Vector database management systems : Fundamental concepts, use-cases, and current challenges
Taipalus, T. (2024). Vector database management systems : Fundamental concepts, use-cases, and current challenges. Cognitive Systems Research, 85, Article 101216. https://doi.org/10.1016/j.cogsys.2024.101216
Published in
Cognitive Systems ResearchAuthors
Date
2024Copyright
© 2024 the Authors
Vector database management systems have emerged as an important component in modern data management, driven by the growing importance for the need to computationally describe rich data such as texts, images and video in various domains such as recommender systems, similarity search, and chatbots. These data descriptions are captured as numerical vectors that are computationally inexpensive to store and compare. However, the unique characteristics of vectorized data, including high dimensionality and sparsity, demand specialized solutions for efficient storage, retrieval, and processing. This narrative literature review provides an accessible introduction to the fundamental concepts, use-cases, and current challenges associated with vector database management systems, offering an overview for researchers and practitioners seeking to facilitate effective vector data management.
Publisher
ElsevierISSN Search the Publication Forum
1389-0417Keywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/207182971
Metadata
Show full item recordCollections
License
Related items
Showing items with similar title or keywords.
-
Convolutional Neural Network Based Sleep Stage Classification with Class Imbalance
Xu, Qi; Zhou, Dongdong; Wang, Jian; Shen, Jiangrong; Kettunen, Lauri; Cong, Fengyu (IEEE, 2022)Accurate sleep stage classification is vital to assess sleep quality and diagnose sleep disorders. Numerous deep learning based models have been designed for accomplishing this labor automatically. However, the class ... -
Process‐Informed Neural Networks : A Hybrid Modelling Approach to Improve Predictive Performance and Inference of Neural Networks in Ecology and Beyond
Wesselkamp, Marieke; Moser, Niklas; Kalweit, Maria; Boedecker, Joschka; Dormann, Carsten F. (Wiley, 2024)Despite deep learning being state of the art for data-driven model predictions, its application in ecology is currently subject to two important constraints: (i) deep-learning methods are powerful in data-rich regimes, but ... -
Estimating Tree Health Decline Caused by Ips typographus L. from UAS RGB Images Using a Deep One-Stage Object Detection Neural Network
Kanerva, Heini; Honkavaara, Eija; Näsi, Roope; Hakala, Teemu; Junttila, Samuli; Karila, Kirsi; Koivumäki, Niko; Alves Oliveira, Raquel; Pelto-Arvo, Mikko; Pölönen, Ilkka; Tuviala, Johanna; Östersund, Madeleine; Lyytikäinen-Saarenmaa, Päivi (MDPI, 2022)Various biotic and abiotic stresses are causing decline in forest health globally. Presently, one of the major biotic stress agents in Europe is the European spruce bark beetle (Ips typographus L.) which is increasingly ... -
DeepFake knee osteoarthritis X-rays from generative adversarial neural networks deceive medical experts and offer augmentation potential to automatic classification
Prezja, Fabi; Paloneva, Juha; Pölönen, Ilkka; Niinimäki, Esko; Äyrämö, Sami (Nature Publishing Group, 2022)Recent developments in deep learning have impacted medical science. However, new privacy issues and regulatory frameworks have hindered medical data sharing and collection. Deep learning is a very data-intensive process ... -
Channel Increment Strategy-Based 1D Convolutional Neural Networks for Seizure Prediction Using Intracranial EEG
Wang, Xiaoshuang; Zhang, Chi; Kärkkäinen, Tommi; Chang, Zheng; Cong, Fengyu (Institute of Electrical and Electronics Engineers (IEEE), 2023)The application of intracranial electroencephalogram (iEEG) to predict seizures remains challenging. Although channel selection has been utilized in seizure prediction and detection studies, most of them focus on the ...