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  • Spatial aggregation: Data model and implementation ,
    Spatial aggregation: Data model and implementation ,

    Data aggregation in Geographic Information Systems (GIS) is a desirable feature, only marginally present in commercial systems nowadays, mostly through ad hoc solutions We address this problem introducing a formal model that integrates, in a natural way, geographic data and non-spatial information contained in a data warehouse external to the GIS

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  • Flow Accumulation (Spatial Analyst)—ArcGIS Pro | Documentation
    Flow Accumulation (Spatial Analyst)—ArcGIS Pro | Documentation

    If the input data is smaller than 5,000 by 5,000 cells in size, fewer cores might be used You can control the number of cores the tool uses with the Parallel processing factor environment See Analysis environments and Spatial Analyst for additional details on the ,

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  • range aggregate processing spatial databases
    range aggregate processing spatial databases

    range range aggregate processing spatial da as Range aggregate processing in spatial databases Article (PDF Available) in IEEE Transactions on Knowledge and Data Engineering 16(12):1555- 1570 January Range Aggregate Processing in Spatial Databas

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  • r - Spatial aggregation with a group by - Stack Overflow
    r - Spatial aggregation with a group by - Stack Overflow

    Sep 17, 2015· by [email protected] 4 000 points ha−1) in agriculture raises new questions as to how to represent this spatial information The objective of this study was to propose a methodology to help define the optimal grid size to map high resolution data in agriculture

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  • Performance tuning - Event streaming with Azure Functions ,
    Performance tuning - Event streaming with Azure Functions ,

    Aug 27, 2019· These processing requirements are simple enough that they don't require a full-fledged stream processing engine In particular, the processing doesn't join streams, aggregate data, or process across time windows Based on these requirements, Azure Functions is a good fit for processing the messag

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  • ACM SIGSPATIAL GIS 2012
    ACM SIGSPATIAL GIS 2012

    May 01, 2012· International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS 2012) November 6-9 2012 — Redondo Beach, California

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  • Spatial distribution of environmental DNA in a nearshore ,
    Spatial distribution of environmental DNA in a nearshore ,

    Nov 28, 2016· We calculated two measures of diversity, richness and evenness, to ask if aggregate metrics of the eDNA community showed evidence of spatial patterning Richness is a measure of the number of distinct types of organisms present and so ranges from 1 (only one taxon observed) to S , the number of taxa observed across all sampl

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  • VITAL
    VITAL

    VITAL is the shared services arm of the Singapore public service serving more than 90,000 public officers over a wide range of corporate and administrative processes ,

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  • The role of particle morphology on concrete fracture ,
    The role of particle morphology on concrete fracture ,

    Aug 01, 2020· A domain composed of 200 Voronoi cells is generated, of which the parental particles are two kinds of real sands, one coarse sand (MA-1117) and one fine sand (MA-106A) in FigA1 of Appendix A Fig 1 illustrates the morphological features of the real and virtual particles, of which shape indices of virtual particulates are compared with their corresponding virtual samples in Fig 2

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  • N94-18350
    N94-18350

    The outer plexiform layer handles spatial processing and dynamic range adjustments, while the inner plexiform layer is involved in , Processing throughout the model is performed on analog da_a, , to the _use _ t-llis Simple, aggregate err0r Signal: (More complex and computationally

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  • How to define the optimal grid size to map high resolution ,
    How to define the optimal grid size to map high resolution ,

    Mar 10, 2018· The development and the release of sensors capable of providing data with high spatial resolution (>4 000 points ha−1) in agriculture raises new questions as to how to represent this spatial information The objective of this study was to propose a methodology to help define the optimal grid size to map high resolution data in agriculture

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  • Benchmarking Spatial Data Warehouses
    Benchmarking Spatial Data Warehouses

    In this paper, we propose a novel spatial data warehouse benchmark, called Spadawan, to address the query processing performance on spatial roll-up and drill-down operations using predefined spatial hierarchies over SDW As spatial predicates, the Spadawan benchmark focuses on intersection, containment and enclosure range queri

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  • aggregate processing and properties
    aggregate processing and properties

    Energy consumption analysis for natural aggregate processing and its results (Atabey, Isparta, Turkey) Article (PDF Available) September 2018 , Mecha range range aggregate processing spatial da ases

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  • Spatial aggregation: Data model and implementation ,
    Spatial aggregation: Data model and implementation ,

    Sep 01, 2009· We assume that non-spatial data are stored in data warehouses , created and maintained separately from the GIS, a so-called loosely coupled approach We formally define the notion of geometric aggregation that characterizes a wide range of aggregate queries over regions defined as semi-algebraic sets We show that our proposal supports .

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  • Research on Novel Method for Forecasting Aggregate Queries ,
    Research on Novel Method for Forecasting Aggregate Queries ,

    Cite this article: FENG Jun,LU Chunyan Research on Novel Method for Forecasting Aggregate Queries over Data Streams in Road Networks*[J] , 2010, 4(11): 1027-1038

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  • Approximate Query Processing: What is New and Where to Go ,
    Approximate Query Processing: What is New and Where to Go ,

    Sep 14, 2018· Online analytical processing (OLAP) is a core functionality in database systems The performance of OLAP is crucial to make online decisions in many applications However, it is rather costly to support OLAP on large datasets, especially big data, and the methods that compute exact answers cannot meet the high-performance requirement To alleviate this problem, approximate query processing .

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  • Thesis - RIT
    Thesis - RIT

    Figure 29: Spatial processing A new spectral band subset was chosen The band selection from code defaults was changed to better match the atmospheric absorption for this particular scene, as shown in the figure below Previously the entire 210-band image was given to the unmixing routines and bad bands were blanked out during processing

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  • Predicting the hotspots of age-adjusted mortality rates of ,
    Predicting the hotspots of age-adjusted mortality rates of ,

    Oct 01, 2020· A significant value of G indicates spatial clustering of LRI mortality rat Both Moran’s I and Getis-Ord General G statistics were calculated in ArcGIS 107 Local measures of spatial autocorrelation such as Getis-ord G i * also were applied to locate the identified spatial autocorrelations of LRI mortality rates (P < 005) as follows [27,28]

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  • Spatial patterns of CTCF sites define the anatomy of TADs ,
    Spatial patterns of CTCF sites define the anatomy of TADs ,

    Aug 12, 2020· Finally, ignoring the top 25% of CTCF sites largely preserved the depletion of short-range (10 3 to 10 5 bp) convergent patterns, indicating that CTCF clusters made of the lowest three quartiles of CTCF sites still follow the same global spatial distribution imbalances as those made with the 25% top-ranking CTCF binding sites (compare .

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  • Exploratory Spatial Analysis of in vitro Respiratory ,
    Exploratory Spatial Analysis of in vitro Respiratory ,

    Dec 22, 2010· where A is any subset, E [] is an expected value, 1 {} is an indicator function of an event, d(, ) is a distance between two points, and r is a radius Intuitively, K(r) captures the spatial accumulation of points in neighborhoods of increasing radiusMore precisely, λK(r) is the expected number of points in a circle of radius r around a “typical” point of the process

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  • Private Spatial Data Aggregation in the Local Setting
    Private Spatial Data Aggregation in the Local Setting

    Private Spatial Data Aggregation Differentially private s-patial data aggregation has been previously studied in the centralized setting Cormode et al [5] design differentially pri-vate spatial decomposition techniques to generate the number of data points within each spatial region in order to answer range queries over arbitrary query regions

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  • (PDF) Range aggregate processing in spatial databases
    (PDF) Range aggregate processing in spatial databases

    A range aggregate query returns summarized information about the points falling in a hyper-rectangle (eg, the total number of these points instead of their concrete ids)

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  • Stimulus Selectivity and Spatial Coherence of Gamma ,
    Stimulus Selectivity and Spatial Coherence of Gamma ,

    Jun 22, 2011· The spatial extent and functional specificity of gamma are critical constraints on the role it may play in cortical processing To function as a global reference signal (eg, an internal clock) ( Hopfield, 2004 ; Fries et al, 2007 ), gamma would need to form a widespread, coherent rhythm, potentially shared among neuronal ensembles with .

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  • Root-mean-square deviation - Wikipedia
    Root-mean-square deviation - Wikipedia

    Formula The RMSD of an estimator ^ with respect to an estimated parameter is defined as the square root of the mean square error: ⁡ (^) = ⁡ (^) = ⁡ ((^)) For an unbiased estimator, the RMSD is the square root of the variance, known as the standard deviation The RMSD of predicted values ^ for times t of a regression's dependent variable, with variables observed over T times, is .

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  • range aggregate processing in spatial databases
    range aggregate processing in spatial databases

    Range aggregate processing in spatial databases IEEE Nov 01 2004 Abstract A range aggregate query returns summarized information about the points falling in a hyperrectangle eg the total number of these points instead of their concrete ids This paper studies spatial indexes that solve such queries efficiently and proposes the aggregate Pointtree aPtree which achieves logarithmic cost to .

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  • Storingand Clustering LargeSpatial Datasets UsingBigData ,
    Storingand Clustering LargeSpatial Datasets UsingBigData ,

    the last years to support horizontal scaling and distributed processing [7,2,8,5] Therefore, storing and quering large geographic datasets can be achieved How-ever, middleware software such as map servers and visualization software such as javascript-based map viewers are not addapted to large datasets and distributed processing

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  • Spatial Aggregate Functions
    Spatial Aggregate Functions

    Each input geometry must be a two-dimensional line or multiline geometry (that is, the SDO_GTYPE value must be 2002 or 2006) This function is not supported for LRS geometri To perform an aggregate concatenation of LRS geometric segments, use the SDO_AGGR_LRS_CONCAT spatial aggregate function

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  • Axonal transport of APP and the spatial regulation of APP ,
    Axonal transport of APP and the spatial regulation of APP ,

    Sep 30, 2011· Proteolytic processing of APP by secretases and the spatial arrangement of APP fragments in neurons (not drawn in proportion) a Schematic depiction of APP cleavage by secretases and the main cleavage fragments derived from APP processing α-secretases cleave APP at a site within the Aβ peptide sequence, thus precluding cleavage by β-secretase and formation of Aβ peptid

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  • Evaluation of pavement surface drainage using an automated ,
    Evaluation of pavement surface drainage using an automated ,

    Feb 01, 2018· The aggregate imaging system (AIMS) introduced by Masad et al is one of the recent methods for measuring the aggregate texture directly using a microscope and a digital image processing technique This method is an important development in texture measurement methods as it allows measuring physical characteristics of the aggregate

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  • Approximate range query processing in spatial network ,
    Approximate range query processing in spatial network ,

    Jul 20, 2012· Spatial range query is one of the most common queries in spatial databases, where a user invokes a query to find all the surrounding interest objects Most studies in range search consider Euclidean distances to retrieve the result in low cost, but with poor accuracy (ie, Euclidean distance less than or equal network distance) Thus, researchers show that range search in network distance .

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