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AI-Powered Event Filtering for Mapping Platform

spyro-soft.com

Hospitality & leisure

Government

GPS

Data Quality and Scalability Challenges in Event Aggregation

Leisure Ordnance Survey aggregates event data from numerous sources, leading to a large volume of information. Manual verification of event relevance (specifically, filtering out events not occurring outdoors) is no longer scalable. The need for automated event filtering to maintain data quality and ensure the platform focuses on relevant activities is critical.

About the Client

A mapping agency providing digital map data and location-based products, focusing on facilitating outdoor activities and events.

Project Goals

Develop an automated system for filtering irrelevant events from the platform's event data.
Improve the accuracy of event categorization to enhance user experience.
Reduce manual effort required for data validation and curation.
Increase the scalability of the event data processing pipeline.

Functional Requirements

Event classification based on natural language processing.
Ability to handle a high volume of event data.
Configurable filtering rules to adjust accuracy and coverage.
API endpoint for integrating with the existing platform
Reporting and monitoring of classification accuracy.

Preferred Technologies

Hugging Face Transformers

Google BERT

Azure Machine Learning Studio

Python

Scikit-learn

PostgreSQL

Integrations Required

Existing event data ingestion pipeline
Platform API for event integration

Key Non-Functional Requirements

High accuracy (target: >95%)
Scalability to handle increasing event data volume
Low latency for event classification
Secure data handling

Expected Business Impact

Automating event filtering will significantly reduce manual effort, improve data quality, enhance user experience by presenting more relevant events, and enable the platform to scale more effectively. This will lead to increased user engagement and better utilization of the platform's resources. The model's adaptability will allow for future expansion into other text-based data filtering tasks.