Publikationen


Suche nach „[Mouzhi] [Ge]“ hat 18 Publikationen gefunden
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    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.

    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.

    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    T. Chondrogiannis, Mouzhi Ge

    Inferring ratings for custom trips from rich GPS traces

    LocalRec '19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-based Recommendations

    2019

    DOI: 10.1145/3356994.3365502

    Abstract anzeigen

    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.

    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    L. Trang, H. Bangui, Mouzhi Ge, B. Buhnova

    Scaling Big Data Applications in Smart City with Coresets

    Proceedings of the 8th International Conference on Data Science, Technology and Applications, vol. Vol. 1: DATA

    2019

    DOI: 10.5220/0007958803570363

    Abstract anzeigen

    With the development of Big Data applications in Smart Cities, various Big Data applications are proposed within the domain. These are however hard to test and prototype, since such prototyping requires big computing resources. In order to save the effort in building Big Data prototypes for Smart Cities, this paper proposes an enhanced sampling technique to obtain a coreset from Big Data while keeping the features of the Big Data, such as clustering structure and distribution density. In the proposed sampling method, for a given dataset and an ε>0, the method computes an ε-coreset of the dataset. The ε-coreset is then modified to obtain a sample set while ensuring the separation and balance in the set. Furthermore, by considering the representativeness of each sample point, our method can helps to remove noises and outliers. We believe that the coreset-based technique can be used to efficiently prototype and evaluate Big Data applications in the Smart City.

    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    Mouzhi Ge, S. Chren, B. Rossi, T. Pitner

    Data Quality Management Framework for Smart Grid Systems

    Business Information Systems (BIS 2019), Cham, Switzerland, vol. 354

    2019

    DOI: 10.1007/978-3-030-20482-2_24

    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    B. Mbarek, Mouzhi Ge, T. Pitner

    Self-adaptive RFID Authentication for Internet of Things

    Advanced information networking and applications, vol. 926

    2019

    ISBN: 978-3-030-15031-0

    Abstract anzeigen

    With the development of wireless Internet of Things (IoT) devices, Radio frequency identification (RFID) has been used as a promising technology for the proliferation and communication in IoT networks. However, the RFID techniques are usually plagued with security and privacy issues due to the inherent weaknesses of underlying wireless radio communications. Although several RFID authentication mechanisms have been proposed to address the security concerns in RFID, most of them are still vulnerable to some active attacks, especially the jamming attack. This paper therefore proposes a novel self-adaptive authentication mechanism with a robust key updating, in order to tackle the security vulnerabilities of key updating algorithms and their inability to jamming attacks. Furthermore, we assess the performance and applicability of our solution with a thorough simulation by taking into account the energy consumption and authentication failure rate.

    DigitalF: Europan Campus Rottal-Inn

    Zeitschriftenartikel

    H. Bangui, Mouzhi Ge, B. Buhnova

    A Research Roadmap of Big Data Clustering Algorithms for Future Internet of Things

    International Journal of Organizational and Collective Intelligence, vol. 9, no. 2, pp. 16-30

    2019

    DOI: 10.4018/IJOCI.2019040102

    Abstract anzeigen

    Due to the massive data increase in different Internet of Things (IoT) domains such as healthcare IoT and Smart City IoT, Big Data technologies have been emerged as critical analytics tools for analyzing the IoT data. Among the Big Data technologies, data clustering is one of the essential approaches to process the IoT data. However, how to select a suitable clustering algorithm for IoT data is still unclear. Furthermore, since Big Data technology are still in its initial stage for different IoT domains, it is thus valuable to propose and structure the research challenges between Big Data and IoT. Therefore, this article starts by reviewing and comparing the data clustering algorithms that can be applied in IoT datasets, and then extends the discussions to a broader IoT context such as IoT dynamics and IoT mobile networks. Finally, this article identifies a set of research challenges that harvest a research roadmap for the Big Data research in IoT domains. The proposed research roadmap aims at bridging the research gaps between Big Data and various IoT contexts.

    DigitalF: Europan Campus Rottal-Inn

    Zeitschriftenartikel

    Mouzhi Ge, F. Persia

    A Generalized Evaluation Framework for Multimedia Recommender Systems

    International Journal of Semantic Computing, vol. 12, no. 04, pp. 541-557

    2018

    DOI: 10.1142/S1793351X18500046

    Abstract anzeigen

    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.