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Garcia Lozano, M., Brynielsson, J., Franke, U., Rosell, M., Tjörnhammar, E., Varga, S. & Vlassov, V. (2020). Veracity assessment of online data. Decision Support Systems, 129, Article ID 113132.
Open this publication in new window or tab >>Veracity assessment of online data
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2020 (English)In: Decision Support Systems, ISSN 0167-9236, E-ISSN 1873-5797, Vol. 129, article id 113132Article in journal (Refereed) Published
Abstract [en]

Fake news, malicious rumors, fabricated reviews, generated images and videos, are today spread at an unprecedented rate, making the task of manually assessing data veracity for decision-making purposes a daunting task. Hence, it is urgent to explore possibilities to perform automatic veracity assessment. In this work we review the literature in search for methods and techniques representing state of the art with regard to computerized veracity assessment. We study what others have done within the area of veracity assessment, especially targeted towards social media and open source data, to understand research trends and determine needs for future research. The most common veracity assessment method among the studied set of papers is to perform text analysis using supervised learning. Regarding methods for machine learning much has happened in the last couple of years related to the advancements made in deep learning. However, very few papers make use of these advancements. Also, the papers in general tend to have a narrow scope, as they focus on solving a small task with only one type of data from one main source. The overall veracity assessment problem is complex, requiring a combination of data sources, data types, indicators, and methods. Only a few papers take on such a broad scope, thus, demonstrating the relative immaturity of the veracity assessment domain.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Veracity assessment, Credibility, Data quality, Online data, Social media, Fake news
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-268789 (URN)10.1016/j.dss.2019.113132 (DOI)000510956500001 ()2-s2.0-85076227196 (Scopus ID)
Note

QC 20200224

Available from: 2020-02-24 Created: 2020-02-24 Last updated: 2020-02-24Bibliographically approved
Trunfio, P. & Vlassov, V. (2019). Clouds for scalable Big Data processing. International Journal of Parallel, Emergent and Distributed Systems, 34(6), 629-631
Open this publication in new window or tab >>Clouds for scalable Big Data processing
2019 (English)In: International Journal of Parallel, Emergent and Distributed Systems, ISSN 1744-5760, E-ISSN 1744-5779, Vol. 34, no 6, p. 629-631Article in journal, Editorial material (Refereed) Published
Place, publisher, year, edition, pages
Taylor & Francis, 2019
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-263499 (URN)10.1080/17445760.2019.1580709 (DOI)2-s2.0-85072069729 (Scopus ID)
Note

QC 20191202

Available from: 2019-12-02 Created: 2019-12-02 Last updated: 2019-12-04Bibliographically approved
Koubarakis, M., Bereta, K., Bilidas, D., Giannousis, K., Ioannidis, T., Pantazi, D.-A. -., . . . Fleming, A. (2019). From copernicus big data to extreme earth analytics. In: Advances in Database Technology - EDBT: . Paper presented at 22nd International Conference on Extending Database Technology, EDBT 2019; Lisbon; Portugal; 26 March 2019 through 29 March 2019 (pp. 690-693). OpenProceedings
Open this publication in new window or tab >>From copernicus big data to extreme earth analytics
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2019 (English)In: Advances in Database Technology - EDBT, OpenProceedings, 2019, p. 690-693Conference paper, Published paper (Refereed)
Abstract [en]

Copernicus is the European programme for monitoring the Earth. It consists of a set of systems that collect data from satellites and in-situ sensors, process this data and provide users with reliable and up-to-date information on a range of environmental and security issues. The data and information processed and disseminated puts Copernicus at the forefront of the big data paradigm, giving rise to all relevant challenges, the so-called 5 Vs: volume, velocity, variety, veracity and value. In this short paper, we discuss the challenges of extracting information and knowledge from huge archives of Copernicus data. We propose to achieve this by scale-out distributed deep learning techniques that run on very big clusters offering virtual machines and GPUs. We also discuss the challenges of achieving scalability in the management of the extreme volumes of information and knowledge extracted from Copernicus data. The envisioned scientific and technical work will be carried out in the context of the H2020 project ExtremeEarth which starts in January 2019.

Place, publisher, year, edition, pages
OpenProceedings, 2019
Series
Advances in Database Technology - EDBT, ISSN 2367-2005
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-251874 (URN)10.5441/002/edbt.2019.88 (DOI)2-s2.0-85064893710 (Scopus ID)9783893180813 (ISBN)
Conference
22nd International Conference on Extending Database Technology, EDBT 2019; Lisbon; Portugal; 26 March 2019 through 29 March 2019
Note

QC 20190528

Available from: 2019-05-28 Created: 2019-05-28 Last updated: 2019-05-28Bibliographically approved
Lin, X., Buyya, R., Yang, L., Tari, Z., Choo, K.-K. -., Vlassov, V., . . . Wang, W. (2019). Message from the BDCloud 2018 Chairs. Paper presented at 11 December 2018 through 13 December 2018. 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018, XXIX-XXX, Article ID 8672358.
Open this publication in new window or tab >>Message from the BDCloud 2018 Chairs
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2019 (English)In: 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018, p. XXIX-XXX, article id 8672358Article in journal, Editorial material (Refereed) Published
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-252211 (URN)10.1109/BDCloud.2018.00009 (DOI)2-s2.0-85063866821 (Scopus ID)
Conference
11 December 2018 through 13 December 2018
Note

QC 20190611

Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-06-11Bibliographically approved
Imtiaz, S., Sadre, R. & Vlassov, V. (2019). On the case of privacy in the iot ecosystem: a survey. In: Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019: . Paper presented at 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019 14-17 July 2019 (pp. 1015-1024).
Open this publication in new window or tab >>On the case of privacy in the iot ecosystem: a survey
2019 (English)In: Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019, 2019, p. 1015-1024Conference paper, Published paper (Refereed)
Abstract [en]

IoT has enabled the creation of a multitude of personal applications and services for a better understanding of urban environments and our personal lives. These services are driven by the continuous collection and analysis of user data in order to provide personalized experiences. However, there is a strong need to address user privacy concerns as most of the collected data is of sensitive nature. This paper provides an overview of privacy preservation techniques and solutions proposed so far in literature along with the IoT levels at which privacy is addressed by each solution as well as their robustness to privacy breaching attacks. An analysis of functional and non-functional limitations of each solution is done, followed by a short survey of machine learning applications designed with these solutions. We identify open issues in the privacy preserving solutions when used in IoT environments. Moreover, we note that most of the privacy preservation solutions need to be adapted in the light of GDPR to accommodate the right to privacy of the users.

National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-268250 (URN)10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00177 (DOI)2-s2.0-85074870716 (Scopus ID)
Conference
2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019 14-17 July 2019
Note

QC 20200427

Available from: 2020-04-27 Created: 2020-04-27 Last updated: 2020-04-28Bibliographically approved
Khan, A. M., Freitag, F., Vlassov, V. & Ha, P. H. (2018). Demo Abstract: Towards IoT Service Deployments on Edge Community Network Microclouds. In: IEEE INFOCOM 2018 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS): . Paper presented at IEEE Conference on Computer Communications (IEEE INFOCOM), APR 15-19, 2018, Honolulu, HI. IEEE
Open this publication in new window or tab >>Demo Abstract: Towards IoT Service Deployments on Edge Community Network Microclouds
2018 (English)In: IEEE INFOCOM 2018 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS), IEEE , 2018Conference paper, Published paper (Refereed)
Abstract [en]

Internet of Things (IoT) services for personal devices and smart homes provided by commercial solutions are typically proprietary and closed. These services provide little control to the end users, for instance to take ownership of their data and enabling services, which hinders these solutions' wider acceptance. In this demo paper, we argue for an approach to deploy professional IoT services on user-controlled infrastructure at the network edge. The users would benefit from the ability to choose the most suitable service from different IoT service offerings, like the one which satisfies their privacy requirements, and third-party service providers could offer more tailored IoT services at customer premises. We conduct the demonstration on microclouds, which have been built with the Cloudy platform in the Guifi. net community network. The demonstration is conducted from the perspective of end users, who wish to deploy professional IoT data management and analytics services in volunteer microclouds.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE Conference on Computer Communications Workshops, ISSN 2159-4228
Keywords
edge cloud computing, community clouds
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-239841 (URN)10.1109/INFCOMW.2018.8406840 (DOI)000450157700008 ()2-s2.0-85050654908 (Scopus ID)978-1-5386-5979-3 (ISBN)
Conference
IEEE Conference on Computer Communications (IEEE INFOCOM), APR 15-19, 2018, Honolulu, HI
Note

QC 20181219

Available from: 2018-12-19 Created: 2018-12-19 Last updated: 2018-12-19Bibliographically approved
Abbas, Z., Sigurdsson, T. T., Al-Shishtawy, A. & Vlassov, V. (2018). Evaluation of the Use of Streaming Graph Processing Algorithms for Road Congestion Detection. In: Chen, JJ Yang, LT (Ed.), 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS: . Paper presented at 16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom, DEC 11-13, 2018, Melbourne, AUSTRALIA (pp. 1017-1025). IEEE COMPUTER SOC
Open this publication in new window or tab >>Evaluation of the Use of Streaming Graph Processing Algorithms for Road Congestion Detection
2018 (English)In: 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS / [ed] Chen, JJ Yang, LT, IEEE COMPUTER SOC , 2018, p. 1017-1025Conference paper, Published paper (Refereed)
Abstract [en]

Real-time road congestion detection allows improving traffic safety and route planning. In this work, we propose to use streaming graph processing algorithms for road congestion detection and evaluate their accuracy and performance. We represent road infrastructure sensors in the form of a directed weighted graph and adapt the Connected Components algorithm and some existing graph processing algorithms, originally used for community detection in social network graphs, for the task of road congestion detection. In our approach, we detect Connected Components or communities of sensors with similarly weighted edges that reflect different states in the traffic, e.g., free flow or congested state, in regions covered by detected sensor groups. We have adapted and implemented the Connected Components and community detection algorithms for detecting groups in the weighted sensor graphs in batch and streaming manner. We evaluate our approach by building and processing the road infrastructure sensor graph for Stockholm's highways using real-world data from the Motorway Control System operated by the Swedish traffic authority. Our results indicate that the Connected Components and DenGraph community detection algorithms can detect congestion with accuracy up to approximate to 94% for Connected Components and up to approximate to 88% for DenGraph. The Louvain Modularity algorithm for community detection fails to detect congestion regions for sparsely connected graphs, representing roads that we have considered in this study. The Hierarchical Clustering algorithm using speed and density readings is able to detect congestion without details, such as shockwaves.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC, 2018
Series
IEEE International Symposium on Parallel and Distributed Processing with Applications, ISSN 2158-9178
Keywords
Streaming, Graph Processing, Congestion, Community Detection, Connected Components
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-252672 (URN)10.1109/BDCloud.2018.00148 (DOI)000467843200134 ()2-s2.0-85063892833 (Scopus ID)978-1-7281-1141-4 (ISBN)
Conference
16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom, DEC 11-13, 2018, Melbourne, AUSTRALIA
Note

QC 20190603

Available from: 2019-06-03 Created: 2019-06-03 Last updated: 2019-06-11Bibliographically approved
Kalavri, V., Vlassov, V. & Haridi, S. (2018). High-Level Programming Abstractions for Distributed Graph Processing. IEEE Transactions on Knowledge and Data Engineering, 30(2), 305-324
Open this publication in new window or tab >>High-Level Programming Abstractions for Distributed Graph Processing
2018 (English)In: IEEE Transactions on Knowledge and Data Engineering, ISSN 1041-4347, E-ISSN 1558-2191, Vol. 30, no 2, p. 305-324Article in journal (Refereed) Published
Abstract [en]

Efficient processing of large-scale graphs in distributed environments has been an increasingly popular topic of research in recent years. Inter-connected data that can be modeled as graphs appear in application domains such as machine learning, recommendation, web search, and social network analysis. Writing distributed graph applications is inherently hard and requires programming models that can cover a diverse set of problems, including iterative refinement algorithms, graph transformations, graph aggregations, pattern matching, ego-network analysis, and graph traversals. Several high-level programming abstractions have been proposed and adopted by distributed graph processing systems and big data platforms. Even though significant work has been done to experimentally compare distributed graph processing frameworks, no qualitative study and comparison of graph programming abstractions has been conducted yet. In this survey, we review and analyze the most prevalent high-level programming models for distributed graph processing, in terms of their semantics and applicability. We review 34 distributed graph processing systems with respect to the graph processing models they implement and we survey applications that appear in recent distributed graph systems papers. Finally, we discuss trends and open research questions in the area of distributed graph processing.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC, 2018
Keywords
Distributed graph processing, large-scale graph analysis, big data
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-221918 (URN)10.1109/TKDE.2017.2762294 (DOI)000422711800008 ()2-s2.0-85040652305 (Scopus ID)
Note

QC 20180131

Available from: 2018-01-31 Created: 2018-01-31 Last updated: 2018-02-02Bibliographically approved
Sozinov, K., Vlassov, V. & Girdzijauskas, S. (2018). Human Activity Recognition Using Federated Learning. In: Chen, JJ Yang, LT (Ed.), 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS: . Paper presented at 16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom, DEC 11-13, 2018, Melbourne, AUSTRALIA (pp. 1103-1111). IEEE COMPUTER SOC
Open this publication in new window or tab >>Human Activity Recognition Using Federated Learning
2018 (English)In: 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS / [ed] Chen, JJ Yang, LT, IEEE COMPUTER SOC , 2018, p. 1103-1111Conference paper, Published paper (Refereed)
Abstract [en]

State-of-the-art deep learning models for human activity recognition use large amount of sensor data to achieve high accuracy. However, training of such models in a data center using data collected from smart devices leads to high communication costs and possible privacy infringement. In order to mitigate aforementioned issues, federated learning can be employed to train a generic classifier by combining multiple local models trained on data originating from multiple clients. In this work we evaluate federated learning to train a human activity recognition classifier and compare its performance to centralized learning by building two models, namely a deep neural network and a softmax regression trained on both synthetic and real-world datasets. We study communication costs as well as the influence of erroneous clients with corrupted data in federated learning setting. We have found that federated learning for the task of human activity recognition is capable of producing models with slightly worse, but acceptable, accuracy compared to centralized models. In our experiments federated learning achieved an accuracy of up to 89 % compared to 93 % in centralized training for the deep neural network. The global model trained with federated learning on skewed datasets achieves accuracy comparable to centralized learning. Furthermore, we identified an important issue of clients with corrupted data and proposed a federated learning algorithm that identifies and rejects erroneous clients. Lastly, we have identified a trade-off between communication cost and the complexity of a model. We show that more complex models such as deep neural network require more communication in federated learning settings for human activity recognition compared to less complex models, such as multinomial logistic regression.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC, 2018
Series
IEEE International Symposium on Parallel and Distributed Processing with Applications, ISSN 2158-9178
Keywords
Federated Learning, Human Activity Recognition, Privacy, Distributed Machine Learning
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-252673 (URN)10.1109/BDCloud.2018.00164 (DOI)000467843200150 ()2-s2.0-85063888507 (Scopus ID)978-1-7281-1141-4 (ISBN)
Conference
16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom, DEC 11-13, 2018, Melbourne, AUSTRALIA
Note

QC 20190603

Available from: 2019-06-03 Created: 2019-06-03 Last updated: 2019-06-03Bibliographically approved
Xhagjika, V., Escoda, O. D., Navarro, L. & Vlassov, V. (2017). Load and video performance patterns of a cloud based WebRTC Architecture. In: Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017: . Paper presented at 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017, 14 May 2017 through 17 May 2017 (pp. 739-744). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Load and video performance patterns of a cloud based WebRTC Architecture
2017 (English)In: Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 739-744Conference paper, Published paper (Refereed)
Abstract [en]

Web Real-Time Communication or Realtime communication in the Web (WebRTC/RTCWeb) is a prolific new standard and technology stack, providing full audio/video agnostic communications for the Web. Service providers implementing such technology deal with various levels of complexity ranging anywhere from: high service distribution, multi-client integration, P2P and Cloud assisted communication backends, content delivery, real-Time constraints and across clouds resource allocation. This work presents a study of the joint factors including multi-cloud distribution, network performance, media parameters and back-end resource loads, in Cloud based Media Selective Forwarding Units for WebRTC infrastructures. The monitored workload is sampled from a large population of real users of our testing infrastructure, additionally the performance data is sampled both by passive user measurements as well as server side measurements. Patterns correlating such factors enable designing adaptive resource allocation algorithms and defining media Service Level Objectives (SLO) spanning over multiple data-centers or servers. Based on our analysis, we discover strong periodical load patterns even though the nature of user interaction with the system is mostly not predetermined with variable user churn.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2017
Keywords
bitrate, load measurements, media, rtp/rtcp, stream allocation, webrtc, Cluster computing, Grid computing, Population statistics, Resource allocation, Systems analysis, Bit rates, Distributed computer systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-216290 (URN)10.1109/CCGRID.2017.118 (DOI)000426912900088 ()2-s2.0-85027437231 (Scopus ID)9781509066100 (ISBN)
Conference
17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017, 14 May 2017 through 17 May 2017
Note

QC 20171211

Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2019-05-20Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6779-7435

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