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检索条件"主题词=compression algorithms"

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Proof of Storage for Video Deduplication in the Cloud

Proof of Storage for Video Deduplication in the Cloud

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IEEE International Conference on Services Economics (SE)

作者： Fatema Rashid Ali Miri Isaac Woungang Department of Computer Science Ryerson University Toronto Ontario Canada

With the advent of cloud computing and its technologies, including data deduplication, more freedom are offered to the users in terms of cloud storage, processing power and efficiency, and data accessibility. The digital data has attained unexceptional growth due to the common use of internet and digital devices giving rise to Big Data problem world wise. These huge volumes of data need some practical platforms for the storage, processing and availability and cloud technology offers all the potentials to fulfil these requirements. Data deduplication is referred to as a strategy offered to cloud storage providers (CSPs) to eliminate the duplicate data and keep only a single unique copy of it for storage space saving purpose to condense Big Data issues. But these benefits also come with data security and privacy issues associated with the cloud technology since the data owner looses the physical control of its data once uploaded in the cloud storage and the CSP gains a complete ownership of the data. In this paper, assuming that the CSP is semi-honest (i.e. Honest but curious and cannot be completely trusted), a proof of retrievability (POR) and a proof of ownership (POW) are proposed for video deduplication in cloud storage environments. The POW protocol is meant to be used by the CSP to authenticate the true owner of the data video before releasing it whereas the POR protocol is meant to allow the user to check that his/her data video stored in the cloud is secured against any malicious user or the semi-honest CSP. These schemes are proposed as complement to our earlier proposed scheme for securing the video deduplication in the cloud storage through the H.264 compression algorithm. Some experimental results are provided, showing the effectiveness of our proposed POR and POW protocols.

关键词： Cloud computing Protocols compression algorithms Encryption Big data Encoding

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Identifying Hierarchical Structures in Sequences on GPU

Identifying Hierarchical Structures in Sequences on GPU

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Prashant Jalan Arihant Kumar Jain Subhajit Roy Department of Computer Science and Engineering Indian Institute of Technology Kanpur

ISBN: (纸本)9781467379533

Identifying hierarchical structures in sequences is an important problem with applications in lossless data-compression to program profiling. A popular algorithm for identifying hierarchical structures in sequences is the Sequitur algorithm developed by Nevill-Manning and Witten. Sequitur is not just a compression algorithm, it attempts to learn the hierarchical structure of the input sequence as a context-free grammar. However, Sequitur is difficult to parallelize. Inspired by Sequitur, we have developed a new GPU algorithm, that reveals the hierarchical structure in sequences and is also concurrency-friendly. Our algorithm, Pequitur, is built as a series of fast kernels (for intermittent synchronization), where each kernel attempts to minimize inter-thread communication and achieve a good load balance among the GPU threads. As opposed to Sequitur, Pequitur follows a greedy strategy to find good productions, that are productions formed by long and frequent substrings. We have implemented and evaluated our algorithm on the NVIDIA K20c card on random strings drawn from multiple distributions. On our benchmarks, Pequitur achieves an average speedup of more than 3X over an optimized Sequitur implementation with similar compression ratios.

关键词： Graphics processing units Grammar Arrays Kernel Synchronization Production compression algorithms

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Evolutionary synthesis of lossless compression algorithms with GP-zip3

Evolutionary synthesis of lossless compression algorithms wi...

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Congress on Evolutionary Computation

作者： Ahmed Kattan Riccardo Poli School of Computer Science and Electronic Engineering University of Essex UK

Here we propose GP-zip3, a system which uses Genetic Programming to find optimal ways to combine standard compression algorithms for the purpose of compressing files and archives. GP-zip3 evolves programs with multiple components. One component analyses statistical features extracted from the raw data to be compressed (seen as a sequence of 8-bit integers) to divide the data into blocks. These blocks are then projected onto a two-dimensional Euclidean space via two further (evolved) program components. K-means clustering is applied to group similar data blocks. Each cluster is then labelled with the optimal compression algorithm for its member blocks. Once a program that achieves good compression is evolved, it can be used on unseen data without the requirement for any further evolution. GP-zip3 is similar to its predecessor, GP-zip2. Both systems outperform a variety of standard compression algorithms and are faster than other evolutionary compression techniques. However, GP-zip2 was still substantially slower than off-the-shelf algorithms. GP-zip3 alleviates this problem by using a novel fitness evaluation strategy. More specifically, GP-zip3 evolves and then uses decision trees to predict the performance of GP individuals without requiring them to be used to compress the training data. As shown in a variety of experiments, this speeds up evolution in GP-zip3 considerably over GP-zip2 while achieving similar compression results, thereby significantly broadening the scope of application of the approach.

关键词： compression algorithms Training Feature extraction Estimation Image coding Data models Predictive models

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Comparison of Vector Data compression algorithms in Mobile GIS

Comparison of Vector Data Compression Algorithms in Mobile G...

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2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： Feixiang CHEN School of Information Science & Technology Beijing Forestry University Beijing,100083,China Haiyan REN School of Information Science & Technology Beijing Forestry University Beijing,100083,China

Spatial data compression is particularly important for the development of Mobile GIS. This paper gives a review on vector data compression of Mobile GIS. Firstly, the definition and classification of vector data compression algorithms are introduced. Secondly, the traditional, new and optimal algorithms are analyzed separately, and their principles, research status, strengths and weaknesses are further discussed in detail. In the end, this paper makes an important comparison among Douglas-Peucker algorithm, wavelet-based algorithm and dynamic programming-based algorithm.

关键词： Mobile GIS vector data compression algorithms

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A Case Study of Optimizing Big Data Analytical Stacks Using Structured Data Shuffling

A Case Study of Optimizing Big Data Analytical Stacks Using ...

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IEEE International Conference on Cluster Computing

作者： Dixin Tang Taoying Liu Rubao Lee Hong Liu Wei Li Institute of Computing Technology Chinese Academy of Sciences Department of Computer Science and Engineering The Ohio State University

ISBN: (纸本)9781467365994

Current major big data analytical stacks often consist of a general purpose, multi-staged computation framework (e.g. Hadoop) and an SQL query system (e.g. Hive) on its top. A key factor of query performance is the efficiency of data shuffling between two execution stages (e.g. Map/Reduce). In current data shuffling, various useful information about the shuffled data and the query on the data is simply wasted. In this paper, we make a strong case of cross-layer optimizations for Hive/Hadoop stack: we have designed and implemented a novel data shuffling mechanism in Hadoop, called Structured Data Shuffling (S-Shuffle), which carefully leverages the rich information in data and queries to optimize the overall query processing. Our experimental results with industry-standard TPC-H benchmark show that, by using S-Shuffle, the performance of SQL query processing on Hadoop can be improved by up to 2.4x.

关键词： Merging Sorting Optimization Data mining Yttrium Big data compression algorithms

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Exploiting compressed block size as an indicator of future reuse

Exploiting compressed block size as an indicator of future r...

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IEEE Symposium on High-Performance Computer Architecture

作者： Gennady Pekhimenko Tyler Huberty Rui Cai Onur Mutlu Phillip B. Gibbons Michael A. Kozuch Todd C. Mowry Carnegie Mellon University Intel Labs Pittsburgh

We introduce a set of new compression-Aware Management Policies (CAMP) for on-chip caches that employ data compression. Our management policies are based on two key ideas. First, we show that it is possible to build a more efficient management policy for compressed caches if the compressed block size is directly used in calculating the value (importance) of a block to the cache. This leads to Minimal-Value Eviction (MVE), a policy that evicts the cache blocks with the least value, based on both the size and the expected future reuse. Second, we show that, in some cases, compressed block size can be used as an efficient indicator of the future reuse of a cache block. We use this idea to build a new insertion policy called Size-based Insertion Policy (SIP) that dynamically prioritizes cache blocks using their compressed size as an indicator. We compare CAMP (and its global variant G-CAMP) to prior on-chip cache management policies (both size-oblivious and size-aware) and find that our mechanisms are more effective in using compressed block size as an extra dimension in cache management decisions. Our results show that the proposed management policies (i) decrease off-chip bandwidth consumption (by 8.7% in single-core), (ii) decrease memory subsystem energy consumption (by 7.2% in single-core) for memory intensive workloads compared to the best prior mechanism, and (iii) improve performance (by 4.9%/9.0%/10.2% on average in single-/two-/four-core workload evaluations and up to 20.1%) CAMP is effective for a variety of compression algorithms and different cache designs with local and global replacement strategies.

关键词： Arrays Bandwidth Radiation detectors compression algorithms System-on-chip Memory management

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Analysis of compressed speech signals in an Automatic Speaker Recognition system

Analysis of compressed speech signals in an Automatic Speake...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Richard A. Metzger John F. Doherty David M. Jenkins Department of Electrical Engineering Pennsylvania State University Applied Research Laboratory University Park Pennsylvania

This paper analyzes the effects popular audio compression algorithms have on the performance of a speaker recognition system. Popular audio compression algorithms were used to compress both clean and noisy speech before being passed to a speaker recognition system. The features extracted from each speaker were 19-dimensional Mel-Frequency Cepstrum Coefficients (MFCC) and the corresponding features were modeled using a 16 mixture Gaussian Mixture Model (GMM). Our experiments show that compression will have a negative effect on recognition rates if the compressed speech is clean. However, if small amounts of white Gaussian noise are added before the speech is compressed, recognition rates can be increased by as much as 7% with certain compression algorithms.

关键词： Speech Speech recognition Digital audio players compression algorithms Hidden Markov models Signal to noise ratio

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Hardware-Software Co-design of 3D Data compression for Real-time Ultrasonic Imaging Applications

Hardware-Software Co-design of 3D Data Compression for Real-...

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IEEE International Ultrasonics Symposium (IUS)

作者： Govindan, Pramod Saniie, Jafar IIT Dept Elect & Comp Engn Chicago IL 60616 USA

ISBN: (纸本)9781479970490

Many of the ultrasonic NDE and imaging applications require processing of huge amount of data in realtime. compression of acquired data helps to reduce the storage and to rapidly transmit information to remote locations for further analysis. Signal fidelity, computational speed and resource utilization are the major parameters to be considered while designing the architecture for the compression algorithm. The objective of this study is to implement discrete wavelet transform (DWT) based ultrasonic 3D data compression algorithm on a reconfigurable ultrasonic systemon- chip hardware platform targeted for real-time ultrasonic imaging applications. The reconfigurable platform allows analysis of multiple architectures to suit various applications. The algorithm is implemented as a hardware-only design and hardware-software co-design. Both implementations provide a high signal compression ratio of about 98% with good quality signal reconstruction. This study demonstrates that, compressing 33 MBytes of experimental ultrasonic 3D data into 0.4 MBytes requires only one-fourth of a second for hardware-only design, and one minute for hardware-software co-design, making both designs highly suitable for real-time ultrasonic imaging applications.

关键词： Acoustics compression algorithms Hardware Program processors System-on-chip Three-dimensional displays Ultrasonic imaging

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compression of Phase Image for Three-dimensional Object

Compression of Phase Image for Three-dimensional Object

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21st International Workshop on Active-Matrix Flatpanel Displays and Devices - TFT Technologies and FPD Materials (AM-FPD)

作者： Tu, Han-Yen Hsieh, Ching-Huang Hoang, Huong-Giang Chinese Culture Univ Dept Elect Engn Taipei 11114 Taiwan

ISBN: (纸本)9784863483958

We present the performances of compression analysis for the phase image resulted from different phase unwrapping algorithms applied to fringe projection profilometry. Since three-dimensional (3D) surface shape information of objects can be extracted from the phase maps of objects, the phase unwrapping is critical to many 3D applications for obtaining accurate phase. To reduce the size of storage and processing for 3D data, these phase images are fed to the compression algorithms. The efficiency is evaluated and compared with compression ratio and normalized root mean square (NRMS) error. Our simulation and experimental results show that the H.264/AVC codec method can maintain low NRMS error while with higher compression ratio than the other methods investigated in this paper.

关键词： Algorithm design and analysis Image coding Image reconstruction Shape Surface reconstruction Three-dimensional displays Transform coding PHASE IMAGING three-dimensional displays Algorithm design and analysis transform coding Surface reconstruction image coding image reconstruction compression ratio Phase compression algorithms

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Large scale trajectory data management

Large scale trajectory data management

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International Conference on Logistics, Informatics and Service Sciences (LISS)

作者： Ayong Ye Yongxing Zheng Fujian Normal University Fuzhou Fujian CN Fujian Normal University Fuzhou China

ISBN: (纸本)9781479918928

In recent years, the wide usage of GPS-enabled devices has generated vast volumes of spatio temporal streams of location data, which raising management challenges, such as efficient storage and querying. Therefore, compression techniques are inevitable also in the field of moving object databases. In this paper, we propose a line simplification technique to compress moving object trajectories, which take the change of moving direction as the judgmental threshold to collect GPS points. The efficiency of our technique is demonstrated through an extensive experimental evaluation on trajectory data using real trajectory datasets collected by Geolife project.

关键词： Trajectory Global Positioning System Mobile communication compression algorithms Time complexity Dead reckoning Data mining

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