Why should you use graph analytics?

Data is growing exponentially, and rising automation is generating a plethora of data from smart phones, mobile and IoT devices, security systems, satellite imagery, vehicles and more. The question becomes: how to rapidly obtain meaningful insights from ever growing data sets across different types and sources?

Graph technology makes it easy to explore relationships and discover connections in data, allowing developers and analysts to gain meaningful insights quickly. Much of the world's data is indeed connected, including financial transactions, social and professional networks of people, manufacturing supply chains and more. Graphs instantly reveal those connections.

Here is an illustration showing how data sets are connected and how complex analysis can get due to data relationships

Graph data platforms provide automation to increase developer productivity. They deliver the performance and scalability to support large deployments, while enhanced query and search capabilities simplify access and accelerate time to insights for connected-data use cases.

Oracle makes it easy to adopt graph technologies. Graphs are part of Oracle's converged database, which supports multi-model, multi-workload, and multi-tenant requirements- all in a single database engine. Oracle Database and Oracle Autonomous Database allow analysts to rapidly discover new insights using the power of built-in graph algorithms, pattern matching queries, and visualization. Developers can easily add graph analytics to existing applications, taking advantage of the performance, scalability, reliability, and security provided by Oracle Database.

Oracle recognized a leader in graph data platforms

Oracle was named a leader in The Forrester Wave™: Graph Data Platforms, Q4 2020 report.

Forrester considered 27 criteria to evaluate graph data platforms. Some of the key criterions include graph model/engine, deployment options, cloud, app development, API/extensibility, data loading/ingestion, data management, transactions, queries/search, analytics, visualization, high availability and disaster recovery, scalability, performance, data security, workloads, and use cases.

Why graph technologies from Oracle?

Key features of the Oracle offering include:

1. Complete graph database with support for both property graph and RDF knowledge graphs. Oracle Database simplifies the process of modeling relational data as graph structures. It automates the discovery, processing, and visualization of connected data sets on-premises or in the cloud

2. Enterprise-level scalability and security. Interactive graph queries can run directly on graph data or in a high-performance in-memory graph server, supporting millions of concurrent users and queries per second. Customers gain fine-grained security, high availability, easy manageability, and integration with other data in business applications. Oracle provides sophisticated, multilevel access control for property graphs vertices and edges, and RDF triples. Oracle also aligns with applicable ISO and Worldwide Web Consortium standards for representing and defining graphs and graph query languages.

3. Comprehensive graph analytics to explore relationships with more than 60 prebuilt algorithms. Analysts can use SQL, native graph languages, JAVA, and Python APIs, as well as Oracle Autonomous Database features to create, query, and analyze graphs. They can display connections easily in data to discover insights, and then use interactive analytics and visualization tools to publish and share analysis results. With Graph Studio in Autonomous Database, almost anyone can get started with graphs to explore relationships in data. Graph Studio automates graph data management and simplifies modeling, analysis, and visualization across the graph analytics lifecycle.

Graph analytics use cases

 

Money laundering detection in financial services: To make fraud detection simpler, users can create a graph from transactions between entities as well as entities that share some information, such as email addresses, passwords, and more. Once a graph is created, running a simple query will find all customers with accounts who have similar information, reveal which accounts are involved in circular payment schemes, and identify patterns used to perpetuate fraud.

 

Traceability in manufacturing: Traceability is of great significance in the manufacturing world. An automobile company might have to issue a recall for a car model because that specific model has a component which was produced from a factory during a limited time slot. Most companies have a production database, a separate retail database, a separate sales database, and a separate shipping database. It is complicated to discover all the relevant information to find the cars with the problem, where they were shipped, and to whom they were sold- unless the company has a graph database to connect all the relationships, and graph algorithms to highlight connections and relevant information.

Criminal investigation: Putting data into graphs provides a natural and efficient way to identify criminal networks and look for patterns. Applying graph-based algorithms makes it easier to identify specific locations, highlight co-traveler relationships, or discover key suspects and criminal gangs. For example, by applying betweenness centrality, users can find the "weakest link," meaning the vertex that the graph relies upon. If you remove that vertex, the entire graph may fall apart, meaning you may have just found the linchpin of a criminal gang.

Data regulation and privacy: Tracking data lineage is a perfect match for a graph. The various steps in the data lifecycle can be tracked and navigated, vertex by vertex, by following the edges. With graph, it becomes possible to follow a path and see where the information originally resided, where it was copied, and where it was utilized. With all this information laid out in a graph, it becomes simpler for data professionals to determine how to fulfill GDPR requests and remain compliant.

Product recommendations in marketing: Graph databases collect all data and form connections to gain speedy insight into customer needs and product trends to provide real-time recommendations. Many large corporations rely upon graph analytics to provide product recommendations because the relationships are already laid out, and the analysis of these relationships to provide recommendations is very fast. Additionally, graph analysis can identify the patterns that reveal trolls, bots, artificially promoted reviews, and information that may distort marketing analysis.

Source: oracle.com