Bashyam et al., 2020 - Google Patents
Fast scalable approximate nearest neighbor search for high-dimensional dataBashyam et al., 2020
- Document ID
- 1191918044556121897
- Author
- Bashyam K
- Vadhiyar S
- Publication year
- Publication venue
- 2020 IEEE International Conference on Cluster Computing (CLUSTER)
External Links
Snippet
K-Nearest Neighbor (k-NN) search is one of the most commonly used approaches for similarity search. It finds extensive applications in machine learning and data mining. This era of big data warrants efficiently scaling k-NN search algorithms for billion-scale datasets …
- 238000000034 method 0 abstract description 85
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30386—Retrieval requests
- G06F17/30424—Query processing
- G06F17/30442—Query optimisation
- G06F17/30445—Query optimisation for parallel queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30386—Retrieval requests
- G06F17/30424—Query processing
- G06F17/30533—Other types of queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30312—Storage and indexing structures; Management thereof
- G06F17/30321—Indexing structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30943—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type
- G06F17/30946—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type indexing structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30587—Details of specialised database models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30575—Replication, distribution or synchronisation of data between databases or within a distributed database; Distributed database system architectures therefor
- G06F17/30584—Details of data partitioning, e.g. horizontal or vertical partitioning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Spann: Highly-efficient billion-scale approximate nearest neighborhood search | |
| Munoz et al. | Hierarchical clustering-based graphs for large scale approximate nearest neighbor search | |
| Pan et al. | Fast GPU-based locality sensitive hashing for k-nearest neighbor computation | |
| US8380643B2 (en) | Searching multi-dimensional data using a parallelization framework comprising data partitioning and short-cutting via early out | |
| Feydy et al. | Fast geometric learning with symbolic matrices | |
| Sismanis et al. | Parallel search of k-nearest neighbors with synchronous operations | |
| Gupta et al. | Bliss: A billion scale index using iterative re-partitioning | |
| Abuzaid et al. | To index or not to index: Optimizing exact maximum inner product search | |
| Bashyam et al. | Fast scalable approximate nearest neighbor search for high-dimensional data | |
| Chatzakis et al. | Odyssey: A journey in the land of distributed data series similarity search | |
| Danopoulos et al. | Fpga acceleration of approximate knn indexing on high-dimensional vectors | |
| Wei et al. | Subspace collision: An efficient and accurate framework for high-dimensional approximate nearest neighbor search | |
| Pan et al. | G-slide: A gpu-based sub-linear deep learning engine via lsh sparsification | |
| Moutafis et al. | Algorithms for processing the group K nearest-neighbor query on distributed frameworks | |
| Gowanlock | KNN-joins using a hybrid approach: exploiting CPU/GPU workload characteristics | |
| Hyvönen et al. | Fast k-nn search | |
| Yagoubi et al. | Radiussketch: massively distributed indexing of time series | |
| Pineau et al. | Efficient and scalable cross-matching of (very) large catalogs | |
| Uribe-Paredes et al. | Similarity search implementations for multi-core and many-core processors | |
| Teixeira et al. | Scalable locality-sensitive hashing for similarity search in high-dimensional, large-scale multimedia datasets | |
| Lin et al. | Toward efficient spmv in sparse llms via block extraction and compressed storage | |
| Wasif et al. | Scalable clustering using multiple GPUs | |
| García-García et al. | MRSLICE: efficient rknn query processing in spatialhadoop | |
| Andrade et al. | Scalable and efficient spatial-aware parallelization strategies for multimedia retrieval | |
| Donnelly et al. | Multi-Space Tree with Incremental Construction for GPU-Accelerated Range Queries |