Semantically based visual tracking of engineering tasks in automotive product lifecycle Selver Softic
Manfred Rosenberger
Andrea Denger
Virtual Vehicle Research Center Graz, Austria
Virtual Vehicle Research Center Graz, Austria
Virtual Vehicle Research Center Graz, Austria
[email protected] [email protected] [email protected] Johannes Fritz Alexander Stocker Virtual Vehicle Research Center Graz, Austria
[email protected]
ABSTRACT Stakeholders in specific disciplines, departments, companies and at different locations within the automotive production process save their results in different data management systems. Project management is currently done separately and does not interact with engineering objects. Our work aims on providing flexible data insights on collaboration tasks between participants within the product lifecycle. We applied semantic technologies RDF1 , OWL2 and SPARQL3 with a specific domain related ontology PROTARES4 (PROject TAsks RESources) to interlink, describe and query domain knowledge about the product. As proof of concept a software prototype is introduced, which resides on the domain ontology and allows knowledge based browsing and visualisation of specific aspects within the production process. With this example we want to demonstrate, how semantically driven customized views can support monitoring and reflection of engineering tasks and decision making within the early phases of the automotive product lifecycle.
Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—information filtering, query formulation, search process; H.4.2 [Information Systems Applications]: Types of Systems—descision support; H.5.2 [Information Interfaces and Presentation]: User Interfaces—grahical user interfaces (GUI), user-centered design; I.2.4 [Artificial Intelligence]: Knowledge Represen1
http://www.w3.org/RDF/ http://www.w3.org/TR/owl-features/ 3 http://www.w3.org/TR/rdf-sparql-query/ 4 http://purl.org/protares/ns/1.0/ 2
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Virtual Vehicle Research Center Graz, Austria
[email protected]
tation Formalisms and Methods—semantic networks; D.2.2 [Software Engineering]: Design Tool and Techniques— user interfaces; D.2.9 [Software Engineering]: Management—life cycle
General Terms DESIGN, HUMAN FACTORS, EXPERIMENTATION, THEORY, ALGORITHMS, MANAGEMENT
Keywords Product Lifecycle Management, Semantic Web, RDF, SPARQL, Information Visualisation
1.
INTRODUCTION
The timescale for development of new vehicles has been drastically shortened and the number of variants and derivatives has multiplied. Doing business globally and sharing information across organization, different disciplines, locations, and partners at that time and place when it is required engages very complex and increasingly dynamic processes. These processes demand a high level of flexibility and adaptability from the involved companies. Nowadays product development is characterized by cross-company collaboration. Distributed development implies extensive exchange of product data and highly related communication efforts within [15]. This requires a high consolidation which is still carried in face to face meetings. The challenge a PLM (Product Lifecycle Management) system is facing is, how to provide stakeholders adequate information to support their decisions and decrease at the same time the administrative effort. Such an approach has to enable views on time, stakeholders, components and processes. The motivation behind this approach is led by the idea that an improved understanding of the whole product system, will improve decision making [3] as well lead to more formal access to the knowledge within the development process [16]. The problem stated here addresses a high demand on interlinked and flexible data. New technologies like Semantic Web could fill the gap, to master this challenge. Beside data structuring Semantic Web offers the flexibility regarding schema, restrictions and relation of different data entities. In this work an
approach with semantic technologies is used to structure and describe the circumstances of collaboration and decision processes of product development on the task level. As result SPARQL queries over semantic structured data deliver input for visualisation which creates selective views on process tasks enables better task analysis and monitoring.
2.
RELATED WORK
The main idea behind the Product Lifecycle Management (PLM) aims at improvement and tracking of develoment and production in different phases of the product lifecycle. It assumes that adequate tools, standards and technologies if involved properly into product lifecycle provide the manufacturers to increase their competitiveness regarding their products and their maintenance. Several studies has shown already that this approach may lead to remarkable improvements [5, 15]. A PLM system requires a high level of coordination and integration as stated in [11]. However the key enabler in PLM are stakeholders, as well as the communication and relations between them [4]. Formalising this relations contributes data reuse and optimisation as reported in [9]. Recent research works with the emergence of Semantic Web based upon ontologies, showed more flexibility than previously offered solutions [10, 13, 12]. The potentials of Semantic Web and its technology stack [2] to rise the implicit knowledge that can be used to optimise particular segments of product lifecycle has been outlined in [17, 19]. The efforts in this field so far were focused to model partial aspects of product life cycle like maintenance [12] or product description [13]. However, to explore serendipity and contribute real lifecycle improvement, aspects of organisation, communication and interaction which include entities, actors, time, process states and relations among them have to be considered as well. Applicability of Semantic Web technologies to design such complex relations between the entities and its flexibility regarding changes in context of information as well inference potentials which has been outlined by numerous previous research efforts [18, 1] makes it an approved choice for current effort.
3.
METHODOLOGY
Agile software development experts suggest to work with uses cases5 . In following we want to introduce the essential use case for semantically driven ”Task Analysis” within product lifecycle. As proof of concept we introduce architecture, context modelling, interlinking, data extraction and visual exploration represented by ”PLM Task Tracking Tool” (see figure 3) .
3.1
Use Case
Every year, ”TU Graz Racing Team” takes part in the FSAE Formula Student6 competition with a self developed and produced racing car. We used their experience and knowledge to define together the ”Task Analysis” use case. The reference data set contains information about around 700 requirements, 60 tasks, 358 related components, 24 resources and 21 persons involved into development process. As primary outcome for proof of concept two different views on provided data were defined. The first one is the view 5 http://tynerblain.com/blog/2008/02/18/cockburn-lovesagile-use-cases/ 6 http://students.sae.org/competitions/formulaseries/
Figure 1: Implementation architecture. of a Project Manager. Project manager needs to know which tasks are currently running and in which state these single tasks are. Based on these views a project manager gets an overview of the project as whole. In order to be able to make decisions and influence the development process a project manager is interested in which persons are involved to specific tasks. He needs to figure out, which persons could provide support in case that an other person needs support. The other actor in this use case is the Engineer. The engineer has to know which tasks are currently related to him and which requirements describe them. In this way we are rounding up this use case from the position of the supervising tracking actor and the actor that is tracked.
3.2
Architecture
Presented use case relies on architecture depicted in figure 1. Generally there are three different layers that can be distinguished: data, entities and view or visualisation layer where the application for ”Task Analysis” resides. The data layer concerns the collection and linking of data into structured form like e.g. XML. This could be done manually by specific tools or automatically by implementing specific algorithms. In our case a manual annotation tool was used. Data in this layer is consumed from different sources and represents different entities: persons, requirements, component specific engineering data, engineering tools and resources as well related tasks in this context. Each component consists of different parts and each part is related with a series of tasks assigned to engineers and specified by requirements. Once the data is pre-structured it will be transformed in entities using the domain ontology and related to each other in order to enable different querying options that can be used as input parameters for different views.
3.3
Modelling the context
After review of existent work [14] on ontologies and formal approaches [16, 6, 13, 12] as well recent research in this area, none of the existent ontologies or vocabularies offered adequate model with sufficient properties and restrictions specific for our application domain. Although some work on task ontology has been done, where TOVE Ontology project7 [7, 8] comes most near to the needs our use case, 7
http://www.eil.utoronto.ca/enterprise-modelling/tove
Listing 1: Retrieving all components from tasks where certain person worked on. PREFIX PREFIX PREFIX PREFIX
rdf: protares: dcterms: foaf:
SELECT ?cn WHERE { ?t rdf:type protares:Task ; protares:respPerson ?p; protares:usesComponent ?c. ?p foaf:name ?name . ?c dcterms:title ?cn . FILTER( ?name = ”Engineer Bob”) }
Figure 2: PROTARES - Ontology for activity modelling.
with its upper level approach it still lacks on the granular precision required for resources and components as well task dependencies reflection needed to describe our use case. In order to enable very precise modelling we decided to create a new domain ontology named PROTARES (PROject TAsks RESources) presented with object related properties in figure 2. The ontology has been combined with wide used FOAF (Friend of A Friend) ontology8 and Requirements Management Ontology from OSLC (Open Services for Lifecycle Collaboration) initiative9 . In this way the whole range of variations around product development has been covered as it will be shown in following subsections. Regarding the conceptualisation the most important process occurs in transformation module of annotator application (see figure 1). The structured data in annotator application is mapped from native XML format using the PROTARES domain ontology depicted in figure 2 into entities layer.
3.4
Data Retrieval and Visualisation
Storing data about engineering artifacts in RDF enables the revealing of different views on data relations and makes this data easier and more flexible retrievable. The triple store used in our case supports the generation of results in various standard formats like XML, CSV or JSON. In our case we use JSON as result format to forward the results of queries to the application called ”PLM Task Tracking Tool” residing on the top of architecture stack which visualises the task related information for the end user. Listing 1 demonstrates with a sample query how easily the data from RDF graphs can be retrieved. In this example the query delivers all components from all tasks on which a certain person has worked on. In order to demonstrate the flexibility we could for instance add an additional filter on time by using the property protares:startDateScheduled from PROTARES ontology and results can be already filtered by specific time span. 8 9
http://xmlns.com/foaf/spec/ http://open-services.net/ns/rm/
Figure 3: ”PLM Task Tracking Tool” graphical user interface.
4.
PRELIMINARY RESULTS
In our preliminary proof of concept we distinguish for now two different views as described in the use case in subsection 3.1. All inputs for the ”PLM Task Tracking Tool” are created with SPARQL queries as the one in listing 1. Figure 3 depicts the current state of ”PLM Task Tracking Tool”. Visualisation in the main window represents the task. Additionally all related entries regarding this task are opened and marked in the tree views on the left side of ”PLM Task Tracking Tool”. The ”PLM Task Tracking Tool” enables the user to browse visually through the development project and retrieve desired information. Different circle colors reflect different types of data: orange color represents tasks, red colored circles with blue border represents requirements, also other entities have their own symbols. The described approach allows the members of the team to analyse dependencies between product components, requirements, used tools and skills. Documented start and end times allow an reverse engineering of the project plan. The created or used documents in any task are stored in related specific directories. In this way at any certain point of time in product lifecycle the project manager has an overview on collaboration issues.
5.
CONCLUSION AND FUTURE WORK
Ontology based interlinking of engineering objects with participants and resources allows getting an overview on product development with small effort. The software prototype ”PLM Task Tracking Tool” gives an idea how this queries can be handled in an easy way by different partici-
pants in the development process. The semantic approach also leads to a three level architecture based on standards and standardised tools. A next step integration will be a role and rights management to provide information access to privileged people only. Documents and other data objects like product components or requirements are managed in Document Management Systems (DMS), Product Data Management (PDM) or Requirement Management Systems (RQM). Information of these systems is still required to display all the relations of a task. Interfaces to the named systems can help to access this data. Such improvement may require also a review of PROTARES ontology. First simple acceptance evaluation of interface has been done by feedback rounds with racing team members, however an extension in form of a survey regarding all roles involved in product development is necessary in order to capture the grade of overall usability acceptance.
ACKNOWLEDGMENTS The authors would like to acknowledge the financial support of the ”COMET K2 - Competence Centres for Excellent Technologies Programme” of the Austrian Federal Ministry for Transport, Innovation and Technology (BMVIT), the Austrian Federal Ministry of Economy, Family and Youth (BMWFJ), the Austrian Research Promotion Agency (FFG), the Province of Styria and the Styrian Business Promotion Agency (SFG). We would furthermore like to express our thanks to our supporting industrial and scientific project partners, namely ”TUG Racing Team” and to the Graz University of Technology.
6.
REFERENCES
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