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Efficient Immediate-Access Dynamic Indexing

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arxiv 2211.06030 v2 pith:EQDM64XJ submitted 2022-11-11 cs.IR cs.DB

classification cs.IRcs.DB
keywords indexdynamicqueriesdescribefastindexingmakepossible
verification ladder T0 review T1 audit T2 compute T3 formal
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In a dynamic retrieval system, documents must be ingested as they arrive, and be immediately findable by queries. Our purpose in this paper is to describe an index structure and processing regime that accommodates that requirement for immediate access, seeking to make the ingestion process as streamlined as possible, while at the same time seeking to make the growing index as small as possible, and seeking to make term-based querying via the index as efficient as possible. We describe a new compression operation and a novel approach to extensible lists which together facilitate that triple goal. In particular, the structure we describe provides incremental document-level indexing using as little as two bytes per posting and only a small amount more for word-level indexing; provides fast document insertion; supports immediate and continuous queryability; provides support for fast conjunctive queries and similarity score-based ranked queries; and facilitates fast conversion of the dynamic index to a "normal" static compressed inverted index structure. Measurement of our new mechanism confirms that in-memory dynamic document-level indexes for collections into the gigabyte range can be constructed at a rate of two gigabytes/minute using a typical server architecture, that multi-term conjunctive Boolean queries can be resolved in just a few milliseconds each on average even while new documents are being concurrently ingested, and that the net memory space required for all of the required data structures amounts to an average of as little as two bytes per stored posting, less than half the space required by the best previous mechanism.

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Cited by 1 Pith paper

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  1. Submitted and Diagnostic Analysis of Full-Text Temporal Retrieval for LongEval-Sci

    cs.IR 2026-07 conditional novelty 3.5 of 10

    Temporalized full-text BM25 is the strongest LongEval-Sci 2026 Task 1 submission, while uncalibrated temporal overlays and citation features remain fragile or unproven.

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