BIpm: Combining BI and Process Mining

Mohammad Reza Harati Nik

1,2

, Wil M. P. van der Aalst

and Mohammadreza Fani Sani

Department of Industrial Management, Allameh Tabataba’i University, Tehran, Iran

PhD visitor at Process and Data Science (PADS) team at RWTH Aachen, Germany

Department of Computer Science, RWTH Aachen, Germany

Keywords: Process Mining, Business Intelligence, Microsoft Power Bi, Process Cubes, Business Analytics.

Abstract: In this paper, we introduce a custom visual for Microsoft Power BI that supports process mining and business

intelligence analysis simultaneously using a single platform. This tool is called BIpm, and it brings the simple,

agile, user-friendly, and affordable solution to study process models over multidimensional event logs. The

Power BI environment provides many self-service BI and OLAP features that can be exploited through our

custom visual aimed at the analysis of process data. The resulting toolset allows for accessing various input

data sources and generating online reports and dashboards. Rather than designing and working with reports

in the Power BI service on the web, it can be possible to view them in the Power BI mobile apps, and this

means BIpm provides a solution to have process mining visualizations on mobiles. Therefore, BIpm can

encourage many businesses and organizations to do process mining analysis with business intelligence

analytics. Consequently, it yields managers and decision makers to translate discovered insights

comprehensively to gain improved decisions and better performance more quickly.

1 INTRODUCTION

Nowadays, process mining is a new and emerging

interdisciplinary field between data science and

business process management. Generally, it bridges

the gap between business process management and

workflow management on the one hand and already

between data mining, business intelligence, and

machine learning on the other hand (van der Aalst,

2016). Process mining can be subdivided into process

discovery, conformance checking, and enhancement.

In process discovery, we aim to discover a process

model that describes the process captured within the

event data. In conformance checking, deviations

between event log and the predefined desirable

process model are discussed, and the enhancement

techniques focus on improving a process by

enhancing the model using the corresponding event

log, e.g., adding timestamps analysis to expose the

bottlenecks and service levels (van der Aalst,

Adriansyah and van Dongen, 2012).

Among these approaches, process discovery plays

a fundamental and significant role in understanding

what occurred in reality. In other words, it helps us to

understand how process instances were executed in

reality. In this branch of process mining, the event log

is used to distinguish “de facto models,” i.e., the

model aims to show real executive business processes

(van der Aalst et al., 2010). The real sequence of

executing activities as a process model is the valuable

output since this yields business owners and service

managers to interpreter desirable insights of hidden

knowledge of the stored event data from various

working information systems. Moreover, there are

different ways to show a process model. The most

widely used type of presentation is process graph that

simply provides interpretable process models

(Agrawal, 1998).

When there are multiple attributes in the given

dataset, and many classes of cases are available in the

event log, the ability to do process mining in a

multidimensional manner becomes more crucial. The

business analyst needs to investigate the multiple case

dimensions on the behaviors of the process.

Multidimensional process mining is related to use the

Online Analytical Processing (OLAP) infrastructure

in process mining (van der Aalst, 2013). Therefore, it

makes sense to integrate process mining into an

existing Business Intelligence (BI) tool that is

supporting OLAP technology. This integration allows

leveraging the scalability and data preprocessing

capabilities for real data science projects.

Harati Nik, M., van der Aalst, W. and Fani Sani, M.

BIpm: Combining BI and Process Mining.

DOI: 10.5220/0007741901230128

In Proceedings of the 8th International Conference on Data Science, Technology and Applications (DATA 2019), pages 123-128

ISBN: 978-989-758-377-3

123

To address this issue, we implemented a process

mining tool building upon the Power BI

infrastructure. We named the tool Business

Intelligence and process mining (BIpm). BIpm can

discover process models from event logs and plot

them understandably and also showing compliance

diagnostics. Moreover, one of the advantages of

BIpm is getting the Power BI users and all BI experts

more familiar with process mining analysis. BIpm

lets BI developers and general data scientists

subsequently apply process mining analysis quickly,

user-friendly and easily in the platform that they are

used to it. According to the available license fees for

process mining commercial tools such as Disco and

Celonis, BIpm is more affordable. It is free custom

visual and only the probable fee might be charged for

using Power BI. Meanwhile, regarding to the current

license policy of Microsoft, using Power BI Desktop

is completely free (Microsoft, 2019).

The idea to relate process mining to OLAP

analysis was introduced firstly by van der Aalst (van

der Aalst, 2013) and it was realized by building the

so-called Process Cube paradigm (Bolt and van der

Aalst, 2015). Process cubes organize event data in the

form of an OLAP cube to allow for discovering,

comparing and analyzing the process models by

applying dice and slice filtering functions on the cube

(cross filtering). Here, we continue this line of

research by providing an integrated process mining

solution with many BI features analysis in a single

platform. This is achieved by our developed custom

visual for Microsoft Power BI. Power BI is the

powerful self-service BI platform for big data-centric

businesses with many interactive visualizations for

graphical figures, data mining tasks, statistical

analyses, and geographical maps and also it has useful

features such as supporting online dashboards,

customized reports and, online alerting (Ferrari and

Russo, 2016). There are many options for connecting

or importing different data sources into Power BI, as

long as the following constraints are satisfied 1) there

is a 1 GB limit for each dataset. 2) The maximum

number of rows when using DirectQuery is 1 million

rows and when not using DirectQuery is 2 billion

rows, 3) The maximum number of columns in a

dataset should not exceed more than 16,000 columns

(Microsoft, 2019). These constraints are not limiting

in most applications.

By using BIpm, business owners, business

analysts, and managers can understand the value of

process mining and come up with the improvement

plans for reengineering the previous and ongoing

processes or designing forerunner ones in the hope to

achieve the better performance and efficiency.

2 BIpm OVERVIEW

In this section, we give an overview of BIpm. Firstly,

we will describe how the input data fields should be

prepared and placed in the Fields pane of Power BI.

After that, we illustrate some functional capabilities

and available opportunities in the BIpm for better

understating of process mining analysis.

2.1 Input Fields

According to the expected attributes of standard event

logs for process mining, given data logs in Power BI

should have these attribute fields: CaseId (i.e., the

identifier for each case), Activity (i.e., activity name

associated to events), and Timestamp (i.e., the

execution time of one activity regarding to the

determined case). Moreover, Path threshold and

Activity threshold are optional fields. Other event and

process attributes such as Resource, Cost, lifecycle,

etc. can be used for multidimensional analysis and to

enrich analysis by adding further insights. An

example of an event log is mentioned in Table 1.

Table 1: Sample rows of an example event log.

Case Id Activity Timestamp Resource Customer

Type

1142 Register 11:25 System Gold

1142 Analyze Defect 12:50 Tester3 Gold

1142 Repair (Simple) 13:25 SolverS1 Gold

1145 Register 11:44 System Silver

1142 Test Repair 17:12 Tester1 Gold

1142 Restart Repair 18:15 System Gold

46 Test Repair 05:47

Tester6

Bronze

46 Inform User 06:00 System

Bronze

46 Archive Repair 06:02 System

Bronze

45 Register 19:36 System

Gold

45 Analyze Defect 19:36 Tester3

Gold

45 Repair (Simple) 20:01 SolverC2

Gold

To get the proper output process model, the following

practical points are recommended to be considered in

the Power BI report designing level:

1. The data type of “CaseId” field should be

numeric for performance reasons, but simple

conversions are available. The data type of

“Timestamp” field can be the time or series of

integers.

2. Generally, CaseId, Activity, and Timestamp

attributes should be set as "Don't summarize" to

be considered as the row based granularity in the

data input gateway for the custom visual. It can be

done in the drop-down menu of each field slot in

the Fields pane.

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3. The values for “Path threshold” and “Activity

threshold” have to be set in the range of 1-100.

This threshold is for determining the percentage

of path or activity based on the unique values of

case identifiers (i.e., distinct count of CaseId) that

should be participated in plotting the final output

graph. Initially, the default values of “Path

threshold” and “Activity threshold” are 80 and

100 respectively.

4. For the “Path threshold,” to avoid plotting

disconnected output graph, even in the lowest

value, main paths are kept in the result process

models.

5. Using "what if parameter" technique of Power

BI for “Path threshold” and “Activity threshold”

(ranged 1-100 and changed it to the single value)

provides the option for end users to change the

thresholds to identify their effects on the output

process model when they are working with

dashboards interactively. If these fields are left

empty, these settings can also be changed through

the Power BI desktop and designing mode of the

Power BI service by the “Thresholds” choice in

the "Format" pane which is located on the right

side of "Fields" pane, below the “Visualizations”

pane.

By dragging all mandatory fields into the visual

custom data field slots, BIpm creates the process

model in the format of the directed flow graph. The

provided output has many user-friendly features to

analyze interactively for better scrutinizing aspects of

processes in a multidimensional manner.

2.2 BIpm Capabilities

In addition to the general capabilities being available

within Power BI and in the produced process model

visualization, we would like to highlight some

important features of doing process mining with

BIpm. All these features are illustrated using a simple

event log containing information about repairs (the

Process Mining Group, Math&CS department, 2016).

For a better understanding of multidimensional

analyses, we added more two fields to the event log,

the first one is the random label of customer-cluster

(Gold, Silver, and Bronze) and the second one is the

random label for repair types (Normal and

Emergency) that both of them are case attributes.

2.2.1 Cross Filtering

Using BIpm provides the opportunity to do process

mining by applying many other visual objects which

are available in the default visualization pane of

Power BI and also at Microsoft AppSource.

Therefore, process mining analysis along with many

self-service BI features becomes ready to use.

Meanwhile, one of the useful capabilities is filtering

the data with many other visualization dice and slice

features. For example, Figure 1 shows the process

model in the downside, for three dices applied to

visual charts related to the input given log, Customer

types=”Silver,” Repair types=”Normal” and

NumberRepairs=0 (Figure 1).

Figure 1: The sample dashboard that is containing the

process model for the three dimension filtered data model

by just clicking on the related top visual charts.

Note that, BIpm not only let us apply process mining

on filtered data based on BI features, it allows to filter

BIpm: Combining BI and Process Mining

125

the given data based on process mining features. For

example, we could filter out process instances with

that two activities are executed directly after each

other in them.

2.2.2 Highlighting the Activity and Its

Related Nodes

If the process model is complicated with many

activities, the ability to analyze each activity with its

following connections can be useful due to the

complexity reduction of the process model.

Therefore, this is offered by BIpm in the way, i.e.,

shown on the left side of Figure 2. Besides, by

clicking on each node, it becomes highlighted in

yellow (Figure 2- the right side). This feature helps to

focus attention.

Figure 2: Left side: a sample of applying activity selection.

Right side: an example of the activity highlighting.

2.2.3 Plotting the Social Network of the

Handover of Work

It is possible to get the social networks of resources

when the original event log has the resource attribute.

When the resource field is chosen instead of the

activity field, the social network of the handover of

work is created and visualized (Figure 3).

2.2.4 Process Models Comparison

The option of “Visual level filters” provided for all

custom visuals in Power BI allows the user to

compare different process models used sliced or diced

data. For instance, it is possible to study the

differences between two process models of gold and

silver customers by setting the filter for the first BIpm

visual instance with the Gold item and another one

with the Silver item as it is shown in Figure 4.

Figure 3: An example of a social network.

Figure 4: Comparing two process models.

2.2.5 Online Process Monitoring

In many industries, for decision makers, it is crucial

to have on-line analysis instead of off-line results. For

example, the number of concurrent tasks of a human

resource could affect the performance of him/her. So

if the number of current works of each employee

could be monitored in a real-time situation could help

managers to distribute works.

The advantage of using Power BI, let business

owners connect their designed business dashboards to

online streams. This type of connections allows us to

monitor the ongoing process models of a business in

a real-time. Note that, this feature can process mining

more applicable.

2.2.6 Sharing Process Mining Analysis

After applying BIpm features, users can share the

corresponding designed dashboard with the fixed or

adjustable settings to others. There are also many

ways to export the process mining analysis. For

example, users can create a PDF file from the

discovered process model, when using Power BI

service, the dashboard can be published on the web

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and it can be put in other webpages as a web part, or

export the CSV file of an event log after applying

different filtering on it.

As it is possible to define different roles in Power

BI, we could apply various access levels for reports.

For example, even the source of data for all reports is

the same, the possible views of users in HR

department may be different from views in the

finance department.

Nowadays confidentiality issues are critical for

companies. As BIpm provides the service integrated

into MS Power BI, there is no need to pass data from

various tools.

3 COMPLEMENTARY

MATERIALS

BIpm was published on Microsoft AppSource under

“Power BI visuals” category and can be obtained via

the following link:

https://appsource.microsoft.com/product/power-bi-

visuals/WA104381928?tab=Overview.

During downloading any custom visual from

AppSource, there are some useful step-by-step

instructions about how to import the custom visuals

into Power BI. Moreover, we have prepared some

complementary guidelines and documents to

empower users to apply BIpm successfully:

1. There are some prerequisites to use BIpm such

as installing R packages and enabling R scripts

running in Power BI. These are described briefly

at http://processm.com/powerbi-custom-visuals/

bipm/installing-bipm/.

2. There are some necessary guidelines for how

to set input data fields which are mentioned at:

http://processm.com/powerbi-custom-visuals/

bipm/.

3. The power BI project sample (.pbix format)

based on repair log scenario is also prepared and

it can be downloaded from this link: https://

github.com/hnreza/ProcessM/blob/master/Proces

sM.pbix.

4. The online demo on the Power BI service is

available at: https://app.powerbi.com/

view?r=eyJrIjoiMzUyZDAyMmQtYjRjNC00YT

YwLWFiOGQtMzVmZmNmYWYyMWFkIiwi

dCI6ImM0ZDAyZmZlLTRlYTctNDViZC1iYTc

wLTg5OWM3NTVkOGNhYiIsImMiOjl9 .

4 CONCLUSION

In this paper, the capabilities and features of BIpm as

a custom visual for doing multidimensional process

mining in Microsoft Power BI are introduced. This

solution provides the opportunity to analyze

complicated event logs with many classes of cases to

distinguish hidden insights of processes in a

multidimensional manner. BIpm offers many

interactive capabilities that tightly integrate BI and

process mining functionalities.

Meanwhile, many significant features of BIpm

such as highlighting, cross-filtering, comparing, and

creating the social network along with some useful

capabilities of Power BI were explained briefly.

Generally, our proposed approach, on the one hand,

enriches BI dashboards with interactive and online

process mining and on the other hand, persuades BI

users to expand their toolset by inferring process

models using BIpm.

As future work, we aim to provide other process

mining analysis e.g., conformance checking and

bottleneck analysis in MS Power BI.

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