process in a trusted way. Furthermore, based on these smart contracts, we abstract and encapsulate a series of common operations and implement a service platform for trustworthy crowd funding and crowd innovation.
{"title":"A Smart Contract-based Service Platform for Trustworthy Crowd Funding and Crowd Innovation","authors":"Wenjie Teng, Hanchuan Xu, Zhe Huang, Yunwen Bai, Zhongjie Wang","doi":"10.1109/ICSS55994.2022.00048","DOIUrl":"https://doi.org/10.1109/ICSS55994.2022.00048","url":null,"abstract":"Crowd funding and crowd innovation can boost creativity of creators at a low cost. However, how to protect rights and benefits of relative stakeholders during the process in a credible way remains a problem. By introducing fungible tokens, non-fungible tokens and on-chain governance based on blockchain, we propose a set of smart contracts supporting crowd funding and crowd innovation to better reward participants and govern the process in a trusted way. Furthermore, based on these smart contracts, we abstract and encapsulate a series of common operations and implement a service platform for trustworthy crowd funding and crowd innovation.","PeriodicalId":327964,"journal":{"name":"2022 International Conference on Service Science (ICSS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132232376","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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