database systems; query processing; crowd-sourcing; human computation; automatic user interface generation; open world data models
Grünheid Anja, Nushi Besmira, Kossmann Donald (2014), Cost-Efficient Querying Strategies for the Crowd, in Proceedings of the Big Uncertain Data Workshop, SIGMOD 2014
Gruenheid A., Dong X.L., Srivastava D. (2014), Incremental Record Linkage, in Proceedings of the VLDB Endowment, Vol. 7 (9), 2014
Nushi Besmira, Singla Adish, Gruenheid Anja, Krause Andreas, Kossmann Donald (2014), Quality Assurance and Crowd Access Optimization: Why does diversity matter?, in Proceedings of the ICML Workshop on Crowdsourcing and Human Computing 2014
Grünheid Anja, Kossmann Donald (2013), Cost and Quality Trade-Offs in Crowdsourcing, in VLDB Workshop on Databases and Crowdsourcing
, Trento, Italy.
Grünheid Anja, Kossmann Donald, Nushi Besmira (2013), When is A=B?, in EATCS Bulletin
, (111), 88-97.
Franklin Michael J., Kossmann Donald, Kraska Tim, Ramesh Sukriti, Xin Reynold (2011), CrowdDB: answering queries with crowdsourcing, in In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, pp. 61-72. ACM
Feng Amber, Franklin Michael, Kossmann Donald, Kraska Tim, Madden Samuel, Ramesh Sukriti, Wang Andrew, Xin Reynold (2011), Crowddb: Query processing with the vldb crowd, in Proceedings of the VLDB Endowment 4, no. 12 (2011). Demo
Nushi B., Singla A., Gruenheid A., Zamanian E., Krause A., Kossmann D., Crowd Access Path Optimization: Diversity Matters, in Proceedings of the Conference on Human Computation & Crowdsourcing (HCOMP) 2015
Nushi Besmira, Alonso Omar, Hentschel Martin, Kandylas Vasileios, CrowdSTAR: A Social Task Routing Framework for Online Communities, in Proceedings of the ICWE 2015.
, Rotterdam, NL.
Database technology hasmaturedover the last twenty tothrirtyyears.Despitethese success stories, thereare still some notoriously hardresearch problems. More specifically, thereare still some queries that state-of-the-art database and information systems such as Oracleor Google cannot answer. Processing these queries involves intelligence in order to deal with inconsistencies or missing data. This research project will explore how to integrate human input effectively in order to process such queries.Specifically, the plan is to develop a new kindof database management system that decomposes queries such that parts of the query are executed by machines and other parts are executed by humans. This way, machines carry out tasks that machines can do well (e.g., numbercrunching, processing large volumes of data) and humansare involvedonly intasks that machines cannot handle well (e.g., resolve inconsistencies and enter missing information). Building such a systemraises many technical research challenges in terms of query language and semantics, optimization, usability, and privacy. We planto address these technical challenges in this project. Buildingsuch a system also raises a number of other challenges; e.g., legal,social,and business. Studying these other challenges is beyond the scope ofthis project.