Talks and Poster Presentations (with Proceedings-Entry):
F. Iglesias Vazquez, D. Ojdanic, A. Hartl, T. Zseby:
"MDCStream: Stream Data Generator for Testing Analysis Algorithms";
Talk: EAI Valuetools 2020,
Tsukuba, Japan;
05-18-2020
- 05-20-2020; in: "Proceedings of the 13th EAI International Conference on Performance Evaluation Methodologies and Tools",
Association for Computing Machinery,
Tsukuba, Japan
(2020),
ISBN: 9781450376464;
56
- 63.
English abstract:
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.
German abstract:
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.
Keywords:
data generation, stream data, synthetic data, multi-dimensional data, concept drift, nonstationarity, MATLAB
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1145/3388831.3388832
Electronic version of the publication:
https://dl.acm.org/doi/10.1145/3388831.3388832
Created from the Publication Database of the Vienna University of Technology.