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Development of Scalable Multimodal Data Analysis Platform for Cognitive Research

sigma.software

Education

Medical

Research

Challenges in Processing Large-Scale Naturalistic Data for Cognitive Research

Existing systems unable to handle massive multimodal data volumes from child development studies, requiring scalable processing infrastructure, secure storage solutions, compliance with healthcare data regulations, and efficient integration of diverse datasets.

About the Client

Research laboratory at Princeton University focused on cognitive science and naturalistic data analysis

Objectives for Developing Advanced Data Analysis Infrastructure

Create a cloud-native platform for scalable multimodal data processing
Enable efficient ingestion and management of heterogeneous datasets
Ensure compliance with CCPA and U.S. Privacy Act standards
Optimize data processing speed for large video datasets
Establish a blueprint for future cognitive science research platforms

Core System Functionalities

Automated data ingestion pipelines for multimodal datasets
Distributed processing framework for video and behavioral data
Secure storage with encryption and access controls
Real-time monitoring and alerting system
Compliance framework for healthcare data regulations

Technology Stack Requirements

AWS Glue

Amazon CloudWatch

AWS CloudTrail

Amazon EventBridge

AWS S3 with Object Lock

System Integration Needs

AWS data processing services
Third-party security compliance tools
Healthcare data management systems

Operational Requirements

Horizontal scalability for petabyte-scale datasets
99.95% system availability
End-to-end data encryption
Sub-second latency for critical processing workflows
Automated disaster recovery with cross-region replication

Expected Impact on Research Efficiency and Data Management

300% increase in historical data processing speed, 50% reduction in storage costs through optimized data architecture, single source of truth for improved analytical accuracy, compliance-ready security framework, and foundation for expanding multimodal dataset research capabilities across behavioral sciences.