US 12,271,382 B2
Query prediction modeling for distributed databases
Charles Howard Cella, Pembroke, MA (US); and Andrew Cardno, San Diego, CA (US)
Assigned to Strong Force VCN Portfolio 2019, LLC, Fort Lauderdale, FL (US)
Filed by Strong Force VCN Portfolio 2019, LLC, Fort Lauderdale, FL (US)
Filed on Mar. 7, 2023, as Appl. No. 18/180,065.
Application 18/180,065 is a continuation of application No. PCT/US2022/028633, filed on May 10, 2022.
Claims priority of provisional application 63/302,013, filed on Jan. 21, 2022.
Claims priority of provisional application 63/299,710, filed on Jan. 14, 2022.
Claims priority of provisional application 63/282,507, filed on Nov. 23, 2021.
Claims priority of provisional application 63/187,325, filed on May 11, 2021.
Claims priority of application No. 202211008709 (IN), filed on Feb. 18, 2022.
Prior Publication US 2023/0252047 A1, Aug. 10, 2023
Int. Cl. G06F 16/2455 (2019.01); G05D 1/00 (2024.01); G05D 1/69 (2024.01); G06F 16/182 (2019.01); G06F 16/2453 (2019.01); G06F 16/2458 (2019.01); G06F 16/27 (2019.01); G06Q 10/0631 (2023.01); G06Q 10/0833 (2023.01); G06Q 10/087 (2023.01); G06Q 20/38 (2012.01); G06Q 30/0201 (2023.01); G06Q 30/0202 (2023.01); G06V 10/774 (2022.01); H04N 23/67 (2023.01)
CPC G06F 16/2455 (2019.01) [G05D 1/0291 (2013.01); G05D 1/69 (2024.01); G06F 16/182 (2019.01); G06F 16/24537 (2019.01); G06F 16/24544 (2019.01); G06F 16/24552 (2019.01); G06F 16/2456 (2019.01); G06F 16/2462 (2019.01); G06F 16/2471 (2019.01); G06F 16/27 (2019.01); G06F 16/278 (2019.01); G06Q 10/06315 (2013.01); G06Q 10/0833 (2013.01); G06Q 10/087 (2013.01); G06Q 20/389 (2013.01); G06Q 30/0202 (2013.01); G06Q 30/0206 (2013.01); G06V 10/774 (2022.01); H04N 23/675 (2023.01); G05B 2219/49023 (2013.01); G06Q 2220/00 (2013.01)] 24 Claims
OG exemplary drawing
 
1. A computer-implemented method for optimizing a distributed database, the method comprising:
receiving, at an aggregator, one or more query logs including one or more past queries received by the distributed database;
generating, by the aggregator, a query prediction model based on the one or more query logs;
predicting, by the aggregator, a future query to be received by a first edge device, wherein the aggregator performs the predicting using the query prediction model;
in response to the predicted future query being directed to a data set stored in edge storage of a second edge device, transmitting, by the aggregator, data for responding to the predicted future query to the first edge device via one or more networks,
wherein the data set includes sensor data, and
wherein the transmitting includes causing, by the aggregator, a subset of the data set to be stored as a redundant data set in edge storage of the first edge device;
generating, by the query prediction model, summary data based on the redundant data set;
storing the summary data on a dynamic ledger maintained by the aggregator,
wherein the dynamic ledger includes location information that indicates storage locations in at least one of the edge storage of the first edge device or the edge storage of the second edge device for the sensor data, and
wherein the dynamic ledger includes edge device role data that defines roles for the first edge device and the second edge device; and
responding, by the first edge device, to a future query based at least partially on the summary data.