Demonstrating Context-aware Process Injection with the CaPI Tool [original]

Demonstrating Context-aware Process Injection

with the CaPI Tool

Klaus Kammerer, Nicolas Mundbrod, and Manfred Reichert

Institute of Databases and Information Systems

Ulm University, Germany

{klaus.kammerer, nicolas.mundbrod, manfred.reichert}@uni-ulm.de

http://www.uni-ulm.de/dbis

Abstract.

Today’s enterprises face individual customer expectations,

high product variability, and an abundance of regulations. Consequently,

they must cope with numerous business process variants, whose design

and execution depend on a multiplicity of influencing factors, like, e.g.,

customer requests, resource availability, compliance rules, or process data.

Moreover, already running processes need to be also adjustable to respond

to contextual changes, emerging regulations, or new customer requests.

With the goal to provide support for process variant management at

both design and run time, this demo paper presents the prototype of the

context-aware process framework (CaPI). The latter, in particular, enables

the sophisticated modeling of process variants based on the context-aware

injection of process fragments into a base process. Thus, executed process

variants may dynamically evolve during run time, considering the current

context of the respective process instance. The CaPI tool was developed

based on existing adaptive process management technology. Overall, CaPI

enables context-aware process injection, and, thus, the specification of

varying processes while providing high process flexibility at run time.

Keywords: Process Flexibility, Process Variability, Process Injection

1Introduction

In a globalized world, enterprises face various challenges like individual customer

expectations, products of increasing complexity, or demanding country-specific

regulations. As a consequence, enterprises need to cope with high process variabil-

ity and a strong demand for process flexibility. In many business processes, the

course of action is influenced by an abundance of process parameters, like, e.g.,

external context factors, intermediate results, and process-related events (e.g.,

successful completion of certain process activities). To deal with this variabil-

ity, usually, process models comprise complex decisions allowing for alternative

courses of actions as well as interdependencies among these decisions. Both the

complex decisions and the interdependencies are often hardly comprehensible for

process modelers. In addition, an automated, controlled and sound adaptation of

(long-running) processes instances is required to address contextual changes, new

regulations, or emerging customer requests at run-time.

2Klaus Kammerer, Nicolas Mundbrod, and Manfred Reichert

This paper presents the concept of context-aware process injection (CaPI)

as well as the proof-of-concept prototype of the CaPI framework. The latter

enables the sophisticated modeling of processes that allow for a context-aware

injection of process fragments into a base process during run time. To ease this

modeling task, CaPI allows experts to model the possible injections from three

different perspectives, i.e., situation-based, location-based, and artifact-based. At

run time, the specified process variants are executed to incorporate up-to-the-

minute context data and to adjust the process based on the given context. The

proof-of-concept prototype was developed based on an advanced adaptive process

management technology, which enables dynamic changes of process instances

during run time. The CaPI tool presentation will demonstrate the features of

the CaPI framework in an integrated and comprehensible way.

2Application Scenario

To ease the understanding of CaPI, a use case from healthcare is introduced first.

Figure 1shows the simplified process of a patient’s medical examination in a

medical center [4]. The process contains the activities for ordering and performing

a medical examination (e.g., A2: Order Medical Examination). Thereby, the latter

is performed differently depending on the kind of examination (i.e., standard,

emergency), the scheduling of examination (on appointment, same day), or activi-

ties supporting the examination (i.e., the preparation of a patient or its transport).

In particular, the process covers four major variants:

V1: Emergency Case

V2:

Standard Case with transportation

V3: Standard Case on Appointment

, and

V4: Standard on Appointment with transportation

. For example, there will be

a emergency case, if the heart rate of a patient is lower than 50 beats/minute or

a laboratory test results smaller than 2.5mmol/l potassium in blood serum.

A2:

Order Medical

Examination

A6:

Perform Medical

Examination

A8:

Create Medical

Report

A9:

Read and Validate

Medical Report

A1: Perform Pre-

Examination

A3-1:

Emergency

Medical

Examination

A3-2:

Examination

A3-3:

Arrange

Appointment for

Medical

Examination

A4-1:

Prepare

Patient

A4-2:

Inform

Patient

A5:

Transport

Patient

A5:

Transport

Patient

A7-1:

Send Condensed

Medical Report

A7-2:

Transport

Patient (Return)

A4-2:

Inform

Patient

A6:

Perform Medical

Examination

A7-2:

Transport

Patient (Return)

A6:

Perform Medical

Examination

A4-1:

Prepare

Patient

A4-2:

Inform

Patient

A5:

Transport

Patient

A6:

Perform Medical

Examination

A7-2:

Transport

Patient (Return)

examination

Type

transportation

Required

Medical

Report

Medical

Protocol

Fig. 1.Process Model of a Medical Examination

For each variant, additional constraints may have to be considered: if variant

shall be performed, an emergency medical examination has to be registered

along with mandatory transportation. Before creating a full medical report, a

condensed report is sent while transporting back the patient during the exe-

cution of variant

. If variant

is executed (scheduled examination with

transportation), no preparation is required. Finally, if a patient is transported to

the examination room, she needs to be transported back to the ward afterwards.

Demonstrating Context-aware Process Injection with the CaPI Tool 3

3Context-Aware Process Injection

To systematically support varying processes requiring (data-driven) run time

flexibility, we introduced the approach of context-aware process injection (CaPI)

[2]. The key objective of CaPI is to ease the modeling of a process family (i.e., a

collection of process variants) at design time and to automate enable controlled

process adaptions during run time, e.g., driven by the data becoming available

during process execution. By taking the current context of a process into account,

CaPI enables the late injection (i.e., insertion) of process fragments into a lean

base process in a controlled manner.

The core artefact of CaPI is the context-aware process family (CPF) (cf. Fig. 2).

More precisely, a CPF comprises a base process model (cf. Fig. 2g) with extension

areas (cf. Fig. 2h), contextual situations (cf. Fig. 2e) characterized through

process parameters (cf. Fig. 2d), a set of process fragments (cf. Fig. 2ij) that may

be injected at specific extension areas during run time, and a set of injection

specifications (cf. Fig. 2f). The latter define process fragments to be injected at

extension areas of choice at a given contextual situation.

If …

PF …

Process Fragment PF_TransPatientRet

Process Fragment PF_SendCondReport

Process Fragment PF_ReqStdMedEx

Process Fragment PF_ReqEmrgMedEx

PF …

A2:

Order Medical

Examination

A6:

Perform Medical

Examination

A8:

Create Medical

Report

A9:

Read and Validate

Medical Report

Extension Area 1

Registration and

Preparation

Extension

Area 2

Transport

Return

A7-1:

Send Condensed

Medical Report

Context Mapping examinationType = (“Emergency“,

“Standard“, “Appointment“ , STRING)

Process Parameters

Mapping Rules

IF (CF1 OR CF2)

THEN examinationType = “Emergency“

Injection Specification

If Contextual Situation EmergencyCase is present at Extension Area 1:

Inject Process Fragment PF_RegEmrgMedEx inline sequential

Process Fragment PF_TransPatient

A7-2:

Transport

Patient (Return)

A5:

Transport

Patient

PF …

Contextual Situation EmergencyCase

Condition: examinationType = “Emergency“

CF1 Context Factor

CFHeartRate CF3 Context Factor

Transportability

CF2 Context Factor

CFLabTestPotassium

Contextual Situation TransportationRequired

Condition: transportationRequired = “TRUE“

Injection Specification

If Contextual Situation TransportationRequired is present at Extension Area 2:

Inject Process Fragment PFtrans inline sequential

A1:

Perform Pre-

Examination

A3-1: Register

Emergency Medical

Examination

A3-2: Register

Medical

Examination

A4-1:

Prepare

Patient

Process Fragment PF_PrepPatient

If …

Base Process

Fig. 2.Illustration of a Context-aware Process Family

Following the separation of concerns design principle, the base process model

solely contains decisions (i.e., branches and gateways) and activities shared by

all variants of the process. In particular, these activities need to be known at

build time, and must not be changed during run time. By contrast, extension

areas represent the dynamic parts of the process. Accordingly, first of all, process

modelers may focus on modeling the predictable parts of the process and then add

the dynamic (i.e., varying) parts of the base process step by step. In particular,

extension areas are used to automatically inject process fragments into the

base process during run time based on the current contextual situation and

well-defined injection specifications. Moreover, an extension area allows for the

dynamic injection of any number of parallel, either the same or different, process

fragments. In turn, contextual situations are defined through conditions expressed

4Klaus Kammerer, Nicolas Mundbrod, and Manfred Reichert

in first-order logic, taking process parameters as well as data objects of the base

process model into account. In this context, process parameters may be linked

to dynamic, external factors (e.g., the availability of a resource) influencing the

process injection’s decision making. When injecting process fragments, CaPI

takes care of correct data flow mappings as well: data objects of an injected

process fragment are automatically connected to existing ones of the base process.

Following this approach, CaPI enables dynamic configurations and changes

of varying processes in a controlled way during run time. By solely enabling

insertions of process fragments, CaPI allows process modelers to focus on the

commonalities of all variants (base process) and the varying process parts instead

of struggling with a complex process model that captures all variants. Furthermore,

process modelers may directly integrate contextual influences into the modeling of

variants. Thereby, complex external context factors are abstracted by meaningful

process parameters and reusable contextual situations. In turn, CaPI is able

to cope with context-driven run time changes based on the late evaluation of

contextual parameters at given extension areas. In this context, the automated

construction of a consistent data flow between the injected process fragments

and the underlying base process mitigates the efforts of involved users. Finally,

CaPI empowers process activities to seamlessly read and write data. A discussion

of related work can be obtained from [2].

4Demonstration

We developed the CaPI prototype to enable empirical studies. Furthermore, the

technical feasibility of the described CaPI framework, was demonstrated in a case

study we conducted in the automotive and electronics industry. In particular,

from this case study we received valuable feedback regarding both the framework

and the proof-of-concept prototype [2].

The CaPI prototype is based on a well-defined architecture (Fig. 3). It is

realized based on Java EE 7technologies and comprises a web-based frontend,

which enables domain experts to intuitively model CPFs (CaPI Modeler), as

well as the following components; representing the CaPI core run time functions:

CPF Repository,CaPI Monitor,CaPI Control, and Context Integrator.

Using the web-based frontend based on Google Polymer, a domain expert may

model the CPFs. First, she needs to specify the mappings of context factors to

process parameters–and mappings of process parameters to contextual situations

accordingly. Then she creates injection specifications, putting together the CPF

components, i.e., extension areas, contextual situations and process fragments,

via drag and drop. For this purpose, we implemented three different perspectives

a domain expert may take to create an injection specification (see our screencast).

To properly execute modeled CPFs, the CaPI prototype integrates AristaFlow

BPM Suite–an advanced adaptive process management technology [1]. The latter

enables the modeling, deployment, and execution of well-structured processes.

Furthermore, it provides sophisticated change operations to adapt running process

instances at run time [3]. Thus, AristaFlow provides the basic execution platform

required to enable the sound injection of process fragments.

Demonstrating Context-aware Process Injection with the CaPI Tool 5

CaPI Control interprets the CPF specifications to continuously monitor the

execution of the process. Therefore, CaPI Control receives any status updates of

activities from AristaFlow and confirms the start of every activity in the base

process. When reaching an extension area, CaPI Control evaluates the contextual

situation, and then injects the appropriate process fragments during run time.

CaPI Application

REST API

CaPI Control

Adaptive Process Management System (AristaFlow)

CPF Repository Context Integrator

CaPI Monitor

BPMS Desktop Client

Desktop Client

BPMS Web Client

Web Client

Modeling

Layer

CaPI

Logic

Layer

Process

Layer

Interaction

Layer End Users

CaPI Modeler Domain Experts

Fig. 3.Overview of the CaPI Architecture

The CaPI modeling and execution functions are illustrated in the screencast

along the presented application scenario (cf. Sect. 2). This screencast can be

retrieved in the following link: bpmdemo2017.capeframework.com

5Conclusion

Taking the context of a process instance into account, CaPI allows for the

controlled injection of process fragments into the given base process. Based on

well-defined extension areas, control and data flow correctness of the process

is ensured after injecting any process fragment. Moreover, CaPI reduces the

complexity of specifying all the variants of a process family. This demo presents

the current state of the CaPI proof-of-concept prototype as well as the feasibility

of the CaPI framework using a real-world process from healthcare. Next, we want

to mature the CaPI tool as a foundation for future empirical studies.

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