System and method for large scale data processing of source data

• US 10,810,225 B2
• Filed: 03/10/2015
• Issued: 10/20/2020
• Est. Priority Date: 03/10/2015
• Status: Active Grant

• Current Assignee: The Boeing Co.
• Original Assignee: The Boeing Co.
• Inventors: Mutschler, Christopher M., Predmore, Brian K., Jacintho, Susan C., Gandhi, Nitin L.
• Primary Examiner(s): Kindred, Alford W
• Assistant Examiner(s): Htay, Lin Lin M
• Application Number:
  US14/643,269
• Publication Number:
  US 20160267157A1
• Time in Patent Office: 2,051 Days
• CPC Class Codes: G06F 16/2477 Temporal data queries
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/20 relating to practices or guidelines
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A system and method for processing source data, which includes a translation processor configured on a server to translate a stream of source data received at the server into a stream of formatted data types based on one or more of parameter definitions for the source data and new parameter information for the source data having an unknown data type. The source data can be received at the server from multiple sources and the data format may not be consistent from one source to another. An analytics processor on the server can be configured to operate on the out-of-tolerance data type to perform one or more of trend data analytics, associated data analytics and preventative action analytics.

Claims

1. A computer system for processing large scale source data, comprising:

a hardware monitor processor configured to monitor a stream of source data received at the computer system from a variety of sources of sensor data from one or more aircraft sensors and determine a data type for each source data in the stream of source data;

a master conversion database configured with definitions and a database query function and a comparator that determines if data contained in the stream of source data is of a known data type or an unknown data type based on a comparison with the definitions, wherein the unknown data type is from a new data source that has not been previously recorded;

a hardware translation processor configured to translate the stream of source data into a stream of formatted data based on one or more of definitions for the source data;

a hardware data conversion processor configured to configure a data definitions database with a new definition to detect the unknown data type and merge the new definition with the master conversion database;

a hardware vital data processor configured to input the stream of formatted data contained in the stream of source data into a dataset database by using an algorithm that identifies vital data using a conversion data map as a key, a data characteristic that identifies vital data using one of a plurality of characteristics of the source data, or a data signature that identifies vital data using a signature of the source data, and to process a trigger function, which detects formatted data associated with a trigger event associated with a physical trigger event that is representative of a physical system that is operating outside of a normal range and outputs, to the dataset database, event data as a vital data parameter that is out of tolerance, the event data being tagged to produce a tagged event data having a tag associated with the source data and the trigger event and retrieves and stores surrounding data associated with the trigger event and having a predetermined relationship to the trigger event to the dataset database; and

a hardware data analytics processor configured to perform one or more of diagnosis, introspection, prediction and prognosis analytics based on the tagged event data and the surrounding data.

2. The computer system for processing source data as recited in claim 1, wherein the hardware monitor processor is further configured to determine a manner in which the data can be stored and an operation to be performed on the source data and the data definition database provides new information for unknown data type to build the conversion data map.

3. The computer system for processing source data as recited in claim 2, further comprising:

a hardware data parsing processor configured to parse and store the stream of source data in the dataset database.

4. The computer system for processing source data as recited in claim 3, further comprising:

a computer workstation receiving analytic results including one or more of trend data analytic results, associated data analytic results and preventative action analytic results having a user interface, which displays the analytic results in a user readable format.

5. The computer system for processing source data as recited in claim 4, further comprising:

a hardware data query processor configured to query and retrieve from the dataset database surrounding data related to the trigger event and associated to the stream of source data.

6. The computer system for processing source data as recited in claim 1, where the hardware data analytics processor comprises an advanced analytic method including one or more of prognostication, introspection, diagnosis and prediction algorithms, whereby the hardware data analytics processor aggregates source data, converts source data into a compressed binary format for storage in a time series database.

7. The computer system for processing source data as recited in claim 6, further comprising:

a data renderer selectively retrieving and providing uncompressed data from selected time series in America Standard Code for Information Interchange (ASCII) format, wherein the uncompressed data that is retrieved is identified based on a time series name and a tag and value pair.

8. The computer system for processing source data as recited in claim 7, where the data renderer down samples compressed sourced data from the time series database at a lower frequency as compared to a higher frequency of stored converted source data.

9. The computer system for processing source data as recited in claim 7, where the data renderer aggregates compressed source data in the time series database by one or more functions including averaging, summing and minimizing.

10. The computer system for processing source data as recited as claim 7, where the data renderer regularizes compressed source data by interpolating the compressed source data.

11. A computer-implemented method for processing source data, comprising:

monitoring, with a hardware monitor processor, a stream of source data received at a computer system from a variety of sources of sensor data from one or more aircraft sensors and determining a data type for each source data in the stream of source data;

configuring a master conversion database with definitions and a database query function and a comparator that determines if data contained in the stream of source data is of a known data type or an unknown data type based on a comparison with the definitions, wherein the unknown data type is from a new data source that has not been previously recorded;

translating, at the computer system with a hardware translation processor, the stream of source data into a stream of formatted data based on the master conversion database and a data definitions database for the source data with known and unknown data;

detecting, with a hardware data conversion processor at a computer server, the unknown data type and configuring the data definitions database with a new definition to detect the unknown data type

detecting, with a hardware vital data processor at the computer server using an algorithm that identifies vital data using a conversion data map as a key, a data characteristic that identifies vital data using one of a plurality of characteristics of the source data, or a data signature that identifies vital data using a signature of the source data, data associated with a trigger event associated with a physical trigger event that is representative of a physical system that is operating outside of a normal range and outputting, to a dataset database, event data as a vital data parameter that is out of tolerance, the event data being tagged to produce a tagged event data having a tag associated with source data and the trigger event and retrieving and storing surrounding data associated with the trigger event to the dataset database; and

performing, on the data type, one or more of trend data analytics, associated data analytics and preventative action analytics.

12. The computer-implemented method for processing source data as recited in claim 11, wherein the monitoring determines a manner in which the data can be stored and an operation to be performed on the source data and the detecting the unknown data type comprises building the conversion data map where the data definition database is configured with new information for the unknown data type.

13. The computer-implemented method for processing source data as recited in claim 12, comprising:

parsing and storing the stream of source data in the dataset database using a parsing processor configured on the computer system wherein the data is parsed based on a predetermined criteria.

14. The computer-implemented method for processing source data as recited in claim 13, comprising:

transmitting analytic results from the computer system to a workstation where the analytic results include one or more of trend data analytic results, associated data analytic results and preventative action analytic results; and

presenting the analytic results on a user interface of the workstation.

15. The computer-implemented method for processing source data as recited in claim 14, comprising:

querying and retrieving from the dataset database surrounding data.

16. The computer-implemented method for processing source data as recited in claim 11, further comprising:

one or more of prognosticating, introspectioning, diagnosing and predicting algorithms;

aggregating source data; and

converting source data into a compressed binary format for storage in a time series database.

17. The computer-implemented method for processing source data as recited in claim 16, further comprising:

a data renderer selectively retrieving and providing uncompressed source data from a selected time series database by identifying data based on a time series name and a tag and value pair.

18. The computer-implemented method for processing source data as recited in claim 17, where the data renderer down samples compressed sourced data from the time series database at a lower frequency as compared to a higher frequency of stored converted source data.

19. The computer-implemented method for processing source data as recited in claim 17, where the data renderer aggregates compressed source data in the time series database by one or more functions including averaging, summing and minimizing.

20. The computer-implemented method for processing source data as recited as claim 17, where the data renderer will regularize compressed source data interpolating the compressed source data.