ArangoDB White Papers
Scalable Fraud Detection With Multi-Model
Across industries, fraud is a growing problem resulting in a global annual loss of $3.7 trillion. Fraudsters became more sophisticated in hiding their activities by forming fraud rings, using stolen identities and other patterns. Traditional approaches still focus on discrete data missing many opportunities to identify or prevent fraud.
Multi-model lets organizations see data from different perspectives, its context and detect fraud patterns with graph database technology even within large scale datasets. In this white paper we will show how to convert data from relational to multi-model graphs, how various fraud detection queries work in ArangoDB’s Query Language (AQL) and how this Fraud Detection can be done at scale.
Operationalizing Knowledge Graphs with Multi-Model
Enterprise Knowledge Graphs (EKGs) have been on the rise and are incredibly valuable tools for harmonizing internal and external data relevant to an organization into a common semantic model to improve operational efficiency for the enterprise and competitive advantage for the business units. On the other hand, EKGs can be difficult to develop and sustain, suffer from scalability issues, and can be difficult for business units to consume.
This White Paper describes some of these challenges and how a flexible data representation of a multi-model graph can address them.
What is a Multi-model Database and Why Use It?
When it comes to choosing the right technology for a new project, it can often be challenging to define the exact right tools that will match set-up criteria from start to finish. In this white paper, we explain what a multi-model database is, including a use case based on aircraft fleet management.
Switching from Relational Databases to ArangoDB
This white paper compares relational database management systems (RDBMS) to native multi-model databases — in particular, MySQL and ArangoDB. It describes key concepts and contrasts at the end of each section, and concludes on what sets ArangoDB apart.
Resilience in ArangoDB 3
In this white paper, we explain the resilience concept of ArangoDB 3. When an application runs on multiple machines or cloud instances, the probability of a machine failure is no longer negligible. Thus, if you run distributed applications and you want to sleep well, these applications need to be fault tolerant or resilient.
ArangoDB Cluster Performance
In our white paper we show that an ArangoDB cluster with 640 vCPUs can sustain a write load of 1.1M JSON documents per second which amounts to approximately 1GB of data per second and that it takes a single short command and around 10 Minutes to deploy such an 80 node cluster using the Mesosphere DCOS.