A Machine Learning Approach to Government Business Process Re-engineering

Governments around the world accumulate large amounts of data but rarely use them to make their daily work more effective. For example, data classification tasks are typically performed manually or with systems that utilize rules created by humans. Public sector business processes are thus often out...

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Vydáno v:International Conference on Big Data and Smart Computing s. 56 - 63
Hlavní autoři: Riyadi, Agus, Kovacs, Mate, Serdult, Uwe, Kryssanov, Victor
Médium: Konferenční příspěvek
Jazyk:angličtina
japonština
Vydáno: IEEE 01.02.2023
Témata:
Big Data
Business process re-engineering
business process reengineering
Data models
Finance
government
Local government
machine learning
Manuals
public sector
Semisupervised learning
ISSN:2375-9356
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Shrnutí:Governments around the world accumulate large amounts of data but rarely use them to make their daily work more effective. For example, data classification tasks are typically performed manually or with systems that utilize rules created by humans. Public sector business processes are thus often outdated and require significant adaptations. One possible approach to move away from current practices is to apply business process re-engineering (BPR). This study proposes a framework for integrating machine learning into government BPR and using big data sets to optimize current public administration procedures. A case study on expenditure data classification was conducted with textual documents from Indonesian local governments. Several different deep learning approaches were examined. The results obtained confirmed that using the proposed framework leads to significant improvements, compared to the traditional labeling method.
ISSN:2375-9356
DOI:10.1109/BigComp57234.2023.00013