Scalable estimation of precision maps in a MapReduce framework Article Swipe
This paper presents a large-scale strip adjustment method for LiDAR mobile mapping data, yielding highly precise maps. It uses several concepts to achieve scalability. First, an efficient graph-based pre-segmentation is used, which directly operates on LiDAR scan strip data, rather than on point clouds. Second, observation equations are obtained from a dense matching, which is formulated in terms of an estimation of a latent map. As a result of this formulation, the number of observation equations is not quadratic, but rather linear in the number of scan strips. Third, the dynamic Bayes network, which results from all observation and condition equations, is partitioned into two sub-networks. Consequently, the estimation matrices for all position and orientation corrections are linear instead of quadratic in the number of unknowns and can be solved very efficiently using an alternating least squares approach. It is shown how this approach can be mapped to a standard key/value MapReduce implementation, where each of the processing nodes operates independently on small chunks of data, leading to essentially linear scalability. Results are demonstrated for a dataset of one billion measured LiDAR points and 278,000 unknowns, leading to maps with a precision of a few millimeters.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1145/2996913.2996990
- OA Status
- green
- Cited By
- 22
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2527622645
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2527622645Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1145/2996913.2996990Digital Object Identifier
- Title
-
Scalable estimation of precision maps in a MapReduce frameworkWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-10-31Full publication date if available
- Authors
-
Claus BrennerList of authors in order
- Landing page
-
https://doi.org/10.1145/2996913.2996990Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1609.07603Direct OA link when available
- Concepts
-
Computer science, Scalability, STRIPS, Quadratic equation, Position (finance), Algorithm, Matching (statistics), Scale (ratio), Lidar, Linear equation, Mathematical optimization, Mathematics, Geometry, Statistics, Remote sensing, Economics, Quantum mechanics, Geology, Physics, Finance, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
22Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 3, 2023: 4, 2022: 3, 2021: 4, 2020: 2Per-year citation counts (last 5 years)
- References (count)
-
16Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2527622645 |
|---|---|
| doi | https://doi.org/10.1145/2996913.2996990 |
| ids.doi | https://doi.org/10.1145/2996913.2996990 |
| ids.mag | 2527622645 |
| ids.openalex | https://openalex.org/W2527622645 |
| fwci | 3.15934835 |
| type | preprint |
| title | Scalable estimation of precision maps in a MapReduce framework |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10191 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| 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/2202 |
| topics[0].subfield.display_name | Aerospace Engineering |
| topics[0].display_name | Robotics and Sensor-Based Localization |
| topics[1].id | https://openalex.org/T11164 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9994999766349792 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2305 |
| topics[1].subfield.display_name | Environmental Engineering |
| topics[1].display_name | Remote Sensing and LiDAR Applications |
| topics[2].id | https://openalex.org/T11211 |
| topics[2].field.id | https://openalex.org/fields/19 |
| topics[2].field.display_name | Earth and Planetary Sciences |
| topics[2].score | 0.9930999875068665 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1907 |
| topics[2].subfield.display_name | Geology |
| topics[2].display_name | 3D Surveying and Cultural Heritage |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6276291608810425 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C48044578 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6148046851158142 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q727490 |
| concepts[1].display_name | Scalability |
| concepts[2].id | https://openalex.org/C200925200 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5785735249519348 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7624170 |
| concepts[2].display_name | STRIPS |
| concepts[3].id | https://openalex.org/C129844170 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5617943406105042 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q41299 |
| concepts[3].display_name | Quadratic equation |
| concepts[4].id | https://openalex.org/C198082294 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5300062894821167 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q3399648 |
| concepts[4].display_name | Position (finance) |
| concepts[5].id | https://openalex.org/C11413529 |
| concepts[5].level | 1 |
| concepts[5].score | 0.5192371010780334 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[5].display_name | Algorithm |
| concepts[6].id | https://openalex.org/C165064840 |
| concepts[6].level | 2 |
| concepts[6].score | 0.43562763929367065 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1321061 |
| concepts[6].display_name | Matching (statistics) |
| concepts[7].id | https://openalex.org/C2778755073 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4341239035129547 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q10858537 |
| concepts[7].display_name | Scale (ratio) |
| concepts[8].id | https://openalex.org/C51399673 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4239999055862427 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q504027 |
| concepts[8].display_name | Lidar |
| concepts[9].id | https://openalex.org/C77346573 |
| concepts[9].level | 2 |
| concepts[9].score | 0.41784900426864624 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q484637 |
| concepts[9].display_name | Linear equation |
| concepts[10].id | https://openalex.org/C126255220 |
| concepts[10].level | 1 |
| concepts[10].score | 0.34842991828918457 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q141495 |
| concepts[10].display_name | Mathematical optimization |
| concepts[11].id | https://openalex.org/C33923547 |
| concepts[11].level | 0 |
| concepts[11].score | 0.2744429111480713 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[11].display_name | Mathematics |
| concepts[12].id | https://openalex.org/C2524010 |
| concepts[12].level | 1 |
| concepts[12].score | 0.11976617574691772 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[12].display_name | Geometry |
| concepts[13].id | https://openalex.org/C105795698 |
| concepts[13].level | 1 |
| concepts[13].score | 0.09447914361953735 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[13].display_name | Statistics |
| concepts[14].id | https://openalex.org/C62649853 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[14].display_name | Remote sensing |
| concepts[15].id | https://openalex.org/C162324750 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[15].display_name | Economics |
| concepts[16].id | https://openalex.org/C62520636 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[16].display_name | Quantum mechanics |
| concepts[17].id | https://openalex.org/C127313418 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[17].display_name | Geology |
| concepts[18].id | https://openalex.org/C121332964 |
| concepts[18].level | 0 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[18].display_name | Physics |
| concepts[19].id | https://openalex.org/C10138342 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q43015 |
| concepts[19].display_name | Finance |
| concepts[20].id | https://openalex.org/C77088390 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q8513 |
| concepts[20].display_name | Database |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.6276291608810425 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/scalability |
| keywords[1].score | 0.6148046851158142 |
| keywords[1].display_name | Scalability |
| keywords[2].id | https://openalex.org/keywords/strips |
| keywords[2].score | 0.5785735249519348 |
| keywords[2].display_name | STRIPS |
| keywords[3].id | https://openalex.org/keywords/quadratic-equation |
| keywords[3].score | 0.5617943406105042 |
| keywords[3].display_name | Quadratic equation |
| keywords[4].id | https://openalex.org/keywords/position |
| keywords[4].score | 0.5300062894821167 |
| keywords[4].display_name | Position (finance) |
| keywords[5].id | https://openalex.org/keywords/algorithm |
| keywords[5].score | 0.5192371010780334 |
| keywords[5].display_name | Algorithm |
| keywords[6].id | https://openalex.org/keywords/matching |
| keywords[6].score | 0.43562763929367065 |
| keywords[6].display_name | Matching (statistics) |
| keywords[7].id | https://openalex.org/keywords/scale |
| keywords[7].score | 0.4341239035129547 |
| keywords[7].display_name | Scale (ratio) |
| keywords[8].id | https://openalex.org/keywords/lidar |
| keywords[8].score | 0.4239999055862427 |
| keywords[8].display_name | Lidar |
| keywords[9].id | https://openalex.org/keywords/linear-equation |
| keywords[9].score | 0.41784900426864624 |
| keywords[9].display_name | Linear equation |
| keywords[10].id | https://openalex.org/keywords/mathematical-optimization |
| keywords[10].score | 0.34842991828918457 |
| keywords[10].display_name | Mathematical optimization |
| keywords[11].id | https://openalex.org/keywords/mathematics |
| keywords[11].score | 0.2744429111480713 |
| keywords[11].display_name | Mathematics |
| keywords[12].id | https://openalex.org/keywords/geometry |
| keywords[12].score | 0.11976617574691772 |
| keywords[12].display_name | Geometry |
| keywords[13].id | https://openalex.org/keywords/statistics |
| keywords[13].score | 0.09447914361953735 |
| keywords[13].display_name | Statistics |
| language | en |
| locations[0].id | doi:10.1145/2996913.2996990 |
| locations[0].is_oa | False |
| locations[0].source | |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems |
| locations[0].landing_page_url | https://doi.org/10.1145/2996913.2996990 |
| locations[1].id | pmh:oai:arXiv.org:1609.07603 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | https://arxiv.org/pdf/1609.07603 |
| locations[1].version | submittedVersion |
| locations[1].raw_type | text |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | http://arxiv.org/abs/1609.07603 |
| indexed_in | arxiv, crossref |
| authorships[0].author.id | https://openalex.org/A5016827109 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6459-0682 |
| authorships[0].author.display_name | Claus Brenner |
| authorships[0].countries | DE |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I114112103 |
| authorships[0].affiliations[0].raw_affiliation_string | Leibniz Universität Hannover, Appelstr., Hannover, Germany |
| authorships[0].institutions[0].id | https://openalex.org/I114112103 |
| authorships[0].institutions[0].ror | https://ror.org/0304hq317 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I114112103 |
| authorships[0].institutions[0].country_code | DE |
| authorships[0].institutions[0].display_name | Leibniz University Hannover |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Claus Brenner |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Leibniz Universität Hannover, Appelstr., Hannover, Germany |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/1609.07603 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Scalable estimation of precision maps in a MapReduce framework |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10191 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| 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/2202 |
| primary_topic.subfield.display_name | Aerospace Engineering |
| primary_topic.display_name | Robotics and Sensor-Based Localization |
| related_works | https://openalex.org/W4319317934, https://openalex.org/W2901265155, https://openalex.org/W2956374172, https://openalex.org/W4281783339, https://openalex.org/W4319837668, https://openalex.org/W4308071650, https://openalex.org/W3188333020, https://openalex.org/W1964041166, https://openalex.org/W4390887692, https://openalex.org/W4210818033 |
| cited_by_count | 22 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 3 |
| counts_by_year[1].year | 2023 |
| counts_by_year[1].cited_by_count | 4 |
| counts_by_year[2].year | 2022 |
| counts_by_year[2].cited_by_count | 3 |
| counts_by_year[3].year | 2021 |
| counts_by_year[3].cited_by_count | 4 |
| counts_by_year[4].year | 2020 |
| counts_by_year[4].cited_by_count | 2 |
| counts_by_year[5].year | 2019 |
| counts_by_year[5].cited_by_count | 1 |
| counts_by_year[6].year | 2018 |
| counts_by_year[6].cited_by_count | 4 |
| counts_by_year[7].year | 2017 |
| counts_by_year[7].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:1609.07603 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| 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 | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/1609.07603 |
| 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 | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/1609.07603 |
| primary_location.id | doi:10.1145/2996913.2996990 |
| primary_location.is_oa | False |
| primary_location.source | |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems |
| primary_location.landing_page_url | https://doi.org/10.1145/2996913.2996990 |
| publication_date | 2016-10-31 |
| publication_year | 2016 |
| referenced_works | https://openalex.org/W2049981393, https://openalex.org/W2009422376, https://openalex.org/W1999478155, https://openalex.org/W2080823437, https://openalex.org/W2065446385, https://openalex.org/W2284939270, https://openalex.org/W2132820324, https://openalex.org/W1511986666, https://openalex.org/W1987648924, https://openalex.org/W2071906076, https://openalex.org/W2094227853, https://openalex.org/W2119851068, https://openalex.org/W4249866455, https://openalex.org/W2124313187, https://openalex.org/W2560019560, https://openalex.org/W2122465391 |
| referenced_works_count | 16 |
| abstract_inverted_index.a | 3, 50, 62, 66, 148, 175, 190, 193 |
| abstract_inverted_index.As | 65 |
| abstract_inverted_index.It | 17, 138 |
| abstract_inverted_index.an | 25, 59, 133 |
| abstract_inverted_index.be | 128, 145 |
| abstract_inverted_index.in | 56, 82, 121 |
| abstract_inverted_index.is | 29, 54, 76, 101, 139 |
| abstract_inverted_index.of | 58, 61, 68, 73, 85, 119, 124, 155, 164, 177, 192 |
| abstract_inverted_index.on | 34, 41, 161 |
| abstract_inverted_index.to | 21, 147, 167, 187 |
| abstract_inverted_index.all | 96, 111 |
| abstract_inverted_index.and | 98, 113, 126, 183 |
| abstract_inverted_index.are | 47, 116, 172 |
| abstract_inverted_index.but | 79 |
| abstract_inverted_index.can | 127, 144 |
| abstract_inverted_index.few | 194 |
| abstract_inverted_index.for | 8, 110, 174 |
| abstract_inverted_index.how | 141 |
| abstract_inverted_index.not | 77 |
| abstract_inverted_index.one | 178 |
| abstract_inverted_index.the | 71, 83, 89, 107, 122, 156 |
| abstract_inverted_index.two | 104 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.each | 154 |
| abstract_inverted_index.from | 49, 95 |
| abstract_inverted_index.into | 103 |
| abstract_inverted_index.map. | 64 |
| abstract_inverted_index.maps | 188 |
| abstract_inverted_index.scan | 36, 86 |
| abstract_inverted_index.than | 40 |
| abstract_inverted_index.this | 69, 142 |
| abstract_inverted_index.uses | 18 |
| abstract_inverted_index.very | 130 |
| abstract_inverted_index.with | 189 |
| abstract_inverted_index.Bayes | 91 |
| abstract_inverted_index.LiDAR | 9, 35, 181 |
| abstract_inverted_index.data, | 12, 38, 165 |
| abstract_inverted_index.dense | 51 |
| abstract_inverted_index.least | 135 |
| abstract_inverted_index.maps. | 16 |
| abstract_inverted_index.nodes | 158 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.point | 42 |
| abstract_inverted_index.shown | 140 |
| abstract_inverted_index.small | 162 |
| abstract_inverted_index.strip | 5, 37 |
| abstract_inverted_index.terms | 57 |
| abstract_inverted_index.used, | 30 |
| abstract_inverted_index.using | 132 |
| abstract_inverted_index.where | 153 |
| abstract_inverted_index.which | 31, 53, 93 |
| abstract_inverted_index.First, | 24 |
| abstract_inverted_index.Third, | 88 |
| abstract_inverted_index.chunks | 163 |
| abstract_inverted_index.highly | 14 |
| abstract_inverted_index.latent | 63 |
| abstract_inverted_index.linear | 81, 117, 169 |
| abstract_inverted_index.mapped | 146 |
| abstract_inverted_index.method | 7 |
| abstract_inverted_index.mobile | 10 |
| abstract_inverted_index.number | 72, 84, 123 |
| abstract_inverted_index.points | 182 |
| abstract_inverted_index.rather | 39, 80 |
| abstract_inverted_index.result | 67 |
| abstract_inverted_index.solved | 129 |
| abstract_inverted_index.278,000 | 184 |
| abstract_inverted_index.Results | 171 |
| abstract_inverted_index.Second, | 44 |
| abstract_inverted_index.achieve | 22 |
| abstract_inverted_index.billion | 179 |
| abstract_inverted_index.clouds. | 43 |
| abstract_inverted_index.dataset | 176 |
| abstract_inverted_index.dynamic | 90 |
| abstract_inverted_index.instead | 118 |
| abstract_inverted_index.leading | 166, 186 |
| abstract_inverted_index.mapping | 11 |
| abstract_inverted_index.precise | 15 |
| abstract_inverted_index.results | 94 |
| abstract_inverted_index.several | 19 |
| abstract_inverted_index.squares | 136 |
| abstract_inverted_index.strips. | 87 |
| abstract_inverted_index.approach | 143 |
| abstract_inverted_index.concepts | 20 |
| abstract_inverted_index.directly | 32 |
| abstract_inverted_index.matrices | 109 |
| abstract_inverted_index.measured | 180 |
| abstract_inverted_index.network, | 92 |
| abstract_inverted_index.obtained | 48 |
| abstract_inverted_index.operates | 33, 159 |
| abstract_inverted_index.position | 112 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.standard | 149 |
| abstract_inverted_index.unknowns | 125 |
| abstract_inverted_index.yielding | 13 |
| abstract_inverted_index.MapReduce | 151 |
| abstract_inverted_index.approach. | 137 |
| abstract_inverted_index.condition | 99 |
| abstract_inverted_index.efficient | 26 |
| abstract_inverted_index.equations | 46, 75 |
| abstract_inverted_index.key/value | 150 |
| abstract_inverted_index.matching, | 52 |
| abstract_inverted_index.precision | 191 |
| abstract_inverted_index.quadratic | 120 |
| abstract_inverted_index.unknowns, | 185 |
| abstract_inverted_index.adjustment | 6 |
| abstract_inverted_index.equations, | 100 |
| abstract_inverted_index.estimation | 60, 108 |
| abstract_inverted_index.formulated | 55 |
| abstract_inverted_index.processing | 157 |
| abstract_inverted_index.quadratic, | 78 |
| abstract_inverted_index.alternating | 134 |
| abstract_inverted_index.corrections | 115 |
| abstract_inverted_index.efficiently | 131 |
| abstract_inverted_index.essentially | 168 |
| abstract_inverted_index.graph-based | 27 |
| abstract_inverted_index.large-scale | 4 |
| abstract_inverted_index.observation | 45, 74, 97 |
| abstract_inverted_index.orientation | 114 |
| abstract_inverted_index.partitioned | 102 |
| abstract_inverted_index.demonstrated | 173 |
| abstract_inverted_index.formulation, | 70 |
| abstract_inverted_index.millimeters. | 195 |
| abstract_inverted_index.scalability. | 23, 170 |
| abstract_inverted_index.Consequently, | 106 |
| abstract_inverted_index.independently | 160 |
| abstract_inverted_index.sub-networks. | 105 |
| abstract_inverted_index.implementation, | 152 |
| abstract_inverted_index.pre-segmentation | 28 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5016827109 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I114112103 |
| citation_normalized_percentile.value | 0.94758761 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |