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17th International Workshop on Ontology Matching, OM 2022

作者： Pour, Mina Abd Nikooie Algergawy, Alsayed Buche, Patrice Castro, Leyla J. Chen, Jiaoyan Dong, Hang Fallatah, Omaima Faria, Daniel Fundulaki, Irini Hertling, Sven He, Yuan Horrocks, Ian Huschka, Martin Ibanescu, Liliana Jiménez-Ruiz, Ernesto Karam, Naouel Laadhar, Amir Lambrix, Patrick Li, Huanyu Li, Ying Michel, Franck Nasr, Engy Paulheim, Heiko Pesquita, Catia Saveta, Tzanina Shvaiko, Pavel Trojahn, Cassia Verhey, Chantelle Wu, Mingfang Yaman, Beyza Zamazal, Ondrej Zhou, Lu Linköping University Swedish E-Science Research Center Linköping Sweden Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany UMR IATE INRAE University of Montpellier France ZB MED Information Centre for Life Sciences Germany Department of Computer Science The University of Manchester United Kingdom Department of Computer Science University of Oxford United Kingdom Information School The University of Sheffield Sheffield United Kingdom University of Lisbon Portugal Institute of Computer Science-FORTH Heraklion Greece Data and Web Science Group University of Mannheim Germany Fraunhofer Institute for High-Speed Dynamics Ernst-Mach-Institut EMI Germany Université Paris-Saclay INRAE AgroParisTech UMR MIA Paris-Saclay France City University of London United Kingdom Fraunhofer FOKUS Institute for Applied Informatics University of Leipzig Germany University of Stuttgart Germany University of Gävle Sweden University Côte d'Azur CNRS Inria France Albert Ludwig University of Freiburg Germany LASIGE Faculdade de Ciências Universidade de Lisboa Portugal Trentino Digitale SpA Trento Italy Institut de Recherche en Informatique de Toulouse France World Data System International Technology Office United States Australian Research Data Commons Australia ADAPT Centre Trinity College Dublin Ireland Prague University of Economics and Business Czech Republic TigerGraph Inc. United States SIRIUS University of Oslo Norway

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2022 campaign offered 14 tracks and was attended by 18 participants. This paper is an overall presentation of that campaign. © 2022 Copyright for this paper by its authors.

关键词： Ontology

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Supporting the DevOps Feedback Loop using Unsupervised Machine Learning

Supporting the DevOps Feedback Loop using Unsupervised Machi...

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2019 IEEE International Symposium on INnovations in Intelligent systems and Applications, INISTA 2019

作者： Figalist, Iris Biesdorf, Andreas Brand, Christoph Feld, Sebastian Kiermeier, Marie Corporate Technology Siemens AG Munich Germany Mobile and Distributed Systems Group Institute for Computer Science Ludwig-Maximilians-University Munich Germany

ISBN: (纸本)9781728118628

Nowadays, software systems and applications need to adapt rapidly to changing requirements and evolve in an agile manner. This creates the need for independent deployments, e.g. as part of DevOps. Due to the flexibility and fast release cycles comprised by DevOps, continuous monitoring and the generation of feedback is crucial to a system's quality, especially if it is continuously developed. For this purpose, we propose a feedback system that combines operations data with development data in order to trace anomalies occurring in production back to their root cause by defining patterns, detecting anomalous behavior, and generating feedback that is transferred back into the development process. To this end, we utilize two different unsupervised machine learning techniques, the k-means clustering and the archetypal analysis, to describe the data set and use the results as a basis to characterize the behavior of new data points as either normal or anomalous. The feedback system was tested and evaluated using real data produced by an application that is currently developed within a large, industrial company and serves as a link to support the loop of continuous planning, development, deployment, monitoring, and feedback. © 2019 IEEE.

关键词： Anomaly detection

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AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

arXiv

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

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Applying COBIT 2019 to IT Governance in Higher Education 50

Applying COBIT 2019 to IT Governance in Higher Education

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50. Jahrestagung der Gesellschaft fur Informatik, INFORMATIK 2020 - 50th Annual Conference of the German Informatics Society, INFORMATIK 2020

作者： Gerl, Armin von der Heyde, Markus Grob, Rainer Seck, Rainer Watkowski, Laura University of Passau Department of Distributed Information Systems Passau94032 Germany vdH-IT Weimar Germany Nuremberg Institute of Technology Computer Science Department Nürnberg90121 Germany CIO CISO University of Applied Sciences Munich Department of Electrical Engineering and Information Technology München80335 Germany University of Bayreuth Department of Information Systems Management Bayreuth95440 Germany

ISBN: (纸本)9783885797012

The Bavarian higher education environment is aiming to renew its IT strategy. The overall objective is to find an organisational solution which allows both local independence and collaborative solutions in those areas which are either commodities or which are too complex or costly to be solved several times in parallel. All Bavarian CIOs are engaged in the development of a model which respects local governance to be included into the overarching IT strategy of Bavaria, potentially Germany and Europe. As a common framework, COBIT was chosen to structure the process and guarantee completeness. The application of COBIT was started by the agreement to a common model. Further, the description of the respective responsibilities and competencies for all stakeholders was defined. We share this current state of the discussion with the broader community of higher education to promote further discussions about methods and objectives. © 2020 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： COBIT Higher education IT governance

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Validating daily social media macroscopes of emotions

arXiv

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arXiv 2021年

作者： Pellert, Max Metzler, Hannah Matzenberger, Michael Garcia, David Sony Computer Science Laboratories 6 Rue Amyot Paris75005 France Institute of Interactive Systems and Data Science Graz University of Technology Inffeldgasse 16C Graz8010 Austria Complexity Science Hub Vienna Josefstädter Straße 39 Vienna1080 Austria Section for Science of Complex Systems Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Spitalgasse 23 Vienna1090 Austria Institute of Globally Distributed Open Research and Education Sweden Der Standard Vordere Zollamtsstraße 13 Vienna1030 Austria

Measuring sentiment in social media text has become an important practice in studying emotions at the macroscopic level. However, this approach can suffer from methodological issues like sampling biases and measurement errors. To date, it has not been validated if social media sentiment can actually measure the temporal dynamics of mood and emotions aggregated at the level of communities. We ran a large-scale survey at an online newspaper to gather daily mood self-reports from its users, and compare these with aggregated results of sentiment analysis of user discussions. We find strong correlations between text analysis results and levels of self-reported feelings, as well as between inter-day changes of both measurements. We replicate these results using sentiment data from Twitter. We show that a combination of supervised text analysis methods based on novel deep learning architectures and unsupervised dictionary-based methods have high agreement with the time series of aggregated mood measured with self-reports. Our findings indicate that macro level dynamics of feelings expressed on an online platform can be tracked with social media text, especially in situations of high mood variability. © 2021, CC BY.

关键词： Sentiment analysis

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SDVADC: Secure Deduplication and Virtual Auditing of Data in Cloud

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Procedia computer science 2020年 171卷 2225-2234页

作者： Geeta C M Shreyas Raju R G Raghavendra S Rajkumar Buyya Venugopal K R S S Iyengar L M Patnaik Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru Cloud Computing and Distributed Systems (CLOUDS) Lab The University of Melbourne Australia Vice Chancellor Bangalore University Bengaluru Department of Computer Science and Engineering Florida International University USA INSA National Institute of Advanced Studies Indian Institute of Science Campus Bangalore India

Over the last few years, deploying data to cloud service for repository is an appealing passion that avoids efforts on significant information sustenance and administration. In distributed repository utilities, deduplication technique is often exploited to minimize the capacity and bandwidth necesseties of amenities by erasing repetitive data and caching only a solitary duplicate of them. Proof-of-Ownership mechanisms authorize any possessor of the identical information to approve to the distributed repository server that he possess the information in a dynamic way. In repository utilities with enormous information, the repository servers may intend to minimize the capacity of cached information, and the customers may want to examine the integrity of their information with a reasonable cost. We propose Secure Deduplication and Virtual Auditing of Data in Cloud (SDVADC) mechanism that realizes integrity auditing and deduplication of information in cloud. The mechanism supports secure deduplication of information and effective virtual auditing of the documents during the download process. In addition, the proposed mechanism lowers the burden of dataowner to audit documents by himself and there is no need to delegate auditing to the Third Party Auditor (TPA). Experimental results demonstrate that the virtual auditing has low auditing time cost relative to the existing public auditing schemes.

关键词： Cloud Computing Deduplication Virtual Auditing Public Auditing Proof of Retrievability Proof of Ownership

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DTCMS: Dynamic traffic congestion management in Social Internet of Vehicles (SIoV)

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Internet of Things (Netherlands) 2021年 16卷

作者： Roopa, M.S. Ayesha Siddiq, S. Buyya, Rajkumar Venugopal, K.R. Iyengar, S.S. Patnaik, L.M. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru 560-001 India Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Melbourne 3053 VIC Australia Bangalore University Bengaluru 560-001 India Department of Computer Science and Engineering Florida International University Miami 33199 FL United States Consciousness Studies Program National Institute of Advanced Studies Indian Institute of Science Bengaluru 560-012 India

With the augmentation of traffic exponentially, we observe that traffic congestion does not guarantee road safety or enhance the driving experience. In the recent past, Social Internet of Vehicles (SIoV), a social network paradigm permits social relationships among every vehicle in the network or with any road infrastructure to render a radically useful environment. SIoV is beneficial for the drivers, in improving road safety, avoiding mishaps, and providing a friendly-driving experience. In this paper, we propose a traffic scheduling algorithm to gain the maximum throughput for the flow of vehicles at a road intersection with the formation of social relationships among the vehicles and with the Road Side Units (RSUs). The algorithm estimates the flow rate of vehicles for lanes at the intersections exploiting the volume of traffic moving through the given road. A condition matrix is designed for the consistent movement of traffic considering different routes on the road segments. Social relationships are devised on various aspects of travel needs for a safe, agile, and better driving experience. Simulation results illustrate the efficacy of the proposed scheme with high traffic throughput, service rate and reduce the total travelling time, delay time, and average waiting time in comparison with Dynamic Throughput Maximization Framework and Adaptive Traffic Control Algorithm. © 2020 Elsevier B.V.

关键词： Dynamic traffic control Social Internet of Vehicles (SIoV) Social relationships Traffic congestion management

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EtherClue: Digital investigation of attacks on Ethereum smart contracts

arXiv

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arXiv 2021年

作者： Aquilina, Simon Joseph Casino, Fran Vella, Mark Ellul, Joshua Patsakis, Constantinos Department of Computer Science University of Malta Msida Malta Centre for Distributed Ledger Technologies University of Malta Msida Malta Department of Informatics University of Piraeus 80 Karaoli & Dimitriou str. Piraeus18534 Greece Information Management Systems Institute Athena Research Center Artemidos 6 Marousi15125 Greece

programming errors in Ethereum smart contracts can result in catastrophic financial losses from stolen cryptocurrency. While vulnerability detectors can prevent vulnerable contracts from being deployed, this does not mean that such contracts will not be deployed. Once a vulnerable contract is instantiated on the blockchain and becomes the target of attacks, the identification of exploit transactions becomes indispensable in assessing whether it has been actually exploited and identifying which malicious or subverted accounts were involved. In this work, we study the problem of post-factum investigation of Ethereum attacks using Indicators of Compromise (IoCs) specially crafted for use in the blockchain. IoC definitions need to capture the side-effects of successful exploitation in the context of the Ethereum blockchain. Therefore, we define a model for smart contract execution, comprising multiple abstraction levels that mirror the multiple views of code execution on a blockchain. Subsequently, we compare IoCs defined across the different levels in terms of their effectiveness and practicality through EtherClue, a prototype tool for investigating Ethereum security incidents. Our results illustrate that coarse-grained IoCs defined over blocks of transactions can detect exploit transactions with less computation;however, they are contract-specific and suffer from false negatives. On the other hand, fine-grained IoCs defined over virtual machine instructions can avoid these pitfalls at the expense of increased computation which are nevertheless applicable for practical use. © 2021, CC BY-NC-ND.

关键词： Blockchain

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Dynamic Management of Traffic Signals through Social IoT

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Procedia computer science 2020年 171卷 1908-1916页

作者： Roopa M S Ayesha Siddiq S Rajkumar Buyya Venugopal K R S S Iyengar L M Patnaik IoT Lab University Visvesvaraya College of Engineering Bangalore University Bengaluru India Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia Bangalore University Bengaluru India Department of Computer Science and Engineering Florida International University USA National Institute of Advanced Studies Indian Institute of Science Campus Bengaluru India

Traffic congestion is a major threat to transportation sector in every urban city around the world. This causes many adverse effects like, heavy fuel consumption, increased waiting time, pollution, etc. and pose an eminent challenge to the movement of emergency vehicles. To achieve better driving we proceed towards a trending research field called Social Internet of Vehicles (SIoV). A social network paradigm that permits the establishment of social relationships among every vehicle in the network or with any road infrastructure can be radically helpful. This holds as the aim of SIoV, to be beneficial for the drivers, in improving the road safety, avoiding mishaps, and have a friendly-driving environment. In this paper, we propose a Dynamic congestion control with Throughput Maximization scheme based on Social Aspect (D-TMSA) utilizing the social, behavioral and preference-based relationships. Our proposed scheme along with the various social relationship types allocates green signal to maximize the traffic flow passing through an intersection. Simulation results show that the D-TMSA outperforms the existing work by achieving high throughput, lowering the total traveling time and reducing the average waiting time to better the flow of traffic based on their social attributes with each other.

关键词： Dynamic Traffic Control Traffic Congestion Management Social Internet of Vehicles (SIoV) Social Relationships

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CLAWER: Context-aware Cloud-Fog based Workflow Management Framework for Health Emergency Services

CLAWER: Context-aware Cloud-Fog based Workflow Management Fr...

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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Shreya Ghosh Jaydeep Das Soumya K Ghosh Rajkumar Buyya Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

ISBN: (数字)9781728160955

ISBN: (纸本)9781728196497

With the major development of sensor technologies and advancements of communication network infrastructures, there is a growing interest to add more intelligence in the e-health monitoring for facilitating an effective healthcare system. While IoT devices are capable of continuous health-parameter sensing and providing notifications to the user, an effective business process management (BPM) facilitates effective system integration and data processing workflow. This paper proposes an efficient framework for managing emergency situations (specifically, health-related) through the analysis of heterogeneous data sources. The proposed framework, named CLAWER (CLoud-Fog bAsed Workflow for Emergency seRvice) aims to bridge the gap between process management and data analytics by providing an automated workflow for personalized health-monitoring and efficient recommendation system. Here, the IoT devices are used for collecting the movement and health data. The smart phone can act as an edge device to acquire data with user movement information. The accumulated data is initially processed inside the fog device, and finally the analysis and recommendations are generated by the cloud. In this paper the indoor health-status of the users are analysed in small cell cloud enhanced eNode B, which is used as fog device. The generated recommendations are stored in the fog device to provide the recommendations to the users with low latency and in timely manner. The experimental analysis of CLAWER yields better precision and recall values than the existing methods.

关键词： Cloud computing Roads Medical services Markov processes Monitoring Delays Biomedical monitoring

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