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Trading Off Accuracy, Timeliness, and Uplink Usage in Online GPS Tracking

IEEE TRANSACTIONS ON MOBILE COMPUTING 2016年第8期15卷 2124-2136页

作者： Musa, A. B. M. Biagioni, James Eriksson, Jakob Univ Illinois Dept Comp Sci Chicago IL 60607 USA

In an online GPS tracking system, a fundamental trade-off exists between timeliness, the average time interval between a recorded change in location and a change in the reported location;accuracy, the average error between the actual location and the reported location;and uplink usage, the average amount of data used per second of tracking. While tracking efficiency has been addressed in the literature, our thrifty tracking system presents the first unified view of timeliness, accuracy, and uplink usage, allowing the user to specify desired targets for any two of these objectives, while optimizing the third. We also provide a closed-form characterization of this three-way trade-off, and demonstrate that our system converges to the predicted performance.

关键词： Spatial databases and GIS mobile communication systems mobile computing client/server data compaction and compression data communications data communications aspects system applications and experience

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Edgebreaker: a simple implementation for surfaces with handles

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COMPUTERS & GRAPHICS-UK 2003年第4期27卷 553-567页

作者： Lopes, H Rossignac, J Safonova, A Szymczak, A Tavares, G Pontificia Univ Catolica Rio de Janeiro Dept Matemat BR-22453900 Rio De Janeiro Brazil Georgia Inst Technol Coll Comp Atlanta GA 30332 USA Georgia Inst Technol GVU Ctr Atlanta GA 30332 USA Carnegie Mellon Univ Dept Comp Sci Pittsburgh PA 15213 USA

The Edgebreaker is an efficient scheme for compressing triangulated surfaces. A surprisingly simple implementation of Edgebreaker has been proposed for surfaces homeomorphic to a sphere. It uses the corner-table data structure, which represents the connectivity of a triangulated surface by two tables of integers, and encodes them with less than 2 bits per triangle. We extend this simple formulation to deal with triangulated surfaces with handles and present the detailed pseudocode for the encoding and decoding algorithms (which take one page each). We justify the validity of the proposed approach using the mathematical formulation of the handlebody theory for surfaces, which explains the topological changes that occur when two boundary edges of a portion of a surface are identified. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： data compaction and compression data storage representations boundary representations graphs and algorithms handlebody theory

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The VPC trace-compression algorithms

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IEEE TRANSACTIONS ON COMPUTERS 2005年第11期54卷 1329-1344页

作者： Burtscher, M Ganusov, I Jackson, SJ Ke, J Ratanaworabhan, P Sam, NB Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

Execution traces, such as are used to study and analyze program behavior, are often so large that they need to be stored in compressed form. This paper describes the design and implementation of four value prediction-based compression (VPC) algorithms for traces that record the PC as well as other information about executed instructions. VPC1 directly compresses traces using value predictors, VPC2 adds a second compression stage, and VPC3 utilizes value predictors to convert traces into streams that can be compressed better and more quickly than the original traces. VPC4 introduces further algorithmic enhancements and is automatically synthesized. Of the 55 SPECcpu2000 traces we evaluate, VPC4 compresses 36 better, decompresses 26 faster, and compresses 53 faster than BZIP2, MACHE, PDATS II, SBC, and SEQUITUR. It delivers the highest geometric-mean compression rate, decompression speed, and compression speed because of the predictors' simplicity and their ability to exploit local value locality. Most other compression algorithms can only exploit global value locality.

关键词： data compaction and compression performance analysis and design aids

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A precompiler to reduce the memory footprint of multiscale PDE solvers in C plus

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2010年第1期26卷 175-182页

作者： Bungartz, H. -J. Eckhardt, W. Weinzierl, T. Zenger, C. Tech Univ Munich D-80748 Garching Germany

A PDE solver's value is increasingly co-determined by its memory footprint, as the increase of computational multicore power overtakes the memory access speed, and as memory restricts the maximum experiment size. Tailoring a code to require less memory is technical challenging, error-prone, and hardware-dependent. Object-oriented code typically consumes much memory, though developers favour such high-level languages offering meaningful models and good maintainability. We augment the language C++ with new keywords branding records to be memory-critical. Our precompiler DaStGen then transforms this augmented specification into plain C++ optimised for low memory requirements. Hereby, it encodes multiple attributes with fixed range within one variable, and it reduces the number of bits per floating point value. The tool also generates one user-defined MPI data type per class and, thus, facilitates the construction of parallel codes with small messages. (C) 2009 Elsevier B.V. All rights reserved.

关键词： data compaction and compression Code generation Multigrid and multilevel methods

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Advanced techniques for high-quality multi-resolution volume rendering

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COMPUTERS & GRAPHICS-UK 2004年第1期28卷 51-58页

作者： Guthe, S Strasser, W Univ Tubingen GRIS WSI D-72076 Tubingen Germany

We present several improvements for compression-based multi-resolution rendering of very large volume data sets at interactive to real-time frame rates on standard PC hardware. The algorithm accepts scalar or multi-variant data sampled on a regular grid as input. The input data are converted into a compressed hierarchical wavelet representation in a pre-processing step. During rendering, the wavelet representation is decompressed on-the-fly and rendered using hardware texture mapping. The level-of-detail used for rendering is adapted to the estimated screen-space error. To.-increase the rendering performance additional visibility tests, such as empty space skipping and occlusion culling, are applied. Furthermore, we discuss how to render the remaining multi-resolution blocks efficiently using modern graphics hardware. Using a prototype implementation of this algorithm we are able to perform a high-quality interactive rendering of large data sets on a single off-the-shelf PC. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： viewing algorithms data compaction and compression

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A Communication Perspective on Automatic Text Categorization

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2009年第7期21卷 1027-1041页

作者： Capdevila, Marta Marquez Florez, Oscar W. Univ Vigo Signal & Commun Proc Dept Telecommun Engn Sch E-36310 Vigo Spain

The basic concern of a Communication System is to transfer information from its source to a destination some distance away. Textual documents also deal with the transmission of information. Particularly, from a text categorization system point of view, the information encoded by a document is the topic or category it belongs to. Following this initial intuition, a theoretical framework is developed where Automatic Text Categorization (ATC) is studied under a Communication System perspective. Under this approach, the problematic indexing feature space dimensionality reduction has been tackled by a two-level supervised scheme, implemented by a noisy terms filtering and a subsequent redundant terms compression. Gaussian probabilistic categorizers have been revisited and adapted to the concomitance of sparsity in ATC. Experimental results pertaining to 20 Newsgroups and Reuters-21578 collections validate the theoretical approaches. The noise filter and redundancy compressor allows an aggressive term vocabulary reduction ( reduction factor greater than 0.99) with a minimum loss ( lower than 3 percent) and, in some cases, gain ( greater than 4 percent) of final classification accuracy. The adapted Gaussian Naive Bayes classifier reaches classification results similar to those obtained by state-of-the-art Multinomial Naive Bayes (MNB) and Support Vector Machines (SVMs).

关键词： data communications text processing data compaction and compression clustering classifier design and evaluation feature evaluation and selection

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Multi-Prediction compression: An Efficient and Scalable Memory compression Framework for GP-GPU

IEEE COMPUTER ARCHITECTURE LETTERS

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IEEE COMPUTER ARCHITECTURE LETTERS 2022年第2期21卷 37-40页

作者： Jin, Hoyong Jeong, Donghun Park, Taewon Ko, Jong Hwan Kim, Jungrae Sungkyunkwan Univ Dept Elect & Comp Engn Suwon 16419 South Korea Sungkyunkwan Univ Dept Semicond & Display Engn Suwon 16419 South Korea

data-intensive applications and throughput-oriented processors demand more memory bandwidth. Memory compression can provide more data beyond physical limits, yet new data types and smaller block sizes are challenging. This paper presents a novel and lightweight memory compression framework, Multi-Prediction compression (MPC), to increase the effective memory bandwidth. Based on multiple prediction models and data-driven algorithm tuning, MPC can provide 31.7% better compression than state-of-the-art (SOTA) algorithms for 32B blocks. Moreover, MPC is hardware-friendly and scalable to support a growing number of data patterns.

关键词： Predictive models data models Correlation Prediction algorithms Transformers Hardware Bandwidth Graphics processors memory hierarchy data compaction and compression

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On linear potential functions for approximating Bayesian computations

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JOURNAL OF THE ACM 1996年第3期43卷 399-430页

作者： Santos, E Department of Electrical and Computer Engineering Air Force Institute of Technology Wright-Patterson AFB Ohio

Probabilistic reasoning suffers from NP-hard implementations. In particular, the amount of probabilistic information necessary to the computations is often overwhelming. For example, the size of conditional probability tables in Bayesian networks has long been a limiting factor in the general use of these networks. We present a new approach for manipulating the probabilistic information given. This approach avoids being overwhelmed by essentially compressing the information using approximation functions called linear potential functions. We can potentially reduce the information from a combinatorial amount to roughly linear in the number of random variable assignments. Furthermore, we can compute these functions through closed form equations. As it turns out, our approximation method is quite general and may be applied to other data compression problems.

关键词： artificial intelligence data compaction and compression integer programming least squares approximation pattern recognition probabilistic reasoning uncertainty

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Clustering-Based compression for Population DNA Sequences

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019年第1期16卷 208-221页

作者： Cheng, Kin-On Law, Ngai-Fong Siu, Wan-Chi Hong Kong Polytech Univ Dept Elect & Informat Engn Ctr Signal Proc Hung Hom Hong Kong Peoples R China

Due to the advancement of DNA sequencing techniques, the number of sequenced individual genomes has experienced an exponential growth. Thus, effective compression of this kind of sequences is highly desired. In this work, we present a novel compression algorithm called Reference-based compression algorithm using the concept of Clustering (RCC). The rationale behind RCC is based on the observation about the existence of substructures within the population sequences. To utilize these substructures, k-means clustering is employed to partition sequences into clusters for better compression. A reference sequence is then constructed for each cluster so that sequences in that cluster can be compressed by referring to this reference sequence. The reference sequence of each cluster is also compressed with reference to a sequence which is derived from all the reference sequences. Experiments show that RCC can further reduce the compressed size by up to 91.0 percent when compared with state-of-the-art compression approaches. There is a compromise between compressed size and processing time. The current implementation in Matlab has time complexity in a factor of thousands higher than the existing algorithms implemented in C/C++. Further investigation is required to improve processing time in future.

关键词： Biology and genetics clustering compression technologies data compaction and compression

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data-Aware Adaptive compression for Stream Processing

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2024年第9期36卷 4531-4549页

作者： Zhang, Yu Zhang, Feng Li, Hourun Zhang, Shuhao Guo, Xiaoguang Chen, Yuxing Pan, Anqun Du, Xiaoyong Renmin Univ China Sch Informat Key Lab Data Engn & Knowledge Engn MOE Beijing 100872 Peoples R China Nanyang Technol Univ NTU Sch Comp Sci & Engn SCSE Singapore 639798 Singapore Tencent Inc Database R&D Dept Shenzhen Peoples R China

Stream processing has been in widespread use, and one of the most common application scenarios is SQL query on streams. By 2021, the global deployment of IoT endpoints reached 12.3 billion, indicating a surge in data generation. However, the escalating demands for high throughput and low latency in stream processing systems have posed significant challenges due to the increasing data volume and evolving user requirements. We present a compression-based stream processing engine, called CompressStreamDB, which enables adaptive fine-grained stream processing directly on compressed streams, to significantly enhance the performance of existing stream processing solutions. CompressStreamDB utilizes nine diverse compression methods tailored for different stream data types and integrates a cost model to automatically select the most efficient compression schemes. CompressStreamDB provides high throughput with low latency in stream SQL processing by identifying and eliminating redundant data among streams. Our evaluation demonstrates that CompressStreamDB improves average performance by 3.84x and reduces average delay by 68.0% compared to the state-of-the-art stream processing solution for uncompressed streams, along with 68.7% space savings. Besides, our edge trials show an average throughput/price ratio of 9.95x and a throughput/power ratio of 7.32x compared to the cloud design.

关键词： Encoding compression algorithms Real-time systems Low latency communication data processing Costs Delays data compaction and compression stream processing edge computing

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