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Text of the page (random words):
international workshop on quality in databases qdb 2026 15th international workshop on quality in databases qdb 26 at the 52nd vldb conference on august 31 2026 boston ma usa about news schedule important dates cfp submission invited speakers people acknowledgement quality in databases data quality has been a major concern of organizations for decades leading to the introduction of standards and quality frameworks recent advances in artificial intelligence ai e g generative ai have brought data quality dq back into the spotlight in enterprises it is particularly important to build data ecosystems that can cope with the emerging challenges posed by ai based systems data quality has been tackled from different perspectives the database community has made significant advances in data profiling and data cleaning and still focuses on dq issues like duplicate detection or missing data handling the information systems community provides solutions for addressing dq at an organizational level the machine learning ml community focuses mainly on the development of robust models that can deal with issues in the data we want to build upon the success of qdb 23 qdb 24 and qdb 25 and continue offering an open format for joint discussions between different communities on the future of dq assessment and improvement qdb 2025 proceedings https www vldb org 2025 workshops vldb html outline container org2a44ac9 qdb 2024 proceedings https vldb org workshops 2024 outline container org90551d8 qdb 2023 proceedings https ceur ws org vol 3462 news we have invited 2 distinguished keynote speakers we have accepted 8 papers from 15 submissions the deadline has been extended to june 1st the qdb 26 workshop proposal has been accepted as a vldb workshop schedule tentative the program consists of talks on the accepted papers and two keynotes we plan to have a poster session to spark more discussion and networking august 31st all times are in boston time 09 00 09 15 opening remarks 09 15 10 15 session 1 morning keynote chair cinzia cappiello keynote title the four paradigms of data quality assessment keynote speaker divesh srivastava at t chief data office keynote abstract scientific discovery uses a variety of methods which have been characterized through four paradigms empirical i e using descriptions of natural phenomena theoretical i e based on mathematical formulas computational i e using computationally intensive methods and data driven i e based on analysis of large complex data sets this talk will draw parallels between methods used for scientific discovery and those used for discovering data errors and present challenges and opportunities within the four paradigms of data quality assessment keynote speaker bio divesh srivastava is the head of database research at at t he is an at t fellow a fellow of the acm on the board of directors of the computing research association cra co chair of the acm publications board and former president of the vldb endowment he is a recipient of the 2021 ieee tcde impact award the 2021 acm sigmod contributions award and the 2023 distinguished alumnus award from the indian institute of technology bombay he has conducted research on a wide variety of topics in data management for three decades and has presented keynotes at several international conferences he received his ph d from the university of wisconsin madison and his bachelor of technology from the indian institute of technology bombay india 10 15 10 45 break 10 45 12 15 session 2 morning research session chair cinzia cappiello can llms serve as a data error detection engine trade offs in accuracy cost and hallucination across datasets maximilian plazotta and meike klettke assessing data quality in relational database migration the data quality score dqs abhinav srivastava and tanya chaudhary measuring credibility in social media platforms through data quality and provenance sebastian garcía and adriana marotta automatic consistency assessment through partial functional dependency mining marcian seeger thorsten papenbrock felix naumann and lisa ehrlinger 12 15 13 45 lunch 13 45 14 45 session 3 afternoon keynote chair steven whang keynote title ad hoc data integration in data lakes keynote speaker ziawasch abedjan tu berlin keynote abstract as data lakes have become a prominent foundation for enterprise and scientific data management organizations increasingly face the challenge of locating relevant datasets and building ad hoc integration pipelines across heterogeneous poorly documented and rapidly evolving data collections in this setting ad hoc data discovery and integration becomes a critical capability for turning raw distributed data assets into usable knowledge in this talk i will give insights into our research on holistic discovery systems task dependent discovery and lake cleaning keynote speaker bio ziawasch abedjan is chair of the data integration and data preparation group at tu berlin and research group lead at berlin institute for foundations of learning and data bifold his research is focused on developing generalizable and scalable techniques for various data integration problems such as data cleaning data science pipeline generation and understanding and data discovery previously he chaired the database and information systems group at the leibniz university hannover he held positions as assistant professor at tu berlin postdoctoral associate at mit research associate at qcri senior researcher at the german center for artificial intelligence dfki and visiting academic at amazon search he received his phd from the hasso plattner institute in potsdam and was awarded the university of potsdam s best dissertation prize in 2014 he has co authored more than 80 peer reviewed papers in leading database and software engineering venues and received recognition with several academic awards his research is supported by the german research council dfg and the german federal ministry of science and education 14 45 15 45 poster session continues during the coffee break 15 15 15 45 break 15 45 17 15 session 4 afternoon research talks chair steven whang scalable goal oriented source selection ambarish singh and romila pradhan improving data preparation for csv files with llms alexander van renen moritz rengert macallyster edmondson and andreas kipf towards inference aware privacy guidance for data preparation vishal chakraborty and felix naumann kgpipe generation of pipelines for data integration into knowledge graphs marvin hofer and erhard rahm 17 15 17 30 closing remarks top important dates submission deadline may 15 2026 june 1 2026 submission website https cmt3 research microsoft com qdb2026 notification of acceptance june 30 2026 july 7 2026 final papers due july 15 2026 workshop monday august 31 2026 top call for papers this workshop aims to exchange novel ideas and best practices about data quality assessment and improvement in the era of ai the event should unite experienced and senior level data quality researchers with junior researchers and phd students we specifically expect junior researchers to benefit since they get to meet the community and continue high quality research on data quality the suggested topics of interest include but are not limited to foundational dq methods and assessment data profiling for data quality measurement statistical methods to detect erroneous data data lineage and provenance tracking industry specific data quality standards and compliance data versioning and quality control benchmark data sets to evaluate dq assurance methods ai ml specific data quality data preparation data quality for foundation models data quality using generative ai bias detection and mitigation in training data data quality for few shot and zero shot learning post training quality fact checking explainable data cleaning ml powered methods for improving data quality integration of data quality tools in mlops pipelines implementation and process optimization automation of dq assessment and improvement methods real time data quality monitoring cost benefit analysis of data quality improvements fairness in data quality we appreciate submissions on all these topics across domains e g healthcare mobility production and data models e g graphs time series top submission submission authors are invited to submit original unpublished research papers not being considered for publication in any other forum please submit your paper as a pdf using microsoft s qdb cmt site format it is the authors responsibility to ensure that their submissions adhere to the format detailed here in particular it is not allowed to modify the format with the objective of squeezing in more material submissions that do not comply with the formatting will be rejected without review note that the limit of up to 6 pages including all figures tables and references must be followed for both full papers and demos publication accepted papers will be distributed via vldb org top invited speakers keynote 1 divesh srivastava at t labs research usa keynote 2 ziawasch abedjan tu berlin germany organization people program chairs lorena etcheverry universidad de la república uruguay steven euijong whang kaist republic of korea cinzia cappiello politecnico di milano italy steering committee ihab ilyas university of waterloo usa felix naumann hasso plattner institute university of potsdam germany program committee felix biessmann berlin university of applied sciences and technology germany antoon bronselaer ghent university belgium lisa ehrlinger hasso plattner institute university of potsdam germanu sandra geisler rwth aachen university germany hazar harmouch university of amsterdam netherlands thomas hütter software competence center hagenberg austria sebastian link university of auckland new zealand adriana marotta universidad de la república uruguay maria angela pellegrino university of salerno italy eduardo pena utfpr brazil romila pradhan purdue university usa anisa rula university of brescia italy flavia serra universidad de la república uruguay wolfram wöß johannes kepler university linz austria top acknowledgement the microsoft cmt service was used for managing the peer reviewing process for this conference this service was provided for free by microsoft and they bore all expenses including costs for azure cloud services as well as for software development and support top