Correlating parallelized data from disparate data sources to aggregate graph data portions to predictively identify entity data
Various embodiments relate generally to data science and data analysis, computer software and systems, and data-driven control systems and algorithms based on graph-based data arrangements, among other things, and, more specifically, to a computing platform configured to receive or analyze datasets in parallel by implementing, for example, parallel computing processor systems to correlate subsets of parallelized data from disparately-formatted data sources to identify entity data and to aggregate graph data portions. In some examples, a method may include classifying data parallelized data to identify a class of observation data, constructing one or more content graphs in a graph data format, correlating parallelized data to other subsets of parallelized data associated with a class of observation data; and aggregating observation data to represent an individual entity.
1. A method comprising:
receiving data from multiple data sources at a computing system including one or more processors and memory configured to process the data in parallel to form parallelized data, the parallelized data being further converted by a format converter to graph-based data from one or more of the multiple data sources;
classifying data representing a subset of the parallelized data to identify a class of observation data;
identifying data representing one or more entity attributes associated with the observation data;
constructing one or more content graphs in a graph data format based on the class of observation data and the one or more entity attributes;
correlating the subset of the parallelized data to other subsets of the parallelized data associated with the class of observation data to form correlated subsets of the parallelized data;
aggregating data representing individual entities to form a set of entities based on the correlated subsets of the parallelized data; and
modifying a graph data arrangement to enrich data stored in association thereof.
2. The method of claim 1 wherein modifying the graph data arrangement further comprises:
modifying a knowledge graph data arrangement.
3. The method of claim 1 further comprising:
receiving the parallelized data from the multiple data sources in different data formats; and
converting the parallelized data in the different data formats into the graph data format.
4. The method of claim 1 wherein receiving the data from the multiple data sources comprises:
ingesting the parallelized data; and
modifying a subset of the parallelized data.
5. The method of claim 1 further comprising:
analyzing a subset of the parallelized data to detect noncompliant data; and
quarantining a subset of the noncompliant data.
6. The method of claim 1 wherein correlating the subset of the parallelized data comprises:
forming an adjacency node for the correlated subsets of the parallelized data; and
linking multiple adjacency nodes to identify an individual entity.
7. The method of claim 6 further comprising:
clustering the correlated subsets of the parallelized data to identify the individual entity.
8. The method of claim 6 further comprising:
clustering the individual entity with data representing other individual entities to form clustered individual entities; and
forming a group of individual entities based on the clustered individual entities.
9. The method of claim 1 wherein correlating the subset of the parallelized data comprises:
identifying data representing a first attribute representing a name; and
identifying data representing a second attribute representing a geographic location.
10. The method of claim 9 further comprising:
forming a first subset of adjacency nodes associated with a subset of data representing the first attribute representing the name; and
forming a second subset of adjacency nodes associated with a subset of data representing the second attribute representing the geographic location.
11. The method of claim 10 further comprising:
implementing the first subset of adjacency nodes and the second subset of adjacency nodes to cluster to aggregate the data representing the individual entities to form the set of entities,
wherein each individual entity includes data representing an individual person.
12. The method of claim 11 further comprising:
aggregating the data representing the individual person with data representing other individual persons to form a group of individual persons.
13. A system comprising:
a data store configured to receive streams of data via a network into an application computing platform; and
a processor configured to execute instructions to implement an application configured to:
receive data from multiple data sources at a computing system including one or more processors and memory configured to process the data in parallel to form parallelized data, the parallelized data being further converted by a format converter to graph-based data from one or more of the multiple data sources;
classify data representing a subset of the parallelized data to identify a class of observation data;
identify data representing one or more entity attributes associated with the observation data;
construct one or more content graphs in a graph data format based on the class of observation data and the one or more entity attributes;
correlate the subset of the parallelized data to other subsets of the parallelized data associated with the class of observation data to form correlated subsets of the parallelized data;
aggregate data representing individual entities to form a set of entities based on the correlated subsets of the parallelized data; and
modify a graph data arrangement to enrich data stored in association thereof.
14. The system of claim 13 wherein the processor is further configured to:
modify a knowledge graph data arrangement implemented at the graph data arrangement.
15. The system of claim 13 wherein the processor is further configured to:
receive the parallelized data from the multiple data sources in different data formats; and
convert the parallelized data in the different data formats into the graph data format.
16. The system of claim 13 wherein the processor is further configured to:
ingest the parallelized data; and
modify a subset of the parallelized data to clean the subset of the parallelized data.
17. The system of claim 13 wherein the processor is further configured to:
analyze a subset of the parallelized data to detect noncompliant data; and
quarantine a subset of the noncompliant data.
18. The system of claim 13 wherein the processor is further configured to:
form an adjacency node for the correlated subsets of the parallelized data; and
link multiple adjacency nodes to identify an individual entity.
19. The system of claim 13 wherein the processor is further configured to:
cluster the correlated subsets of the parallelized data to identify an individual entity based on linked adjacency nodes.
20. The system of claim 13 wherein the processor is further configured to:
identify data representing a first attribute representing a name; and
identify data representing a second attribute representing a geographic location,
wherein the data representing the first attribute and the second attribute may be associated with the class of observation data and an adjacency node.