Context-free data analysis with Transcendental Information Cascades.
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In order to discover hidden relationships and patterns in data streams from multiple heterogenous sources, we work on a method for exploratory data analysis. We disregard any system-specific context to generate generic networks of information co-occurrence. These networks allow for more informed sampling and filtering. Case specific context can be added once these networks have been created to support accurate decision making.
![Context-free data analysis with Transcendental Information
Cascades.
Markus Luczak-Roesch
University of Southampton, Web and Internet Science
@mluczak | http://markus-luczak.de
System A
System B
System C
Related activity?
t
[1] Markus Luczak-Roesch, Ramine Tinati, and Nigel Shadbolt. 2015. When Resources Collide:
Towards a Theory of Coincidence in Information Spaces. In WWW’15 Companion.
[2] Markus Luczak-Roesch, Ramine Tinati, Max Van Kleek, and Nigel Shadbolt. 2015. From
Coincidence to Purposeful Flow? Properties of Transcendental Information Cascades. In IEEE/ACM
International Conference on Advances in Social Networks Analysis and Mining 2015.](https://image.slidesharecdn.com/2016-01-06-usrg-pitch-tic-160107135255/85/Context-free-data-analysis-with-Transcendental-Information-Cascades-1-320.jpg)
![HF LF
[3] Kleinberg, Jon. "Bursty and hierarchical structure in streams." Data
Mining and Knowledge Discovery 7.4 (2003): 373-397.
[4] Subašić, I., & Berendt, B. (2013). Story graphs: Tracking document
set evolution using dynamic graphs. Intelligent Data Analysis, 17(1),
125-147.
Time
Numberofobserveddocuments
Content streams as automata [3]
“The key notion of TTM is
burstiness – sudden increases in
frequency of text fragments, and
all TTM methods aim to model
burstiness.” [4]
t](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Context-free data analysis with Transcendental Information Cascades.
- 1. Context-free data analysis with Transcendental Information Cascades. Markus Luczak-Roesch University of Southampton, Web and Internet Science @mluczak | http://markus-luczak.de System A System B System C Related activity? t [1] Markus Luczak-Roesch, Ramine Tinati, and Nigel Shadbolt. 2015. When Resources Collide: Towards a Theory of Coincidence in Information Spaces. In WWW’15 Companion. [2] Markus Luczak-Roesch, Ramine Tinati, Max Van Kleek, and Nigel Shadbolt. 2015. From Coincidence to Purposeful Flow? Properties of Transcendental Information Cascades. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015.
- 2. HF LF [3] Kleinberg, Jon. "Bursty and hierarchical structure in streams." Data Mining and Knowledge Discovery 7.4 (2003): 373-397. [4] Subašić, I., & Berendt, B. (2013). Story graphs: Tracking document set evolution using dynamic graphs. Intelligent Data Analysis, 17(1), 125-147. Time Numberofobserveddocuments Content streams as automata [3] “The key notion of TTM is burstiness – sudden increases in frequency of text fragments, and all TTM methods aim to model burstiness.” [4] t
- 3. F1 Fn … Transcendental Information Cascades Generic time-ordered networks of information co- occurrence t … C11 C21 C22 C23 t6 - t0 t2 - t1 t8 - t2 t4 - t2 t7 - t4 t5 - t3 t1 - t0 t2 - t1 t4 - t1 t4 - t3 t6 - t5 t8 - t6 t7 - t4 t5 - t4 t3 - t2