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ZeroMove Technology

ZeroMove hashing algorithm is an innovative novel data distribution technique for distributed systems. In contrast to the traditional consistent hashing algorithm, which is widely used in distributed data systems and requires data migration when scaling the system, ZeroMove hashing enables the addition of new clusters of nodes without moving data between nodes. A cluster is identified using an encoded unique identifier, while a node is found with a hash function within a cluster. This approach ensures that data remains in the node where it is hashed, thereby increasing availability and improving system performance. Furthermore, the ZeroMove hashing technique can significantly reduce facility and administrative expenses, making it an excellent option for large-scale distributed systems. Our tests on consistent hashing and ZeroMove hashing have shown that scaling from one node to six nodes with 480,000 data records took 6100 seconds in a system based on consistent hashing. In contrast, it took only 1.2 seconds for ZeroMove hashing to achieve similar scaling under the same settings. With consistent hashing, the time taken and amount of data moved increase proportionally with the amount of data stored in the system. However, with ZeroMove hashing, these values does not increase in proportion to the amount of data being stored. This is because ZeroMove hashing only involves the exchange of small amount of metadata between nodes during scaling processes.





AI Data

Artificial intelligence (AI) systems are trained using large amounts of data to learn and improve their performance. This is because AI algorithms use statistical techniques to find patterns and make predictions based on the data they have been trained on. The more diverse and representative the data is, the better the AI will be able to learn and generalize from that data. To create accurate and reliable AI models, it is important to ensure that the data used for training is of high quality, well-structured, and covers a wide range of scenarios and use cases. This allows the AI to learn from a variety of perspectives and make more accurate predictions or decisions when applied to new data. Therefore, having lots of good data is essential for developing robust and accurate AI models that can be applied in a variety of contexts and provide value to businesses and individuals alike. Good data comes from a well-managed database where knowledge and facts are maintained and fed to the AI systems to reach another level of intelligence. The vector database in JaguarDB can store and index the embeddings of image and text data for fast search in a multi-node distributed architecture which can be easily scaled out horizonally more than a million time faster than any other distributed databases. The distributed storage technology also makes storing large volumes of raw data like videos, images easier than ever.









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