=Paper=
{{Paper
|id=Vol-1985/BPM17industry06
|storemode=property
|title=Introducing Semantic Services for Continuous Agile Enterprise and Process Modeling
|pdfUrl=https://ceur-ws.org/Vol-1985/BPM17industry06.pdf
|volume=Vol-1985
|authors=Julia Bilinkis (Stavenko),Nikolay Kazantsev
|dblpUrl=https://dblp.org/rec/conf/bpm/BilinkisK17
}}
==Introducing Semantic Services for Continuous Agile Enterprise and Process Modeling==
https://ceur-ws.org/Vol-1985/BPM17industry06.pdf
Introducing Semantic Services for Continuous Agile
Enterprise and Process Modeling
Yulia Bilinkis (Stavenko)1[0000-0001-9983-829X] and Nikolai Kazantsev2[0000-0002-6812-8786]
1 National Research University "Higher School of Economics" (NRU HSE)
Moscow, Russia
ybiliniks@hse.ru
2 National Research University "Higher School of Economics" (NRU HSE)
nkazantsev@hse.ru
Abstract. This article is a final report of process enhancement project in one of
the leading Russian banks. It was done with the set of tools and techniques for
enriching process models with semantic information and adjusting them on re-
quest. We propose an approach for binding these models with corresponding doc-
uments and expert profiles define factors that trigger models’ changes using com-
pany’s information field.
Keywords: Agility; BPM; Context-awareness; Process modelling.
1 Introduction
Changes in client behavior towards application of services drive banks to rethink their
financial strategies and digitize their value chains. In the past digital disruption occurred
on levels of discrete product and service technologies (e.g., online banking). Today, it
occurs on the level of ecosystems and the banking business model is a good example
of this change. Current banking customers look for agility and the bank’s value propo-
sition depends on building ecosystem-level strategies that encompass many partners
that should be able to react quickly on customer trends.
Nowadays, neither their BPM office nor process managers or valuable process partici-
pants cannot predict process changes that appear due to execution variability. Brander
et al. [3] claims that gaps between process specifications and practical requirements are
inevitable and noncompliant behavior occurs to bridge this gap. Melão and Pidd [4]
recognize that social interaction fosters debates and collaboration and, thus, deviations
from intended structures need to be expected. Intended noncompliance, often in the
form of workarounds, receives considerable attention in literature, cf. (Alter [5];
Röder[6]).
Since those changes are not detected in advance and become visible only after customer
complains - it causes potential financial and image risks to the company. Furthermore,
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2
changing customer expectations expends this gap and nowadays more flexibility re-
quired from employee’s performance often driven by the context. For the bank analyzed
we covered this gap, by application of the context-aware modelling approach [1].
To detect the main obstacles for current process modelling in industries we interviewed
more than 70 companies in various economic sectors in Russia [1]. We identified that
the major constraints refer to lack of understanding of modelling value and absence the
appropriate mindset (see Figure 1).
Constrains
No constrains
Require more staff
High dynamics of changes in business processes
Low level of maturity of the company
The effect from implementation is not obvious
There is no understanding the need to use
No budget or no time to implement
Employees’ inertia
Top managers have no interest to this topic
0% 5% 10% 15% 20% 25% 30% 35% 40%
Сonstrains
Fig. 1 Modern constrains for business modelling
We claim that to overcome the above modelling challenges we require the new ap-
proach to enterprise modelling exactly the one we applied in the case study.
2 The case study
The bank we consulted provides a full range of financial services in Russia in consumer
lending. It has a large network of touchpoints including branches, ATMs, loan officers
that serve 5 million customers by 30 000 employees. Its business goals are decomposed
from the strategic level to the level of business process design through specifications.
3
On this level, all business processes are described with the accuracy required to imple-
ment them on the production level. Due to the competitive struggle, bank faces many
challenges with respect to the management of its business processes, e.g.:
- noncompliance with current process models and specifications
- deviations from established work procedures.
- both options above impact KPIs
We decided not to concentrate on developing perfect process landscape, but to de-
liver models that are useful in a period on time [1]. Therefore, we formulated the main
requirements for our approach in this Bank:
1. cross-organisational ad-hoc collaboration
2. iterative task resolution
3. workflow supports changes
4. every employee can initiate an improvement and get support by experts
5. decisions are the outcomes of collaborative processes
The proposed approach involves following steps to make enterprise models evolving
synchronously with the changes (see Figure 2) [2]:
Model owner
Analyze Find Choose Change
changes experts experts model
New request Model
changed
Done
Subjects
Receive Accept
Expert
massage invitation
Invitation declined
Expert community
Analyze
changes
Identify
Change Detection
Notify model
potential
owner
changes
Service
Change profile
Model
No
profiles
changes
Expert Search Service
Send list of Create
Search Send
potential expert
experts invitations
experts community
Change profile
Expert
profiles No experts
Fig. 2 Swim lanes of context -aware business processes
4
Context-aware process management identifies preconditions for potential changes and
ensures quality process execution. That is why, in the context-aware process paradigm
any deviation from the model is not an exception but an expected option to detect im-
plicit process changes. They are mostly caused by noncompliant behavior of employ-
ees, that includes skipping activities, performing additional activities, outdated proce-
dures, lack of staff, outdated equipment, fatigue, inexperience or performing activities
without proper authorization, etc. There is also malicious behavior such as lying, cheat-
ing, and stealing for the personal benefit or overcoming inadequate IT functionality or
other obstacles and preventing future mishaps. Unintended behavior occurs in the form
of mistakes and often due to a lack of knowledge about procedures. Implicit changes
may not show themselves for a long time, often they can be identified only by experts.
If they are not identified in advance they create operational risks for the system process
model.
2.1 Information field analysis
To identify these changes, we provide natural language processing of all specifications
and key terms stored in employee’s emails and document attachments. Enterprise ar-
chitecture in practice is often left unstructured, being more represented through the in-
formation field generated by employees. This field encompasses live communication
in natural language within the ad-hoc tasks, e-mail correspondence, chats, and various
types of documents (policies, tasks, actions). Companies who analyze their information
field regularly might identify emerging change-requests in advance.
One of the main elements of text mining is corpus – a huge structured set of texts,
usually containing tags with morphological (POS-tags) and syntactic information. In
our case, corpus is based on relevant terms and messages containing model-related
terms identified by experts. Original text is tokenized – divided into parts, minimal
fragments (not always words!): words, stable phrases, prepositions (‘because of’), ab-
breviations (‘e.g.’) and so on. A special list of words and elements (usually punctuation,
the most common and the rarest elements of the text, interjections, etc.) called “stop
words” is skipped. After that, tokens are lemmatizing - presented in the initial form
(“better” -> “good”). It is important to identify what part of speech is token and what
meaning it has, otherwise lemmatization may be done in a wrong way. POS-tags in
corpus help to get morphological information about elements of our text. For syntactic
and semantic analysis, a vector form of words/sentences/documents is often used – they
are presented as a point with coordinates in space. Word–context, pair–pattern and
term-document matrix may be created. Vector model allow to compare documents and
words, to define their similarity, to recognize patterns and association rule (famous ex-
ample: “king” - “men” + “woman” = “queen”). There are several types of vector docu-
ment models, for example: Random Projections (RP), Latent Dirichlet Allocation
(LDA), Hierarchical Dirichlet Process, (HDP). For words vectorization, a Google’s in-
strument “word2vec” may be used. At the final step, the algorithm is learned on our
corpus and then works with our text, our data, making morphological, syntactic and
semantic analysis.
5
The results of work of the linguistic engine are used in Change Detection service and
Expert Search service.
2.1.1 Change Detection
The Change Detection service is intended for continuous monitoring of the organi-
zational unstructured content to identify changes, associated with the emergence of new
themes, events, and description of the domain objects. This set of documents would be
the main data source for change control: it should be regularly updated with new doc-
uments, created by employees. It also could be fully substituted by a new set of mes-
sages, collected inside the expert network. Its service interface allows prediction of
changes. This information is a set of new terms associated with a model. For handling
process exceptions and cases when higher knowledge from process actors is needed for
decision making, we applied expert search service.
2.1.2 Identification of experts
An Expert search service parses the document content and returns the list of persons
sorted by their discovered capabilities in this query topic. Searching for experts aims to
assess the “tacit knowledge” in organizations through artifacts of “explicit knowledge”:
organizational documents associated with the model. The tacit knowledge of multiple
experts is combined with explicit information from project databases, documents, man-
uals and other external sources. It uses the confirmed hypothesis that person’s qualifi-
cation strongly correlates with set of characteristic concepts which he uses; these terms
are specific to the industry. Considering relative frequency of term usage and many
other factors, our approach can identify the true experts. Significant terms have strong
non-uniform distribution of relative frequency of usage among employees, and com-
mon ones – approximately identical relative frequency of application. It can be used:
- To find potential experts giving a new content as input. It’s possible to send a
message to each expert with the request when the advice in the new process
part is urgently required.
- To form expert communities for analyzing changes and adjusting process
models. Newly-established team estimates work volumes, decide on priorities,
and decide whether the change is important based on change context (annota-
tion).
6
3 Conclusion
As we have figured out, the classical process modelling approach suffers from the lack
of process models robustness when the changes are not precisely identified. This paper
makes two contributions:
a.an original changes identification method, called Context-aware process
modelling, that solves several challenges through the annotation of models
with experts and artifacts
b. two independent services: Change Detection and Expert Search that sup-
port the business processes management by integrating semantic technol-
ogies into models.
Currently we are working on self-adapting business process intelligence system that
evaluates and selects best possible process scenario for process execution in the real-
time, supported by the accumulation of semantically described processes in the process
repository, using the services presented in this case study.
4 Acknowledgement
This work was supported partially by the Russian Foundation for Basic Research (No.
1707-01441) and by the European Commission under the European Union's Horizon
2020 research and innovation programme (grant agreement n° 723336).
References
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