30 research outputs found
LiteMat: a scalable, cost-efficient inference encoding scheme for large RDF graphs
The number of linked data sources and the size of the linked open data graph
keep growing every day. As a consequence, semantic RDF services are more and
more confronted with various "big data" problems. Query processing in the
presence of inferences is one them. For instance, to complete the answer set of
SPARQL queries, RDF database systems evaluate semantic RDFS relationships
(subPropertyOf, subClassOf) through time-consuming query rewriting algorithms
or space-consuming data materialization solutions. To reduce the memory
footprint and ease the exchange of large datasets, these systems generally
apply a dictionary approach for compressing triple data sizes by replacing
resource identifiers (IRIs), blank nodes and literals with integer values. In
this article, we present a structured resource identification scheme using a
clever encoding of concepts and property hierarchies for efficiently evaluating
the main common RDFS entailment rules while minimizing triple materialization
and query rewriting. We will show how this encoding can be computed by a
scalable parallel algorithm and directly be implemented over the Apache Spark
framework. The efficiency of our encoding scheme is emphasized by an evaluation
conducted over both synthetic and real world datasets.Comment: 8 pages, 1 figur
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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
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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
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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
Natural capital metrics. Phase 1 final report: central components
This is a condensed version of the Natural Capital Metrics (NCMet) project Phase 1 report. It focuses on three key components: i) the conceptual framework, ii) development of six example evidence-chains and their associated data and model inventories, and iii) early development work towards a Natural Capital Portal to provide access to relevant data, models and maps of natural capital.
All outputs are preliminary and are undergoing considerable refinement in the second phase of the project. Phase 2 outputs will be available in 2018
Investigating the universality of a semantic web-upper ontology in the context of the African languages
Ontologies are foundational to, and upper ontologies provide semantic integration across, the Semantic Web. Multilingualism has been shown to be a key challenge to the development of the Semantic Web, and is a particular challenge to the universality requirement of upper ontologies. Universality implies a qualitative mapping from lexical ontologies, like WordNet, to an upper ontology, such as SUMO. Are a given natural language family's core concepts currently included
in an existing, accepted upper ontology? Does SUMO preserve an ontological non-bias with respect to the multilingual challenge, particularly in the context of the African languages? The approach to developing WordNets mapped to shared core concepts in the non-Indo-European language families has highlighted these challenges and this is examined in a unique new context: the Southern African
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Acts and joint resolutions of the General Assembly of the state of South Carolina 2021 regular session volume I
The General Assembly of South Carolina regularly publishes acts and join resolutions passed during the regular session of legislation