Data compressions on machines with limited memory

We consider two problems in which machines with limited internal memory are used to compress and decompress data. In the first application, a powerful encoder transmits a coded file to a decoder that has severely constrained memory. A data structure that achieves minimum storage is presented, and alternative methods that sacrifice a small amount of storage to attain faster decoding are described. The second problem we address is that of encoding and decoding in limited memory. Methods for representing context models succinctly are described. These methods provide compression performance that is superior to state-of-the-art techniques, and competitive with newer approaches that use five times as much internal memory

New Algorithms and Lower Bounds for Sequential-Access Data Compression

This thesis concerns sequential-access data compression, i.e., by algorithms that read the input one or more times from beginning to end. In one chapter we consider adaptive prefix coding, for which we must read the input character by character, outputting each character's self-delimiting codeword before reading the next one. We show how to encode and decode each character in constant worst-case time while producing an encoding whose length is worst-case optimal. In another chapter we consider one-pass compression with memory bounded in terms of the alphabet size and context length, and prove a nearly tight tradeoff between the amount of memory we can use and the quality of the compression we can achieve. In a third chapter we consider compression in the read/write streams model, which allows us passes and memory both polylogarithmic in the size of the input. We first show how to achieve universal compression using only one pass over one stream. We then show that one stream is not sufficient for achieving good grammar-based compression. Finally, we show that two streams are necessary and sufficient for achieving entropy-only bounds.Comment: draft of PhD thesi

Data structures

We discuss data structures and their methods of analysis. In particular, we treat the unweighted and weighted dictionary problem, self-organizing data structures, persistent data structures, the union-find-split problem, priority queues, the nearest common ancestor problem, the selection and merging problem, and dynamization techniques. The methods of analysis are worst, average and amortized case

Learning, Categorization, Rule Formation, and Prediction by Fuzzy Neural Networks

National Science Foundation (IRI 94-01659); Office of Naval Research (N00014-91-J-4100, N00014-92-J-4015) Air Force Office of Scientific Research (90-0083, N00014-92-J-4015

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

A review of clustering techniques and developments

© 2017 Elsevier B.V. This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. Clustering is defined as an unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. The approaches used in these methods are discussed with their respective states of art and applicability. The measures of similarity as well as the evaluation criteria, which are the central components of clustering, are also presented in the paper. The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted

DCT Implementation on GPU

There has been a great progress in the field of graphics processors. Since, there is no rise in the speed of the normal CPU processors; Designers are coming up with multi-core, parallel processors. Because of their popularity in parallel processing, GPUs are becoming more and more attractive for many applications. With the increasing demand in utilizing GPUs, there is a great need to develop operating systems that handle the GPU to full capacity. GPUs offer a very efficient environment for many image processing applications. This thesis explores the processing power of GPUs for digital image compression using Discrete cosine transform

간결한 자료구조를 활용한 반구조화된 문서 형식들의 공간 효율적 표현법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 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.셀 수 없는 빅 데이터가 다양한 원본로부터 생성되고 있다. 이들 데이터의 대부분은 고정되지 않은 종류의 스키마를 포함한 파일 형태로 저장되는데, 이로 인하여 반구조화된 문서 형식을 이용하여 파일을 유지하는 것이 적합하다. XML, JSON 및 YAML과 같은 종류의 반구조화된 문서 형식이 데이터에 내재하는 구조를 유지하기 위하여 제안되었다. 수집된 데이터를 구조화하는 RDF와 같은 여러 데이터 모델들은 사후 처리를 위한 저장 및 전송을 위하여 반구조화된 문서 형식에 의존한다. 반구조화된 문서 형식은 가독성과 다변성에 집중하기 때문에, 문서를 구조화하고 유지하기 위하여 추가적인 공간을 필요로 한다. 문서를 압축시키기 위하여 일반적인 압축 기법들이 널리 사용되고 있으나, 이들 기법들을 적용하게 되면 문서의 내부 구조의 손실로 인하여 데이터의 사후 처리가 어렵게 된다. 데이터를 정보이론적 하한에 가까운 공간만을 사용하여 저장을 가능하게 하면서 질의에 대한 응답을 제공하는 간결한 자료구조는 이론적으로 널리 연구되고 있는 분야이다. 비트열과 트리가 널리 알려진 간결한 자료구조들이다. 그러나 반구조화된 문서들을 저장하는 데 간결한 자료구조의 아이디어를 적용한 연구는 거의 진행되지 않았다. 본 학위논문을 통해 우리는 다양한 종류의 반구조화된 문서 형식을 통일되게 표현하는 공간 효율적 표현법을 제시한다. 이 기법의 주요한 기능은 간결한 자료구조가 강점으로 가지는 특성에 기반한 간결성과 질의 가능성이다. 비트열로 인덱싱된 배열, 간결한 순서 있는 트리 및 다양한 압축 기법을 통합하여 해당 표현법을 고안하였다. 이 기법은 실재적으로 구현되었고, 실험을 통하여 이 기법을 적용한 반구조화된 문서들은 최대 60% 적은 디스크 공간과 90% 적은 메모리 공간을 통해 표현될 수 있다는 것을 보인다. 더불어 본 학위논문에서 반구조화된 문서들은 분할적으로 표현이 가능함을 보이고, 이를 통하여 제한된 환경에서도 빅 데이터를 표현한 문서들을 처리할 수 있다는 것을 보인다. 앞서 언급한 공간 효율적 반구조화된 문서 표현법을 구축함과 동시에, 본 학위논문에서 이미 존재하는 압축 기법 중 일부를 추가적으로 개선한다. 첫째로, 본 학위논문에서는 정렬 여부에 관계없는 정수 배열을 부호화하는 아이디어를 제시한다. 이 기법은 이미 존재하는 범용 코드 시스템을 개선한 형태로, 간결한 비트열 자료구조를 이용한다. 제안된 알고리즘은 기존 범용 코드 시스템에 비해 최대 44\% 적은 공간을 사용할 뿐만 아니라 15\% 적은 부호화 시간을 필요로 하며, 기존 시스템에서 제공하지 않는 부호화된 배열에서의 임의 접근을 지원한다. 또한 본 학위논문에서는 비트맵 인덱스 압축에 사용되는 SBH 알고리즘을 개선시킨다. 해당 기법의 주된 강점은 부호화와 복호화 진행 시 중간 매개인 슈퍼버켓을 사용함으로써 여러 압축된 비트맵 인덱스에 대한 질의 성능을 개선시키는 것이다. 위 압축 알고리즘의 중간 과정에서 진행되는 분할에서 영감을 얻어, 본 학위논문에서 CPU 및 GPU에 적용 가능한 개선된 병렬화 압축 매커니즘을 제시한다. 실험을 통해 CPU 병렬 최적화가 이루어진 알고리즘은 압축된 형태의 변형 없이 4코어 컴퓨터에서 최대 38\%의 압축 및 해제 시간을 감소시킨다는 것을 보인다. GPU 병렬 최적화는 기존에 존재하는 GPU 비트맵 압축 기법에 비해 48\% 빠른 질의 처리 시간을 필요로 함을 확인한다.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 요약 109 Acknowledgements 111Docto

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts