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226 results on '"Ge, Mouzhi"'

52. Context Dependent Preference Acquisition with Personality-Based Active Learning in Mobile Recommender Systems

Author

Braunhofer, Matthias, Elahi, Mehdi, Ge, Mouzhi, Ricci, Francesco, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Zaphiris, Panayiotis, editor, and Ioannou, Andri, editor

Published
2014
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63. Alignment of Ontology Design Patterns: Class As Property Value, Value Partition and Normalisation

Author

Rodriguez-Castro, Bene, Ge, Mouzhi, Hepp, Martin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Meersman, Robert, editor, Panetto, Hervé, editor, Dillon, Tharam, editor, Rinderle-Ma, Stefanie, editor, Dadam, Peter, editor, Zhou, Xiaofang, editor, Pearson, Siani, editor, Ferscha, Alois, editor, Bergamaschi, Sonia, editor, and Cruz, Isabel F., editor

Published
2012
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64. Understanding Recommendations by Reading the Clouds

Author

Gedikli, Fatih, Ge, Mouzhi, Jannach, Dietmar, van der Aalst, Wil, Series editor, Mylopoulos, John, Series editor, Rosemann, Michael, Series editor, Shaw, Michael J., Series editor, Szyperski, Clemens, Series editor, Huemer, Christian, editor, and Setzer, Thomas, editor

Published
2011
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72. Interoperability-oriented Quality Assessment for Czech Open Data

Author

Kusnirakova, Dasa, Ge, Mouzhi, Walletzky, Leonard, and Buhnova, Barbora

Subjects
FOS: Computer and information sciences, Computer Science - Databases, Databases (cs.DB)
Abstract

With the rapid increase of published open datasets, it is crucial to support the open data progress in smart cities while considering the open data quality. In the Czech Republic, and its National Open Data Catalogue (NODC), the open datasets are usually evaluated based on their metadata only, while leaving the content and the adherence to the recommended data structure to the sole responsibility of the data providers. The interoperability of open datasets remains unknown. This paper therefore aims to propose a novel content-aware quality evaluation framework that assesses the quality of open datasets based on five data quality dimensions. With the proposed framework, we provide a fundamental view on the interoperability-oriented data quality of Czech open datasets, which are published in NODC. Our evaluations find that domain-specific open data quality assessments are able to detect data quality issues beyond traditional heuristics used for determining Czech open data quality, increase their interoperability, and thus increase their potential to bring value for the society. The findings of this research are beneficial not only for the case of the Czech Republic, but also can be applied in other countries that intend to enhance their open data quality evaluation processes., In Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA, 446-453, 2022 , Lisbon, Portugal

Published
2022

75. Applying Scrum in Data Science Projects

Author

Baijens, Jeroen, Helms, Remko W., Iren, Deniz, Guédria, Wided, Proper, Henderik A., Verelst, Jan, Hacks, Simon, Timm, Felix, Sandkuhl, Kurt, Fellmann, Michael, Serapiao, Gabriel, Payan, Mathias, Komarov, Mikhail, Maltseva, Svetlana, Uskenbayeva, Raissa, Nazarov, Dmitry, Ge, Mouzhi, Helfert, Markus, Ehrlinger, Lisa, Department of Information Science, RS-Research Program Learning and Innovation in Resilient systems (LIRS), RS-Research Line Innovation (part of LIRS program), Guédria, Wided, Proper, Henderik A., Verelst, Jan, Hacks, Simon, Timm, Felix, Sandkuhl, Kurt, Fellmann, Michael, Serapiao, Gabriel, Payan, Mathias, Komarov, Mikhail, Maltseva, Svetlana, Uskenbayeva, Raissa, Nazarov, Dmitry, Ge, Mouzhi, Helfert, Markus, and Ehrlinger, Lisa

Subjects
Service (systems architecture), Agile, Process modeling, business.industry, Computer science, 05 social sciences, Big data, Data Science, Software development, 02 engineering and technology, Data science, Domain (software engineering), Scrum, 020204 information systems, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, Design science research, business, 050203 business & management, Agile software development
Abstract

The rise of big data has led to an increase in data science projects conducted by organizations. Such projects aim to create valuable insights by improving decision making or enhancing an organization’s service offering through data-driven services. However, the majority of data science projects still fail to deliver the expected value. To increase the success rate of projects, the use of process models or methodologies is recommended in the literature. Nevertheless, organizations are hardly using them because they are considered too rigid and they do not support the typical iterative and open nature of data science projects. To overcome this problem, this research suggests applying Agile methodologies to data science projects. Agile methodologies were originally developed in the software engineering domain and are characterised by their iterative approach towards software development. In this study, we selected the Scrum approach and integrated it into the CRISP-DM methodology for data science projects using a Design Science Research approach. This new methodology was then evaluated in three different case organizations using expert interviews. Analysis of the expert interviews resulted in a further refinement of the Agile data science methodology proposed by this research.

Published
2020

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

Author

Popescu, Mirona Ana-Maria, Ge, Mouzhi, Helfert, Markus, Popescu, Mirona Ana-Maria, Ge, Mouzhi, and Helfert, Markus

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.

Published
2019

97. Analysis of data warehouse architectures: modeling and classification

Author

Filipe, Joaquim, Smialek, Michal, Brodsky, Alexander, Hammoudi, Slimane, Yang, Qishan, Ge, Mouzhi, Helfert, Markus, Filipe, Joaquim, Smialek, Michal, Brodsky, Alexander, Hammoudi, Slimane, Yang, Qishan, Ge, Mouzhi, and Helfert, Markus

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 representativeDWHAs 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.

Published
2019
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