@conference{macek2013visualizing,
  address = {New York, NY, USA},
  author = {Macek, Björn-Elmar and Atzmueller, Martin},
  booktitle = {Proc. ASONAM 2013},
  interhash = {d8cd9de635a391360c1663c0ec1ba35d},
  intrahash = {04a24a6fa8abc228ea70b7d1c2ca7455},
  publisher = {ACM Press},
  title = {Visualizing The Impact of Time Series Data
for Predicting User Interactions},
  year = 2013
}

@article{Fuchs2010379,
  abstract = {Subspace representations that preserve essential information of high-dimensional data may be advantageous for many reasons such as improved interpretability, overfitting avoidance, acceleration of machine learning techniques. In this article, we describe a new subspace representation of time series which we call polynomial shape space representation. This representation consists of optimal (in a least-squares sense) estimators of trend aspects of a time series such as average, slope, curve, change of curve, etc. The shape space representation of time series allows for a definition of a novel similarity measure for time series which we call shape space distance measure. Depending on the application, time series segmentation techniques can be applied to obtain a piecewise shape space representation of the time series in subsequent segments. In this article, we investigate the properties of the polynomial shape space representation and the shape space distance measure by means of some benchmark time series and discuss possible application scenarios in the field of temporal data mining.},
  author = {Fuchs, Erich and Gruber, Thiemo and Pree, Helmuth and Sick, Bernhard},
  doi = {10.1016/j.neucom.2010.03.022},
  interhash = {88c499ac1dc9e9708e70187967494219},
  intrahash = {fdf6865c1bece3f77cc3e29365a2c6b3},
  issn = {0925-2312},
  journal = {Neurocomputing},
  note = {Artificial Brains},
  number = {1–3},
  pages = {379 - 393},
  title = {Temporal data mining using shape space representations of time series},
  url = {http://www.sciencedirect.com/science/article/pii/S0925231210002237},
  volume = 74,
  year = 2010
}

@article{Mucha14052010,
  abstract = {Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows studies of community structure in a general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales.},
  author = {Mucha, Peter J. and Richardson, Thomas and Macon, Kevin and Porter, Mason A. and Onnela, Jukka-Pekka},
  doi = {10.1126/science.1184819},
  eprint = {http://www.sciencemag.org/content/328/5980/876.full.pdf},
  interhash = {7cc01f266e3a745d2be16a9a3b377695},
  intrahash = {c5b7cfb584d5aee1a941a8e5d3e856b1},
  journal = {Science},
  number = 5980,
  pages = {876-878},
  title = {Community Structure in Time-Dependent, Multiscale, and Multiplex Networks},
  url = {http://www.sciencemag.org/content/328/5980/876.abstract},
  volume = 328,
  year = 2010
}

@inproceedings{dkmnrt06visualizing,
  author = {Dubinko, M. and Kumar, R. and Magnani, J. and Novak, J. and Raghavan, P. and Tomkins, A.},
  booktitle = {Proc. of the 15th International WWW Conference},
  day = {23-25},
  interhash = {b9ff2f72831a1406013a86c8202d6276},
  intrahash = {e14f92577c8819bfd9753a047c6a8cea},
  title = {Visualizing Tags over Time},
  year = 2006
}