ETD

• information retrieval
• inverted index
• query processing
• rust

Inverted indexes are a crucial data structure for efficient information retrieval in large text corpora. They enable fast full-text searches by mapping terms to their occurrences within documents and then using different algorithms to quickly retrieve relevant documents based on user queries.
They have been widely studied and implemented in various programming languages, each offering different trade-offs in terms of performance, memory usage, and ease of use.
Their use has also been extended to modern retrieval methods, i.e. vector search with sparse embeddings, where they help in efficiently finding nearest neighbors in high-dimensional spaces, often outperforming traditional methods.
We present RISE, a novel inverted index library implemented in Rust, designed to deliver high performance and efficiency for information retrieval tasks. RISE leverages Rust's safety and performance to provide a robust solution for building and querying inverted indexes, along with providing more accessible extensibility through its expressive and clear trait system.
While developing RISE, we retraced the steps of the literature on inverted indexes, therefore reproducing numerous works using this new test bench.
We evaluated RISE against existing libraries, demonstrating its competitive speeds across various datasets and query workloads.
Our results indicate that RISE is a promising tool for researchers and practitioners in the field of information retrieval.