  Proximity full-text search is commonly implemented in contemporary full-text
search systems. Let us assume that the search query is a list of words. It is
natural to consider a document as relevant if the queried words are near each
other in the document. The proximity factor is even more significant for the
case where the query consists of frequently occurring words. Proximity
full-text search requires the storage of information for every occurrence in
documents of every word that the user can search. For every occurrence of every
word in a document, we employ additional indexes to store information about
nearby words, that is, the words that occur in the document at distances from
the given word of less than or equal to the MaxDistance parameter. We showed in
previous works that these indexes can be used to improve the average query
execution time by up to 130 times for queries that consist of words occurring
with high-frequency. In this paper, we consider how both the search performance
and the search quality depend on the value of MaxDistance and other parameters.
Well-known GOV2 text collection is used in the experiments for reproducibility
of the results. We propose a new index schema after the analysis of the results
of the experiments.
  This is a pre-print of a contribution published in Supplementary Proceedings
of the XXII International Conference on Data Analytics and Management in Data
Intensive Domains (DAMDID/RCDL 2020), Voronezh, Russia, October 13-16, 2020, P.
336-350, published by CEUR Workshop Proceedings. The final authenticated
version is available online at: http://ceur-ws.org/Vol-2790/
