Data Model Logger - Data Discovery for Extract-Transform-Load

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Madhikermi, Manik
dc.contributor.author Buda, Andrea
dc.contributor.author Dave, Bhargav
dc.contributor.author Främling, Kary
dc.date.accessioned 2018-08-21T13:47:07Z
dc.date.available 2018-08-21T13:47:07Z
dc.date.issued 2018
dc.identifier.citation Madhikermi , M , Buda , A , Dave , B & Främling , K 2018 , Data Model Logger - Data Discovery for Extract-Transform-Load . in 2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS) . IEEE , pp. 629-630 , IEEE International Conference on Data Science and Systems , Bangkok , Thailand , 18/12/2017 . DOI: 10.1109/HPCC-SmartCity-DSS.2017.87 en
dc.identifier.isbn 978-1-5386-2588-0
dc.identifier.other PURE UUID: b18cb720-601d-4a5f-a312-41147f3be8da
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/data-model-logger--data-discovery-for-extracttransformload(b18cb720-601d-4a5f-a312-41147f3be8da).html
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/33548
dc.description | openaire: EC/H2020/688203/EU//BIoTope
dc.description.abstract Information Systems (ISs) are fundamental to streamline operations and support processes of any modern enterprise. Being able to perform analytics over the data managed in various enterprise ISs is becoming increasingly important for organisational growth. Extract, Transform, and Load (ETL) are the necessary pre-processing steps of any data mining activity. Due to the complexity of modern IS, extracting data is becoming increasingly complicated and time-consuming. In order to ease the process, this paper proposes a methodology and a pilot implementation, that aims to simplify data extraction process by leveraging the end-users’ knowledge and understanding of the specific IS. This paper first provides a brief introduction and the current state of the art regarding existing ETL process and techniques. Then, it explains in details the proposed methodology. Finally, test results of typical data-extraction tasks from 4 commercial ISs are reported. en
dc.format.extent 629-630
dc.language.iso en en
dc.relation info:eu-repo/grantAgreement/EC/H2020/688203/EU//BIoTope
dc.relation.ispartof IEEE International Conference on Data Science and Systems en
dc.relation.ispartofseries 2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS) en
dc.rights restrictedAccess en
dc.subject.other 113 Computer and information sciences en
dc.title Data Model Logger - Data Discovery for Extract-Transform-Load en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.contributor.department Professorship Främling K.
dc.subject.keyword ETL
dc.subject.keyword Database
dc.subject.keyword Trigger
dc.subject.keyword Reverse Engineering
dc.subject.keyword Data Warehouse
dc.subject.keyword Information System
dc.subject.keyword Information Retrieval
dc.subject.keyword Process Mapping
dc.subject.keyword Data Discovery
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201808214681
dc.identifier.doi 10.1109/HPCC-SmartCity-DSS.2017.87


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