Monotone Descent Path Queries on Dynamic Terrains

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Xiangzhi Wei ◽  
Ajay Joneja ◽  
Yaobin Tian ◽  
Yan-An Yao
Keyword(s):  
Data Structure ◽  
Engineering Design ◽  
Linear Time ◽  
Monotone Path ◽  
Path Queries ◽  
Contour Tree ◽  
Monotone Paths

Monotone paths are useful in many engineering design applications. In this paper, we address the problem of answering monotone descent path queries on terrains that are continually changing. A terrain can be represented by a unique contour tree. Such a contour tree belongs to a class of graphs called arbitrarily directed trees (ADTs). Let T be an ADT with n nodes. In this paper, we present a new linear time preprocessing algorithm for decomposing a static ADT T into a forest F, with which we can answer lowest common descendent (LCA) queries in O(1) time. This is useful in answering monotone path queries on the corresponding terrain. We show how to maintain this data structure, and thereby answer LCA queries efficiently, for dynamic ADTs. We also show how to maintain the data structure of dynamic terrains, while simultaneously maintaining the corresponding contour tree. This allows us to efficiently answer monotone path queries between any two points on dynamic terrains.

AN OPTIMAL DATA STRUCTURE FOR SHORTEST RECTILINEAR PATH QUERIES IN A SIMPLE RECTILINEAR POLYGON

1996 ◽  
Vol 06 (02) ◽  
pp. 205-225 ◽  
Author(s):  
SVEN SCHUIERER
Keyword(s):  
Data Structure ◽  
Shortest Path ◽  
Linear Time ◽  
Time Algorithm ◽  
Query Time ◽  
Linear Time Algorithm ◽  
Link Length ◽  
N Storage ◽  
Path Queries ◽  
Rectilinear Polygon

We present a data structure that allows to preprocess a rectilinear polygon with n vertices such that, for any two query points, the shortest path in the rectilinear link or L1-metric can be reported in time O( log n+k) where k is the link length of the shortest path. If only the distance is of interest, the query time reduces to O( log n). Furthermore, if the query points are two vertices, the distance can be reported in time O(1) and a shortest path can be constructed in time O(1+k). The data structure can be computed in time O(n) and needs O(n) storage. As an application we present a linear time algorithm to compute the diameter of a simple rectilinear polygon w.r.t. the L1-metric.

LOGARITHMIC-TIME LINK PATH QUERIES IN A SIMPLE POLYGON

1995 ◽  
Vol 05 (04) ◽  
pp. 369-395 ◽  
Author(s):  
ESTHER M. ARKIN ◽  
JOSEPH S.B. MITCHELL ◽  
SUBHASH SURI
Keyword(s):  
Data Structure ◽  
Shortest Path ◽  
Simple Polygon ◽  
Additive Constant ◽  
Time And Space ◽  
Link Distance ◽  
Path Distance ◽  
Path Queries ◽  
Shortest Path Distance ◽  
Small Additive

We develop a data structure for answering link distance queries between two arbitrary points in a simple polygon. The data structure requires O(n3) time and space for its construction and answers link distance queries in O(log n) time, after which a minimum-link path can be reported in time proportional to the number of links. Here, n denotes the number of vertices of the polygon. Our result extends to link distance queries between pairs of segments or polygons. We also propose a simpler data structure for computing a link distance approximately, where the error is bounded by a small additive constant. Finally, we also present a scheme for approximating the link and the shortest path distance simultaneously.

scholarly journals Fast Fréchet Distance Between Curves with Long Edges

2019 ◽  
Vol 29 (02) ◽  
pp. 161-187
Author(s):  
Joachim Gudmundsson ◽  
Majid Mirzanezhad ◽  
Ali Mohades ◽  
Carola Wenk
Keyword(s):  
Data Structure ◽  
Approximation Algorithm ◽  
Special Class ◽  
Linear Time ◽  
Time Algorithm ◽  
Linear Time Algorithm ◽  
Fréchet Distance ◽  
Frechet Distance ◽  
Polygonal Curves

Computing the Fréchet distance between two polygonal curves takes roughly quadratic time. In this paper, we show that for a special class of curves the Fréchet distance computations become easier. Let [Formula: see text] and [Formula: see text] be two polygonal curves in [Formula: see text] with [Formula: see text] and [Formula: see text] vertices, respectively. We prove four results for the case when all edges of both curves are long compared to the Fréchet distance between them: (1) a linear-time algorithm for deciding the Fréchet distance between two curves, (2) an algorithm that computes the Fréchet distance in [Formula: see text] time, (3) a linear-time [Formula: see text]-approximation algorithm, and (4) a data structure that supports [Formula: see text]-time decision queries, where [Formula: see text] is the number of vertices of the query curve and [Formula: see text] the number of vertices of the preprocessed curve.

scholarly journals d-PBWT: dynamic positional Burrows-Wheeler transform

2020 ◽  
Author(s):  
Ahsan Sanaullah ◽  
Degui Zhi ◽  
Shaojie Zhang
Keyword(s):  
Data Structure ◽  
Time Complexity ◽  
Linear Time ◽  
Genotype Imputation ◽  
Worst Case ◽  
Average Case ◽  
Insertion And Deletion ◽  
Static Data ◽  
Efficient Retrieval ◽  
Burrows Wheeler Transform

AbstractDurbin’s PBWT, a scalable data structure for haplotype matching, has been successfully applied to identical by descent (IBD) segment identification and genotype imputation. Once the PBWT of a haplotype panel is constructed, it supports efficient retrieval of all shared long segments among all individuals (long matches) and efficient query between an external haplotype and the panel. However, the standard PBWT is an array-based static data structure and does not support dynamic updates of the panel. Here, we generalize the static PBWT to a dynamic data structure, d-PBWT, where the reverse prefix sorting at each position is represented by linked lists. We developed efficient algorithms for insertion and deletion of individual haplotypes. In addition, we verified that d-PBWT can support all algorithms of PBWT. In doing so, we systematically investigated variations of set maximal match and long match query algorithms: while they all have average case time complexity independent of database size, they have different worst case complexities, linear time complexity with the size of the genome, and dependency on additional data structures.

scholarly journals Group Degree Centrality and Centralization in Networks

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1810
Author(s):  
Matjaž Krnc ◽  
Riste Škrekovski
Keyword(s):  
Data Structure ◽  
Approximation Algorithm ◽  
Linear Time ◽  
Centrality Measures ◽  
Degree Centrality ◽  
Central Group ◽  
Fast Running ◽  
Or Groups ◽  
Optimal Value ◽  
The One

The importance of individuals and groups in networks is modeled by various centrality measures. Additionally, Freeman’s centralization is a way to normalize any given centrality or group centrality measure, which enables us to compare individuals or groups from different networks. In this paper, we focus on degree-based measures of group centrality and centralization. We address the following related questions: For a fixed k, which k-subset S of members of G represents the most central group? Among all possible values of k, which is the one for which the corresponding set S is most central? How can we efficiently compute both k and S? To answer these questions, we relate with the well-studied areas of domination and set covers. Using this, we first observe that determining S from the first question is NP-hard. Then, we describe a greedy approximation algorithm which computes centrality values over all group sizes k from 1 to n in linear time, and achieve a group degree centrality value of at least (1−1/e)(w*−k), compared to the optimal value of w*. To achieve fast running time, we design a special data structure based on the related directed graph, which we believe is of independent interest.

scholarly journals Compressing Exact Cover Problems with Zero-suppressed Binary Decision Diagrams

2021 ◽  
Author(s):  
Masaaki Nishino ◽  
Norihito Yasuda ◽  
Kengo Nakamura
Keyword(s):  
Data Structure ◽  
State Of The Art ◽  
Linear Time ◽  
Binary Decision Diagrams ◽  
Experimental Results ◽  
Decision Diagrams ◽  
Binary Decision ◽  
Running Time ◽  
Order Of Magnitude ◽  
Exact Cover

Exact cover refers to the problem of finding subfamily F of a given family of sets S whose universe is D, where F forms a partition of D. Knuth’s Algorithm DLX is a state-of-the-art method for solving exact cover problems. Since DLX’s running time depends on the cardinality of input S, it can be slow if S is large. Our proposal can improve DLX by exploiting a novel data structure, DanceDD, which extends the zero-suppressed binary decision diagram (ZDD) by adding links to enable efficient modifications of the data structure. With DanceDD, we can represent S in a compressed way and perform search in linear time with the size of the structure by using link operations. The experimental results show that our method is an order of magnitude faster when the problem is highly compressed.

scholarly journals SHORTEST PATH QUERIES IN RECTILINEAR WORLDS

1992 ◽  
Vol 02 (03) ◽  
pp. 287-309 ◽  
Author(s):  
MARK DE BERG ◽  
MARC VAN KREVELD ◽  
BENGT J. NILSSON ◽  
MARK OVERMARS
Keyword(s):  
Data Structure ◽  
Shortest Path ◽  
Simple Solution ◽  
Single Shot ◽  
Fixed Target ◽  
Higher Dimensional ◽  
Axis Parallel ◽  
Fixed Constant ◽  
Path Queries

In this paper, a data structure is given for two and higher dimensional shortest path queries. For a set of n axis-parallel rectangles in the plane, or boxes in d-space, and a fixed target, it is possible with this structure to find a shortest rectilinear path avoiding all rectangles or boxes from any point to this target. Alternatively, it is possible to find the length of the path. The metric considered is a generalization of the L1-metric and the link metric, where the length of a path is its L1-length plus some (fixed) constant times the number of turns on the path. The data structure has size O((n log n)d−1), and a query takes O( log d−1 n) time (plus the output size if the path must be reported). As a byproduct, a relatively simple solution to the single shot problem is obtained; the shortest path between two given points can be computed in time O(nd log n) for d≥3, and in time O(n2) in the plane.

scholarly journals Optimal Shortest Path and Minimum-Link Path Queries between Two Convex Polygons inside a Simple Polygonal Obstacle

1997 ◽  
Vol 07 (01n02) ◽  
pp. 85-121 ◽  
Author(s):  
Yi-Jen Chiang ◽  
Roberto Tamassia
Keyword(s):  
Data Structure ◽  
Shortest Path ◽  
Dynamic Environment ◽  
Simple Polygon ◽  
Efficient Algorithms ◽  
Optimal Time ◽  
Convex Polygons ◽  
Separation Problem ◽  
Dynamic Case ◽  
Path Queries

We present efficient algorithms for shortest-path and minimum-link-path queries between two convex polygons inside a simple polygon P, which acts as an obstacle to be avoided. Let n be the number of vertices of P, and h the total number of vertices of the query polygons. We show that shortest-path queries can be performed optimally in time O( log h + log n) (plus O(k) time for reporting the k edges of the path) using a data structure with O(n) space and preprocessing time, and that minimum-link-path queries can be performed in optimal time O( log h + log n) (plus O(k) to report the k links), with O(n3) space and preprocessing time. We also extend our results to the dynamic case, and give a unified data structure that supports both queries for convex polygons in the same region of a connected planar subdivision [Formula: see text]. The update operations consist of insertions and deletions of edges and vertices. Let n be the current number of vertices in [Formula: see text]. The data structure uses O(n) space, supports updates in O( log 2 n) time, and performs shortest-path and minimum-link-path queries in times O( log h+ log 2n) (plus O(k) to report the k edges of the path) and O( log h + k log 2 n), respectively. Performing shortest-path queries is a variation of the well-studied separation problem, which has not been efficiently solved before in the presence of obstacles. Also, it was not previously known how to perform minimum-link-path queries in a dynamic environment, even for two-point queries.

scholarly journals An external memory data structure for shortest path queries

2003 ◽  
Vol 126 (1) ◽  
pp. 55-82 ◽  
Author(s):  
David Hutchinson ◽  
Anil Maheshwari ◽  
Norbert Zeh
Keyword(s):  
Data Structure ◽  
Shortest Path ◽  
External Memory ◽  
Path Queries
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