Beibei Pang

Hamideh Afsarmanesh
email: h.afsarmanesh@uva.nl

Juanqiong Gou
email: jqgou@bjtu.edu.cn

Wenxin Mu
email: wxmu@bjtu.edu.cn

Supporting Transparent Information/Knowledge Federation in Collaborative Administrative Environments

Keywords: Collaborative network, Knowledge federation, Unified ontology

Leading edge ICT facilitates obtaining and interoperating information within collaborative networks (CNs), providing the base to tackle more advanced challenges. The paper addresses provision of transparent federated information/knowledge within administrative CNs. We introduce a methodology and mechanisms for incremental ontology development. The paper first identifies four typical sources of information/knowledge at the organizations involved in targeted emerging CNs, including: (i) database schemas, (ii) mission statements and main application scenarios, (iii) textual communications, and (iv) governance policies. It then introduces a systematic methodology to develop their meta-data and unify them into an ontology. This methodology consists of four semi-automated steps to gradually develop and enhance an ontology for the environment. The paper describes and exemplifies these steps and their mechanisms. An example real emerging case in the field of higher education administration in China is presented to serve as the proof of concept and verification of our proposed solution approach.

Introduction

"A collaborative network (CN) is an alliance constituted by a variety of entities (e.g. organizations and people) that are largely autonomous, geographically distributed, and heterogeneous in terms of their operating environments, culture, values, and goals, but that collaborate to better achieve common or compatible goals, and whose interactions are achieved through computer networks [START_REF] Camarinha-Matos | On reference models for collaborative networked organizations[END_REF]." Advanced ICT and emerging technologies provide the base to facilitate obtaining, sharing and exchange of various types of information/knowledge in collaborative networks [START_REF] Unal | Semi-automated schema integration with SASMINT[END_REF]. Focusing on administrative CNs, we consider the application case of federating varied information/ knowledge sources as for instance illustrated in Fig. 1. Suppose that there are several organizations including some universities and enterprises that collaborate to achieve the following common goals: i) obtaining the progress track of students throughout their life cycle; ii) generating training advices to universities on how to supervise and promote good students, and iii) producing advices to employers for their recruitment plans, according to students' background experience and school performance. To achieve these common goals, it is necessary to first identify the information/ knowledge to be shared among these organizations, such as the undergraduate, masters, PhD records of students in different universities, their working experience records at various enterprises, as well as the missions and governing policies at these universities related to supervising/promoting students. Furthermore, it is necessary to then integrate and federate these information/knowledge, generating both a unified and transparent pool that can be accessed by all actors in this environment, e.g. from the students and staff at universities to analysts and decision makers at enterprises, while supporting fair analysis of all students in this environment.

Shared Information / knowledge from each organization in a CN legend:

Integrated information / knowledge set for a CN to assist collaboration

Organizations: several universities, and several enterprises

Common goals: • Analyze the progress track of students throughout their life cycle • Generate training advices for universities • Produce advices on the employers for their recruitment plan

Shared information / knowledge from each node • Undergraduate, masters, PhD records of students in different universities • Their working experience records at various enterprises • Missions and governing policies at these universities related to supervising/promoting students

The suggested approach for information / knowledge sources integration: p First add all the relational DBs (from all the nodes in the collaborative network), and all the policies from all these nodes, etc. p Second, based on step 1, further find interrelationship among DBs, governing policies, etc. Then federate these sources.

University-2

Enterprise-A Employer-B University-3 University-4

In t egration

University-1 Fig. 1. An example application case of CN in higher education environment

In this process, we first identify four main kinds of information/knowledge sources, including: (i) relational databases, (ii) existing mission statements and example application scenarios, usually characterized by their fragmented, lightweight and behavior-intensive features, (iii) textual communications among its stakeholders, mostly gathered through fragmented application cases and (iv) governance policies. Each of the above organizations, being a university or an enterprise, in this CN has one or more kinds of these four information/knowledge sources. In order to realize the federation of all these heterogeneous knowledge sources, we first analyze each data source, identify some inherent challenges, and define its related meta-data applying object-oriented principles, and second extract semantic relations among all these different pieces of information/knowledge. We then create a unified ontology for this collaborative environment, and formalize it using the OWL [START_REF] Ekaterina | Management of information in virtual organizations breeding environments[END_REF]. In our approach, we consider and apply current state of the art approaches for integrating databases, and for gathering governing policies [START_REF] Unal | Semi-automated schema integration with SASMINT[END_REF] [START_REF] Sandkuhl | Enterprise Modeling: Tackling Business Challenges with the 4EM Method[END_REF]. Thus, the paper does not address these aspects in details, and rather focuses on challenges that are not yet addressed. We specifically describe and tackle the following obstacles in this paper, since they are faced in achieving the information/knowledge federation goal in administrative CNs:

•

For relational database schemas, knowledge is typically represented as tables, and attributes are classified as primary key, foreign key, etc. These are typically captured using the data definition language (DDL). The challenge faced here is to automate transforming relational schemas represented as DDL information, into the OWL elements for the unified ontology.

•

For extracting meta-data from application scenarios, typically their knowledge from every source has completely different organizational structure, and the relationship between different knowledge pieces is not well expressed. There are also usually some knowledge overlaps with the knowledge presented through the relational schema. The challenge faced here is how to semiautomatically deal with resolving these problems.

•

For textual data gathered from different communications in the environment, data is usually recorded together with some timestamps. The challenge faced here is first to convert these into structured formats, and then to automatically extract semantic information from text corpora, and generate their meta-data.

•

For generating meta-data related to the governing policies of the environment, since their expression formats are quite flexible, only few studies have so far treated them. But in fact these represent an important knowledge source in administrative environments. This is especially the case for capturing the temporal data behavior that is usually hidden elsewhere, and only present in governing policies. Therefore, the challenges faced here are complex and at present we can only manually identify and formalize these temporal data behaviors in order to represent them in OWL format for the ontology. Aiming to address the above-mentioned obstacles, we introduce our systematic methodology to knowledge source's meta-data unification that consists of four semiautomated steps, that gradually develop a unified ontology for the environment, formalized in OWL. This article is structured according to the following sections. Section 2 represents the related work. Sections 3 and 4 describe a methodology to facilitate identification and resolution of the main encountered typical inconsistencies among heterogeneous knowledge sources within collaborative environments. Section 5 concludes this research work and provides some perspectives for future plans.

Related Work

Influenced by [START_REF] Ekaterina | Management of information in virtual organizations breeding environments[END_REF], we define knowledge as the set of collected information together with their context, which could be understood, formatted, and shared without ambiguity by the environment stakeholders. In this paper, we aim at developing an ontology to support collaboration within administrative CNs. We address different kinds of information and/or knowledge that can be shared by the involved organizations. Please note that for simplicity reasons, in the remaining of this paper we mostly refer to the information/knowledge as "knowledge", and to the sources of information and/or knowledge as "knowledge sources". We then focus on generating the common/unified meta-data from all addressed sources. Our related research review focuses on three main challenging aspects: (i) unification of heterogeneous knowledge sources, (ii) specification and management of governing policies, and (iii) topic modeling for the content of textual communications.

Unification of Heterogeneous Knowledge Sources

Research areas related to unification of heterogeneous knowledge sources in collaborative networks either address the ontology based knowledge integration [START_REF] García | An ontology-based data integration approach for web analytics in e-commerce[END_REF], or the ontology based data base integration [2] [START_REF] Li | A Framework for Ontology-Based Top-K Global Schema Generation[END_REF]. The unified ontology represents all types of data in uniform format and realizes intelligent analysis on integrated data sets [START_REF] García | An ontology-based data integration approach for web analytics in e-commerce[END_REF]. It can also guide the integration of heterogeneous data bases [START_REF] Li | A Framework for Ontology-Based Top-K Global Schema Generation[END_REF]. State of the art in ontology based unification methods can be summarized as: first identifying different knowledge sources [START_REF] Unal | Semi-automated schema integration with SASMINT[END_REF][6], second formalizing and generating ontology for each source, and third integrating ontologies by using semantic similarity (i.e. graph based or content based) research methods [2][7]. However, the state of the art research primarily focuses on addressing similar types of knowledge sources, such as research on integrating a number of databases [START_REF] Li | A Framework for Ontology-Based Top-K Global Schema Generation[END_REF], or research on integrating several XML documents [START_REF] Tekli | Building Semantic Trees from XML Documents[END_REF]. In our research however, we design a methodology that handles four representative knowledge sources that are typically heterogeneous, and we aim to unify all these varied meta-data into one ontology. To the best of our knowledge, the current existing approaches have not yet addressed this problem area. We therefore propose a systematic approach to knowledge source unification for administrative CNs, through a methodology consisting of four semi-automated unification steps, gradually developing and enhancing the unified ontology for this environment.

Specification and Management of Governing Policies

A few studies capture and model the governance policies in the environment. one closely related research addresses enterprise modeling field [START_REF] Sandkuhl | Enterprise Modeling: Tackling Business Challenges with the 4EM Method[END_REF], in which three relations are identified for business rules, namely the is-a relation, support relation, and hinder relation. For example, a governing rule states that: "if the training plan of students that can be updated systematically every four years support our planned goal, then you must organize relevant revision work every four year". Since policies are typically defined flexibly at every node in the network, they do not typically have a uniform format. In our research, we focus on extracting relevant semantics from governing policies in the environment, in order to integrate these with their related concepts in relational schemas or other meta-data. This in turn helps knowledge transfer between policy makers and executors, as well as benefiting the intelligent check whether these policies are being executed as expected. Semantics like temporal data behavior, complex causal relationship are identified by our methodology. In this paper however, we only address the temporal data behavior, and our approach is rooted in temporal data bases [START_REF] Date | Temporal Data & the Relational Model[END_REF]. We will describe and formalize temporal data behavior patterns, reflected from governing policies in the environment.

Topic Modelling for the Content of Textual Communications

Topic models are commonly used to extract topics from texts, by simulating the human thinking process. Related topic models include Latent Semantic Analysis (LSA) [START_REF] Landauer | An introduction to latent semantic analysis[END_REF], Probability Latent Semantic Analysis (PLSA) [START_REF] Hofmann | Probabilistic latent semantic analysis[END_REF], and Latent Dirichlet Distribution (LDA) [START_REF] Blei | Latent dirichlet allocation[END_REF]. LSA breaks the previous thinking of text representation based on "dictionary space", and introduces a semantic dimension. However, the basis of the LSA methodology is derived from linear algebra, and the results of the operation are negative in many dimensions. Hofmann proposes a new method PLSA based on reliable probability statistics for the defects of LSA. But PLSA does not provide a probabilistic model at the document level, which leads to a linear increase in the number of parameters to be estimated in the model, depending on the size of the corpus. LDA has further extended the PLSA model by introducing a Dirichlet prior distribution. This approach overcomes the shortage of PLSA parameters as the document set grows linearly, thus forming a widely used probability topic model [START_REF] Della Rocca | A semantic-grained perspective of latent knowledge modeling[END_REF].

In our previous work, LDA model has been used in the first step of extracting domain knowledge in education field [START_REF] Pang | Extracting Topics and Their Relationship from College Student Mentoring[END_REF]. In this paper, LDA model will be used to enhance the ontology's data properties through parsing the content of textual communications.

Research Approach

We use the application case mentioned in section 1 as the input and proof of concept for our approach. The considered four knowledge sources include: source #1: integrated relational schemas of databases from universities and enterprises; source #2: gathered meta-data from application scenarios, such as students take part in lectures information; source #3: gathered textual communications between students and education staff; source #4: gathered governing policies from universities.

Fig. 2. Unification methodology flowchart

We introduce our step-wise knowledge federation methodology as depicted in Fig. 2. The methodology starts by first tackling the main source #1. This source contains the basic structure and conceptual model of the main entities in the environment, thus providing the tarp for our unification process. Second, the meta-data from source #2 will be generated from application scenarios, in order to extend the basic structure from source #1. Third, from source #3, some semantic information can be extracted to further extend the generated unified model. Furthermore, another important source is the governing policies. In our approach, temporal data behaviors are extracted from these policies. Since description objects related to governing policies are mainly defined on top of the knowledge sources mentioned above, it is necessary to first formalize the above knowledge and then integrate the governing policies. As shown on the right half of Fig. 2, in our proposed methodology, steps 1 and 3 are fully automated, while step 2 is semi-automated, and step 4 is manual. Every step enhances/extends the ontology generated in previous step, as shown by O1 to O4.

Detailed Meta-data Unification Methodology

In order to better explain the addressed knowledge types and their meta-data, from each of the four knowledge sources, we provide some simple and easy to understand examples for each discussed aspects. Please note that to help with better understanding of the examples in this section, a partial information/knowledge from each of the four sources are provided as annex at the end of this paper.

Step 1: Extract Relational Meta-data to OWL Through DDL Information

This step turns relational schemas into OWL. Some relevant examples are shown in Table 1. The algorithm 1 in Fig. 3 represents this process and its three functions. user defined relation This is defined in special situations of a field in a table when the domain class is not the class that corresponds to its own table in the meta-data. e.g. mobile field in table take_extracurricular_lecture is intended to describe the students' mobile contact information. Therefore, this field is not semantically related to taking extracurricular lectures. Rather, it will be converted to a data property of the class Student in OWL. simple relation We call all other fields that do not satisfy the above three classifications simple fields, e.g. the lec_type of table extracurricular_lecture.

After manually sorting out the above four kinds of fields, our algorithm 2 will be executed to extend the output of step 1 of the methodology, as also shown in Fig. 4. There are four functions defined in algorithm 2. Function F2.1 converts tables one by one to classes, if their corresponding classes are not yet present in O1. Based on result of function F2.1, Function F2.2 handles the simple and user defined relations, and accordingly adds their suitable domain classes. In function F2.3, fields with sameAs relation will appear as annotations on their related data properties. In function F2.4, each field that plays a connecting role will be converted to an object property, and its domain and classes will be defined according to its role.

4.3

Step 3: Extend Data Properties of O2 with Knowledge Source #3

Rooted in our earlier study [START_REF] Pang | Extracting Topics and Their Relationship from College Student Mentoring[END_REF], we introduce our approach in step 3. There are three main sub steps that generate new meta-data from the recorded text provided through this knowledge source, and further extend the O2 as sub data properties. We first briefly address how the gathered texts are preprocessed and then describe how semantics are extracted from them, in order to create new meta-data elements in OWL. Some relevant examples are shown in Table 3. More information about our approach to automate step 3, related to functions F3.1 and F3.2 are described below.

Algorithm 3: Enhance data properties of O2 by applying information extracted from text content in knowledge source #3

F3.1

i. Editing domain dictionary table ii. Word segmentation iii. Removing stop words

Input: communication pieces Output: preprocessed document set F3.2

i. Train the LDA model ii. Generate and name topics iii. Generate matrix: document-topicprobability

Input: text vector space from F3.2

Output: document-topic-possibility

F3.3

Add each topic as sub data property on relevant property in O2.

Input: topics from F3.3

Output: new OWL sub data properties e.g. add life, job etc. as sub data properties of property content in O2. e.g. given two simple documents: 1) this semester, his performance dropped sharply, etc. 2) he gives up treatment, etc. First add phrase"give up" to the domain dictionary table, then execute word segmentation tool, and remove stop words: "this""his","he","sharply" thus obtain text vector space:

'semester' 'performance' 'dropped' … 'give up' 'treatment'…

d1 d2 0.44 … … … … 0.31 … … … … d1 d2 life … … job …
discovered topics e.g. the alogrithm identifies many words and their appearance rate in all preprocessed documents. Suppose some extracted words are friend, sleep, and lunch. Then an expert names a topic t1 as "life", being related to friend, sleep and lunch. LDA algorithm will be trained accordingly and generated matrix [topic-word-probability], e.g. {life: (friend, 0.24), (sleep, 0.12), (lunch, 0.11)}. consequently the matrix [document-topic-probability] is generated indicating the total probability for each topic, as follows. (3) Generating topics distribution of each document -We apply the LDA topic model to generate the set of , which represent the probability distribution of each document under each topic. Combined with the set generated in (2), we therefore produce: .

4.4

Step 4: Extend O3 by Governing Policies from Knowledge Source #4

This step focuses on extraction of "temporal data behavior" concepts, e.g. related to environment policies, as necessary regulation constraints can enhance the conceptual model of the collaborative environment. For the interest of this paper, three kinds of behavior for temporal entities are considered and classified, as described below: (i) Discrete temporal behavior -Discrete temporal data properties represent events that can be recorded only at specific points in time, such as check in time of every student for a lecture or the lecture's start time, then for example, a discrete temporal behavior rule related to such a lecture will state that the value of check in time for a student should be minimum 15 minutes earlier than the value of time. This time behavior can be modeled as a time constraint on data property on the defined values. b) Situation2: variable step duration -As an example of a time constraint on data properties, consider different governing policies related to the minimum required outcomes to pass a postgraduate innovation project at a school. Suppose that between Sept.2013 and Sept.2017, the minimum required outcomes were two papers, but that after Sept.2018, the required outcomes are either 1 high impact publication or 3 papers. Valid time for the requirements can be represented by the interval: (t1, t2), where t1 and t2 correspond to the start and end date of the period respectively. In order to also support the case when the end time is not known, we introduce ** symbol that indicates the expiration date would be the date of next potential start time.

Postgraduate Innovation Project

Temporal data behavior -Stepwise & constant: minimume required outcomes:

• The algorithm 4 and its three functions in Fig. 10 represents the process we introduce for step 4 of our approach. In function F4.1, temporal behavior concepts mentioned above are added into O3 as annotation properties. Function F4.2 extracts discrete or period based temporal behavior from governing policies. In function F4.3, two situations of stepwise & constant temporal behavior are considered, compared with function F4.2, an important process here is adding of step duration or valid time constraints on each related rule. Therefore, O4 is generated as the output of this step, as well as the final output of our knowledge federation approach.

Discussion

This section provides details of our proposed methodology. We extract/federate metadata from four kinds of knowledge sources, as mentioned in section 1. The meta-data extracted from integrated relational schemas provide the tarp for our unification approach, while information from other sources are used to extract semantics to further enhance the unified ontology. The other considered sources include: i) metadata generated from mission statements and application scenarios, ii) meta-data extracted from textual communications, through LDA model that are converted to sub data properties used to enhance already generated data property, and iii) meta-data generated from governing policies used to enhance conceptual models by adding time constraints on class instances or on data property values. Through our approach, these four kinds of heterogeneous knowledge sources are unified, and in turn effectively supporting knowledge interoperability in administrative CNs. Nevertheless, our proposed methodology is relatively general and applicable to other relevant administrative CN cases. A point of caution for the current approach is related to data privacy. In other words, the approach to providing information/knowledge transparency needs to be carefully adjusted to the type of environment stakeholders. This means that not all users, e.g. student, administrative staff, etc., can see everything transparently, rather the interface accessible to every kind of user, must concisely correspond to the user's level of information/ knowledge visibility.

Conclusion and Future Works

To realize federation of varied knowledge sources in administrative CNs, we propose a systematic methodology and a set of mechanisms for federation of four typical types of knowledge sources shared within collaborative environments. Our introduced mechanisms support semi-automation of the methodology steps and incremental generation of a unified ontology capturing all shared knowledge from heterogeneous sources. As a proof of concept, our approach is exemplified for a real emerging case in higher education administration environment. As the next steps of our research, we intend to address knowledge unification for several other types of sources that we identify in collaborative administration networks. These include: entity relationship diagrams (ERD), data dictionaries accessible from relational data bases, standard regulation documents, and data captured through cyber physical devices. We also intend to further tackle other kinds of temporal data behaviors, including those with complex causal relations, as well as addressing interactive application scenarios and information communicated among the environment stakeholders. We are currently in the process of developing mostly automated mechanisms to handle knowledge unification, which will be addressed in forthcoming publications.

Acknowledgments. The present research has been partially supported by the Fundamental Research Funds for the Central Universities of China (2017YJS081) and Research on Behavioral Intelligence Based on Collaborative Scenarios in High Education Environment (2018A02008). This research is also partially supported by the ARCON-ACM project at the University of Amsterdam.

Annexed Tables:

Table 1. Example of integrated relational schemas related to knowledge source #1 student (people_unique_identifier (PK), name, birth_date) student_degree (student_id(PK), people_unique_identifier (FK), discipline_id(FK), entry_year, university_name, degree_type) discipline(discipline_id(PK), title, start_time, end_time, teaching_language) graduate_innovation_project (innovation_project_id (PK), project_manager_id(FK), published_papers, published_high_impact) training plan (training_plan_id(PK), discipline_id(FK), training_start_date, tot_credit, length_of_study) student_status_change (student_status_change_id(PK), student_id(FK), status_change_type, start_time, end_time) employee (employee_id (PK), people_unique_identifier (FK), position, enterprise_name, salary_level, entry_date) Example of one document: Yesterday, I had a conversation with Student A. He just received an intern offer from a company ranked in Fortune 500. This is an opportunity that many students dream of. However, since he is going to take an important National Civil Servant Examination, it seems hard for him to balance both, and he looked really anxious.

  Function F1.1 converts each table to a class. Function F1.2 converts attributes (if not foreign keys) to data properties, and adds the related class generated by function F1.1 as the domain class for each data property. Function F1.3 converts the relational foreign keys to object properties one by one, and specifies their respective domain and range classes according to the reference relation specified in the schema.

Fig. 3 . 2 Step 2 : 2

 3222 Fig. 3. Algorithm 1: Generates OWL classes and properties from relational schemas

Fig. 5 .

 5 Fig. 5. Algorithm 3: Enhances data properties of O2, by applying information extracted from text content in knowledge source #3 (for more description of these examples, see [14])

Fig. 7 .Fig. 8a .

 78a Fig. 7. Example of temporal rule on discrete behavior (ii) Stepwise & constant temporal behavior -Here we mainly consider two specific cases of stepwise & constant temporal behavior, as addressed below. a) Situation1: constant step duration -As an example of a time constraint on class instances, suppose that since a decade ago the definition of the students' training plan changes once every 4 years. Given this policy, the behavior of every instance in class "Training plan" while being constant, changes with steps of exactly 4 years.

Fig. 9 .

 9 Fig. 9. Example of temporal rule on period based behavior

Algorithm 4 : 4 F4. 1 Fig. 10 .

 44110 Fig. 10. Algorithm 4: Extracts temporal data behavior from governing policies through knowledge source #4

Algorithm 1: Generate owl classes and properties from relational schemas from knowledge source #1

  Each relational foreign key is converted to an OWL object property.

			F1.3
			Input: relational foreign key
			Output:OWL object property
	no	All attributes considered?	yes

e.g. relational table : "student" is converted to OWL class: Student and relational table : "employee" is converted to OWL class: Employee. e.g. relational attribute : "position" of relational table "employee" is converted to its corresponding OWL data property: position, having Employee as its domain class. e.g. relational foreign key : "people_unique_identifer" of table "employee" which references the "people_unique_identifier" of table "student" is converted to OWL object property: hasStudent having Employee as its domain class, and Student as its range class. F1.1 Each relational table is converted to an OWL class. Input: relational tables, one at a time Output: OWL classes F1.2 Each relational attribute is converted to an OWL data property. Input: relational attribute Output: OWL data property For each attribute, if attribute = relational attribute(except foreign key) relational foreign key

Table 2 .

 2 In our proposed method, fields of tables from the source #2 are specifically classified into the following four categories: a) sameAs relation The sameAs relation means that the field is already addressed and exists either in O1 or in another already categorized extracted table from the source #2, e.g. user_name of table take_extracurricular_lecture is sameAs student_name of class Student in O1.

b)

Algorithm 2: Extend O1 by considering meta data from knowledge source #2 e

.g. the field "user_name"from knowledge source #2 is same as data property student_name already introduced in OWL as element O1, then add this field as annotation property sameAs on the student_name.

e.g. both lec_id and user_id in

table"take_extracurricular_ lecture" play connecting roles. So both would be converted to object properties: hasStudent and has_extracurricular_lecture, with hasStudent having domain Take_extracurricular_lecture and range Student, and has_extracurricular_lecture having domain Take_extracurricular_lecture and range Extracurricular_lecture e.g. if corresponding class for tables "extracurricular_ lecture" or "take_extracurricular lecture" does not yet exist in O1, then, create new owl classes Extracurricular_lecture and Take_extracurricular_lecture. F2.1 For each table R, if corresponding class does not already exist in O1, create new class for R. Input: tables, one at a time Output: new OWL classes F2.3 For each field that is a "sameAs" relation, add annotation on related data property. Input: field with sameAs relation Output: add sameAs on OWL data property F2.2 Input: field with either simple relation or user defined relation Output: new OWL data property For each field, create data property. if it is simple field, add domain class corresponding to its table. If it is user defined assignment, add domain class according to demand e.g. simple field "lec_type" of table "extracurricular_lecture", it will be converted to data property

lec_type, with the domain Extracurricular_ lecture.

  Some fields play the role of connecting two tables, or connecting a table with a class. e.g. consider if table take_extracurricular_lecture and table extracurricular_lecture both have the field lec_id, and the lec_id in table take_extracurricular_lecture is used to describe which lectures are considered as extra-curricular. So in OWL the lec_id in table take_extracurricular_lecture will be converted to an object property, in order to link the class take_ extracurricular_lecture and the class extracurricular_lecture with each other, as created by function F2.1 in Fig.4.

e.g. the field "mobile" of table "take_extracurricular_ecture" is converted to data property mobile, with the domain Student.

For each field, if field = either simple relation or user defined relation connecting role relation sameAs relation

All fields considered? yes F2.4

For each field playing the connecting role, it will be converted to object property, with its correct domain and range. Input: field with connecting role relation Output: new connecting OWL object property no Fig. 4. Algorithm 2: Extends O1 by considering meta-data from knowledge source #2 c) connecting role relation d)

data behavior -Period based: for each

  As another example of time constraint on class instances, consider a period-based governing policy to capture the behavior of events that may occur on each entity (e.g. a student) in an environment, over a period of time (e.g. study in a program). For example, a policy can state that for each student, the total number of months of leave from school cannot go over 24 months.

	Data property constraint			owl: class
				innovation_project_id	data property
				published_papers	data property
		example variable step duration	published_high_impact	data property
	2 papers
				2 papers,
	1 high impact or 3 papers	Sept.2013 Sept.2018	Real time line	valid interval (Sept.2013-Sept.2017) • 1 high impact publication or 3 papers, valid interval (Sept.2018-**)
	Fig. 8b. Example of temporal rule on variable step duration behavior
	(iii) Period based temporal behavior -Student_status_change: Class instance constraint	owl: class
				hasStudent:	object property
					Student_status_change
		example period based event	hasStudent Domain:
					Student
				hasStudent range:
				status_change_type:	data property
	Student S1
				start_time:	data property
				end_time:	data property
			Real time line	Temporal student, total leave time from all its status-change
	Sep.2009 Jan.2010 Jan.2011 Jan.2012
				instances should be less than 24 months
		leave

Table 2 .

 2 Example of gathered meta-data related to knowledge sources #2

	extracurricular_lecture	take_extracurricular_lecture communication_information
	lec_id	end_time	lec_id	check_in_time	staff_id	end_time
	lec_title	total_nmu	user_id	mobile	stu_id	content
	start_time lec_type	user_name		start_time

Table 3 .

 3 Example of communication's content related to knowledge source #3

Table 4 .

 4 Example of gathered governing policies related to knowledge source #4 a. Between Sept.2013 and Sept.2017, the minimum required outcomes to pass a innovation project were two papers, but that after Sept.2018, the required outcomes are either 1 high impact publication or 3 papers. (from university-1) b. The value of check in time for a student should be minimum 15 minutes earlier than the value of extracurricular lecture's start time. (from university-2) c. The definition of the students' training plan systematically changes once every 4 years. (from university-3) d. For each student, total leave time from all its status-change instances should be less than 24 months. (from university-1, university-2 and university-3)

4.3.1

Data Pre-processing by Function F3.1

Preprocessing of the acquired text data involves the following tasks. Each piece of recorded communication represents a "document" in this process. Pre-processing program first removes all carriage return characters in each document, then assembles all documents into one "integrated document", while separating the original documents by adding carriage return characters. In the next step, the document set is word segmented. In order to improve the accuracy of word segmentation, a domain dictionary table and a stop-use dictionary table are manually constructed, as described below under (1) and [START_REF] Unal | Semi-automated schema integration with SASMINT[END_REF]. Clearly, the language for the content presented in the two tables must be with the same as the language for the text being processed.

(1) The domain dictionary table avoids incorrect segmentation of domain-specific words (e.g. in English, "give up" into "give" and "up") by word segmentation tools.

(2) The stop-use dictionary keeps track of meaningless words such as "the" and "in" that appear in the document of word segmentation.

Topic Based Semantic Extraction by Function F3.2

We apply the LDA model[17], mentioned in section 2.3, as follows:

(1) LDA topic modeling -"Topic" represents a concept and the conditional probability of a series of words. Each word in a document is characterized by the process of "selecting a topic with a certain probability, and selecting a certain word from the topic with a certain probability." So for a document , the probability of each word appearing in it can be calculated by: . In this formula, is word, is document, is topic, and is probability. For one document, this can be represented by , as in the following matrix:

Fig. 6. Topic modeling theory

The "document-word" matrix represents the word appearance's frequency in each document. The "topic-word" matrix represents the probability of occurrence of each word in each topic. The "document-topic" matrix represents the frequency of each topic appearing in each document. Given the pre-processed document set, the "document-word" matrix on the left can be obtained by segmenting different documents, and calculating the frequency of each word in each document.

Our topic model is then trained by learning from the matrix on the left, to derive the two matrices on the right. For this training, we apply the Dirichlet distribution[15], which identifies appropriate number of topics in document set. The basic idea there is to identify all topics when similarity between the topics is the smallest. Therefore, appropriate numbers of topics will be identified by the LDA method.

(2) Naming each topic -Here, we apply the LDA model and generate the set of , that describe the distribution of words that are related to each topic. Combined with the domain knowledge, the topic names are defined.


Henrique Diogo
email: henrique.d.silva@inesctec.pt

António Lucas Soares
email: asoares@inesctec.pt

Andrea Bettoni
email: andrea.bettoni@supsi.ch

Andrea Barni
email: andrea.barni@supsi.ch

Serena Albertario
email: serena.albertario@holonix.it

A Digital Platform Architecture to Support Multi-Dimensional Surplus Capacity Sharing

Keywords: Digital platforms, Digital platform architecture, Manufacturing as a service

The highly disruptive transformation that digital platforms are imposing on entire sectors of the economy, along with the broad digitalization of industrial business processes, is having an impact on supply chains around the world. To take advantage of this new aggregated market paradigm new business models with a heavy focus on servitization are changing the value proposition of businesses. In this paper, we describe a reference architectural framework designed to support a digital platform fostering the optimization of supply chains by the pairing of unused industrial capacity with production demand. This framework aims at harmonizing stakeholder requirements with specifications of different levels in order to set up a coherent reference blueprint that serves as a starting point for development activities. A four-layer approach is used to articulate between technical components, with the data and tools layers, and the ecosystem, with the business and interfaces layers. The overall architecture and component description is presented as extensions of the initial set of affordances.

Introduction

One of the more prevalent effects of the platform paradigm in the economy is the separation of physical assets from the value they create, the separation of function from the form [START_REF] Parker | Platform Revolution[END_REF]. In the industrial sector, this switch is evidenced by the widespread shift of income generation from the sale of physical products to the charging of customers for the availability of functionalities of a product [START_REF] Vargo | On value and value co-creation: A service systems and service logic perspective[END_REF]. From the perspective of companies that chose to invest on hefty fixed assets like top-of-the-line laser cutting machines or a 5-axis CNC machine this selling of capacity allows for better resource distribution while for other businesses it provides facilitated access to costly equipment that can help in alleviating initial costs of business or even the ability to meet seasonal peak demands. The growing willingness of companies to both buy and sell manufacturing capacity has precipitated the development of the Manufacturing as a Service (MaaS) paradigm [START_REF] Bettoni | Multi-Sided Digital Manufacturing Platform Supporting Exchange of Unused Company Potential[END_REF] that both boosts and leverages the platform economy.

The explosion of the platform business has had a profound impact on businesses structures. The traditional pipeline perspective where processes were arranged step-bystep with producers at one end and consumers at the other has given way to new platform mediated structures [START_REF] Parker | Platform Revolution[END_REF]. In this new paradigm, the linearity of the value chain is twisted and tangled to the point where the boundaries of user and producer are regularly crossed or in some cases inexistent.

Having this landscape as the starting point, and taking digital platforms as stimulants for the transition of industry businesses towards service-oriented approaches [START_REF] Cenamor | Adopting a platform approach in servitization: Leveraging the value of digitalization[END_REF][START_REF] Barrett | Service Innovation in the Digital Age: Key Contributions and Future Directions[END_REF][START_REF] Lenka | Digitalization Capabilities as Enablers of Value Co-Creation in Servitizing Firms[END_REF][START_REF] Vendrell-Herrero | Servitization, digitization and supply chain interdependency[END_REF] it becomes clear that a platform centered ecosystem is needed in order to further advance the MaaS business model. In [START_REF] Bettoni | Multi-Sided Digital Manufacturing Platform Supporting Exchange of Unused Company Potential[END_REF] authors lay out their vision for a digital platform in the MaaS realm that leverages this disruption of the value chain and fluidity of user roles. By adopting a holistic perspective of the value network, and going beyond the simple matchmaking of manufacturing resources, the sharing potential is extended to the whole manufacturing ecosystem network. The fulfillment of this vision, in turn, requires a platform that can establish the bridge between the expected affordances of digital platforms and the cross-sectoral environment, in an ecosystem able to generate added value to its users. By bringing together tools and ecosystem, we are promoting a better and more sustainable use of resources, the reintegration in the loop of unused manufacturing capacity, leading to the creation of local, more efficient value networks, and the seamless involvement of different actors along the value network for crossfertilization of product-service solutions and underlying technologies.

Leveraging the ensued entanglement of the value chain, this paper aims at describing the design approach and resulting core components that will constitute the backbone architecture of a MaaS platform. The paper is divided into a first section that puts into perspective the manufacturing domain that will represent the platform's ecosystem, as well as the features the platform is expected to support, followed by the core section where the components that make up each one of the four layers are described in detail.

2

The Manufacturing Digital Platform Landscape

Digital Platform Affordances

The development of the sharing economy is a clear example of how digital platforms have played a fundamental role in the development of the market [START_REF] Sutherland | The sharing economy and digital platforms: A review and research agenda[END_REF]. Scholars point to six crucial affordances that stride the balance between the rigidity of the technological and the human components of platform ecosystems [START_REF] Pee | Affordances for sharing domain-specific and complex knowledge on enterprise social media[END_REF]: (1) generation of flexibility; (2) matchmaking; (3) scale and reach extension; (4) transaction management; (5) trust building; and (6) community creation support. Digital platforms have the capacity to generate flexibility, not only in how and when users can interact with the platforms [START_REF] Ke | Service Providers of the Sharing Economy: Who Joins and Who Benefits?[END_REF]. The fluidity between user roles within the platform has also shown a measurable impact in the interactions and consumption habits. Authors such as [START_REF] Cheng | Sharing economy: A review and agenda for future research[END_REF] and [START_REF] Philip | Examining temporary disposition and acquisition in peer-to-peer renting[END_REF] point to ability to regularly access sharing economy platforms as an essential component of its success as a business model, while [START_REF] Bauer | Crowdsourcing: Global search and the twisted roles of consumers and producers[END_REF] show how the ability to easily change roles within the platform between client and producer has the beneficial effect of incentivizing the engagement on different levels.

The matching of users, along with pulling them to the platform and facilitating their actions and interactions, are some of the main functions of a digital platforms [START_REF] Parker | Platform Revolution[END_REF]. The ability to perform matchmaking based on a set of attributes then becomes a fundamental function of digital platforms [START_REF] Benoit | A triadic framework for collaborative consumption (CC): Motives, activities and resources & capabilities of actors[END_REF][START_REF] Puschmann | Sharing Economy[END_REF]. Different mechanisms for matchmaking currently exist based on algorithmic assignment and powerful searching and sorting tools [START_REF] Sutherland | The sharing economy and digital platforms: A review and research agenda[END_REF]. This automation of processes then becoming the entire value proposition of many platforms. For industry-focused platforms, perspectives like the one presented by [START_REF] Bettoni | Multi-Sided Digital Manufacturing Platform Supporting Exchange of Unused Company Potential[END_REF] are starting to rethink this process beyond the matching of manufacturing resources to the whole manufacturing ecosystem value network.

The scale and reach brought about by the facilitated access to an extensive network of organizations, consumers, and resources that compose a platform's ecosystem is also one of its main competitive advantages [START_REF] Cohen | Ride On! Mobility Business Models for the Sharing Economy[END_REF]. By striking the balance between the benefits of network externalities and the automation capabilities, made possible by its technological constructs, platforms are able to create a scaling loop that, after crossing the initial hurdle of the point of critical mass, have the potential to grow indefinitely [START_REF] Botsman | What's Mine Is Yours -How Collaborative Consumption is Changing the Way we live[END_REF][START_REF] Cusumano | How Traditional Firms Must Compete in the Sharing Economy[END_REF][START_REF] Irani | The cultural work of microwork[END_REF];

The management of the transactions involved in the transmission and securing of goods, information or labor is another widespread functionality of sharing economy platforms [START_REF] Täuscher | Understanding platform business models: A mixed methods study of marketplaces[END_REF]. In this sense, platforms double up as marketplace and bookkeeper by bringing both parties together while also keeping records of all transactions, ensuring the validity of all the exchanges [START_REF] Carroll | Creating Value Together: The Emerging Design Space of Peer-to-Peer Currency and Exchange[END_REF][START_REF] Weber | Intermediation in a Sharing Economy: Insurance, Moral Hazard, and Rent Extraction[END_REF];

Trust and trustworthiness are a contested point in the digital realm. Where anonymity has always been an obstacle in the conduction of transactions through this medium [START_REF] Kim | Why People Participate in the Sharing Economy: A Social Exchange Perspective[END_REF], and in-person meetings made for the more trustworthy method, in the last decades the advent of several trust-based mechanisms has started to invert this trend. The popularization of features like user profiles as extensions of resumes [START_REF] Ma | Self-Disclosure and Perceived Trustworthiness of Airbnb Host Profiles[END_REF][START_REF] Sarasua | Microtask Available, Send us your CV![END_REF], the utilization of subjective and non-subjective user reviews system [START_REF] Ikkala | Monetizing Network Hospitality[END_REF] along with the implementation of more strict governance directives for the management of platform's communities [START_REF] Lee | Characterizing and automatically detecting crowdturfing in Fiverr and Twitter[END_REF][START_REF] Rosenblat | Algorithmic labor and information asymmetries: A case study of Uber's drivers[END_REF] have tipped the scales in favor of the digital. When thinking about the impact of trust in digital platforms it's also essential to distinguish between trust between users of the ecosystem and the trust users deposit in the platform itself, as both play an essential role when it comes to the adoption of these systems.

The human component of digital platforms is what truly elevates them to the status of sociotechnical constructs [START_REF] Barnes | Building tribal communities in the collaborative economy: an innovation framework[END_REF][START_REF] Sutherland | The Gig Economy and Information Infrastructure[END_REF]. Community building structures that serve as venues for community interactions and participation play an import role in onboarding new users and the facilitation of new relationships between them [START_REF] Moser | Community Commerce: Facilitating Trust in Mom-to-Mom Sale Groups on Facebook[END_REF]. This is a crucial aspect to keep in mind at the platform design stage as previous studies suggest that, for sharing economy platforms, not only economic profit but also community involvement play a critical role as motivators in platform adoption [START_REF] Carroll | Creating Value Together: The Emerging Design Space of Peer-to-Peer Currency and Exchange[END_REF][START_REF] Hamari | The sharing economy: Why people participate in collaborative consumption[END_REF], even in platforms with minimal community interaction capabilities.

3

A MaaS Platform Supporting Sharing of Unused Resources

The MANU-SQUARE Platform

Building on the MaaS concept, the MANU-SQUARE project [START_REF] Bettoni | Multi-Sided Digital Manufacturing Platform Supporting Exchange of Unused Company Potential[END_REF] aims at establishing a European ecosystem of organizations and other relevant stakeholders that, in a marketplace environment, can act as both supplier and client. Through this approach, the platform moves available capacity closer to production demand, further disrupting the traditional linear value network, allowing for the rapid and efficient creation of local value networks for innovative providers of products and services and the optimization and reintroduction in the loop of unused capacity that would otherwise be lost.

The MANU-SQUARE platform goes far beyond the partner search and matching, and supply-chain/virtual enterprises formation proposed in the last 20 years of virtual enterprise literature in three crucial points: (1) extending the sharing potential to the whole manufacturing ecosystem value network; (2) by focusing on surplus capacity; while (3) adopting a multi-dimensional and cross-sectoral vision of capacity.

Current approaches to the sharing of manufacturing capacity have narrowed down their scope to both specific sectors of the industrial ecosystem, and the sharing of unused production resources. This limited view of surplus capacity leaves out, however, much of the wealth that the European industry has been building through the years. Our perspective scopes this vision back up to not only include all the actors that make up the European manufacturing value chain, such as manufacturing organizations, knowledge providers, innovation facilitators, etc. but also to enlarge the concept of capacity beyond production to surplus know-how, technology, and by-products.

This broader scope carries with it the necessity of an architecture able to cope with an increasingly nuanced system. To answer these demands, tried and true standards, such as semantic infrastructures, need to be articulated with state-of-the-art technologies like distributed ledger systems, to produce new and better trust-based, platforms for negotiation, networking and community building.

In this sense, the value proposition of the platform becomes:(1) from a user's perspective, be able to, among a European-wide pool, quickly find trustworthy suppliers according to a set of requirements. This matchmaking would help to manage fluctuating production demand or build/extend production capacity without owning production means relying on a structured RFQ and information sharing system and a transaction management system. (2) From a supplier's perspective, access to a broader cross-sectorial market becomes the main value proposition. This wider access gets complemented by the ability to sell unused capacity, access to up-to-date client information, structured and trustworthy processes for the dissemination of documentation such as RFQs, plus reputation management, and transaction management systems.

Stakeholders & Functionalities

The vast literature on stakeholder analysis has yet to catch up with the platform reality. Very much focused on stakeholders for small and medium enterprises (SMEs), [START_REF] Zibuschka | Stakeholder Analysis of a Platform and Ecosystem for Open Innovation in SMEs BT -Collaborative Networks for a Sustainable World[END_REF] define five stakeholders roles: Innovation Commercialiser; Innovation Funder; Innovation Generator; End User; and Platform Operator. On a 2017 report, the World Economic Forum divides the roles in a platform ecosystem into four, non-mutually exclusive, categories: Orchestrator; Producer; Consumer; or Infrastructure provider.

From this theoretical underpinning, and supported by interviews and workshops with industry players, eight stakeholder typologies were identified. Manufacturing organizations, consisting of producers of products, components, and technology, are the leading stakeholder group. A second group of stakeholders consists of Service and knowledge providers, ranging from IT Laboratories, legal and consultancy organizations to research institutes and universities. By integrating joint research projects and offering their services through the platform, these stakeholders become critical in the development of new and improved value chains. In this same vein, start-ups and innovation facilitators also become essential users of the platform by bringing together innovation/technology hubs that facilitate and promote innovation.

In order to build self-sustaining, thriving communities of both customers and suppliers on the platform, achieving a critical mass of users is essential. This continuous task of community building is supported by two stakeholder roles: multipliers and investors. Clusters and sectorial network organizations, industry associations and investors that are looking for new business and investment ideas are essential elements in enabling access to larger a pool of ideas and business opportunities.

Also, in supporting roles of the central platform functionalities, auditors and regulators, plus consumers are relevant stakeholder groups. Regulatory compliance and audit authorities place complex sets of constraints on organizations. With these supervisory bodies as platform stakeholders, organizations can take advantage of the privileged contact in order to facilitate compliance, that can even lead to added value for customers in the case of certifications. On the other hand, the presence of consumers in the platform becomes relevant for the development or improvement of products and services.

Based on the defined stakeholders, a set of base 14 functionalities are described in Table 1. Table 2 maps the relationships of each functionality to the relevant affordance.

Table 1. Platform functionalities

Functionality Description Matching Production capacity matching

Matchmaking between suppliers of available manufacturing capacity and customers that aims to exploit that capacity. The platform recommends potential compliant suppliers, filtering them according to user selected parameters.

Know-how capability matching

Matchmaking among suppliers of available knowledge and customers that require support in the related field of expertise. By-product matching Matchmaking between companies whose manufacturing processes generate one or more by-products, and customers that can exploit these byproducts as an input resource.

Optimization Sustainability assessment

The platform supports the optimization of matchings according to an environmental sustainability assessment.

Ecosystem optimization

The platform supports the ecosystem optimization, ranking suppliers and suggesting the most sustainable matchings.

Management User profile management

The platform supports each user in the development of its profile.

Reputation management

The platform allows for both user subjective and quantitative, KPI based evaluations of involved parties in transactions, for establishing a reputation level of users.

Certifications management

The platform allows Auditors and Regulators to certify players through a verified and secure certifications management system.

Trust management

The functionality supports the management of information across the platform giving users the right to define the level of accessibility to provide to their information.

Communication support

The platform supports communication among platform users, streamlining connections and mediating the interactions among parties.

Innovation management

Starting from a user introduced idea, different users can provide tracked and structured contributions. The platform administrates the flow of contributions.

RFQ management

The platform provides the infrastructure to enable the definition and management of quotations, managing the level of visibility of the quotations and partners exchanging requests and transactions.

Transactions management

The platform supports the creation of traceable transactions across the platform value network.

Platform expansion

The platform supports the expansibility of its core functionalities through a complete expansion SKD.

• Certifications management • Trust management • • Communication support • • Innovation management • • RFQ management • • Transactions management • • Platform expansion •

Platform Architecture

Given the socio-technical nature of digital platforms, the architecture design process needs to take into account not only all the technological underpinnings that serve as a platform infrastructure but also all the social and business elements that eventually will develop into the ecosystem. In many ways we may akin the process of platform design to city planning: infrastructure is an intrinsic and essential component of the project, but if focused to the detriment of other components, it may give way to problematic cities. Expansion, of both population and industry/services, equal distribution of services and natural resources and the development of functional transportation networks, are some of the challenges that can be exacerbated by this lack of human perspective. The adopted four-layer architecture, shown in Fig. 1, can further be divided into two groups. A first group, consisting of the Data and Tools layers, corresponds the technological, infrastructure backbone of the platform, while a second group, corresponding of the Business and Web Portal players, are responsible for the ecosystem management, the human and business component of the platform. Each of these four layers houses components that, through their interplay, allow for all the functionalities of the platform.

Fig. 1. Low-level platform architecture

The Data layer sits at the heart of any information system. Where more traditional paradigms of data storage/management were static abstractions where value was derived from the read/write logic, with the development of technologies like the semantic-WEB and distributed ledger systems, in between reads and writes we can gather context, inferences, and accountability. Through this layer, we leverage a semantically described ecosystem of actors, interactions and resources flow to feed an inference reasoning engine capable of uncovering non-trivial and previously unknown opportunities. The developed MANU-SQUARE core ontology, presented by [START_REF] Landolfi | An ontology based semantic data model supporting a MaaS digital platform[END_REF], acts as the first step in the description of a MaaS ecosystem and along with standard services and interface options such as an RDF data store and a SPARQL endpoint, it will feed other platform tools with rich data for other functionalities.

The Blockchain platform, although still part of the Data layer, spills into the tools layer, due to its very nature. At a high level, this component works to ensure provenance, immutability, and finality of data, by guaranteeing that only mutually agreed upon transactions become part of a consensual and cryptographically secure shared ledger. Features like these make blockchain, and distributed ledger technologies in general, an ideal fit for digital platforms where affordances as trust and trustworthiness are a must, even more, when considering how they can be articulated with other components. Acting as the single point of trust for the ecosystem, from simple operations such as logging user access or storing stakeholder's reputation data in a immutable manner, to automating complex transactional operations that involve the exchange of sensitive information, the integration of the blockchain platform will help to fill trust building, transaction management and flexibility generation functionalities.

Given the modular architecture of the platform and both the data persistence methods previously presented, an extra abstraction to facilitate the access to information independently of its location is needed. By abstracting all the underlying data structures, the ecosystem data manager becomes the data broker for the platform and, by exposing a structured API to other components, allows for ubiquitous access to data, regardless of storage infrastructure, while preserving all of the inherent benefits of each storage method. Because this makes the ecosystem data manager aware of all the data flows within the platform, it will work in conjunction with the blockchain platform as a control point for data access.

The tools layer houses the modular tools that will provide many of the core services of the MANU-SQUARE platform. In an on-demand perspective, these services will be in constant communication with both the data layer and the business layer to fulfill many of the functionalities proposed in Table 1 and cover affordances presented in 2.1. The five tools that make up this layer are: [START_REF] Barnes | Building tribal communities in the collaborative economy: an innovation framework[END_REF] The Unified Flow Ecosystem Orchestrator that provides functionalities to analyze the needs of the different companies to propose ecosystem (re-)configurations that better link availability of resources with their optimal environmental performance, working closely together with the matchmaking mechanism; (2) the Matchmaking Tool that provides the production, know-how and by-product capacity matching functionalities. Feeding off of all the stakeholder profiling information, this tool is responsible for the optimal pairing of user's needs with available resources in the ecosystem; (3) the Sustainability Assessment Layer that provides functionalities to support the evaluation of the environmental impact of new chains established through the platform; (4) the Open innovation & Co-design Idea Management Tool provides the Innovation Management functionality by leveraging the open innovation paradigm; and (5) the user profiler and reputation mechanism that provide the user profile management, reputation management, and certifications management functionalities. Because establishing trust and trustworthiness between organizations is a complex, time and resource intensive process, by integrating blockchaincontrolled transactions to keep track of quantitative KPIs such as on-time delivery and quality of products, beside qualitative platform user feedback, based on the perceived quality of interactions with other actors of the ecosystem, we can strike the balance between the technological with the human components of trust.

The business layer, standing between the user-facing interfaces and the core services of the platform is responsible for the orchestration between tools and the complex set of functionalities. Composed by a combination of the gateway orchestrator and a set of outward facing APIs, this engine is responsible for the implementation of business processes relevant the platform's stakeholders through the use of the tools from the Tools Layer. From a modular architecture standpoint, this layer is essential in realizing the full potential of the ecosystem as it alights the flexibility provided by the decoupling of services with the flexibility in the reorganization of services to better fit different business process needs. At its heart, the gateway orchestrator is powered by decision automation software that can interpret business processes codified in standard business process modeling notation (BPMN) and, according to the services offered by the platform, provide users with the optimal experience.

The web portal directly provides the platform expansion functionality and empowers all the remaining by representing the primary interface through which users will interact with the platform. Leveraging the ubiquitous and flexibility of the WEB platform, this layer provides both general graphical interfaces in the form of web pages as well as platform expansion points for external tools. This layer, in direct contact with the gateway orchestrator, will be able to trigger different business processes and run users through the involved tasks.

Conclusions & Next Steps

In this paper, a description of the underlying, layered architecture that serves as the backbone of a MaaS platform has been presented. An initial six affordances, drawn from the gig/sharing economy paradigm, were transposed to the industrial sector and served as guiding principles for the definition of a core set of functionalities required for the introduction of digital platforms as added value tools for the MaaS paradigm. With the actualized architecture, composed by four different layers and eighth individual tools, this framework retains the flexibility of its modular design for the application in different manufacturing sectors, business processes or use cases, all the while maintaining its reliability through the use of state-of-the-art trust-based information technologies in conjunction with tried and true standards. Next steps will consider the development and integration of the software tools, followed by an iterative approach to the demonstration pilots made possible by the development of the architecture alongside the MANU-SQUARE project.

Table 2 .

 2 Mapping of functionalities and relevant affordances.

	Generation of	flexibility	Matchmaking	Scale and reach	extension	Transaction management	Trust building	Community	creation support
	Production capacity matching		•	•
	Know-how capability match-ing		•	•
	By-product matching		•
	Sustainability assessment		•	•
	Ecosystem optimization			•
	User profile management						•
	Reputation management						•

Acknowledgments

The work presented here was part of the project "MANU-SQUARE -MANUfacturing ecoSystem of QUAlified Resources Exchange" and received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements No 761145.


Dariusz Szybicki
email: dszybicki@prz.edu.pl

Krzysztof Kurc
email: kkurc@prz.edu.pl

Piotr Gierlak
email: pgierlak@prz.edu.pl

Andrzej Burghardt
email: andrzejb@prz.edu.pl

Magdalena Muszyńska

Marek Uliasz
email: marek.uliasz@pwrze.utc.com

Application of Virtual Reality in Designing and Programming of Robotic Stations

Keywords: Virtual reality, robots, robotic stations, collaborative network, design of robotic stations

The article presents the subject of the practical application of virtual reality in the design and programming of robotic stations. Robotic stations designed with the use of virtual reality were created in a collaborative network study consisting of a university, enterprise and government organization -National Centre for Research and Development. An example of a project implemented within the framework described in the collaboration article is the robotisation of the manufacturing processes of aircraft engine components.

Introduction

The article presents an example of the use of virtual reality in the design of robotic stations. The project was carried out as part of a collaborative network consisting of a university, a company and a government organization -the National Centre for Research and Development (Fig. 1).

The network of cooperating units in question consists of independent institutions that differ in their goals, environment, organization and culture [START_REF] Shuman | Collaborative networks are the organization: an innovation in organization design and management[END_REF], [START_REF] David | Collaborate and Innovate: A New World of Sourcing[END_REF]. The company that is included in this network has clearly defined goals. The modern theory of the company is based on the contention that the most important goal in the company is to maximize its market value. Besides this general goal, there are also marketing goals, i.e. a level increase in sales, innovative goals -product innovation, and financial goals -profitability. The aim of the University is to educate students, conduct scientific research and create favourable conditions for innovation. The National Centre for Research and Development (NCRD) established in Poland has the following objectives:

• management and implementation of strategic research and development programs that directly translate into the development of innovation;

• supporting commercialization and other forms of transfer of research results to the economy, management of applied research programs and implementation of projects in the field of defence and state security; • ensuring good conditions for the development of academic staff, implementing, inter alia, international mobility programs for researchers.

Fig. 1. Diagram of the functioning of the discussed collaborative network

In pursuing these goals, the NCRD ensures that public money spent on research and development work brings the greatest benefits to the Polish economy. Through several dozen programs, the Centre can provide financial support for a project at all levels of technological readiness -from initial industrial research to the development of an innovative product, service or technology. This offer is complemented by programs supporting the financing of international protection of industrial property and foreign expansion of young innovative companies. The Rzeszow University of Technology, together with industrial partners, has implemented and continues to implement several dozen projects that are co-financed and implemented in cooperation with the NCRD. The team involved in the presented in this work project focuses on the design of advanced robotic stations and robotization of various types of processes in the aviation industry. An example of projects implemented within this collaborative network is the robotic quality control of jet engine components presented in the works [START_REF] Burghardt | Robot-operated quality control station based on the UTT method[END_REF], [START_REF] Burghardt | Software for the robot-operated inspection station for engine guide vanes taking into consideration the geometric variability of parts[END_REF], [START_REF] Burghardt | Robot-operated inspection of aircraft engine turbine rotor guide vane segment geometry[END_REF]. Jointly implemented projects also included autodiagnostics of robotic stations and robotic machining processes in the aviation industry which are the subject of publications [START_REF] Burghardt | Monitoring the parameters of the robot-operated quality control process[END_REF], [START_REF] Teti | Advanced monitoring of machining operations[END_REF], [START_REF] Gierlak | Hybrid position/force control in robotised machining[END_REF], [START_REF] Barnfather | A performance evaluation methodology for robotic machine tools used in large volume manufacturing[END_REF], [START_REF] Burghardt | Experimental Study of Inconel 718 Surface Treatment by Edge Robotic Deburring with Force Control[END_REF], [START_REF] Kurc | Non-contact Robotic Measurement of Jet Engine Components with 3D Optical Scanner and UTT Method[END_REF], [START_REF] Szybicki | Calibration and verification of an original module measuring turbojet engine blades geometric parameters[END_REF], [START_REF] Hui-Ping | Communication of multi-robot system on the TCP/IP In: mechatronic science, electric engineering and computer (MEC)[END_REF], [START_REF] Zhsao | Composite adaptive control of belt polishing force for aero-engine blade[END_REF].

Functionally, the collaborative network based on NCRD allows companies to implement innovative solutions related to robots, for example, and researchers can conduct advanced research using the latest technologies. Projects implemented thanks to financial and substantive support of the NCRD allow the creation of cooperation networks and the development of the units involved. Patents, scientific articles or PhD theses are often the result of cooperation. Companies thanks to the collaborative network have access to laboratories, substantive support and can acquire students as future employees. The implementation of joint projects is very important for didactics. Students may be familiarized with the work being carried out, and they may also take part in the tasks performed. An example of a project implemented within the discussed collaborative network is the robotization of the manufacturing processes of aircraft engine components. During the design and programming of the station for the machining of aircraft engine components, virtual reality techniques were used.

Virtual Reality

Virtual reality (VR) is an artificial image of reality created using information technology. It involves the multimedia creation of a computer visualisation of objects, space and events. It can represent both elements of the real world and completely fictional ones. In virtual reality it is possible to simulate the presence and operation of the user and feedback information is sent to one or more senses in such a way that the user has a sense of immersion in the simulation.

The virtual reality system exposes objects to the user with the help of image, sound and sensory stimuli, and allows interaction, giving the impression of being in a simulation. Sensory feedback provides users with direct sensory information depending on their physical location in a virtual environment. Sensory information is transmitted through synthetic stimuli, that is computer-generated visual, audio or tactile information. Most of the feedback is implemented via visual information, although some environments only use tactile information. In addition to simulation, the virtual reality system requires an interface that allows users to "enter" virtual reality. One of the first such devices was an invention by Morton Heilig, named the Sensorama Simulator from 1962, which was the first video game allowing immersion in virtual reality [START_REF] Burdea | Virtual reality technology[END_REF]. The device displayed a colourful, three-dimensional graphic on a stereoscopic display, it was equipped with a stereo sound system, an odour generator, fans that simulated breeze and a movable seat. The game consisted of riding a motorcycle on the streets of New York. The players felt the unevenness of the road, the smells of food from restaurants and the air movement resulting from the movement of the motorcycle. However, it was not possible to interact with the virtual environment objects. Heilig also started work on virtual reality glasses mounted on the head equipped with headphones. He did not complete his invention, but the potential of his solutions was noticed. The development of technology allowed for the construction and improvement of virtual reality interfaces. Nowadays VR devices are widely available and systematically increasing the number of their applications, both commercial and specialist.

One of the most frequently used examples of the practical application of virtual reality are flight simulators [START_REF] Mihelj | Virtual reality technology and applications[END_REF]. Pilots can practice flying in different conditions, simulate failures and ways of responding to them. Simulators of surgical operations are another important example [START_REF] Gallagher | Virtual reality simulation for the operating room proficiencybased training as a paradigm shift in surgical skills training[END_REF]. An operation is a dangerous situation for the patient, because a single error can lead to his or her death. Following the example of flight simulators, surgical simulators provide a virtual environment in which the surgeon can use realistic touch interfaces (which look like real surgical tools) to perform surgical procedures on various patients. Virtual patients do not necessarily have to be imaginary objects. Modern imaging methods, such as computed tomography, can be used to create a three-dimensional image of the human body. Before a real operation, surgeons can practice on a virtual patient with very similar characteristics to the real patient being prepared for surgery. Surgical simulators have become particularly widespread thanks to the creation of surgical robots that allow the whole operation to be performed using a touch interface and a screen.

In [START_REF] Koźlak | Virtual Reality Technology for Military and Industry Training Programs[END_REF] the authors proposed the use of virtual reality to train employees in positions where they cooperate with robots. The reason for this approach was to improve safety. Thanks to VR, an inexperienced employee is able to learn the principles of safe cooperation with machines. The work [START_REF] Ortiz | Virtual training for industrial automation processes through[END_REF] presents a VR application for training in the field of management of industrial processes, with particular emphasis on pneumatic systems. The virtual application consists of a virtual laboratory and a virtual industrial plant.

Design and Software of a Robotic Station Created with the Help of Virtual Reality

For designing and programming a robotic station, it was decided to use currently available tools for designing and programming robots off-line. Off-line programming is recommended for use in the construction and modification of complex robotic systems. The direct benefits of using off-line programming are:

• shortening break times in production;

• automatic generation of NC code;

• numerical (CAD) representation of elements of the position -easier and faster integration of the robotic station; • use of graphic editor in testing (verification) and optimization of the work program; • facilitated interchangeability of programs between robots; • the ability to create complex work programs, complex position logics, and advanced, large sensory systems.

Using modern computer programs for programming industrial robots, fast and accurate generation of control programs can be obtained. It is possible to test various organizational variants of the position and work scenarios while maintaining both easy and quick corrections and the safety of testing (e.g. collision detection) of the program, thanks to simulation in a virtual environment. The design method of advanced robotic stations is shown in Figure 2.

Fig. 2. The design method of robotic stations

After a review of the available solutions for designing and programming a robotic station, the ABB tool, RobotStudio, was chosen (Fig. 3). Earlier, the use of other available off-line programming tools was considered. The possibilities of using virtual reality of RoboGuide software by Fanuc, Kuka Sim Pro by Kuka and K-Roset by Kawasaki were analyzed. Unfortunately, none of these tools has such options. Connectivity to a computer can be implemented by cable using HDMI and USB interfaces or wirelessly. The movements of the user's head are tracked by two sets of sensors that are responsible for tracking the rotation and position of the head in space.

The Virtual Reality Meeting turned out to be a very useful option used during the design of the station. This solution, which is a function of the RobotStudio software, allows a virtual meeting of several people in one robotic station. People can be in different locations, and distance is not important. Thanks to the Virtual Reality Meeting, users can see their avatars in the form of coloured glasses, talk, draw and annotate in a virtual model of a robotic station (Fig. 5).

Fig. 5. View of the station designed using Virtual Reality Meeting

The RobotStudio application with the Virtual Reality Meeting option allows users to run "live simulations" where robots perform paths, objects move, and the virtual reality provides accurate perception of dimensions. Checking of ergonomics and accessibility for cleaning and servicing equipment are available. The meeting function helps in design analysis and error correction at an early stage. This results in a much shorter installation and start-up phase.

An example of a project implemented with the use of the off-line design, virtual reality and Virtual Reality Meeting tools, discussed above, can be seen in a robotic station for machining aircraft engine components. Pratt & Whitney Rzeszow S.A., which is part of UTC, in its operations performs technological operations involving the machining of V2500 engine diffuser castings. One of many technological operations is edge deburring. In the process of making a diffuser, there are a number of edges that require deburring, which is an operation that is carried out manually. As part of a joint venture, coordinated and co-financed by the NCRD, a robotic station for carrying out this process was designed and constructed (Fig. 6).

Fig. 6. View of a station designed for machining aircraft engine components

The operation of the station is divided into three stages: removal of oversizes (flash), chamfering and grinding of the machined surfaces. The whole process is performed by the ABB IRB140 robot, and the implementation of subsequent processing stages is possible thanks to the exchange of tools located in a four-position changer.

As part of the work, a precise model of the station was prepared using VR, the location of the station elements and the method of loading the workpieces were discussed with the industrial partner, and scenarios of failures, repairs and service inspections were analysed. Due to the need for meetings with engineers from the USA and Canada, the Virtual Reality Meeting function proved useful.

Conclusions

The article presents the available possibilities and an example of the use of virtual reality in the design of robotic stations. This innovative project was created as part of a collaborative network consisting of the Rzeszow University of Technology, the company Pratt & Whitney Rzeszow S.A. and the National Centre for Research and Development. The concept and way of functioning of this network are discussed in the article. The approach based on the cooperation of a technical university and a company, supported by the assistance of a government institution, results in interesting and advanced projects. Thanks to the cooperation of scientists with specialist knowledge and appropriate tools with a company dealing with high technologies, the benefits are shared by both sides and the level of innovation in the state is raised. The method of designing robotic stations shown in the article can be applied in the design of various robotic stations and automated systems. The use of off-line design and programming tools, virtual reality and Virtual Reality Meeting capabilities allows designing and planning in a way that was previously unachievable. The station and similar discussed in the article, developed in cooperation with the industry, are used in the education process. In the case of the station described in the article, students took part in the design of the station elements' layout and prepared some CAD models. In addition, students can see how the design and implementation of the advanced robotic station looked like. Thanks to the use of virtual reality, you can show the way the station is operated and the operation of security systems.

As part of subsequent works, techniques for designing and programming robotic stations using virtual reality will be developed. Improvement of the possibilities of joint and remote work on a given project is intended to be prepared. Advanced avatars of team members implementing the project will be added. The students will be more involved in the project. A base of virtual-interactive objects will be built, from which it will be possible to build more robotic stations as if from blocks.

Fig. 3 .

 3 Fig. 3. View of the station designed in the RobotStudio software

Fig. 4 .

 4 Fig. 4. The concept of cooperation of RobotStudio software with VR tools A PC with RobotStudio software and VR Oculus Rift glasses were used to design and program the robotic station. After the glasses are put on, they display the image of the computer-generated world in front of the user's eyes. The user can look around in a natural way, moving their head or body. The Oculus Rift model used has a screen displaying to each eye an independent image with a resolution of 1080 × 1200 pixels. Connectivity to a computer can be implemented by cable using HDMI and USB interfaces or wirelessly. The movements of the user's head are tracked by two sets of sensors that are responsible for tracking the rotation and position of the head in space.The Virtual Reality Meeting turned out to be a very useful option used during the design of the station. This solution, which is a function of the RobotStudio software, allows a virtual meeting of several people in one robotic station. People can be in different locations, and distance is not important. Thanks to the Virtual Reality Meeting, users can see their avatars in the form of coloured glasses, talk, draw and annotate in a virtual model of a robotic station (Fig.5).

pneumatic controls. In International Conference on Augmented Reality, Virtual Reality and Computer Graphics (pp. 516-532). Springer, Cham (2018).