Onto how compression yields energy-efficient text search

Abstract

[Abstract]: In the last two decades, word-based text compression has shown to be the key to efficiently handling large collections of text not only due to yielding important storage savings but, more importantly, because it allowed boosting the performance of some traditional text retrieval systems. The reason is that when the appropriate compression techniques are chosen, compressed text search becomes much faster than searches on plain text, and retrieval/decompression could start at any part of the compressed data, hence allowing to keep the text collection compressed all the time. Word-based text compressors have been typically compared in terms of their compression effectiveness, encoding/decoding speed, and performance when searching for words. In this paper, we show that compression also has benefits in terms of energy efficiency when performing word-based searches. Particularly, our experiments considering searches performed over uncompressed text and text compressed with the most well-suited compressors for text databases showed energy savings of around 30–70%. These savings are obtained thanks to improvements in search time but also, rather unexpectedly, because compressed text searches typically require less power from the processor. We have also analyzed how small modifications to the Horspool search algorithm can lead to time savings and can reduce even further the power needs of the processor (up to 5–10%), and, consequently, the overall energy consumption.