Authors:
Ami Pandat
1
;
Minal Bhise
1
and
Sanjay Srivastava
2
Affiliations:
1
Distributed Databases Group, DAIICT, Gandhinagar, India
;
2
DAIICT, Gandhinagar, India
Keyword(s):
Analytics, Centrality, Connectivity, Graph Database, Graph Analytics, Path Analytics.
Abstract:
Data management solutions are becoming increasingly necessary as more Big Data applications are developed. One such area that deals with Big Data is Big Graphs. Complex relationships exist in graph-based applications. Analytics and data extraction are better solutions for understanding such complex applications. Data from Avian Science has shown significant growth in recent years. Graph analytics can be used to interpret complex scientific data and their relationships. This paper uses graph analytics to discuss the application of graph analytics in avian science. For the eBird Dataset, four Graph Analytics techniques were identified and implemented. These methods extract information about path patterns, node popularity, connections to other nodes, and clustering. The Dataset includes real-time data on bird observation and distribution. Each analytics technique extracts data from the birds’ observations. The findings show that graph analytics for avian science data can aid in predicti
ng a wide range of crowd-sourced information. Additionally, the work can be expanded using machine learning methods.
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