LOD-a-lot democratizes access to the Linked Open Data (LOD) Cloud by serving more than 28 billion unique triples from 650K datasets from a single self-indexed file. This corpus can be queried online with a sustainable Linked Data Fragments interface, or it can be downloaded and consumed locally: LOD-a-lot is easy to deploy and only requires limited resources (524 GB of disk space and 15.7 GB of RAM), enabling web-scale repeatable experimentation and research from a high-end laptop.
LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, and L1-loss linear SVM.
Main features of LIBLINEAR include
* Same data format as LIBSVM, our general-purpose SVM solver, and also similar usage
* Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
* Cross validation for model selection
* Probability estimates (logistic regression only)
* Weights for unbalanced data
* MATLAB/Octave, Java interfaces
Kowari is an Open Source, massively scalable, transaction-safe, purpose-built database for the storage, retrieval and analysis of metadata. Kowari is written in Java and licensed under the Mozilla Public License.
S. Rendle, L. Marinho, A. Nanopoulos, и L. Schmidt-Thieme. KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, стр. 727--736. New York, NY, USA, ACM, (2009)