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teaching:projh402 [2021/09/08 09:09] ezimanyi [MobilityDB on Google Cloud Platform] |
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| - | ====== MA Computer Science Projects (PROJ-H-402) ====== | + | ====== MA Computer Science Projects (PROJ-H-402) ===== |
| + | ====== Master's Thesis Projects (MEMO-H-503) ====== | ||
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| Links: | Links: | ||
| - | * {{:teaching:symbolic_trajectories.pdf|}} | + | * R.H. Guting, F Valdés, M.L. Damiani, {{:teaching:symbolic_trajectories.pdf|Symbolic Trajectories}}, ACM Transactions on Spatial Algorithms Systems, (1)2, Article 7, 2015 |
| ===== Trajectory Data Warehouses ===== | ===== Trajectory Data Warehouses ===== | ||
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| The goal of this project is to survey the state of the art in geospatial trajectory data cleaning, both model-based and machine learning. The work also includes prototyping and empirically evaluating a selection of these methods in the MobilityDB system, and on different real datasets. These outcomes should serve as a base for a thesis project to enhance geospatial trajectory data cleaning. | The goal of this project is to survey the state of the art in geospatial trajectory data cleaning, both model-based and machine learning. The work also includes prototyping and empirically evaluating a selection of these methods in the MobilityDB system, and on different real datasets. These outcomes should serve as a base for a thesis project to enhance geospatial trajectory data cleaning. | ||
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| + | ===== Dynamic Time Warping for Trajectories ===== | ||
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| + | The dynamic time warping (DTW) algorithm is able to find the optimal alignment between two time series. It is often used to determine time series similarity, classification, and to find corresponding regions between two time series. Several dynamic time warping implementations are available. However, DTW has a quadratic time and space complexity that limits its use to small time series data sets. Therefore, a fast approximation of DTW that has linear time and space complexity has been proposed. | ||
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| + | The goal of this project is to survey and to prototype in MobilityDB the state of art methods in dynamic time warping. | ||
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| + | * T. Giorgino, [[https://www.jstatsoft.org/article/view/v031i07|Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package]], Journal of Statistical Software, (31)7, 2009. | ||
| + | * S. Salvador, P. Chan, [[https://cs.fit.edu/~pkc/papers/tdm04.pdf|FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space]], Intelligent Data Analysis, 11(5):561-580, 2007. | ||
| + | * D.F. Silva, G.E.A.P.A. Batista, [[http://sites.labic.icmc.usp.br/dfs/pdf/SDM_PrunedDTW.pdf|Speeding Up All-Pairwise Dynamic Time Warping Matrix Calculation]], Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 837-845, 2016. | ||
| + | * G. Al-Naymat, S. Chawla, J. Taheri (2012). [[https://arxiv.org/abs/1201.2969|SparseDTW: A Novel Approach to Speed up Dynamic Time Warping]]. CoRR abs/1201.2969, 2012. | ||
| + | * M. Müller, H. Mattes, F. Kurth, [[https://www.audiolabs-erlangen.de/content/05-fau/professor/00-mueller/03-publications/2006_MuellerMattesKurth_MultiscaleAudioSynchronization_ISMIR.pdf|An Efficient Multiscale Approach to Audio Synchronization]]. Proceedings of the International Conference on Music Information Retrieval (ISMIR), pp. 192-197, 2006. | ||
| + | * Thomas Prätzlich, Jonathan Driedger, and Meinard Müller, [[https://www.researchgate.net/publication/303667579_Memory-Restricted_Multiscale_Dynamic_Time_Warping|Memory-Restricted Multiscale Dynamic Time Warping]], Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 569-573, 2016. | ||
| ===== Geospatial Trajectory Similarity Measure ===== | ===== Geospatial Trajectory Similarity Measure ===== | ||