User interface for building an enhanced data orchestration enterprise service bus
A system of building an enhanced data orchestration (EDO) enterprise service bus. The system also comprises an EDO builder application stored in the at least one non-transitory memory that when executed by the at least one processor, presents a visual interface that responds to drag and drop command inputs that build a sequence of data stream processes, presents a command input that when activated causes a properties dialog box to be presented that promotes a user defining data from a data stream to be passed in to a selected data stream process and defining data to be output by the selected data stream process, and builds an EDO enterprise service bus application based on pre-trained ML models, based on a base enterprise service bus application, based on the drag and drop command inputs, and based on user definitions of data provided through the properties dialog box.
1. A method for building and using an enhanced data orchestration (EDO) enterprise service bus, comprising:
presenting, by an EDO builder application stored in a non-transitory memory and executable by a processor, a visual interface;
receiving, by the EDO builder application, drag and drop command inputs that build a sequence of data stream processes;
presenting, by the EDO builder application, a command input that when activated causes a properties dialog box to be presented;
receiving, by the EDO builder application, user inputted definitions via the properties dialog box defining data from a data stream to be passed in to a selected data stream process and defining data to be output by the selected data stream process;
building, by the EDO builder application, an EDO enterprise service bus application based on a plurality of pre-trained machine learning (ML) models stored in the non-transitory memory, based on a base enterprise service bus application stored in the non-transitory memory, based on the drag and drop command inputs, and based on the user inputted definitions of data provided through the properties dialog box;
rebuilding, by the EDO builder application, the EDO enterprise service bus application based on at least one of a new pre-trained ML model stored in the non-transitory memory, one or more updated pre-trained ML models stored in the non-transitory memory, a proprietary ML model stored in the non-transitory memory, or an updated base enterprise service bus application stored in the non-transitory memory;
processing, by the EDO enterprise service bus application, at least one message using one or more of the new pre-trained ML model, the one or more updated pre-trained ML model, or the proprietary ML model to obtain metadata associated with the at least one message; and
attaching, by the EDO enterprise service bus application, the metadata to the at least one message, wherein the processing of the at least one message to obtain the metadata and attaching the metadata to the at least one message occurs in-flight without the EDO enterprise service bus application storing the at least one message in permanent storage.
2. The method of claim 1 , wherein the drag and drop command inputs position icons corresponding to different data orchestration utilities sequentially to build the sequence of data stream processes, and wherein when the EDO enterprise service bus application is executed in a live production computer environment, each data orchestration utility executes sequentially according to the sequential position of the icons relative to each other.
3. The method of claim 2 , wherein a drag and drop command input positions a first icon corresponding to a first data orchestration utility after a previously positioned icon corresponding to a second data orchestration utility, and wherein when the EDO enterprise service bus application is executed in a live production computer environment, the second data orchestration utility is executed before the first data orchestration utility.
4. The method of claim 2 , wherein a drag and drop command input positions a first icon corresponding to a first data orchestration utility before a previously positioned icon corresponding to a second data orchestration utility, and wherein when the EDO enterprise service bus application is executed in a live production computer environment, the first data orchestration utility is executed before the second data orchestration utility.
5. The method of claim 1 , wherein the pre-trained ML models comprise at least one of an entity extraction ML model, a sentiment analysis ML model, or a behavioral anomaly ML model.
6. The method of claim 1 , wherein the EDO enterprise service bus application comprises:
a subset of the plurality of pre-trained ML models;
a data format conversion component that, when executed by the processor, receives a message in a first format and converts the content of the message to a second format, wherein the data format conversion component is configured to convert a plurality of different formats to the second format;
a mediation component that, when executed by the processor, receives the message in the second format from the data format conversion component, processes the message using at least some of the subset of pre-trained ML models to obtain metadata associated with the message, and attaches the metadata to the message in the second format; and
a message reassembly component that, when executed by the at least one processor, reassembles the message with the attached metadata and transmits the reassembled message and the attached metadata.
7. A system for building and using an enhanced data orchestration (EDO) enterprise service bus, comprising:
at least one processor;
at least one non-transitory memory;
a plurality of pre-trained machine learning (ML) models stored in the at least one non-transitory memory;
a base enterprise service bus application stored in the at least one non-transitory memory;
an EDO builder application stored in the at least one non-transitory memory that when executed by the at least one processor:
presents a visual interface that responds to drag and drop command inputs that build a sequence of data stream processes,
presents a command input that when activated causes a properties dialog box to be presented that promotes a user defining data from a data stream to be passed in to a selected data stream process and defining data to be output by the selected data stream process, and
builds an EDO enterprise service bus application based on the pre-trained ML models, based on the base enterprise service bus application, based on the drag and drop command inputs, and based on user definitions of data provided through the properties dialog box; and
the EDO enterprise service bus application stored in the at least one non-transitory memory that when executed by the at least one processor processes at least one message using one or more of the pre-trained ML models to obtain metadata associated with the at least one message and attaches the metadata to the at least one message, wherein the processing of the at least one message to obtain the metadata and attaching the metadata to the at least one message occurs in-flight without the EDO enterprise service bus application storing the at least one message in permanent storage.
8. The system of claim 7 , wherein the drag and drop command inputs position icons corresponding to different data orchestration utilities sequentially to build the sequence of data stream processes, and wherein when the EDO enterprise service bus application is executed in a live production computer environment, each data orchestration utility executes sequentially according to the sequential position of the icons relative to each other.
9. The system of claim 8 , wherein a drag and drop command input positions a first icon corresponding to a first data orchestration utility after a previously positioned icon corresponding to a second data orchestration utility, and wherein when the EDO enterprise service bus application is executed in a live production computer environment, the second data orchestration utility is executed before the first data orchestration utility.
10. The system of claim 8 , wherein a drag and drop command input positions a first icon corresponding to a first data orchestration utility before a previously positioned icon corresponding to a second data orchestration utility, and wherein when the EDO enterprise service bus application is executed in a live production computer environment, the first data orchestration utility is executed before the second data orchestration utility.
11. The system of claim 7 , wherein the pre-trained ML models comprise at least one of an entity extraction ML model, a sentiment analysis ML model, or a behavioral anomaly ML model.
12. The system of claim 7 , wherein the EDO enterprise service bus application comprises:
a subset of the plurality of pre-trained ML models;
a data format conversion component that, when executed by the at least one processor, receives a message in a first format and converts the content of the message to a second format, wherein the data format conversion component is configured to convert a plurality of different formats to the second format;
a mediation component that, when executed by the at least one processor, receives the message in the second format from the data format conversion component, processes the message using at least some of the subset of pre-trained ML models to obtain metadata associated with the message, and attaches the metadata to the message in the second format; and
a message reassembly component that, when executed by the at least one processor, reassembles the message with the attached metadata and transmits the reassembled message and the attached metadata.
13. A method for building and using an enhanced data orchestration (EDO) enterprise service bus, comprising:
presenting, by an EDO builder application stored in a non-transitory memory and executable by a processor, a visual interface;
receiving, by the EDO builder application, drag and drop command inputs that build a sequence of data stream processes;
presenting, by the EDO builder application, a command input that when activated causes a properties dialog box to be presented;
receiving, by the EDO builder application, user inputted definitions via the properties dialog box defining data from a data stream to be passed in to a selected data stream process and defining data to be output by the selected data stream process;
building, by the EDO builder application, an EDO enterprise service bus application based on a plurality of pre-trained machine learning models stored in the non-transitory memory, based on a base enterprise service bus application stored in the non-transitory memory, based on the drag and drop command inputs, and based on the user inputted definitions of data provided through the properties dialog box;
processing, by the EDO enterprise service bus application, at least one message using one or more of the new pre-trained ML model, the one or more updated pre-trained ML model, or the proprietary ML model to obtain metadata associated with the at least one message; and
attaching, by the EDO enterprise bus application, the metadata to the at least one message, wherein the processing of the at least one message to obtain the metadata and attaching the metadata to the at least one message occurs in-flight without the EDO enterprise service bus application storing the at least one message in permanent storage.
14. The method of claim 13 , wherein the drag and drop command inputs position icons corresponding to different data orchestration utilities sequentially to build the sequence of data stream processes, and wherein when the EDO enterprise service bus application is executed in a live production computer environment, each data orchestration utility executes sequentially according to the sequential position of the icons relative to each other.
15. The method of claim 14 , wherein a drag and drop command input positions a first icon corresponding to a first data orchestration utility after a previously positioned icon corresponding to a second data orchestration utility, and wherein when the EDO enterprise service bus application is executed in a live production computer environment, the second data orchestration utility is executed before the first data orchestration utility.
16. The method of claim 14 , wherein a drag and drop command input positions a first icon corresponding to a first data orchestration utility before a previously positioned icon corresponding to a second data orchestration utility, and wherein when the EDO enterprise service bus application is executed in a live production computer environment, the first data orchestration utility is executed before the second data orchestration utility.
17. The method of claim 13 , wherein the pre-trained ML models comprise at least one of an entity extraction ML model, a sentiment analysis ML model, or a behavioral anomaly ML model.
18. The method of claim 13 , wherein the EDO enterprise service bus application comprises:
a subset of the plurality of pre-trained ML models;
a data format conversion component that, when executed by the processor, receives a message in a first format and converts the content of the message to a second format, wherein the data format conversion component is configured to convert a plurality of different formats to the second format;
a mediation component that, when executed by the processor, receives the message in the second format from the data format conversion component, processes the message using at least some of the subset of pre-trained ML models to obtain metadata associated with the message, and attaches the metadata to the message in the second format; and
a message reassembly component that, when executed by the at least one processor, reassembles the message with the attached metadata and transmits the reassembled message and metadata.