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# Visual Analytics Meets Process Mining: Challenges and Opportunities

Theresia Gschwandtner

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Theresia Gschwandtner. Visual Analytics Meets Process Mining: Challenges and Opportunities. 5th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA), Dec 2015, Vienna, Austria. pp.142-154, 10.1007/978-3-319-53435-0_7. hal-01651890

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# Visual Analytics Meets Process Mining: Challenges and Opportunities

Theresia Gschwandtner

Vienna University of Technology,
CVAST - Centre for Visual Analytics Science and Technology
gschwandtner@cvast.ac.at
http://www.cvast.tuwien.ac.at/cvast

**Abstract.** Event data or traces of activities often exhibit unexpected behavior and complex relations. Thus, before and during the application of automated analysis methods, such as process mining algorithms, the analyst needs to investigate and understand the data at hand in order to decide which analysis methods might be appropriate. Visual analytics integrates the outstanding capabilities of humans in terms of visual information exploration with the enormous processing power of computers to form a powerful knowledge discovery environment. The combination of visual data exploration with process mining algorithms makes complex information structures more comprehensible and facilitates new insights. In this position paper I portray various concepts of interactive visual support for process mining, focusing on the challenges, but also the great opportunities for analyzing process data with visual analytics methods.

**Keywords:** Visual Process Mining, Visual Analytics, Challenges

## 1 Introduction

Today we are confronted with an overload of data and information. The amount of information produced on a daily basis is just too much for anyone to process in order to answer specific questions. Examples include data from electronic health records, real time sensors, communication logs, and financial transactions. However, somewhere within this huge amounts of data, there is valuable information to solve specific tasks and answer specific questions. Yet, in order to make sense of the data, we need to find appropriate ways to process it. There are two opposed approaches to tackle this problem: (1) machine learning and data mining, and (2) visualization of the data.

The first approach, i.e. using machine learning and data mining, taps the computational power of the computer for statistical analysis of the data. As a result, a report is generated, giving - at best - the answer to the user's question. However, the second approach, i.e. visualizing the data, utilizes the power of human perception to simultaneously process large amounts of data. When visualizations are carefully designed (e.g., using a suitable combination of preattentive visual attributes to encode important information), the human perception
is capable of processing large amounts of data in parallel and of quickly identify-
ing patterns and outliers [34]. Thus, visualization helps to provide an overview
of large and complex data. Furthermore, interactive controls allow the user to
explore different aspects of the data in more detail.

Visual analytics is defined as 'the science of analytical reasoning facilitated by visual interactive interfaces' [45, p. 4]. It exploits both, the computational power of the computer and the human's perception system to facilitate insights and enable knowledge discovery in large and complex bodies of data. Thus, visual analytics combines both of the aforementioned approaches: machine learning and data mining, and visualization of the data.

1.1 Benefits of Visualization

One example that motivates the use of visualizations nicely, is the Anscombe's Quartet: In 1973 the statistician Francis Anscombe constructed four data sets [5] to demonstrate the necessity to analyze data with visual means as well as the impact of outliers on statistical properties. The summary statistic properties of these four data sets are nearly identical which could lead to the conclusion that these data sets are nearly identical. However, visualizing these data sets with scatter-plots reveals very different behavior of all four sets.

Another example is given by Spence and Garrison [44]: The Hertzsprung Russel Diagram shows the temperature of stars plotted against their magnitude. When looking at this diagram humans can easily distinguish different clusters of types of stars, while automatic means fail due to the noise and artifacts within the data. Card et al. summarize the ways is which information visualization amplifies human cognition like this [10]:

- Increasing working resources such as by using a visual resource to offload work from the cognitive to the perceptual system

- Reducing search such as by representing large amounts of data and grouping information

- Enhancing the recognition of patterns such as by visually organizing data by structural relationships

- Supporting perceptual inference of relationships by suiting the human's per-
  perceptual abilities

- Perceptual monitoring of a large number of potential events such as by mak-
  ing changes stand out visually

- Providing a manipulable medium that, unlike static diagrams, allows for interactively exploring the data

In contrast to pure question answering tools, such as IBM Watson [13] (a computing system that uses machine learning technologies to answer specific questions), visual analytics combines these technologies with interactive presentations of data and results, and thus, it enables users to derive additional insights from interacting with the data. Hence, users are able to browse the data and they may find answers to questions they did not even know they were looking for.
## 2 Visual Approaches for Process Mining

In their process mining manifesto [47] van der Aalst et al. emphasize the potential of visual analytics, i.e. the combination of visualizations, interactions, and mining techniques, to enhance process mining. We conducted a literature research starting with the process mining manifesto and references to relevant systems we already knew. Moreover, we used libraries and search engines such as Google Scholar [16] and IEEE Xplore [24] to search for various related keywords and we went through proceedings of important conferences such as IEEE VIS [23], EuroVis [12] conference, and the BPM conference [11], as well as through the list of references of relevant papers.

Most process mining approaches employ some kind of visual representation due to the wealth and complexity of the data. Yet, they usually do not explicitly consider visualization as a key factor and rather put emphasis on the mining methods [46, 47]. Visualizations are used, for instance, for process discovery: to explore the event log data and to understand, reason about, and fine-tune the derived models (e.g. [26, 20, 51, 32, 22, 48, 21]). To this end, flow charts [36] and directed graphs are used most frequently. Other examples use visualizations to identify interesting behavior and patterns in the event log data (e.g., [43, 7]), or to investigate process conformance (e.g. [49, 1, 8]). In this position paper I give by no means a comprehensive list of visualization approaches in the context of process mining. Yet, I introduce a selection of our own approaches, highlighting some alternative visual representations and interaction techniques in order to spark ideas how to combine process mining with visual analytics.

### 2.1 Visual Analysis of Guideline Conformance

A related topic to the analysis of process conformance is the analysis of conformance of clinical actions with the recommended care process. Clinical practice guidelines give specific recommendations on how to treat a patient in a given clinical situation. They are aimed at assisting the care process and ensuring its quality [14]. Hence, they can be seen as process models. Bodesinsky et al. [8] presented a visual interactive approach to analyze the compliance of clinical care with these guidelines. The approach is aimed at supporting (1) physicians at the point of care to display clinical guidelines and point them to omitted clinical actions, and (2) medical experts in retrospective analysis of the quality of the treatment and also of the quality of the clinical practice guideline. It visualizes the recommended sequence of actions (see Figure 1 (a)) together with the actual clinical actions that were applied (see Figure 1 (c)). Applied clinical actions are represented by diamonds on a time axis (a pop-up window gives the user details about these actions when hovering them). Moreover, the patient's parameters (see Figure 1 (b)) and the compliance of actions with the clinical guidelines (see Figure 1 (c)) are represented on a time axis. To aid the analysis of conformance, they visually distinguish conform and non-conform actions, as well as missing actions. Conform actions are indicated by diamonds and non-conform actions
are indicated by diamonds marked with an X. Any delay of application is indi-
cated by two parallel colored bars, marking the time span between the intended
time of application and the actual time of application (see Figure 1 (c)).

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Visual analysis of guideline conformance [8]. The recommended care process is visualized in a flow chart like graph in (a). In this screenshot the graph is zoomed in to show two repeated actions that should be carried out in parallel. (b) shows the patient's parameters that are affected by these actions. (c) represents actual clinical actions on a time axis. Actions that conform to the guideline are indicated by diamonds, non-conform actions are diamonds marked with an X, and the delay of actions is indicated by colored horizontal bars. A pop-up window gives the user details about these actions when hovering them.</figcaption></figure>

## 2.2 Plan Strips

Another example from the medical domain is the Plan Strips visualization [39]. This interactive approach visualizes the complex hierarchies of processes – again using the example of clinical practice guidelines – by a set of nested rectangles. These guidelines recommend treatment plans (groups of clinical actions) which are composed of sub-plans. The execution order of a plan determines in which of the following ways its sub-plans are to be carried out: in sequence, in parallel (all sub-plans start at the same time), unordered (in sequence, in parallel, or a mixture of both), or in any-order (in sequence but the order is not defined) [40]. In addition, cyclical plans represent plans which are carried out in loops. Plan Strips encode these different kinds of sub-plan synchronization by color (see Figure 2): Gray rectangles indicate recommended clinical actions or activities while colored containers represent the corresponding parent-plans as well as the
execution order. Interactive selection and highlighting techniques are used to ease
navigation. One limitation of this approach is that the visualization demands
for some learning effort, however, Plan Strips demonstrate an alternative way
of representing highly specified and complex conditions of a process model in a
space efficient way.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Plan Strips [39] represent the hierarchical structure (a) of clinical plans and sub-plans as nested rectangles (b). User performed actions are indicated by gray rectangles (light gray when selected), while the color of parent containers indicate the execution order.</figcaption></figure>

2.3 EventExplorer

EventExplorer [7] was designed to aid the analyst in exploring event log data, in order to gain some understanding of the data and the dependencies between events before deciding for concrete analysis methods or process mining algorithms. To this end, it combines pattern mining with interactive visualizations. Moreover, it supports the exploration of events on a temporal scale to aid the identification of important temporal dependencies of events (e.g., two events always occur together). Figure 3 shows a screenshot of EventExplorer. Each case (i.e., sequence of events) of the dataset is represented by a horizontal line composed of colored rectangles which represent the different events: The color indicates the type of event, while the order indicates the sequence of events.
The user can switch the representation to lay out events along a time axis to investigate their precise temporal behavior. EventExplorer uses pattern mining techniques to identify patterns within sequences of events. When a pattern is selected, all instances of this pattern within one case are highlighted and connected by gray arcs which emphasize the recurrence of this pattern within the case. EventExplorer also supports fuzzy pattern mining by allowing the use of wildcards when entering a pattern of interest. The approach presents interactive visualizations to (1) get familiar with the data, (2) to cluster and sort cases, and (3) to identify temporal patterns. The arcs visually emphasize recurring patterns as well as temporal distances (when using the time axis view).

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** EventExplorer [7]. In (a) EventExplorer represents all cases of the dataset as horizontal lines with color coded events (scrollable). (b) shows the different patterns (together with their number of occurrences) which match the settings in (c), i.e., the length of the pattern and the minimum number of occurrences. Alternatively, the user can enter a specific pattern of interest into the text field in (b). Selected patterns (in this case 'V-R-V') are highlighted by gray arcs in (a). The user can switch the x-axis in (a) from a sequential axis to a time axis (d) in order to explore the precise temporal characteristics of event sequences.</figcaption></figure>

The three approaches outlined in detail were designed for very diverse purposes and give only examples of applications of visual analytics in process mining. However, there are still many open challenges but also opportunities when looking at the combination of these two research fields. We derived these challenges from the literature review, our previous work, and our long lasting experience in the research field of visual analytics.
# 3 Challenges and Opportunities

Researchers have identified challenges of today's process mining research in various works (e.g., [33, 47]). In [47] van der Aalst et al. even mention the potential of visual analytics to support process mining. Others again have summarized open challenges in the field of visual analytics (e.g., [30, 45, 50]). When focusing on the combination of visual analytics and process mining, we face related challenges.

## C1: Intertwining Process Mining with Visual Analytics

Van der Aalst et al. identify the combination of process mining with other types of analysis as one of the challenges in process mining [47]. Moreover, they explicitly mention the potential of process mining combined with visual analytics for leading to further insights. Yet, how to intertwine process mining with visual analytics techniques is still an open problem. There are a couple of approaches which already tackle this problem. Among these visual approaches to aid process mining, the use of flow charts [36] or directed graphs to represent the process model are most common (e.g., [20, 32, 22, 21, 39]). Visualizing the process model helps to understand and fine-tune the model, but also to check conformance. Other visual approaches show an overview of individual event sequences (e.g., [43, 26, 51, 48, 7]) which can be used to identify common paths and thus, to derive a process model. Visualizations that show the conformance of individual event sequences (e.g., [8, 1]), however, help to identify shortcomings of the existing model, but also variations of individual cases. Yet, there is still much unexploited potential of visual analytics to better support pattern discovery and the derivation of process models, fine-tuning and enhancing these models, as well as identifying conformance problems and finding alternative solutions. To this end, visualizations need to be tightly coupled to analytical techniques and mining algorithms in an iterative loop, giving immediate visual feedback to changes and adjustments made to the algorithms. Appropriate views and interactions need to be tailored to specific user groups with respect to their specific tasks and data [35].

## C2: Scalability and Aggregation

The scalability of visualizations is a well-known challenge in visual analytics [30, 45, 50]. This is also true in the context of process mining, which usually deals with huge amounts of event sequences that need to be analyzed. On the one hand, process mining algorithms tackle this problem by reducing these huge event logs to a manageable amount of patterns and process models. On the other hand, the analysis of the raw data and single event sequences may yield some important insights, and thus, visualizations need to support the simultaneous analysis of huge amounts of individual event sequences. There are some approaches that tackle this problem to some extent (e.g., [26, 51, 7]) by simultaneously showing a number of individual event sequences at minimum display space. Still the number of individual event sequences that can be displayed simultaneously is
limited and it is an open challenge how to design interactive visualizations to support the whole analysis cycle from single event sequences to multiple event logs. There is a need for scalable visualizations able to represent a big number of cases while allowing zoom-in and drill-down to investigate details about single event sequences, in analogy to the famous Information Seeking Mantra by Shneiderman: 'overview first, zoom and filter, then details-on-demand' [41, p.337].

### C3: Interaction to Support Process Discovery and Enhancement

This challenge is tightly coupled with the last challenge of finding appropriate visualization techniques to support the different tasks. Data exploration is an iterative process with trial and error loops. Suitable interaction techniques need to support drilling down from an overview visualization to the investigation of single events. Commonly used techniques include zoom and filter functionalities, however, other interaction techniques may be needed depending on the data, user, and task [35]. Further interaction techniques include *select*, *explore*, *re-configure*, *encode*, *abstract/elaborate*, and *connect* [52]. Allowing the analyst to interactively investigate the raw event data as well as the effects of changes made to models and mining algorithms may foster new insights and improve process mining results.

EventExplorer [7] allows for sorting cases by pattern count or by time. Another interesting way of sorting would be by similarity of event sequences in order to aid the identification of different groups of cases. Moreover, it supports the investigation of events in sequence or on a temporal scale. An interesting addition would be allowing the analyst to apply different ways of aligning cases vertically, for instance, aligning cases with the first appearance of a specific event.

### C4: Data Quality and Uncertainty

Data quality is a general challenge in visual analytics [27], as well as in process mining [47], and thus, data quality is just as important when it comes to the combination of both fields. While process mining algorithms require good data quality and well-structured event logs, in practice these can be erroneous and badly structured. Instances of the same event type may have different names, event granularities may vary, and quite often the event log contains missing, incorrect, imprecise, uncertain, or irrelevant data. Data quality control can be divided into (1) data profiling (i.e., identifying and communicating quality problems), (2) data cleansing (i.e., correcting erroneous data), and (3) data transformation (i.e., transforming the data into appropriate formats for automatic processing) [19, 27]. There are visual approaches that tackle the problem of data quality (e.g., [17, 9, 29, 28]), however, the specific needs in process mining are hardly considered, such as case heterogeneity, event granularity, or concept drift.

On the other hand, data often contains some amount of uncertainty. Event log data may, for instance, contain uncertainties about which event type corresponds to which log entry or about the exact time of an event. Visually communicating
these uncertainties to the analyst (e.g., [18]) is important for a better informed
reasoning.

C5: Complexity of Time-Oriented Data

Usually event log data has an inherent temporal structure. For any such event
log at least the sequence of events is known and used to analyze paths and pro-
cesses. Yet, there are also event logs with precise timestamps for each event,
allowing for a detailed analysis regarding the temporal dimension of events and
event sequences. Time, however, is a complex data domain with very special fea-
tures that require special consideration [2, 4, 41]. Some examples of these special
characteristics are: time can be given as time points or intervals, conventionally
time is aggregated irregularly (i.e., 60 minutes per hour, 24 hours per day, 28-31
days per month, 356/366 days per year, ...), leap seconds and days, different time
zones, recurrences (e.g., seasonal cycles), and it has a strong social component
(e.g., weekends, holidays) [2]. Ailenei et al. [3] outline use cases with special con-
sideration of the temporal behavior of processes that need to be considered in
process mining. When it comes to the visualization of time-oriented data, Aigner
et al. [2] give a comprehensive overview. However, considering these peculiar
characteristics of events (e.g., type of day, some cyclic behavior synchronized
with the calendar, or events that co-occur with a certain delay) and adequately
visualizing them for the task at hand is still an open challenge.

C6: Evaluation

Since visual analytics aims at supporting human reasoning and gaining new in-
sights, the quality of visual analytics approaches is hard to quantify. Plaisant [37]
outlines different challenges of evaluating visualizations and emphasizes that
evaluation strategies need to take the exploratory nature of tasks and the added
value of visualization such as overall awareness and potential discoveries into
account. Moreover, praxis related aspects such as a successful adoption of these
visualizations need to be considered. There are different approaches for evalu-
ating visualizations [42, 25, 15] which usually involve target user participation
and are highly task dependent (see [4] for a task framework and [31] for a time-
specific task framework). In any case it is recommended to pursue an iterative
design approach which involves feedback of the target user as early as possible.

On the other hand, evaluation in process mining is an open challenge too.
There are different approaches on how to evaluate derived process models (e.g., [38,
6]), and Ailenei et al. [3] present a set of task specific use cases for the evalua-
tion of process mining tools. These use cases may as well help to evaluate visual
analytics solutions for process mining from a technical point of view. However,
the evaluation of gained awareness and insights is still an open challenge.
## 4 Conclusion

Handling huge amounts of process data can be tackled in different ways: automatic mining techniques, visualizations, or by visual analytics which combines the benefits of both fields. Especially the latter has potential to improve the results of process analysis in many ways. In this paper I highlighted some examples of how this was approached in recent years. Moreover, I outlined six open challenges which reveal opportunities for further research. I believe, visual analytics is beneficial to many tasks of process mining and – if done right – will have substantial impact on the quality of process analysis.

## ACKNOWLEDGMENTS

The research leading to these results has received funding from the Centre for Visual Analytics Science and Technology CVAST, funded by the Austrian Federal Ministry of Science, Research, and Economy in the exceptional Laura Bassi Centres of Excellence initiative (#822746).

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