To index or not to index

Positional ranking functions, widely used in web search engines and related search systems, improve result quality by exploiting the positions of the query terms within documents. However, it is well known that positional indexes demand large amounts of extra space, typically about three times the space of a basic nonpositional index. Textual data, on the other hand, is needed to produce text snippets. In this paper, we study time–space trade-offs for search engines with positional ranking functions and text snippet generation. We consider both index-based and non-index based alternatives for positional data. We aim to answer the question of whether positional data should be indexed, and how. We show that there is a wide range of practical time–space trade-offs. Moreover, we show that using about 1.30 times the space of positional data, we can store everything needed for efficient query processing, with a minor increase in query time. This yields considerable space savings and outperforms, both in space and time, recent alternatives from literature. We also propose several efficient compressed text representations for snippet generation, which are able to use about half of the space of current state-of-the-art alternatives with little impact in query processing time. • Positional ranking and text snippets can be generated efficiently from the text. • Compression boosting+LZ4 compression offers the most attractive trade-off. • Search process can be supported using 1.3 times the space of positional indexes.