'Range tree' presentation slideshows

Computational Geometry Chapter 12

Computational Geometry Chapter 12. employee. age salary start date city address. Motivation Range Trees. e.g. Database Database Record Suppose you want to know all employees with Salary ? [40K, 50K] ? Scan records & pick out those in range *slow*. employee. age salary

905 views • 77 slides

Approximating the Depth via Sampling and Emptiness

Approximating the Depth via Sampling and Emptiness. Approximating the Depth via Sampling and Emptiness. Approximating the Depth via Sampling and Emptiness. Approximating the Depth via Sampling and Emptiness. Example: Range tree. S = Set of points in the plane r = Query Rectangle.

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Computational Geometry Chapter 12

Computational Geometry Chapter 12. Range queries. How do you efficiently find points that are inside of a rectangle? Orthogonal range query ([ x 1 , x 2 ], [ y 1 ,y 2 ]): find all points ( x, y ) such that x 1 <x<x 2 and y 1 <y<y 2 Useful also as a multi-attribute database query. y. y 2.

634 views • 52 slides

Range Search Algorithms

Range Search Algorithms. Shmuel Wimer Bar Ilan Univ. Eng. Faculty Technion, EE Faculty. VLSI designer is drawing schematics or designing layout. He / she wishes to focus on a small portion of data by zooming in. How to find efficiently the objects of interest?. How to store the objects?.

456 views • 38 slides

Orthogonal Range Searching I

Orthogonal Range Searching I. Range Trees. Range Searching. S = set of geometric objects Q = query object Report/Count objects in S that intersect Q. Query Q. Report/Count answers. Single-shot Vs Repeatitive. Query may be: Single-shot (one-time). No need to preprocess

534 views • 38 slides

The Buffer Tree

The Buffer Tree. Lars Arge. Presented by Or Ozery. I/O Model. Previously defined: N = # of elements in input M = # of elements that fit into memory B = # of elements per block Measuring in terms of # of blocks: n = N / B m = M / B. I/O Model vs. RAM Model.

551 views • 37 slides

Finding Objects

Finding Objects. Shmuel Wimer Bar Ilan Univ., School of Engineering. Here is a floor (can also be space). Objects are located in the room. The Problem. What objects located here?. Need efficient way to store objects and apply queries. Outline. 1D range search 2D Kd-Tree 2D Range Tree

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Special Cases of the Hidden Line Elimination Problem

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Special Cases of the Hidden Line Elimination Problem

418 views • 36 slides

Multidimensional Range Search

Multidimensional Range Search. Static collection of records. No inserts, deletes, changes. Only queries. Each record has k key fields. Multidimensional query. Given k ranges [ l i , u i ] , 1 <= i <= k . Report all records in collection such that l i <= k i <= u i , 1 <= i <= k .

404 views • 30 slides

COSC 6114

COSC 6114. Prof. Andy Mirzaian. More Geometric Data Structures: Windowing. References :. [M. de Berge et al] chapter 10. Data Structures: Interval Trees Priority Search Trees Segment Trees. Applications : Windowing Queries Vehicle navigation systems

389 views • 27 slides

Orthogonal Range Searching

Orthogonal Range Searching. Reading: Chapter 5 of the Textbook Driving Applications Querying a Database Related Application Crystal Structure Determination. Interprete DB Queries Geometrically. Transform records in database into points in multi-dimensional space.

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Generalized Index-Set Splitting

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Fractional Cascading and Its Applications

Fractional Cascading and Its Applications. G. S. Lueker. A data structure for orthogonal range queries. In Proc. 19 th annu. IEEE Sympos. Found. Comput. Sci., pages 28-34, 1978.

409 views • 18 slides

Fractional Cascading and Its Applications

Fractional Cascading and Its Applications. G. S. Lueker. A data structure for orthogonal range queries. In Proc. 19 th annu. IEEE Sympos. Found. Comput. Sci., pages 28-34, 1978.

201 views • 18 slides

Orthogonal Range Searching

Orthogonal Range Searching. Linear Range Search : 1-dim Range Trees 2-dimensional Range Search : kd-trees 2-dimensional Range Search : 2-dim Range Trees Range Search in Higher Dimensions. A ssumption : no two points have the same x or y coordinates. y. x. Range Trees.

317 views • 13 slides