Main Goal:
Provide a systematic understanding of the main ideas of data structure design.
Understand, design, implement, apply and propose innovative data structures to solve problems related to multidimensional data processing, information retrieval by similarity, search engines and other computational problems.
Papers:
"An Overview Of Quadtrees, Octrees, And Related Hierarchical Data Structures" by Samet H.(pdf)
Review: "Spatial Data Structures" by Samet H. (pdf)
"R-Trees. A dynamic index structure for spatial searching" by Guttman A. (pdf)
"The R*-tree: An Efficient and Robust Access Method for Points and Rectangles+" (pdf)
"The R+ -Tree: A Dynamic Index for Multi-dimensional objects" by Sellis T. et al. (pdf)
"A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis pdf" by Fahad et al. (pdf)
"Confluently Persistent Tries for Efficient Version Control" by Demain E. et al (pdf)
"Retroactive Data Structures" by Demain E. et al. (pdf)
"GPU Data Structures GPU Data Structures" by Aaron Lefohn (pdf)
"Implementing Efficient Parallel Data Structures on GPUs" by Lefohn et al. (pdf)
Datasets:
- Document Collection - AP_BBC_CNN_Reuters (zip)
- Image Collection - Caltech 101 (tar.gz)
- 50k Chicago Crimes: 6.2M reported crimes from the City of Chicago Data Portal (extracted from Nanocubes.net)
Testing Dataset:
Subset:
https://archive.ics.uci.edu/ml/datasets/yearpredictionmsd
Full Dataset:
https://labrosa.ee.columbia.edu/millionsong/
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(2017-I) Final Project: "A persistent data structure to support a Version Control System"