Let’s say you have to analyze customer buying behavior, uncover collusive crime in financial transactions, provide product recommendations, or discover information about social networks.
Discovering the answer to each of these questions can be complicated with typical systems.
How Do Graphs Work?
Taking data out of the restrictions of the relational database makes it easier to traverse your data to find connections—that is, if you map the data into the form of a graph with entities represented as vertices and relationships represented as edges.
By doing so, you can discover relationships that weren’t obvious before, which is is why graph technology has become increasingly popular and important in today’s world of connected data. For the last three years running, Gartner has selected graph technology as one of its top ten data and analytics trends.
Why Have Graphs Become Popular?
Graph theory has been around for centuries, and graph technology has been around for a few decades. But it has recently grown in popularity for a few key reasons:
◉ Graphs enable new kinds of analysis that are more needed than ever in today’s connected world. They complement relational technology and can become the basis for machine learning, especially with neural networks.
◉ Graphs are very visual, often with easily observable results and they provide simple data modeling.
◉ Graphs have a very flexible data model with no predefined schema, making them extensible and also useful in cases with sparse data.
What Do Graphs Do?
Graphs are designed to help you model relationships in your applications. They are purpose-built to handle highly connected data. Because of the increase in the volume and connectedness of today’s data and because so much of today’s data is connected through relationships, there is a tremendous opportunity for graphs to provide extraordinary value for your organization. Uses include:
◉ Use graph analytics to discover social media bots
◉ Find fraud payments quickly
◉ Track the use of sensitive data for compliance purposes
◉ Discover tax evasion
◉ Improve datacenter management
◉ Provide product recommendations
◉ Detect anomalies in social networks, healthcare, and more
◉ Discover outages in utilities networks
◉ Spot vulnerabilities in IP networks
◉ Perform turnover analysis to keep valuable employees
◉ Create portals for citizens to access data from multiple sources, including statistical data, gender, or demographic data
◉ Track chemical and drug names for pharmaceutical companies
◉ Track proteins for vaccine development
The possibilities are virtually limitless. If your data is connected via relationships in some way, graph databases can likely help. Questions that graph can answer include:
◉ Which supplier am I most dependent upon?
◉ Who is the most influential customer?
◉ Do my products appeal to certain communities?
◉ Which anomalous patterns are there that could indicate fraudulent behavior?
Best of all, you can answer these questions with queries that run many times faster. For more use cases, download our ebook on graph technology use cases but keep in mind that our suggestions are only the beginning.
Graph Analytics in Oracle Database and Oracle Autonomous Database
Oracle provides a graph analytics engine with Oracle Database and Oracle Autonomous Database so users can discover more insights in their data by using the power of graph algorithms, pattern matching queries, and visualization.
Having graph technologies in Oracle Database and Oracle Autonomous Database provides scalability, performance, and security. Users can run graph analytics with more than 60 graph algorithms, use a query language and visualizations. Standard interfaces simplify this process and integration with machine learning tools makes it simpler to apply graph to machine learning.
How Does Graph Technology Work?
Oracle is built upon the idea of a multi-model database, meaning there is one database that supports multiple languages and protocols.
Of course there is the relational database. But Oracle also supports additional data models, including JSON, XML, spatial data, and of course, graph structures.
Oracle’s graph database has an in-memory server that holds data in a graph format, providing easy access and high speed analytics along with the power of the database. This combines the best of both worlds—graph data held in graph structures, and the power of Oracle Database.
Storing data as a graph and loading it in memory requires only a few easy steps. You can directly load data from standard relational tables into the in-memory server as a graph.
Alternatively, load the data into a property graph schema, for a persisted copy of the graph, and load from the property graph schema into the in-memory graph server.
Soon, Oracle will release Graph Studio, which builds on existing graph capabilities to make graph analytics and graph database management easier for everyone. It includes automated modeling, integrated visualization, and pre-built workflows for different use cases.
With graph technologies, you can unfold the data landscape in a completely new way. Discover insights. Solve complex problems. Unlock endless possibilities.
Source: oracle.com
Posts
0 comments:
Post a Comment