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detecting complex fraud patterns with ArangoDB

Detecting Complex Fraud Patterns with ArangoDB

00General, Graphs, how to, Query LanguageTags: , ,

Introduction

This article presents a case study of using AQL queries for detecting complex money laundering and financial crime patterns. While there have been multiple publications about the advantages of graph databases for fraud detection use cases, few of them provide concrete examples of implementing detection of complex fraud patterns that would work in real-world scenarios. 

This case study is based on a third-party transaction data generator, which is designed to simulate realistic transaction graphs of any size. The generator disguises complex financial fraud patterns of two kinds: 

  • Circular money flows: a big amount of money is going through different nodes and comes back to the source node.
  • Indirect money transfers: a big amount of money is sent from source node to a target node over a multi-layered network of intermediate accounts.
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Sort-Limit Optimization in AQL

00GeneralTags:

Sometimes we want sorted output from a query and, for whatever reason, cannot use an index to do the sorting. In ArangoDB, we already cover this critical case with finely tuned query execution code. Sometimes though, we do not need to return all output, and follow our SORT clause with LIMIT. In ArangoDB 3.4 and earlier, we did not handle this case any differently from returning the full data, at least with respect to sorting – we would sort the full input, then apply the limit afterwards.

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Using the WebUI AQL Editor – Basics

00GeneralTags: , ,

The ArangoDB query language (AQL) can be used to retrieve and modify data that is stored in ArangoDB. The AQL editor in the web interface is useful for running ad hoc AQL queries and trying things out.

The editor is split into three parts. The center section allows you to write your query and modify your query bind parameters. At the bottom you can either run the query or explain it, allowing to explain the query and inspect its execution plan. This can be used to check if the query uses indexes, and which. Here more information about optimizing a query. Read more

From Zero to Advanced Graph Query Knowledge with ArangoDB

10General, Graphs, Query LanguageTags: , ,

Thinking about your data as a highly connected set of information is a powerful way to gain insights, solve problems and bring products faster into the hands of your users.

Unlike other databases, relationships take the first priority in graph databases and with ArangoDBs multi-model approach for graphs, documents and key/value pairs you can even switch between models or combine them in a single query.

The graph concept is booming but still new to many. So we invested a few bazillion coffees and some night shifts to come up with a good plan for a Graph Course:

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Arangochair – a tool for listening to changes in ArangoDB

04GeneralTags: , , ,

The ArangoDB team gave me an opportunity to write a tutorial about arangochair. Arangochair is the first attempt to listen for changes in the database and execute actions like pushing a document to the client or execute an AQL query. Currently it is limited to single nodes.

This tutorial is loosely based on the example at baslr/arangochair-serversendevents-demo

arangochair is a Node.js module hosted on npm which make it fairly easy to install. Just run
npm install arangochair and its installed. Read more

Killing a long-running query

01General, Query LanguageTags: ,

Suppose there is an AQL query that’s executing in the server for a long time already and you want to get rid of it. What can be done to abort that query?

If a connection to the server can still be established, the easiest is to use the ArangoShell to fetch the list of currently executing AQL queries and send a kill command to the server for the correct query. Read more

AQL optimizer improvements for 2.8

00PerformanceTags: , ,

With the 2.8 beta phase coming to an end it’s time to shed some light on the improvements in the 2.8 AQL optimizer. This blog post summarizes a few of them, focusing on the query optimizer. There’ll be a follow-up post that will explain dedicated new AQL features soon. Read more

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