[go: up one dir, main page]

Arfat et al., 2017 - Google Patents

Parallel shortest path graph computations of united states road network data on apache spark

Arfat et al., 2017

Document ID
6732891736841810525
Author
Arfat Y
Mehmood R
Albeshri A
Publication year
Publication venue
International Conference on Smart Cities, Infrastructure, Technologies and Applications

External Links

Snippet

Big data is being generated from various sources such as Internet of Things (IoT) and social media. Big data cannot be processed by traditional tools and technologies due to their properties, volume, velocity, veracity, and variety. Graphs are becoming increasingly popular …
Continue reading at link.springer.com (other versions)

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
    • G06F17/30533—Other types of queries
    • 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
    • G06F9/5066—Algorithms for mapping a plurality of inter-dependent sub-tasks onto a plurality of physical CPUs
    • 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
    • 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]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • 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
    • G06F17/30861—Retrieval from the Internet, e.g. browsers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a programme unit and a register, e.g. for a simultaneous processing of several programmes
    • G06F15/163—Interprocessor communication
    • G06F15/173—Interprocessor communication using an interconnection network, e.g. matrix, shuffle, pyramid, star, snowflake
    • 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
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/40—Transformations of program code
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management

Similar Documents

Publication Publication Date Title
Arfat et al. Parallel shortest path graph computations of united states road network data on apache spark
Dafir et al. A survey on parallel clustering algorithms for big data
US9053067B2 (en) Distributed data scalable adaptive map-reduce framework
Xia et al. A high-performance cellular automata model for urban simulation based on vectorization and parallel computing technology
Usman et al. ZAKI: A smart method and tool for automatic performance optimization of parallel SpMV computations on distributed memory machines
Simuni Batch Processing with Hadoop MapReduce: A Performance and Scalability Study
Arfat et al. Parallel shortest path big data graph computations of US road network using apache spark: survey, architecture, and evaluation
Bakhthemmat et al. Decreasing the execution time of reducers by revising clustering based on the futuristic greedy approach
Zhang et al. MapReduce-based distributed tensor clustering algorithm
Vennila et al. Symmetric Matrix-based Predictive Classifier for Big Data computation and information sharing in Cloud
Nguyen et al. An efficient and scalable approach for mining subgraphs in a single large graph
Cheng et al. A big graph clustering method to support parallel processing by perceiving graph’s application algorithm semantics
Adoni et al. MRA*: Parallel and distributed path in large-scale graph using mapReduce-A* based approach
Sarnovsky et al. Distributed algorithm for text documents clustering based on k-means approach
Bendechache et al. Performance evaluation of a distributed clustering approach for spatial datasets
El Handri et al. Top kws algorithm in the map-reduce paradigm for cloud computing qos recommendation system
Shehab et al. Big data analytics concepts, technologies challenges, and opportunities
Arfat et al. Parallel Shortest Path Graph Computations
Gupta et al. Evaluation of MapReduce-based distributed parallel machine learning algorithms
Saketh et al. Spark-based scalable algorithm for link prediction
Luckow et al. Data infrastructure for connected transport systems
Zhao et al. Finding and counting tree-like subgraphs using MapReduce
Lin et al. A parallel and scalable CAST-based clustering algorithm on GPU
Talan et al. An overview of Hadoop MapReduce, spark, and scalable graph processing architecture
Wu et al. ACO-DPDGW: an ant colony optimization algorithm for data placement of data-intensive geospatial workflow