Shangguan et al., 2018 - Google Patents
SPARK processing of computing-intensive classification of remote sensing images: The case on K-means clustering algorithmShangguan et al., 2018
- Document ID
- 18033984782025672864
- Author
- Shangguan B
- Yue P
- Publication year
- Publication venue
- 2018 26th International Conference on Geoinformatics
External Links
Snippet
High performance processing of remote sensing images is an important topic in remote sensing applications. One typical type of remote sensing processing is the iterative computing algorithms such as image classification algorithms, which are often computing …
- 238000004422 calculation algorithm 0 title abstract description 12
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
- G06F17/30312—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
- G06F17/30286—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
- G06F17/30289—Database design, administration or maintenance
-
- 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
-
- 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
- G06F17/30067—File systems; File servers
-
- 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
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- 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
- G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
-
- 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
- G06F17/10—Complex mathematical operations
-
- 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
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US12099906B2 (en) | Parallel development and deployment for machine learning models | |
| Eldawy et al. | A demonstration of spatialhadoop: An efficient mapreduce framework for spatial data | |
| Perez et al. | Ringo: Interactive graph analytics on big-memory machines | |
| Arnaiz-González et al. | MR-DIS: democratic instance selection for big data by MapReduce | |
| Ying et al. | Accelerating the image processing by the optimization strategy for deep learning algorithm DBN | |
| Shangguan et al. | SPARK processing of computing-intensive classification of remote sensing images: The case on K-means clustering algorithm | |
| Lin et al. | A hybrid recommendation algorithm based on hadoop | |
| Underwood et al. | Evostore: Towards scalable storage of evolving learning models | |
| He et al. | Parallel outlier detection using kd-tree based on mapreduce | |
| Wang et al. | A Hadoop-based distributed framework for efficient managing and processing big remote sensing images | |
| Perwej et al. | An extensive investigate the mapreduce technology | |
| Ketu et al. | Performance enhancement of distributed K-Means clustering for big Data analytics through in-memory computation | |
| Schoeneman et al. | Solving all-pairs shortest-paths problem in large graphs using Apache Spark | |
| Pan et al. | A remote sensing image cloud processing system based on Hadoop | |
| US10339107B2 (en) | Multi-level colocation and processing of spatial data on MapReduce | |
| Xiong et al. | HiGIS: An open framework for high performance geographic information system | |
| Bousbaci et al. | A parallel sampling-pso-multi-core-k-means algorithm using mapreduce | |
| Jha et al. | Fuzzy-based kernelized clustering algorithms for handling big data using apache spark | |
| Fu et al. | DiscFinder: A data-intensive scalable cluster finder for astrophysics | |
| Ma et al. | Live data migration approach from relational tables to schema-free collections with MapReduce | |
| Liu et al. | A distributed and parallel anomaly detection in hyperspectral images based on low-rank and sparse representation | |
| Bhardwaj et al. | BDT3V—A Technique for big data testing considering 3V’s | |
| Lee et al. | A hadoop-based output analyzer for large-scale simulation data | |
| Fonał et al. | Distributed nonnegative matrix factorization with HALS algorithm on apache spark | |
| Khan et al. | Computational performance analysis of cluster-based technologies for big data analytics |