Browsing by Author "Blanco, R."

Now showing 1 - 3 of 3

Open Access
Characterizing web search queries that match very few or no results
(ACM, 2012-11) Altıngövde, İ. Ş.; Blanco, R.; Cambazoğlu, B. B.; Özcan, Rıfat; Sarıgil, Erdem; Ulusoy, Özgür
Despite the continuous efforts to improve the web search quality, a non-negligible fraction of user queries end up with very few or even no matching results in leading web search engines. In this work, we provide a detailed characterization of such queries based on an analysis of a real-life query log. Our experimental setup allows us to characterize the queries with few/no results and compare the mechanisms employed by the major search engines in handling them.
Open Access
Characterizing, predicting, and handling web search queries that match very few or no results
(John Wiley & Sons, 2018) Sarıgil, Erdem; Altıngövde, I. S.; Blanco, R.; Cambazoğlu, B. B.; Özcan, R.; Ulusoy, Özgür
A non‐negligible fraction of user queries end up with very few or even no matching results in leading commercial web search engines. In this work, we provide a detailed characterization of such queries and show that search engines try to improve such queries by showing the results of related queries. Through a user study, we show that these query suggestions are usually perceived as relevant. Also, through a query log analysis, we show that the users are dissatisfied after submitting a query that match no results at least 88.5% of the time. As a first step towards solving these no‐answer queries, we devised a large number of features that can be used to identify such queries and built machine‐learning models. These models can be useful for scenarios such as the mobile‐ or meta‐search, where identifying a query that will retrieve no results at the client device (i.e., even before submitting it to the search engine) may yield gains in terms of the bandwidth usage, power consumption, and/or monetary costs. Experiments over query logs indicate that, despite the heavy skew in class sizes, our models achieve good prediction quality, with accuracy (in terms of area under the curve) up to 0.95.
Open Access
Energy-price-driven query processing in multi-center web search engines
(IEEE, 2011-07) Kayaaslan, Enver; Cambazoglu, B. B.; Blanco, R.; Junqueira, F. P.; Aykanat, Cevdet
Concurrently processing thousands of web queries, each with a response time under a fraction of a second, necessitates maintaining and operating massive data centers. For large-scale web search engines, this translates into high energy consumption and a huge electric bill. This work takes the challenge to reduce the electric bill of commercial web search engines operating on data centers that are geographically far apart. Based on the observation that energy prices and query workloads show high spatio-temporal variation, we propose a technique that dynamically shifts the query workload of a search engine between its data centers to reduce the electric bill. Experiments on real-life query workloads obtained from a commercial search engine show that significant financial savings can be achieved by this technique.