Search Results for "data collection algorithm"

...HugeGraph supports fast import performance in the case of more than 10 billion Vertices and Edges Graph, millisecond-level OLTP query capability, and can be integrated into big data platforms like Hadoop or Spark for OLAP analysis. The main scenarios of HugeGraph include correlation search, fraud detection, and knowledge graph. Not only supports Gremlin graph query language and RESTful API but also provides commonly used graph algorithm APIs. To help users easily implement various queries and analyses, HugeGraph has a full range of accessory tools, such as supporting distributed storage, data replication, scaling horizontally, and supports many built-in backends of storage engines.

The Lightweight Encryption Algorithm (also known as LEA) is a 128-bit block cipher developed by South Korea in 2013 to provide confidentiality in high-speed environments such as big data and cloud computing, as well as lightweight environments such as IoT devices and mobile devices. LEA is one of the cryptographic algorithms approved by the Korean Cryptographic Module Validation Program (KCMVP) and is the national standard of Republic of Korea (KS X 3246).

Snowplow is ideal for data teams who want to manage the collection and warehousing of data across all their platforms and products.

...In conclusion, RBF is a novel framework that performs strongly especially on data with large size.

...This project tries to realize these algorithms with statistical programming. Overcoming the long span has important meanings. First, we can verify the validity of algorithm with big data. Second, we can put them into unprecedented wide application in the age of data. Third, the unique inspection of I Ching that linked "all the universe" open new eyes for us in thought and methodology about interdisciplinary use of data. This program is open-source for human. If you are interested in it, welcome to discuss and work together.

How to extract meaningful information from big data has been a popular open problem. Decision tree, which has a high degree of knowledge interpretation, has been favored in many real world applications. However noisy values commonly exist in high-speed data streams, e.g. real-time online data feeds that are prone to interference. When processing big data, it is hard to implement pre-processing and sampling in full batches.

...This project tries to realize these algorithms with statistical programming. Overcoming the long span has important meanings. First, we can verify the validity of algorithm with big data. Second, we can put them into unprecedented wide application in the age of data. Third, the unique inspection of I Ching that linked "all the universe" open new eyes for us in thought and methodology about interdisciplinary use of data. This program is open-source for human. If you are interested in it, welcome to discuss and work together.