scholarly journals FASTSET: A Fast Data Structure for the Representation of Sets of Integers

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 91
Author(s):  
Giuseppe Lancia ◽  
Marcello Dalpasso
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Optimal Time ◽  
Time Performance ◽  
Set Operations

We describe a simple data structure for storing subsets of { 0 , … , N − 1 } , with N a given integer, which has optimal time performance for all the main set operations, whereas previous data structures are non-optimal for at least one such operation. We report on the comparison of a Java implementation of our structure with other structures of the standard Java Collections.

scholarly journals DenseZDD: A Compact and Fast Index for Families of Sets

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 128 ◽  
Author(s):  
Shuhei Denzumi ◽  
Jun Kawahara ◽  
Koji Tsuda ◽  
Hiroki Arimura ◽  
Shin-ichi Minato ◽  
...  
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Information Integration ◽  
Decision Diagrams ◽  
Web Information Retrieval ◽  
Binary Decision ◽  
Web Information ◽  
Set Operations ◽  
Succinct Data Structure ◽  
The Family

In this article, we propose a succinct data structure of zero-suppressed binary decision diagrams (ZDDs). A ZDD represents sets of combinations efficiently and we can perform various set operations on the ZDD without explicitly extracting combinations. Thanks to these features, ZDDs have been applied to web information retrieval, information integration, and data mining. However, to support rich manipulation of sets of combinations and update ZDDs in the future, ZDDs need too much space, which means that there is still room to be compressed. The paper introduces a new succinct data structure, called DenseZDD, for further compressing a ZDD when we do not need to conduct set operations on the ZDD but want to examine whether a given set is included in the family represented by the ZDD, and count the number of elements in the family. We also propose a hybrid method, which combines DenseZDDs with ordinary ZDDs. By numerical experiments, we show that the sizes of our data structures are three times smaller than those of ordinary ZDDs, and membership operations and random sampling on DenseZDDs are about ten times and three times faster than those on ordinary ZDDs for some datasets, respectively.

scholarly journals A Comparative Study about Data Structures Used for Efficient Management of Voxelised Full-Waveform Airborne LiDAR Data during 3D Polygonal Model Creation

2021 ◽  
Vol 13 (4) ◽  
pp. 559
Author(s):  
Milto Miltiadou ◽  
Neill D. F. Campbell ◽  
Darren Cosker ◽  
Michael G. Grant
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Airborne Lidar ◽  
Memory Allocation ◽  
Lidar Data ◽  
Full Waveform ◽  
Waveform Lidar ◽  
Airborne Lidar Data ◽  
Full Waveform Lidar ◽  
Polygonal Model

In this paper, we investigate the performance of six data structures for managing voxelised full-waveform airborne LiDAR data during 3D polygonal model creation. While full-waveform LiDAR data has been available for over a decade, extraction of peak points is the most widely used approach of interpreting them. The increased information stored within the waveform data makes interpretation and handling difficult. It is, therefore, important to research which data structures are more appropriate for storing and interpreting the data. In this paper, we investigate the performance of six data structures while voxelising and interpreting full-waveform LiDAR data for 3D polygonal model creation. The data structures are tested in terms of time efficiency and memory consumption during run-time and are the following: (1) 1D-Array that guarantees coherent memory allocation, (2) Voxel Hashing, which uses a hash table for storing the intensity values (3) Octree (4) Integral Volumes that allows finding the sum of any cuboid area in constant time, (5) Octree Max/Min, which is an upgraded octree and (6) Integral Octree, which is proposed here and it is an attempt to combine the benefits of octrees and Integral Volumes. In this paper, it is shown that Integral Volumes is the more time efficient data structure but it requires the most memory allocation. Furthermore, 1D-Array and Integral Volumes require the allocation of coherent space in memory including the empty voxels, while Voxel Hashing and the octree related data structures do not require to allocate memory for empty voxels. These data structures, therefore, and as shown in the test conducted, allocate less memory. To sum up, there is a need to investigate how the LiDAR data are stored in memory. Each tested data structure has different benefits and downsides; therefore, each application should be examined individually.

scholarly journals Shape Neutral Analysis of Graph-based Data-structures

2018 ◽  
Vol 18 (3-4) ◽  
pp. 470-483 ◽  
Author(s):  
GREGORY J. DUCK ◽  
JOXAN JAFFAR ◽  
ROLAND H. C. YAP
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Program Analysis ◽  
Constraint Handling ◽  
C Programs ◽  
Wide Range ◽  
Target Program ◽  
Target Data ◽  
Structure Graph ◽  
Structure Properties

AbstractMalformed data-structures can lead to runtime errors such as arbitrary memory access or corruption. Despite this, reasoning over data-structure properties for low-level heap manipulating programs remains challenging. In this paper we present a constraint-based program analysis that checks data-structure integrity, w.r.t. given target data-structure properties, as the heap is manipulated by the program. Our approach is to automatically generate a solver for properties using the type definitions from the target program. The generated solver is implemented using a Constraint Handling Rules (CHR) extension of built-in heap, integer and equality solvers. A key property of our program analysis is that the target data-structure properties are shape neutral, i.e., the analysis does not check for properties relating to a given data-structure graph shape, such as doubly-linked-lists versus trees. Nevertheless, the analysis can detect errors in a wide range of data-structure manipulating programs, including those that use lists, trees, DAGs, graphs, etc. We present an implementation that uses the Satisfiability Modulo Constraint Handling Rules (SMCHR) system. Experimental results show that our approach works well for real-world C programs.

Integrating Image Computation in Undergraduate Level Data-Structure Education

1998 ◽  
Vol 12 (08) ◽  
pp. 1071-1080 ◽  
Author(s):  
Sudeep Sarkar ◽  
Dmitry Goldgof
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Data Structure ◽  
Data Structures ◽  
Science Curriculum ◽  
Early Stage ◽  
Real Life ◽  
South Florida ◽  
The University ◽  
Structure Education

There is a growing need for expertise both in image analysis and in software engineering. To date, these two areas have been taught separately in an undergraduate computer and information science curriculum. However, we have found that introduction to image analysis can be easily integrated in data-structure courses without detracting from the original goal of teaching data structures. Some of the image processing tasks offer a natural way to introduce basic data structures such as arrays, queues, stacks, trees and hash tables. Not only does this integrated strategy expose the students to image related manipulations at an early stage of the curriculum but it also imparts cohesiveness to the data-structure assignments and brings them closer to real life. In this paper we present a set of programming assignments that integrates undergraduate data-structure education with image processing tasks. These assignments can be incorporated in existing data-structure courses with low time and software overheads. We have used these assignment sets thrice: once in a 10-week duration data-structure course at the University of California, Santa Barbara and the other two times in 15-week duration courses at the University of South Florida, Tampa.

scholarly journals Spaced Seed Data Structures forDe NovoAssembly

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Inanç Birol ◽  
Justin Chu ◽  
Hamid Mohamadi ◽  
Shaun D. Jackman ◽  
Karthika Raghavan ◽  
...  
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
De Novo ◽  
Bloom Filters ◽  
De Bruijn Graph ◽  
Sequence Specificity ◽  
Sequencing Errors ◽  
Spaced Seeds ◽  
Read Error Correction ◽  
Seed Data

De novoassembly of the genome of a species is essential in the absence of a reference genome sequence. Many scalable assembly algorithms use the de Bruijn graph (DBG) paradigm to reconstruct genomes, where a table of subsequences of a certain length is derived from the reads, and their overlaps are analyzed to assemble sequences. Despite longer subsequences unlocking longer genomic features for assembly, associated increase in compute resources limits the practicability of DBG over other assembly archetypes already designed for longer reads. Here, we revisit the DBG paradigm to adapt it to the changing sequencing technology landscape and introduce three data structure designs for spaced seeds in the form of paired subsequences. These data structures address memory and run time constraints imposed by longer reads. We observe that when a fixed distance separates seed pairs, it provides increased sequence specificity with increased gap length. Further, we note that Bloom filters would be suitable to implicitly store spaced seeds and be tolerant to sequencing errors. Building on this concept, we describe a data structure for tracking the frequencies of observed spaced seeds. These data structure designs will have applications in genome, transcriptome and metagenome assemblies, and read error correction.

Suffix Tree Constructing Algorithm for Datasets with Discrete Contents

2015 ◽  
Vol 733 ◽  
pp. 867-870
Author(s):  
Zhen Zhong Jin ◽  
Zheng Huang ◽  
Hua Zhang
Keyword(s):  
Data Structure ◽  
Sensor Network ◽  
Association Analysis ◽  
Data Structures ◽  
Suffix Tree ◽  
Analysis Data ◽  
Large Datasets ◽  
Intermediate Data ◽  
Input Strings

The suffix tree is a useful data structure constructed for indexing strings. However, when it comes to large datasets of discrete contents, most existing algorithms become very inefficient. Discrete datasets are need to be indexed in many fields like record analysis, data analyze in sensor network, association analysis etc. This paper presents an algorithm, STD, which stands for Suffix Tree for Discrete contents, that performs very efficiently with discrete input datasets. It imports several wonderful intermediate data structures for discrete strings; we also take care of the situation that the discrete input strings have similar characteristics. Moreover, STD keeps the advantages of existing implementations which are for successive input strings. Experiments were taken to evaluate the performance and shown that the method works well.

Efficient Data Structures for Range Selections Problem

2013 ◽  
Vol 756-759 ◽  
pp. 1387-1391
Author(s):  
Xiao Dong Wang ◽  
Jun Tian
Keyword(s):  
Data Structure ◽  
Linear Space ◽  
Data Structures ◽  
Computation Model ◽  
Word Size ◽  
Selection Problems ◽  
Efficient Data ◽  
Space Data ◽  
Efficient Data Structures ◽  
Range Selection

Building an efficient data structure for range selection problems is considered. While there are several theoretical solutions to the problem, only a few have been tried out, and there is little idea on how the others would perform. The computation model used in this paper is the RAM model with word-size . Our data structure is a practical linear space data structure that supports range selection queries in time with preprocessing time.

Markup Discontinued

2009 ◽  
Author(s):  
C. M. Sperberg-McQueen ◽  
Claus Huitfeldt
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
The Other ◽  
Discontinuous Elements ◽  
Parent Child

That the textual phenomena of interest for markup are not always hierarchically arranged is well known and widely discussed. Less frequently discussed is the fact that they are also not always contiguous, so that the units of our analysis cannot always correspond to single elements in the document. Various notations for discontinuous elements exist, but the mapping from those notations to data structures has not been well analysed or understood. And as far as we know, there are no standard mechanisms for validating discontinuous elements. We propose a data structure (a modification of the Goddag structure) to better handle discontinuous elements: we relax the rule that every pair of elements where one contains the other be related by a path of parent/child links. Parent/child links are then not an automatic result of containment. We conclude with a brief sketch of the issues involved in extending current validation mechanisms to handle discontinuity.

Description Mirror Features and Data Structures in an Optical CAD System

2014 ◽  
Vol 513-517 ◽  
pp. 796-799
Author(s):  
Jun Hai Jiang ◽  
Xiao Hui Yang
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Optical System ◽  
Simple Structure ◽  
Optical Design ◽  
Object Oriented ◽  
Optical Lens ◽  
Cad System ◽  
System Components ◽  
Object Oriented Methods

CAD is presented in optical design, the use of object-oriented methods, optical lens structure described. Using object-oriented techniques to describe the object lens, and with VC++ language features to build such systems, construct a data structure of the optical system components. This method has a simple structure, portability, reusability of characteristics.

EXTENDING THE DOUBLY LINKED FACE LIST FOR THE REPRESENTATION OF 2-PSEUDOMANIFOLDS AND 2-MANIFOLDS WITH BOUNDARIES

2011 ◽  
Vol 21 (04) ◽  
pp. 467-494
Author(s):  
THIAGO R. DOS SANTOS ◽  
HANS-PETER MEINZER ◽  
LENA MAIER-HEIN
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Point Of View ◽  
Minimal Amount ◽  
Computer Memory ◽  
Memory Usage ◽  
Surgery Simulation ◽  
Practical Applications ◽  
Software Applications ◽  
Behavioral Observations

The Doubly Linked Face List (DLFL) is a data structure for mesh representation that always ensures topological 2-manifold consistency. Furthermore, it uses a minimal amount of computer memory and allows queries to be performed very efficiently. However, the use of the DLFL for the implementation of practical applications is very limited, mainly because of two drawbacks: (1) the DLFL is only able to represent 2-manifold objects; (2) its operators may be ambiguous, modifying the structure in an unexpected way from the user's point of view. In order to overcome these drawbacks, we present the Extended Doubly Linked Face List (XDLFL), which extends the DLFL for the representation of 2-pseudomanifolds and 2-manifolds with boundaries, increasing its applicability for practical software applications. Using these extensions, we also show how to avoid ambiguities in the original DLFL's operators. A new set of intuitive operators for the manipulation of the extensions and for the unambiguous manipulation of the data structure is also presented. The implementation of these extensions is straightforward, since the modifications to the DLFL are trivial and based on behavioral observations of the DLFL's operators. After integrating the extensions to the DLFL, memory usage increases very slightly, while is still smaller than the memory usage of other well-known data structures. Furthermore, queries related to the new extensions, such as whether an edge belongs to a boundary, may be performed very efficiently. The proposed extensions and their operators are very beneficial for applications such as surgery simulation softwares, where the interactions with the models, such as cutting or appending objects to each other, must be performed in an efficient and transparent manner.

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