Decompression Cost Research Articles

Edge-Enhanced QoS Aware Compression Learning for Sustainable Data Stream Analytics

Existing Cloud systems involve large volumes of data streams being sent to a centralised data centre for monitoring, storage and analytics. However, migrating all the data to the cloud is often not feasible due to cost, privacy, and performance concerns. However, Machine Learning (ML) algorithms typically require significant computational resources, hence cannot be directly deployed on resource-constrained edge devices for learning and analytics. Edge-enhanced compressive offloading becomes a sustainable solution that allows data to be compressed at the edge and offloaded to the cloud for further analysis, reducing bandwidth consumption and communication latency. The design and implementation of a learning method for discovering compression techniques that offer the best QoS for an application is described. The approach uses a novel modularisation approach that maps features to models and classifies them for a range of Quality of Service (QoS) features. An automated QoS-aware orchestrator has been designed to select the best autoencoder model in real-time for compressive offloading in edge-enhanced clouds based on changing QoS requirements. The orchestrator has been designed to have diagnostic capabilities to search appropriate parameters that give the best compression. A key novelty of this work is harnessing the capabilities of autoencoders for edge-enhanced compressive offloading based on portable encodings, latent space splitting and fine-tuning network weights. Considering how the combination of features lead to different QoS models, the system is capable of processing a large number of user requests in a given time. The proposed hyperparameter search strategy (over the neural architectural space) reduces the computational cost of search through the entire space by up to 89%. When deployed on an edge-enhanced cloud using an Azure IoT testbed, the approach saves up to 70% data transfer costs and takes 32% less time for job completion. It eliminates the additional computational cost of decompression, thereby reducing the processing cost by up to 30%.

Implementation of Outpatient Minimally Invasive Lumbar Decompression at an Academic Medical Center Without Ambulatory Surgery Centers

Background Lumbar decompressions are increasingly performed at ambulatory surgery centers (ASCs). We sought to compare costs of open and minimally invasive (MIS) lumbar decompressions performed at a university without dedicated ASCs. Methods Lumbar decompressions performed at a tertiary academic hospital or satellite university hospital dedicated to outpatient surgery were retrospectively reviewed. Care pathways were same-day, overnight observation, or inpatient admission. Patient demographics, American Society of Anesthesiologists classification, Charlson Comorbidity Index, surgical characteristics, 30-day readmission, and costs were collected. A systematic review of lumbar decompression cost literature was performed. Results A total of 354 patients, mean age 55 years with 128 women (36.2%), were reviewed. There was no significant difference in age, gender, body mass index, American Society of Anesthesiologists classification, or Charlson Comorbidity Index between patients treated with open and minimally invasive surgery. Open decompression was associated with higher total cost ($21,280 vs. $14,407; P Conclusions When accounting for care pathway, the cost of open and MIS decompression were no different. Transition from a tertiary academic hospital to a university hospital specializing in outpatient surgery was not associated with lower costs. Academic departments may consider transitioning lumbar decompressions to a dedicated ASC to maximize cost savings; however, additional studies are needed.

Implementation of Outpatient Minimally Invasive Lumbar Decompression at an Academic Medical Center without Ambulatory Surgery Centers: A Cost Analysis and Systematic Review

Lumbar decompressions are increasingly performed at ambulatory surgery centers (ASCs). We sought to compare costs of open and minimally invasive (MIS) lumbar decompressions performed at a university without dedicated ASCs. Lumbar decompressions performed at a tertiary academic hospital or satellite university hospital dedicated to outpatient surgery were retrospectively reviewed. Care pathways were same-day, overnight observation, or inpatient admission. Patient demographics, American Society of Anesthesiologists classification, Charlson Comorbidity Index, surgical characteristics, 30-day readmission, and costs were collected. A systematic review of lumbar decompression cost literature was performed. A total of 354 patients, mean age 55 years with 128 women (36.2%), were reviewed. There was no significant difference in age, gender, body mass index, American Society of Anesthesiologists classification, or Charlson Comorbidity Index between patients treated with open and minimally invasive surgery. Open decompression was associated with higher total cost ($21,280 vs. $14,407; P<0.001); however, this was driven by care pathway and length of stay. When stratifying by care pathway, there wasno difference in total cost between open versus minimally invasive surgery among same-day ($10,609 vs. $11,074; P=0.556), overnight observation ($14,097 vs. $13,992; P=0.918), or inpatient admissions ($24,507 vs. $27,929; P= 0.311). When accounting for care pathway, the cost of open and MIS decompression were no different. Transition from a tertiary academic hospital to a university hospital specializing in outpatient surgery was not associated with lower costs. Academic departments may consider transitioning lumbar decompressions to a dedicated ASC to maximize cost savings; however, additional studies are needed.

Compression-based integral curve data reuse framework for flow visualization

Currently, by default, integral curves are repeatedly re-computed in different flow visualization applications, such as FTLE field computation, source-destination queries, etc., leading to unnecessary resource cost. We present a compression-based data reuse framework for integral curves, to greatly reduce their retrieval cost, especially in a resource-limited environment. In our design, a hierarchical and hybrid compression scheme is proposed to balance three objectives, including high compression ratio, controllable error, and low decompression cost. Specifically, we use and combine digitized curve sparse representation, floating-point data compression, and octree space partitioning to adaptively achieve the objectives. Results have shown that our data reuse framework could acquire tens of times acceleration in the resource-limited environment compared to on-the-fly particle tracing, and keep controllable information loss. Moreover, our method could provide fast integral curve retrieval for more complex data, such as unstructured mesh data.

Content aware texture compression

Efficient and high quality texture compression is important because computational powers are always limited, especially for embedded systems such as mobile phones. To access random texels instantly, down sampling and S3TC are the most commonly used methods due to the fixed compression ratio at every local region. However, the methods are content oblivious, which uniformly discard texel information. Therefore, we present content aware warp to reduce a texture resolution, where homogeneous regions are squeezed more to retain more structural information. We then relocate texture coordinates of the 3D model as the way of relocating texels, such that rendering the model with our compressed texture requires no additional decompression cost. Our texture compression technique can cooperate with existing methods such as S3TC since the compression strategies are independent. By reducing texture resolution in a content aware manner, followed by texture compression using S3TC, as the experiments show, we achieve higher compression ratios without degrading visual quality significantly.

DNA-COMPACT: DNA COMpression based on a pattern-aware contextual modeling technique.

Genome data are becoming increasingly important for modern medicine. As the rate of increase in DNA sequencing outstrips the rate of increase in disk storage capacity, the storage and data transferring of large genome data are becoming important concerns for biomedical researchers. We propose a two-pass lossless genome compression algorithm, which highlights the synthesis of complementary contextual models, to improve the compression performance. The proposed framework could handle genome compression with and without reference sequences, and demonstrated performance advantages over best existing algorithms. The method for reference-free compression led to bit rates of 1.720 and 1.838 bits per base for bacteria and yeast, which were approximately 3.7% and 2.6% better than the state-of-the-art algorithms. Regarding performance with reference, we tested on the first Korean personal genome sequence data set, and our proposed method demonstrated a 189-fold compression rate, reducing the raw file size from 2986.8 MB to 15.8 MB at a comparable decompression cost with existing algorithms. DNAcompact is freely available at https://sourceforge.net/projects/dnacompact/for research purpose.

Survey of Compression of DNA Sequence

Compression of large collections of data can lead to improvements in retrieval times by offsetting the CPU decompression costs with the cost of seeking and retrieving data from disk. In this paper, the author has study the different compression method which can compress the large DNA sequence. In this paper, authors have explored the DNA compression method that is COMRAD, which is used to compare with the dictionary based compression method i.e. LZ77, LZ78, LZW and general purpose compression method RAY. In this, authors have analyzed which one algorithm is better to compress the large collection of the DNA Sequence. Compression table and the line graph show that which compression algorithm has a better compression ratio and the compression size. It also shows that which one has better compression and decompression time.

Probe into the Problem of Water-Saving and Energy-Saving in Building

With the fast development of economy in China, some questions have come out gradually, and more and more obviously. For instance the energy in short supply, the water resource exhausted. I have made a simple analysis on the issue that some extant energy is wasted and the water resource wasted in the water supply and sewerage engineering in China, and have put forward some corresponding measures, such as full use of available municipal water supply network head, reasonable control of the flow of overpressure and decompression cost saving measures, promotion of the technology of water reuse and rainwater reuse and other measures. These measures can effectively reduce the energy and water waste in the water supply and sewerage engineering.

Periodic Streaming Data Reduction Using Flexible Adjustment of Time Section Size

Much of the research regarding streaming data has focused only on real time querying and analysis of recent data stream allowable in memory. However, as data stream mining, or tracking of past data streams, is often required, it becomes necessary to store large volumes of streaming data in stable storage. Moreover, as stable storage has restricted capacity, past data stream must be summarized. The summarization must be performed periodically because streaming data flows continuously, quickly, and endlessly. Therefore, in this paper, we propose an efficient periodic summarization method with a flexible storage allocation. It improves the overall estimation error by flexibly adjusting the size of the summarized data of each local time section. Additionally, as the processing overhead of compression and the disk I/O cost of decompression can be an important factor for quick summarization, we also consider setting the proper size of data stream to be summarized at a time. Some experimental results with artificial data sets as well as real life data show that our flexible approach is more efficient than the existing fixed approach.

A general-purpose compression scheme for large collections

Compression of large collections can lead to improvements in retrieval times by offsetting the CPU decompression costs with the cost of seeking and retrieving data from disk. We propose a semistatic phrase-based approach called xray that builds a model offline using sample training data extracted from a collection, and then compresses the entire collection online in a single pass. The particular benefits of xray are that it can be used in applications where individual records or documents must be decompressed, and that decompression is fast. The xray scheme also allows new data to be added to a collection without modifying the semistatic model. Moreover, xray can be used to compress general-purpose data such as genomic, scientific, image, and geographic collections without prior knowledge of the structure of the data. We show that xray is effective on both text and general-purpose collections. In general, xray is more effective than the popular gzip and compress schemes, while being marginally less effective than bzip2. We also show that xray is efficient: of the popular schemes we tested, it is typically only slower than gzip in decompression. Moreover, the query evaluation costs of retrieval of documents from a large collection with our search engine is improved by more than 30% when xray is incorporated compared to an uncompressed approach. We use simple techniques for obtaining the training data from the collection to be compressed and show that with just over 4% of data the entire collection can be effectively compressed. We also propose four schemes for phrase-match selection during the single pass compression of the collection. We conclude that with these novel approaches xray is a fast and effective scheme for compression and decompression of large general-purpose collections.

General-purpose compression for efficient retrieval

Compression of databases not only reduces space requirements but can also reduce overall retrieval times. In text databases, compression of documents based on semistatic modeling with words has been shown to be both practical and fast. Similarly, for specific applications—such as databases of integers or scientific databases—specially designed semistatic compression schemes work well. We propose a scheme for general-purpose compression that can be applied to all types of data stored in large collections. We describe our approach—which we call RAY—in detail, and show experimentally the compression available, compression and decompression costs, and performance as a stream and random-access technique. We show that, in many cases, RAY achieves better compression than an efficient Huffman scheme and popular adaptive compression techniques, and that it can be used as an efficient general-purpose compression scheme.

General‐purpose compression for efficient retrieval

Compression of databases not only reduces space requirements but can also reduce overall retrieval times. In text databases, compression of documents based on semistatic modeling with words has been shown to be both practical and fast. Similarly, for specific applications-such as databases of integers or scientific databases-specially designed semistatic compression schemes work well. We propose a scheme for general-purpose compression that can be applied to all types of data stored in large collections. We describe our approach-which we call RAY-in detail, and show experimentally the compression available, compression and decompression costs, and performance as a stream and random-access technique. We show that, in many cases, RAY achieves better compression than an efficient Huffman scheme and popular adaptive compression techniques, and that it can be used as an efficient general-purpose compression scheme.