Reproducible Ranked Retrieval for Evolving Corpora Using a Hybrid Lucene-MonetDB System

Core Concepts

This paper introduces a hybrid information retrieval system that combines Lucene and a versioned column-store database (VCBR) built on MonetDB to enable reproducible ranked retrieval results in evolving document collections, addressing the limitations of traditional IR systems in research contexts requiring result traceability and replicability.

Abstract

Bibliographic Information:

Staudinger, M., Piroi, F., & Rauber, A. (2024). Reproducible Hybrid Time-Travel Retrieval in Evolving Corpora. In Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP ’24), December 9–12, 2024, Tokyo, Japan. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3673791.3698421

Research Objective:

This paper presents a novel hybrid information retrieval (IR) system designed to address the challenge of result reproducibility in evolving document collections, a critical issue in research areas reliant on consistent data subsets for analysis and validation. The authors aim to demonstrate the feasibility of combining a traditional IR system (Lucene) with a versioned column-store database (MonetDB) to achieve both efficient query processing and result reproducibility.

Methodology:

The proposed hybrid system leverages Lucene for fast, ranked retrieval and a VCBR system implemented on MonetDB for storing historical corpus statistics, enabling the recreation of past index states. The system synchronizes document preprocessing and term statistics between Lucene and MonetDB, tracking changes over time. Queries are primarily handled by Lucene, with results stored in a query store alongside metadata and hash keys for reproducibility verification. Re-execution of queries utilizes the VCBR system to retrieve historical corpus statistics and reproduce identical ranked lists.

Key Findings:

Evaluation using a subset of the German Wikipedia corpus demonstrated the system's ability to reproduce identical ranked lists while maintaining acceptable performance. Lucene's indexing and query processing times remained relatively stable with increasing corpus size, while MonetDB exhibited linear increases. Despite minor score variations due to floating-point inaccuracies, the system successfully reproduced identical ranked lists, with a hash-based error correction mechanism addressing rare discrepancies.

Main Conclusions:

The hybrid Lucene-VCBR system effectively addresses the reproducibility challenge in evolving corpora, offering a viable solution for research domains requiring result traceability and replicability. The system's ability to recreate past index states enables time-travel search, further enhancing its utility.

Significance:

This research contributes significantly to the field of information retrieval by providing a practical solution for reproducible ranked retrieval in dynamic data environments. The proposed system has the potential to impact research areas such as systematic literature reviews, patent analysis, and scientific studies relying on evolving data collections.

Limitations and Future Research:

Future work includes expanding the system's support for additional retrieval models beyond BM25, particularly dense retrieval models. Performance optimization of the VCBR system is crucial for handling larger datasets. Further investigation into the reproducibility of ranked lists using neural reranking techniques is also warranted.

Stats

The study used the first 520,000 articles of the German Wikipedia corpus dump of May 2020 (3.72GB).
The vocabulary consisted of 7,787,028 unique terms and a total of 159,150,478 terms.
Documents were inserted in 26 batches of 20,000 documents each.
Four predefined sets of 100 queries each, comprising 1, 2, 5, and 10 terms, were used.
The study used BM25 as the retrieval model and retrieved the top-20 documents.
Lucene's indexing time remained stable at approximately 13 minutes per 20,000 documents.
MonetDB's indexing time increased linearly from 2:30 minutes to 8 minutes per batch.
The storage footprint of the MonetDB solution was 4.05 GB, while Lucene's index size was 3.82 GB.
Lucene's query processing time was approximately 80 ms per query.
MonetDB's query processing time increased linearly from 2 seconds to 20 seconds.
Score differences between Lucene and VCBR were within the range of 10^-5.
The average difference in score between consecutive documents in the ranked list was on the order of 10^-2.
Only one case of document swapping occurred in over 10,400 queries due to floating-point inaccuracies.

Quotes

"Although Boolean retrieval allows for the replication and reproduction of search results, they usually return large sets of documents, of which many are not relevant to the user’s actual information need."
"Reproducibility of IR ranked results, as argued for above, necessitates keeping track of all changes. This is a highly challenging task, considering the rate of change in the document collections."

Key Insights Distilled From

Reproducible Hybrid Time-Travel Retrieval in Evolving Corpora

by Moritz Staud... at arxiv.org 11-07-2024

https://arxiv.org/pdf/2411.04051.pdf

Deeper Inquiries

How can this hybrid system be adapted to handle real-time updates in rapidly evolving document collections, such as social media feeds or news streams?

Adapting the hybrid system to handle real-time updates in rapidly evolving document collections like social media feeds or news streams presents a significant challenge. The current system, while excelling at reproducibility, is optimized for batch updates and may struggle with the constant influx of new data in real-time scenarios. Here's a breakdown of potential adaptations and considerations:
1. Indexing Pipeline Optimization:

Stream Processing: Instead of batch processing, implement a stream processing pipeline using technologies like Apache Kafka or Apache Flink. This allows for continuous ingestion and processing of new documents as they arrive.
Incremental Indexing: Lucene, the primary search engine, supports incremental indexing. This feature should be leveraged to update the index with new documents without rebuilding it entirely.
Microservices Architecture: Decompose the system into smaller, independent microservices responsible for specific tasks like data ingestion, preprocessing, indexing, and query processing. This enhances scalability and fault tolerance.
2. Database Considerations for VCBR:

Columnstore Limitations: MonetDB, being a column-oriented database, might face performance bottlenecks with high-frequency writes typical of real-time updates. Explore alternative database technologies like Apache Cassandra or HBase, designed for high write throughput.
Data Partitioning: Implement data partitioning strategies (e.g., by time or topic) to distribute the data across multiple database nodes, improving scalability and handling larger data volumes.
Eventual Consistency: In a real-time setting, consider adopting an eventual consistency model for the VCBR system. This means accepting that the reproduced results might not always reflect the absolute latest state of the corpus but will eventually become consistent.
3. System Monitoring and Performance Tuning:

Real-time Monitoring: Implement robust monitoring tools to track system performance metrics like indexing latency, query response times, and resource utilization. This helps identify bottlenecks and areas for optimization.
Load Balancing: Employ load balancing techniques to distribute incoming queries across multiple instances of the search engine and database, preventing overload and ensuring consistent performance.
4. Trade-offs and Considerations:

Reproducibility vs. Real-timeness: Achieving perfect reproducibility in a real-time environment might be impractical. Define acceptable levels of latency for reproducibility and prioritize real-time updates for the primary search engine.
Cost and Complexity: Implementing these adaptations introduces complexity and potentially higher infrastructure costs. Carefully evaluate the trade-offs between real-time performance, reproducibility requirements, and budget constraints.

While the system addresses reproducibility, could the reliance on a centralized database system introduce potential vulnerabilities or limitations in terms of scalability and fault tolerance?

Yes, the reliance on a centralized database system like MonetDB for the VCBR component does introduce potential vulnerabilities and limitations:
Scalability:

Centralized Bottleneck: As the document collection grows, a single database server can become a bottleneck, limiting the system's ability to handle increasing data volumes and query loads.
Vertical Scaling Limits: While vertical scaling (upgrading hardware) can provide some relief, it eventually reaches limits, and scaling out horizontally (adding more servers) becomes more complex with a centralized database.
Fault Tolerance:

Single Point of Failure: A centralized database represents a single point of failure. If the database server fails, the entire VCBR system becomes unavailable, impacting reproducibility features.
Data Loss Risk: Without proper backups and redundancy mechanisms, a database failure could lead to data loss, compromising the ability to reproduce past search results.
Potential Solutions and Mitigations:

Distributed Databases: Consider migrating to a distributed database system like Apache Cassandra or Amazon DynamoDB. These systems offer better scalability and fault tolerance by distributing data across multiple nodes.
Database Replication: Implement database replication to create redundant copies of the data on different servers. This ensures high availability and protects against data loss in case of server failures.
Sharding: Partition the database into smaller, more manageable chunks (shards) distributed across multiple servers. This improves scalability by allowing parallel processing of queries and data updates.
Database Backups and Disaster Recovery: Implement regular database backups and a robust disaster recovery plan to minimize downtime and data loss in case of unexpected failures.

Could the principles of version control systems, commonly used in software development, be applied to manage evolving document collections and further enhance reproducibility in information retrieval?

Yes, the principles of version control systems (VCS) like Git, commonly used in software development, can be effectively applied to manage evolving document collections and significantly enhance reproducibility in information retrieval. Here's how:
1. Document Versioning and History Tracking:

Track Changes: Similar to how Git tracks code changes, a VCS can track every addition, deletion, and modification made to documents within a collection. This provides a complete audit trail of document evolution.
Revert to Previous Versions: Researchers can easily revert to previous versions of documents or the entire collection, enabling them to analyze information as it existed at a specific point in time.
Branching and Merging: Different versions of the document collection can be maintained as branches, allowing parallel research on different snapshots of the data. These branches can later be merged, incorporating changes from different research efforts.
2. Reproducible Research Workflows:

Link Queries to Document Versions: Queries can be linked to specific versions of the document collection, ensuring that the same query executed at a later time retrieves results from the same document set.
Capture Experiment State: Along with document versions, VCS can store metadata about queries, ranking algorithms, and other experimental parameters, providing a comprehensive snapshot of the research environment.
Collaboration and Sharing: Researchers can easily share their research workflows, including document versions and experiment configurations, fostering collaboration and ensuring reproducibility across teams.
3. Implementation Considerations:

Storage Backend: Traditional VCS like Git might not be suitable for storing large document collections. Explore specialized VCS designed for large files or integrate with cloud storage solutions like Amazon S3 or Google Cloud Storage.
Metadata Management: Develop robust metadata schemas to capture relevant information about document versions, queries, and experimental settings. This metadata is crucial for efficient search and reproducibility.
User Interface and Tools: Provide user-friendly interfaces and tools that abstract away the complexities of the underlying VCS, making it easy for researchers to interact with different document versions and track their research workflows.
Benefits of Applying VCS Principles:

Enhanced Reproducibility: Provides a robust mechanism to recreate past research experiments by accessing specific document versions and experimental configurations.
Improved Collaboration: Facilitates collaboration among researchers by enabling easy sharing and merging of research workflows and document collections.
Better Data Management: Offers a structured approach to manage evolving document collections, track changes over time, and ensure data integrity.
By adopting the principles of version control systems, information retrieval systems can significantly enhance reproducibility, enabling researchers to confidently build upon past work and accelerate scientific discovery.