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Large-scale spatial join query processing in Cloud

https://doi.org/10.1109/icdew.2015.7129541
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-11538-2_19openalex-snapshot
On Massive Spatial Data Retrieval Based on Spark
inferredcites →https://doi.org/10.1007/978-3-319-53538-8_6openalex-snapshot
Quantitative Approach
inferredcites →https://doi.org/10.1016/j.is.2014.01.005openalex-snapshot
Parallel online spatial and temporal aggregations on multi-core CPUs and many-core GPUs
inferredcites →https://doi.org/10.1145/1206049.1206056openalex-snapshot
Spatial join techniques
inferredcites →https://doi.org/10.1145/1629175.1629198openalex-snapshot
MapReduce
inferredcites →https://doi.org/10.1145/2447481.2447485openalex-snapshot
Speeding up large-scale point-in-polygon test based spatial join on GPUs
inferredcites →https://doi.org/10.1145/2523616.2523629openalex-snapshot
Scale-up vs scale-out for Hadoop
inferredcites →https://doi.org/10.1145/2588555.2594513openalex-snapshot
OceanRT
inferredcites →https://doi.org/10.14778/2536222.2536227openalex-snapshot
Hadoop GIS
inferredcites →https://doi.org/10.14778/2536274.2536283openalex-snapshot
A demonstration of SpatialHadoop
inferredcites →https://doi.org/10.14778/2732951.2732956openalex-snapshot
Efficient in-memory data management
inferredcites →https://doi.org/10.5441/002/edbt.2014.03openalex-snapshot
Optimization Techniques for "Scaling Down" Hadoop on Multi-Core, Shared-Memory Systems

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Hadoop Framework for Querying Network-Constrained Moving-Object Trajectories
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Performance Evaluation of MongoDB and PostgreSQL for Spatio-temporal Data
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OLAP parallel query processing in clouds with C‐ParGRES
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Indexing
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Architectures
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Time-Based Trajectory Data Partitioning for Efficient Range Query
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Big Spatial and Spatio-Temporal Data Analytics Systems
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How good are modern spatial analytics systems?
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High-Performance Spatial Data Analytics: Systematic R&D for Scale-Out and Scale-Up Solutions from the Past to Now
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The Era of Big Spatial Data: A Survey
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Agent-Based versus Data-Streamed Big-Data Parallelization in Computational Geometry
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An Efficient Partitioning Technique in SpatialHadoop
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Using computer clusters for processing spatial-temporal data streams in data acquisition systems
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Simulated Evaluation of Ubiquitous Sensed Situational Awareness Systems
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Map-Matching on Big Data: a Distributed and Efficient Algorithm with a Hidden Markov Model
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Perfomance Evaluation of Java Serialization Frameworks on Geospatial Big Data
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Spatiotemporal Aspects of Big Data
inferred← citeshttps://doi.org/10.32657/10220/47970openalex-snapshot
Distributed systems for spatio-textual data streams
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Towards advanced distributed data processing: framework, optimization, and application
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Effective and efficient topic mining and exploration from geo-textual data
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USE OF DISTRIBUTED DBMS FOR SPATIAL DATA PROCESSING
inferred← citeshttps://doi.org/10.3389/fdata.2020.00014openalex-snapshot
Accelerating Spatial Cross-Matching on CPU-GPU Hybrid Platform With CUDA and OpenACC
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A Resilient Large-Scale Trajectory Index for Cloud-Based Moving Object Applications
inferred← citeshttps://doi.org/10.3390/app9204261openalex-snapshot
Parallel Insertion and Indexing Method for Large Amount of Spatiotemporal Data Using Dynamic Multilevel Grid Technique
inferred← citeshttps://doi.org/10.3390/app9224857openalex-snapshot
An Efficient Parallel Algorithm for Polygons Overlay Analysis
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THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
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Efficient Group K Nearest-Neighbor Spatial Query Processing in Apache Spark
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Real-Time Spatial Queries for Moving Objects Using Storm Topology
inferred← citeshttps://doi.org/10.3390/ijgi6030084openalex-snapshot
Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis
inferred← citeshttps://doi.org/10.3390/ijgi6040096openalex-snapshot
An Effective High-Performance Multiway Spatial Join Algorithm with Spark
inferred← citeshttps://doi.org/10.3390/ijgi6090285openalex-snapshot
GeoSpark SQL: An Effective Framework Enabling Spatial Queries on Spark
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Approach to Accelerating Dissolved Vector Buffer Generation in Distributed In-Memory Cluster Architecture
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Review of Big Data and Processing Frameworks for Disaster Response Applications
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An Efficient Method of Sharing Mass Spatio-Temporal Trajectory Data Based on Cloudera Impala for Traffic Distribution Mapping in an Urban City
inferred← citeshttps://doi.org/10.3390/s21020365openalex-snapshot
A Parallel Computing Approach to Spatial Neighboring Analysis of Large Amounts of Terrain Data Using Spark
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A New Design of High-Performance Large-Scale GIS Computing at a Finer Spatial Granularity: A Case Study of Spatial Join with Spark for Sustainability
inferred← citeshttps://doi.org/10.4018/978-1-7998-1954-7.ch004openalex-snapshot
High-Level Languages for Geospatial Analysis of Big Data
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Applying a Methodology in Data Transmission of Discrete Events From the Perspective of Cyber-Physical Systems Environments
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A Novel Query Method for Spatial Database Based on Improved K-Nearest Neighbor Algorithm
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Vegetation change detection based on time series analysis by Apache Spark and RasterFrame
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A Two-level Spatial In-Memory Index
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The Case for Learned Spatial Indexes
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A SPARK BASED COMPUTING FRAMEWORK FOR SPATIAL DATA
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SIDELOADING – INGESTION OF LARGE POINT CLOUDS INTO THE APACHE SPARK BIG DATA ENGINE
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PARALLEL PROCESSING OF BIG POINT CLOUDS USING Z-ORDER-BASED PARTITIONING
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A Big Spatial Data Processing Framework Applying to National Geographic Conditions Monitoring
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SparkNN: A Distributed In-Memory Data Partitioning for KNN Queries on Big Spatial Data
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SmarT: Machine Learning Approach for Efficient Filtering and Retrieval of Spatial and Temporal Data in Big Data
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A Heterogeneous High Performance Computing Framework For Ill-Structured Spatial Join Processing
inferred← citeshttps://doi.org/10.70675/3bec36a9zadffz4226z90c3zcc2ee6b07495openalex-snapshot
Efficient Query Processing when Spatial Data Meets Rdf Graph
inferred← citeshttps://doi.org/10.70675/a470dc02z791cz49e9z9d9fza71176c6f82bopenalex-snapshot
Un modèle spatio-temporel hybride de SIG temporel : application à la géomorphologie marine
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