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

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A4 Artikkeli konferenssijulkaisussa
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Date
2018
Major/Subject
Mcode
Degree programme
Language
en
Pages
629-630
Series
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)
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.
Description
| openaire: EC/H2020/688203/EU//BIoTope
Keywords
ETL, Database, Trigger, Reverse Engineering, Data Warehouse, Information System, Information Retrieval, Process Mapping, Data Discovery
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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 . https://doi.org/10.1109/HPCC-SmartCity-DSS.2017.87