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
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.
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, and L. Schmidt-Thieme. KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, page 727--736. New York, NY, USA, ACM, (2009)