Publikationen


7032 Publikationen gefunden
    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    Q. Yang, Mouzhi Ge, M. Helfert

    Developing Reliable Taxonomic Features for Data Warehouse Architectures

    2020 IEEE 22nd Conference on Business Informatics (CBI)

    2020

    ISBN: 978-1-7281-9926-9

    DOI: 10.1109/CBI49978.2020.00033

    Abstract anzeigen

    Since there is a large variety of data warehouse architectures with different structures and components, it is very difficult and time-consuming to systematically analyse them and obtain insights from those architectures. One effective way to understand those architectures is using a taxonomy to classify them. However, most of the taxonomic features are derived in an ad-hoc way and the reliability of those features is unknown. This paper therefore is to develop a set of reliable features by modeling different data warehouse architectures and further generate the structural knowledge represented by a taxonomy. This taxonomy is further validated by evaluating two real-world data warehouse architectures from IBM and Facebook.

    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    M. Elahi, N. El Ioini, A. Alexander Lambrix, Mouzhi Ge

    Exploring Personalized University Ranking and Recommendation

    UMAP '20: 28th ACM Conference on User Modeling, Adaptation and Personalization

    2020

    DOI: 10.1145/3386392.3397590

    Abstract anzeigen

    Finding the right university to study is still a challenge for many people due to the large number of universities worldwide. Although there exist a number of global university rankings, they provide non# personalized rankings as one-size-fits-all solution. This becomes an issue since different people may have different preferences and considerations in mind, when choosing the university to study. This paper addresses this problem and presents a Recommender System to generate a personalized ranking list based on users particular preferences. The system is capable of eliciting users preferences, provided as ratings for universities, building predictive models on the preference data, and generating a personalized university ranking list that is tailored to the particular preferences and needs of the users. We performed two sets of experiments. First, we conducted an offline experiment using a dataset of user preferences, collected by the early version of our system. This allowed us to cross-validate and compare different recommender algorithms and choose the most accurate recommender algorithm that can better suit the particular problem at hand. We integrated the chosen algorithm in the final implementation of our system. As the follow-up, we performed a user study in order to analyze whether or not the final version of our system is usable from the perception of users. The results showed that the system has scored well above the benchmark and users assessed it as "good" in term of usability.

    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    F. Persia, D. D'Auria, Mouzhi Ge

    Improving Learning System Performance with Multimedia Semantics

    2020 IEEE 14th International Conference on Semantic Computing (ICSC)

    2020

    DOI: 10.1109/ICSC.2020.00050

    Abstract anzeigen

    Nowadays, different new learning methodologies have been proposed to achieve effective learning in University education. One of the most promising methodologies for teaching computer science is multimedia-based education. In order to empower the performance within the online learning platforms, such as Moodle or OLE, this paper proposes to integrate the multimedia-based education to learning systems, and conducts an experiment with the operating system course. We show that exploiting multimedia, such as educational video and smart text, can significantly improve the student's learning performance in terms of exam grade and knowledge transfer. Further, the paper presents a real-world case study depicting how to enhance the performance of learning platform with multimedia semantics.

    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    H. Bangui, Mouzhi Ge, B. Buhnova

    Improving Big Data Clustering for Jamming Detection in Smart Mobility

    ICT Systems Security and Privacy Protection, Cham, Switzerland, vol. 580

    2020

    ISBN: 978-3-030-58200-5

    Abstract anzeigen

    Smart mobility, with its urban transportation services ranging from real-time traffic control to cooperative vehicle infrastructure systems, is becoming increasingly critical in smart cities. These smart mobility services thus need to be very well protected against a variety of security threats, such as intrusion, jamming, and Sybil attacks. One of the frequently cited attacks in smart mobility is the jamming attack. In order to detect the jamming attacks, different anti-jamming applications have been developed to reduce the impact of malicious jamming attacks. One important step in anti-jamming detection is to cluster the vehicular data. However, it is usually very time-consuming to detect the jamming attacks that may affect the safety of roads and vehicle communication in real-time. Therefore, this paper proposes an efficient big data clustering model, coresets-based clustering, to support the real-time detection of jamming attacks. We validate the model efficiency and applicability in the context of a typical smart mobility system: Vehicular Ad-hoc Network, known as VANET.

    Extern

    M. Hölbl, K. Rannenberg, T. Welzer

    ICT Systems Security and Privacy Protection

    35th IFIP TC-11 SEC 2020 International Information Security and Privacy Conference

    IFIP Advances in Information and Communication Technology, Cham, Switzerland, vol. 580

    2020

    ISBN: 978-3-030-58200-5

    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    B. Mbarek, Mouzhi Ge, T. Pitner

    Enhanced network intrusion detection system protocol for internet of things

    SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

    2020

    ISBN: 978-1-4503-6866-7

    DigitalF: Europan Campus Rottal-Inn

    Zeitschriftenartikel

    Mouzhi Ge, W. Lewoniewski

    Developing the Quality Model for Collaborative Open Data

    Procedia Computer Science, vol. 176, pp. 1883-1892

    2020

    DOI: 10.1016/j.procs.2020.09.228

    Abstract anzeigen

    Nowadays, the development of data sharing technologies allows to involve more people to collaboratively contribute knowledge on the Web. The shared knowledge is usually represented as Collaborative Open Data (COD), for example, Wikipedia is one of the well-known sources for COD. The Wikipedia articles can be written in different languages, updated in real time, and originated from a vast variety of editors. However, COD also bring different data quality problems such as data inconsistency and low data objectiveness due to the crowd-based and dynamic nature. These data quality problems such as biased information may lead to sentimental changes or social impacts. This paper therefore proposes a new measurement model to assess the quality of COD. In order to evaluate the proposed model, A preliminary experiment is conducted with a large scale of Wikipedia articles to validate the applicability and efficiency of this proposed quality model in the real-world scenario.

    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    B. Mbarek, Mouzhi Ge, T. Pitner

    Blockchain-Based Access Control for IoT in Smart Home Systems

    Database and Expert Systems Applications

    2020

    ISBN: 978-3-030-59050-5

    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    L. Walletzký, F. Romanovská, A. Toli, Mouzhi Ge

    Research Challenges of Open Data as a Service for Smart Cities

    Proceedings of the 10th International Conference on Cloud Computing and Services Science (CLOSER 2020)

    2020

    ISBN: 978-989-758-424-4

    DigitalF: Europan Campus Rottal-Inn

    Zeitschriftenartikel

    B. Mbarek, Mouzhi Ge, T. Pitner

    An Efficient Mutual Authentication Scheme for Internet of Things

    Internet of Things, vol. 9, no. 1

    2020

    DOI: 10.1016/j.iot.2020.100160

    Abstract anzeigen

    The Internet of Things (IoT) is developed to facilitate the connections and data sharing among people, devices, and systems. Among the infrastructural IoT techniques, Radio Frequency IDentification (RFID) has been used to enable the proliferation and communication in IoT networks. However, the RFID techniques usually suffer from security issues due to the inherent weaknesses of underlying wireless radio communications. One of the main security issues is the authentication vulnerability from the jamming attack. In order to tackle the vulnerabilities of key updating algorithms, this paper therefore proposes an efficient authentication scheme based on the self-adaptive and mutual key updating. Furthermore, we evaluate the performance and applicability of our solution with a thorough simulation by taking into account the energy consumption, authentication failure rate and authentication delay. The feasibility and applicability are demonstrated by implementing the proposed authentication scheme in smart home IoT systems.

    DigitalF: Europan Campus Rottal-Inn

    Zeitschriftenartikel

    M. Macak, Mouzhi Ge, B. Buhnova

    A Cross-Domain Comparative Study of Big Data Architectures

    International Journal of Cooperative Information Systems, vol. 29, no. 04

    2020

    DOI: 10.1142/S0218843020300016

    Abstract anzeigen

    Nowadays, a variety of Big Data architectures are emerging to organize the Big Data life cycle. While some of these architectures are proposed for general usage, many of them are proposed in a specific application domain such as smart cities, transportation, healthcare, and agriculture. There is, however, a lack of understanding of how and why Big Data architectures vary in different domains and how the Big Data architecture strategy in one domain may possibly advance other domains. Therefore, this paper surveys and compares the Big Data architectures in different application domains. It also chooses a representative architecture of each researched application domain to indicate which Big Data architecture from a given domain the researchers and practitioners may possibly start from. Next, a pairwise cross-domain comparison among the Big Data architectures is presented to outline the similarities and differences between the domain-specific architectures. Finally, the paper provides a set of practical guidelines for Big Data researchers and practitioners to build and improve Big Data architectures based on the knowledge gathered in this study.

    DigitalF: Europan Campus Rottal-Inn

    Zeitschriftenartikel

    F. Persia, G. Pilato, Mouzhi Ge, P. Bolzoni, D. D’Auria, S. Helmer

    Improving orienteering-based tourist trip planning with social sensing

    Future Generation Computer Systems, vol. 110, no. 9, pp. 931-945

    2020

    DOI: 10.1016/j.future.2019.10.028

    Abstract anzeigen

    We enhance a tourist trip planning framework based on orienteering with category constraints by adding social sensing. This allows us to customize a user’s experience without putting the burden of preference elicitation on the user. We identify the interests of a user by analyzing their Tweets and then match these interests to descriptions of points of interests. For this analysis we adapt different schemes for social sensing to the needs of our orienteering context and compare them to find the most suitable approach. We show that our technique is fast enough for use in real-time dynamic settings and also has a higher accuracy compared to previous approaches. Additionally, we integrate a more efficient algorithm for solving the orienteering problem, boosting the overall performance and utility of our framework further, as demonstrated by the positive user satisfaction received by real users.

    DigitalF: Europan Campus Rottal-Inn

    Zeitschriftenartikel

    M. Drăgoicea, L. Walletzký, L. Carrubbo, Nabil Badr, Angeliki T., F. Romanovská, Mouzhi Ge

    Service Design for Resilience: A Multi-Contextual Modeling Perspective

    IEEE Access, vol. 8, pp. 185526-185543

    2020

    DOI: 10.1109/ACCESS.2020.3029320

    Abstract anzeigen

    This paper introduces a conceptual framework aiming to broaden the discussion on resilience for the design of public services. From a theoretical point of view, the paper explores service design with a Systems Thinking lens. A multi-contextual perspective aiming to analyze, decompose, and design smart cities services where resilience is an input at the service design level is described and the four diamondsof-context model for service design (4DocMod) is introduced. This service model accommodates various actors' contexts in public service design and consists of four design artefacts, the diamonds (See, Recognize, Organize, Do). From a practical point of view, guidelines for the application of the 4DocMod service model extension for resilience are described along with two case studies addressing the recent COVID-19 pandemic that illustrates a clear situation of resilience with insights in multiple contexts. According to the findings of this paper, it is obvious that resilience is not “just”a request. Instead, it plays a higher role within the service system. It is not “just”another Context, either. Instead, it goes through many contexts with different circumstances. In this manner, it is possible to address the qualities through which actors can become resilient, at the service design stage, to ensure continuity of the public services in times of emergency. As our approach using the 4DocMod is proposing, resilience may be is achieved when specific properties are provisioned at information service design level.

    DigitalGesundF: Europan Campus Rottal-Inn

    Zeitschriftenartikel

    R. Istepanian, M. Kulhandjian, Georgi Chaltikyan

    Mobile Health (mHealth) in the Developing World: Two Decades of Progress or Retrogression

    Journal of the International Society for Telemedicine and eHealth (JISfTeH), vol. 8, no. e24, pp. 1-5

    2020

    DOI: 10.29086/JISfTeH.8.e24

    Abstract anzeigen

    Mobile healthcare, or mHealth, is one of the key pillars of information and communication technologies for healthcare that consists of telemedicine, telehealth, eHealth, and mHealth. In the past two decades, mobile health has become a transformative concept for healthcare delivery innovations on a global scale. The success was based on the market-driven strategies that utilised the advances in mobile communications, computing, and sensor technologies, especially in recent years. Those market-driven mobile health systems were also closely associated with the global proliferation of smartphones, and based on the correlated usage principle of the smartphone applications for healthcare and wellbeing. However, the global commercial success of the smartphone-based mHealth model was not widely translated into successful scaled-up and tangible healthcare benefits, especially in low- and-middle income countries, compared to the consumer mobile health markets. The numerous healthcare challenges in the developing world remained largely untackled by the existing mobile health systems and models. The much-hyped transformative benefits of these systems remain largely unfulfilled. For two decades since the inception of this concept, the majority of the population in resource-limited healthcare settings still remain in poorer health and live in worsened conditions, with limited if any access to basic healthcare services. The much-hyped mobile health services that promised transforming these fragile and limited healthcare conditions, did not come to wider fruition globally. The COVID-19 pandemic, with its devastating human and economic impact worsened this status. An overview of the origin and the basic principles of mobile health, its current landscape and status in the developing world is presented. The impact of the smartphone-centric model that dominated the landscape of mobile health systems in these countries is discussed, and a critical view on the limitation of this mobile health model adopted widely in these settings is provided.

    DigitalGesundF: Europan Campus Rottal-Inn

    Zeitschriftenartikel

    Fara Fernandes, Georgi Chaltikyan

    Analysis of Legal and Regulatory Frameworks in Digital Health: A Comparison of Guidelines and Approaches in the European Union and United States

    Journal of the International Society for Telemedicine and eHealth (JISfTeH), vol. 8, no. e11, pp. 1-13

    2020

    DOI: 10.29086/JISfTeH.8.e11

    Abstract anzeigen

    The advent of digital technology in healthcare presents opportunities for the improvement of healthcare systems around the world and the move towards value-based treatment. However, this move must be accompanied by strong legal and regulatory frameworks that will not only facilitate but encourage the good use of technology. The goal of the study was to assess the amenability and furtherance of regulatory frameworks in digital health by evaluating and comparing the processes, effectiveness and outcomes of these frameworks in the European Union and United States. Methods: This study incorporated two research methodologies. The first was a research of current legal and regulatory frameworks in digital health in the European Union and United States. A comprehensive online search for publications was carried out which included laws, regulations, policies, green papers, guidelines and recommendations. This research was complemented with interviews of five purposively sampled key informants in the legal and regulatory landscape. Results: Mind-maps revealed key features and challenges of the digital health field in the topics of the current state of regulation of digital health in the EU, Germany and US, regulatory pathways for digital health devices, protection and privacy of health data, mobile health validation, risk-based classification of medical devices, regulation of clinical decision support systems, telemedicine, artificial intelligence and emerging technologies, reimbursement for digital health services and liability for digital health products. The experts expressed and explained key points where current regulation is deficient. The review of the legal frameworks revealed deficiencies which provide opportunities and recommendations to further develop and strengthen the regulatory landscape. Conclusions: A key element to a robust regulatory framework is the ability to ensure trust and confidence in using digital health technology. Technology must measure the impact on quality of life and burden of disease and not merely involve the collection of data.

    DigitalF: Angewandte Wirtschaftswissenschaften

    Zeitschriftenartikel

    Christian Mandl

    Datengetriebene Planungsmethoden zur Steuerung der Rohstoffbeschaffung und des physischen Rohstoffhandels unter Preisrisiko

    OR News (Das Magazin der Gesellschaft für Operations Research e.V.), vol. 70, no. November

    2020

    DigitalF: Angewandte Wirtschaftswissenschaften

    Beitrag (Sammelband oder Tagungsband)

    Christian Mandl

    Datengetriebene Ansätze zur Optimierung der Beschaffung und Lagerhaltung von Rohstoffen in volatilen Märkten

    Supply Management Research

    2020

    ISBN: 978-3-658-31897-0

    Abstract anzeigen

    Preisschwankungen stellen sowohl für rohstoffverarbeitende als auch für rohstoffhandelnde Unternehmen eine große Herausforderung dar. Die vorliegende Arbeit untersucht die Auswirkungen von Preisunsicherheit auf optimale Beschaffungs- und Lagerhaltungsstrategien. Eine zentrale Erweiterung der existierenden Literatur sind der Fokus auf Preismodellunsicherheit, sprich unvollständige Information über den zugrundeliegenden stochastischen Preisprozess, sowie auf kostenoptimale Beschaffungsentscheidungen statt optimaler Preisprognosen. Die entwickelten stochastischen und datengetriebenen Analytics-Ansätze kombinieren hierbei mathematische Optimierungsverfahren des Operations Research mit Methoden aus dem Bereich des Maschinellen Lernens und liefern als Ergebnis effektive und interpretierbare Entscheidungsregeln. Das primäre Ziel dieser Arbeit ist es, Rohstoffeinkäufern im digitalen Zeitalter Entscheidungsunterstützungstools an die Hand zu geben, mit deren Hilfe Big Data für verbesserte Beschaffungs- und Lagerhaltungsentscheidungen genutzt werden kann. Die entwickelten Algorithmen wurden dabei auf der Basis von Echtdaten für verschiedene Rohstoffklassen (Metalle, Energie, Agrar) validiert.

    DigitalF: Angewandte Wirtschaftswissenschaften

    Zeitschriftenartikel

    Christian Mandl, S. Minner

    Data-Driven Optimization for Commodity Procurement Under Price Uncertainty

    Manufacturing and Service Operations Management, no. Published Online:18 Aug 2020

    2020

    DOI: 10.1287/msom.2020.0890

    Abstract anzeigen

    Problem definition: We study a practice-motivated multiperiod stochastic commodity procurement problem under price uncertainty with forward and spot purchase options. Existing approaches are based on parametric price models, which inevitably involve price model misspecification and generalization error. Academic/practical relevance: We propose a nonparametric, data-driven approach (DDA) that is consistent with the optimal procurement policy structure but without requiring the a priori specification and estimation of stochastic price processes. In addition to historical prices, DDA is able to leverage real-time feature data, such as economic indicators, in solving the problem. Methodology: This paper provides a framework for prescriptive analytics in dynamic commodity procurement, with optimal purchase policies learned directly from data as functions of features, via mixed integer linear programming (MILP) under cost minimization objectives. Hence, DDA focuses on optimal decisions rather than optimal predictions. Furthermore, we combine optimization with regularization from machine learning (ML) to extract decision-relevant data from noise. Results: Based on numerical experiments and empirical data, we show that there is a significant value of feature data for commodity procurement when procurement policy parameters are learned as functions of features. However, overfitting deteriorates the performance of data-driven solutions, which asks for ML extensions to improve out-of-sample generalization. Compared with an internal best-practice benchmark, DDA generates savings of on average 9.1 million euros per annum (4.33%) for 10 years of backtesting. Managerial implications: A practical benefit of DDA is that it yields simple but optimally structured decision rules that are easy to interpret and easy to operationalize. Furthermore, DDA is generalizable and applicable to many other procurement settings.

    DigitalF: Angewandte WirtschaftswissenschaftenS: TC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    Diane Ahrens

    Digitale Dörfer. Gleichwertige Lebensverhältnisse durch Digitalisierung im ländlichen Raum?

    Zukunft vor Ort. Kommunalpolitik in Bayern, München

    2020

    NachhaltigF: Angewandte WirtschaftswissenschaftenI: Institut GMRC

    Internetdokument

    Josef Scherer

    GRC in der Praxis – von der Resilienz und dem Nachhaltigen Handeln

    Ein Interview mit Prof. Dr. Josef Scherer

    2020