Scalable Methods for Storing and Retrieving Wikipedia Revision Histories for Large-Scale Analysis

The history-based content of Wikipedia has long served as a valuable resource for a wide range of research efforts in Natural Language Processing (NLP). However, due to Wikipedia’s dynamic and ever-evolving nature, conducting large-scale temporal analyses requires the efficient retrieval and processing of historical article versions. One of the major challenges in this domain arises from the lack of robust and scalable tools capable of managing the vast volume of revision data that Wikipedia provides. This limitation often acts as a significant bottleneck in both data management and downstream applications. In this work, we present a comprehensive analysis of online algorithms designed to enable efficient compression and retrieval of Wikipedia’s revision history. Our methods are supported by theoretical guarantees, demonstrating their ability to achieve high compression rates while maintaining favorable time–space trade-offs. The analysis emphasizes the practicality and scalability of our approach, particularly in environments constrained by memory or processing time. To operationalize these theoretical findings, we develop and release WikECD (Wikipedia Efficient Compression and Decompression), an open-source Python library that implements our proposed algorithms for large-scale Wikipedia revision compression, retrieval, and analysis. Experimental evaluations on sampled Wikipedia articles show that the method can reduce dataset size to as little as 6% of its original volume, achieving up to 94% compression while preserving reconstructive accuracy. To the best of our knowledge, this work constitutes the first detailed and rigorous study to analyze and quantify the compression of Wikipedia’s complete revision history dataset, paving the way for scalable and reproducible NLP research based on historical web data.