OPEN SOURCE SOFTWARE

Some of our products are available for anyone to use.

LeoFS

LeoFS

LeoFS is a distributed object storage which offers highly available, distributed, eventually consistent storage system. It suppors S3-API, Multi Data Center Replication, Built-in the Cache Mechanism and so on. It's possible to build in-house fast object storage in stead of S3 and to deal with secure files. LeoFS will make a great contribution to reduce cost.

Egison

Egison

Egison is the pattern-matching-oriented, pure functional programming language. Egison simplifies code by replacing complex nested loops and conditional branches with an intuitive pattern-matching expression. Egison is improving day by day. Recently, we added the facility to handle databases with intuitive Egison pattern-matching. We also released Ruby extension for Egison pattern-matching.

ROMA

ROMA

ROMA is a distributed key-value store implemented by Ruby. It offers high availability and scalability with pure P2P architechture. The most significant feature of ROMA is flexibility to add customable functions on ROMA developed by engineers. You can use not only simple string but also list on ROMA. Furthermore, ROMA project also provides various of clients such as Ruby, PHP, and Java.

fairy

fairy

fairy is a framework for distributed processing like MapReduce in Ruby. fairy supports programming model, called filter IF, and various built-in filters. It offers high productivity and simplicity because of its API like Ruby programming. It's expected to increase productivity.

Rakuten MA

Rakuten MA

Rakuten MA is a morphological analyzer (word segmentor + PoS Tagger) for Chinese and Japanese written purely in JavaScript. It works on modern browsers and node.js, and implements incremental update of models by online machine learning. It also supports compact model representations and is bundled with Chinese and Japanese models trained from general corpora and E-commerce corpora.

Category2Vec

Category2Vec

Category2Vec is an implementation of the category vector models [Marui and Hagiwara 2015], and the paragraph vector models [Le and Mikolov 2014]. These programs are based on word2vec [Mikolov et al. 2013a,b] in gensim project [Rahurek 2013]. After training, you can obtain distributed representations for categories, paragraphs, and words. You can also infer a category from a description.