Abstract: A computing platform may perform computer database file management by implementing statistics maintenance and column similarity. The computing platform may receive an input table and determine a data type for the received input table. The computing platform may determine statistics relevant to the input table, and statistics relevant to historical tables of the data type. Statistics may include overlap, distribution, mean, standard deviation, percentile, median, and the like. Based on the determined statistics, the computing platform may determine a similarity score between the input table and the historical tables. Based on the statistics and column similarity, the computing platform may perform database file management, such as storing records, updating records, identifying data anomalies, sending alerts related to identified data anomalies, moving records, tagging records, and the like. The computing platform may send alerts related to identified data anomalies to remote devices.

Abstract: A computing platform may perform computer database file management by implementing statistics maintenance and column similarity. The computing platform may receive an input table and determine a data type for the received input table. The computing platform may determine statistics relevant to the input table, and statistics relevant to historical tables of the data type. Statistics may include overlap, distribution, mean, standard deviation, percentile, median, and the like. Based on the determined statistics, the computing platform may determine a similarity score between the input table and the historical tables. Based on the statistics and column similarity, the computing platform may perform database file management, such as storing records, updating records, identifying data anomalies, sending alerts related to identified data anomalies, moving records, tagging records, and the like. The computing platform may send alerts related to identified data anomalies to remote devices.

Abstract: Systems and methods are provided for transforming historical data collected in response to one or more triggering events, in order to classify textual values. Embodiments access a plurality of textual values from historical transaction data; identify one or more distinct patterns within the plurality of textual values; group the textual values based on the one or more distinct patterns, thereby forming one or more clusters; apply a similarity gauge to the textual values of each of the clusters to determine similarity or dissimilarity among the textual values of each cluster; and filter the textual values of each cluster to determine which textual values belong in each cluster, wherein the textual values that belong are cluster values. Some embodiments also remove undesired characters from the textual values, and in some cases identifying the distinct patterns includes comparing pronunciations and/or phonetics of the textual values.

Abstract: Systems and methods are provided for transforming historical data collected in response to one or more triggering events, in order to analyze resource utilization, inflow and outflow. Embodiments access inflow and outflow information corresponding to a primary node from historical data; analyze the inflow and outflow information corresponding to the primary node in order to identify one or more channels of flow out of a plurality of channels of flow, each channel connecting the primary node and a secondary node, the one or more channels of flow identified when they exhibit one or more predetermined characteristics; determine that the one or more channels of flow, based on the exhibited predetermined characteristics should be categorized into one or more categories out of a plurality of categories.

Abstract: Systems and methods are provided for transforming historical data collected in response to one or more triggering events, in order to classify textual values. Embodiments access a plurality of textual values from historical transaction data; identify one or more distinct patterns within the plurality of textual values; group the textual values based on the one or more distinct patterns, thereby forming one or more clusters; apply a similarity gauge to the textual values of each of the clusters to determine similarity or dissimilarity among the textual values of each cluster; and filter the textual values of each cluster to determine which textual values belong in each cluster, wherein the textual values that belong are cluster values. Some embodiments also remove undesired characters from the textual values, and in some cases identifying the distinct patterns includes comparing pronunciations and/or phonetics of the textual values.

Abstract: Aspects of the present disclosure are directed to methods and systems for macroeconomic indication, including electronically selectively mapping a plurality of merchant classification data to sub-sector economic data for payment card transactions classification. The method may electronically receive a plurality of retail sales data based on the payment card transactions classification over a predetermined period of time to define an economic time series dataset; electronically adjust the economic time series dataset based on an autoregressive integrated moving average; and electronically transform the economic time series dataset after the adjusting step by using linear regression to define a predefined time period percentage change in the periodic data.