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| <p>A graph is a structure composed of vertices and edges. Both vertices and edges can have an |
| arbitrary number of key/value-pairs called properties. Vertices denote discrete objects such as |
| a person, a place, or an event. Edges denote relationships between vertices. For instance, a |
| person may know another person, have been involved in an event, and/or have recently been at a |
| particular place. Properties express non-relational information about the vertices and edges. |
| Example properties include a vertex having a name and an age, and an edge having a timestamp |
| and/or a weight. Together, the aforementioned graph is known as a property graph and it is the |
| foundational data structure of Apache TinkerPop. </p> |
| <p>If a user's domain is composed of a heterogeneous set of objects (vertices) that can be related |
| to one another in a multitude of ways (edges), then a graph may be the right representation to |
| use. In a graph, each vertex is seen as an atomic entity (not simply a "row in a table") that |
| can be linked to any other vertex or have properties added or removed at will. This empowers the |
| data modeler to think in terms of actors within a world of complex relations as opposed to, in |
| relational databases, statically-typed tables joined in aggregate. Once a domain is modeled, |
| that model must then be exploited in order to yield novel, differentiating information. Graph |
| computing has a rich history that includes not only query languages devoid of table-join |
| semantics, but also algorithms that support complex reasoning: path analysis, vertex clustering |
| and ranking, subgraph identification, and more. The world of applied graph computing offers a |
| flexible, intuitive data structure along with a host of algorithms able to effectively leverage |
| that structure.</p> |
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| <h3 class="ft-18 bold text-blue">Social Networks</h3> |
| <p>Unleash the power of a social network by using relationships between individuals to infer and |
| recommend connections.</p> |
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| <img src="img/use-cases/fraud.png" class="img-fluid mb-3" alt="img"> |
| <h3 class="ft-18 bold text-blue">Fraud Detection</h3> |
| <p>Leverage the connections inherent in a financial transaction to provide real-time analysis and |
| detection for known fraudulent patterns of activity.</p> |
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| <img src="img/use-cases/knowledge.png" class="img-fluid mb-3" alt="img"> |
| <h3 class="ft-18 bold text-blue">Knowledge Graphs</h3> |
| <p>Unite disperate data sources and data silos to provide contextually relevant information to |
| queries.</p> |
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| <img src="img/use-cases/recommendation.png" class="img-fluid mb-3" alt="img"> |
| <h3 class="ft-18 bold text-blue">Recommendation Engines</h3> |
| <p>Provide real-time personalized recommendations for products, services and content by utilizing |
| connections within data.</p> |
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| <h3 class="ft-18 bold text-blue">Identity/Access Management</h3> |
| <p>Effectively and effciently track user activity and access using a graph of assets and |
| relationships.</p> |
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| <img src="img/use-cases/360-view.png" class="img-fluid mb-3" alt="img"> |
| <h3 class="ft-18 bold text-blue">Customer 360</h3> |
| <p>Leverage disconnected data sources to provide a holisitic 360 degree view of customer |
| interactons</p> |
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| <img src="img/use-cases/master-data.png" class="img-fluid mb-3" alt="img"> |
| <h3 class="ft-18 bold text-blue">Master Data Management</h3> |
| <p>Organize and provide access to canonical master data by combining data in a schemaless and |
| flexible manner.</p> |
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| <h3 class="ft-18 bold text-blue">Supply Chain Management</h3> |
| <p>Manage and visualize complex interconnected supply chains to identify optimizations and assess |
| risk.</p> |
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| <img src="img/use-cases/compliant.png" class="img-fluid mb-3" alt="img"> |
| <h3 class="ft-18 bold text-blue">Compliance</h3> |
| <p>Ensure effective compliance by examining and evaluating entities against a network of applicable |
| interconnected regulations.</p> |
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| <p class="mt-2 mb-4">Apache TinkerPop™ is a graph computing framework for both graph databases |
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