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Real-Time Election Data Processing and Visualization System Development
  1. case
  2. Real-Time Election Data Processing and Visualization System Development

Real-Time Election Data Processing and Visualization System Development

yalantis
Government

Challenge Overview for Electoral Data Management and Analysis

The client faces manual data entry and processing inefficiencies during elections, leading to extensive operational hours (up to 600 hours for large datasets). There is a need to reduce manual effort, enhance system scalability during high traffic periods, secure election results against potential fraud, and improve overall user experience with interactive and customizable result visualization tools.

About the Client

A government electoral commission overseeing national or regional elections, requiring high-volume, real-time election data processing and visualization.

Goals for Automated, Scalable, and Secure Election Data Systems

  • Automate manual processes such as data validation, chart generation, and file uploads to significantly reduce operational hours, aiming for processing times under 3 hours.
  • Implement a scalable system architecture capable of handling over 100,000 requests per second and supporting simultaneous user requests with response times less than two seconds.
  • Enhance data security through role-based access controls and comprehensive activity logging to prevent fraud and facilitate audit trails.
  • Develop interactive geographic and electoral district mapping features allowing users to filter and customize election result reports.
  • Create an intuitive admin interface for polling officers to manage district data, preview charts, and configure electoral regions.

Core Functional Specifications for Election Data Processing Platform

  • Automated scripts for validating and processing large datasets, including CSV file intake and ballot chart creation.
  • A GraphQL API for flexible data object interaction, supporting concurrent data access and updates.
  • A robust database solution (e.g., managed relational database) with adequate data field capacity and performance optimization.
  • Integration with scalable cloud infrastructure (e.g., AWS Lambda and AWS AppSync) for on-demand resource allocation and high load handling.
  • Load testing to validate system performance under expected and peak traffic scenarios.
  • Role-based access control including polling station-level permissions, audit logging, and activity tracking.
  • Interactive, clickable maps for filtering election results by district, with SVG processing for custom map overlays.
  • Chart customization capabilities, allowing users to reorder, preview, and configure electoral district names and sizes before publication.

Technical Stack and Architectural Approach Recommendations

GraphQL API for flexible and efficient data querying and mutation
AWS Lambda and AWS AppSync for scalable, serverless backend services
Aurora MySQL or equivalent relational database with appropriate data field capacity
React.js for building interactive client-side components, including maps and charts
SVG file format for detailed electoral district mapping and clickable interactive maps

External Systems and Data Source Integration Needs

  • Official social survey and election data sources for data ingestion
  • Authentication and authorization systems for role management
  • Audit logging systems for activity tracking and data integrity verification

Non-Functional System Attributes and Performance Criteria

  • System scalability to handle over 100,000 requests per second
  • High system availability with minimal latency, ensuring responses under 2 seconds for peak loads
  • Secure role-based access control with detailed activity logs
  • Error-free automation scripts to reduce manual effort from 600 hours to approximately 3 hours
  • Ability to add or remove server resources dynamically based on election cycle demands

Projected Business Outcomes and System Benefits

The implementation of this automated, scalable, and secure election data processing system is expected to drastically reduce manual operational effort from 600 hours to under 3 hours, support real-time visualization of election results with interactive maps, and ensure system stability under high-volume traffic. Resulting in faster reporting, improved data accuracy, enhanced security against manipulation, and a superior user experience for election officials and the public.

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