{"id":"https://openalex.org/W3210910216","doi":"https://doi.org/10.1007/s13222-021-00391-x","title":"Feature Engineering Techniques and Spatio-Temporal Data Processing","display_name":"Feature Engineering Techniques and Spatio-Temporal Data Processing","publication_year":2021,"publication_date":"2021-10-20","ids":{"openalex":"https://openalex.org/W3210910216","doi":"https://doi.org/10.1007/s13222-021-00391-x","mag":"3210910216"},"language":"en","primary_location":{"id":"doi:10.1007/s13222-021-00391-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s13222-021-00391-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s13222-021-00391-x.pdf","source":{"id":"https://openalex.org/S73012565","display_name":"Datenbank-Spektrum","issn_l":"1610-1995","issn":["1610-1995","1618-2162"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Datenbank-Spektrum","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s13222-021-00391-x.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054183581","display_name":"Chris-Marian Forke","orcid":null},"institutions":[{"id":"https://openalex.org/I70451448","display_name":"HAW Hamburg","ror":"https://ror.org/00fkqwx76","country_code":"DE","type":"education","lineage":["https://openalex.org/I70451448"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Chris-Marian Forke","raw_affiliation_strings":["Hochschule f\u00fcr Angewandte Wissenschaften Hamburg, Berliner Tor 7, 20099, Hamburg, Germany","Hochschule f\u00fcr Angewandte Wissenschaften Hamburg, Berliner Tor\u00a07, 20099, Hamburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hochschule f\u00fcr Angewandte Wissenschaften Hamburg, Berliner Tor 7, 20099, Hamburg, Germany","institution_ids":["https://openalex.org/I70451448"]},{"raw_affiliation_string":"Hochschule f\u00fcr Angewandte Wissenschaften Hamburg, Berliner Tor\u00a07, 20099, Hamburg, Germany","institution_ids":["https://openalex.org/I70451448"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067255721","display_name":"Marina Tropmann-Frick","orcid":"https://orcid.org/0000-0003-1623-5309"},"institutions":[{"id":"https://openalex.org/I70451448","display_name":"HAW Hamburg","ror":"https://ror.org/00fkqwx76","country_code":"DE","type":"education","lineage":["https://openalex.org/I70451448"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Marina Tropmann-Frick","raw_affiliation_strings":["Hochschule f\u00fcr Angewandte Wissenschaften Hamburg, Berliner Tor 7, 20099, Hamburg, Germany","Hochschule f\u00fcr Angewandte Wissenschaften Hamburg, Berliner Tor\u00a07, 20099, Hamburg, Germany"],"raw_orcid":"https://orcid.org/0000-0003-1623-5309","affiliations":[{"raw_affiliation_string":"Hochschule f\u00fcr Angewandte Wissenschaften Hamburg, Berliner Tor 7, 20099, Hamburg, Germany","institution_ids":["https://openalex.org/I70451448"]},{"raw_affiliation_string":"Hochschule f\u00fcr Angewandte Wissenschaften Hamburg, Berliner Tor\u00a07, 20099, Hamburg, Germany","institution_ids":["https://openalex.org/I70451448"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5067255721"],"corresponding_institution_ids":["https://openalex.org/I70451448"],"apc_list":{"value":3190,"currency":"USD","value_usd":3190},"apc_paid":{"value":3190,"currency":"USD","value_usd":3190},"fwci":0.7027,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.69861395,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"21","issue":"3","first_page":"237","last_page":"244"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7761709094047546},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.6752191781997681},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6074426174163818},{"id":"https://openalex.org/keywords/data-processing","display_name":"Data processing","score":0.5573049187660217},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.537746250629425},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.5091621279716492},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.502068042755127},{"id":"https://openalex.org/keywords/data-type","display_name":"Data type","score":0.4564930498600006},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4104015529155731},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36863207817077637},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3526384234428406},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10090944170951843},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.1006198525428772},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.07479876279830933}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7761709094047546},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.6752191781997681},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6074426174163818},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.5573049187660217},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.537746250629425},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.5091621279716492},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.502068042755127},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.4564930498600006},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4104015529155731},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36863207817077637},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3526384234428406},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10090944170951843},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.1006198525428772},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.07479876279830933},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s13222-021-00391-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s13222-021-00391-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s13222-021-00391-x.pdf","source":{"id":"https://openalex.org/S73012565","display_name":"Datenbank-Spektrum","issn_l":"1610-1995","issn":["1610-1995","1618-2162"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Datenbank-Spektrum","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s13222-021-00391-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s13222-021-00391-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s13222-021-00391-x.pdf","source":{"id":"https://openalex.org/S73012565","display_name":"Datenbank-Spektrum","issn_l":"1610-1995","issn":["1610-1995","1618-2162"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Datenbank-Spektrum","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3210910216.pdf","grobid_xml":"https://content.openalex.org/works/W3210910216.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W223385549","https://openalex.org/W1487321909","https://openalex.org/W1730693163","https://openalex.org/W1964155876","https://openalex.org/W2087576375","https://openalex.org/W2118561568","https://openalex.org/W2122825543","https://openalex.org/W2155261478","https://openalex.org/W2167101736","https://openalex.org/W2521003103","https://openalex.org/W2557913829","https://openalex.org/W2618995589","https://openalex.org/W2752848290","https://openalex.org/W2782791108","https://openalex.org/W2784026266","https://openalex.org/W2786780230","https://openalex.org/W2788381291","https://openalex.org/W2795016801","https://openalex.org/W2808862972","https://openalex.org/W2883786748","https://openalex.org/W2886368623","https://openalex.org/W2896393885","https://openalex.org/W2950619241","https://openalex.org/W2953072840","https://openalex.org/W2962834725","https://openalex.org/W2963214893","https://openalex.org/W3022394370","https://openalex.org/W3105524694","https://openalex.org/W3206101061","https://openalex.org/W6610450269","https://openalex.org/W6633340474"],"related_works":["https://openalex.org/W4387478977","https://openalex.org/W2380784125","https://openalex.org/W2914806737","https://openalex.org/W3004057759","https://openalex.org/W4250495829","https://openalex.org/W2889085215","https://openalex.org/W4360615883","https://openalex.org/W2100274982","https://openalex.org/W2219019889","https://openalex.org/W1760579403"],"abstract_inverted_index":{"Abstract":[0],"More":[1],"and":[2,17,32,40,81],"more":[3],"applications":[4],"nowadays":[5],"use":[6,71],"spatio-temporal":[7,38,45,76],"data":[8,24,39,46,62,77,85],"for":[9,34,44,61],"different":[10],"purposes.":[11],"In":[12],"order":[13,69],"to":[14,70,89],"be":[15,90],"processed":[16],"used":[18],"efficiently,":[19],"this":[20],"unique":[21],"type":[22],"of":[23,37,58,93],"requires":[25],"special":[26],"handling.":[27],"This":[28],"paper":[29],"summarizes":[30],"methods":[31],"approaches":[33,60,73],"feature":[35,99],"selection":[36],"machine":[41],"learning":[42],"algorithms":[43],"engineering.":[47,100],"Furthermore,":[48],"it":[49],"highlights":[50],"relevant":[51],"work":[52],"in":[53,68,78],"specific":[54],"domains.":[55],"The":[56],"range":[57],"possible":[59],"processing":[63,86],"is":[64,98],"quite":[65],"wide.":[66],"However,":[67],"these":[72],"with":[74],"the":[75,94],"a":[79],"meaningful":[80],"practical":[82],"way,":[83],"individual":[84],"steps":[87,97],"need":[88],"adapted.":[91],"One":[92],"most":[95],"important":[96]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}