{"doi":"10.1145/3318464.3380598","title":"Timely Reporting of Heavy Hitters using External Memory","abstract":"Given an input stream of size N, a φ-heavy hitter is an item that occurs at least φ N times in S. The problem of finding heavy-hitters is extensively studied in the database literature. We study a real-time heavy-hitters variant in which an element must be reported shortly after we see its T = φ N-th occurrence (and hence becomes a heavy hitter). We call this the Timely Event Detection (TED) Problem. The TED problem models the needs of many real-world monitoring systems, which demand accurate (i.e., no false negatives) and timely reporting of all events from large, high-speed streams, and with a low reporting threshold (high sensitivity). Like the classic heavy-hitters problem, solving the TED problem without false-positives requires large space (Ω(N) words). Thus in-RAM heavy-hitters algorithms typically sacrifice accuracy (i.e., allow false positives), sensitivity, or timeliness (i.e., use multiple passes). We show how to adapt heavy-hitters algorithms to external memory to solve the TED problem on large high-speed streams while guaranteeing accuracy, sensitivity, and timeliness. Our data structures are limited only by I/O-bandwidth (not latency) and support a tunable trade-off between reporting delay and I/O overhead. With a small bounded reporting delay, our algorithms incur only a logarithmic I/O overhead. We implement and validate our data structures empirically using the Firehose streaming benchmark. Multi-threaded versions of our structures can scale to process 11M observations per second before becoming CPU bound. In comparison, a naive adaptation of the standard heavy-hitters algorithm to external memory would be limited by the storage device's random I/O throughput, i.e., ~100K observations per second.","journal":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","year":2020,"id":119899,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":14,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.945,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":556059,"name":"Shikha Singh","orcid":"0000-0002-3704-7131","position":1,"is_corresponding":false},{"id":556060,"name":"Michael A. Bender","orcid":"0000-0001-7639-530X","position":2,"is_corresponding":false},{"id":556061,"name":"Jonathan W. Berry","orcid":"0000-0002-1768-685X","position":3,"is_corresponding":false},{"id":556062,"name":"Martı́n Farach-Colton","orcid":"0000-0003-3616-7788","position":4,"is_corresponding":false},{"id":556063,"name":"Rob Johnson","orcid":"0000-0002-0784-7410","position":5,"is_corresponding":false},{"id":556064,"name":"Thomas M Kroeger","orcid":"0000-0003-3218-6412","position":6,"is_corresponding":false},{"id":556065,"name":"Cynthia A. Phillips","orcid":"0000-0001-7348-9079","position":7,"is_corresponding":false},{"id":556058,"name":"Prashant Pandey","orcid":"0000-0001-5576-0320","position":0,"is_corresponding":true}],"reference_count":53,"raw_metadata":null,"created_at":"2026-07-18T23:14:13.002105Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}