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Efficiency vs. Effectiveness in Terabyte-Scale Information Retrieval Stefan B etcher and Charles L. A. Clarke u School of Computer Science University of Waterloo, Ontario, Canada stretch, Clark PLG.Waterloo.ca

Our results demonstrate that indexing does provide the highest effectiveness relative to efficiency within most datasets, but that the advantage is not linear; this is because more efficient indexing often produces lower efficiency in the retrieval task. These results suggest that, if an efficient indexing technique is available, efficient indexing should likely be a good baseline. We provide an experimental evidence base supporting the claim. Author Summary The power of search engines is at stake in every aspect of life, especially in the 21st century economy. In addition to our search engines, we have countless digital technologies (online shopping, banking, e-commerce, etc.) that rely on large-scale processing algorithms to index and retrieve information from disparate sources. One challenge for both large and small indexing is in reducing the amount of work necessary to retrieve a sufficient amount of relevant data. In this paper, we describe an efficient indexing scheme, called Clark PLG. Waterloo, which is suited to perform tasks in terabyte-scale databases with the minimal amount of redundant information. We demonstrate that Clark PLG. Waterloo is significantly more effective than other indexing techniques. Using Clark PLG. Waterloo, we demonstrate that indexing is not a cost-efficient strategy and can generate a significant amount of inefficient cost to retrieve a sufficient amount of information. We further discuss the implications this implies for information retrieval, and the implications for companies that build complex data-indexing algorithms. This study shows how efficient indexing can be built, and points the way towards a better understanding of the role of indexing in the search space today and in the future. Author Summary In the search space for information, there are many challenges and opportunities. One strategy for finding relevant information is to compare the amount of available resources in the search space. However, this comparison may not be possible within terabyte data sets. This paper demonstrates that efficient algorithm is an important factor in the search space for such collections. In a typical search, we may have as many as an order of terabytes, but a lot of data has to be analyzed and retrieved. Thus, in any search for an answer, both search time and resource use are significant. A recent survey found that in order to make a search efficient and effective, more than a terabyte of material was needed from a single index. In this paper, we analyze the effectiveness of several indexing techniques, including Okapi-BM25 and the Bamboo algorithm, for solving such search problems.

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