Ranked Document Retrieval in (Almost) No Space
This addresses the space inefficiency in search engines, offering a significant improvement for large-scale document retrieval systems.
The paper tackles the problem of ranked document retrieval by reducing the space overhead of inverted indexes, achieving query times of tens of milliseconds with only 6%-18% extra space over compressed text.
Ranked document retrieval is a fundamental task in search engines. Such queries are solved with inverted indexes that require additional 45%-80% of the compressed text space, and take tens to hundreds of microseconds per query. In this paper we show how ranked document retrieval queries can be solved within tens of milliseconds using essentially no extra space over an in-memory compressed representation of the document collection. More precisely, we enhance wavelet trees on bytecodes (WTBCs), a data structure that rearranges the bytes of the compressed collection, so that they support ranked conjunctive and disjunctive queries, using just 6%-18% of the compressed text space.