Abstract
Real-time deduplication of search results is beneficial in the field of information retrieval. This process can deliver unique and relevant outputs to users with minimal delay. For instance, if a news aggregator is processing hundreds of incoming articles each hour, it can use an online deduplication process to sift out redundant content.
Present methods often rely on offline deduplication, which may not scale well for massive, frequently updating data streams. Repeated entries can diminish user satisfaction by cluttering query outputs with similar or identical items. Large-scale, real-time deduplication can help address this by filtering exact duplicates and improving clarity, although balancing high throughput and precise detection of near-duplicates is challenging.
In some implementations, the system can offer an efficient framework that reduces redundant processing in dynamic environments. It can provide a structured set of procedures that adapts to high-volume traffic without imposing excessive computational overhead. Such an approach can help present meaningful search results in real-time.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Intrator, Yotam and Cohen, Regev, "Online Cascaded Deduplicating", Technical Disclosure Commons, (May 22, 2025)
https://www.tdcommons.org/dpubs_series/8151