My research dashboard
Reed Elsevier

A personal dashboard for millions of users

The request

Reed Elsevier wanted to show authors of research articles the success of their scientific work in one simple dashboard. By combining data from various sources, authors are given an overview of the success of their scientific work, presented in a dynamic dashboard.

An additional challenge was the expected volume of users, potentially more than two million. This, combined with the fact that some 400 external documents had to be collected and interpreted to assemble a dashboard, was a technical challenge.
 

Approach

The project was addressed with an Agile approach. At the beginning, only general information was available about the various data sources and the desired end result. Five sprints were used to reach a worthy system. The team consisted of four developers.
 

The development

In order to be able to process the requested amount of information, a significant investment in hardware and software would be required. It was decided in consultation to use the Amazon AWS platform for its strong Platform-as-a-Service. Amazon AWS is able to provide a strongly scalable platform, depending on the demand, without the need to investment in hardware.

The decision was made to host parts of the application architecture at AWS. This means that no software packages are used for components of the system, but that services have been integrated into the platform. Thanks to this approach, a platform could be delivered which was able to process large amounts of data without an initial investment in hardware and software licenses at relatively low costs with the underlying AWS platform growing along with the use.

The presentation layer has been developed by Clever Franke from Utrecht, specialists in data visualisation. It has been created in JavaScript and communicates with the middleware of Ritense using APIs. This means that both parties can work completely independently.

 

Technology

Amazon AWS infrastructure & PaaS services

  • AWS Elastic Load Balancing
  • EC2 (7+ instances)
  • EBS – Elastic Block Storage
  • ElastiCache – in memory cache
  • SQS – simple message queue
  • DynamoDB – NoSQL database
  • RDS MySQL - master/slave setup
  • S3 – file storage
  • SES – simple email service for bulk notfications
  • Multi A/Z deployment
  • Auto-scaling groups

 

Programming

  • Java WAR deployement (OpenBD + native Java)

 

Facts & figures

  • Maximum intended users: 2.000.000
  • Connected source systems: 5
  • Calls per Dashboard: ~ 400
  • Number of servers: 7 to 40 parallel
  • In collaboration with Clever Franke from Utrecht (frontend)
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