Dr. Fathoni A. Musyaffa – Smart Data Analytics

Dr. Fathoni A. Musyaffa left SDA. The profile below reflects the status at the point of his departure and is no longer updated.

PhD Student
Computer Science Institute
University of Bonn

Profile: LinkedIn, Google Scholar, DBLP

Endenicher Allee 19a, 53115 Bonn
University of Bonn, Computer Science

Short CV

Fathoni A. Musyaffa is a PhD Student at the University of Bonn. Fathoni’s research interests are in the area of Data Management and Analytics for Heterogenous Public Fiscal Data Sources.

Research Interests

Linked Data and Semantic Web
Open Data
Concepts Interlinking
Distributed Computing
Cluster Computing
e-Government

Projects

Publications

2021

Musyaffa, Fathoni Arief

Comparative Analysis of Open Linked Fiscal Data PhD Thesis

Rheinische Friedrich-Wilhelms-Universität Bonn, 2021.

Abstract | Links | BibTeX

@phdthesis{Musyaffa2021,

title = {Comparative Analysis of Open Linked Fiscal Data},

author = {Fathoni Arief Musyaffa},

url = {https://bonndoc.ulb.uni-bonn.de/xmlui/handle/20.500.11811/9114},

year  = {2021},

date = {2021-06-01},

school = {Rheinische Friedrich-Wilhelms-Universität Bonn},

abstract = {The open data movement within public administrations has provided data regarding governance publicly. As public administrators and governments produce data and release the data as open data, the volume of the data is highly increasing. One of these datasets is budget and spending data, which has been gaining interest to the extent that several working groups and CSO/NGOs started working on this particular open data domain. The majority of these datasets are part of the open budget and spending datasets, which laid out data regarding how public administrations plan, revise, allocate, and expense their governance funding. The disclosure of public administration budget and spending data is expected to improve governance transparency, accountability, law enforcement, and political participation. 



Unfortunately, the analysis of budget and spending datasets is not a trivial task to do for several reasons. First, the quality of open fiscal data varies. Standards and recommendations for publishing open data are available, however, these standards are often not met and no framework specifically addresses fiscal data quality measurements. Second, the datasets are heterogeneous, since it is produced by different public administrations with different business process, accounting practice, requirements, and language. This lead to a challenging task in data integration across public budget and spending data. The structural and linguistic heterogeneity of open budget and spending data makes comparative analysis across datasets difficult to perform. Third, datasets within the budget and spending domain are complicated. To be able to comprehend such data, expertise is needed both from the public accounting/budgeting domain, as well as the technical domain to digest the datasets properly. Fourth, a platform to transform, store, analyze, and visualize datasets is necessary, especially those that make the utilization of semantic analysis is possible. Fifth, there is no conceptual association between datasets, which can be used as a comparison point to analyze fiscal records between compared public administrations. Lastly, there is a lack of methodology to consume and compare linked open fiscal data records across different public administrations. 



Our focus in this thesis is hence to perform research to help the community gain a better understanding of open fiscal data, provide analysis of their quality, suggest a way to publish open fiscal data in an improved manner, analyze the open fiscal data heterogeneity while also laying out lessons learned regarding their current state and supporting data formats that are capable for open fiscal data integration. Consequently, a platform to digest, analyze and visualize these datasets is devised, continued with performing experiments on multilingual fiscal data concept mapping and wrapped up with a proof-of-concept description of comparative analysis over linked open fiscal data.

},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

The open data movement within public administrations has provided data regarding governance publicly. As public administrators and governments produce data and release the data as open data, the volume of the data is highly increasing. One of these datasets is budget and spending data, which has been gaining interest to the extent that several working groups and CSO/NGOs started working on this particular open data domain. The majority of these datasets are part of the open budget and spending datasets, which laid out data regarding how public administrations plan, revise, allocate, and expense their governance funding. The disclosure of public administration budget and spending data is expected to improve governance transparency, accountability, law enforcement, and political participation.

Unfortunately, the analysis of budget and spending datasets is not a trivial task to do for several reasons. First, the quality of open fiscal data varies. Standards and recommendations for publishing open data are available, however, these standards are often not met and no framework specifically addresses fiscal data quality measurements. Second, the datasets are heterogeneous, since it is produced by different public administrations with different business process, accounting practice, requirements, and language. This lead to a challenging task in data integration across public budget and spending data. The structural and linguistic heterogeneity of open budget and spending data makes comparative analysis across datasets difficult to perform. Third, datasets within the budget and spending domain are complicated. To be able to comprehend such data, expertise is needed both from the public accounting/budgeting domain, as well as the technical domain to digest the datasets properly. Fourth, a platform to transform, store, analyze, and visualize datasets is necessary, especially those that make the utilization of semantic analysis is possible. Fifth, there is no conceptual association between datasets, which can be used as a comparison point to analyze fiscal records between compared public administrations. Lastly, there is a lack of methodology to consume and compare linked open fiscal data records across different public administrations.

Our focus in this thesis is hence to perform research to help the community gain a better understanding of open fiscal data, provide analysis of their quality, suggest a way to publish open fiscal data in an improved manner, analyze the open fiscal data heterogeneity while also laying out lessons learned regarding their current state and supporting data formats that are capable for open fiscal data integration. Consequently, a platform to digest, analyze and visualize these datasets is devised, continued with performing experiments on multilingual fiscal data concept mapping and wrapped up with a proof-of-concept description of comparative analysis over linked open fiscal data.

2020

Musyaffa, Fathoni A.; Vidal, Maria-Esther; Orlandi, Fabrizio; Lehmann, Jens; Jabeen, Hajira

IOTA: Interlinking of heterogeneous multilingual open fiscal DaTA Journal Article

In: Expert Syst. Appl., 147 , pp. 113135, 2020.

Links | BibTeX

Musyaffa, Fathoni A.; Lehmann, Jens; Jabeen, Hajira

Cross-administration comparative analysis of open fiscal data Inproceedings

In: ICEGOV 2020: 13th International Conference on Theory and Practice of Electronic Governance, Athens, Greece, 23-25 September, 2020, pp. 459–468, ACM, 2020.

Links | BibTeX

2018

Musyaffa, Fathoni A.

Towards Comparative Analysis of Open Fiscal Data Inproceedings

In: Proceedings of the 11th International Conference on Theory and Practice of Electronic Governance, ICEGOV 2018, Galway, Ireland, April 04-06, 2018, pp. 644–647, ACM, 2018.

Links | BibTeX

Musyaffa, Fathoni A.; Orlandi, Fabrizio; Jabeen, Hajira; Vidal, Maria-Esther

Classifying Data Heterogeneity within Budget and Spending Open Data Inproceedings

In: Proceedings of the 11th International Conference on Theory and Practice of Electronic Governance, ICEGOV 2018, Galway, Ireland, April 04-06, 2018, pp. 81–90, ACM, 2018.

Links | BibTeX

Musyaffa, Fathoni A.; Halilaj, Lavdim; Li, Yakun; Orlandi, Fabrizio; Jabeen, Hajira; Auer, Sören; Vidal, Maria-Esther

OpenBudgets.eu: A Platform for Semantically Representing and Analyzing Open Fiscal Data Inproceedings

In: Web Engineering - 18th International Conference, ICWE 2018, Cáceres, Spain, June 5-8, 2018, Proceedings, pp. 433–447, Springer, 2018.

Links | BibTeX

Musyaffa, Fathoni A.; Engels, Christiane; Vidal, Maria-Esther; Orlandi, Fabrizio; Auer, Sören

Experience: Open Fiscal Datasets, Common Issues, and Recommendations Journal Article

In: J. Data and Information Quality, 9 (4), pp. 19:1–19:10, 2018.

Links | BibTeX

2016

Musyaffa, Fathoni A.; Halilaj, Lavdim; Siebes, Ronald; Orlandi, Fabrizio; Auer, Sören

Minimally Invasive Semantification of Light Weight Service Descriptions Inproceedings

In: IEEE International Conference on Web Services, ICWS 2016, San Francisco, CA, USA, June 27 - July 2, 2016, pp. 672–677, IEEE Computer Society, 2016.

Links | BibTeX

Mynarz, Jindrich; Kl'imek, Jakub; Dudás, Marek; Skoda, Petr; Engels, Christiane; Musyaffa, Fathoni A.; Svátek, Vojtech

Reusable transformations of Data Cube Vocabulary datasets from the fiscal domain Inproceedings

In: Proceedings of the 4th International Workshop on Semantic Statistics co-located with 15th International Semantic Web Conference, SemStats@ISWC 2016, Kobe, Japan October 18, 2016, CEUR-WS.org, 2016.

Links | BibTeX

2013

Lee, Changhyeon; Musyaffa, Fathoni Arief; Kwon, Yong-Moo

Web based collaborative social album authoring system using Facebook photos Inproceedings

In: 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, Austin, TX, USA, October 20-23, 2013, pp. 221–230, ICST / IEEE, 2013.

Links | BibTeX

Kwon, Yong-Moo; Lee, Changhyeon; Musyaffa, Fathoni Arief

Collaborative Content Creation Using Web-Based Distributed User Interface (DUI) Incollection

In: Distributed User Interfaces: Usability and Collaboration, pp. 39–52, Springer, 2013.

Links | BibTeX