=Paper=
{{Paper
|id=Vol-1224/paper7
|storemode=property
|title=iGraph: Intelligent Enterprise Information Logistics
|pdfUrl=https://ceur-ws.org/Vol-1224/paper7.pdf
|volume=Vol-1224
|dblpUrl=https://dblp.org/rec/conf/i-semantics/MichelbergerMBM14
}}
==iGraph: Intelligent Enterprise Information Logistics==
https://ceur-ws.org/Vol-1224/paper7.pdf
iGraph: Intelligent Enterprise
Information Logistics?
Bernd Michelberger1 , Bela Mutschler1 , Daniel Binder1 ,
Jan Meurer1 , and Markus Hipp2
1
University of Applied Sciences Ravensburg-Weingarten, Weingarten, Germany
{michelbe,mutschlb,binderd,meurerj}@hs-weingarten.de
2
Group Research & Advanced Engineering Daimler AG, Ulm, Germany
markus.hipp@daimler.com
Abstract. Engineers in the automotive domain are confronted with a
huge load of information making it difficult for them to identify the
information relevant for performing their tasks. Particularly challenging
is the alignment of process information, such as offices files, checklists,
and guidelines with business processes. In previous work, we introduced
the concept of process-oriented information logistics (POIL) enabling
the intelligent delivery of process information along business processes.
In this paper, we present iGraph, an application implementing POIL.
Specifically, iGraph demonstrates how engineers can be supported with
relevant process information during the review of product requirements.
Key words: information logistics, semantic technology
1 Introduction
The amount of information engineers are confronted with, makes it a challenging
task to identify and handle the exact information needed to perform their daily
work. During a review, for example, engineers not only have to consider office
files and best practices, but also guidelines and handbooks. This information may
be accessed through shared drives, databases, or enterprise portals. However, en-
gineers are not only interested in quickly accessing information, but additionally
require comprehensive and aggregated information when conducting a review.
To tackle this challenge, information logistics (IL) concepts have been intro-
duced by researchers and practitioners in recent decades [1]. IL aims at delivering
the information to knowledge workers fitting their demands best. Information
awareness (e.g., awareness of information quality and flows) and, to a smaller
extent, context awareness (e.g., awareness of the user context for which per-
sonalized information shall be delivered) adopt key roles in IL. However, what
has been neglected by contemporary IL approaches is process-awareness, i.e. the
integrated support of business processes and their tasks.
?
This paper was done in the niPRO research project. The research is funded by the
German Federal Ministry of Education and Research under grant number 17102X10.
28 Michelberger et al.
This weakness has guided our development of process-oriented information
logistics (POIL) as a new paradigm for delivering the right process information,
in the right format and quality, at the right place, at the right point in time,
and to the right people [2]. Specifically, POIL enables a process-oriented and
context-aware delivery of relevant process information to knowledge workers [3].
The core component of POIL is a semantic information network (SIN) [2],
a labeled and weighted digraph comprising unified information objects (e.g.,
guidelines, best practices), process objects (e.g., tasks, lanes, events), and the
relationships (e.g., “is similar to”, “is used after”) between them. In particular,
a SIN allows identifying objects linked to each other in the one or other way,
e.g., information objects addressing the same topic or needed when performing
a particular process task. Overall, the SIN constitutes the basis for delivering
relevant information objects to knowledge workers [2].
Section 2 introduces the application scenario. Section 3 presents iGraph.
Section 4 discusses related work. Section 5 concludes with a summary.
2 Application Scenario
The iGraph scenario deals with the review of product requirements documented
as functional specifications at a large automotive manufacturer. Goal is to
both improve and approve such specifications. The underlying review process
is knowledge-intensive as it comprises large amounts of process information,
user interaction (e.g., “perform review meeting”), and decision-making (e.g.,
should the document be approved or not?). Three roles are involved: The author
provides the specification to be reviewed. The review moderator organizes the
review meetings. The reviewer finally analyzes the provided specification and
documents errors, ambiguities, and uncertainties.
Specifically, we consider a scenario with one process schema (modeled with
Signavio Process Editor), three process instances (created and managed with
the Activiti Business Process Management (BPM) Platform), and about 300
documents (i.e., process information) such as reviews, templates, and guidelines.
3 The iGraph Application
iGraph1 is a web-based Java application based on the semantic middleware iQser
GIN server 2.0, the web framework Play! 2.1.1, jQuery 1.8.3, D3 3.1.1, HTML5,
and CSS3. The three main features of iGraph are as follows: (1) comprehensive
integration of process information and business processes from heterogeneous
data sources, (2) intelligent syntactic and semantic analysis of integrated infor-
mation and process objects, and (3) process-oriented delivery of needed process
information and business processes to knowledge workers.
iGraph implements the architectural layers of our POIL framework [2]: a data
layer, a semantic integration layer, and an application layer. The data layer con-
cerns the set of data sources to be integrated. For each data source, a so called
1
A screencast of iGraph is available at http://nipro.hs-weingarten.de/screencast.
Posters & Demos Track @ SEMANTiCS2014 29
ContentProvider 2 is implemented. Its main task is to transform proprietary pro-
cess information or business processes into generic information and process ob-
jects. The semantic integration layer, in turn, is responsible for the syntactic and
semantic analysis of information and process objects. For this purpose, we use the
semantic middleware iQser GIN server. Goal is to classify and group correlated
objects (e.g., filled-out review templates). Finally, user behavior is investigated,
for example, the frequency of using certain information in the context of specific
process tasks. Details regarding the semantic integration layer can be found in
[2]. Finally, the application layer concerns the delivery of process information.
(a) Table-based view of iGraph. (b) Graph-based view of iGraph.
Fig. 1. Screenshots of iGraph.
In the following we show how iGraph is used. Particularly, we consider a
specific task of the review process: the author prepares a functional specification
for the review and needs a review template for guidance.
Goal is to identify relevant information objects supporting the review prepa-
ration. For this purpose, iGraph provides a search box. The reviewer enters, for
example, the term “template” into the search box and executes the query. Search
results are listed in a table-based view (cf. Fig. 1a). Each row represents a search
result (i.e., an information object), whereas each column contains detailed meta-
data of the found information objects, such as the author or title of an object.
In order to identify related information objects (e.g., addressing the same topic),
iGraph provides a graph-based view (cf. Fig. 1b) showing related information
objects starting from a specific information object (e.g., a template).
In order to quickly identify relevant information objects, iGraph provides two
fundamental key indicators: the first algorithm determines the link popularity
(SIN LP for short) of information objects based on the SIN. The second algo-
rithm determines the rate popularity (SIN RP for short) of information objects
based on user ratings. In [4], we presented both algorithms in detail as well as
as an empirical investigation proving that our algorithms can replace the costly
and time-intensive human determination of relevant information objects.
2
Our ContentProviders are available at http://sourceforge.net/directory/?q=nipro.
30 Michelberger et al.
4 Related Work
Various approaches have been proposed in the field of IL. As examples consider
data warehousing (DWH), business intelligence (BI) solutions, decision support
systems (DSS), and enterprise content management (ECM). However, these ap-
proaches suffer from several weaknesses. For example, DWH rather focuses on
the creation of an integrated database. Traditional BI, in turn, addresses data
analytics and is usually isolated from business process execution. Conventional
DSS support complex business decision-making at the management level. By
contrast, ECM deals with the management of information across enterprises re-
ferring to related strategies, methods, and tools. Applications enabling IL are
available, for example, in the fields of wearable computing [5], weather forecast
[6], or the healthcare domain [7]. A more detailed overview can be found in our
comprehensive literature survey [1].
5 Summary
This paper presents iGraph, an application applying semantic technology to
enable the integration, analysis, and delivery of process information to knowledge
workers. The simple visualization of iGraph, both in a table-based and graph-
based view, as well as the two key indicators SIN LP and SIN RP make it easy
to identify relevant process information during business process execution.
References
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Information Logistics. in: Proc. 16th Int’l Conf. on Business Information Systems
(BIS’13), pp. 138-150, Poznań (2013)
2. Michelberger, B., Mutschler, B., Reichert, M.: Process-oriented Information Logis-
tics: Aligning Enterprise Information with Business Processes. in: Proc. 16th IEEE
Int’l EDOC Conf. (EDOC’12), pp. 21-30, Beijing (2012)
3. Hipp, M., Michelberger, B., Mutschler, B., Reichert, M.: A Framework for the
Intelligent Delivery and User-adequate Visualization of Process Information. in:
Proc. 28th Symp. On Applied Computing (SAC’13), pp. 1383-1390, Coimbra (2013)
4. Michelberger, B., Mutschler, B., Hipp, M., Reichert, M.: Determining the Link and
Rate Popularity of Enterprise Process Information. in: Proc. 21st Int’l Conf. on
Cooperative Information Systems (CoopIS’13), pp. 112-129, Graz (2013)
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