In previous works, we have presented a catalogue of patterns aimed at improving context models' construction by departing from a set of predefined elements, instead of starting from scratch. Patterns were identified from i*- based context models constructed for several industrial cases (where the DHARMA method was used), packaged and stored in the form of i* SD actors and dependencies. In this paper we go one step further and present a framework, based in semantic technologies, designed to improve utilization of the catalog, which has been deployed as a semantic repository. The framework makes easier the construction of i*-based context models, by recommending knowledge elements stored in the repository.
Modern enterprises rely on Information Systems (IS) specifically designed to manage
the increasing interactions with their context. Design of such systems requires deep
understanding of the enterprise context and strategies. To support this process, we have
proposed the DHARMA method [
Copyright © 2016 for this paper by its authors. Copying permitted for private and academic purposes. 2
There are several tools supporting i*-based modeling and its variants (TROPOS,
Service Oriented i*, etc.), e.g., OpenOME [
In [
The DHARMA (Discovering Hybrid ARchitectures by Modelling Actors) method (see
Fig.1), aims at the definition of enterprise architectures using the i* framework [
S-DHARMA is a web framework that allows the creation and maintenance of i*
models, following the activities defined in the DHARMA method. Its main objective is to
make easier the construction of organizational CMs and to visualize the models
resulting from each activity of the method.
As starting point, the framework recommends the dependencies stored in a semantic
repository, and then, in a refinement phase, the user can create additional dependencies
or modify existing ones using the tabular form of i* SD model presented in [
Architecture Architecture of the semantic DHARMA framework (see Fig.2) is defined in three layers, client, server and data layers. The client layer allows the interaction with the user, and gives him the possibility to create, edit, store, and visualize context models based in the i* notation, it has been constructed using JavaScript and makes use of the D3.js library to generate i* diagrams.
CLIENT LAYER DATA LAYER
Apache Marmotta
SERVER LAYER
DHARMA
Ontology SPARQL Generator
Mapping
Process Result
The server layer works as a bridge between the client and the data layers. It receives
requests from the client layer in JSON format by invoking RESTful Web Services, and
then, through a mapping process between the data and the DHARMA ontology, creates
SPARQL queries [
The data layer stores and retrieves information from the triplestore based in Apache Marmotta. The SPARQL queries generated in the server layer, either for insert, update or delete, are processed in this layer and results are returned to the server in JSON format. Immediately, the server layer retrieves the response to the client-layer. classification offer-job
As mentioned before, the S-DHARMA framework supports the activities of the DHARMA method; in this subsection we will focus on the support for activities 1 and 2. The first activity of the DHARMA method, “Context Modelling”, requires a deep analysis of the organization and its OAs, so, as first step, the organization must be created, by inputting its name, size, sector and industry. Immediately the framework will show a group of OAs of interest for the organization under study, which are automatically recommended and retrieved from the catalog and displayed as a tree, which enables a consultor (user conducting the modeling activities) for its selection and the creation of new OAs as needed. Next, the description of each OA and the specification of a responsible for each of them is required; this information is stored in order to identify the administrative stakeholder to be interviewed by the consultant during the modeling process. Then, the analysis and modeling of each OA should be performed; the process starts by selecting a group of actors, external and internal, with whom the OAs interact (also retrieved and recommended from the semantic repository and displayed as a tree). After that, the framework queries the semantic repository (see figure 4a for a query example) and presents a group of generic dependencies of interest for each pair of related actors (also retrieved from the semantic repository), the user can accept or not the dependencies suggested.
After saving the dependencies, the framework will show the tabular form of the CM (see fig.4b), and additional options to support a refinement phase, where the user can either create specific dependencies needed to improve the model, or parametrize dependencies and actors. The framework also includes an option to show i* diagrams for each pair of actors modelled in this activity (see fig. 4c).
The second activity of the DHARMA method, “System Context Modelling”, consists in the identification of the dependencies that can be solved by the IS, so, the framework allows its identification using radio buttons to specify whether a dependency can be totally or partially covered by the IS. After that, new i* diagrams can be generated, including the IS as an actor.
SELECT distinct ?URI ?Actor1 ?Dependum ?Actor2 ?IntentionalType ?direction WHERE { GRAPH <http://www.ucuenca.edu.ec/graph/DHARMA/production> { ?DependerLink OntoIStar:has_Dependency_DependerLink_target_ref ?DependerE . ?DependerLink OntoIStar:has_Dependency_DependerLink_source_ref ?URI . ?DependeeLink OntoIStar:has_Dependency_DependeeLink_target_ref ?DependeeE . ?DependeeLink OntoIStar:has_Dependency_DependeeLink_source_ref ?URI . ?URI OntoIStar:Node_sannotation ?direction . ?URI dharma:has_Dependency_Area_source_ref ?URIArea . ?DependerE OntoIStar:Node_label ?Actor2 . ?DependeeE OntoIStar:Node_label ?Actor1 . ?DependumLink OntoIStar:has_Dependency_DependumLink_target_ref ?DependumE . ?DependumE OntoIStar:Node_label ?Dependum . ?DependumE OntoIStar:has_IntentionalElement_IntentionalType ?IntentionalTypeE .
?IntentionalTypeE rdfs:label ?IntentionalType . }}
(a) (b) (c)
Fig. 4. Screenshots of the Semantic DHARMA Framework 5
In this paper, we presented the S-DHARMA framework, a web framework to support the construction of CM based in the activities of the DHARMA method. Its interactive interface, helps users to create models in short time, reducing typical errors commonly associated to the i* notation. The framework makes use of semantic technologies that allow the retrieval, recommendation and instantiation of i* SD context models elements which have been annotated, packaged and stored in a semantic repository.
Currently, the S-DHARMA framework supports the construction of SD models required by activity 1 and 2 of the DHARMA method. As future work, we plan to extend it to support SR models and the identification of the system architecture required by activities 3 and 4 of the method. Also, we pretend to incorporate semantic reasoners and include synonymy modules to avoid dependency duplication. Furthermore, we pretend to adapt the constructors in order to implement the iStar2.0 standard and allow the user to import and export i* models in existing formats, such as iStarML. Finally, we pretend to incorporate the framework in academic activities aiming at improving the DHARMA Method and the i* framework teaching-learning process.
This work has been conducted with the support of a grant from the Research Direction of the University of Cuenca (DIUC), in the framework of the project “Hacia la identificación, validación, y enriquecimiento semántico de patrones de modelos de contexto a ser utilizados como base para la definición de arquitecturas de sistemas de información empresariales”.