Which three are use cases for Graph Studio? (Choose three.)

Oracle Graph Studio is a tool for modeling, managing, and analyzing data as a graph. Its primary use cases revolve around understanding complex relationships and patterns within datasets. Pattern matching is a core function, using languages like PGQL to find specific structures or sequences, such as fraud rings or complex supply chains. Clustering (or community detection) is a built-in analytical capability used to identify densely connected groups, like social communities or customer segments. Churn analysis leverages graph analytics to model customer relationships and influence, helping to predict which customers are likely to leave based on the behavior of their network connections.

A. Facial recognition is a computer vision and machine learning problem, not a typical use case for graph databases which focus on relationship analysis.
E. 3-D modelling deals with geometric and spatial data for computer graphics, which is fundamentally different from the network-based data managed by Graph Studio.

1. Oracle Property Graph Developer's Guide

Release 23c

Chapter 1

Introduction to Oracle Property Graph

Section 1.2 What Can You Do with Graph Analytics? This section explicitly lists "Identifying communities through clustering analysis" and describes "pattern matching" as a key capability of graph queries. It also discusses identifying influencers in a network

a core component of advanced churn analysis.

2. Oracle LiveLabs

"Get Started with Graph Analytics on Autonomous Database"

Lab 2: Querying and Visualizing Graphs. This lab demonstrates running PGQL queries for pattern matching and executing built-in graph algorithms

including community detection (clustering)

directly within Graph Studio.

3. Oracle Autonomous Database Documentation

"Use Oracle Graph Studio"

Chapter 4

Analyzing Graphs. This chapter details the use of the 40+ built-in algorithms available in Graph Studio

which include community detection algorithms (clustering) and path-finding algorithms often used in pattern matching and network influence analysis (relevant to churn).