Business Process Management Workshops

Process discovery algorithms typically aim at discovering a process model from an event log that best describes the recorded behavior. However, multiple quality dimensions can be used to evaluate a process model. In previous work we showed that there often is not one single process model that describes the observed behavior best in all quality dimensions. Therefore, we present an extension to our flexible ETM algorithm that does not result in a single best process model but in a collection of

mutually non-dominating

process models. This is achieved by constructing a Pareto front of process models. We show by applying our approach on a real life event log that the resulting collection of process models indeed contains several good candidates. Furthermore, by presenting a collection of process models, we show that it allows the user to investigate the different trade-offs between different quality dimensions.

Capturing the performance of a system or business process as accurately as possible is important, as models enriched with performance information provide valuable input for analysis, operational support, and prediction. Due to their computationally nice properties, memoryless models such as exponentially distributed stochastic Petri nets have earned much attention in research and industry. However, there are cases when the memoryless property is clearly not able to capture process behavior, e.g., when dealing with fixed time-outs.

We want to allow models to have generally distributed durations to be able to capture the behavior of the environment and resources as accurately as possible. For these more expressive process models, the execution policy has to be specified in more detail. In this paper, we present and evaluate process discovery algorithms for each of the execution policies. The introduced approach uses raw event execution data to discover various classes of stochastic Petri nets. The algorithms are based on the notion of alignments and have been implemented as a plug-in in the process mining framework ProM.

Automated process discovery methods aim at extracting business process models from execution logs of information systems. Existing methods in this space are designed to discover synchronization conditions over a set of events that is fixed in number, such as for example discovering that a task should wait for two other tasks to complete. However, they fail to discover synchronization conditions over a variable-sized set of events such as for example that a purchasing decision is made only if at least three out of an a priori undetermined set of quotes have been received. Such synchronization conditions arise in particular in the context of artifact-centric processes, which consist of collections of interacting artifacts, each with its own life cycle. In such processes, an artifact may reach a state in its life cycle where it has to wait for a variable-sized set of artifacts to reach certain states before proceeding. In this paper, we propose a method to automatically discover such synchronization conditions from event logs. The proposed method has been validated over actual event logs of a research grant assessment process.

The research field of process mining deals with the extraction of knowledge from event logs. Event logs consist of the recording of activities that took place in a certain business environment and as such, one of process mining’s main goals is to get an insight on the execution of business processes. Although a substantial effort has been put on developing techniques which are able to mine event logs accurately, it is still unclear how exactly characteristics of the latter influence a technique’s performance. In this paper, we provide a robust methodology of analysis and subsequently derive useful insights on the role of event log characteristics in process discovery tasks by means of an exhaustive comparative study.

A relevant topic in business process management is the ability to predict the outcome of a process in order to establish

a priori

recommendations about how to go forward from a certain point in the process. Recommendations are made based on different predictions, like the process remaining time or the process cost. Predicting remaining time is an issue that has been addressed by few authors, whose approaches have limitations inherent to their designs. This article presents a novel approach for predicting process remaining time based on query catalogs that store the information of process events in the form of partial trace tails, which are then used to estimate the remaining time of new executions of the process, ensuring greater accuracy, flexibility and dynamism that the best methods currently available. This was tested in both simulated and real process event logs. The methods defined in this article may be incorporated into recommendation systems to give a better estimation of process remaining time, allowing them to dynamically learn with each new trace passing through the system.

Given an event log describing observed behaviour, process discovery aims to find a process model that ‘best’ describes this behaviour. A large variety of process discovery algorithms has been proposed. However, no existing algorithm returns a sound model in all cases (free of deadlocks and other anomalies), handles infrequent behaviour well and finishes quickly. We present a technique able to cope with infrequent behaviour and large event logs, while ensuring soundness. The technique has been implemented in ProM and we compare the technique with existing approaches in terms of quality and performance.

For the third time, the Business Process Intelligence workshop hosted the Business Process Intelligence Challenge. The goal of this challenge is twofold. On the one hand, the challenge allows researchers and practitioners in the field to show their analytical capabilities to a broader audience. On the other hand, the challenge (and it’s data) allows for researchers to prove that their techniques work on real-life data sets.

Social networks have emerged as a new paradigm for everyday communication, as well as for promoting collaboration within enterprises and organizations. Using current technology, social network participants not only exchange information but also invoke external applications in the form of gadgets. In order to provide enhanced, complex functionality to participants, enabling them to complete a specific business task, workflow composition based on gadget combination could be supported. In such case, gadgets executed in the context of a user profile may add or update information stored in it, while the profile owner is responsible to combine them. In this paper, we explore how to support such features, based on a recommendation mechanism, which automatically produces gadget composition plans, based on intelligent techniques. This context-driven process provides efficiency in cases when a large number of gadgets is available in a social network. The main contribution of the proposed framework lies in the fact that users are only expected to state the content to be added in their profile; the recommendation mechanism can then automatically discover and combine the appropriate gadgets, proposing a solution; no predefined workflows or any other knowledge of the available gadgets is required. The proposed mechanism supporting gadget composition promotes the adoption of social networks as a novel paradigm not only for content sharing and collaboration but also for business process management within the enterprise.

The traditional approach towards BPM has proven to have a number of limitations such as the processes being imposed on the user and therefore do not benefit from first hand user experience in the process discovery stage. In recent years, proposals to integrate BPM and Social Software have promised to overcome these limitations. One such idea proposed in this paper is driving BPM enactment by social goals. This paper presents a goal-based approach to social BPM that combines flexibility with the element of collaboration as found in social software.

This paper describes an experiment on the capture of business processes’ tacit knowledge owned by knowledge workers. During the experiment, knowledge workers used a functional prototype to achieve a set of goals described to them in natural language. To achieve these goals, they needed to create a set of informational objects. By inspecting these objects, it is possible to decide on the success of a business process instance. The experiment hypothesis states that by enhancing the tool with a recommender system, the nomenclature used by the knowledge workers converges. The experiment took 13 subjects and a confederate. Results show that the proposed approach, empowered with a contextual recommender system, allows work practices to converge, and thus, tacit knowledge to be captured using a practice-oriented strategy.

In today’s rapidly changing environment and constant development of new technologies, organizations must be operationally efficient and agile to respond efficiently and evolve their processes to changing requirements.

Specialists argue that BPM business process management provides the base that enables the business to adapt, optimize and evolve. Our research suggests that stakeholders recognize the importance of creating, sharing, collaborating and maintaining process maps, but no-one seems to be ready and motivated to become genuinely involved and to invest the corresponding time and effort. Moreover, the multidisciplinary team of actors involves professionals in different domains who use different vocabularies, and their lack of a common language results in communication problems.

This paper presents a BPM approach incorporated into a social network built around organizational processes and participants who share common interests. We exploit the motivational characteristics of, participative end-user modeling, gamification and social media tools in order to motivate and encourage people to perform process formalization and analysis, process modeling or simulation.

Data- and artifact-centric business processes are gaining momentum due to their ability of explicitly capturing the interplay between the process control-flow and the manipulated data. In this paper, we rely on the framework of Data-Centric Dynamic Systems (DCDSs), which has been recently introduced for the formal specification and verification of data-centric processes, showing how it can be lifted towards run-time execution support. In particular, we focus on the problem of database update as induced by the action execution, introducing a set of patterns that allow for an incremental management of the update. At the same time, we discuss the natural correspondence between DCDSs and state-of-the-art rule engines, e.g., JBoss Drools, which paves the way towards a reference implementation for data- and artifact-centric processes, where the model used for analysis and verification is fully aligned with the one adopted for the execution.

Business process models expressed in UML activity diagrams can specify the flow of multiple stateful business objects among activities. Such business process models implicitly specify not only the life cycles of those objects, but also their communication. This paper presents a semi-automated approach that synthesizes an object-centric system design from a business process model referencing multiple objects. The object-centric design can be used to perform the process in a flexible way.

Currently, two major process modeling paradigms exist: activity-centric and artifact-centric. They focus on different first class modeling constructs and therefore, they are eligible for different scenarios. Nevertheless, both paradigms compete for users raising the own capabilities over the other’s ones neglecting that both paradigms are compatible to each other such that one can transform one into the other one. In this paper, we provide a set of algorithms to allow these transformations as roundtrip, ie from an artifact-centric process model to an activity-centric one and back and vice versa. To this end, we utilize a synchronized object life cycle as mediator between both paradigms. We show applicability of our algorithms by discussing them in combination with an example.

Attribute-Relationship Diagrams (ARD) aim at capturing dependencies between attributes describing an information system. They were proposed to prototype rule bases in an iterative manner. In the paper, we propose to apply the ARD method to business processes. We describe a technique of automatic generation of a BPMN model with decision table schemas for business rule tasks and form attributes for user tasks from the ARD diagram. In our approach, processes and rules are generated simultaneously. Thanks to this, they are complementary and can be directly executed using our hybrid execution environment.

Making the right decisions in time is one of the key tasks in every business. In this context, decision theory fosters decision-making based on well-defined decision rules. The latter evaluate a given set of input parameters and utilize evidenced data in order to determine an optimal alternative out of a given set of choices. In particular, decision rules are relevant in the context business processes as well. Contemporary process modeling languages, however, have not incorporated decision theory yet, but mainly consider rather simple, guard-based decisions that refer to process-relevant data. To remedy this drawback, this paper introduces an approach that allows embedding decision problems in business process models and applying decision rules to deal with them. As a major benefit, it becomes possible to automatically determine optimal execution paths during run time.

Contemporary organizations have been implementing a wide variety of process-aware information systems in order to streamline their operations. The current organizational environment is often characterized by a multitude of internal and external directives which impose restrictions through business rules on the operations and as such define declarative business process models. We present a twofold methodology which can be applied towards the validation and enhancement of process models which are expressed in a declarative form in order to improve their correctness and completeness. Our approach is based on validation of real-life behavior using rule property checking, and on allowed behavior by the process model which was not encountered in real-life cases by matching rule-generated rejected activity occurrences with absent behavior in the event log. Our methodology retains the ability to correspond retrieved findings to decision-makers in a clear and comprehensible manner (i.e. in the form of a new rule), rather than a formal revision of an implemented procedural model, which is a significant advantage when considering business-IT alignment concerns.

This paper demonstrates that the discovery technique using historical event logs can be extended to predict business performance and recommend performers for running instances. For the prediction and recommendation, we adopt decision trees, which is a decision support tool in management science. Decision trees are commonly used to help identify the most likely alternative to reach a goal. To provide effective performer recommendation, we use several filters with previous performers and key tasks to the decision tree. These filters allow for a suitable recommendation according to the characteristics of the processes. The proposed approach is implemented on ProM framework and it is then evaluated through an experiment using real-life event logs, taken from a Dutch Financial Institute. The main contribution of this paper is to provide a real-time decision support tool by recommendation of the best performer for a target performance indicator during process execution based on historical data.

In this paper, a novel approach towards enabling the exploratory understanding of the dynamics inherent in the capture of customers’ data at different points in time is outlined. The proposed methodology combines state-of-art data mining clustering techniques with a tuned sequence mining method to discover prominent customer behavior trajectories in data bases, which – when combined – represent the “behavior process” as it is followed by particular groups of customers. The framework is applied to a real-life case of an event organizer; it is shown how behavior trajectories can help to explain consumer decisions and to improve business processes that are influenced by customer actions.

A cloud workflow system is designed to provide WaaS (Workflow as a Service) which harnesses the power of cloud computing for efficient and cost-effective workflow execution. The current cloud workflow systems are mainly used by people who possess the knowledge about the business processes such as the process structures and functional/non-functional requirements because they need to create the executable workflow applications by themselves. However, most end-users do not acquire such kind of knowledge and thus limits the system usability and hinders the implementation of complete WaaS. To address this problem, this paper proposes the design of a novel online workflow recommendation system which can help end-users to create their own executable workflow applications by only providing some keywords to describe their requirements without the specific workflow modeling process. The system framework and its core components are illustrated in this paper. A detailed case study on an online ordering business workflow is demonstrated to successfully prove the feasibility of our system design.

Connected to cloud, mobile devices enable workers to manage the business processes hosted on remote process engines. However, traditional business process execution is not tailored for mobile devices. Typically the business logic remains in the cloud, making the process execution on mobile devices vulnerable to unreliable network connection. In this paper, we propose a framework that assigns, deploys business process from cloud onto mobile devices and executes them in disconnected environments. To model the process assignment and execution on mobile devices, we extend BPMN with context constraints such as location and hardware resources. The proposed framework benefits from centralized process model management and the distributed process execution on mobile devices, regardless of the constant access to cloud. We implemented a prototype of mobile process engine with an application for blood pressure examination used by nurses in rural areas.

Business process monitoring enables a fast and specific overview of the process executions in an enterprise. Traditionally, this kind of monitoring requires a coherent event log. Yet, in reality, execution information is often heterogeneous and distributed. In this paper, we present an approach that enables monitoring of business processes with execution data, independently of the structure and source of the event information. We achieve this by implementing an open source event processing platform combining existing techniques from complex event processing and business process management. Event processing includes transformation for abstraction as well as correlation to process instances and BPMN elements. Monitoring rules are automatically created from BPMN models and executed by the platform.

In software development, crosscutting concerns, such as security, audit, access control, authentication, logging, persistence, transaction and error handling, can be modularized using aspect-oriented approaches, which can also benefit the business process from the reduction in complexity. Literature refers to techniques that address aspects on process implementation and modeling. However, they adopt different semantic representation of aspects and related elements, making it difficult to provide integration between these phases of the business process lifecycle. This paper aims at addressing the issue of crosscutting concerns during the lifecycle of a business process with an approach based on an ontology for aspect-orientation and a semantic service invocation. Our main contribution is a bridge for the semantic gap between the modeling and implementation steps of aspect-oriented business processes.

Logistics processes have some characteristics which are fundamentally challenging from a business process management perspective. Their execution usually involves multiple parties and information exchanges and has to ensure a certain level of flexibility in order to respond to unexpected events. On the level of monitoring, potential disruptions have to be detected and reactive measures be taken in order to avoid delays and contract penalties. However, current business process management systems do not exactly address these general requirements which call for the integration of techniques from event processing. Unfortunately, activity-based and event-based execution paradigms are not thoroughly in line. In this paper, we untangle conceptual issues in aligning both. We present a set of three challenges in the monitoring of process-oriented complex logistics chains identified based on a real-world scenario consisting of a three-leg intermodal logistics chain for the transportation of goods. Required features that such a monitoring system should provide, as well as related literature referring to these challenges, are also described.

This paper presents an approach that explains the synergy between process mining and data mining for the investigation of the service behavior variances in the context of port logistics. The huge variances in service behaviors are identified and regrouped by the trace clustering technique applied to the operational processes. By incorporating domain information, the unsupervised process mining result is considerably improved in both accuracy and comprehensibility. Data mining techniques are then used for investigating the correlations between the variation in services and the contributing factors. The applicability of the proposed approach is demonstrated using an extensive case study carried out at an important Chinese port.

Green supply chain has become a promising and challenging field during the last decade driven by rising environmental conscious business and governmental legislation. In this paper, we develop a Petri-net based model to describe the green supply chain and to evaluate the essential performance. Generalized stochastic Petri nets (GSPN) are introduced to model the network of a general green supply chain with time characteristics. Performance analysis is carried out using the embedded Markov chain. Furthermore, we perform a comparison between green and normal supply chain to assert the superiority of the green supply chain in terms of profits.

A (home) care plan defines the health cares or supportive cares delivered by health care professionals in patients’ homes. Such a care plan is usually constructed through a complex process involving a comprehensive assessment of patient’s needs as well as its social and physical environment. Managing home care plans is challenging because care plans are inherently non-structured processes which require complex interdisciplinary cooperation. This paper addresses the problems underlying the design and management of home care plans. First, we present a DSL (Domain Specific Language) based approach tailored to express home care plans using high level and user-oriented abstractions. Then, we describe and discuss preliminary results regarding formalization of the proposed DSL abstractions using timed automata in order to provide basic services to support analysis, verification, enactment and management of home care plans.

This study analyzes the traffic characteristics and management within Bejaia metropolis (Algeria). Large scale spatial and temporal land-use data were used to investigate the dynamics of land-use change in this area. In this paper, we considered the case of the intersection of Aamriw (Bejaia’s city), using discrete event simulation. This allowed us to calculate the main performance of the system with traffic lights and with the construction of a hopper. We present simulation results that show the validity of the queueing models in the computation of average travel times. These results allowed us to make a comparison between different versions, with traffic lights or with hopper.

This paper discusses a typical supply chain system based on Auto-Regressive Integrated Moving Average (ARIMA) demand process. Minimum Mean Square Error principle and stochastic optimal control theory are introduced to build a new framework for supply chain uncertainty study under general ARIMA demand process. After formulating the order and inventory quantity at time period

t

, this paper analyzes the optimal order policy as to decrease the bullwhip effect and stock fluctuations under non-stationary demand. The theoretical analysis reveals that a reasonable order quantity can reduce the bullwhip effect generated by demand uncertainty. We also show the negative correlation between the bullwhip effect and inventory stability in the discussed supply chain model.

Process model search has received significant interest in the scientific community, recently, due to its precursory role for process model reuse. An abundance of diverse techniques has been proposed. Yet, few of them have been evaluated thoroughly, and a commonly agreed evaluation method is missing. Based on a literature review, we present measures and propose methods for evaluating the quality and performance of process model search techniques, and discuss their relevance and suitability by a case study. This work aims at improving the analysis of future process model search techniques and comparability between them.

Nowadays, organizations utilize process model collections with several hundreds or thousands of models. To reduce model complexity, large process models are typically decomposed into smaller ones. Moreover, processes are modeled at different levels of abstraction by means of hierarchization. Although the hierarchization and decomposition concepts result in a reuse of processes and foster a better understanding, their applications can lead to various syntactic and semantic issues concerning the concatenation of the resulting processes. In this article, the decomposition of event-driven process chains (EPCs) is examined by an analysis of an established reference model as well as the hierarchization concept. Furthermore, a classification of the identified issues will be presented. The awareness about these issues can be used to avoid modeling weaknesses of new models or for the evaluation of other (reference) models.

Nowadays, it is common for an organization to maintain thousands of business processes. Technologies that provide automatic management for such amount of models are required. The objective of this paper is to deal with the problem of process model fragmentization, clustering and merging for the consolidation of Office Automation (OA) systems in China Mobile Communications Corporation (CMCC). After investigating the structural statistics of real-life process model samples, we propose an approach, based on the refined process structure tree (RPST) and software product line (SPL), to automatically identify reusable process fragments and merge similar ones into master fragments. These fragments can, for example, be used to facilitate the (re)design of numerous process models. Special attention is paid to the empirical study and statistics from the experiment on a sample set of 37 real-life OA processes. Lesson learned and problems to be further considered are also proposed.

Nowadays business process modeling is an integral part of many organizations to document and redesign complex organizational processes. Particularly due to the large number of process models, quality assurance represents an important issue in many organizations. While many quality aspects are well understood and can be automatically checked with existing tools, there is currently no possibility to support modelers in maintaining a consistent degree of granularity. In this paper, we leverage natural language analysis in process models to introduce a novel set of metrics that indicate the granularity of process models. We evaluate the proposed metrics using two hierarchically organized process model collections from practice. Statistical tests demonstrate the expressive power of the proposed metrics.

Many organizations build up their business process management activities in an incremental way. As a result, there is no overarching structure defined at the beginning. However, as business process modeling initiatives often yield hundreds to thousands of process models, there is a growing need for such a structure. This challenge calls for a technique to extract process categories from a set of process models automatically which yields an up-to-date view of the structure of a collection of process models. It also provides a means to check whether pre-defined process categories are still reasonable. In this paper, we introduce a technique for automatically extracting process categories from process model collections and test it using a collection from industry. The results demonstrate the usefulness of the technique by revealing issues of the pre-existing process categories. In this way, we contribute to the field of process model management and quality assurance.

Process model matching refers to the creation of correspondences between activities of process models. Applications of process model matching are manifold, reaching from model validation over harmonization of process variants to effective management of process model collections. Recently, this demand led to the development of different techniques for process model matching. Yet, these techniques are heuristics and, thus, their results are inherently uncertain and need to be evaluated on a common basis. Currently, however, the BPM community lacks established data sets and frameworks for evaluation. The Process Model Matching Contest 2013 aimed at addressing the need for effective evaluation by defining process model matching problems over published data sets.

This paper summarizes the setup and the results of the contest. Besides a description of the contest matching problems, the paper comprises short descriptions of all matching techniques that have been submitted for participation. In addition, we present and discuss the evaluation results and outline directions for future work in this field of research

Although the development and expansion for the time-based RBAC policy has enhanced the security greatly, there are hardly any paper paying attention to the conflicts that produced by the time feature in multi-domain RBAC model. In this paper, we focus on temporal constraints and the role inheritance constraints that occurred in the inter-operation domains based on Petri nets. The approach can check whether an inter-domain access requirement has violated its local RBAC policy or the inter-domain access control policy has improper temporal constraints. In order to illustrate this approach, an applicable example is shown for the specification and conflict detection.

Web services composition allows a software designer for combining atomic services, for instance taken from a marketplace, in a complex business process fulfilling a desired functional goal. Moreover, among a large number of possible compositions, the designer may want to consider only those which satisfy specific non-functional requirements.

In our work we consider verification of security properties and evaluation quantitative security metrics in a single framework. The main focus of this article is the verification of a composition with several security metrics at once. We provide a general solution for the problem and show how such verification can be made more efficient in specific cases (e.g., when a metric is an abstraction of another one). We employ a mathematical structure called c-semirings granting the generality of our approach.

Some business process modeling languages offer explicit constructs for expressing time-out conditions and other termination semantics with regard to process execution. However, the use of these language elements is usually optional, and most languages allow to model business processes without any time-outs or other termination conditions at all. This leads to an underspecification of execution semantics with negative impact on execution safety and security, because it remains open how processes will behave, if some of the involved process steps terminate other than expected, or do not terminate at all. The work presented in this article motivates the obligatory use of termination semantics in business process models and newly created business process modeling languages, especially in domain-specific process modeling languages.

Compliance specifications concisely describe selected aspects of what a business operation should adhere to. To enable automated techniques for compliance checking, it is important that these requirements are specified correctly and precisely, describing exactly the behavior intended. Although there are rigorous mathematical formalisms for representing compliance rules, these are often perceived to be difficult to use for business users. Regardless of notation, however, there are often subtle but important details in compliance requirements that need to be considered. The main challenge in compliance checking is to bridge the gap between informal description and a precise specification of all requirements. In this paper, we present an approach which aims to facilitate creating and understanding formal compliance requirements by providing configurable templates that capture these details as options for commonly-required compliance requirements. These options are configured interactively with end-users, using question trees and natural language. The approach is implemented in the Process Mining Toolkit ProM.

Today privacy is a key issue when securing business processes. It has received increasing attention from consumers, companies, researchers and legislators. Organizations claim to have their own privacy policy as well as guarantee its proper enforcement. In this work we consider privacy features at the early stages of the systems development and specifically focus on modelling and analysis of the system requirements. A framework for modelling privacy access control policies was created through (

i

) defining access control policies that satisfy privacy requirements (

ii

) verification of designed privacy access control policy, and (

iii

) a set of heuristics for defining policy.

Nowadays most of documents are held in digital form and stored in document repositories or databases. The documents can contain delicate information such that not all the actors on the partner side should see and edit. This leads to the necessity to restrict the actions that users could perform with the document content. In this work we research a possibility to integrate existing technologies in order to dynamically define forms and their security permissions. As the solution to the problem we introduce a dynamic way to define security permissions on the XML documents. We propose to use a role-based access control application on the document structure components and introduce a merging strategy to maintain the document’s integrity.

State-of-the-art business process monitoring systems usually base on different types of real-time visualizations, in which data is typically presented using various graphical elements such as speedometers. However, these systems have several drawbacks, such as the inability to constantly monitor process executions while at the same time working on other things. This is why this paper proposes to enhance visual process monitoring with techniques from the area of sonification (the presentation of data using sound). Even though sonification has already successfully been evaluated in several domains for real-time monitoring, there is so far no comprehensive research for its usage in business process monitoring. This paper proposes sonification techniques and user interactions that can be implemented in future applications.

Mobile devices have enabled the development of a new breed of enterprise solutions. Oracle, SAP, IBM, and others are offering mobile clients (e.g. ERP, BI, CRM) for iPhone and Android devices. Nonetheless, in the field of workflow management systems (WfMS) the progresses do not support well mobile workers. In this paper we explore how metaphors can be used to drive the development of mobile workflow systems. Our approach relies on the use of the TomTom metaphor to establish an

isomorphism

between car navigation systems and WfMS. Based on the isomorphism, we used the Technology Acceptance Model (TAM) to provide a first validation of the approach. The positive results led us to implement an early prototype to be used as a proof of concept and to identify important requirements such as context information.

Modeling Knowledge-Intensive Processes (KIP) is not an easy task due to its essential characteristics: unstructured, based on social interactions, experience and intentions of the actors involved. The previously developed Knowledge Intensive Process Ontology (KIPO) proposed all the concepts (and relationships among them) to make a KIP explicit. However, KIPO does not provide a graphical notation, which is crucial for the stakeholders to reach a common understanding about it. This paper presents the Knowledge Intensive Process Notation (KIPN), a notation for building knowledge-intensive processes graphical models. We discuss the results of an exploratory study where a KIP was modeled by its participants. The goal was to explore the use of the notation as a basis for identifying the elements within the process modeling.