Abstract
In the last two decades, the IR community has seen numerous advances in top-k query processing and inverted index compression techniques. While newly proposed methods are typically compared against several baselines, these evaluations are often very limited, and we feel that there is no clear overall picture on the best choices of algorithms and compression methods. In this paper, we attempt to address this issue by evaluating a number of state-of-the-art index compression methods and safe disjunctive DAAT query processing algorithms. Our goal is to understand how much index compression performance impacts overall query processing speed, how the choice of query processing algorithm depends on the compression method used, and how performance is impacted by document reordering techniques and the number of results returned, keeping in mind that current search engines typically use sets of hundreds or thousands of candidates for further reranking.
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This research was supported by NSF Grant IIS-1718680 and a grant from Amazon.
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Mallia, A., Siedlaczek, M., Suel, T. (2019). An Experimental Study of Index Compression and DAAT Query Processing Methods. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11437. Springer, Cham. https://doi.org/10.1007/978-3-030-15712-8_23
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