Pub Date : 2022-02-18DOI: 10.1007/s13222-022-00404-3
D. Petković
{"title":"Specification of Row Pattern Recognition in the SQL Standard and its Implementations","authors":"D. Petković","doi":"10.1007/s13222-022-00404-3","DOIUrl":"https://doi.org/10.1007/s13222-022-00404-3","url":null,"abstract":"","PeriodicalId":72771,"journal":{"name":"Datenbank-Spektrum : Zeitschrift fur Datenbanktechnologie : Organ der Fachgruppe Datenbanken der Gesellschaft fur Informatik e.V","volume":"42 1","pages":"163 - 174"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85982165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-16DOI: 10.1007/s13222-022-00407-0
P. Dadam, Klaus Küspert, H. Schek
{"title":"Nachruf auf Prof. Dr. Albrecht Blaser, Hirschhorn","authors":"P. Dadam, Klaus Küspert, H. Schek","doi":"10.1007/s13222-022-00407-0","DOIUrl":"https://doi.org/10.1007/s13222-022-00407-0","url":null,"abstract":"","PeriodicalId":72771,"journal":{"name":"Datenbank-Spektrum : Zeitschrift fur Datenbanktechnologie : Organ der Fachgruppe Datenbanken der Gesellschaft fur Informatik e.V","volume":"46 1","pages":"93 - 94"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76920056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-17DOI: 10.1007/s13222-021-00402-x
Wolfgang Lehner, Kai-Uwe Sattler, J. Freytag
{"title":"BTW2021 erstmals als digitale Vortragsreihe","authors":"Wolfgang Lehner, Kai-Uwe Sattler, J. Freytag","doi":"10.1007/s13222-021-00402-x","DOIUrl":"https://doi.org/10.1007/s13222-021-00402-x","url":null,"abstract":"","PeriodicalId":72771,"journal":{"name":"Datenbank-Spektrum : Zeitschrift fur Datenbanktechnologie : Organ der Fachgruppe Datenbanken der Gesellschaft fur Informatik e.V","volume":"75 1","pages":"67 - 71"},"PeriodicalIF":0.0,"publicationDate":"2022-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77365115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1007/s13222-022-00430-1
Carsten Binnig, Alexander Böhm, Tilmann Rabl, Pınar Tözün
With the ever increasing complexity of database systems and their pervasive use in industry, testing them has been an important issue for a long time. Recognizing this relevance, researchers and industry have started the Workshop Series on Testing Database Systems in 2008 collocated with ACM SIGMOD. Six instances of the workshop were successfully run until 2013. Five years later, in 2018, we revived the workshop in a new, biannual format. Today, the DBTest workshop consistently has high-quality submissions, expert presenters, and active participants across both academia and industry. Going forward, we plan to open the workshop up to an even more diverse audience, especially the research communities that focus on software testing and debugging in general, and not only on database systems.
{"title":"Reviving the Workshop Series on Testing Database Systems - DBTest.","authors":"Carsten Binnig, Alexander Böhm, Tilmann Rabl, Pınar Tözün","doi":"10.1007/s13222-022-00430-1","DOIUrl":"https://doi.org/10.1007/s13222-022-00430-1","url":null,"abstract":"<p><p>With the ever increasing complexity of database systems and their pervasive use in industry, testing them has been an important issue for a long time. Recognizing this relevance, researchers and industry have started the Workshop Series on Testing Database Systems in 2008 collocated with ACM SIGMOD. Six instances of the workshop were successfully run until 2013. Five years later, in 2018, we revived the workshop in a new, biannual format. Today, the DBTest workshop consistently has high-quality submissions, expert presenters, and active participants across both academia and industry. Going forward, we plan to open the workshop up to an even more diverse audience, especially the research communities that focus on software testing and debugging in general, and not only on database systems.</p>","PeriodicalId":72771,"journal":{"name":"Datenbank-Spektrum : Zeitschrift fur Datenbanktechnologie : Organ der Fachgruppe Datenbanken der Gesellschaft fur Informatik e.V","volume":"22 3","pages":"257-260"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9713739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9259691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01Epub Date: 2022-01-17DOI: 10.1007/s13222-021-00401-y
Nico Lässig, Sarah Oppold, Melanie Herschel
To obtain accurate predictions of classifiers, model ensembles comprising multiple trained machine learning models are nowadays used. In particular, dynamic model ensembles pick the most accurate model for each query object, by applying the model that performed best on similar data. Dynamic model ensembles may however suffer, similarly to single machine learning models, from bias, which can eventually lead to unfair treatment of certain groups of a general population. To mitigate unfair classification, recent work has thus proposed fair model ensembles, that instead of focusing (solely) on accuracy also optimize global fairness. While such global fairness globally minimizes bias, imbalances may persist in different regions of the data, e.g., caused by some local bias maxima leading to local unfairness. Therefore, we extend our previous work by including a framework that bridges the gap between dynamic model ensembles and fair model ensembles. More precisely, we investigate the problem of devising locally fair and accurate dynamic model ensembles, which ultimately optimize for equal opportunity of similar subjects. We propose a general framework to perform this task and present several algorithms implementing the framework components. In this paper we also present a runtime-efficient framework adaptation that keeps the quality of the results on a similar level. Furthermore, new fairness metrics are presented as well as detailed informations about necessary data preparations. Our evaluation of the framework implementations and metrics shows that our approach outperforms the state-of-the art for different types and degrees of bias present in training data in terms of both local and global fairness, while reaching comparable accuracy.
{"title":"Metrics and Algorithms for Locally Fair and Accurate Classifications using Ensembles.","authors":"Nico Lässig, Sarah Oppold, Melanie Herschel","doi":"10.1007/s13222-021-00401-y","DOIUrl":"https://doi.org/10.1007/s13222-021-00401-y","url":null,"abstract":"<p><p>To obtain accurate predictions of classifiers, model ensembles comprising multiple trained machine learning models are nowadays used. In particular, <i>dynamic model ensembles</i> pick the most accurate model for each query object, by applying the model that performed best on similar data. Dynamic model ensembles may however suffer, similarly to single machine learning models, from bias, which can eventually lead to unfair treatment of certain groups of a general population. To mitigate unfair classification, recent work has thus proposed <i>fair model ensembles</i>, that instead of focusing (solely) on accuracy also optimize <i>global fairness</i>. While such global fairness globally minimizes bias, imbalances may persist in different regions of the data, e.g., caused by some local bias maxima leading to <i>local unfairness</i>. Therefore, we extend our previous work by including a framework that bridges the gap between dynamic model ensembles and fair model ensembles. More precisely, we investigate the problem of devising locally fair and accurate dynamic model ensembles, which ultimately optimize for equal opportunity of similar subjects. We propose a general framework to perform this task and present several algorithms implementing the framework components. In this paper we also present a runtime-efficient framework adaptation that keeps the quality of the results on a similar level. Furthermore, new fairness metrics are presented as well as detailed informations about necessary data preparations. Our evaluation of the framework implementations and metrics shows that our approach outperforms the state-of-the art for different types and degrees of bias present in training data in terms of both local and global fairness, while reaching comparable accuracy.</p>","PeriodicalId":72771,"journal":{"name":"Datenbank-Spektrum : Zeitschrift fur Datenbanktechnologie : Organ der Fachgruppe Datenbanken der Gesellschaft fur Informatik e.V","volume":"22 1","pages":"23-43"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8762451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39851213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-22DOI: 10.1007/s13222-021-00399-3
Meike Klettke, U. Störl
{"title":"Four Generations in Data Engineering for Data Science","authors":"Meike Klettke, U. Störl","doi":"10.1007/s13222-021-00399-3","DOIUrl":"https://doi.org/10.1007/s13222-021-00399-3","url":null,"abstract":"","PeriodicalId":72771,"journal":{"name":"Datenbank-Spektrum : Zeitschrift fur Datenbanktechnologie : Organ der Fachgruppe Datenbanken der Gesellschaft fur Informatik e.V","volume":"66 1","pages":"59 - 66"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84762486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-21DOI: 10.1007/s13222-021-00398-4
Lucas Woltmann, P. Volk, Michael Dinzinger, Lukas Gräf, S. Strasser, Johannes Schildgen, Claudio Hartmann, Wolfgang Lehner
{"title":"Data Science Meets High-Tech Manufacturing – The BTW 2021 Data Science Challenge","authors":"Lucas Woltmann, P. Volk, Michael Dinzinger, Lukas Gräf, S. Strasser, Johannes Schildgen, Claudio Hartmann, Wolfgang Lehner","doi":"10.1007/s13222-021-00398-4","DOIUrl":"https://doi.org/10.1007/s13222-021-00398-4","url":null,"abstract":"","PeriodicalId":72771,"journal":{"name":"Datenbank-Spektrum : Zeitschrift fur Datenbanktechnologie : Organ der Fachgruppe Datenbanken der Gesellschaft fur Informatik e.V","volume":"28 1","pages":"5 - 10"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83306129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1007/s13222-021-00386-8
Ioannis Prapas, Behrouz Derakhshan, Alireza Rezaei Mahdiraji, V. Markl
{"title":"Continuous Training and Deployment of Deep Learning Models","authors":"Ioannis Prapas, Behrouz Derakhshan, Alireza Rezaei Mahdiraji, V. Markl","doi":"10.1007/s13222-021-00386-8","DOIUrl":"https://doi.org/10.1007/s13222-021-00386-8","url":null,"abstract":"","PeriodicalId":72771,"journal":{"name":"Datenbank-Spektrum : Zeitschrift fur Datenbanktechnologie : Organ der Fachgruppe Datenbanken der Gesellschaft fur Informatik e.V","volume":"9 1","pages":"203 - 212"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77765420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-29DOI: 10.1007/s13222-021-00395-7
Ralf Schenkel, Stefanie Scherzinger, M. Tropmann-Frick
{"title":"„Data Engineering“ in der Hochschullehre","authors":"Ralf Schenkel, Stefanie Scherzinger, M. Tropmann-Frick","doi":"10.1007/s13222-021-00395-7","DOIUrl":"https://doi.org/10.1007/s13222-021-00395-7","url":null,"abstract":"","PeriodicalId":72771,"journal":{"name":"Datenbank-Spektrum : Zeitschrift fur Datenbanktechnologie : Organ der Fachgruppe Datenbanken der Gesellschaft fur Informatik e.V","volume":"56 1","pages":"251 - 253"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80423155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-27DOI: 10.1007/s13222-021-00396-6
{"title":"Dissertationen","authors":"","doi":"10.1007/s13222-021-00396-6","DOIUrl":"https://doi.org/10.1007/s13222-021-00396-6","url":null,"abstract":"","PeriodicalId":72771,"journal":{"name":"Datenbank-Spektrum : Zeitschrift fur Datenbanktechnologie : Organ der Fachgruppe Datenbanken der Gesellschaft fur Informatik e.V","volume":"83 1","pages":"261 - 264"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81084335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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