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dc.contributor.authorJess, Torben
dc.date.accessioned2017-12-08T14:48:36Z
dc.date.available2017-12-08T14:48:36Z
dc.date.issued2017-11-01
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/270103
dc.description.abstractIndustrial companies are dealing with an increasing data overload problem in all aspects of their business: vast amounts of data are generated in and outside each company. Determining which data is relevant and how to get it to the right users is becoming increasingly difficult. There are a large number of datasets to be considered, and an even higher number of combinations of datasets that each user could be using. Current techniques to address this data overload problem necessitate detailed analysis. These techniques have limited scalability due to their manual effort and their complexity, which makes them unpractical for a large number of datasets. Search, the alternative used by many users, is limited by the user’s knowledge about the available data and does not consider the relevance or costs of providing these datasets. Recommender systems and so-called market approaches have previously been used to solve this type of resource allocation problem, as shown for example in allocation of equipment for production processes in manufacturing or for spare part supplier selection. They can therefore also be seen as a potential application for the problem of data overload. This thesis introduces the so-called RecorDa approach: an architecture using market approaches and recommender systems on their own or by combining them into one system. Its purpose is to identify which data is more relevant for a user’s decision and improve allocation of relevant data to users. Using a combination of case studies and experiments, this thesis develops and tests the approach. It further compares RecorDa to search and other mechanisms. The results indicate that RecorDa can provide significant benefit to users with easier and more flexible access to relevant datasets compared to other techniques, such as search in these databases. It is able to provide a fast increase in precision and recall of relevant datasets while still keeping high novelty and coverage of a large variety of datasets.
dc.language.isoen
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.subjectData allocation
dc.subjectData management
dc.subjectRecommender systems
dc.subjectMarket-based algorithms
dc.subjectData overload
dc.titleRecommender systems and market approaches for industrial data management
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridge
dc.publisher.departmentInstitute for Manufacturing, Department of Engineering
dc.date.updated2017-12-08T12:38:33Z
dc.identifier.doi10.17863/CAM.16918
dc.publisher.collegeRobinson College
dc.type.qualificationtitlePhD in Engineering
cam.supervisorMcFarlane, Duncan
rioxxterms.freetoread.startdate2018-12-08


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