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.
Unicorn is an HTTP server for Rack applications designed to only serve fast clients on low-latency, high-bandwidth connections and take advantage of features in Unix/Unix-like kernels. Slow clients should only be served by placing a reverse proxy capable of fully buffering both the the request and response in between Unicorn and slow clients.
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.
M. Keller, J. Beutel, O. Saukh, and L. Thiele. Local Computer Networks Workshops (LCN Workshops), 2012 IEEE 37th Conference on, page 925--933. IEEE, (2012)
S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-Thieme. Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, page 635--644. New York, NY, USA, ACM, (2011)
H. Tong, C. Faloutsos, and J. Pan. ICDM '06: Proceedings of the Sixth International Conference on Data Mining, page 613--622. Washington, DC, USA, IEEE Computer Society, (2006)
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)
A. Troy, G. Zhang, and Y. Tian. Proceedings of the 15th International Conference on Conceptual Structures (ICCS 2007), volume 4604 of Lecture Notes in Artificial Intelligence, page 206--219. Berlin, Heidelberg, Springer-Verlag, (2007)