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This article shows you the application example analysis of graphic database, the content is concise and easy to understand, it can definitely brighten your eyes. I hope you can get something through the detailed introduction of this article.

When you hear the word database, what you usually think of is a table, where each row is a separate record and the column header is the field name-basically a spreadsheet. When the data is too complex to fit into a single table, we move to a relational database-multiple tables linked by fields.

Building a relational database requires people who understand the data structure. And, when new information is added, or new relationships become important, the database administrator will need to change the structure of the database and most likely update the user interface.

So, what if the dataset cannot map the relationship in advance? Can people connect through things you can't predict in advance, rather than through a single data point?

Maybe two people are on the same baseball team, or like the same type of books, or live in the same city. For database administrators, adding these items as separate fields and creating new relationships for them can be a time-consuming and never-ending task.

One solution is a graphical database.

In a graphical database, any data point can connect to any other data point, and business users can establish a connection at any time without the need for the database administrator to rewrite the entire schema.

The graphics database is designed to be extensible, making it very suitable for today's big data applications. And they are fast, allowing users to move quickly along the chain of connections, allowing businesses to gain insights faster and more efficiently.

"in a regular relational database, if I want to add something or change relationships, I need to make a lot of plans," said Karen Panetta, dean of the Graduate School of Engineering at Tufts University and a researcher at IEEE. "the graphical database allows you to add new relationships quickly."

The following are the main use cases of a graphical database.

Fraud and anomalies

Panetta says fraud detection is one of the most powerful use cases for graph databases so far.

Traditional fraud detection methods rely on simple lists. For example, if the transaction amount exceeds a certain amount or involves an entity on the government watch list, the transaction is suspicious. This simplified approach may miss more subtle fraud attempts, but databases designed to detect abnormal connections between transactions may be able to identify these frauds.

For example, many e-commerce transactions may come from different accounts, but all from the same IP address or IP address cluster. Alternatively, several amounts of the same amount of cash may be withdrawn from the same community and deposited in different accounts on the same day.

There may be no suspicious signs of these transactions alone, but the correlation requires attention.

"large money laundering schemes may use one person's name, another person's social security number, and the address of a third person," Panetta said. "how do you identify it? the structure of the chart allows you to identify these things as anomalies, allowing us to explore meaningless relationships."

We can not only detect shopping or bank fraud in this way. In network security, enterprises can defend against hacker attacks by looking for a set of events that are associated in an unusual way.

For example, when the relational database used by the network security provider Brinqa reaches its limit of flexibility, it switches to the Neo4j graphical database system.

"our platform is dynamic, but not enough to handle all types of situations," said Syed Abdur Rahman, product director of Brinqa. "with a graph database, you can define schemas dynamically, and you can define nodes and relationships. You don't have to define it in advance, you can do that when you introduce data."

Recommendation engine

Unusual connections can also be positive. Today's advanced recommendation engines recommend music, books, movies, clothes, and other products and services based on connections to other transactions. They can go beyond simple direct connections.

For example, people who buy dog food may also buy dog collars, and they may also be interested in comfortable walking shoes or sofa covers.

Recommendation engines are starting to appear in many different places, not just streaming applications and e-commerce sites.

Fabric is a consumer data market that connects brands and customers, using graphical databases to reduce development time for new features, interfaces, and analytics.

Paul Taylor, founder and CEO of Fabric, said: "for 90% of emerging enterprise projects, graphical databases are better than relational databases."

Replacing traditional relational databases with graphical databases can also reduce the need for middleware. "Graphics are a strong foundation."

'but that doesn't mean they apply to all use cases,'he said.

"Graphics are not very suitable for writing heavy applications, where only a few data queries are needed throughout the lifecycle," he said. "

Data synchronization

Privacy regulations such as the European General data Protection regulations (GDPR) and the California Consumer Privacy Act require companies to aggregate all personal data they collect and delete it as required. Because enterprises usually store this information in different data islands, this can be a daunting task.

But it's not just compliance requirements that make companies want to link different data sets. Justin Richie, director of data science at Nerdery, a digital services consultancy, says wearable companies, IoT suppliers, healthcare companies and financial companies all need the technology.

"graphic databases are most commonly used for real-time data synchronization," he said. "

look into the future

'The graphical database is still in its infancy, and we will see more applications emerge, 'said Panetta of Tufts University.

She predicts that future use cases for graphical databases will include upgrading AI to a new level.

"the way we now do artificial intelligence on data is to use old-fashioned relational databases," she said. " However, artificial intelligence is based on relationships. "graphical databases will help build better AI systems."

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