Venue Duplicate Detection: Audit And Verification Success

by Alex Johnson 58 views

Duplicate data can be a real headache, especially when you're trying to keep track of venues and events. This article dives into a successful audit and verification process for venue duplicate detection, ensuring a clean and accurate dataset. We'll explore how the system effectively deduplicates venues, collides events from multiple sources, and prevents false positives, all while using clever name matching techniques. So, if you're grappling with duplicate data or just curious about how it's tackled, read on!

Summary of Venue Duplicate Detection Fixes

The core goal here was to eliminate duplicate venue entries and ensure that events are correctly associated with their respective locations. The good news? All the fixes implemented have been thoroughly verified and are working like a charm! The updated system now boasts the following capabilities:

  • Deduplicates cinema venues across various scrapers, preventing redundant entries.
  • Collides events from multiple sources that occur at the same venues, providing a unified view.
  • Prevents false positive matches, ensuring that distinct locations aren't incorrectly merged.
  • Employs a token-based name matching system that intelligently handles naming variations and inconsistencies.

This means a cleaner, more accurate database, which translates to a better experience for users and more reliable data for analysis. Let's dive into the specifics of the verification results to see how these improvements play out in practice.

Verification Results: A Deep Dive

To ensure the effectiveness of the implemented fixes, a series of verification tests were conducted. These tests covered various aspects of venue and event handling, and the results are quite impressive.

1. Venue Deduplication: Eliminating the Clutter

Venue deduplication was a primary focus. The aim was to ensure that there are no duplicate cinema venue entries in the system. To achieve this, the baseline was set to "No duplicate cinema venues", and the results speak for themselves.

For instance, Cinema City Kazimierz is now represented by a single venue (id=51), Cinema City Bonarka by another unique venue (id=55), and Cinema City Zakopianka by yet another (id=96). In total, the system now accurately reflects 3 unique venues, a significant improvement from the previous state where there were 6 duplicates. This streamlined representation makes it much easier to manage and track events at these popular cinema locations.

2. Venue Collision (Cross-Scraper): Unifying Event Data

Venue collision is crucial when dealing with data from multiple sources. The goal here was to ensure that events from different scrapers (like Cinema City and Kino Krakow) are correctly associated with the same venues. The baseline was set to "Cinema City and Kino Krakow using the same venues," and the results confirm the successful collision of events.

An impressive 36 events at Cinema City venues now contain showtimes from both scrapers. A prime example is Event 95, "Home Sweet Home at Kraków - Bonarka." This event, held at Bonarka City Center (id=55), boasts a total of 88 showtimes sourced from both Cinema City and Kino Krakow. This cross-scraper collision ensures that users see a comprehensive view of event schedules, regardless of the source.

3. Event Collision: Ensuring Uniqueness

Event collision aims to prevent duplicate event entries for the same movie at the same venue. The baseline for this test was "Only one event per movie per venue", and the results are encouraging.

Bugonia events, for example, are now represented by 9 unique events across 9 unique venues. No duplicate events were detected, and the original complaint regarding duplicate Bugonia events has been successfully resolved through merging. This ensures that users aren't bombarded with redundant event listings, making the browsing experience much smoother.

4. False Positive Prevention: Keeping Venues Distinct

Preventing false positives is essential to maintain data integrity. The objective here is to ensure that distinct venues, such as bars and museums, are not incorrectly matched with cinemas. The baseline was set to "Bars/museums stay separate from cinemas," and the system passed with flying colors.

The closest bar, BroPub, is located a considerable 1,236.6m away from the Kazimierz cinema. All non-cinema venues were correctly kept separate, demonstrating the system's ability to distinguish between different types of locations. The VenueNameMatcher plays a crucial role here, correctly rejecting dissimilar names with a 0% similarity score. This prevents erroneous merges and ensures that each venue maintains its unique identity.

5. Algorithm Performance: The Power of Token-Based Matching

The performance of the algorithm used for venue name matching is critical to the success of duplicate detection. The VenueNameMatcher, a key component of the system, has proven its mettle in this area. Let's look at some examples:

  • "Cinema City Bonarka" vs "Kraków - Bonarka": 100.0% match ✅
  • "Cinema City Kazimierz" vs "Kraków - Galeria Kazimierz": 100.0% match ✅
  • "Cinema City Zakopianka" vs "Kraków - Zakopianka": 100.0% match ✅

These results demonstrate the VenueNameMatcher's ability to accurately identify venue matches despite variations in naming conventions. In contrast, the old PostgreSQL trigrams approach only yielded similarity scores in the 29-32% range, which would not have resulted in a match. This highlights the significant improvement achieved by the new token-based matching system.

To further enhance matching accuracy, the search radius has been expanded to 3000m to account for geocoding variations. This ensures that venues in close proximity are correctly identified as potential duplicates, even if their GPS coordinates differ slightly.

Data Statistics: A Snapshot of the System

To provide a comprehensive overview of the system's current state, let's look at some key data statistics:

  • Total movie events: 125
  • Total venues: 14
  • Cinema City venues: 3 (no duplicates)
  • Collision rate: 28.8% (36 events from both scrapers)

These numbers paint a clear picture of a well-organized and efficient system. The absence of duplicate Cinema City venues and the high collision rate (28.8%) indicate that events from different sources are being effectively merged. This leads to a more complete and accurate view of event schedules for users.

Changes Implemented: Behind the Scenes

Several key changes were implemented to achieve the impressive results described above. These changes span multiple modules and functionalities within the system.

lib/eventasaurus_app/venues/duplicate_detection.ex

This module underwent significant modifications to improve duplicate detection capabilities:

  1. ✅ Replaced calculate_name_similarity() to use VenueNameMatcher: This was a crucial step in enhancing the accuracy of name matching.
  2. ✅ Updated similarity thresholds (0.4, 0.5, 0.6): Fine-tuning the similarity thresholds ensures that matches are neither too strict nor too lenient.
  3. ✅ Expanded search radius to 3000m in find_duplicate(): The expanded search radius accounts for geocoding variations and ensures that nearby venues are considered potential duplicates.
  4. ✅ Added documentation explaining 3km geocoding variations: Clear documentation helps developers understand the rationale behind the expanded search radius.

lib/eventasaurus_discovery/scraping/processors/venue_processor.ex

This module, responsible for processing venue data from scrapers, also saw key improvements:

  1. ✅ Added VenueNameMatcher to aliases: Making VenueNameMatcher readily available simplifies its usage within the module.
  2. ✅ Fixed Geocoder.Coords struct access bug (struct_to_map conversion): Addressing this bug ensures correct handling of geocoding data.
  3. ✅ Replaced name-only fallback matching (lines 145-179) to use VenueNameMatcher: This ensures that VenueNameMatcher is used consistently for name matching, even in fallback scenarios.

These changes, while technical in nature, have a profound impact on the accuracy and reliability of venue duplicate detection.

Additional Robustness Improvements (Optional): A Safety Net

While the core venue matching fix is working well, there's always room for improvement, especially when it comes to robustness. CodeRabbit, a code review tool, identified a potential crash scenario in venue_store.ex, highlighting the importance of defensive programming.

VenueStore.ex Safety Enhancement

  • Location: Lines 97 and 116 use Decimal.to_float(distance) directly
  • Issue: Could crash if PostGIS returns non-Decimal type (plain float, integer, etc.)
  • Fix: Use existing to_float/1 helper (lines 434-446) with nil-guarding

The suggested code changes involve replacing direct calls to Decimal.to_float(distance) with the existing to_float/1 helper function, which includes nil-guarding. This prevents potential crashes if PostGIS returns a non-Decimal type, such as a plain float or integer.

This enhancement, while optional, is a valuable addition to the system's robustness. It's a proactive step towards preventing unexpected issues and ensuring smooth operation.

Final Recommendation: Mission Accomplished!

After rigorous testing and verification, the final recommendation is clear: YES - This issue can be successfully closed.

All verification checks have passed, confirming the effectiveness of the implemented fixes:

  • ✅ No duplicate venues
  • ✅ Events colliding correctly
  • ✅ Showtimes aggregating from multiple sources
  • ✅ False positives prevented
  • ✅ User complaints resolved
  • ✅ No regressions detected

The venue duplicate detection system is now functioning as designed, leveraging token-based name matching and an expanded GPS search radius. This translates to a cleaner, more accurate, and more reliable dataset, ultimately benefiting users and the overall system.

By addressing duplicate data, this project enhances data quality and usability, ensuring that event information is accurate and readily accessible. The successful implementation of these fixes demonstrates a commitment to data integrity and user satisfaction.

In conclusion, tackling duplicate data is crucial for maintaining a healthy and efficient system. This audit and verification process showcases the importance of thorough testing and the effectiveness of a well-designed duplicate detection system. The result is a cleaner, more reliable dataset that benefits both users and the organization.

For further reading on data deduplication and its best practices, consider exploring resources on websites such as Data Management Body of Knowledge (DMBOK)