OPEN SOURCE SOFTWARE

Some of our products are available for anyone to use.

LeoFS

LeoFS

LeoFS is a distributed object storage system which offers highly available, distributed, eventually consistent storage. It supports S3-API, multi data center replication, built-in cache mechanism and so on. LeoFS also has family products: LeoInsight (QoS) and LeoCenter (Web Console). It's possible to build in-house fast object storage instead of S3 and to deal with secure files. LeoFS can make a great contribution to reducing costs.

ROMA

ROMA

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

Egison

Egison

Egison is a 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 a Ruby extension for Egison pattern-matching.

fairy

fairy

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

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 [Rehurek 2013]. After training, you can obtain distributed representations for categories, paragraphs, and words. You can also infer a category from a description.