=Paper=
{{Paper
|id=Vol-2405/14_paper
|storemode=property
|title=Enterprise Architecture modelling with ArchiMate
|pdfUrl=https://ceur-ws.org/Vol-2405/14_paper.pdf
|volume=Vol-2405
|authors=Kaïs Chaabouni,Alessandra Bagnato,Stale Walderhaug,Arne J. Berre,Caj Sodergard,Andrey Sadovykh
|dblpUrl=https://dblp.org/rec/conf/staf/ChaabouniBWBSS19
}}
==Enterprise Architecture modelling with ArchiMate ==
https://ceur-ws.org/Vol-2405/14_paper.pdf
Enterprise Architecture modelling with ArchiMate
Kaïs Chaabouni1, Alessandra Bagnato1, Ståle Walderhaug2, Arne J. Berre2,
Caj Södergård3, and Andrey Sadovykh4,1
1Softeam R&D Department, France
{kais.chaabouni,alessandra.bagnato}@softeam.fr
2 SINTEF, Norway
{arne.j.berre,stale.walderhaug}@sintef.no
3 VTT, Finland
caj.sodergard@vtt.fi
4 Innopolis University, Russia
a.sadovykh@innopolis.ru
Abstract. The Data-Driven Bio-economy project (DataBio) focuses on
developing new technologies and services for agriculture, fishery and forestry
by exploiting the huge potential of Big Data technologies. This Lighthouse
project includes 27 pilots and 91 technological components provided by 27 of
the 48 project partners. It applies a standard Enterprise Architecture modelling
language: “ArchiMate 3.0”. ArchiMate models are created with the tool
“Modelio” which allows contributors to create ArchiMate diagrams and
collaborate on a synchronized version of the models. The DataBio models cover
different aspects of the project from the specification phase including
requirements, goals and strategies, to the implementation phase by describing
the different processes of the tasks included in the work packages and
representing the technological components. This paper describes the use of
ArchiMate modelling applied in the context of the DataBio research project.
Keywords: Enterprise Architecture, Modelling, ArchiMate, Modelio.
Project data
- Acronym: DataBio
- Title: Data-Driven Bio-economy
- Start date: 1 January 2017, Duration: 36 months
- Partners: INTRASOFT International S.A. Belgium (project coordinator), VTT
Technical Research Centre of Finland LTD, SINTEF and 45 more partners
including IT companies and research institutes [1]
1 Introduction
The Data-Driven Bio-economy project (DataBio) [2] focuses on exploiting big data
technologies for improving the production of raw materials from agriculture, forestry
and fishery for the bio-economy industry to produce food, energy and biomaterials in
a responsible and sustainable way. DataBio takes advantage of recent innovative big
data technologies applied in bio-economy sectors and aims to develop a big data
platform on top of partners infrastructures and solutions. The technologies consist of
91 technological, mostly software, components provided by 27 partners including 37
datasets of Earth Observation and sensor data as well as 13 component pipelines. The
27 DataBio pilots have been classified into three categories:
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
80 K. Chaabouni et al.
- The agriculture pilots which aim to improve precision farming based on
observational data and predictive analysis.
- The forestry pilots which aim to improve forest monitoring, predict risks and
optimize tree resources.
- The fishery pilots which aim to improve vessel energy economy, logistic
efficiency and predictive analysis of the fishery market as well as decreasing the
environmental impact.
Taking into consideration the complexity of these tasks and the big number of
heterogeneous technological components involved, we chose ArchiMate 3.0 [3] as an
enterprise architecture modelling language in order to represent the components and
the processes of the different pilots. These models can be useful for requirements
elicitation, technical solutions design, interactions between stakeholders, facilitating
communications between partners and for reporting results. This paper is structured as
follows: section 2 presents the ArchiMate modelling approach in the context of
DataBio project, section 3 illustrates the modelling of the pilots and Section 4
contains the modelling of the technological components used in DataBio project.
2 DataBio modelling approach with “ArchiMate 3.0”
The enterprise architecture methods are used for providing organizational structure,
business processes and the IT infrastructure of an enterprise. These methods can be
applied to DataBio context, where we are interested in modelling the case studies
business objectives, processes, requirements, data pipelines and IT components.
ArchiMate language has been proven to be particularly helpful for modelling
organizations having complex IT infrastructures [4]. Moreover, the ArchiMate
framework provides a wide range of modelling concepts which represent different
layers of the enterprise such as: strategy, application, motivation, technology,
business, etc [5]. The modelling environment used for this task is the Modelio
ArchiMate modelling tool [6] which allows developers to collaborate on a
synchronized remote version of the models. At the first stage of the project, first six
months, the modelling process was focusing on the specification phase which meant
to provide diagrams to specify the objectives, the requirements and the desired
outcomes of the pilots. This helps to understand the big picture of the pilot, the
guidelines and the interactions between the stakeholders. In the second stage of the
modelling process, the technological design of the pilots became more mature and the
partners started working on diagrams that illustrate the software components, their
interactions and their deployment environment. The models are structured in five
ArchiMate projects described as follows: three projects (agriculture, forestry and
fishery) for pilot description, one project for modelling software and IoT system
components and finally one project for modelling Earth Observation data services.
3 DataBio pilot models
DataBio pilots allow to experiment with different technologies on real case studies
with the objective of analyzing the feasibility, the efficiency and the economic
impact. The project’s goal is to have a big data platform that integrates these
technologies at the end of the project. We define this platform as an environment in
which a combination of software components are developed to be deployed in
hardware, virtualized infrastructure, operating system, middleware or a cloud. This
environment provides through the DataBio Hub (http://databiohub.eu) [7], a big data
toolset which offers functionalities primarily for services in agriculture, forestry and
fishery. The functionalities enable new software components to be easily and
effectively combined with open source, standards-based big data, and proprietary
components and infrastructures based on generic and domain specific components.
Enterprise Architecture modelling with ArchiMate 81
The DataBio toolset supports the forming of reusable and deployable pipelines of
interoperable and replaceable components, that can integrate the technologies adopted
in the pilots. Each pilot has been modelled in the specification phase with diagrams
that emphasize the motivation and strategy point of view. The motivation views
provide elements that motivate the choices of the pilot such as objectives and
requirements. The strategy views complete the motivation views by planning long
term actions to meet the specified objectives. Some of the pilots went further in their
design into providing additional models such as application views, data views and
business processes.
3.1 Pilot motivation views
The pilot motivation views present the reasons and factors that justify and guide the
pilot choices. This step is important for introducing the pilot and for explaining the
relevance of the pilot concept. The motivation diagrams contain goals which specify
the main objectives of the pilots. In relation to goals, these diagrams contain certain
outcomes that realize the specified goals. Moreover, these views contain the
stakeholders which are the individuals, teams and organizations involved in the pilot.
There are also internal and external factors represented by “driver” elements which
motivate an organization to define its goals and implement the changes necessary to
achieve them. In addition to these concepts, we use “requirement” elements which are
functionalities that need to be implemented and “constraint” elements which are
factors that limit the realization of the defined goals.
Fig. 1. Fishery pilot B1: “Oceanic tuna fisheries planning” Motivation View
An example of the motivation view can be found in the published presentation of
the “tuna fisheries planning” pilot [8]. The purpose of this pilot is to improve
profitability of tuna fisheries by saving fuel based on fish observation and vessels
route optimization. Observe, that the catch volume cannot normally be increased as it
is limited by quota. Therefore, the main goal of this pilot is “improving catch
revenue”. More specifically we want to “reduce energy consumption” and “improving
catch efficiency” (see Fig. 1). In this pilot there are two major stakeholders: the
“Vessel Owner” and “Vessel master” who both want to improve catch revenue. The
vessel master is motivated by logistics on the ship and reducing the time spent on
operations. Similarly, the vessel owner is concerned with reducing energy
consumption and improving catch efficiency. These objectives are realized by
optimizing route cost efficiency and species distribution forecasting.
82 K. Chaabouni et al.
3.2 Pilot strategy views
The pilot strategy views allow decision makers to elaborate a global roadmap to
implement the announced objectives by defining course of actions for implementing
the tasks at hand considering the capabilities of the organization and the available
resources. For example, the strategy view of the fishery pilot mentioned in the
previous section demonstrate how strategy elements provide leads for implementing
the objective of “Improving the revenue of fish catching” (see Fig. 2). This objective
can be realized by providing a decision support system on vessel operations. Given
our capabilities in data collection and data analytics, we can develop the specified
decision support system by collecting sensor data and extract useful information for
decision makers.
Fig. 2. Fishery pilot B1: “Oceanic tuna fisheries planning” Strategy View
4 Technological components models
DataBio pilots include various technological components with different interfaces,
different data formats (sensors data, satellite imagery, etc) and different deployment
environments. For each component, ArchiMate diagrams were created for modelling
the interface view, the deployment view and the data view. The interface view shows
the external interfaces of the component which are designed for interactions with
users or with other components through various communication protocols. For
example, the component “C07.04: Data Manager” is used for downloading and
preprocessing earth observation data in forestry and fishery pilots. Its identification
code “C07.04” is formed from component (=C), DataBio partner number (=07) and
component number of that partner (=4). This component offers three interfaces for
downloading data via Java API, REST API and command line interface (CLI). It also
offers three interfaces for consuming data from other components (see Fig. 3) [9]. The
deployment view describes the application executables and the software and physical
environment required for running the application. The data view describes the format,
the source and the content of the data processed by the component.
Enterprise Architecture modelling with ArchiMate 83
Fig. 3. Component C07.04: Data Manager Interface View
Fig. 4. Oceanic tuna fisheries planning - Pipeline View
In addition to components models, more ArchiMate diagrams were created in
order to represent the data pipelines adopted by each pilot. As mentioned before, the
different pilots are working on integrating several components into their workflow
processes. These processes are referred to as “pipelines”, as they integrate different
tasks along the data value chain from data collecting to analysing and visualizing. The
pipelines models represent the pilot lifecycle, the integrated components and the data
flow between components. For example, the component “C07.04: Data Manager”
mentioned previously is integrated in the pipeline of the fishery pilot “Oceanic tuna
fisheries planning” (see Fig. 4) [10]. “Data Manager” acquires data from different
components such as the component “C07.01: FedEO Gateway” which is a unique
endpoint that retrieves geographical data from several backend providers such as
“Copernicus Open Access Hub”. Moreover, “Data Manager” stores data in network
file systems or HDFS distributed file systems. Finally, the component “C07.06
84 K. Chaabouni et al.
Ingestion Engine” consumes the collected data by “Data Manager” via REST API
interface.
5 Future work and concluding remarks
In this paper, we outlined the contribution of ArchiMate models in DataBio pilots
specification and technological design. These models helped to provide productivity
and clarity in the project by contributing to the analysis of the case studies and to the
production of design documentation. This approach of ArchiMate modelling is
currently being adopted by DataBio partners in national and European projects. On
the other hand, based on our experience with this project, we have identified a
potential for improvement with regard to both modelling process and model quality:
- Providing a holistic view of the project in addition to the pilot centered views and
adding “Business Process” views to illustrate platform exploitation by end users.
- Ensuring component reuse in models to avoid duplication.
- Establishing quality metrics for “Enterprise Architecture” based on modelling
experience from DataBio project and from other projects [11,12] and taking into
consideration existing metrics described in the literature such as the “6C quality
goals” described by Mohagheghi et al. [13].
Acknowledgments
This work is partially funded by the DataBio project (No. 732064) under European
Commission’s Horizon 2020 research and innovative programme.
References
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