Data transformation techniques for event data in multi-system computing environments
An event restraint-delivery computing system receives event data in a high-volume event stream from source computing systems. For each event data object in the high-volume event data stream, the event restraint-delivery computing system identifies at least one event data object subset associated with a respective recipient computing system. The event restraint-delivery computing system withholds the event data object subset from the respective recipient computing system based on restraint status data for the event data object subset. Based on a change in the respective restraint status data, such as a change indicating that the event data object subset is modified to fulfill a trigger criterion, the event restraint-delivery computing system provides the event data object subset to the respective recipient computing system. In addition, the respective recipient computing system performs a computing function based on event data in the event data object subset.
1 . A method of coordinating high-volume streams of event data, the method including operations executed by at least one processor, the operations comprising:
monitoring a high-volume event data stream that includes multiple event data objects generated via multiple source computing systems, wherein each monitored event data object in the monitored high-volume event data stream is compared to multiple sets of criteria, wherein each particular set of criteria is associated with a respective recipient system in a group of multiple recipient computing systems, wherein each one of the multiple recipient computing systems is excluded from the multiple source computing systems, and wherein the multiple sets of criteria include a) a first set of criteria associated with a first recipient computing system and b) a second set of criteria associated with a second recipient computing system;
identifying, from the monitored high-volume event data stream, an event data object subset (“EDO subset”) in which each event data object identified for inclusion in the EDO subset matches the first set of criteria;
during an evaluation of restraint status data of the EDO subset, withholding the EDO subset from the first recipient computing system associated with the first set of criteria, wherein the first recipient computing system is excluded from the multiple source computing systems;
responsive to identifying a change in the restraint status data of the EDO subset, providing the EDO subset to the first recipient computing system, wherein the first recipient computing system is configured to perform one or more computing functions based on the EDO subset; and
responsive to determining that each event data object identified for inclusion in the EDO subset fails to match the second set of criteria, withholding the EDO subset from the second recipient computing system.
2 . The method of claim 1 , further comprising:
performing the evaluation of the restraint status data of the EDO subset, wherein the evaluation includes one or more of:
determining an elapsed time calculated from an identification of a first event data object included in the EDO subset,
determining, in the EDO subset, a quantity of event data objects received from a particular group of source computing systems, or
identifying from the monitored high-volume event data stream an additional event data object for inclusion in the EDO subset.
3 . The method of claim 1 , wherein the one or more computing functions that the first recipient computing system is configured to perform include at least one of: fraud detection, identity verification, restricting access to digital data by an additional computing device, or restricting access by the additional computing device to an online service.
4 . The method of claim 1 , further comprising:
modifying a ledger data repository to include, for each monitored event data object in the monitored high-volume event data stream, an event-specific data record indicating an occurrence of each monitored event data object,
wherein modifying the ledger data repository occurs during the monitoring of the high-volume event data stream.
5 . The method of claim 1 , further comprising:
determining a first transformation technique associated with the first recipient computing system; and
modifying the EDO subset based on the first transformation technique, wherein providing the EDO subset to the first recipient computing system includes providing the modified EDO subset to the first recipient computing system.
6 . The method of claim 5 , wherein the first transformation technique includes one or more of: an encryption technique, a communication channel selection technique, or a data value normalization technique.
7 . The method of claim 1 , further comprising:
applying, to each monitored event data object in the monitored high-volume event data stream, a trained machine-learning model that is configured to:
a) determine one or more characteristics of each monitored event data object, and
b) based on the one or more characteristics, determine classification output data of each monitored event data object,
wherein the first set of criteria includes at least one criterion indicating a particular classification outcome determined for each event data object identified for inclusion in the EDO subset.
8 . An event restraint-delivery computing system comprising:
a processing device; and
a memory device in which instructions executable by the processing device are stored for causing the processing device to perform operations comprising:
monitoring a high-volume event data stream that includes multiple event data objects generated via multiple source computing systems, wherein each monitored event data object in the monitored high-volume event data stream is compared to multiple sets of criteria, wherein each particular set of criteria is associated with a respective recipient system in a group of multiple recipient computing systems, wherein each one of the multiple recipient computing systems is excluded from the multiple source computing systems, and wherein the multiple sets of criteria include a) a first set of criteria associated with a first recipient computing system and b) a second set of criteria associated with a second recipient computing system,
identifying, from the monitored high-volume event data stream, an event data object subset (“EDO subset”) in which each event data object identified for inclusion in the EDO subset matches the first set of criteria;
during an evaluation of restraint status data of the EDO subset, withholding the EDO subset from the first recipient computing system associated with the first set of criteria;
responsive to identifying a change in the restraint status data of the EDO subset, providing the EDO subset to the first recipient computing system, wherein the first recipient computing system is configured to perform one or more computing functions based on the EDO subset; and
responsive to determining that each event data object identified for inclusion in the EDO subset fails to match the second set of criteria, withholding the EDO subset from the second recipient computing system.
9 . The event restraint-delivery computing system of claim 8 , the operations further comprising:
performing the evaluation of the restraint status data of the EDO subset, wherein the evaluation includes one or more of:
determining an elapsed time calculated from an identification of a first event data object included in the EDO subset,
determining, in the EDO subset, a quantity of event data objects received from a particular group of the multiple source computing systems, or
identifying from the monitored high-volume event data stream an additional event data object for inclusion in the EDO subset.
10 . The event restraint-delivery computing system of claim 8 , the operations further comprising:
modifying a ledger data repository to include, for each monitored event data object in the monitored high-volume event data stream, an event-specific data record indicating an occurrence of each monitored event data object,
wherein modifying the ledger data repository occurs during the monitoring of the high-volume event data stream.
11 . The event restraint-delivery computing system of claim 8 , the operations further comprising:
determining a first transformation technique associated with the first recipient computing system; and
modifying the EDO subset based on the first transformation technique, wherein providing the EDO subset to the first recipient computing system includes providing the modified EDO subset to the first recipient computing system.
12 . The event restraint-delivery computing system of claim 8 , the operations further comprising:
applying, to each monitored event data object in the monitored high-volume event data stream, a trained machine-learning model that is configured to:
a) determine one or more characteristics of each monitored event data object, and
b) based on the one or more characteristics, determine classification output data of each monitored event data object,
wherein the first set of criteria includes at least one criterion indicating a particular classification outcome determined for each event data object identified for inclusion in the EDO subset.
13 . A non-transitory computer-readable storage medium having program code that is executable by a processor device to cause a computing device to perform operations, the operations comprising:
monitoring a high-volume event data stream that includes multiple event data objects generated via multiple source computing systems, wherein each monitored event data object in the monitored high-volume event data stream is compared to multiple sets of criteria, wherein each particular set of criteria is associated with a respective recipient system in a group of multiple recipient computing systems, wherein each one of the multiple recipient computing systems is excluded from the multiple source computing systems, and wherein the multiple sets of criteria include a) a first set of criteria associated with a first recipient computing system and b) a second set of criteria associated with a second recipient computing system;
identifying, from the monitored high-volume event data stream, an event data object subset (“EDO subset”) in which each event data object identified for inclusion in the EDO subset matches the first set of criteria;
during an evaluation of restraint status data of the EDO subset, withholding the EDO subset from the first recipient computing system associated with the first set of criteria, wherein the first recipient computing system is excluded from the multiple source computing systems;
responsive to identifying a change in the restraint status data of the EDO subset, providing the EDO subset to the first recipient computing system, wherein the first recipient computing system is configured to perform one or more computing functions based on the EDO subset; and
responsive to determining that each event data object identified for inclusion in the EDO subset fails to match the second set of criteria, withholding the EDO subset from the second recipient computing system.
14 . The non-transitory computer-readable storage medium of claim 13 , the operations further comprising:
performing the evaluation of the restraint status data of the EDO subset, wherein the evaluation includes one or more of:
determining an elapsed time calculated from an identification of a first event data object included in the EDO subset,
determining, in the EDO subset, a quantity of event data objects received from a particular group of the multiple source computing systems, or
identifying from the monitored high-volume event data stream an additional event data object for inclusion in the EDO subset.
15 . The non-transitory computer-readable storage medium of claim 13 , the operations further comprising:
modifying a ledger data repository to include, for each monitored event data object in the monitored high-volume event data stream, an event-specific data record indicating an occurrence of each monitored event data object,
wherein modifying the ledger data repository occurs during the monitoring of the high-volume event data stream.
16 . The non-transitory computer-readable storage medium of claim 13 , the operations further comprising:
determining a first transformation technique associated with the first recipient computing system; and
modifying the EDO subset based on the first transformation technique, wherein the providing the EDO subset to the first recipient computing system includes providing the modified EDO subset to the first recipient computing system.
17 . The non-transitory computer-readable storage medium of claim 13 , the operations further comprising:
applying, to each monitored event data object in the monitored high-volume event data stream, a trained machine-learning model that is configured to:
a) determine one or more characteristics of each monitored event data object, and
b) based on the one or more characteristics, determine classification output data of each monitored event data object,
wherein the first set of criteria includes at least one criterion indicating a particular classification outcome determined for each event data object identified for inclusion in the EDO subset.