The document discusses graph databases and their properties. Graph databases are structured to store graph-based data by using nodes and edges to represent entities and their relationships. They are well-suited for applications with complex relationships between entities that can be modeled as graphs, such as social networks. Key graph database technologies mentioned include Neo4j, OrientDB, and TinkerPop which provides graph traversal capabilities.
The document discusses graph databases and their properties. Graph databases are structured to store graph-based data by using nodes and edges to represent entities and their relationships. They are well-suited for applications with complex relationships between entities that can be modeled as graphs, such as social networks. Key graph database technologies mentioned include Neo4j, OrientDB, and TinkerPop which provides graph traversal capabilities.
Jubatus is an open source machine learning framework that allows for distributed, online machine learning. It features algorithms like classification, recommendation, anomaly detection, and clustering. The architecture uses a feature extractor to transform data into feature vectors which are then used to train machine learning models. Models are combined with feature extractors and accessed via client libraries using an RPC interface, enabling applications in languages like Ruby, Python, Perl, and JavaScript.
Jubatus is a distributed online machine learning framework that is distributed, fault tolerant, and allows for fixed time computation. It combines a machine learning model with a feature extractor. Jubatus uses a shared-everything architecture that allows it to be fast and fault tolerant. The architecture allows clients to access Jubatus through a single RPC interface even as the number of Jubatus servers scales out dynamically. Jubatus supports various machine learning algorithms including classification, recommendation, anomaly detection, clustering, and regression.
The document describes SkipGraph, a distributed hash table based on SkipLists. SkipGraph uses a SkipList data structure to store key-value pairs and distribute them across nodes through consistent hashing of keys to nodes. SkipGraph assigns each node a membership vector that describes which keys it stores, allowing efficient lookups of O(log n) by routing queries through nodes with overlapping membership vectors.