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1 Compression Ratio

The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform

Petko Bogdanov – 1st expert on this subject based on the ideXlab platform

  • Efficient spectrum summarization using compressed spectrum scans
    IEEE INFOCOM 2018 – IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2018
    Co-Authors: Mariya Zheleva, Timothy Larock, Paul Schmitt, Petko Bogdanov

    Abstract:

    We present AirPress, a spectrum scan Compression method that leverages wavelet decomposition for lossy Compression of spectrum data and allows up to 64:1 Compression Ratio of power spectral density traces without adversely impacting the spectrum summarization accuracy. We demonstrate the utility of AirPress on real-world spectrum measurements and show that it enables high-accuracy spectrum summarization of real-world transmitters while reducing the corresponding trace by 94%.

  • INFOCOM Workshops – Efficient spectrum summarization using compressed spectrum scans
    IEEE INFOCOM 2018 – IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2018
    Co-Authors: Mariya Zheleva, Timothy Larock, Paul Schmitt, Petko Bogdanov

    Abstract:

    We present AirPress, a spectrum scan Compression method that leverages wavelet decomposition for lossy Compression of spectrum data and allows up to 64:1 Compression Ratio of power spectral density traces without adversely impacting the spectrum summarization accuracy. We demonstrate the utility of AirPress on real-world spectrum measurements and show that it enables high-accuracy spectrum summarization of real-world transmitters while reducing the corresponding trace by 94%.

  • AirPress: High-accuracy spectrum summarization using compressed scans
    2018 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2018
    Co-Authors: Mariya Zheleva, Timothy Larock, Paul Schmitt, Petko Bogdanov

    Abstract:

    Spectrum summarization is the analysis of a wide-band spectrum scan to determine the number of transmitters, their time-frequency characteristics, approximate modulation and legitimacy of opeRation. Spectrum summarization has emerged as a critical functionality to enable next-geneRation dynamic spectrum access technologies and legislation. Typically, spectrum summarization is performed in a cloud-based manner, requiring full-scan transmission from the spectrum sensors to the cloud. As spectrum scans generate large volumes of data, full-scan transmission quickly incurs prohibitively-high cost in terms of bandwidth and storage requirements. To address this problem we design AirPress, a spectrum scan Compression method that leverages wavelet decomposition for lossy Compression of spectrum data and allows up to 64:1 Compression Ratio of power spectral density traces without adversely impacting the spectrum summarization accuracy. We demonstrate the utility of AirPress on real-world spectrum measurements and show that it enables high-accuracy spectrum summarization of real-world transmitters while reducing the corresponding trace by 94%.

Christos Faloutsos – 2nd expert on this subject based on the ideXlab platform

  • Efficiently supporting ad hoc queries in large datasets of time sequences
    ACM SIGMOD Record, 2005
    Co-Authors: Flip Korn, Hosagrahar Visvesvaraya Jagadish, Christos Faloutsos

    Abstract:

    Ad hoc querying is difficult on very large datasets, since it is usually not possible to have the entire dataset on disk. While Compression can be used to decrease the size of the dataset, compressed data is notoriously difficult to index or access. In this paper we consider a very large dataaet compris- ing multiple distinct time sequences. Each point in the sequence is a numerical value. We show how to compress such a dataset into a format that supports ad hoc query- ing, provided that a small error can be tolerated when the data is uncompressed. Experiments on large, real world datasets (AT&T customer calling patterns) show that the proposed method achieves an average of less thau 5% error in any data value after compressing to a mere 2.5% of the original space (i. e., a 40:1 Compression Ratio), with these numbers not very sensitive to dataset size. Experiments on aggregate queries achieved a 0.5% reconstruction error with a space requirement under 2%

  • SIGMOD Conference – Efficiently supporting ad hoc queries in large datasets of time sequences
    Proceedings of the 1997 ACM SIGMOD international conference on Management of data – SIGMOD '97, 1997
    Co-Authors: Flip Korn, Hosagrahar Visvesvaraya Jagadish, Christos Faloutsos

    Abstract:

    Ad hoc querying is difficult on very large datasets, since it is usually not possible to have the entire dataset on disk. While Compression can be used to decrease the size of the dataset, compressed data is notoriously difficult to index or access. In this paper we consider a very large dataset comprising multiple distinct time sequences. Each point in the sequence is a numerical value. We show how to compress such a dataset into a format that supports ad hoc querying, provided that a small error can be tolerated when the data is uncompressed. Experiments on large, real world datasets ( AT&T customer calling patterns) show that the proposed method achieves an average of less than 5% error in any data value after compressing to a mere 2.5% of the original space ( i.e. , a 40:1 Compression Ratio), with these numbers not very sensitive to dataset size. Experiments on aggregate queries achieved a 0.5% reconstruction error with a space requirement under 2%.

Mariya Zheleva – 3rd expert on this subject based on the ideXlab platform

  • Efficient spectrum summarization using compressed spectrum scans
    IEEE INFOCOM 2018 – IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2018
    Co-Authors: Mariya Zheleva, Timothy Larock, Paul Schmitt, Petko Bogdanov

    Abstract:

    We present AirPress, a spectrum scan Compression method that leverages wavelet decomposition for lossy Compression of spectrum data and allows up to 64:1 Compression Ratio of power spectral density traces without adversely impacting the spectrum summarization accuracy. We demonstrate the utility of AirPress on real-world spectrum measurements and show that it enables high-accuracy spectrum summarization of real-world transmitters while reducing the corresponding trace by 94%.

  • INFOCOM Workshops – Efficient spectrum summarization using compressed spectrum scans
    IEEE INFOCOM 2018 – IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2018
    Co-Authors: Mariya Zheleva, Timothy Larock, Paul Schmitt, Petko Bogdanov

    Abstract:

    We present AirPress, a spectrum scan Compression method that leverages wavelet decomposition for lossy Compression of spectrum data and allows up to 64:1 Compression Ratio of power spectral density traces without adversely impacting the spectrum summarization accuracy. We demonstrate the utility of AirPress on real-world spectrum measurements and show that it enables high-accuracy spectrum summarization of real-world transmitters while reducing the corresponding trace by 94%.

  • AirPress: High-accuracy spectrum summarization using compressed scans
    2018 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2018
    Co-Authors: Mariya Zheleva, Timothy Larock, Paul Schmitt, Petko Bogdanov

    Abstract:

    Spectrum summarization is the analysis of a wide-band spectrum scan to determine the number of transmitters, their time-frequency characteristics, approximate modulation and legitimacy of opeRation. Spectrum summarization has emerged as a critical functionality to enable next-geneRation dynamic spectrum access technologies and legislation. Typically, spectrum summarization is performed in a cloud-based manner, requiring full-scan transmission from the spectrum sensors to the cloud. As spectrum scans generate large volumes of data, full-scan transmission quickly incurs prohibitively-high cost in terms of bandwidth and storage requirements. To address this problem we design AirPress, a spectrum scan Compression method that leverages wavelet decomposition for lossy Compression of spectrum data and allows up to 64:1 Compression Ratio of power spectral density traces without adversely impacting the spectrum summarization accuracy. We demonstrate the utility of AirPress on real-world spectrum measurements and show that it enables high-accuracy spectrum summarization of real-world transmitters while reducing the corresponding trace by 94%.