=Paper=
{{Paper
|id=Vol-2797/paper4
|storemode=property
|title=Bayesian-belief Networks for Supporting Decision-making of the Opening Data by the
Customs
|pdfUrl=https://ceur-ws.org/Vol-2797/paper4.pdf
|volume=Vol-2797
|authors=Ahmad Luthfi,Boriana Rukanova,Marcel Molenhuis,Marijn Janssen,Yao-Hua Tan
|dblpUrl=https://dblp.org/rec/conf/egov/LuthfiRMJT20
}}
==Bayesian-belief Networks for Supporting Decision-making of the Opening Data by the
Customs==
https://ceur-ws.org/Vol-2797/paper4.pdf
Bayesian-belief Networks for Supporting Decision-
making of the Opening Data by the Customs
Ahmad Luthfi*, Boriana Rukanova**, Marcel Molenhuis***,
Marijn Janssen****, Yao-Hua Tan*****
*Delft University of Technology, the Netherlands/Universitas Islam Indonesia, Indonesia
a.luthfi@tudelft.nl/ahmad.luthfi@uii.ac.id
**Delft University of Technology, the Netherlands, b.d.rukanova@tudelft.nl
***Customs Administration of the Netherlands, the Netherlands, jm.molenhuis@belastingdienst.nl
****Delft University of Technology, the Netherlands, m.f.w.h.a.janssen@tudelft.nl
*****Delft University of Technology, the Netherlands, y.tan@tudelft.nl
Abstract: Open government data initiatives are part of the endeavor process of governments to
show that they are accountable and transparent organizations. Opening more datasets to external
data analytics providers or other government organizations holds the potential to help
governments to improve their processes by promoting a better understanding and enhancing the
decision-making. Nevertheless, the decision-making to disclose datasets is challenging. Decision-
makers often refuse to open their datasets due to several potential risks. In situations like the
Dutch Customs, a dataset can contain competitive sensitive data, and multiple parties have to
agree to open it. Given this complex situation, in this paper, we test a Bayesian-belief Network
method for supporting the decision to open data. Our work contributes to Customs in their efforts
to disclose more datasets and helping decision-makers in the process of evaluating data and
defining strategies of how to move from closed to open decisions.
Keywords: Bayesian-belief Networks, Decision-making, Open Data, Customs, Risks
Acknowledgement: This research was funded by Indonesia Endowment Fund for Education (LPDP),
the Ministry of Finance of Republic of Indonesia. This study was also partially funded by the
innovation program.
1. Introduction
Government institutions play an essential role and have the power in opening of public data. Being
both a data publisher and a policy-maker, the government has a particular locus to define strategies
and tools for opening its data that improves the decision-making process (Luthfi & Janssen, 2019).
Besides, opening more datasets can promote a better understanding, stimulate great ideas, enhance
transparency, and other value proportions (Janssen, Charalabidis, & Zuiderwijk, 2012; Kucera &
Copyright ©2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
52 Ongoing Research
Chlapek, 2014). However, during the decision-making process for opening datasets, the
governments and external stakeholders can have different roles and motivations (Gonzales-Zapata
& Heeks, 2015).
Regardless of the underlying motivation for opening data, analyzing and making decisions on
the status of the dataset before releasing it to the appropriate stakeholders is often challenging and
not trivial. The government should take into account several risks (Martin, Foulonneau, Turki, &
Ihadjadene, 2013). The possible risks could include unlocking sensitive personal data, competitive
information, and opening inaccurate data (Luthfi & Janssen, 2017). As a result, these potential risk
factors can influence accountability and even degrade the reputation of the government institutions
(Martin et al., 2013).
In this study, we introduce a supporting tool where a conceptual model was previously
developed based on the healthcare case study (Luthfi & Janssen, 2017). At that time, the proposed
decision-making model was still described in a high-level overview. The prior model employed
sequential steps to analyze the selected dataset. For the analysis of the dataset, a non-actual dataset
sample and simulated model using the Bayesian-beliefs network method was used. Besides, a
quantitative approach was used to estimate the possible adverse-risks level while constructing the
causal Bayesian networks. The empirical setting for this study is a pilot project (called the Dutch
Living Lab) which is part of the PROFILE1 EU-funded research project for developing data analytics
solutions for Customs (Rukanova et al., 2019). The main research question that we set to explore in
this paper is "to what extent the decision support tool for opening data is applicable to the Customs
context?". Hence, the objective of this study is to explore the feasibility of the decision supporting
tool using Bayesian-beliefs network that was developed for the context of opening data in the
healthcare domain (Luthfi & Janssen, 2017) to the context of the Customs case study.
2. Theoretical Background
In this section we first review existing models that have been applied so far in the context of opening
data. We then present in more detail the decision-making model using Bayesian Networks method,
which is the method we further develop and enhance in this paper.
2.1. Decision-making Models for Opening Data
In the literature study, we found that there are various models for making decisions to open data.
The five systematic models that contribute to the open data domain were identified, as follows: (1)
Trade-off the risks values (Zuiderwijk & Janssen, 2015). This model provides structured steps for
analyzing the benefits and risks of disclosing data. (2) Decision-support framework (Buda et al.,
2015). This model provided a prototype that was based on the insight of open data ecosystems. (3)
Multiple Criteria for decision-making (Luthfi, Janssen, & Crompvoets, 2018). This model used a
fuzziness theory to analyze the uncertainty problems and provide decision alternatives. (4) Costs
and benefits of opening data (Luthfi & Janssen, 2019). This model was developed based on the
1 https://www.profile-project.eu/
Ongoing Research 53
Decision Tree Analysis method. This model is used to estimate the potential advantages and
disadvantages of releasing data. (5) Interactive decision-making process (Luthfi & Janssen, 2017;
Luthfi et al., 2018). This model proposed a Bayesian-belief Networks method to construct the causal
relationships of the decision-making process to open data in the case of health patient records. This
model contributes an interesting perspective of how to examine the risks and benefits of opening
data by providing sequential iteration process. The model uses a suppression technique like k-
anonymity to anonymize such sensitive attributes.
The prior research listed above has explored the feasibility of these models in the context of
opening data. In this paper we focus specifically on further developing the last model that we listed,
namely the one using Bayesian-belief Networks method for opening data (Luthfi & Janssen, 2017)2.
2.2. Prior Study to Open Data Using Bayesian-belief Networks
In prior research (Luthfi & Janssen, 2017) a conceptual model was developed to analyze the
possibility of adverse-risks in the open data domain that makes use of Bayesian-belief Networks
theory. The main motivation of the prior research was to deliver new knowledge to the decision-
makers and other related stakeholders on how to make decisions to open data by using a scientific
and structural manner. This model proposed four main sequential steps to analyze the potential
risks, namely retrieving and decomposing dataset, evaluating, assessing, and decision-making. This
model examined the health patient records dataset as an example case, and developed a systematic
simulation to test the conceptual model.
Besides, to estimate the level of possible risks, quantitative approach was employed. In the
assessment step, during the iterative process of decision-making, the model normalizes the table by
removing several sensitive attributes of the dataset based on the Bayesian network employment.
While this model demonstrates an initial application of how Bayesian Networks theory can be
applied in the context of open data (Luthfi & Janssen, 2017), this previous research it did not fully
integrate all the Bayesian-belief Network rules.
3. A Novel Conceptual Model
In this study, we use a systematic approach to apply the decision-making process to open data from
the insight of Dutch Customs. As a starting point for developing our model we used the conceptual
model that was initially developed in the previous study by using the health patient records dataset
(Luthfi & Janssen, 2017). In this paper, we are adapting the prior decision-support model by
modifying some steps to make it more effective but still take into account the comprehensive
overview. In the previous model, there are four main steps, namely retrieving and decomposing
dataset, evaluation, assessment, and decision-making. In this paper, we propose a new more
effective process by merging the evaluation and assessment steps to become a single evaluation
2 In this paper we focus only on further developing the use of Bayesian Networks theory in the context of
opening data. Further research can also examine and further develop the use of the other models or
combination thereof in the context of the opening data but this is out of the scope of the current paper.
54 Ongoing Research
process by implementing the Bayesian-belief Network rules. The objective to combine these steps is
because the tool can employ the steps in the same process. Besides, the previous model was focused
on the binary decision (open and closed), whereas in this new model, we can provide more dynamic
decisions. The four decisions that are possible to take are as follows: (a) open the dataset; (b) maintain
limited access to the dataset; (c) introduce additional screening; and (d) remain closed. The new
proposed conceptual model for this study is presented in Figure 1.
Figure 2: A Novel Conceptual Model of Decision Support to Open Data (adapted from (Luthfi & Janssen,
2017))
In the first step (Initialization), we retrieve the datasets from the data provider. In this step, the
decision support tool will extract the selected dataset into a machine- readable structure. In the
second step (Evaluation), we analyze the dataset using Bayesian-belief networks method. There are
four sub-steps, namely (a) determine the risk factors, (b) construct the causal relationship of the risk
factors, (c) determine the risks level, and (d) interrogate the structure. Next, we evaluate the latest
status of the dataset based on the single classification result in Step 2. In this step, the constructed
Bayesian-
t from this
step is to provide a single classification of the dataset status, namely open, limited access, additional
screening, and closed dataset. In the case that the data providers consider reanalyzing the dataset
because of potential other risk effects, the decision support tool can iterate the process. The iteration
process aims to update the dataset status (back to step 2) to keep certain parts of the dataset is able
to be disclosed.
Ongoing Research 55
4. Case Study Analysis
4.1. Examine the Decision Support Tool
In order to observe and evaluate the decision support model, we apply the tool to the context of the
Dutch Customs case study. In this paper, we employ the three main steps from the conceptual model
shown in Figure 1.
Step 1. Initialization
In this step, the authentication process is required to indicate the groups and levels of the users
namely: administrator, data analysist (experts), and decision-makers. For example, we give a
privilege level from the data analyst or expert. Then, the tool selects the datasets from the data
provider. The original dataset structure used in this case study is derived from the Dutch Living
Lab, namely Vereeenvoudigde Aangifte e-Commerce. In this process, the decision support tool will
extract the selected dataset into a readable and machine structure. The tool can select a data source
from multiple database platforms like CSV, XML, JSON, etc. and ensure that the metadata of the
dataset is well structured. Afterward, the tool constructs the dataset structure and its relationships.
Step 2. Evaluation
The first sub-step of this process is determining the risk factors. In this step, the tool asks the data
analyst or expert to select a single or multiple risks category of the attribute. There are several risk
factors provided in this tool, namely privacy infringement, data inaccuracy, data misinterpretation,
data sensitivity, and data ownership. In this case, the expert selects the data sensitivity issue as we
want to examine the selected dataset in terms of sensitive level. In the second sub-step, the tool
constructs a causal network of the risk factors that are determined in the previous sub-step. This
causal networks are developed based on the Bayesian-belief Networks formulation. The causal
networks can play a role to represent a set of risks variables and their conditional dependencies via
a directed acyclic graph, as shown in Figure 2. The third sub-step of this process is determining the
risks level. The earlier studies conducted in the healthcare sector (Luthfi & Janssen, 2017) used a
quantitative approach to determine the risks level of attributes. For that study the availability of the
experts and data analysts in this field was sufficient enough to quantify and estimate the of risk level
of the selected dataset. Nevertheless, in the Dutch Customs case, such accurate expertise for
estimating the details the risks level is limited. Besides, in practice, doing quantitative approach will
take an effort and is time consuming. Therefore, in this paper we adapt the approach to a qualitative
in Figure 4.
The last sub-step of this process is interrogating the dataset structure. In this sub-step, the tool
develops the group of the Dutch Customs declaration dataset including the risks level. The goal of
this interrogation is to visualize the explicit status of each attribute in terms of the data sensitivity
issues. There are three color signals shown by the tool to indicate the risks level. The red attributes
represent the high risk level, the yellow attributes indicate the moderate risk, and the green
attributes reflect the low risk.
56 Ongoing Research
Figure 3: Bayesian Networks Causal Relationships
Figure 4: Determine Risk Level
Step 3. Decision-making
In this final step, the tool provides the information of the status of dataset attributes into four
decision alternatives. Based on the analysis process (step 2), the tool recommends to use additional
screening with respect to some sensitive attributes like recipient_name, recipient_address,
sender_name, and sender_address. In order to help the data analyst to follow up the additional
screening decision, the tool also take into account the action plan. In this case, we propose a salted
cryptography algorithm to train the attributes. This method uses concatenating technique to
randomly blur the plain text of the data value (Dubrawsky, 2009).
Ongoing Research 57
5. Discussion and Conclusions
Based on the entire process of the decision-making process of opening data in this study, we found
some positive results. First, for the decision-support tool, it is applicable to use a qualitative
approach in estimating the potential risks level of the dataset. The qualitative work has proven to
make the time more efficient because the Customs expert does not require such high expertise to
define and compare the risk by reflecting on the numbers (quantitative). Second, Bayesian-belief
Networks is a suitable method that can analyze multiple datasets with different case studies by using
systematic steps. The tool is not only able to construct the causal relationships of the risk factors, but
also to interrogate the attributes by grouping the risks level. Third, by examining the real-life
environment of the Dutch Customs declaration dataset, the decision-support tool has shown the
more rigorous result and is recommended to be used by decision-makers.
Regarding contribution to theory, in this study we extend the method developed earlier (Luthfi
& Janssen, 2017) by: (1) merging steps in the method to simplify the process and make it more
efficient; (2) by incorporating a more sophisticated use of the Bayesian-belief Networks in the
method compared to what was done earlier; (3) by extending the tool and demonstrating that it can
support also qualitative analysis in addition to quantitative analysis. Our study also demonstrated
a broader applicability of the method and the tool beyond the healthcare domain where it was
originally developed also to the Customs domain. which is a very different domain of application.
This increases our confidence that the tool can be applied across domains.
With respect to practice, this study contributes new insights to policy-makers and decision-
makers, in particular in the Customs domain, to support their decision-making process in opening
data. The decision-support tool is applicable to multiple datasets and different case studies. The use
of a synthetic qualitative approach could be beneficial for government organizations who have a
limited number of experts to assess the risks level. Besides, the tool can make a better understanding
of the decision-makers regarding the possibility of changing policy from closed to open data sets.
For future work, we recommend using multiple methods to evaluate the dataset, such as Multi-
Criteria Decision Making and K-Nearest Neighbour algorithm, to get more rigorous results and
findings of the proposed conceptual model to open data.
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About the Authors
Ahmad Luthfi
Ahmad Luthfi is a PhD researcher at Delft University Technology, the Netherlands. He holds his master's in
the field of Computer Science at Gadjah Mada University, Indonesia. His research interests are in the area
of Open Government Data, Decision-making Process, and Decision Support Systems.
Boriana Rukanova
Dr. Boriana Rukanova is a researcher at Delft University of Technology. Her research interest include digital
infrastructure innovations in international supply chains, upscaling of innovations, and value of data
analytics for government supervision.
Marcel Molenhuis
Marcel Molenhuis is a senior consultant data & analytics at Secretary Coordination Group Innovation (CGI)
in Customs Administration of the Netherlands.
Marijn Janssen
Prof. dr. Marijn Janssen is full professor in ICT & Governance at the Delft University of Technology and head
of the Information and Communication Technology section. His research is focused on ICT-architecting which
multiple public and private organizations.
Yao-Hua Tan
Prof. dr. Yao-Hua Tan is full professor of Information and Communication Technology at the department of
Technology, Policy and Management of the Delft University of Technology. His research fields are IT
innovation for e-customs to make international trade more secure and safe; IT architectures for data sharing
and compliance management for international supply chains; artificial intelligence and data analytics for
customs risk targeting and improve logistic efficiency in international trade.