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Ultipa Graph Documentation -- Use Cases

Introduction

In this chapter, we'll be covering selective scenarios that Ultipa Graph excels in. In general, Ultipa Graph can be used for all graph computing use cases, especially in those cases that deep data correlations (and/or real-time computing) are sought after, but there is no need to limit Ultipa Graph to that kind of scenarios only.

Keep in mind the following characteristics of Ultipa Graph:

  • Super-fast graph search:  High QPS/TPS. (100x to competitions)
  • Super-deep search against any type of graph.  (10x deeper)
  • Highly concurrent graph system: Extremely high throughput. (10x higher concurrency)
  • Relatively small run-time in-memory footprint. (50% less RAM)
  • Very stable graph system.
  • Highly visualized graph database manager & high-performance KG-embedded front-end.

There are other features, the list can be very long, but for now, the aforementioned 5 things are to bear in your mind, and in the benchmarks article, more concrete and quantitative reports are shared.

Generally speaking, Ultipa Graph system serves at least the following domains:

  • BFSI sector:  Banking, Financial, Security and Insurance sectors, with scenarios like anti-fraud, asset management, anti-money-laundering, risk control, risk budgeting and risk management, IT auditing, and more.
  • Telco carrier: customer 360, smart recommendation, anti-fraud, network monitoring and management with knowledge graph, and etc.
  • IoT: The IOT problem is both big data and fast data, and to dig more value out of the IOT network, network analytics is a must-have/must-do, it makes every sense to leverage graph database for this purpose. 
  • Supply Chain Management: Supply chain tends to form a gigantic network, and to do data analytics against this network, graph is your best friend!
  • Internet sector:  Features like knowledge graph, smart search & recommendation, chatbot, fraud-detection and etc.

We have collected a few use cases for your reference, they are:

  • Deep Graph Traversal – UBO Identification.
  • Fraud Prevention – Corporate Guarantee-Chain Detection
  • Fraud Detection – Real-Time Decision Making
  • Knowledge Graph & Real-time Computing
  • Anti-Fraud – Real-time Fraud Call Detection
  • Crime Fighting – Real-time Crime-Ring/Network Detection(To Be Done)
  • Other assorted use cases.

Use Case: Deep Graph Traversal, UBO Finding

Technically speak, DGT (Deep Graph Traversal) is not a use case, it is a unique feature by graph computing system. In high performance systems like Ultipa Graph, real-time DGT can be very beneficial to easily solve real-world business challenges.

Here is one such challenge faced by local, state-wide and federal authorities in San Francisco, in the past decade San Francisco’s real-estate properties that were supposedly allocated to low-income local families have been bought up by LLCs (Limited Liability Companies) that are hard to trace their UBOs (Ultimate Beneficial Owners). The issues were so prevalent that this has become a major concern, government agencies like IRS (Internal Revenue Services) and local law enforcements are interested in understanding what parties are hiding behind these LLCs, manual exploration process can be very labor intensive and time consuming, because the eventual UBO parties may hide many hops (layers) behind the surface LLCs, and often times these UBOs intentionally hide and cross-owning their shares, making the overall owning structures highly complicated. An automated white-box AI solution is desired.

Similar cases are also found popular in other markets, for instance, China has seen the rise of Three-Cha (namely Tianyancha, Qichacha and Qixinbao), the top players that offering semi-automatic business background investigation services online).

A Company’s Ownership Network Topology

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