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κ°κ²°ν μλ£κ΅¬μ‘°λ₯Ό νμ©ν λ°κ΅¬μ‘°νλ λ¬Έμ νμλ€μ κ³΅κ° ν¨μ¨μ ννλ²
νμλ
Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν μ κΈ°Β·μ»΄ν¨ν°κ³΅νλΆ, 2021. 2. Srinivasa Rao Satti.Numerous big data are generated from a plethora of sources. Most of the data stored as files contain a non-fixed type of schema, so that the files are suitable to be maintained as semi-structured document formats. A number of those formats, such as XML (eXtensible Markup Language), JSON (JavaScript Object Notation), and YAML (YAML Ain't Markup Language) are suggested to sustain hierarchy in the original corpora of data. Several data models structuring the gathered data - including RDF (Resource Description Framework) - depend on the semi-structured document formats to be serialized and transferred for future processing.
Since the semi-structured document formats focus on readability and verbosity, redundant space is required to organize and maintain the document. Even though general-purpose compression schemes are widely used to compact the documents, applying those algorithms hinder future handling of the corpora, owing to loss of internal structures.
The area of succinct data structures is widely investigated and researched in theory, to provide answers to the queries while the encoded data occupy space close to the information-theoretic lower bound. Bit vectors and trees are the notable succinct data structures. Nevertheless, there were few attempts to apply the idea of succinct data structures to represent the semi-structured documents in space-efficient manner.
In this dissertation we propose a unified, space-efficient representation of various semi-structured document formats. The core functionality of this representation is its compactness and query-ability derived from enriched functions of succinct data structures. Incorporation of (a) bit indexed arrays, (b) succinct ordinal trees, and (c) compression techniques engineers the compact representation. We implement this representation in practice, and show by experiments that construction of this representation decreases the disk usage by up to 60% while occupying 90% less RAM. We also allow processing a document in partial manner, to allow processing of larger corpus of big data even in the constrained environment.
In parallel to establishing the aforementioned compact semi-structured document representation, we provide and reinforce some of the existing compression schemes in this dissertation. We first suggest an idea to encode an array of integers that is not necessarily sorted. This compaction scheme improves upon the existing universal code systems, by assistance of succinct bit vector structure. We show that our suggested algorithm reduces space usage by up to 44% while consuming 15% less time than the original code system, while the algorithm additionally supports random access of elements upon the encoded array.
We also reinforce the SBH bitmap index compression algorithm. The main strength of this scheme is the use of intermediate super-bucket during operations, giving better performance on querying through a combination of compressed bitmap indexes. Inspired from splits done during the intermediate process of the SBH algorithm, we give an improved compression mechanism supporting parallelism that could be utilized in both CPUs and GPUs. We show by experiments that this CPU parallel processing optimization diminishes compression and decompression times by up to 38% in a 4-core machine without modifying the bitmap compressed form. For GPUs, the new algorithm gives 48% faster query processing time in the experiments, compared to the previous existing bitmap index compression schemes.μ
μ μλ λΉ
λ°μ΄ν°κ° λ€μν μλ³Έλ‘λΆν° μμ±λκ³ μλ€. μ΄λ€ λ°μ΄ν°μ λλΆλΆμ κ³ μ λμ§ μμ μ’
λ₯μ μ€ν€λ§λ₯Ό ν¬ν¨ν νμΌ ννλ‘ μ μ₯λλλ°, μ΄λ‘ μΈνμ¬ λ°κ΅¬μ‘°νλ λ¬Έμ νμμ μ΄μ©νμ¬ νμΌμ μ μ§νλ κ²μ΄ μ ν©νλ€. XML, JSON λ° YAMLκ³Ό κ°μ μ’
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λ°κ΅¬μ‘°νλ λ¬Έμ νμμ κ°λ
μ±κ³Ό λ€λ³μ±μ μ§μ€νκΈ° λλ¬Έμ, λ¬Έμλ₯Ό ꡬ쑰ννκ³ μ μ§νκΈ° μνμ¬ μΆκ°μ μΈ κ³΅κ°μ νμλ‘ νλ€. λ¬Έμλ₯Ό μμΆμν€κΈ° μνμ¬ μΌλ°μ μΈ μμΆ κΈ°λ²λ€μ΄ λ리 μ¬μ©λκ³ μμΌλ, μ΄λ€ κΈ°λ²λ€μ μ μ©νκ² λλ©΄ λ¬Έμμ λ΄λΆ ꡬ쑰μ μμ€λ‘ μΈνμ¬ λ°μ΄ν°μ μ¬ν μ²λ¦¬κ° μ΄λ ΅κ² λλ€.
λ°μ΄ν°λ₯Ό μ 보μ΄λ‘ μ ννμ κ°κΉμ΄ 곡κ°λ§μ μ¬μ©νμ¬ μ μ₯μ κ°λ₯νκ² νλ©΄μ μ§μμ λν μλ΅μ μ 곡νλ κ°κ²°ν μλ£κ΅¬μ‘°λ μ΄λ‘ μ μΌλ‘ λ리 μ°κ΅¬λκ³ μλ λΆμΌμ΄λ€. λΉνΈμ΄κ³Ό νΈλ¦¬κ° λ리 μλ €μ§ κ°κ²°ν μλ£κ΅¬μ‘°λ€μ΄λ€. κ·Έλ¬λ λ°κ΅¬μ‘°νλ λ¬Έμλ€μ μ μ₯νλ λ° κ°κ²°ν μλ£κ΅¬μ‘°μ μμ΄λμ΄λ₯Ό μ μ©ν μ°κ΅¬λ κ±°μ μ§νλμ§ μμλ€.
λ³Έ νμλ
Όλ¬Έμ ν΅ν΄ μ°λ¦¬λ λ€μν μ’
λ₯μ λ°κ΅¬μ‘°νλ λ¬Έμ νμμ ν΅μΌλκ² νννλ κ³΅κ° ν¨μ¨μ ννλ²μ μ μνλ€. μ΄ κΈ°λ²μ μ£Όμν κΈ°λ₯μ κ°κ²°ν μλ£κ΅¬μ‘°κ° κ°μ μΌλ‘ κ°μ§λ νΉμ±μ κΈ°λ°ν κ°κ²°μ±κ³Ό μ§μ κ°λ₯μ±μ΄λ€. λΉνΈμ΄λ‘ μΈλ±μ±λ λ°°μ΄, κ°κ²°ν μμ μλ νΈλ¦¬ λ° λ€μν μμΆ κΈ°λ²μ ν΅ν©νμ¬ ν΄λΉ ννλ²μ κ³ μνμλ€. μ΄ κΈ°λ²μ μ€μ¬μ μΌλ‘ ꡬνλμκ³ , μ€νμ ν΅νμ¬ μ΄ κΈ°λ²μ μ μ©ν λ°κ΅¬μ‘°νλ λ¬Έμλ€μ μ΅λ 60% μ μ λμ€ν¬ 곡κ°κ³Ό 90% μ μ λ©λͺ¨λ¦¬ 곡κ°μ ν΅ν΄ ννλ μ μλ€λ κ²μ 보μΈλ€. λλΆμ΄ λ³Έ νμλ
Όλ¬Έμμ λ°κ΅¬μ‘°νλ λ¬Έμλ€μ λΆν μ μΌλ‘ ννμ΄ κ°λ₯ν¨μ 보μ΄κ³ , μ΄λ₯Ό ν΅νμ¬ μ νλ νκ²½μμλ λΉ
λ°μ΄ν°λ₯Ό ννν λ¬Έμλ€μ μ²λ¦¬ν μ μλ€λ κ²μ 보μΈλ€.
μμ μΈκΈν κ³΅κ° ν¨μ¨μ λ°κ΅¬μ‘°νλ λ¬Έμ ννλ²μ ꡬμΆν¨κ³Ό λμμ, λ³Έ νμλ
Όλ¬Έμμ μ΄λ―Έ μ‘΄μ¬νλ μμΆ κΈ°λ² μ€ μΌλΆλ₯Ό μΆκ°μ μΌλ‘ κ°μ νλ€. 첫째λ‘, λ³Έ νμλ
Όλ¬Έμμλ μ λ ¬ μ¬λΆμ κ΄κ³μλ μ μ λ°°μ΄μ λΆνΈννλ μμ΄λμ΄λ₯Ό μ μνλ€. μ΄ κΈ°λ²μ μ΄λ―Έ μ‘΄μ¬νλ λ²μ© μ½λ μμ€ν
μ κ°μ ν ννλ‘, κ°κ²°ν λΉνΈμ΄ μλ£κ΅¬μ‘°λ₯Ό μ΄μ©νλ€. μ μλ μκ³ λ¦¬μ¦μ κΈ°μ‘΄ λ²μ© μ½λ μμ€ν
μ λΉν΄ μ΅λ 44\% μ μ 곡κ°μ μ¬μ©ν λΏλ§ μλλΌ 15\% μ μ λΆνΈν μκ°μ νμλ‘ νλ©°, κΈ°μ‘΄ μμ€ν
μμ μ 곡νμ§ μλ λΆνΈνλ λ°°μ΄μμμ μμ μ κ·Όμ μ§μνλ€.
λν λ³Έ νμλ
Όλ¬Έμμλ λΉνΈλ§΅ μΈλ±μ€ μμΆμ μ¬μ©λλ SBH μκ³ λ¦¬μ¦μ κ°μ μν¨λ€. ν΄λΉ κΈ°λ²μ μ£Όλ κ°μ μ λΆνΈνμ 볡νΈν μ§ν μ μ€κ° 맀κ°μΈ μνΌλ²μΌμ μ¬μ©ν¨μΌλ‘μ¨ μ¬λ¬ μμΆλ λΉνΈλ§΅ μΈλ±μ€μ λν μ§μ μ±λ₯μ κ°μ μν€λ κ²μ΄λ€. μ μμΆ μκ³ λ¦¬μ¦μ μ€κ° κ³Όμ μμ μ§νλλ λΆν μμ μκ°μ μ»μ΄, λ³Έ νμλ
Όλ¬Έμμ CPU λ° GPUμ μ μ© κ°λ₯ν κ°μ λ λ³λ ¬ν μμΆ λ§€μ»€λμ¦μ μ μνλ€. μ€νμ ν΅ν΄ CPU λ³λ ¬ μ΅μ νκ° μ΄λ£¨μ΄μ§ μκ³ λ¦¬μ¦μ μμΆλ ννμ λ³ν μμ΄ 4μ½μ΄ μ»΄ν¨ν°μμ μ΅λ 38\%μ μμΆ λ° ν΄μ μκ°μ κ°μμν¨λ€λ κ²μ 보μΈλ€. GPU λ³λ ¬ μ΅μ νλ κΈ°μ‘΄μ μ‘΄μ¬νλ GPU λΉνΈλ§΅ μμΆ κΈ°λ²μ λΉν΄ 48\% λΉ λ₯Έ μ§μ μ²λ¦¬ μκ°μ νμλ‘ ν¨μ νμΈνλ€.Chapter 1 Introduction 1
1.1 Contribution 3
1.2 Organization 5
Chapter 2 Background 6
2.1 Model of Computation 6
2.2 Succinct Data Structures 7
Chapter 3 Space-efficient Representation of Integer Arrays 9
3.1 Introduction 9
3.2 Preliminaries 10
3.2.1 Universal Code System 10
3.2.2 Bit Vector 13
3.3 Algorithm Description 13
3.3.1 Main Principle 14
3.3.2 Optimization in the Implementation 16
3.4 Experimental Results 16
Chapter 4 Space-efficient Parallel Compressed Bitmap Index Processing 19
4.1 Introduction 19
4.2 Related Work 23
4.2.1 Byte-aligned Bitmap Code (BBC) 24
4.2.2 Word-Aligned Hybrid (WAH) 27
4.2.3 WAH-derived Algorithms 28
4.2.4 GPU-based WAH Algorithms 31
4.2.5 Super Byte-aligned Hybrid (SBH) 33
4.3 Parallelizing SBH 38
4.3.1 CPU Parallelism 38
4.3.2 GPU Parallelism 39
4.4 Experimental Results 40
4.4.1 Plain Version 41
4.4.2 Parallelized Version 46
4.4.3 Summary 49
Chapter 5 Space-efficient Representation of Semi-structured Document Formats 50
5.1 Preliminaries 50
5.1.1 Semi-structured Document Formats 50
5.1.2 Resource Description Framework 57
5.1.3 Succinct Ordinal Tree Representations 60
5.1.4 String Compression Schemes 64
5.2 Representation 66
5.2.1 Bit String Indexed Array 67
5.2.2 Main Structure 68
5.2.3 Single Document as a Collection of Chunks 72
5.2.4 Supporting Queries 73
5.3 Experimental Results 75
5.3.1 Datasets 76
5.3.2 Construction Time 78
5.3.3 RAM Usage during Construction 80
5.3.4 Disk Usage and Serialization Time 83
5.3.5 Chunk Division 83
5.3.6 String Compression 88
5.3.7 Query Time 89
Chapter 6 Conclusion 94
Bibliography 96
μμ½ 109
Acknowledgements 111Docto
Advances in Large-Scale RDF Data Management
One of the prime goals of the LOD2 project is improving the performance and scalability of RDF storage solutions so that the increasing amount of Linked Open Data (LOD) can be efficiently managed. Virtuoso has been chosen as the basic RDF store for the LOD2 project, and during the project it has been significantly improved by incorporating advanced relational database techniques from MonetDB and Vectorwise, turning it into a compressed column store with vectored execution. This has reduced the performance gap (βRDF taxβ) between Virtuosoβs SQL and SPARQL query performance in a way that still respects the βschema-lastβ nature of RDF. However, by lacking schema information, RDF database systems such as Virtuoso still cannot use advanced relational storage optimizations such as table partitioning or clustered indexes and have to execute SPARQL queries with many self-joins to a triple table, which leads to more join effort than needed in SQL systems. In this chapter, we first discuss the new column store techniques applied to Virtuoso, the enhancements in its cluster parallel version, and show its performance using the popular BSBM benchmark at the unsurpassed scale of 150 billion triples. We finally describe ongoing work in deriving an βemergentβ relational schema from RDF data, which can help to close the performance gap between relational-based and RDF-based storage solutions
Database system architecture supporting coexisting query languages and data models
SIGLELD:D48239/84 / BLDSC - British Library Document Supply CentreGBUnited Kingdo
A Compressed Sampling and Dictionary Learning Framework for WDM-Based Distributed Fiber Sensing
We propose a compressed sampling and dictionary learning framework for
fiber-optic sensing using wavelength-tunable lasers. A redundant dictionary is
generated from a model for the reflected sensor signal. Imperfect prior
knowledge is considered in terms of uncertain local and global parameters. To
estimate a sparse representation and the dictionary parameters, we present an
alternating minimization algorithm that is equipped with a pre-processing
routine to handle dictionary coherence. The support of the obtained sparse
signal indicates the reflection delays, which can be used to measure
impairments along the sensing fiber. The performance is evaluated by
simulations and experimental data for a fiber sensor system with common core
architecture.Comment: Accepted for publication in Journal of the Optical Society of America
A [ \copyright\ 2017 Optical Society of America.]. One print or electronic
copy may be made for personal use only. Systematic reproduction and
distribution, duplication of any material in this paper for a fee or for
commercial purposes, or modifications of the content of this paper are
prohibite
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