Key Summary
- Support for high dimensionality and sparsity
- Describe rich data such as texts, images and video in various domains such as recommender systems, similarity search, and chatbots.
- Vector data is in geospatial applications
- Two dimensional points such as the location of the end-user and points-of-interest may be represented as vectors
- High-dimensional vectors can be used to represent more complex data such as text, image, audio and video features
- VDBMSs typically support similarity search through indexing methods that enable rapid and accurate searching of similar vectors
- Search for vectors that closely resemble a given query vector based on specific distance metrics such as Euclidean distance or cosine similarity.
- In natural language processing, words and phrases are vectorized into vectors in such a way that similar words have similar vector representations.
- Word2vec [7], FastText, and Doc2vec [8] are examples of techniques that create vector embeddings for words in natural language
- From a developer perspective, queries in VDBMSs are more closely related to simple document or keyvalue store queries than to complex queries in relational databases
- Vectors are retrieved using one or several query vectors
Use-cases
- Similarity search in general
- Image and video similarity search
- Voice recognition
- Chatbots and long-term memory
Current challenges
- Balancing between speed and accuracy
- Growing dimensionality and sparsity
- Information security
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