# US 12,111,848 B2 · Active management of files being processed in enterprise data warehouses utilizing time series predictions > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,111,848 B2 - Document ID: US12111848B2 - Status: Granted - Kind: B2 - Application number: 18/102,901 - Filed: 2023-01-30 - Published: 2024-08-01 - Granted: 2024-10-08 - Earliest claimed: 2023-01-30 - Assignees: Cerner Innovation, Inc. - Applicants: Cerner Innovation, Inc. - Inventors: Wasimakram Binnal; Karthik Kolar Nagaraja; Pradeep Revanna Premakumar - Web page: https://patentgrep.com/patent/US12111848B2 ## CPC - G06F 16/283: {Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP} - G06F 16/219: {Managing data history or versioning (querying versioned data G06F16/2474; querying temporal data G06F16/2477)} - H04L 67/10: in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract Techniques are provided for determining a delay in a data process flow at an enterprise data warehouse. An example method includes a device receiving historical data from a staging area and a target database of a data warehouse. The device can generate a feature for a machine learning model based at least in part on the historical data. The device can generate a forecasted time interval between receipt of data at the staging area and receipt of the data at the target database using the machine learning model and based at least in part on the generated feature, the data to be transmitted from the staging area to the target database. The device can compare the forecasted time interval with an expected time interval. The device can generate a work ticket based at least in part on the comparison. ## Claims ### Claim 1 A method, comprising: receiving, by a computing device, historical data from a staging area and a target database of a data warehouse; generating, by the computing device, a feature for a machine learning model based at least in part on the historical data; generating, by the computing device, a forecasted time interval between receipt of data at the staging area and receipt of the data at the target database using the machine learning model and based at least in part on the generated feature, the data to be transmitted from the staging area to the target database; comparing, by the computing device, the forecasted time interval with an expected time interval for a file received at the staging area; and generating, by the computing device, a work ticket associated with the file based at least in part on the comparison. ### Claim 2 (depends on 1) The method of claim 1, wherein the method further comprises: training the machine learning model using the historical data; and generating the forecasted time interval based at least in part on the training, wherein the forecasted time interval is generated on a same day as the training. ### Claim 3 (depends on 1) The method of claim 1, wherein the forecasted time interval is with respect to a source of the data transmitted, and wherein the method further comprises: determining a first confidence score for an accuracy of the machine learning model; determining a threshold confidence score based at least in a part on the first confidence score; determining a second confidence score for the forecasted time interval; comparing the first confidence score with the second confidence score; and determining whether to compare the first confidence and the second confidence based at least in a part on the comparison. ### Claim 4 (depends on 3) The method of claim 3, wherein determining threshold confidence score is based at least in a part on a percentile of the first confidence score. ### Claim 5 (depends on 1) The method of claim 1, wherein the machine learning model uses a regression analytics of the historical data to output to the forecasted time interval. ### Claim 6 (depends on 1) The method of claim 1, wherein the data warehouse is an enterprise data warehouse. ### Claim 7 (depends on 1) The method of claim 1, wherein the expected time interval is an agreed upon by a sender of the data and an operator of the data warehouse. ### Claim 8 A computing device, comprising: a processor; and a computer-readable medium including instructions that, when executed by the processor, cause the processor to perform operations comprising: receiving historical data from a staging area and a target database of a data warehouse; generating a feature for a machine learning model based at least in part on the historical data; generating a forecasted time interval between receipt of data at the staging area and receipt of the data at the target database using the machine learning model and based at least in part on the generated feature, the data to be transmitted from the staging area to the target database; comparing the forecasted time interval with an expected time interval for a file received at the staging area; and generating a work ticket associated with the file based at least in part on the comparison. ### Claim 9 (depends on 8) The computing device of claim 8, wherein instructions that, when executed by the processor, further cause the processor to perform operations comprising: training the machine learning model using the historical data; and generating the forecasted time interval based at least in part on the training, wherein the forecasted time interval is generated on a same day as the training. ### Claim 10 (depends on 8) The computing device of claim 8, wherein the forecasted time interval is with respect to a source of the data transmitted, and wherein instructions that, when executed by the processor, further cause the processor to perform operations comprising: determining a first confidence score for an accuracy of the machine learning model; determining a threshold confidence score based at least in a part on the first confidence score; determining a second confidence score for the forecasted time interval; comparing the first confidence score with the second confidence score; and determining whether to compare the first confidence and the second confidence based at least in a part on the comparison. ### Claim 11 (depends on 10) The computing device of claim 10, wherein determining threshold confidence score is based at least in a part on a percentile of the first confidence score. ### Claim 12 (depends on 8) The computing device of claim 8, wherein the machine learning model uses a regression analytics of the historical data to output to the forecasted time interval. ### Claim 13 (depends on 8) The computing device of claim 8, wherein the data warehouse is an enterprise data warehouse. ### Claim 14 (depends on 8) The computing device of claim 8, wherein the expected time interval is an agreed upon by a sender of the data and an operator of the data warehouse. ### Claim 15 A non-transitory computer-readable medium having stored thereon a sequence of instructions that, when executed by a processor, causes the processor to perform operations comprising: receiving historical data from a staging area and a target database of a data warehouse; generating a feature for a machine learning model based at least in part on the historical data; generating a forecasted time interval between receipt of data at the staging area and receipt of the data at the target database using the machine learning model and based at least in part on the generated feature, the data to be transmitted from the staging area to the target database; comparing the forecasted time interval with an expected time interval for a file received at the staging area; and generating a work ticket associated with the file based at least in part on the comparison. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein instructions that, when executed by the processor, further cause the processor to perform operations comprising: training the machine learning model using the historical data; and generating the forecasted time interval based at least in part on the training, wherein the forecasted time interval is generated on a same day as the training. ### Claim 17 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the forecasted time interval is with respect to a source of the data transmitted, and wherein instructions that, when executed by the processor, further cause the processor to perform operations comprising: determining a first confidence score for an accuracy of the machine learning model; determining a threshold confidence score based at least in a part on the first confidence score; determining a second confidence score for the forecasted time interval; comparing the first confidence score with the second confidence score; and determining whether to compare the first confidence and the second confidence based at least in a part on the comparison. ### Claim 18 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein determining threshold confidence score is based at least in a part on a percentile of the first confidence score. ### Claim 19 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the machine learning model uses a regression analytics of the historical data to output to the forecasted time interval. ### Claim 20 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the data warehouse is an enterprise data warehouse.