Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):
F. Iglesias Vazquez, D. Ojdanic, A. Hartl, T. Zseby:
"MDCStream: Stream Data Generator for Testing Analysis Algorithms";
Vortrag: EAI Valuetools 2020,
Tsukuba, Japan;
18.05.2020
- 20.05.2020; in: "Proceedings of the 13th EAI International Conference on Performance Evaluation Methodologies and Tools",
Association for Computing Machinery,
Tsukuba, Japan
(2020),
ISBN: 9781450376464;
S. 56
- 63.
Kurzfassung deutsch:
The establishment of modern technological paradigms like ubiquitous computing, big data, cyber-physical systems, or communication networks has strongly increased the need for efficient, effective data stream analysis. MDCStream is a MATLAB tool for generating temporal-dependent numerical datasets in order to stress-test stream data classification, clustering, and outlier detection algorithms. MDCStream is built on MDCGen, therefore showing a high flexibility for creating a wide diversity of data scenarios. To show an example of the potential of MDCStream, we tested a stream data clustering algorithm recently proposed in the literature with datasets generated with MDCStream. Datasets were designed to draw challenges related to space geometries and concept drift.
Kurzfassung englisch:
The establishment of modern technological paradigms like ubiquitous computing, big data, cyber-physical systems, or communication networks has strongly increased the need for efficient, effective data stream analysis. MDCStream is a MATLAB tool for generating temporal-dependent numerical datasets in order to stress-test stream data classification, clustering, and outlier detection algorithms. MDCStream is built on MDCGen, therefore showing a high flexibility for creating a wide diversity of data scenarios. To show an example of the potential of MDCStream, we tested a stream data clustering algorithm recently proposed in the literature with datasets generated with MDCStream. Datasets were designed to draw challenges related to space geometries and concept drift.
Schlagworte:
data generation, stream data, synthetic data, multi-dimensional data, concept drift, nonstationarity, MATLAB
"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1145/3388831.3388832
Elektronische Version der Publikation:
https://dl.acm.org/doi/10.1145/3388831.3388832
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.