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A vector database, vector store or vector search engine is a database that can store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more Approximate Nearest Neighbor algorithms, [1] [2] [3] so that one can search the database with a query vector to retrieve the closest matching database records.
Data sharding; Streaming data ingestion, which allows to process and ingest data in real-time as it arrives; A dynamic schema, which allows inserting the data without a predefined schema; Independent storage and compute layers; Multi-tenancy scenarios (database-oriented, collection-oriented, partition-oriented) [14] Memory-mapped data storage
TerminusDB is an in-memory graph database management system with a rich query language. The design of the underlying data structure, which is implemented in a Rust library, uses a succinct data structures and delta encoding approach drawing inspiration from software source control systems like Git. [21]
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The database schema is the structure of a database described in a formal language supported typically by a relational database management system (RDBMS). The term " schema " refers to the organization of data as a blueprint of how the database is constructed (divided into database tables in the case of relational databases ).
It is designed to provide high availability, scalability, and low-latency access to data for modern applications. Unlike traditional relational databases, Cosmos DB is a NoSQL (meaning "Not only SQL", rather than "zero SQL") and vector database, [1] which means it can handle unstructured, semi-structured, structured, and vector data types. [2]
The main file (.shp) contains the geometry data. Geometry of a given feature is stored as a set of vector coordinates. [1]: 5 The binary file consists of a single fixed-length header followed by one or more variable-length records. Each of the variable-length records includes a record-header component and a record-contents component.
Distributional–relational models were first formalized, [3] [4] as a mechanism to cope with the vocabulary/semantic gap between users and the schema behind the data. In this scenario, distributional semantic relatedness measures, combined with semantic pivoting heuristics can support the approximation between user queries (expressed in their own vocabulary), and data (expressed in the ...