Most recommendation systems excessively pursue the recommendation accuracy and give rise to over-specialization. However, the existing recommendation systems research has not studied serendipity much. Hence, the serendipitous item recommendation has received more attention in recent years. The serendipitous recommendation of our research is not included in the area that the user predict easily but recommends the keywords that match the potential preferences. Potential preferences are those that are present in the user profile, which the user may not know. In this research, we recommend keywords that can express serendipity by intersecting the relation between keywords mainly. Furthermore, we propose the related page recommendation method on Web IndeX System for recommending linked pages related to these serendipitous keywords based on the user's potential preferences.
{"title":"Serendipitous Page Recommendation on Web IndeX System with Potential Preferences","authors":"Xingyu Chen, Jun Nemoto, Motomichi Toyama","doi":"10.1145/3428757.3429132","DOIUrl":"https://doi.org/10.1145/3428757.3429132","url":null,"abstract":"Most recommendation systems excessively pursue the recommendation accuracy and give rise to over-specialization. However, the existing recommendation systems research has not studied serendipity much. Hence, the serendipitous item recommendation has received more attention in recent years. The serendipitous recommendation of our research is not included in the area that the user predict easily but recommends the keywords that match the potential preferences. Potential preferences are those that are present in the user profile, which the user may not know. In this research, we recommend keywords that can express serendipity by intersecting the relation between keywords mainly. Furthermore, we propose the related page recommendation method on Web IndeX System for recommending linked pages related to these serendipitous keywords based on the user's potential preferences.","PeriodicalId":212557,"journal":{"name":"Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services","volume":"578 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127085085","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 natural way, how JSON documents can be queried and modified is to store them first in relational environment. In such a case, the features of relational DBMSs such as transaction processing, can be used. In this paper we compare two different mapping techniques: Adjacency List and the Single-Table Data Mapping (STDM) algorithm, which can be used, among other techniques, to store JSON documents in relational tables. The reason to choose and compare these two techniques is due to their origin: both are representatives of two different non-native storing techniques. The former is a general technique, which can be applied to any data presented in hierarchical form, while the latter is a representative of the family of XML-to-Relational storage algorithms, which can be used for JSON, too. Our results show that using the STDM algorithm the size of resulting relational table is approximately 70% of the size of the corresponding table generated with Adjacency List. Additionally, the STDM algorithm significantly outperforms Adjacency List concerning time.
{"title":"Non-native Techniques for Storing JSON Documents into Relational Tables","authors":"D. Petković","doi":"10.1145/3428757.3429103","DOIUrl":"https://doi.org/10.1145/3428757.3429103","url":null,"abstract":"The natural way, how JSON documents can be queried and modified is to store them first in relational environment. In such a case, the features of relational DBMSs such as transaction processing, can be used. In this paper we compare two different mapping techniques: Adjacency List and the Single-Table Data Mapping (STDM) algorithm, which can be used, among other techniques, to store JSON documents in relational tables. The reason to choose and compare these two techniques is due to their origin: both are representatives of two different non-native storing techniques. The former is a general technique, which can be applied to any data presented in hierarchical form, while the latter is a representative of the family of XML-to-Relational storage algorithms, which can be used for JSON, too. Our results show that using the STDM algorithm the size of resulting relational table is approximately 70% of the size of the corresponding table generated with Adjacency List. Additionally, the STDM algorithm significantly outperforms Adjacency List concerning time.","PeriodicalId":212557,"journal":{"name":"Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129569568","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}
Mahdi Bennara, Antoine Zimmermann, Maxime Lefrançois, Nurten Messalti
The advent of the Web of Things as an application layer for the Internet of Things has led to the proliferation of Web services exposing data and functionality of the networked objects. Since resource-oriented architectures align well with the WoT architectures, RESTful services have been the go-to interface to expose the connected devices on the Web. However, the heterogeneity of descriptions of devices and services as well as underlying IoT-level protocols has led to a number of interoperability issues. Recently, the growing popularity of semantically-enabled services has led to the emergence of services described with and exchanging RDF. In this position paper, we attempt to frame the challenges encountered in enabling semantic interoperability of heterogeneous WoT services, with the help of a real world production line scenario. We also propose preliminary solutions to the main issues hampering the establishment of true semantic interoperability on the WoT.
{"title":"Interoperability of Semantically-Enabled Web Services on the WoT: Challenges and Prospects","authors":"Mahdi Bennara, Antoine Zimmermann, Maxime Lefrançois, Nurten Messalti","doi":"10.1145/3428757.3429199","DOIUrl":"https://doi.org/10.1145/3428757.3429199","url":null,"abstract":"The advent of the Web of Things as an application layer for the Internet of Things has led to the proliferation of Web services exposing data and functionality of the networked objects. Since resource-oriented architectures align well with the WoT architectures, RESTful services have been the go-to interface to expose the connected devices on the Web. However, the heterogeneity of descriptions of devices and services as well as underlying IoT-level protocols has led to a number of interoperability issues. Recently, the growing popularity of semantically-enabled services has led to the emergence of services described with and exchanging RDF. In this position paper, we attempt to frame the challenges encountered in enabling semantic interoperability of heterogeneous WoT services, with the help of a real world production line scenario. We also propose preliminary solutions to the main issues hampering the establishment of true semantic interoperability on the WoT.","PeriodicalId":212557,"journal":{"name":"Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115291366","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}
Quality distant education has received growing scientific attention in the recent decades, which is accelerated in present times as a result of the COVID-19 pandemic; traditional in-class teaching methods needed a rapid transformation to be effective in a distant educational context as well, to reduce the existing and deepening digital divide. Convinced by the "FAIR-principles" and the "CARE" Standard, the present paper searches for gamified applicational examples in forecasting and business studies. Through the discussion of the presently existing literature reviews in this domain, the article sheds light upon the development and current scientific standing of this research area. During the search for ethical implications, the present paper aims to formulate future considerations, believing in the idea that gamification, holding the possibility of successful individual outcomes for learners in knowledge acquisition and also in the form of psychological benefits, could serve as a tool for humanity to reach a global society where no one is left behind.
{"title":"Gamification as an enabler of quality distant education: The need for guiding ethical principles towards an education for a global society leaving no one behind","authors":"A. Tjoa, Flora Poecze","doi":"10.1145/3428757.3429145","DOIUrl":"https://doi.org/10.1145/3428757.3429145","url":null,"abstract":"Quality distant education has received growing scientific attention in the recent decades, which is accelerated in present times as a result of the COVID-19 pandemic; traditional in-class teaching methods needed a rapid transformation to be effective in a distant educational context as well, to reduce the existing and deepening digital divide. Convinced by the \"FAIR-principles\" and the \"CARE\" Standard, the present paper searches for gamified applicational examples in forecasting and business studies. Through the discussion of the presently existing literature reviews in this domain, the article sheds light upon the development and current scientific standing of this research area. During the search for ethical implications, the present paper aims to formulate future considerations, believing in the idea that gamification, holding the possibility of successful individual outcomes for learners in knowledge acquisition and also in the form of psychological benefits, could serve as a tool for humanity to reach a global society where no one is left behind.","PeriodicalId":212557,"journal":{"name":"Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122037173","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}
Matteo Paganelli, Francesco Del Buono, F. Guerra, N. Ferro
Evaluation of the quality of data integration processes is usually performed via manual onerous data inspections. This task is particularly heavy in real business scenarios, where the large amount of data makes checking all the tuples infeasible and the frequent updates, i.e. changes in the sources and/or new sources, impose to repeat the evaluation over and over. Our idea is to address this issue by providing the experts with an unsupervised measure, based on word frequencies, which quantifies how much a dataset is representative of another dataset, giving an indication of how good is the integration process and whether deviations are happening and a manual inspection is needed. We also conducted some preliminary experiments, using shared datasets, that show the effectiveness of the proposed measures in typical data integration scenarios.
{"title":"Unsupervised Evaluation of Data Integration Processes","authors":"Matteo Paganelli, Francesco Del Buono, F. Guerra, N. Ferro","doi":"10.1145/3428757.3429129","DOIUrl":"https://doi.org/10.1145/3428757.3429129","url":null,"abstract":"Evaluation of the quality of data integration processes is usually performed via manual onerous data inspections. This task is particularly heavy in real business scenarios, where the large amount of data makes checking all the tuples infeasible and the frequent updates, i.e. changes in the sources and/or new sources, impose to repeat the evaluation over and over. Our idea is to address this issue by providing the experts with an unsupervised measure, based on word frequencies, which quantifies how much a dataset is representative of another dataset, giving an indication of how good is the integration process and whether deviations are happening and a manual inspection is needed. We also conducted some preliminary experiments, using shared datasets, that show the effectiveness of the proposed measures in typical data integration scenarios.","PeriodicalId":212557,"journal":{"name":"Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116968156","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}
A. Khiat, Mirette Elias, A. Foldenauer, M. Koehm, I. Blumenstein, Giulio Napolitano
Inflammatory bowel disease (IBD) is a chronic disease characterized by numerous, hard to predict periods of relapse and remission. "Digital twin" approaches, leveraging personalized predictive models, would significantly enhance therapeutic decision-making and cost-effectiveness. However, the associated computational and statistical methods require high quality data from a large population of patients. Such a comprehensive repository is very challenging to build, though, and none is available for IBD. To overcome this, a promising approach is to employ a knowledge graph, which is built from the available data and would help predicting IBD episodes and delivering more relevant personalized therapy at the lowest cost. In this research, we present a knowledge graph developed on the basis of patient records which are collected from one of the largest German gastroentologic outpatient clinic. First, we designed IBD ontology that encompasses the vocabulary, specifications and characteristics associated by physicians with IBD patients, such as disease classification schemas (e.g., Montreal Classification of IBD), status of the disease activity, and medications. Next, we defined the mappings between ontology entities and database variables. Physicians and project members participating in the Fraunhofer MED2ICIN project, validated the ontology and the knowledge graph. Furthermore, the knowledge graph has been validated against the competency questions compiled by physicians.
{"title":"Towards an Ontology Representing Characteristics of Inflammatory Bowel Disease","authors":"A. Khiat, Mirette Elias, A. Foldenauer, M. Koehm, I. Blumenstein, Giulio Napolitano","doi":"10.1145/3428757.3429110","DOIUrl":"https://doi.org/10.1145/3428757.3429110","url":null,"abstract":"Inflammatory bowel disease (IBD) is a chronic disease characterized by numerous, hard to predict periods of relapse and remission. \"Digital twin\" approaches, leveraging personalized predictive models, would significantly enhance therapeutic decision-making and cost-effectiveness. However, the associated computational and statistical methods require high quality data from a large population of patients. Such a comprehensive repository is very challenging to build, though, and none is available for IBD. To overcome this, a promising approach is to employ a knowledge graph, which is built from the available data and would help predicting IBD episodes and delivering more relevant personalized therapy at the lowest cost. In this research, we present a knowledge graph developed on the basis of patient records which are collected from one of the largest German gastroentologic outpatient clinic. First, we designed IBD ontology that encompasses the vocabulary, specifications and characteristics associated by physicians with IBD patients, such as disease classification schemas (e.g., Montreal Classification of IBD), status of the disease activity, and medications. Next, we defined the mappings between ontology entities and database variables. Physicians and project members participating in the Fraunhofer MED2ICIN project, validated the ontology and the knowledge graph. Furthermore, the knowledge graph has been validated against the competency questions compiled by physicians.","PeriodicalId":212557,"journal":{"name":"Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125717303","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}
Data integration is one of the key problems in the era of Big Data analytics. The key challenge of data integration is the identification of records representing the same entities (e.g. person). This task is referred to as Record Linkage. It is uncommon for different data sources to share a unique identifier hence the records must be matched by comparing their corresponding values. Most of the existing methods assume that records across different sources are structured and represented by the same set of attributes (e.g. name, date of birth). However, nowadays majority of the data comes without structure (e.g. social media sites). We propose a new approach to Record Linkage based on application of Siamese Neural Network. The model can be applied with structured, semi-structured and unstructured records and it does not assume a common format across different data sources. We demonstrate that the model performs on par with other approaches, which make constraining assumptions regarding the data.
{"title":"Siamese Neural Network for Unstructured Data Linkage","authors":"Anna Jurek-Loughrey","doi":"10.1145/3428757.3429106","DOIUrl":"https://doi.org/10.1145/3428757.3429106","url":null,"abstract":"Data integration is one of the key problems in the era of Big Data analytics. The key challenge of data integration is the identification of records representing the same entities (e.g. person). This task is referred to as Record Linkage. It is uncommon for different data sources to share a unique identifier hence the records must be matched by comparing their corresponding values. Most of the existing methods assume that records across different sources are structured and represented by the same set of attributes (e.g. name, date of birth). However, nowadays majority of the data comes without structure (e.g. social media sites). We propose a new approach to Record Linkage based on application of Siamese Neural Network. The model can be applied with structured, semi-structured and unstructured records and it does not assume a common format across different data sources. We demonstrate that the model performs on par with other approaches, which make constraining assumptions regarding the data.","PeriodicalId":212557,"journal":{"name":"Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124467979","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}
There have been numerous studies that have examined the performance of distribution frameworks. Most of these studies deal with the processing of large amounts of data. This work compares two of these frameworks for their ability to implement CPU-intensive distributed algorithms. As a case study for our experiments we used a simple but computationally intensive puzzle. To find all solutions using brute-force search, 15! permutations had to be calculated and tested against the solution rules. Our experimental application was implemented in the Java programming language using a simple algorithm and having two distributed solutions with the paradigms MapReduce (Apache Hadoop) and RDD (Apache Spark). The implementations were benchmarked in Amazon-EC2/EMR clusters for performance and scalability measurements, where the processing time of both solutions scaled approximately linearly. However, according to our experiments, the number of tasks, hardware utilization and other aspects should also be taken into consideration when assessing scalability. The comparison of the solutions with MapReduce (Apache Hadoop) and RDD (Apache Spark) under Amazon EMR showed that the processing time measured in CPU minutes with Spark was up to 30 % lower, while the performance of Spark especially benefits from an increasing number of tasks. Considering the efficiency of using the EC2 resources, the implementation via Apache Spark was even more powerful than a comparable multithreaded Java solution.
{"title":"Performance evaluation of Apache Hadoop and Apache Spark for parallelization of compute-intensive tasks","authors":"Alexander Döschl, Max-Emanuel Keller, P. Mandl","doi":"10.1145/3428757.3429121","DOIUrl":"https://doi.org/10.1145/3428757.3429121","url":null,"abstract":"There have been numerous studies that have examined the performance of distribution frameworks. Most of these studies deal with the processing of large amounts of data. This work compares two of these frameworks for their ability to implement CPU-intensive distributed algorithms. As a case study for our experiments we used a simple but computationally intensive puzzle. To find all solutions using brute-force search, 15! permutations had to be calculated and tested against the solution rules. Our experimental application was implemented in the Java programming language using a simple algorithm and having two distributed solutions with the paradigms MapReduce (Apache Hadoop) and RDD (Apache Spark). The implementations were benchmarked in Amazon-EC2/EMR clusters for performance and scalability measurements, where the processing time of both solutions scaled approximately linearly. However, according to our experiments, the number of tasks, hardware utilization and other aspects should also be taken into consideration when assessing scalability. The comparison of the solutions with MapReduce (Apache Hadoop) and RDD (Apache Spark) under Amazon EMR showed that the processing time measured in CPU minutes with Spark was up to 30 % lower, while the performance of Spark especially benefits from an increasing number of tasks. Considering the efficiency of using the EC2 resources, the implementation via Apache Spark was even more powerful than a comparable multithreaded Java solution.","PeriodicalId":212557,"journal":{"name":"Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117055295","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}
To support sustainable in-home long-term care, it is essential to monitor mental states of elderly people at home, and to encourage their ability of self-care. However, many challenges exist in practice, including limitations of human interventions, sensor-based monitoring, the daily recording and externalization of mental states. In the previous research, we have proposed Mind Monitoring Service, which aims to monitor mental states and promote self-care of elderly people at home. In the proposed service, a chatbot asks a user specific questions to acquire his/her mental state. Based on the answers, the service assesses the mental state. In this research, we develop a new feature of weekly feedback. The feature automatically reviews answers of past one week, and sends advice to improve the current situation. We conduct an experiment to evaluate the effectiveness. Through the experiment, it was confirmed that the weekly feedback encouraged self-care consciousness and increased motivation of subjects.
{"title":"Implementing and Evaluating feedback feature of Mind Monitoring Service for Elderly People at Home","authors":"C. Miura, S. Saiki, Masahide Nakamura, K. Yasuda","doi":"10.1145/3428757.3429124","DOIUrl":"https://doi.org/10.1145/3428757.3429124","url":null,"abstract":"To support sustainable in-home long-term care, it is essential to monitor mental states of elderly people at home, and to encourage their ability of self-care. However, many challenges exist in practice, including limitations of human interventions, sensor-based monitoring, the daily recording and externalization of mental states. In the previous research, we have proposed Mind Monitoring Service, which aims to monitor mental states and promote self-care of elderly people at home. In the proposed service, a chatbot asks a user specific questions to acquire his/her mental state. Based on the answers, the service assesses the mental state. In this research, we develop a new feature of weekly feedback. The feature automatically reviews answers of past one week, and sends advice to improve the current situation. We conduct an experiment to evaluate the effectiveness. Through the experiment, it was confirmed that the weekly feedback encouraged self-care consciousness and increased motivation of subjects.","PeriodicalId":212557,"journal":{"name":"Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116279122","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}
Christian Dienbauer, Benedikt Pittl, W. Mach, E. Schikuta
Today, traded cloud services are described by service level agreements that specify the obligations of providers such as availability or reliability. Violations of service level agreements lead to penalty payments. The recent development of prominent cloud platforms such as the re-design of Amazon's spot marketspace underpins a trend towards dynamic cloud markets where consumers migrate their services continuously to different marketspaces and providers to reach a cost-optimum. This leads to a heterogeneous IT infrastructure and consequently aggravates the monitoring of the delivered service quality. Hence, there is a need for a transparent penalty management system, which ensures that consumers automatically get penalty payments from providers in case of service violations. In the paper at hand, we present a cloud monitoring system that is able to execute penalty payments autonomously. In this regard, we revert to smart contracts hosted on blockchains. They continuously monitor cloud services and trigger penalty payments to consumers in case of service violations. We implemented the presented approach with the IBM Hyperledger Fabric framework and created a use case with Amazon's cloud services as well as Azures cloud services to illustrate the universal design of the introduced approach.
{"title":"A Penalty-Aware Cloud Monitoring System based on Blockchains","authors":"Christian Dienbauer, Benedikt Pittl, W. Mach, E. Schikuta","doi":"10.1145/3428757.3429130","DOIUrl":"https://doi.org/10.1145/3428757.3429130","url":null,"abstract":"Today, traded cloud services are described by service level agreements that specify the obligations of providers such as availability or reliability. Violations of service level agreements lead to penalty payments. The recent development of prominent cloud platforms such as the re-design of Amazon's spot marketspace underpins a trend towards dynamic cloud markets where consumers migrate their services continuously to different marketspaces and providers to reach a cost-optimum. This leads to a heterogeneous IT infrastructure and consequently aggravates the monitoring of the delivered service quality. Hence, there is a need for a transparent penalty management system, which ensures that consumers automatically get penalty payments from providers in case of service violations. In the paper at hand, we present a cloud monitoring system that is able to execute penalty payments autonomously. In this regard, we revert to smart contracts hosted on blockchains. They continuously monitor cloud services and trigger penalty payments to consumers in case of service violations. We implemented the presented approach with the IBM Hyperledger Fabric framework and created a use case with Amazon's cloud services as well as Azures cloud services to illustrate the universal design of the introduced approach.","PeriodicalId":212557,"journal":{"name":"Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116935607","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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