Pub Date : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-191
Y. Teranishi, Takashi Kimata, Eiji Kawai, H. Harai
In this paper, we propose a spatio-temporal data aggregation protocol in Vehicular Delay Tolerant Network (VDTN). We focus on Asynchronous Vehicular Crowdsensing Service (AVCS) to collect volume sensor data (e.g., images captured by on-board cameras) from VDTN-enabled vehicles. In AVCS, it is critical to cope with the huge redundant traffic generated by a large number of vehicles. We propose a novel protocol to aggregate volume spatio-temporal sensor data in Hybrid DTN data collection architecture. By assigning spatio-temporal identifiers (STI) to the aggregation targets in AVCS and extending the message exchange protocol to treat STI in VDTN, the redundant traffic can be significantly improved. Simulation results using a real taxi trace dataset showed the effectiveness of the proposed data aggregation protocol. The coverage of the crowdsensing was improved around 20-35% with 80% traffic reduction compared with the baseline aggregation protocol.
{"title":"Spatio-Temporal Volume Data Aggregation for Crowdsensing in VDTN","authors":"Y. Teranishi, Takashi Kimata, Eiji Kawai, H. Harai","doi":"10.1109/COMPSAC48688.2020.0-191","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-191","url":null,"abstract":"In this paper, we propose a spatio-temporal data aggregation protocol in Vehicular Delay Tolerant Network (VDTN). We focus on Asynchronous Vehicular Crowdsensing Service (AVCS) to collect volume sensor data (e.g., images captured by on-board cameras) from VDTN-enabled vehicles. In AVCS, it is critical to cope with the huge redundant traffic generated by a large number of vehicles. We propose a novel protocol to aggregate volume spatio-temporal sensor data in Hybrid DTN data collection architecture. By assigning spatio-temporal identifiers (STI) to the aggregation targets in AVCS and extending the message exchange protocol to treat STI in VDTN, the redundant traffic can be significantly improved. Simulation results using a real taxi trace dataset showed the effectiveness of the proposed data aggregation protocol. The coverage of the crowdsensing was improved around 20-35% with 80% traffic reduction compared with the baseline aggregation protocol.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125634304","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00027
Tyler Morrow, A. Hurson, Sahra Sedigh Sarvestani
The work presented in this paper demonstrates the use of context-aware recommendation to facilitate personalized education, by assisting students in selecting courses and course content and mapping a trajectory to graduation. The recommendation algorithm considers a student's profile and their program's curricular requirements in generating a schedule of courses, while aiming to reduce attributes such as cost and time-to-degree. The resulting optimization problem is solved using integer linear programming and graph-based heuristics. The course selection algorithm has been developed for the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS), which can assist or supplement the degree planning actions of an academic advisor, with assurance that recommended selections are always valid.
{"title":"Algorithmic Support for Personalized Course Selection and Scheduling","authors":"Tyler Morrow, A. Hurson, Sahra Sedigh Sarvestani","doi":"10.1109/COMPSAC48688.2020.00027","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00027","url":null,"abstract":"The work presented in this paper demonstrates the use of context-aware recommendation to facilitate personalized education, by assisting students in selecting courses and course content and mapping a trajectory to graduation. The recommendation algorithm considers a student's profile and their program's curricular requirements in generating a schedule of courses, while aiming to reduce attributes such as cost and time-to-degree. The resulting optimization problem is solved using integer linear programming and graph-based heuristics. The course selection algorithm has been developed for the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS), which can assist or supplement the degree planning actions of an academic advisor, with assurance that recommended selections are always valid.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122929844","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00-71
Neda Tavakoli
Sequence classification has been widely used in numerous application domains. There exists a good number of classification algorithms that can be applied to feature vectors. However, these classification algorithms cannot be directly applied to the sequence classification problem, mainly because of the difficulties to capture feature vectors from sequences. More specifically, due to the sequential nature of features that exist in a sequence, the clustering problem in sequences suffers from the curse of dimensionality, which makes the sequence classification task more challenging compared to a typical classification on feature vectors. In this paper, we present a novel idea of transforming sequences to images, called Seq2Image, a simple yet effective method to perform genomic sequence classification using Convolutional Neural Network (CNN). We first convert a given genomic sequence to a tensor, and then the obtained tensor is transformed into an image. We then employ the CNN deep learning-based image processing techniques to classify the created images of sequences. The results of our preliminary experimental study are very promising achieving 95.78% training accuracy, 95.76% validation accuracy, and 95.83% testing accuracy for classification of human genome of 166 samples with six different sequence families.
{"title":"Seq2Image: Sequence Analysis using Visualization and Deep Convolutional Neural Network","authors":"Neda Tavakoli","doi":"10.1109/COMPSAC48688.2020.00-71","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00-71","url":null,"abstract":"Sequence classification has been widely used in numerous application domains. There exists a good number of classification algorithms that can be applied to feature vectors. However, these classification algorithms cannot be directly applied to the sequence classification problem, mainly because of the difficulties to capture feature vectors from sequences. More specifically, due to the sequential nature of features that exist in a sequence, the clustering problem in sequences suffers from the curse of dimensionality, which makes the sequence classification task more challenging compared to a typical classification on feature vectors. In this paper, we present a novel idea of transforming sequences to images, called Seq2Image, a simple yet effective method to perform genomic sequence classification using Convolutional Neural Network (CNN). We first convert a given genomic sequence to a tensor, and then the obtained tensor is transformed into an image. We then employ the CNN deep learning-based image processing techniques to classify the created images of sequences. The results of our preliminary experimental study are very promising achieving 95.78% training accuracy, 95.76% validation accuracy, and 95.83% testing accuracy for classification of human genome of 166 samples with six different sequence families.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127560991","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00026
Giuseppe Attanasio, F. Giobergia, Andrea Pasini, F. Ventura, Elena Baralis, Luca Cagliero, P. Garza, D. Apiletti, T. Cerquitelli, S. Chiusano
During the last years an increasing number of university-level and post-graduation courses on Data Science have been offered. Practices and assessments need specific learning environments where learners could play with data samples and run machine learning and data mining algorithms. To foster learner engagement many closed-and open-source platforms support the design of data science competitions. However, they show limitations on the ability to handle private data, customize the analytics and evaluation processes, and visualize learners' activities and outcomes. This paper presents Data Science Lab Environment (DSLE, in short), a new open-source platform to design and monitor data science competitions. DSLE offers a easily configurable interface to share training and test data, design group works or individual sessions, evaluate the competition runs according to customizable metrics, manage public and private leaderboards, monitor participants' activities and their progress over time. The paper describes also a real experience of usage of DSLE in the context of a 1st-year M.Sc. course, which has involved around 160 students.
{"title":"DSLE: A Smart Platform for Designing Data Science Competitions","authors":"Giuseppe Attanasio, F. Giobergia, Andrea Pasini, F. Ventura, Elena Baralis, Luca Cagliero, P. Garza, D. Apiletti, T. Cerquitelli, S. Chiusano","doi":"10.1109/COMPSAC48688.2020.00026","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00026","url":null,"abstract":"During the last years an increasing number of university-level and post-graduation courses on Data Science have been offered. Practices and assessments need specific learning environments where learners could play with data samples and run machine learning and data mining algorithms. To foster learner engagement many closed-and open-source platforms support the design of data science competitions. However, they show limitations on the ability to handle private data, customize the analytics and evaluation processes, and visualize learners' activities and outcomes. This paper presents Data Science Lab Environment (DSLE, in short), a new open-source platform to design and monitor data science competitions. DSLE offers a easily configurable interface to share training and test data, design group works or individual sessions, evaluate the competition runs according to customizable metrics, manage public and private leaderboards, monitor participants' activities and their progress over time. The paper describes also a real experience of usage of DSLE in the context of a 1st-year M.Sc. course, which has involved around 160 students.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132590816","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-228
Ming Zhu, Pengyu Wan, Xiangyang Feng, Zhengyu Wang, Wenpei Shao
This article focuses on the background of commercial banks' assessment of pre-loan risk capabilities. In order to reduce the risk of bank loans, scientific and reasonable assessment of loan companies is required. The evaluation of enterprises requires the establishment of a complete set of indicators, which can reflect the full picture of enterprise capabilities. The key need is to ensure the rationality of the data analyzed, which is the premise of capacity assessment. Therefore, this article guarantees the rationality, scientificity, accuracy, and applicability of the index system data from the perspective of network perception, which lay the foundation for the data mining stage.
{"title":"Research on Network Awareness of Enterprise Evaluation System Indicators","authors":"Ming Zhu, Pengyu Wan, Xiangyang Feng, Zhengyu Wang, Wenpei Shao","doi":"10.1109/COMPSAC48688.2020.0-228","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-228","url":null,"abstract":"This article focuses on the background of commercial banks' assessment of pre-loan risk capabilities. In order to reduce the risk of bank loans, scientific and reasonable assessment of loan companies is required. The evaluation of enterprises requires the establishment of a complete set of indicators, which can reflect the full picture of enterprise capabilities. The key need is to ensure the rationality of the data analyzed, which is the premise of capacity assessment. Therefore, this article guarantees the rationality, scientificity, accuracy, and applicability of the index system data from the perspective of network perception, which lay the foundation for the data mining stage.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132634007","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00-15
Ricardo Buettner, Florian Klenk, M. Ebert
To analyze the state of the art of machine learning-based disease profiling and personalized treatments, we review the relevant literature included in top peer-reviewed journals and evaluate the coverage according to the ICD-11 framework. We identify advantages, but also research needs and limitations within the ICD-11 disease categories to foster the adaptation of these new E-health technologies.
{"title":"A Systematic Literature Review of Machine Learning-Based Disease Profiling and Personalized Treatment","authors":"Ricardo Buettner, Florian Klenk, M. Ebert","doi":"10.1109/COMPSAC48688.2020.00-15","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00-15","url":null,"abstract":"To analyze the state of the art of machine learning-based disease profiling and personalized treatments, we review the relevant literature included in top peer-reviewed journals and evaluate the coverage according to the ICD-11 framework. We identify advantages, but also research needs and limitations within the ICD-11 disease categories to foster the adaptation of these new E-health technologies.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133422784","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-121
R. Fatima, Affan Yasin, Lin Liu, Jianmin Wang
Objective of the study is to calculate: a) grey literature evidence in the selected Systematic literature reviews (SLRs); b) Google Scholar indexing for the extracted primary studies from the selected SLRs. We have randomly selected 20+ SLRs from Science Direct, IEEE Xplore, Springer Link and ACM. Result: a) Random selection of 20+ SLRs and grey literature calculation verifies that the grey literature percentage ranges around 5.7% to 9.1%; b) The second phase showed that Google Scholar was successful in retrieving around ~91% of the primary studies.
{"title":"The Use of Grey Literature and Google Scholar in Software Engineering Systematic Literature Reviews","authors":"R. Fatima, Affan Yasin, Lin Liu, Jianmin Wang","doi":"10.1109/COMPSAC48688.2020.0-121","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-121","url":null,"abstract":"Objective of the study is to calculate: a) grey literature evidence in the selected Systematic literature reviews (SLRs); b) Google Scholar indexing for the extracted primary studies from the selected SLRs. We have randomly selected 20+ SLRs from Science Direct, IEEE Xplore, Springer Link and ACM. Result: a) Random selection of 20+ SLRs and grey literature calculation verifies that the grey literature percentage ranges around 5.7% to 9.1%; b) The second phase showed that Google Scholar was successful in retrieving around ~91% of the primary studies.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133604040","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-152
Xiao‐Liang Cheng, N. Liu, Lin Guo, Zhou Xu, Tao Zhang
With a growing number of software projects, software quality is increasingly crucial. Researchers and engineers in the software engineering field often pay much attention to bug management tasks, such as bug localization, bug triage, and duplicate bug detection. However, there are few researchers to study blocking bug prediction. Blocking bugs prevent other bugs from being fixed and usually need more time to be fixed. Thus, developers need to identify blocking bugs and reduce the impact of blocking bugs. The previous studies utilized supervised algorithms to implement this task. However, they did not consider the dependencies among individual classifiers so that they cannot get the perfect accuracy for blocking bug prediction. In this paper, we propose a new framework XGBlocker that includes two stages. In the first stage, XGBlocker collects more features from bug reports to build an enhanced dataset. In the second stage, XGBlocker exploits XGBoost technique to construct an effective model to perform the prediction task. We conduct experiments on four projects with three evaluation metrics. The experimental results show that our method XGBlocker achieves promising performance compared with baseline methods in most cases. In detail, XGBlocker achieves F1-score, ER@20%, and AUC of up to 0.808, 0.944, and 0.975, respectively. On average across the four projects, XGBlocker improves F1-score, ER@20%, and AUC over the state-of-the-art method ELBlocker by 17.27%, 12.67%, and 4.85%, respectively.
{"title":"Blocking Bug Prediction Based on XGBoost with Enhanced Features","authors":"Xiao‐Liang Cheng, N. Liu, Lin Guo, Zhou Xu, Tao Zhang","doi":"10.1109/COMPSAC48688.2020.0-152","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-152","url":null,"abstract":"With a growing number of software projects, software quality is increasingly crucial. Researchers and engineers in the software engineering field often pay much attention to bug management tasks, such as bug localization, bug triage, and duplicate bug detection. However, there are few researchers to study blocking bug prediction. Blocking bugs prevent other bugs from being fixed and usually need more time to be fixed. Thus, developers need to identify blocking bugs and reduce the impact of blocking bugs. The previous studies utilized supervised algorithms to implement this task. However, they did not consider the dependencies among individual classifiers so that they cannot get the perfect accuracy for blocking bug prediction. In this paper, we propose a new framework XGBlocker that includes two stages. In the first stage, XGBlocker collects more features from bug reports to build an enhanced dataset. In the second stage, XGBlocker exploits XGBoost technique to construct an effective model to perform the prediction task. We conduct experiments on four projects with three evaluation metrics. The experimental results show that our method XGBlocker achieves promising performance compared with baseline methods in most cases. In detail, XGBlocker achieves F1-score, ER@20%, and AUC of up to 0.808, 0.944, and 0.975, respectively. On average across the four projects, XGBlocker improves F1-score, ER@20%, and AUC over the state-of-the-art method ELBlocker by 17.27%, 12.67%, and 4.85%, respectively.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134220637","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.000-5
I. Mitrea, Giovanni Zenezini, A. Marco, Filippo Maria Ottaviani, Tiziana Delmastro, C. Botta
The widespread diffusion of the online channel in the retail marketplace is impacting modern society considerably in recent years. Given the growing demand, Business to Consumer (B2C) e-commerce entails a much higher complexity of the delivery process due to significant fragmentation of parcel shipments in the last mile, especially in urban areas, where traffic and congestion problems are arising together with environmental issues. All these aspects rise interest not only from companies – which strive to maintain a high target service level for their customers - but also for public administrations, that aim to foresee the implications of this phenomenon. In this context, the purpose of the study is to investigate the potential of an alternative solution to the traditional home delivery, namely the self-collection delivery service through automated parcel lockers. The research study is based on data gathered from an online survey submitted to a sample of residents living in the metropolitan city of Turin, Italy. The potential of parcel lockers to capture the actual demand will be assessed to determine the feasibility of the delivery solution under consideration.
{"title":"Estimating e-Consumers' Attitude Towards Parcel Locker Usage","authors":"I. Mitrea, Giovanni Zenezini, A. Marco, Filippo Maria Ottaviani, Tiziana Delmastro, C. Botta","doi":"10.1109/COMPSAC48688.2020.000-5","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.000-5","url":null,"abstract":"The widespread diffusion of the online channel in the retail marketplace is impacting modern society considerably in recent years. Given the growing demand, Business to Consumer (B2C) e-commerce entails a much higher complexity of the delivery process due to significant fragmentation of parcel shipments in the last mile, especially in urban areas, where traffic and congestion problems are arising together with environmental issues. All these aspects rise interest not only from companies – which strive to maintain a high target service level for their customers - but also for public administrations, that aim to foresee the implications of this phenomenon. In this context, the purpose of the study is to investigate the potential of an alternative solution to the traditional home delivery, namely the self-collection delivery service through automated parcel lockers. The research study is based on data gathered from an online survey submitted to a sample of residents living in the metropolitan city of Turin, Italy. The potential of parcel lockers to capture the actual demand will be assessed to determine the feasibility of the delivery solution under consideration.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133025900","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00-31
Siyuan Wang, Xuehan Zhang, Wei Yu, Kai Hu, Jian Zhu
A smart contract is a computable protocol that automatically enforces contract terms in a computer, transforming real-world contract terms into digital promises of the virtual world. Early smart contracts have been stuck in the theoretical phase due to the lack of a credible execution environment and the means to control digital assets. With the emergence of blockchain technology, it has solved the problems mentioned above. Smart contracts are stored on blockchain, ensuring the credibility of contract execution through the joint execution of contracts by the various nodes in the blockchain network. However, the current technology of blockchain-based smart contracts is still not mature enough and faces many major challenges. Among them, the extensibility and performance of smart contracts are the most important and most concerned ones. This paper studies the extensibility and performance of smart contracts by combining blockchain-based smart contracts with cloud technologies to address the extensibility and performance issues of smart contracts. Combined with micro-service technology, a new type of smart contract architecture is proposed, and then the key technologies in each layer of the architecture are further studied.
{"title":"Smart Contract Microservitization","authors":"Siyuan Wang, Xuehan Zhang, Wei Yu, Kai Hu, Jian Zhu","doi":"10.1109/COMPSAC48688.2020.00-31","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00-31","url":null,"abstract":"A smart contract is a computable protocol that automatically enforces contract terms in a computer, transforming real-world contract terms into digital promises of the virtual world. Early smart contracts have been stuck in the theoretical phase due to the lack of a credible execution environment and the means to control digital assets. With the emergence of blockchain technology, it has solved the problems mentioned above. Smart contracts are stored on blockchain, ensuring the credibility of contract execution through the joint execution of contracts by the various nodes in the blockchain network. However, the current technology of blockchain-based smart contracts is still not mature enough and faces many major challenges. Among them, the extensibility and performance of smart contracts are the most important and most concerned ones. This paper studies the extensibility and performance of smart contracts by combining blockchain-based smart contracts with cloud technologies to address the extensibility and performance issues of smart contracts. Combined with micro-service technology, a new type of smart contract architecture is proposed, and then the key technologies in each layer of the architecture are further studied.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133082603","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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