Datacomp: Locally Independent Adaptive Compression for Real-World Systems

Petérson, Peter A.; Reiher, Peter

doi:10.1109/icdcs.2016.106

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…The Lempel-Ziv scheme puts a pointer into the place of each previewed string. We use a version of Lempel-Ziv that compresses these pointers by Huffman coding (Peterson, 2013). The pointers of Lempel-Ziv look like (345, 8), which means go backward 345 bytes and copy 8 characters.…”

Section: Lempel-ziv Methodsmentioning

confidence: 99%

Can Flight Data Recorder Memory Be Stored on the Cloud?

Wiseman¹

2016

Journal of Aviation Technology and Engineering

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Flight data recorders (FDRs, or black boxes) generate data that is collected on an embedded memory device. A well-known difficulty with these devices is that the embedded memory device runs out of space. To avoid getting into this problematic situation, the software of the FDR is designed to operate in a watchful mode, constantly working to minimize the use of memory space; otherwise a larger FDR would be needed. However, larger FDRs can be a problem because they have very rigorous requirements; thus, enlargement is costly. Outcomes of this research include the recommendation to send FDR data to a remote cloud storage system, so the data memory device will be unbounded.

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Section: Lempel-ziv Methodsmentioning

confidence: 99%

Can Flight Data Recorder Memory Be Stored on the Cloud?

Wiseman¹

2016

Journal of Aviation Technology and Engineering

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“…A research conducted by Peter et al proposed an adaptive compression system that searches for the most appropriate compression algorithm by considering the type of the target data and system conditions [13]. This system chooses the compression algorithm that maximizes network throughput based on CPU utilization and frequency, available network bandwidth, and data compressibility.…”

Section: Related Workmentioning

confidence: 99%

“…Features related to Huffman coding includes symbol count, symbol frequency distribution, etc. [12], [13], [45], [47].…”

Section: ) Enhanced Heuristic-based Compressibility Predictionmentioning

confidence: 99%

“…• Computation complexity reduction: many previous researches have studied compressibility prediction [12], [13], [22], [45]- [47]. However, these studies only discussed software-centric implementations, such as log, square, division, and floating-point operations, all requiring significant computational time and hardware costs.…”

Section: ) Hardware Cost-reduction Techniquementioning

confidence: 99%

“…Many previous studies have investigated techniques to predict the compressibility of data chunks and improve the performance of data compression based on prediction results [12], [13], [22], [45]- [47]. However, these previous studies have two limitations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Low-Overhead Compressibility Prediction for High-Performance Lossless Data Compression

et al. 2020

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As big data has evolved over the past few years, a lack of storage space and I/O bandwidth has become one of the most important challenges to overcome. To mitigate these problems, data compression schemes reduce the amount of data to be stored and transmitted at the cost of additional CPU overhead. Many researchers have attempted to reduce the computational load imposed on the CPU by data compression using specialized hardware. However, space savings through data compression often comes from only a small portion of data. Therefore, compressing all data, regardless of data compressibility, can waste computational resources. Our work aims to decrease the cost of data compression by introducing a selective data compression scheme based on data compressibility prediction. The proposed compressibility prediction method provides more fine-grained selectivity for combinational compression. Additionally, our method reduces the amount of resources consumed by the compressibility predictor, enabling selective compression at a low cost. To verify the proposed scheme, we implemented a DEFLATE compression system on a field-programmable gate array platform. Experimental results demonstrate that the proposed scheme improves compression throughput by 34.15% with a negligible decrease in compression ratio. INDEX TERMS Data compression, Huffman coding, LZ77 encoding, accelerator architecture, field programmable gate array, estimation, compressibility.

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Unlimited and protected memory for flight data recorders

Wiseman

2016

AEAT

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Purpose -Extensive enlargement and safety of Flight Data Recorder memory.Design/methodology/approach -Moving the memory of Flight Data Recorders from an internal embedded device to a cloud.Findings -Our implementation has made the embedded memory device of Flight Data Recorder effectively unlimited and hence much more information can be stored.Research limitations/implications -The possibility of a flight data recorder to be damaged or lost in a crash is not so high, but our implementation can be very helpful in cases such as aerial disappearances. Practical implications -Larger and protected memory for Flight Data Recorders.Social implications -Faster finding of reasons for crashes and enabling to take immediate actions so as to remedy the failures.Originality/value -The use of Internet and cellphones in airplanes is nothing special in our time. It is suggested to take this technology for Flight Data Recorders as well.

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Datacomp: Locally Independent Adaptive Compression for Real-World Systems

Cited by 7 publications

References 13 publications

Can Flight Data Recorder Memory Be Stored on the Cloud?

Can Flight Data Recorder Memory Be Stored on the Cloud?

Low-Overhead Compressibility Prediction for High-Performance Lossless Data Compression

Unlimited and protected memory for flight data recorders

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