Abstract: A computer-implemented method includes determining a plurality of bulk sets for querying database records. The method also includes assigning a plurality of keysets to the plurality of bulk sets, with each keyset comprising a unique set of dimension attribute values from the database records. The method also includes calculating a predicted load score of each bulk set. The method also includes performing a transfer of a keyset from a first bulk set to a second bulk set when the transfer reduces a difference between predicted load scores of the first bulk set and the second bulk set. The method also includes, after the transfer, executing bulk queries using the plurality of bulk sets.

Abstract: An example process for aligning time-series datasets includes receiving a first time-series dataset and a second time-series dataset. The first time-series dataset can include a first set of values associated with respective time stamps and the second time-series dataset can include a second set of values associated with respective time stamps. The process also includes determining degrees of variance of the first and second sets of values, and comparing each degree of variance with a threshold. The process also includes selecting among multiple time alignment processes based on the comparisons, and processing the time-series datasets according to the selected process to thereby generate an aligned time-series dataset.

Abstract: An example process for selecting chart scalings for plotting two time-series datasets includes determining a first log difference between a logarithm of a maximum value of a first dataset and a logarithm of a minimum value of the first dataset. The process also includes determining a second log difference between a logarithm of a maximum value of a second dataset and a logarithm of a minimum value of the second dataset. The process also includes selecting, based at least in part on the first and second log differences, between a linear chart scaling and a logarithmic chart scaling for the first dataset. The process also includes selecting, based at least in part on the first and second log differences, between a linear chart scaling and a logarithmic chart scaling for the second dataset.

Abstract: A computer-implemented method includes determining a plurality of bulk sets for querying database records. The method also includes assigning a plurality of keysets to the plurality of bulk sets, with each keyset comprising a unique set of dimension attribute values from the database records. The method also includes calculating a predicted load score of each bulk set. The method also includes performing a transfer of a keyset from a first bulk set to a second bulk set when the transfer reduces a difference between predicted load scores of the first bulk set and the second bulk set. The method also includes, after the transfer, executing bulk queries using the plurality of bulk sets.

Abstract: An example process for selecting chart scalings for plotting two time-series datasets includes determining a first log difference between a logarithm of a maximum value of a first dataset and a logarithm of a minimum value of the first dataset. The process also includes determining a second log difference between a logarithm of a maximum value of a second dataset and a logarithm of a minimum value of the second dataset. The process also includes selecting, based at least in part on the first and second log differences, between a linear chart scaling and a logarithmic chart scaling for the first dataset. The process also includes selecting, based at least in part on the first and second log differences, between a linear chart scaling and a logarithmic chart scaling for the second dataset.

Abstract: An example process for aligning time-series datasets includes receiving a first time-series dataset and a second time-series dataset. The first time-series dataset can include a first set of values associated with respective time stamps and the second time-series dataset can include a second set of values associated with respective time stamps. The process also includes determining degrees of variance of the first and second sets of values, and comparing each degree of variance with a threshold. The process also includes selecting among multiple time alignment processes based on the comparisons, and processing the time-series datasets according to the selected process to thereby generate an aligned time-series dataset.