Towards Process-oriented Information Logistics:
Why Quality Dimensions of Process Information Matter∗
Bernd Michelberger1, Bela Mutschler1, Manfred Reichert2
1University of Applied Sciences Ravensburg-Weingarten, Germany
{bernd.michelberger; bela.mutschler}@hs-weingarten.de
2Institute of Databases and Information Systems, University of Ulm, Germany
Abstract: An increasing data overload makes it difficult to deliver needed information
to knowledge-workers and decision-makers in process-oriented enterprises. The main
problem is to identify information being relevant for process participants and their
activities. To cope with this challenge, enterprises crave for an intelligent and process-
oriented information logistics. The major challenge is to provide the right process
information in the right format and level of granularity at the right place and accurate
point in time to the right actors. When realizing such process-oriented information
logistics it becomes crucial to take into account quality dimensions of process infor-
mation (e.g., completeness, topicality, punctuality). Reason is that these dimensions
determine process information quality and thus also the overall relevance of process
information for a particular process participant and his activities. This paper picks up
this issue and analyzes different quality dimensions of process information and their
impact on process-oriented information logistics.
1 Introduction
Market globalization has led to massive cost pressure and increased competition for en-
terprises. Products and services must be developed in ever-shorter cycles and new forms
of collaboration within and across organizations are continuously emerging. As exam-
ples consider product engineering processes [MHHR06] or the treatment of patients in an
integrated healthcare network [LR07]. To cope with these challenges, effective business
process management (BPM) [Wes07] becomes success-critical for enterprises.
BPM technology has focused on the modeling, analysis, and execution of processes (e.g.,
using BPM systems) [MRB08] in recent years. What has been neglected so far is the
support of knowledge-workers and decision-makers by providing personalized process in-
formation to them depending on their current work context. The latter determines the
information a process participant needs in order to perform current and newly upcoming
∗This research was performed in the niPRO project. This project is funded by the German Federal Min-
istry of Education and Research (BMBF) under grant number 17102X10. More information can be found at
http://www.nipro-project.org.
activities. For example, to prepare his ward round a doctor needs information about his
patients. Besides process-related information (e.g., the current activity), the work context
also comprises device-related information (e.g., display size, bandwidth), location-based
information (e.g., GPS location), and user-related information (e.g., user name, role, de-
partment). Overall, an extensive amount of process information is provided within and
across organizations using techniques and tools such as e-mail, shared drives, Web 2.0
applications, and enterprise information systems [LL09].
Before characterizing the notion of process information, we first have to define the terms
data and information. In literature, respective definitions are broadly diversified [Row06].
In the context of our research we apply the definitions suggested in [BCGH06, RT08,
AG04]: data are raw facts or observations of things, events, activities, and transactions that
are recorded and stored, but are not organized and processed, and therefore do not convey
any specific meaning. Information, in turn, refers to data that has been organized and
processed for a specific purpose. Consequently, it has a meaning and provides some value
to the recipient. Generally, data turns into information, if someone is interested in this data.
For example, a doctor might be interested in the blood group of a particular patient or the
patient’s maximum and minimum body temperature during a day. Besides, information
can be also derived from data. As example consider the average body temperature that can
be calculated from the individual temperature data items. Consequently, the difference
between data and information is not structural, but functional [Ack89].
Analogously, we define process information as follows: process information refers to data
that has been processed to support process users in the modeling, execution, monitoring,
optimization, and design of processes. Hence, the data gets a meaning and has a value
with respect to the process users’ activities. Typical process information includes, for ex-
ample, process descriptions, working guidelines, process models, operational instructions,
forms, checklists, and best practices (e.g., documented in text documents, spreadsheets,
presentations, and e-mails).
However, the mere availability of process information is not sufficient to adequately sup-
port knowledge-workers and decision-makers in their daily activities. What enterprises
need is an intelligent, process-oriented information logistics, i.e., the right process infor-
mation must be provided in the right format and level of granularity at the right place
and accurate point in time. More precisely, process-oriented information logistics deals
with the planning, execution, and control of process information flows within or between
enterprises to support knowledge-intense business processes implying human interaction
and decision making [BD08]. In this paper, we address one first aspect of an effective
process-oriented information logistics: quality dimensions of process information.
Based on the question whether process information fulfills certain quality requirements,
the overall relevance of process information can be determined. Picking up our health-
care example, typically, it becomes necessary that patient information is up-to-date and
complete in order to be able to charge certain services through the accounting and billing
department. Therefore, patient information which is out-of-date or incomplete is not rel-
evant, since it cannot be processed by the medical accounting. However, depending on a
specific work context, different quality dimensions might be more or less important than
others. For example, for a surgeon, patient information should be available punctual and
up-to-date. Conversely, for an employee being responsible for patient admission, informa-
tion about the patient must be complete and error-free. Therefore, depending on the work
context, a different weighting of the individual quality dimensions becomes necessary.
In fact, the consideration of work context and quality dimensions of process information is
key to identify relevant process information. Figure 1 shows the relationship between work
context and process information quality. On the one hand, the work context determines
the process information a process participant needs to perform current activities. On the
other hand, the use of quality dimensions allows to determine process information quality.
Together, both aspects allow to determine the overall relevance of process information.
Work Context
Process
User
task x
task
task
Process
Information
Quality Dimension #1
Goal: selection
of relevant …
(overall relevance) Quality Dimension #2
Quality Dimension #n
Process
Information
Quality
...
Figure 1: Determining the relevance of process information.
The presented research is performed in the niPRO project. In this project we apply se-
mantic technology to integrate process information within intelligent, user-adequate pro-
cess information portals. Our overall goal is to support knowledge-workers and decision-
makers with the needed process information depending on their current work context. So
far, both research and practice do not address how processes and related process informa-
tion can be effectively merged. Currently, conventional methods of information retrieval or
enterprise search engines are mainly used for this purpose. Opposed to this, the niPRO pro-
cess information portal aims at determining required information for knowledge-workers
and decision-makers dynamically and automatically. Key challenges include the person-
alized provision of process information, flexible visualization of process information, and
innovative design approaches for different levels of information granularity.
This paper is organized as follows. Section 2 presents a running example that will be used
throughout the paper. Section 3 investigates quality dimensions of process information.
Section 4 discusses why contextual quality dimensions are particularly important. Section
5 discusses related work. Section 6 concludes the paper with a summary and an outlook.
2 Running Example
To illustrate different quality dimensions of process information, we use a running exam-
ple from the clinical domain. This example is based on experiences we gathered during
an exploratory case study at a large German university hospital [MMR11]. In this case
study we analyzed the process of an unplanned, stationary hospitalization, including pa-
tient admission, medical indication in the anesthesia, surgical intervention, post-surgery
treatment, patient discharge, and financial accounting and management.
Our running example (cf. Figure 2) specifically picks up the doctor’s ward round. First,
the ward round is prepared, i.e., the doctor has a look at patient information and current
medical instructions (e.g., endoscope, physical therapy). Next, the doctor communicates
with the patient and asks for information about his status. Afterwards, the patient is exam-
ined. This activity includes the analysis of blood values, vital values, and further follow-up
diagnosis. Finally, the doctor creates medical instructions and updates patient information.
prepare ward
round communicate
with patient examine
patient create medical
instructions update patient
information
Patient
Record Notes ... Notes Instruc-
tions Notes Patient
Record
......
Figure 2: Our running example.
For each of these activities different process information is needed. For example, to per-
form activity ”create medical instructions” a doctor needs blood values, vital values, notes,
and current medical instructions. Conversely, to perform activity ”update patient informa-
tion”, the doctor needs output information (e.g., notes, instructions) of other process ac-
tivities and also access to the patient record. Note that the illustrated process information
(e.g., notes, instructions, patient record) from our running example should be viewed only
as a small part of all relevant process information.
To determine which process information is relevant in a specific work context, we now take
a closer look at quality dimensions of process information. This way, we can ensure that
process information meets necessary quality requirements (e.g., contextual relevance, free-
of-error, objectivity, and believability), fits with the work context, is suitable for specific
activities, and can so be easily used by process users.
3 Process Information Quality
Apart from very broad characterizations such as ”fitness for use” [TB98] there exists no
commonly accepted understanding of the term information quality. In fact, giving a single
definition of information quality is difficult as this term is widely used in many areas. In
this paper we define process information quality as a set of quality dimensions.
Process information quality can be investigated from various viewpoints, e.g., integration,
transmission, security, storage, access, and representation. According to the goals of the
niPRO project, we focus on the viewpoints of integration,semantic processing, and provi-
sion. Integration concerns the collection of process information from different data sources
(e.g., databases, enterprise information systems, shared drives). The viewpoint semantic
processing implies semantic analysis, processing and linking of process information. The
provision viewpoint, finally, deals with the technical provision of process information.
Quality categories and quality dimensions described in the following were gathered based
on a literature study, two qualitative exploratory case studies, and an additional online
survey [MMR11, HMR11].
3.1 Quality Categories of Process Information
Quality dimensions of process information can be combined into different quality cate-
gories. Each category subsumes a set of dimensions. All dimensions belonging to the
same category are affected by the same influencing factors such as work context (e.g.,
process- and user-related information) or information systems characteristics (e.g., repre-
sentation of information). Specifically, we apply the classification of Wang [WS96] and
Moore & Benbasat [MB91] and introduce four quality categories (cf. Figure 3):
•The Intrinsic quality category (QC1) integrates self-contained quality dimensions
of process information. Quality dimensions from this category are independent on
the work context. Examples include believability (e.g., to improve the believability
of a medical diagnosis several doctors have to approve it) and objectivity (e.g., to
guarantee the objectivity the health of patients must be determined by certain criteria
and not by estimation). Another example is free-of-error (e.g., to achieve error-free
patient lists, name and identification number of the patient must match).
Quality Categories of
Process Information
Contextual
(QC4)
Intrinsic
(QC1) Accessible
(QC2)
Representational
(QC3)
independent on
work context
dependent on
work context
Figure 3: Quality categories of process information.
•The Accessible quality category (QC2) combines quality dimensions being impor-
tant for the access to process information. These are mainly affected by the informa-
tion systems providing process information. Examples of respective quality dimen-
sions are accessibility (e.g., to treat a patient the doctor needs the patient record)
and security (e.g., ensure the security so that specific process information is only
accessible to authorized users).
•The Representational quality category (QC3) subsumes quality dimensions con-
cerning the representation of process information. This quality category is again
mainly influenced by the information systems providing process information. As
examples of respective quality dimensions consider interpretability (e.g., the exact
unit of measurement is always indicated for the given values), understandability
(e.g., addresses should not be displayed as GPS coordinates), consistent representa-
tion (e.g., patient information should be display consistently), and concise represen-
tation (e.g., current diseases are displayed separately from pre-existing diseases).
•The Contextual quality category (QC4) integrates quality dimensions which are in-
fluenced by the work context of process users. Contextual quality dimensions are,
for example, contextual relevance (e.g., a doctor performing activity ”prepare ward
round” gets other process information than in activity ”create medical instructions”),
completeness (e.g., patient information must be completely available), and punctu-
ality (e.g., blood values must be available when the doctor needs it). These quality
dimensions always depend on the current work context.
In the next section we restrict ourselves to the contextual quality category. We do this
because this quality category is particularly influenced by the work context and thus also
by process-related information.
3.2 Contextual Quality Dimensions of Process Information
We distinguish between nine contextual quality dimensions of process information (cf.
Figure 4). Each dimension is described in the following.
Punctuality
(QD1) Topicality
(QD2) Contextual
Relevance (QD3)
Completeness
(QD4) Value-Added
(QD5) Appropriate
Amount (QD6)
Granularity
(QD7) Neighbourhood
(QD8) Methods-of-Use
(QD9)
Contextual Quality
Category (QC4)
Figure 4: Contextual quality dimensions of process information.
3.2.1 Punctuality
The Punctuality quality dimension (QD1) indicates whether process information is pro-
vided punctually when the process participant needs it. Specifically, three different time
points (t) have to be distinguished: (a) the point in time at which the process user requests
the process information, (b) the point in time at which the process information is provided,
and (c) the point in time at which the process user applies the process information. Based
on this we can determine whether process information is punctual or not.
Additionally, it becomes necessary to distinguish between ad-hoc process information and
regular one. The former is requested spontaneously. For example, a doctor may request
blood values in order to be able to make decisions. Ad-hoc process information is accurate
in time if it is provided between the point in time it is requested and it is used (cf. Figure
5). The length of this period depends on the process participant.
t t+1 t+5t+2 t+4t+3 t+6
request (a) use (c)
punctual unpunctual
time
Figure 5: Punctuality of ad-hoc process information.
Conversely, regular process information is provided at pre-defined points in time. For ex-
ample, every morning a doctor may receive a patient list in order to know which patients
he has to visit. The punctuality of regular process information can be distinguished be-
tween two time points: (a) punctual in respect to the provision and (b) punctual in respect
to the use (cf. Figure 6).
t t+1 t+5t+2 t+4t+3 t+6
provision (b) use (c)
punctual to the provision unpunctual
punctual to the use
time
Figure 6: Punctuality of regular process information.
3.2.2 Topicality
The Topicality quality dimension (QD2) indicates whether process information captures
the current characteristics (e.g., name, insurance agreement) of an object (e.g., patient) at
the current point in time (t). Process information is out-of-date if the characteristics of
the object have changed between the time point of capture and the time point at which
the process user applies the process information (cf. Figure 7). For example, a body
temperature of the patient measured two days ago is most likely obsolete. In practice,
the capture of characteristics is often time-consuming. Characteristics of an object may
continuously change (e.g., body temperature of a patient, health of patient). The capture
can be done either in real-time (e.g., using heart rate monitor) or at pre-defined time points
(e.g., during the ward round).
3.2.3 Contextual Relevance
The Contextual Relevance quality dimension (QD3) indicates whether process information
is relevant in a specific work context. Process information has a high contextual relevance
t t+1 t+5t+2 t+4t+3 t+6
create
object update
object
up-to-date out-of-date
use (2)use (1)capture
time
Figure 7: Topicality of process information.
if it is needed to perform or support an activity. For example, for preparing the ward
round a doctor needs current diagnoses and medical instructions. The more precise a
work context can be defined the more accurate the contextual relevance can be determined.
Therefore it becomes necessary to consider not only process- and user-related information,
but also location-based, device-related and time information.
Unlike the overall relevance (cf. Figure 1 on page 3), the contextual relevance is not influ-
enced by other quality dimensions. As an example consider again the preparation of the
ward round for which the doctor needs the patient record. Let us assume that the patient
record is punctually available. In this case the patient record has high contextual relevance
and high overall relevance. Let us assume that the patient record is not punctually avail-
able. In this case the contextual relevance is still high, but no overall relevance can be
identified since the quality dimension punctuality is not fulfilled.
3.2.4 Completeness
The quality dimension Completeness (QD4) indicates whether or not all parts of a com-
plex process information (comprising several information parts) are available. In order
to perform the activity ”create medical instructions”, for example, different blood values
(together representing a process information ”blood values”) must be available. Process
information is incomplete if some parts of a process information are missing. It is impor-
tant to mention that completeness thereby depends on the currently needed information,
i.e., it depends on the current work context. Picking up again our example, this does not
mean that all possible blood values must be available, but only those being needed for
current patient treatment.
3.2.5 Value-Added
The Value-Added quality dimension (QD5) indicates whether it is possible to increase
some ”value” (e.g., patient satisfaction, diagnostic accuracy) by using process informa-
tion. For example, information about patient needs is value added because the fulfillment
of the needs increases patient satisfaction. The value-added amount is calculated as the dif-
ference between the value that can be realized without using specific process information
and the value that can be realized with specific process information. Figure 8 shows this
relationship. However it is quite difficult to determine the value-added quality dimension
since respective effects often cannot be exactly estimated.
70%60%50% 80%
without
use
value-added
patient
satisfaction
use
Figure 8: Value-Added of process information.
3.2.6 Appropriate Amount
The quality dimension Appropriate Amount (QD6) of process information indicates
whether the amount of available process information is sufficient. This is the case if
the amount meets the requirements of process participants. For example, a doctor needs
the name of the patient and pre-existing diseases. The appropriate amount of process
information is not sufficient if he gets the entire patient record. In practice, this problem
is solved by extracting process information via software (e.g., a document is divided into
individual information objects). In our case studies we analyzed the appropriate amount
of process information. Obviously, decision-makers are confronted with too much in-
formation. Knowledge-workers, by contrast, have the problem of being confronted with
insufficient information.
3.2.7 Granularity
The Granularity quality dimension (QD7) indicates whether the aggregation of process
information meets the requirements of process users. Process information has the right
granularity if immediate use is possible (cf. Figure 9). For example, if a doctor needs to
know the average body temperature a patient had during the past week he should immedi-
ately get the calculated average value but not the individual values.
c
b
f
df
amethods for
aggregation 2xf
1xc
Day
01
02
03
04
05
methods for
aggregation
P. Miller
Temp.
37.3
38.2
38.4
38.1
37.9
P. Miller
Current Temp.:
37.9
Average Temp.:
38.0
Prognosis:
Figure 9: Granularity of process information.
According to [Jun06], three aggregation dimensions need to be distinguished: (a) time
dimension, (b) area-specific dimension, and (c) value and quantity dimension. As an ex-
ample of the time dimensions consider the aggregation based on emergencies per day,
month, or year. Examples of the area-specific dimension include aggregation by organiza-
tion units (e.g., number of patients on ward A) or patients (e.g., patients’ age and gender).
As an example for the value and quantity dimension consider aggregations relating to cost
centers (e.g., research and development, patient service).
Unlike the granularity, the appropriate amount (QD6) meets the requirements if non-
aggregated information is provided. Let us assume that the doctor wants to know the
average body temperature of a patient. If individual data items are provided, only QD7
meets the requirements. If the average body temperature is provided, both QD6 and QD7
meet the requirements.
3.2.8 Neighbourhood
The Neighbourhood quality dimension (QD8) indicates how strong and how frequently
process information is linked to other process information. Process information which is
strongly and frequently linked tends to be more important. In addition, the semantic of the
relation is important. Examples include metadata-matching (e.g., author, keyword), text
similarity, and usage-pattern [WM09]. Figure 10 illustrates the relations between process
information along a simple example. Starting from a process information (doctor in our
example) we can identify the neighbourhood being relevant.
planning best practices
doctor
email John Doe
project expert
article
wiki
patient
Jane Doe
guideline
examination document
Figure 10: Neighbourhood of process information.
3.2.9 Methods-of-Use
The Methods-of-Use quality dimension (QD9) indicates how a process participant uses
the process information. Suitable use cases are, for example, to read, create, update, and
delete the process information. For example, a process user cannot use read-only process
information if he wants to edit process information.
4 Evaluating Quality Dimensions
Quality dimensions are an important means to determine process information quality and
the overall relevance of process information for a particular process participant. They are
thus also an important means to realize process-oriented information logistics.
Generally, there exist several approaches which can be used to decide whether process
information fulfills our defined quality dimensions: (a) algorithmic methods, (b) seman-
tic technologies, (c) social methods, and (d) convergent methods.Algorithmic methods
are, for example, the vector space model, the term frequency algorithm, and methods
of clustering. The use of semantic technologies is another possibility to determine pro-
cess information quality (e.g., via ontologies) [WM09]. Social methods include collabora-
tive tagging or human-based rating of process information [WZY06]. Finally, convergent
methods improve the aforementioned methods through their combination (e.g., algorith-
mic detected relationships between process information are editable by process users).
Table 1 illustrates which of these methods can be used to determine our introduced quality
dimensions.
Table 1: Methods to determine process information quality.
Shortcut Quality Dimension Algorithmic Semantic Social
QD1 Punctuality x x
QD2 Topicality x x
QD3 Contextual Relevance x x x
QD4 Completeness x x x
QD5 Value-Added x
QD6 Appropriate Amount x x
QD7 Granularity x x
QD8 Neighbourhood x
QD9 Methods-of-Use x
5 Related Work
Different authors investigate process-oriented information logistics in general. The state-
of-the-art regarding information logistics is summarized in [DW09]. Empirical evidence
on benefits generated by information logistics concepts is provided in [BD08]. Context-
awareness in particular is discussed by Haselhoff [Has05] and Meisen et. al [MPVW05].
Problems of data quality and solutions for data quality improvements are described in
[Red01]. Scannapieco et. Al [SVM+04] present an architecture for managing data quality
in cooperative information system.
Quality dimensions of information in general have been considered in literature. Jung
[Jun06], for example, investigates data integration architectures and also sketches quality
dimensions. Wang et. al [WW96, Wan98] identify aspects of data quality based on em-
pirical research and integrate their findings into a data quality framework. Naumann et.
al [NLF99] describe a framework for multidatabase query processing taking information
quality into consideration. Table 2 compares our quality dimensions with the ones in the
aforementioned papers.
Table 2: Contextual Quality Dimensions from different viewpoints.
Shortcut Quality Dimension Our Paper Jung Wang Naumann
[Jun06] [PLW02] [NLF99]
QD1 Punctuality x x x x
QD2 Topicality x x x x
QD3 Contextual Relevance x
QD4 Completeness x x x x
QD5 Value-Added x x
QD6 Appropriate Amount x x x x
QD7 Granularity x x
QD8 Neighbourhood x
QD9 Methods-of-Use x x
* Relevance x x x
* Periodicity x
* Price x
Wang subsume QD1 and QD2 under the term timeliness. In our paper, relevance is not a
separate quality dimension. Rather, relevance results from the combination of all quality
dimensions. Our QD3 is not influenced by other quality dimensions (cf. Section 3.2.3
on page 7). According to Jung, the relevance can be affected by other quality dimensions
(e.g., by QD1). In the paper of Wang, the influence towards the relevance is unclear. Wang
et. al [PLW02] define relevance as ”the extent to which information is applicable and
helpful for the task at hand”. Jung subsumed QD4 and QD6 under the term completeness.
In our research the quality dimension periodicity is based on the information sources and is
therefore not a quality dimension of process information. The quality dimension price can
be omitted because commercial data providers are not in focus. Naumann closely follows
Wang. Due to different perspectives some quality dimensions of Wang (e.g., value-added)
are omitted in the paper of Naumann.
6 Summary and Outlook
Enterprises are confronted with a continuously increasing data overload making it difficult
for them to provide the needed information to their employees. Thereby, relevant infor-
mation is often closely related to the execution of business processes. Hence, the main
problem is to identify information being relevant for a process user and his activities in a
given work context. To solve this problem, enterprises crave for an intelligent and process-
oriented information logistics enabling them to provide the right process information, in
the right format and level of granularity, at the right place and accurate point of time to the
right people. To realize such process-oriented information logistics, quality dimensions
of process information adopt a key role. This paper picks up this issue and investigates
quality dimensions and discusses their role for process-oriented information logistics.
Future research includes an evaluation of the proposed contextual quality dimensions with
respect to the development of an intelligent, process-oriented information logistics. We
will investigate the factors determining the work context of process users. Only a precise
understanding of a work context allows us to accurately determine the overall relevance of
process information. Future research will also pick up again Table 1 and will address the
application of the discussed methods to determine our dimensions in detail.
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