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Prof. Dr. Mouzhi Ge

Google Scholar: https://scholar.google.com/citations?user=tFLaZJYAAAAJ DBLP: https://dblp.uni-trier.de/pid/35/6880.html Scopus: https://www.scopus.com/authid/detail.uri?authorId=36666262900

Faculty of European Campus Rottal-Inn

Professors

Professor

Head of Data Science and Intelligent Systems Research Group

AA 221

0991/3615-8883

Sortierung:
Journal article
  • F. Gedikli
  • Mouzhi Ge
  • D. Jannach

Explaining Online Recommendations Using Personalized Tag Clouds.

In: icom vol. 10 pg. 3-10

  • (2011)

DOI: 10.1524/icom.2011.0002

show abstract

Recommender systems are sales-supporting applications that are usually integrated into online shops and are designed to point the visitor to products or services she or he might be interested in but has not bought yet. In the last decade, many techniques have been developed to improve the predictive accuracy of such systems. However, there are also factors other than accuracy that infl uence the user-perceived quality of such a system. In particular, system-generated explanations as to why a certain item has been recommended have shown to be a valuable tool to improve both the user's satisfaction and the system's effi ciency. This paper reports the results of a fi rst user study which was conducted to evaluate whether personalized tag clouds are an appropriate means to visually explain recommendations. The evaluation reveals that using tag clouds as explanation mechanism leads to higher user satisfaction and recommendation effi ciency than previous keyword-style explanations.
Contribution
  • Mouzhi Ge
  • M. Helfert
  • D. Jannach

Information quality assessment: validating measurement dimensions and processes.

In: Proceedings of the 19th European Conference on Information Systems (ECIS 2011). pg. 75

  • Eds.:
  • M. Rossi
  • V. Tuunainen
  • J. Nandhakumar

  • (2011)
Contribution
  • M. Braunhofer
  • M. Elahi
  • Mouzhi Ge
  • F. Ricci
  • T. Schievenin

STS: Design of Weather-Aware Mobile Recommender Systems in Tourism.

In: Proceedings of the First International Workshop on Intelligent User Interfaces: Artificial Intelligence meets Human Computer Interaction (AI*HCI 2013), A workshop of the XIII International Conference of the Italian Association for Artificial Intelligence (AI*IA 2013). (CEUR Workshop Proceedings)

  • Eds.:
  • C. Gena
  • B. Carolis

  • (2013)
Contribution
  • M. Elahi
  • Mouzhi Ge
  • F. Ricci
  • D. Massimo
  • S. Berkovsky

Interactive Food Recommendation for Groups.

In: Poster Proceedings of the 8th ACM Conference on Recommender Systems (RecSys 2014). (CEUR Workshop Proceedings)

  • Eds.:
  • J. Mahmud
  • L. Chen

  • (2014)
Contribution
  • Mouzhi Ge
  • F. Ricci
  • D. Massimo

Health-aware Food Recommender System.

In: Proceedings of the 9th ACM Conference on Recommender Systems (RecSys 2015). pg. 333-334

  • Eds.:
  • G. Semeraro
  • H. Werthner
  • M. Zanker
  • J. Golbeck

  • (2015)

DOI: 10.1145/2792838.2796554

show abstract

With the rapid changes in the food variety and lifestyles, many people are facing the problem of making healthier food decisions to reduce the risk of chronic diseases such as obesity and diabetes. To this end, our recommender system not only offers recipe recommendations that suit the user's preference but is also able to take the user's health into account. It is developed on a mobile platform by considering that our application may be directly used in the kitchen. This demo paper summarizes the complete human-computer interaction design, the implemented health-aware recommendation algorithm and preliminary user feedback.
Journal article
  • Mouzhi Ge
  • H. Bangui
  • B. Buhnova

Big Data for Internet of Things: A Survey.

In: Future Generation Computer Systems vol. 87 pg. 601-614

  • (2018)

DOI: 10.1016/j.future.2018.04.053

show abstract

With the rapid development of the Internet of Things (IoT), Big Data technologies have emerged as a critical data analytics tool to bring the knowledge within IoT infrastructures to better meet the purpose of the IoT systems and support critical decision making. Although the topic of Big Data analytics itself is extensively researched, the disparity between IoT domains (such as healthcare, energy, transportation and others) has isolated the evolution of Big Data approaches in each IoT domain. Thus, the mutual understanding across IoT domains can possibly advance the evolution of Big Data research in IoT. In this work, we therefore conduct a survey on Big Data technologies in different IoT domains to facilitate and stimulate knowledge sharing across the IoT domains. Based on our review, this paper discusses the similarities and differences among Big Data technologies used in different IoT domains, suggests how certain Big Data technology used in one IoT domain can be re-used in another IoT domain, and develops a conceptual framework to outline the critical Big Data technologies across all the reviewed IoT domains.
Contribution
  • M. Popescu
  • Mouzhi Ge
  • M. Helfert

The Social Media Perception and Reality – Possible Data Quality Deficiencies between Social Media and ERP.

In: 20th International Conference on Enterprise Information Systems (ICEIS). pg. 198-204

  • Eds.:
  • Institute of Electrical and Electronics Engineers Inc.

  • (2018)

DOI: 10.5220/0006788801980204

show abstract

With the increase of digitalisation, data in social media are often seen as more updated and realistic than the information system representations. Due to the fast changes in the real world and the increasing Big Social media data, there is usually certain misalignment between the social media and information system in the enterprise such as ERP, therefore there can be data deficiencies or data quality problems in the information systems, which is caused by the differences between the external social media and internal information system. In this paper, underpinned by the work of ontological data quality from Wang and Wand 1996, we investigate a set of data quality problems between two representations Social Media and ERP. We further discuss how ERP system can be improved from the data quality perspective.
Journal article
  • Mouzhi Ge
  • F. Persia

A Generalized Evaluation Framework for Multimedia Recommender Systems.

In: International Journal of Semantic Computing vol. 12 pg. 541-557

  • (2018)

DOI: 10.1142/S1793351X18500046

show abstract

With the widespread availability of media technologies, such as real-time streaming, new Internet-of-Thing devices and smart phones, multimedia data are extensively increased and the big multimedia data rapidly spread over various social networks. This has created complexity and information overload for users to choose the suitable multimedia objects. Thus, different multimedia recommender systems have been emerging to help users find the useful multimedia objects that are possibly preferred by the user. However, the evaluation of these multimedia recommender systems is still in an ad-hoc stage. Given the distinct features of multimedia objects, the evaluation criteria adopted from the general recommender systems might not be effectively used to evaluate multimedia recommendations. In this paper, we therefore review and analyze the evaluation criteria that have been used in the previous multimedia recommender system papers. Based on the review, we propose a generalized evaluation framework to guide the researchers and practitioners to perform evaluations, especially user-centric evaluations, for multimedia recommender systems.
Contribution
  • T. Chondrogiannis
  • Mouzhi Ge

Inferring ratings for custom trips from rich GPS traces.

In: LocalRec '19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-based Recommendations. pg. 1-4

  • Eds.:
  • D. Sacharidis
  • Yaron K.
  • P. Bouros
  • Tamraparni Dasu
  • M. Renz

  • (2019)

DOI: 10.1145/3356994.3365502

show abstract

Trip planning services are employed extensively by users to compute paths between locations and navigate within a road network. In some real-world scenarios such as planning for a hiking trip or running training, users usually require personalized trip planning. Although some existing systems can recommend trips that other users have posted, along with a set of ratings w.r.t. the difficulty of the route, conditions, or the enjoyment it provides. Very often though users want to define a custom trip that fits their personal needs, for which existing systems are unable to provide any rating. In this paper we therefore define the problem of inferring ratings for custom trips. We also outline a solution to infer ratings by utilizing the ratings of trips previously posted by users and their similarity with a given custom trip. Finally, we present the results of preliminary experiments were we evaluate the efficiency of our proposed approach on inferring ratings for trips related to hiking and other similar activities.
Contribution
  • Q. Yang
  • Mouzhi Ge
  • M. Helfert

Analysis of Data Warehouse Architectures: Modeling and Classification.

In: Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS). pg. 604-611

  • Eds.:
  • Institute of Electrical and Electronics Engineers Inc.

  • (2019)

DOI: 10.5220/0007728006040611

show abstract

With decades of development and innovation, data warehouses and their architectures have been extended to a variety of derivatives in various environments to achieve different organisations’ requirements. Although there are some ad-hoc studies on data warehouse architecture (DWHA) investigations and classifications, limited research is relevant to systematically model and classify DWHAs. Especially in the big data era, data is generated explosively. More emerging architectures and technologies are leveraged to manipulate and manage big data in this domain. It is therefore valuable to revisit and investigate DWHAs with new innovations. In this paper, we collect 116 publications and model 73 disparate DWHAs using Archimate, then 9 representative DWHAs are identified and summarised into a”big picture”. Furthermore, it proposes a new classification model sticking to state-of-the-art DWHAs. This model can guide researchers and practitioners to identify, analyse and compare differences and trends of DWHAs from componental and architectural perspectives.
Contribution
  • B. Mbarek
  • Mouzhi Ge
  • T. Pitner

Blockchain-Based Access Control for IoT in Smart Home Systems.

In: Database and Expert Systems Applications. Proceedings of the 31th International Conference DEXA 2020, Bratislava, Slovakia, September 14-17, 2020, Part II (Lecture Notes in Computer Science) pg. 17-32

  • Eds.:
  • G. Kotsis
  • A. Tjoa
  • S. Hartmann
  • I. Khalil
  • J. Küng

Springer International Publishing

  • (2020)
Journal article
  • H. Bangui
  • Mouzhi Ge
  • B. Buhnova

A hybrid machine learning model for intrusion detection in VANET.

In: Computing pg. 1-29

  • (2021)

DOI: 10.1007/s00607-021-01001-0

show abstract

While Vehicular Ad-hoc Network (VANET) is developed to enable effective vehicle communication and traffic information exchange, VANET is also vulnerable to different security attacks, such as DOS attacks. The usage of an intrusion detection system (IDS) is one possible solution for preventing attacks in VANET. However, dealing with a large amount of vehicular data that keep growing in the urban environment is still an critical challenge for IDSs. This paper, therefore, proposes a new machine learning model to improve the performance of IDSs by using Random Forest and a posterior detection based on coresets to improve the detection accuracy and increase detection efficiency. The experimental results show that the proposed machine learning model can significantly enhance the detection accuracy compared to classical application of machine learning models.
Journal article
  • B. Mbarek
  • Mouzhi Ge
  • T. Pitner

Proactive trust classification for detection of replication attacks in 6LoWPAN-based IoT.

In: Internet of Things vol. 16 pg. 100442

  • (2021)

DOI: 10.1016/j.iot.2021.100442

show abstract

The 6LoWPAN standard has been widely applied in different Internet of Things (IoT) application domains. However, since the nodes in the IoT are mostly resource constrained, 6LoWPAN is vulnerable to a variety of security attacks. Among others, replication attack is one of the severe security threads to IoT networks. This paper therefore proposes a trust-based detection strategy against replication attacks in IoT, where a number of replica nodes are intentionally inserted into the network to test the reliability and response of witness nodes. We further assess the feasibility of the proposed detection strategy and compare with two other strategies such as brute-force and first visited strategy via a thorough simulation. The evaluation takes into account the detection probability for compromised attacks, the execution time of transactions and rate of communication failure. The simulation results show that while maintaining detection runtime on average 60 s for up to 1000 nodes, the proposed trust-based strategy can significantly increase the detection probability to 90% on average against replication attacks and in turn significantly reduce the communication failure.
Journal article
  • J. Blanco
  • Mouzhi Ge
  • T. Pitner

Modeling Inconsistent Data for Reasoners in Web of Things.

In: Procedia Computer Science pg. 1265-1273

  • (2021)

DOI: 10.1016/j.procs.2021.08.130

show abstract

With the recent developments of the Internet of Things and its integration in the web environment, the Web of Things and the real-time data submissions to Reasoners are enabled. However, the data that are fed to the Reasoners are often inconsistent. This can be possibly caused by the malfunction of certain Internet of Things device or by human errors. The data consistency issue is becoming more complex in the Web of Things network. This paper, therefore, proposes a new data processing model to tackle the inconsistent data, so that the processed data can be further used in Reasoners. The data processing model introduces an oversimplification of the Shramko-Wansing sixteen-valued trilattice, which is an extension of Belnap’s four-valued bilattice to assign the data classical truth-values. A preliminary implementation is demonstrated to validate the proposed model. The result shows that our model can avoid system collapse when contradictory outputs exist.
Journal article
  • H. Bangui
  • Mouzhi Ge
  • B. Buhnova
  • L. Trang

Towards faster big data analytics for anti‐jamming applications in vehicular ad‐hoc network.

In: Transactions on Emerging Telecommunications Technologies vol. 32 pg. 1-22

  • 15 April 2021 (2021)

DOI: 10.1002/ett.4280

show abstract

Nowadays, Wireless Vehicular Ad-Hoc Network (VANET) has become a valuable asset for transportation systems. However, this advanced technology is characterized by highly distributed and networked environment, which makes VANET communications vulnerable to malicious jamming attacks. Although Big Data Analytics has been used to solve this critical security issue by supporting the development of anti-jamming applications, as the amount of vehicular data is growing exponentially, the anti-jamming applications face many challenges (i., reactions in real-time) due to the lack of specific solutions that can keep up with the fast advancement of VANET. In this paper, we propose a new vehicular data prioritization model based on coresets to accelerate the Big Data Analytics in VANET. Our experimental evaluation shows that our solution can significantly increase the efficiency for clustering in jamming detection while keeping and improving the clustering quality. Also, the proposed solution can enable the real-time detection and be integrated to anti-jamming applications.
Journal article
  • B. Mbarek
  • Mouzhi Ge
  • T. Pitner

An adaptive anti-jamming system in HyperLedger-based wireless sensor networks.

In: Wireless Networks

  • 20.01.2022 (2022)

DOI: 10.1007/s11276-022-02886-1

show abstract

Using new methodologies such as Blockchain in data communications in wireless sensor networks (WSN) has emerged owing to the proliferation of collaborative technologies. However, the WSN is still vulnerable to denial of service cyber attacks, in which jamming attack becomes prevalent in blocking data communications in WSN. The jamming attack launches malicious sensor nodes to block legitimate data communications by intentional interference. This can in turn cause monitoring disruptions, data loss and other safety-critical issues. In order to address the malicious attacks, this paper proposes an adaptive anti-jamming solution based on Hyperledger Fabric-based Blockchain, named as ABAS, to ensure the reliability and adaptivity of data communication in case of jamming attacks. In order to validate the ABAS solution, we applied the algorithm in healthcare WSN and showed that ABAS has significantly reduce the jamming coverage and energy consumption while maintaining high computational performance.
Journal article
  • Mouzhi Ge
  • F. Persia
  • G. Pilato

Guest Editors’ Introduction.

In: International Journal of Semantic Computing vol. 16 pg. 161-162

  • (2022)

DOI: 10.1142/S1793351X22020020

Contribution
  • Q. Yang
  • Mouzhi Ge
  • M. Helfert

Classification Methodology for Architectures in Information Systems: A Statistical Converging Technique.

In: Information Systems Development, Organizational Aspects and Societal Trends. ISD2023 Proceedings

  • Eds.:
  • C. Schneider
  • C. Barry
  • H. Linger
  • A. Da Silva
  • J. Estima
  • M. Lang
  • M. Da Silva

  • (2023)

DOI: 10.62036/ISD.2023.11

show abstract

Architectures are critical to the Information System (IS) domain because they represent funda- mental structures and interactions of systems. Since analysing architecture similarities is chal- lenging and time-consuming even in one domain, IS architecture classifications are paramount to understanding architectural complexity. However, classification approaches used in existing research commonly rely on manual interventions, and thus architectural classification reliability is hampered. We propose a novel methodology based on component modelling and applica- tion of a statistical converging technique, which ensures reliable IS architectural classification and minimises subjective interventions. We demonstrate the methodology by classifying data warehouse architectures.
Contribution
  • T. Chondrogiannis
  • Mouzhi Ge

Rating Inference for Custom Trips from Enriched GPS Traces using Random Forests.

In: Proceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising. pg. 50-57

ACM New York, NY, USA

  • (2023)

DOI: 10.1145/3615896.3628344

Lecture
  • Mouzhi Ge

Trust Management with Deep Learning in the Internet of Behavior. Invited Keynote.

In: Summer School on Applied Informatics

CERIT Science Park II Brno, Czech Republic

  • 12.-13.09.2023 (2023)
Journal article
  • H. Bangui
  • Mouzhi Ge
  • B. Buhnova

When Trustless meets Trust: Blockchain Consensus Review and Reconsideration.

In: Procedia Computer Science vol. 246 pg. 3351-3360

  • (2024)

DOI: 10.1016/j.procs.2024.09.222

show abstract

Blockchain, as a trustless network, has provided diverse benefits for a wide range of application domains, such as enhancing data management in terms of data security, traceability, accountability, transparency, and decentralization. However, the detection of cybersecurity vulnerabilities in blockchain has initiated a debate on whether this inherently trustless technology needs further trust support or not. In this work, we explore mutual trust and trustless cooperation. First, we examine blockchain-assisted trust management to highlight the specific trustless trait of blockchain. Then, as consensus is an important component of blockchain technology, we examine the role of trust in evaluating the trustworthiness of peer participants in the blockchain consensus process and enhancing the growth of a consistent chain. Finally, we derive research findings in the promising cooperation between trustless and trust.

Vita

Dr. Mouzhi Ge is a Professor for Data Analytics at the Deggendorf Institute of Technology in Germany. He was previously an Associate Professor (Tenured) at the Masaryk University in Czech Republic, where he obtained his Habilitation. He received his Ph.D. from Dublin City University in Ireland. Afterwards he has conducted research and practice on data engineering and intelligent systems in UK, USA and Italy. His research is mainly focused on Big Data Analytics, Intelligent Healthcare Systems, Internet of Things and Health-aware Recommender Systems. Within those research areas he has published more than 100 research papers. Over the last 10 years, his works have been published in a variety of journals such as Future Generation Computer Systems, International Journal of Human-Computer Studies, International Journal of Cooperative Information Systems, IEEE Access, Wireless Personal Communications, International Journal of Semantic Computing, Internet of Things Journal, Journal of Sensor and Actuator Networks, Transactions on Emerging Telecommunications Technologies, Computing Journal, Wireless Networks, Journal of Computer Information Systems, IEEE Transactions on Technology and Society, ACM Transactions on Management Information Systems, Multimedia Tools and Applications, etc.

Other

Academic Activities

Demo Chair of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD 2026. Call for Papers: https://ecmlpkdd.org/2026/organisation/ Chair of Smart Cities and Critical Infrastructures Track at the 41st ACM/SIGAPP Symposium On Applied Computing, Thessaloniki, Greece, 2026. Call for Papers: https://sites.google.com/view/sac-scci-2026 Program Chair of 27th IEEE International Symposium on Multimedia, Naples, Italy, 2025. Call for Papers: https://www.ieee-ism.org UNUM Award 2025 for Mentoring Excellent Research and Outstanding Experimenting with AI, Fake News and Disinformation at IVI Summit, Ireland, 2025. Award and Trophy: https://nextcloud.th-deg.de/s/MWnbt5R4zwtRrCD Program Chair of IEEE International Conference on Artificial Intelligence x Multimedia, Laguna Hills, California, USA, 2025. Call for Papers: https://semanticcomputing.wixsite.com/aixmm2025 Chair of Smart Cities and Critical Infrastructures Track at the 40th ACM/SIGAPP Symposium On Applied Computing, Sicily, Italy, 2025. Call for Papers: https://sites.google.com/view/sac-scci-2025/ Chair of Semantic Applications for Critical Infrastructures at 29th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Osaka, Japan, 2025 Call for Papers: https://sites.google.com/view/kes2025-saci/ Chair of Ph.D. Exam Committee, Free University of Bozen-Bolzano, Italy, 2025 Organizer of Erasmus+ Blended Intensive Programmes (BIP): AI-Driven Service Innovation, Telč, Czech Republic, 2025. Call for Applications to Participate: https://seslab.fi.muni.cz/en/bip-summer-school-2025 Guest Editor of AI-Driven Innovations in Cyber-Physical Systems at Journal Engineering Applications of Artificial Intelligence, Elsevier, 2024. Certificate: https://nextcloud.th-deg.de/s/o3n2tMXHBqcD9yi Program Chair of 26th IEEE International Symposium on Multimedia, Hitachi Central Research Laboratory, Tokyo, Japan, 2024 Best Paper Award at the 14th International Conference on Exploring Service Science, Brno, Czech Republic 2024. Certificate: https://nextcloud.th-deg.de/s/9K8smLbjnqZqEiY Guest Editor of Special Issue on Multimedia Computing at International Journal of Semantic Computing, 2024. Editorial: https://doi.org/10.1142/S1793351X24020057 Chair of Smart Cities and Critical Infrastructures at the 39th ACM/SIGAPP Symposium On Applied Computing, Avila, Spain, 2024. Call for Papers: https://sites.google.com/view/sac-scci2024 In 2023, I am Reviewer and Program Committee for ACM TIST, ACM JDIQ, Journal of Big Data, ACM UMAP, ACM SAC, IEEE ISM, IEEE BigMM, IEEE AIMHC, ECIS, IESS, IEEE FedCSIS, ICEIS, ADBIS, etc. I am also in the Ph.D. Exam Commision for University of Naples Federico II in Italy, University of L'Aquila in Italy, and Masaryk University in Czech Republic. Award of Highly Ranked Conference Paper, THD Dies academicus 2023. Editor of Big Multimedia Data and Applications for the journal of Frontiers in Big Data, 2023. Call for Papers: https://www.frontiersin.org/research-topics/49519/big-multimedia-data-and-applications Program Chair of 9th IEEE International Conference on Multimedia Big Data, California, USA, December 2023. IEEE Message: https://ieeexplore.ieee.org/document/10411795 Chair of Semantic Models for the Web of Things at the 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Athens, Greece, 2023. Call for Papers: http://kes2023.kesinternational.org Program Chair of 25th IEEE International Symposium on Multimedia, Laguna Hills, USA, 2023. Call for Papers: https://www.ieee-ism.org Chair of Critical Infrastructures at the 38th ACM/SIGAPP Symposium On Applied Computing, Tallinn, Estonia 2023. Call for Papers: https://sites.google.com/view/sac-ci2023 Best Conference Paper Award at THD, Dies Academicus 2022 Best Paper Award at the 11th International Conference on Smart Cities and Green ICT Systems, 2022 Certificate: https://nextcloud.th-deg.de/s/9d5QKFfbzdWpqLR Guest Editor for the International Journal of Semantic Computing, 2022. Editorial: https://doi.org/10.1142/S1793351X22020020 Program Chair of 24th IEEE International Symposium on Multimedia, Naples, Italy, 2022. IEEE Message: https://ieeexplore.ieee.org/document/10019623 Chair of International Workshop of Critical Infrastructure Dependability in conjunction with 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, Maryland, USA, 2022 Chair of Critical Infrastructures at the 37th ACM/SIGAPP Symposium On Applied Computing, Brno, Czech Republic, 2022. https://sites.google.com/view/sac-ci-2022/ Chair of Semantic Models for the Web of Things Session at the 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Verona, Italy, 2022 Program Chair of 23rd IEEE International Symposium on Multimedia, Online, 2021 Chair of 6th International Conference on Internet of Things, Big Data and Security, Online, 2021 International Liaison & Publicity Chair of IEEE International Conference on Cloud and Big Data Computing, Calgary, Canada, 2020 and 2021 Chair of International Workshop on Trust, Ethics and Information Quality in Smart Environments at the 22nd IEEE Conference on Business Informatics, 2020 Guest Editor of the Special Issue "Information Value Management" in International Journal of Information System Modeling and Design, 2019 Top 100 World-wide AMiner Most Influential Scholars in Recommender System - Artificial Intelligence, 2018 Chair of the International Symposium on Big Data in Cloud and Services Computing Applications at the 13th Federated Conference on Computer Science and Information Systems, Poznań, Poland, 2018 Chair of the International Workshop on Data Engineering meets Intelligent Food and Cooking Recipe in conjunction with 34th IEEE International Conference on Data Engineering, Paris, France, 2018 Chair of International Workshop on Big Data in Smart Cities and Smart Buildings in conjunction with IEEE Big Data Conference, Boston, USA, 2017 Chair of International Workshop on Geospatial Data Processing for Tourist Applications in conjunction with 21st European Conference on Advances in Databases and Information Systems, Nicosia, Cyprus, 2017 Chair of 3rd International Workshop on Information Value Management in conjunction with 19th International Conference on Enterprise Information Systems, Porto, Portugal, 2017 Guest Editor of the Special Issue "Quality Management in Big Data" in Informatics Journal, 2017 Chair of Doctoral Consortium in 5th International Conference on Smart Cities and Green ICT Systems, Rome, Italy, 2016 Chair of the International Workshop on Decision Making and Recommender Systems, Bolzano, Italy, 2014, 2015 Director of European Industry-University Research Association, 2013-2016