This application discloses a data compression method and a computing device. The disclosed method is applied to the computing device. The method includes receiving, by the computing device, to-be-compressed data, and identifying a data type of the to-be-compressed data. The method further includes selecting one or more data compression models based on the identified data type and compressing the to-be-compressed data based on the selected one or more data compression models.

The present disclosure includes analyzing client instance performance trends to predict future client instance performance and adjusting thresholds used to send resource utilization alerts based on analyzing the client instance performance trends. In particular, a data center providing a platform as a service includes a database that stores performance data associated with client instances. The data center also includes alignment logic that temporally aligns the performance data, and a frequency based filter that compresses the aligned performance data based on frequency of values. The data center further includes dynamic threshold adjustment logic that adjusts thresholds associated with sending performance trend alerts based on analyzing the compressed set of performance data. In this manner, the thresholds may be dynamically adjusted for changing circumstances and/or relevant details associated with resource usage, and thus may more accurately send performance trend alerts indicative of situations when resource utilization becomes high and resources become low.

The invention relates to the representation of digital signals. In order to improve the perception by a user of the quality of a digital signal, a first sample of first digital signal is approximated to a second sample of a second digital signal having a second number of significant bits lower than the first number of significant bits of the first sample. The second number of significant bits is also lower than a number of significant bits allowing the second digital signal, or a signal derived therefrom to match an expected bit depth of a processing unit said second digital signal, or a signal derived therefrom is to be sent to.

The invention relates to improved convolutions of digital signals. When a first digital signal is convoluted with a second digital signal to obtain an output digital signal, to be converted afterwards using a limited number of bits. In order to prevent a loss of information, and therefore a degradation of the output digital signal upon the future conversion, at least one of the first and the second digital signal is formed of suitable values that store the information from the first digital signal within the most significant bits of the output digital signal.

A method for storing data includes: generating data for current data based on historical data and a change rule of the historical data; obtaining a delta between the data and the current data; storing the delta instead of the current data.

An ECG signal lossless compression system includes: a signal difference value generating module and a compression module. The signal difference value generating module performs an adaptive linear prediction encoding on an ECG signal, so as to generate a plurality of signal difference values corresponding to each datum of the ECG signal; the compression module divides the signal difference values into a plurality of groups and performs an adaptive linear lossless compression encoding on each group, so as to generate a plurality of window compression streams, wherein each group corresponds to a bit reference index configured to be a compression encoding parameter of the adaptive linear lossless compression encoding.

A disclosure for lossless data compression can include receiving a data block by a processor, performing, by the processor, a sparse transform extraction on the data block, selecting, by the processor, a transform matrix for the data block, modeling, by the processor, the selected transform matrix for the data block, selecting, by the processor, a transform coefficient model for the data block, modeling, by the processor, the selected transform coefficient model for the data block, compressing, by the processor, the data in the data block using the selected transform matrix and the selected transform coefficient model.

Data may be efficiently analyzed and compressed as part of a data compression service. A data compression request may be received from a client indicating data to be compressed. An analysis of the data or metadata associated with the data may be performed. In at least some embodiments, this analysis may be a rules-based analysis. Some embodiments may employ one or more machine learning techniques to historical compression data to update the rules-based analysis. One or more compression techniques may be selected out of a plurality of compression techniques to be applied to the data. Data compression candidates may then be generated according to the selected compression techniques. In some embodiments, a compression service restriction may be enforced. One of the data compression candidates may be selected and sent in a response.

The invention relates to improved convolutions of digital signals. When a first digital signal is convoluted with a second digital signal to obtain an output digital signal, to be converted afterwards using a limited number of bits. In order to prevent a loss of information, and therefore a degradation of the output digital signal upon the future conversion, at least one of the first and the second digital signal is formed of suitable values that store the information from the first digital signal within the most significant bits of the output digital signal.

The invention relates to the representation of digital signals. In order to improve the perception by a user of the quality of a digital signal, a first sample of first digital signal is approximated to a second sample of a second digital signal having a second number of significant bits lower than the first number of significant bits of the first sample. The second number of significant bits also depends upon the value of the first sample.