@article{GrunzkeBreuersGesingetal.2013, author = {Grunzke, Richard and Breuers, Sebastian and Gesing, Sandra and Herres-Pawlis, Sonja and Kruse, Martin and Blunk, Dirk and de la Garza, Luis and Packschies, Lars and Sch{\"a}fer, Patrick and Sch{\"a}rfe, Charlotta and Schlemmer, Tobias and Steinke, Thomas and Schuller, Bernd and M{\"u}ller-Pfefferkorn, Ralph and J{\"a}kel, Ren{\´e} and Nagel, Wolfgang and Atkinson, Malcolm and Kr{\"u}ger, Jens}, title = {Standards-based metadata management for molecular simulations}, series = {Concurrency and Computation: Practice and Experience}, journal = {Concurrency and Computation: Practice and Experience}, doi = {10.1002/cpe.3116}, year = {2013}, language = {en} } @misc{Schaefer, author = {Sch{\"a}fer, Patrick}, title = {Bag-Of-SFA-Symbols in Vector Space (BOSS VS)}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-54984}, abstract = {Time series classification mimics the human understanding of similarity. When it comes to larger datasets, state of the art classifiers reach their limits in terms of unreasonable training or testing times. One representative example is the 1-nearest-neighbor DTW classifier (1-NN DTW) that is commonly used as the benchmark to compare to and has several shortcomings: it has a quadratic time and it degenerates in the presence of noise. To reduce the computational complexity lower bounding techniques or recently a nearest centroid classifier have been introduced. Still, execution times to classify moderately sized datasets on a single core are in the order of hours. We present our Bag-Of-SFA-Symbols in Vector Space (BOSS VS) classifier that is robust and accurate due to invariance to noise, phase shifts, offsets, amplitudes and occlusions. We show that it is as accurate while being multiple orders of magnitude faster than state of the art classifiers. Using the BOSS VS allows for mining massive time series datasets and real-time analytics.}, language = {en} }