Abstract: When a merchant ships a product to a buyer, the merchant may wish to push shipping status updates to the buyer. Moreover, the buyer may wish to receive a notification when a shipping event has occurred in order to remain informed regarding the shipping status of their package without actively checking the package's status with the carrier. In some embodiments, there is provided a computer-implemented system and method that obtains a tracking identifier for a package, transmits the tracking identifier to a carrier's computing interface (e.g. the carrier's API), receives back an indication of the most recent shipping event, predicts the time of a next shipping event at least based on the most recent shipping event, and retransmits the tracking identifier to the computing interface based on the predicted time of the next shipping event.

Abstract: When a merchant ships a product to a buyer, the merchant may wish to push shipping status updates to the buyer. Moreover, the buyer may wish to receive a notification when a shipping event has occurred in order to remain informed regarding the shipping status of their package without actively checking the package's status with the carrier. In some embodiments, there is provided a computer-implemented system and method that obtains a tracking identifier for a package, transmits the tracking identifier to a carrier's computing interface (e.g. the carrier's API), receives back an indication of the most recent shipping event, predicts the time of a next shipping event at least based on the most recent shipping event, and retransmits the tracking identifier to the computing interface based on the predicted time of the next shipping event.

Abstract: The current document is directed to methods and systems that collect metric data within computing facilities, including large data centers and cloud-computing facilities. In a described implementation, lower and higher metric-data-value thresholds are used to partition collected metric data into outlying metric data and inlying metric data. The inlying metric data is quantized to compress the inlying metric data and adjacent data points having the same quantized metric-data values are eliminated, to further compress the inlying metric data. The resulting compressed data includes original metric-data representations for outlier data points and compressed metric-data representations for inlier data points, providing accurate restored metric-data values for significant data points when compressed metric data is decompressed.

Abstract: The current document is directed to methods and systems that collect metric data within computing facilities, including large data centers and cloud-computing facilities. In a described implementation, lower and higher metric-data-value thresholds are used to partition collected metric data into outlying metric data and inlying metric data. The inlying metric data is quantized to compress the inlying metric data and adjacent data points having the same quantized metric-data values are eliminated, to further compress the inlying metric data. The resulting compressed data includes original metric-data representations for outlier data points and compressed metric-data representations for inlier data points, providing accurate restored metric-data values for significant data points when compressed metric data is decompressed.

Abstract: The current document is directed to methods and systems that collect metric data within computing facilities, including large data centers and cloud-computing facilities. In a described implementation, two or more metric-data sets are combined to generate a multidimensional metric-data set. The multidimensional metric-data set is compressed for efficient storage by clustering the multidimensional data points within the multidimensional metric-data set to produce a covering subset of multidimensional data points and by then representing the multidimensional-data-point members of each cluster by a cluster identifier rather than by a set of floating-point values, integer values, or other types of data representations. The covering set is constructed to ensure that the compression does not result in greater than a specified level of distortion of the original data.

Abstract: The current document is directed to methods and systems that collect metric data within computing facilities, including large data centers and cloud-computing facilities. In a described implementation, two or more metric-data sets are combined to generate a multidimensional metric-data set. The multidimensional metric-data set is compressed for efficient storage by clustering the multidimensional data points within the multidimensional metric-data set to produce a covering subset of multidimensional data points and by then representing the multidimensional-data-point members of each cluster by a cluster identifier rather than by a set of floating-point values, integer values, or other types of data representations. The covering set is constructed to ensure that the compression does not result in greater than a specified level of distortion of the original data.

Abstract: The current document is directed to methods and systems that collect metric data within computing facilities, including large data centers and cloud-computing facilities. In a described implementation, lower and higher metric-data-value thresholds are used to partition collected metric data into outlying metric data and inlying metric data. The inlying metric data is quantized to compress the inlying metric data and adjacent data points having the same quantized metric-data values are eliminated, to further compress the inlying metric data. The resulting compressed data includes original metric-data representations for outlier data points and compressed metric-data representations for inlier data points, providing accurate restored metric-data values for significant data points when compressed metric data is decompressed.

Abstract: The current document is directed to a multi-stage metric-data compression method and subsystem for compressing metric data collected and stored within distributed computing systems to facilitate computer-system management and administration. In a described implementation, metric data is partitioned into constant metric data, low-variability metric data, and high-variability metric data. High-variability metric data is compressed by identifying a set of basis metrics, or independent metrics, with respect to which a remaining set of dependent metrics can be expressed using coefficient multipliers. The high-variability metric data can then be stored as a set of independent metrics and set of coefficients, along with a small amount of additional data.