Real-time Spread Burst Detection in Data Streaming Article Swipe
Haibo Wang
,
Dimitrios Melissourgos
,
Chaoyi Ma
,
Shigang Chen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3589979
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3589979
Data streaming has many applications in network monitoring, web services, e-commerce, stock trading, social networks, and distributed sensing. This paper introduces a new problem of real-time burst detection in flow spread, which differs from the traditional problem of burst detection in flow size. It is practically significant with potential applications in cybersecurity, network engineering, and trend identification on the Internet. It is a challenging problem because estimating flow spread requires us to remember all past data items and detecting bursts in real time requires us to minimize spread estimation overhead, which was not the priority in most prior work. This paper provides the first efficient, real-time solution for spread burst detection. It is designed based on a new real-time super spreader identifier, which outperforms the state of the art in terms of both accuracy and processing overhead. The super spreader identifier is in turn based on a new sketch design for real-time spread estimation, which outperforms the best existing sketches.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3589979
- OA Status
- green
- Cited By
- 11
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4377224210
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4377224210Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1145/3589979Digital Object Identifier
- Title
-
Real-time Spread Burst Detection in Data StreamingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-19Full publication date if available
- Authors
-
Haibo Wang, Dimitrios Melissourgos, Chaoyi Ma, Shigang ChenList of authors in order
- Landing page
-
https://doi.org/10.1145/3589979Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/11075074Direct OA link when available
- Concepts
-
Computer science, Real-time computing, Overhead (engineering), Identifier, The Internet, Sketch, Data mining, Computer network, World Wide Web, Operating system, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 2, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4377224210 |
|---|---|
| doi | https://doi.org/10.1145/3589979 |
| ids.doi | https://doi.org/10.1145/3589979 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/38716481 |
| ids.openalex | https://openalex.org/W4377224210 |
| fwci | 4.39552292 |
| type | article |
| title | Real-time Spread Burst Detection in Data Streaming |
| awards[0].id | https://openalex.org/G4696872293 |
| awards[0].funder_id | https://openalex.org/F4320332161 |
| awards[0].display_name | |
| awards[0].funder_award_id | R01 LM014027 |
| awards[0].funder_display_name | National Institutes of Health |
| awards[1].id | https://openalex.org/G1316849842 |
| awards[1].funder_id | https://openalex.org/F4320306076 |
| awards[1].display_name | |
| awards[1].funder_award_id | SCC-2124858, CNS-1909077 |
| awards[1].funder_display_name | National Science Foundation |
| biblio.issue | 2 |
| biblio.volume | 7 |
| biblio.last_page | 31 |
| biblio.first_page | 1 |
| topics[0].id | https://openalex.org/T10400 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1705 |
| topics[0].subfield.display_name | Computer Networks and Communications |
| topics[0].display_name | Network Security and Intrusion Detection |
| topics[1].id | https://openalex.org/T12761 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9990000128746033 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Data Stream Mining Techniques |
| topics[2].id | https://openalex.org/T10742 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9979000091552734 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1705 |
| topics[2].subfield.display_name | Computer Networks and Communications |
| topics[2].display_name | Peer-to-Peer Network Technologies |
| funders[0].id | https://openalex.org/F4320306076 |
| funders[0].ror | https://ror.org/021nxhr62 |
| funders[0].display_name | National Science Foundation |
| funders[1].id | https://openalex.org/F4320332161 |
| funders[1].ror | https://ror.org/01cwqze88 |
| funders[1].display_name | National Institutes of Health |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8166154026985168 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C79403827 |
| concepts[1].level | 1 |
| concepts[1].score | 0.6302736401557922 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q3988 |
| concepts[1].display_name | Real-time computing |
| concepts[2].id | https://openalex.org/C2779960059 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6059088110923767 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7113681 |
| concepts[2].display_name | Overhead (engineering) |
| concepts[3].id | https://openalex.org/C154504017 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5626423954963684 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q853614 |
| concepts[3].display_name | Identifier |
| concepts[4].id | https://openalex.org/C110875604 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5330554246902466 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q75 |
| concepts[4].display_name | The Internet |
| concepts[5].id | https://openalex.org/C2779231336 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5243844389915466 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7534724 |
| concepts[5].display_name | Sketch |
| concepts[6].id | https://openalex.org/C124101348 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3905491530895233 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[6].display_name | Data mining |
| concepts[7].id | https://openalex.org/C31258907 |
| concepts[7].level | 1 |
| concepts[7].score | 0.3062996566295624 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1301371 |
| concepts[7].display_name | Computer network |
| concepts[8].id | https://openalex.org/C136764020 |
| concepts[8].level | 1 |
| concepts[8].score | 0.11299806833267212 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q466 |
| concepts[8].display_name | World Wide Web |
| concepts[9].id | https://openalex.org/C111919701 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0887044370174408 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[9].display_name | Operating system |
| concepts[10].id | https://openalex.org/C11413529 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[10].display_name | Algorithm |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8166154026985168 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/real-time-computing |
| keywords[1].score | 0.6302736401557922 |
| keywords[1].display_name | Real-time computing |
| keywords[2].id | https://openalex.org/keywords/overhead |
| keywords[2].score | 0.6059088110923767 |
| keywords[2].display_name | Overhead (engineering) |
| keywords[3].id | https://openalex.org/keywords/identifier |
| keywords[3].score | 0.5626423954963684 |
| keywords[3].display_name | Identifier |
| keywords[4].id | https://openalex.org/keywords/the-internet |
| keywords[4].score | 0.5330554246902466 |
| keywords[4].display_name | The Internet |
| keywords[5].id | https://openalex.org/keywords/sketch |
| keywords[5].score | 0.5243844389915466 |
| keywords[5].display_name | Sketch |
| keywords[6].id | https://openalex.org/keywords/data-mining |
| keywords[6].score | 0.3905491530895233 |
| keywords[6].display_name | Data mining |
| keywords[7].id | https://openalex.org/keywords/computer-network |
| keywords[7].score | 0.3062996566295624 |
| keywords[7].display_name | Computer network |
| keywords[8].id | https://openalex.org/keywords/world-wide-web |
| keywords[8].score | 0.11299806833267212 |
| keywords[8].display_name | World Wide Web |
| keywords[9].id | https://openalex.org/keywords/operating-system |
| keywords[9].score | 0.0887044370174408 |
| keywords[9].display_name | Operating system |
| language | en |
| locations[0].id | doi:10.1145/3589979 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S4210193547 |
| locations[0].source.issn | 2476-1249 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 2476-1249 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Proceedings of the ACM on Measurement and Analysis of Computing Systems |
| locations[0].source.host_organization | https://openalex.org/P4310319798 |
| locations[0].source.host_organization_name | Association for Computing Machinery |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319798 |
| locations[0].source.host_organization_lineage_names | Association for Computing Machinery |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Proceedings of the ACM on Measurement and Analysis of Computing Systems |
| locations[0].landing_page_url | https://doi.org/10.1145/3589979 |
| locations[1].id | pmid:38716481 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Proceedings of the ACM on measurement and analysis of computing systems |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/38716481 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:11075074 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Proc ACM Meas Anal Comput Syst |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11075074 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5100328812 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-4809-4897 |
| authorships[0].author.display_name | Haibo Wang |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I33213144 |
| authorships[0].affiliations[0].raw_affiliation_string | University of Florida, Gainesville, FL, USA |
| authorships[0].institutions[0].id | https://openalex.org/I33213144 |
| authorships[0].institutions[0].ror | https://ror.org/02y3ad647 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I33213144 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | University of Florida |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Haibo Wang |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | University of Florida, Gainesville, FL, USA |
| authorships[1].author.id | https://openalex.org/A5029447247 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-1040-5779 |
| authorships[1].author.display_name | Dimitrios Melissourgos |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I8606887 |
| authorships[1].affiliations[0].raw_affiliation_string | Grand Valley State University, Allendale, MI, USA |
| authorships[1].institutions[0].id | https://openalex.org/I8606887 |
| authorships[1].institutions[0].ror | https://ror.org/001m1hv61 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I8606887 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Grand Valley State University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Dimitrios Melissourgos |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Grand Valley State University, Allendale, MI, USA |
| authorships[2].author.id | https://openalex.org/A5074529126 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-3572-0046 |
| authorships[2].author.display_name | Chaoyi Ma |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I33213144 |
| authorships[2].affiliations[0].raw_affiliation_string | University of Florida, Gainesville, USA |
| authorships[2].institutions[0].id | https://openalex.org/I33213144 |
| authorships[2].institutions[0].ror | https://ror.org/02y3ad647 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I33213144 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | University of Florida |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Chaoyi Ma |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | University of Florida, Gainesville, USA |
| authorships[3].author.id | https://openalex.org/A5006184530 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-7867-7765 |
| authorships[3].author.display_name | Shigang Chen |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I33213144 |
| authorships[3].affiliations[0].raw_affiliation_string | University of Florida, Gainesville, FL, USA |
| authorships[3].institutions[0].id | https://openalex.org/I33213144 |
| authorships[3].institutions[0].ror | https://ror.org/02y3ad647 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I33213144 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | University of Florida |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Shigang Chen |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | University of Florida, Gainesville, FL, USA |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11075074 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Real-time Spread Burst Detection in Data Streaming |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10400 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1705 |
| primary_topic.subfield.display_name | Computer Networks and Communications |
| primary_topic.display_name | Network Security and Intrusion Detection |
| related_works | https://openalex.org/W2378994405, https://openalex.org/W2385974820, https://openalex.org/W2373478030, https://openalex.org/W2378679551, https://openalex.org/W3149739944, https://openalex.org/W2392363776, https://openalex.org/W2063051341, https://openalex.org/W2591066345, https://openalex.org/W1494563618, https://openalex.org/W4390399609 |
| cited_by_count | 11 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 6 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 2 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 2 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:pubmedcentral.nih.gov:11075074 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764455111 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | PubMed Central |
| best_oa_location.source.host_organization | https://openalex.org/I1299303238 |
| best_oa_location.source.host_organization_name | National Institutes of Health |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I1299303238 |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | Text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | Proc ACM Meas Anal Comput Syst |
| best_oa_location.landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11075074 |
| primary_location.id | doi:10.1145/3589979 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S4210193547 |
| primary_location.source.issn | 2476-1249 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 2476-1249 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Proceedings of the ACM on Measurement and Analysis of Computing Systems |
| primary_location.source.host_organization | https://openalex.org/P4310319798 |
| primary_location.source.host_organization_name | Association for Computing Machinery |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319798 |
| primary_location.source.host_organization_lineage_names | Association for Computing Machinery |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Proceedings of the ACM on Measurement and Analysis of Computing Systems |
| primary_location.landing_page_url | https://doi.org/10.1145/3589979 |
| publication_date | 2023-05-19 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W3196328488, https://openalex.org/W2150191781, https://openalex.org/W2111678491, https://openalex.org/W2025051251, https://openalex.org/W2119351095, https://openalex.org/W2155746806, https://openalex.org/W2205557629, https://openalex.org/W2327315875, https://openalex.org/W2083477206, https://openalex.org/W2002231822, https://openalex.org/W4211131942, https://openalex.org/W4289533969, https://openalex.org/W3145761337, https://openalex.org/W6712758098, https://openalex.org/W2799723114, https://openalex.org/W2168305032, https://openalex.org/W2008365755, https://openalex.org/W2762095987, https://openalex.org/W3011354655, https://openalex.org/W2337150356, https://openalex.org/W2753629899, https://openalex.org/W4232254113, https://openalex.org/W3173382529, https://openalex.org/W4244556018, https://openalex.org/W1980065740, https://openalex.org/W2917458986, https://openalex.org/W37503093, https://openalex.org/W4292262069, https://openalex.org/W4286447321 |
| referenced_works_count | 29 |
| abstract_inverted_index.a | 21, 62, 116, 146 |
| abstract_inverted_index.It | 43, 60, 111 |
| abstract_inverted_index.in | 5, 28, 40, 50, 80, 95, 129, 142 |
| abstract_inverted_index.is | 44, 61, 112, 141 |
| abstract_inverted_index.of | 24, 37, 126, 131 |
| abstract_inverted_index.on | 57, 115, 145 |
| abstract_inverted_index.to | 71, 85 |
| abstract_inverted_index.us | 70, 84 |
| abstract_inverted_index.The | 137 |
| abstract_inverted_index.all | 73 |
| abstract_inverted_index.and | 15, 54, 77, 134 |
| abstract_inverted_index.art | 128 |
| abstract_inverted_index.for | 107, 150 |
| abstract_inverted_index.has | 2 |
| abstract_inverted_index.new | 22, 117, 147 |
| abstract_inverted_index.not | 92 |
| abstract_inverted_index.the | 34, 58, 93, 102, 124, 127, 156 |
| abstract_inverted_index.was | 91 |
| abstract_inverted_index.web | 8 |
| abstract_inverted_index.Data | 0 |
| abstract_inverted_index.This | 18, 99 |
| abstract_inverted_index.best | 157 |
| abstract_inverted_index.both | 132 |
| abstract_inverted_index.data | 75 |
| abstract_inverted_index.flow | 29, 41, 67 |
| abstract_inverted_index.from | 33 |
| abstract_inverted_index.many | 3 |
| abstract_inverted_index.most | 96 |
| abstract_inverted_index.past | 74 |
| abstract_inverted_index.real | 81 |
| abstract_inverted_index.time | 82 |
| abstract_inverted_index.turn | 143 |
| abstract_inverted_index.with | 47 |
| abstract_inverted_index.based | 114, 144 |
| abstract_inverted_index.burst | 26, 38, 109 |
| abstract_inverted_index.first | 103 |
| abstract_inverted_index.items | 76 |
| abstract_inverted_index.paper | 19, 100 |
| abstract_inverted_index.prior | 97 |
| abstract_inverted_index.size. | 42 |
| abstract_inverted_index.state | 125 |
| abstract_inverted_index.stock | 11 |
| abstract_inverted_index.super | 119, 138 |
| abstract_inverted_index.terms | 130 |
| abstract_inverted_index.trend | 55 |
| abstract_inverted_index.which | 31, 90, 122, 154 |
| abstract_inverted_index.work. | 98 |
| abstract_inverted_index.bursts | 79 |
| abstract_inverted_index.design | 149 |
| abstract_inverted_index.sketch | 148 |
| abstract_inverted_index.social | 13 |
| abstract_inverted_index.spread | 68, 87, 108, 152 |
| abstract_inverted_index.because | 65 |
| abstract_inverted_index.differs | 32 |
| abstract_inverted_index.network | 6, 52 |
| abstract_inverted_index.problem | 23, 36, 64 |
| abstract_inverted_index.spread, | 30 |
| abstract_inverted_index.accuracy | 133 |
| abstract_inverted_index.designed | 113 |
| abstract_inverted_index.existing | 158 |
| abstract_inverted_index.minimize | 86 |
| abstract_inverted_index.priority | 94 |
| abstract_inverted_index.provides | 101 |
| abstract_inverted_index.remember | 72 |
| abstract_inverted_index.requires | 69, 83 |
| abstract_inverted_index.sensing. | 17 |
| abstract_inverted_index.solution | 106 |
| abstract_inverted_index.spreader | 120, 139 |
| abstract_inverted_index.trading, | 12 |
| abstract_inverted_index.Internet. | 59 |
| abstract_inverted_index.detecting | 78 |
| abstract_inverted_index.detection | 27, 39 |
| abstract_inverted_index.networks, | 14 |
| abstract_inverted_index.overhead, | 89 |
| abstract_inverted_index.overhead. | 136 |
| abstract_inverted_index.potential | 48 |
| abstract_inverted_index.real-time | 25, 105, 118, 151 |
| abstract_inverted_index.services, | 9 |
| abstract_inverted_index.sketches. | 159 |
| abstract_inverted_index.streaming | 1 |
| abstract_inverted_index.detection. | 110 |
| abstract_inverted_index.efficient, | 104 |
| abstract_inverted_index.estimating | 66 |
| abstract_inverted_index.estimation | 88 |
| abstract_inverted_index.identifier | 140 |
| abstract_inverted_index.introduces | 20 |
| abstract_inverted_index.processing | 135 |
| abstract_inverted_index.challenging | 63 |
| abstract_inverted_index.distributed | 16 |
| abstract_inverted_index.e-commerce, | 10 |
| abstract_inverted_index.estimation, | 153 |
| abstract_inverted_index.identifier, | 121 |
| abstract_inverted_index.monitoring, | 7 |
| abstract_inverted_index.outperforms | 123, 155 |
| abstract_inverted_index.practically | 45 |
| abstract_inverted_index.significant | 46 |
| abstract_inverted_index.traditional | 35 |
| abstract_inverted_index.applications | 4, 49 |
| abstract_inverted_index.engineering, | 53 |
| abstract_inverted_index.cybersecurity, | 51 |
| abstract_inverted_index.identification | 56 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.88964511 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |