The impact of caching on search engines
By Ricardo A. Baeza-Yates, Aristides Gionis, Flavio Paiva Junqueira, Vanessa Murdock, Vassilis Plachouras, Fabrizio Silvestri. 2007
234 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Fabrizio Silvestri
- Ricardo A. Baeza-Yates
- Benjamin Livshits
- Jimmy J. Lin
- Aristides Gionis
- Flavio Paiva Junqueira
- Aleksandra Korolova
In Information Systems
- Fabrizio Silvestri
- Jimmy J. Lin
- Aleksandra Korolova
- Wenwu Ou
- Ricardo A. Baeza-Yates
- Benjamin Livshits
- Aristides Gionis
- Vanessa Murdock
Citations in the corpus
- BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model (2017, background): “Such caching of the most frequently used queries locally has a disproportionately positive impact on the expected query latency [36, 3] as queries to a search engine follow a power-law distribution [4].”
- Mining Query Logs: Turning Search Usage Data into Knowledge (2010, methodology): “[15, 16] use a static cache containing the 128,000 most frequent queries from a Yahoo! log has been used to test the hit-ratio trends on an hourly basis.”
- Automatic management of partitioned, replicated search services (2011, methodology): “For simplicity, we opted for a one-toone index server to physical machine mapping and use other techniques to more fully utilize resources: index servers are multi-threaded to exploit multi-core processors, and partitions are appropriately-sized to fit in memory.”
- A cascade ranking model for efficient ranked retrieval (2011, unknown): “Caching [2, 21] reduces query latency significantly.”
- Learning to efficiently rank (2010, background): “Another way to speed up query evaluation is through caching [2] (i.e., term posting lists or query search results caching).”
- Cascade Ranking for Operational E-commerce Search (2017, methodology): “ere are some other approaches for improving eciency in ranking such as caching [2] and index pruning [5].”
- Design trade-offs for search engine caching (2008, methodology): “Baeza-Yates et al. [2007] introduce a caching mechanism for query answers where the cache memory is split in two parts.”