Wilfrid Utz
email: wilfrid.utz@boc-eu.com

Robert Woitsch
email: robert.woitsch@boc-eu.com

A Model-based Environment for Data Services: Energy-Aware Behavioral Triggering using ADOxx

Keywords: Data as a Service, IoT, Sensor Network, Big Data, Service Composition, Energy Rating and Certificates, Modelling, Meta-modelling

This paper demonstrates an application case for the concept of Data Service design and composition techniques established by the Big-Data Data Service (BD-DS) modelling method realized using the ADOxx meta-modelling platform. In the domain of energy-efficiency assessments of buildings and their operations, the collection of energy related data does not pose a problem anymore as the necessary infrastructure is available in a non-intrusive way, at low installation and operation costs. An issue identified relates to the realization of value-adding services based on these continuous data streams in such distributed, heterogeneous environments. In the context of the ORBEET project, a dynamic, close-to-real-time data access/composition design and exploration framework has been realized. This framework builds upon the concept of Energy Data as a Service combining different sources for a dynamic and enhanced operational rating deployed in 4 different pilot sites in Europe.

Introduction

The design of data access and processing mechanism has gained importance in the past years as a result of the technological evolution on device and infrastructure level. This evolution allows to install and use sensors in various situations and settings, to access, to store and to process larger amount of diversely structured data from the environment surrounding us. A technological challenge that has been identified in [START_REF] Roussopoulos | Design Semantics on Accessibility in Unstructured Data Environments[END_REF] relates to the continuous evolution and update of schema information. Since any connected device, application or service deployed online is potentially a data source that could be used to as input to implement value-adding capabilities as an identified trend in [START_REF] Camarinha-Matos | Collaborative smart grids -A survey on trends[END_REF], (e.g. enable understanding and decision support) a novel approach is required to handle this variability in access and composition. Data is not hand-crafted anymore, but continuously generated by computers, devices, sensors, search results [START_REF] Roussopoulos | Conceptual Modeling: Past, Present and the Continuum of the Future[END_REF]; The related schema is evolving over time along the development of new devices and sensors. When realizing functionality that builds on these dynamically changing and/or attached sources, a flexible approach is needed to continuously adapt access and processing mechanisms accordingly. The "Big-Data Data Service" (BD-DS) modelling method as described in [START_REF] Roussopoulos | Design Semantics on Accessibility in Unstructured Data Environments[END_REF] established an approach and tool support for the concept of Data Services. Using diagrammatic, conceptual models as put forward in [START_REF] Karagiannis | Domain-Specific Conceptual Modeling: Concepts, Methods and Tools[END_REF], following a well-structured modelling procedure, it is possible to design access to data sources of any type, classify composition techniques and provide standardized interfaces for integration, having the format, syntactical and semantical characteristics of the sources and intended target schema as a guiding frame; at the same time the design is readable by the user, externalizing the calculation techniques and methods. The stakeholders involved along the line of realization of novel functionalities (visualization, analysis) are involved and work collaboratively on the design.

This paper demonstrates the application of the above method in an application case from the energy efficiency assessment domain. BD-DS acts as a mitigation layer between energy sensors, visualization/prediction techniques and the end-user in a close to real-time setting. Based on the requirements of the project, the modelling method has been extended and adapted to the specifics of the domain.

The remainder of paper is structured as follows: section II provides background information on the OrbEEt project and related work to set the results presented in context of its objectives. Section III introduces the challenges and requirements from a conceptual perspective, observed and mapped to the BD-DS approach. Section IV describes the prototypical implementation and preliminary results of evaluation in the pilot settings. The paper concludes by outlining future work in the context of the project and beyond.

Energy Data as a Service: The OrbEEt Case

The contribution presented in this paper is based on the work performed in the OrbEEt project (http://www.orbeet.eu/), a research and innovation action funded by the European Commission's Horizon 2020 Energy Efficiency programme. The objective of the project is to introduce an innovative ICT solution to facilitate public and social engagement to action for energy efficiency by providing real-time assessments of the energy impact and energy-related organisational behaviour [START_REF] Tsatsakis | ORBEET: Organizational Behaviour improvement for Energy Efficient administrative public offices[END_REF]. As such it combines and integrates building and operations/business process information with energy data on high granularity level. In current energy assessment techniques three major deficiencies have been identified: a) Assessment Approach: as put forward in the legal framework [START_REF][END_REF] by the European Commission, the objective is to reduce energy use by 20%. Current assessments are performed by expert teams in a static way. Static means that it is done periodically (e.g. yearly) by expert consultants, information of results achieved are not set in context of the changing organisation and occupants' behaviour. Therefore the impact of the results is limited; b) Correlation capabilities are limited and do not allow for a combination of the information with operational behavioural aspects. The solution presented in this paper aims to propose a solution to this challenge. c) Collaboration techniques and analysis of the environment in a virtualized setting: energy-efficiency initiatives need to be run in a balanced way, therefore collaboration is required between involved stakeholders in the business ecosystem [START_REF] Graça | A Proposal of Performance Indicators for Collaborative Business Ecosystems[END_REF]. Such balancing must consider behaviour of the stakeholders, strategy considerations of the organisation, operation and business process adaptation and impact of the supply-chain and distribution network, resulting in a multi-dimensional collaborative composition and aggregation of information as discussed in [START_REF] Matei | Collaborative Data Mining for Intelligent Home Appliances[END_REF]. Fig. 1 shows the overall concept of the project. In this paper we focus on the Systemic Enterprise Operational Rating (SEOR) Engine that acts as a mitigation layer between data sources (energy, operation, building characteristics and location, baselining) and the representation/interaction layer (enhanced Display Energy Certificate, behavioral triggering, gamification). Further details on the project's objective are available in [START_REF] Tsatsakis | ORBEET: Organizational Behaviour improvement for Energy Efficient administrative public offices[END_REF].

The SEOR Engine accesses data from different sources, granularity and variability: Energy data is provided in 10min intervals by sensors, business process/operation information is less variable but space and people aspects needs to be considered and annotated. As output from the engine, harmonized data streams, are required that feed into visualization and behavior triggering frameworks, updating and changing the energy behavior of occupants and operations. As a collaborative project, the consortium is composed of nine partners from six European countries with different competences and experiences. A strong aspect in the consortium relates to the involvement of pilots. These pilot building are located in 4 different countries (Spain, Austria, Germany, and Bulgaria) with different operational usages and construction characteristics. The project started on March 1, 2015 and will run for a period of 36 month with a budget of 1.7 MEUR.

Concept: Extending BD-DS for Energy Data as a Service

As a starting point for conceptualizing the of the SEOR mitigation layer, the BD-DS modelling method has been selected. Based on the generic modelling method framework developed by Karagiannis and Kühn in [START_REF] Kühn | Enterprise model integration[END_REF], the modelling method comprises of the modelling procedure realized as a domain-independent guideline, supported by a corresponding model structure and model processing mechanisms/algorithms. Further details on the modelling method components are available in [START_REF] Roussopoulos | Design Semantics on Accessibility in Unstructured Data Environments[END_REF], complemented by a proof-of-concept implementation (modelling toolkit and operation/deployment mechanisms for data services) available online via the Open Models Initiative Laboratory (www.omilab.org) at [START_REF] Omilab | Project BD-DS[END_REF]. Following the Agile Modelling Method Engineering approach [START_REF] Karagiannis | Agile modeling method engineering[END_REF], BD-DS is iteratively extended to include domain-specific aspects.

Hierarchical Composition and Calculation

Fig. 2. SEOR Data Service Definition Example

For the domain of energy data and assessment, specific requirements have been identified from the project's pilot cases. Fig. 2 shows these requirements visually, detailed below. The core challenge relates to support decision making and behavior triggering in a distributed, heterogeneous environment, whereas distribution is understood from a technical observation but also organizational (e.g. skills, competences, involved stakeholders). The user of the system should have the possibility to understand the meaning of data and its impact on the behavioral triggers that are put in place in a transparent way. Graphical models act here as the knowledge-bearing entity: they can be interpreted by the end-user, providing means to reason and derive action from the provided results and are also machine executable by a real-time access and composition engine. The requirements for the extension are:

1. Temporal Variability: as data sources with varying temporal dimensions are input to the composition, harmonization techniques need to be applied to create combinable data series. An example for this challenge relates to the different update intervals of energy sensors: electricity sensors provide their input in 10min intervals, whereas heating data is captured on a bi-monthly to monthly basis. Operation information is captured manually by analyzing the business processes with a lower variability and the building structure is described as the Building Information Model (BIM) using the gbXML standard [START_REF]gbXML -An industry supported standard for storing and sharing building properties between 3D Architectural and Engineering Analysis Software[END_REF]. 2. Semantic Annotation: during the design of data service compositions, modelling support mechanisms are required. Through semantic annotation of data sources, initial compositions and filters become feasible using the temporal, spatial and operational context annotations as input. 3. Data Quality, Provenance: due to the technical distribution of providing sources and integration via a loosely-coupled architecture as detailed in [START_REF] Utz | Conceptualisation of Hybrid Service Models: An Open Models Approach[END_REF] and [START_REF] Woitsch | Process oriented knowledge management: A service based approach[END_REF], quality and provenance information needs to be captured dynamically and composed along the calculation logic. 4. Hierarchical Composition: the calculation of complex data services is designed in a hierarchical manner. Calculation techniques are made available as plugins to provide flexible extension and modification mechanisms. The processing engine interprets the hierarchy, binds the calculation plugins dynamically and performs the composition as well as orchestration. The extensions to BD-DS on a conceptual level based on the above requirements are discussed in the following.

Modelling Language Extension: Metadata Annotation

The BD-DS modelling language as presented in [START_REF] Tsatsakis | ORBEET: Organizational Behaviour improvement for Energy Efficient administrative public offices[END_REF]Fig. 4] is extended to provide means for annotation of data-services with energy related metadata and contextual information. The objective of this extension is to keep the core meta-model stable and provide dynamic means to extend the semantic expressiveness of artefacts created. The Semantic Lifting approach from [START_REF] Woitsch | Business Process as a Service (BPaaS)[END_REF] is re-purposed for BD-DS, extending the available "Metadata" view. The characteristic of the extension is twofold: a) it establishes on tool-level a dynamic and adaptive mechanisms to cope with changing domain-specific semantics and their evolution, and b) enriches the semantic expressiveness of the artefacts created to realize model-value functionality in the form of domain-specific analysis features, graph-rewriting techniques and composition layouts. Model-value capabilities aim to elevate the model quality from a user interaction and interpretation point of view with the graphical conceptual models used to specify the data service logic.

This repository for annotation is made available as an ontological representation using RDF syntax [START_REF]W3C Consortium: RDF 1.1 concepts and abstract syntax[END_REF] on vocabulary and instance level: the energy domain semantics of OrbEEt are captured in a custom, high-level vocabulary that can be imported into the system, updated and modified. Related instances are derived from the domain context of the observed building, device sensor and operation. Instance information is considered to reside in an external environment. The combination of both vocabulary and instance information represents the annotation and tagging base as shown in Fig. 3.

BD-DS Annotation Extension using RDF

Mechanisms and Algorithms: Annotation-based Modelling Support

The annotation technique of above is used as input to dynamically derive data service compositions. Based on the context made available in the model, composite services are dynamically created and added to the model. The algorithmic solution is available as pseudo code below. The algorithm uses defined data source services from a common information layer (an abstraction from actual sensor information) and their annotations as input.

Pseudo-code: Annotation-based Composite Data Services

# retrieve all source services set sources to GET_BASE_DATA_SOURCES set annotations to map (a_key, a_value_list) for i=0 to source.size set a_src to GET_DATA_SOURCE_ANNOTATIONS (source(i)) set annotations to union(annotations, a_src) endfor # create aggregate service set r_list to annotations.get(r_key).values for k=0 to r_list.size CREATE_AGGREGATE_DATA_SERVICE (l, r) set rel_src to GET_SERVICE_BY_ANNOTATION (r_key, room(k))

CREATE_INPUT_RElATlON (new_aggrate_ds, rel_src) endfor RADIAL_WEDGE_LAYOUT (model)

The algorithm is available for the end-user to create complex design, validate the composition logic and visualize results interactively.

Through these extensions on meta-model and mechanism/algorithm level, the requirements of the energy domain in data service design can be satisfied. Evaluation was performed by realizing a prototypical implementation of the extension and validating its operation with the OrbEEt framework.

Evaluation: SEOR Data Service Designer and Engine

The proof-of-concept implementation has been performed as the SEOR Data Services Designer, supporting the modeling of energy data as a service, and Engine supporting the operationalization of these services in the cloud.

SEOR Data Service Designer

The realization of the prototype builds upon the available implementation of BD-DS on ADOxx [START_REF] Adoxx | ADOxx Metamodelling Platform[END_REF] resulting in the SEOR Data Service Designer. The extensions have been implemented using metamodeling concepts and techniques, adapting the metamodel with required constructs for annotation and temporal update definitions for dynamic data series.

Fig. 4 shows the results of the implementation in the SEOR Data Service Designer: 1. Data sources are retrieved from the abstract sensor information layer. The step is performed through automated import mechanisms (detection) and manual interaction of the energy expert. 2. The domain expert annotates the data source services with semantic constructs, imported in RDF Turtle syntax. Annotation is performed for location, operational and temporal attributes as well as energy type, based using RDF subjects and objects (see Fig. 4 a)); 3. The tool derives additional properties are using ADOxx Expressions (composition serial, temporal cron expressions). 4. The system automatically creates the composed data services based on the annotation (see Fig. 4 b)) and performs a radial layout algorithm on the model for better readability.

SEOR Data Service Engine

The SEOR Data Service Engine has been implemented as a web-application using Java 8.0 on the server side and JavaScript/HTML5/CSS for the user interface. The applications logic is dynamic upon the design artifacts and operationalizes them using web-service technologies. As an interaction layer with the engine, all data services (source-and composed service) are accessible through standard service protocols (SOAP and ReST) to reduce integration effort. A validation UI is provided to assess the operational status of each service, values and historical information. The characteristics of this implementation are summarized below:

─ Interpretation of conceptual model: the engine interprets the diagrammatic representation in the designer and executes them. This approach provides flexibility to the designer as artifacts created can be verified and validated in operation without further implementation/coding ─ Plugin-based Composition Logic: Composition and aggregation logic in the engine is supported by a plugin-based approach, meaning that arbitrary composition techniques for data services can be realized as individual plugins in the engine, dynamically made available in the designer following [START_REF] Utz | A model-driven data marshalling approach for capturing learning activities in heterogeneous environments[END_REF]. ─ Time-series handling: to support the requirement of temporal variability and data quality, the engine builds on locally cached, streamed data. According to the derived temporal variability, the engine runs update threads for each service in the

Conclusion and Outlook

The work presented in this paper provides insights into results achieved within the energy data modelling and composition task of the OrbEEt project, applying the BD-DS Data Service concept in a practical setting. An initial technical and user evaluation of the results took place, resulting in the finding that due to the flexible approach, an efficient design and deployment process as well as agile adaptation in the system could be performed. A challenge identified during the work relates to the observed knowledge gap between business views captured in the operation and data processing experts. Interpretation of finding and therefore optimization techniques on both ends are currently only of limited value due to this gap. Different levels of visual and conceptual techniques are going to be developed in the coming period, summarized under the keyword "Conceptual Analytics", to foster the creation of value-adding, innovative service offerings in the domain and their application in networked organisational settings. This work contributes to elevate (big) data related work to a conceptual and decision making level, involving stakeholders from different background, expertise and create organisational value, not only through data analytics but also in conceptually understanding data assets in the company.

Fig. 1 .

 1 Fig. 1. OrbEEt Overall Concept [9]: Positioning the SEOR Layer

Fig. 3 .

 3 Fig. 3. Extended BD-DS meta-model with RDF annotations

Fig. 4 .

 4 Fig. 4. SEOR Data Service Designer: a) Annotation and b) Composite Service Generation The SEOR Data Service Designer enables additional interaction capabilities, provided by the meta-modelling platform ADOxx such as visualization options, analysis and query techniques, reporting and open interfaces.

  background and enables close to real-time processing. Additional aggregation techniques are supported to condense/expand a time-series. In the evaluation environment for the project, growing time-series data of 531 data services (atomic and composed) consisting of 9700 entries per series (as of writing this publication) are handled in a performant and efficient way. A re-design of the composition logic is by the designer is supported at any point in time. ─ Dynamic filtering and visualization techniques: review results of the SEOR engine execution for specific time-frames and visualize results as charts.Composed Energy Data Service: Electricity Consumption in Administrative RoomAtomic Data Service -Sensor-based: Electricity Consumption for a specific PC

Fig. 5 .Fig. 5

 55 Fig. 5. SEOR Data Service Engine: Data Explorer (atomic and composed) Fig. 5 shows the SEOR Data Service Engine's user interface. The Data Explorer provides functionality to access data services in a hierarchical manner, visualize the change history and drill-down/up to related services.

Variability 1 4

		request	output
			<<Align>>
		Energy-Consumption	Calculation Rules
			Per Process
		input		input
		Time: min				Time: hourly
	<<Source>>	Operation: Business Process X		<<Source>>	Operation: Business Process Y
	Heat Cost A Data Service	Location: Room K Unit: wH Quality		Sensor B Data Service	Location: Room L Unit: wH Quality
	Access Technique: Text Mining	Provenance	Time Annotation	2	Access Technique: API Retrieval	Provenance	(Provenance, Quality) Pre-processing	3
			Operation
			Location
					API

Acknowledgments

. The OrbEEt project has received funding from the European Community Horizon 2020 Program for European Research and Technological Development (2014-2020) within the funding line H2020-EU.3.3.1. -Reducing energy consumption and carbon footpint by smart and sustainable use. Contract no. 649753-OrbEEt-H2020-EE-2014-2015/H2020-EE-2014-2-RIA


Günther Schuh

Anne Bernardy

Violett Zeller

Volker Stich
email: volker.stich@fir.rwth-aachen.de

Violett Bernardy

Zeller

New Requirement Analysis Approach for Cyber-Physical Systems in an Intralogistics Use Case

Keywords: CPS, Matching, Collaboration, Requirements

Nowadays, cyber physical systems support the improvement of efficiency in intralogistics by controlling and manipulating the production and logistic environment autonomously. Due to the complexity of the individual production processes, designing suitable cyber-physical systems based on their existing production environment is a challenge for companies. This paper presents a new methodology on how to design cyber-physical systems conceptually to suit an individual production environment. Compared to existing design approaches, this methodology matches immediately the required functions to existing information and communication technology's components insisting on the neutral assimilation of requirements. Therefore, the requirement specification asks for needed functions in relating to offered functions of information and communication technology (ICT) components. The paper focusses the use case of implementing a cutting-edge mobile network technology into an existing tracking and tracing process

Introduction

Nowadays, companies pursue the implementation of intelligent systems in their production to keep up with the changing demands towards the flexible production of tomorrow. Through a dedicated use of information technologies like monitoring systems to supervise the production status of orders and material flows, the production status can be communicated and visualized in real-time, and an optimized production line can be analysed per order [START_REF] Frey | IKT in der Fabrik der Zukunft[END_REF]. These potentials are essential factors for small and medium-sized enterprises (SMEs) to maintain their competitiveness.

However, most companies are limited in time and financial resources to integrate CPS into the intralogistics of their production. One major challenge is the conceptual design and the use of information technologies due to the constantly growing market of CPS. Furthermore, the benefit and deployment depend on many factors as individual required functionalities so that SMEs need to rely on external expertise for inte-gration [START_REF] Frey | IKT in der Fabrik der Zukunft[END_REF][START_REF]Chancen zur Bewältigung des Fachkraeftemangels in KMU[END_REF].

This complexity is caused by the unclear definition how to design CPS [START_REF] Akkaya | Systems Engineering for Industrial Cyber-Physical Systems Using Aspects[END_REF][START_REF] Derler | Modeling Cyber-Physical Systems[END_REF]. Historically CPS are an evolution of embedded systems, so that existing design methods are based on system engineering methodologies. Using existing modelling approaches requires the configuration of process models [START_REF] Derler | Modeling Cyber-Physical Systems[END_REF][START_REF] Baum | Industrie 4.0: Beherrschung der industriellen Komplexität mit SysLM[END_REF][START_REF] Akkaya | Systems Engineering for Industrial Cyber-Physical Systems Using Aspects[END_REF] but do not support the choice of suitable information and communication technologies (ICT). In addition, the design of complex CPSs requires a comprehensive knowledge of these production processes beneath the knowledge of system engineering, which is usually only known by the user company itself. Since the market for information and communication technologies grows exponentially, a specialized knowledge of these technologies is needed to ensure the best possible solutions for a CPS concept and realization. The process of integrating individual CPSs in manufacturing companies is accompanied by the challenge of knowing and taking into account all relevant interfaces in advance [START_REF] Acatech | Cyber-Physical Systems: Innovationsmotor für Mobilität, Gesundheit, Energie und Produktion[END_REF]. Hence, this paper proposes a new method to introduce CPS. Since one common use case for CPS is to connect a tracking and tracing system, this use case serves as an example to illustrate the provided approach explaining how to choose systematically information and communicating systems in the intralogistics to transform it to a CPS.

State of the Art

These days, the tracking and tracing of products in the intralogistics is essential for different reasons. On the one hand, products in the reclamation allow the interference to the reasons of quality lack. On the other hand, tracking and tracing orders and material helps to point out difficulties of the production and logistic processes. The global logistics use standard identification systems, that can register their status autonomously and which are standardized like the EPCIS, for about 20 years [START_REF] Kropp | Entwicklung eines generischen Ereignismodells als Grundlage der Produktionsregelung[END_REF]. In contrast, tracking and tracing systems for the intralogistics are mostly insulated applications referring to the individual company [START_REF] Bauernhansl | Industrie 4.0 in Produktion, Automatisierung und Logistik: Anwendung, Technologien und Migration[END_REF]. Due to the changing requirements toward the productions effectivity caused by the digitalization, tracking and tracing systems need to get connected to the industrial internet as well as the material and orders of the company. Therefore, the tracking and tracing system needs to be transformed into a cyber-physical system.

The conception of a CPS however requires a common definition for all stakeholders, but no commonly acknowledged definition has been established so far [START_REF] Bettenhausen | Cyber-Physical Systems: Chancen und Nutzen aus Sicht der Automation[END_REF]. To develop the approach to match applicant's requirements and technology functions, the FIR INSTITUTE FOR INDUSTRIAL MANAGEMENT invented a functional definition based on the definitions of [START_REF] Lee | Cyber Physical Systems: Design Challenges[END_REF][START_REF]Cyber-Physical Systems (CPS)[END_REF][START_REF]Cyber-Physical Systems (CPS)[END_REF], which presents six main functions in a CPS [START_REF]Requirements-based matching approach to configurate cyber-physical systems for SMEs[END_REF].

Currently, the only designing and modelling methods for CPS refer to configurations of the chosen components in a simulation environment, e.g. in Modelica, Matlab or Simulink [START_REF] Akkaya | Systems Engineering for Industrial Cyber-Physical Systems Using Aspects[END_REF][START_REF] Derler | Modeling Cyber-Physical Systems[END_REF][START_REF] Lee | A Cyber-Physical Systems architecture for Industry 4.0based manufacturing systems[END_REF]. Since these tools require user skills to simulate the interdependencies between already selected components, they neither support the selection of technologies nor are they suitable for the use by untrained system designers. In addition, they don't involve the applicant into the designing process and do not support requirement engineering approaches to ensure an entire, solution-neutral requirement recording. The selection of the technologies needs to factor new, upcoming ICT because the implementation process of a cyber-physical system takes up to two years in average [START_REF] Stich | Studie -Cyber physial Systems in der Produktionspraxis[END_REF][START_REF] Lee | The Past, Present and Future of CPS: A focus on Models[END_REF][START_REF] Stich | Big Data Technology for Resilient Failure Management in Production Systems[END_REF]. Screening the ICT market is moreover incessant to ensure the selection of a sustainable and effective technology portfolio.

For a proper decision-making, the alignment between applicant's requirements and technology provided functions needs to be supported by a cost-value-analysis of given matching results between problems and solutions. The following section will describe the foundation of the matching methodology.

Scientific Approach -The Matching Method Applied to Tracking and Tracing

As mentioned before, a CPS contains technologies from the cyber and the physical world. Implemented to tracking and tracing systems, they enable more flexibility and environment independence when networking transmission technologies are used to globally connect the tracking and tracing system. Concerning the example use case in this paper of the connected material tracking in a production, the desired cyberphysical system needs sensors to identify the location of the material. Suitable technologies are e.g. Auto-ID technologies such as RFID or barcode systems. Furthermore, the identification has to be connected to the central local or global controlling system.

Fig. 1: The six technology clusters and their tasks

Hence, transmission technologies have to be used like Bluetooth or mobile connectivity. To fulfil the aim of the tracking and tracing, the data needs to be firstly transformed by tools like e.g. diagrams and then displayed e.g. on a smart device or a terminal. Finally, the cyber-physical system contains an actuator like e.g. a robot which can interact due to implemented logics. If there is no autonomic, technical system to interact, there is at least a human to react to the events during the production process. Finally, the CPS needs a stable IT infrastructure as a backbone. This example illustrates the different functions a CPS has to fulfil. Based on case research, the FIR Institute for Industrial Management at the Aachen University formed six technology clusters to categorize information-and communication systems [START_REF]Requirements-based matching approach to configurate cyber-physical systems for SMEs[END_REF]. Fig. 1 gives an overview of the definitions and example technologies for the six technology clusters developed at FIR in Aachen. They were conceived to increase accuracy in describing CPS technologies, allowing for a more systematic approach towards categorizing CPSs. As shown in Fig. 1 the technology clusters are:

-Actuators: Actuators represent the means of a CPS to interact and manipulate the physical world via hard-and software applications. On the one hand, this can be a hardware like a robot or a motor control. On the other hand, software like a MES (manufacturing execution system) can control production systems and change its setup. -Data Analysis and processing: The raw data needs to be analysed referring to an individual task. Referring to the tracking and tracing system, methods like deep learning build the basic to reduce order or material stockings automatically. Artificial intelligences (AIs) can predict shifts in machine occupancy and change the job distribution inside the production system to re-establish an equilibrium on their own. -IT infrastructure: Essentially, all the data assimilation, processing and transfer refers to a stable IT infrastructure. The aim of the technologies of this cluster is to ensure the data, information and knowledge in defined storages like central servers or in the decentral cloud to keep it retrievable at any time. Corresponding to the connected tracking and tracing systems, the data needs to be stored and available for different stakeholders like the production manager to interact if any flow is stacked or the customer supports to calculate the delivery time. E.g. cloud technologies offer the new flexibility to scale the IT infrastructure according to current requirements. However, cloud services decrease the control over the processed data. -Transmission Technologies: Finally, the transmission of data between admission localization and users need to be realized. Transmission technologies such as mo-bile communication, Wireless LAN, Ethernet or bus-systems like ProfiNet© provide the technical infrastructure to exchange data and information within the different agents inside the cyber-physical system. In respect to tracking and tracing systems, the requirement towards more flexible ways of data transmission increases so that wireless communication technologies such as 4G, 5G, and Wireless LAN gain in importance. Depending on the environment, their reliability and their security function lack the requirement so that Ethernet or Fieldbus are the preferable options. To match the right technologies to individual requirements, a systematical alignment approach is obligatory. Since available technologies of the market can contain several functions of different technology clusters, the matching needs to consider functional overlapping to avoid redundant technology suggestions. Therefore, for all clusters morphologies are designed with categories, attributes and characteristics.

Fig. 2: Example of the matchmaker

The morphologies serve as a pattern to categorize technologies. If a technology is sorted into a technology cluster, it creates an own pattern which later on can be compared to the requirement pattern (see Fig. 2). Every technology assigns for at least one of the clusters. New developments regarding e.g. RFID systems are covering multiple technology cluster aims. They are sorted into several clusters and the matching algorithm proves the redundancy of the functionalities. In relation to the tracking and tracing use case, typical technologies like identification and localization systems are defined by the physical measurement task, the data transmission quality, the existing clients to run IT systems in general and the desired interaction like visualization via a touchable display of the humans.

Theses morphologies form the structure for the systematic requirement assimilation presented in section 0 in this paper and shape the framework of the matchmaking algorithm. Based on the typologies for both the requirements and the technologies, the matching algorithm can then identify the best matches for requirements and solutions. This means that the requirements need to be assimilated into the same kind of pattern like the technologies are clustered. The applicant has to fulfil a questionnaire matching the categories, attributes and characteristics with requirements and to prioritize these requirements by a paired comparison. The ranking decides the alignment process in which or Hence, every question of that requirement guidance is a translation from an available function to a customer need. Mostly, one question asks for one attribute. Since the six clusters contain more than 15 characteristics per cluster with at least two attributes, the applicant needs to answer more than 200 questions. To minimize the effort for the applicant, the questionnaire fades out questions corresponding to answered ones. The fulfilled questionnaire forms than the requirement pattern. The process in the configurator to extract the requirement pattern efficiently is explained in the next section.

Requirement Extraction

For the requirement extraction, three overhead-use-case-types are given by the magic triangle of logistic problems [START_REF] Pfeifer | Qualitätsmanagement: Strategien -Methoden -Techniken[END_REF]. The triangle shows that logistic problems can transferred to an initial issue such as:

-A quality process improvement getting better -An efficiency increase  getting faster -A cost decrease  getting less expensive The first use-case-type Quality requires e.g. all kinds of proving and checking processes as well as measurement processes. Problems of quality management in the intralogistics are e.g. producing lots of waste or certify critical products. Stage-Gate processes are known in the quality management to decide the status of a product. The second use-case-type Time requires support systems and automation systems. The pre-set questions focus on Human-Machine-Interfaces and the possibilities to assist the worker with wearables.

Talking about industrie 4.0, this use-case-type includes questions about flexibility and an efficient one-piece production [START_REF]Industrie 4.0 im globalen Kontext:: Strategien derZusammenarbeit mit internationalen Partnern[END_REF]. The third use-case-type costs brings into focus the problems like too high costs. Compared to the use-case-type time material flows are analyzed and have to be improved. The recorded IT infrastructure is essential to detect redundant or unused systems. The data analysis need to be specified by the individual applicant's problem so that the questionnaire is just reduced to typical visualization tasks like the overview of e.g. correlations between environment data (e.g. temperature, humidity) and failures of the products.

Summary and Outlook

The matchmaking supports the networking of technology suppliers and applicants and is realized by an online platform; via whose Web front the requirements are recorded. For an efficient determination of requirements, the user selects a problem description from a list of reference application cases, which corresponds to his individual problem situation (see Fig. 2). The pre-selection limits the following questionnaire to a selection of relevant questions. The structure of the questionnaire provides a continuous containment of the questions so that the effort of the filling is reduced to a minimum for the applicant.

Six functional technology clusters built the framework for both, the requirement assimilation and the technology sorting. Hence, the requirement recording is structured and implemented in a way that gradually limits the number of possible technologies to the compatible and application-specific components from the existing framework conditions and the specification of the optimization target.

The questionnaire asserts a solution-neutral requirement assessment to ensure that users are offered the most efficient and effective solution. The CPS matchmaker compares the requirements profile with its broad portfolio of technologies. The solutionneutral formulation prevents companies from using well-known technologies without knowing all the technologies that functionally fulfill the required task.

Currently, the CPS matchmaker is in the process of recording and validating application cases as well as technologies and their providers. According to the project sponsored by the Leitmarkt.IKT agency and the EFRE, the CPS matchmaker will be completed as an online platform by the end of 2018 and will be continually validated by summer 2019. In addition to the application-oriented reference application cases of the application partners, further applications for test purposes may be included in the case of interest. Current information can be viewed at: cyberKMU.de

  -Sensors: Sensors build the antagonist to the actuators cluster. The aim of sensor technologies is the generation of data. Concerning tracking and tracing use cases, sensors especially assimilate localization and identification data. Auto-ID sensors e.g. like RFID-chips support the detection of the location of an item or identification the product being handled, as well as camera systems that automatically recognize the positioning of the work piece and which are monitoring the piece's quality are examples for sensors in the industrial context.-Human-Machine-Interface (HMI): Human-Machine-Interfaces support the decision support. The visualization of aggregated data is a central aim of the technologies referring to this cluster. Corresponding to the tracking and tracing systems, HMI can appear in the form of a dashboard giving a real-time overview of material and order flows, and can uncover material, information or orders stacks. Theses dashboards can be displayed e.g. on smart devices (smartphone, table) or on BDEterminals. Other interaction possibilities are realized via speech (e.g. pick-byvoice).

Fig. 3 :

 3 Fig. 3: The questionnaire process The three types of use-cases require different pre-sets of combinations of attributes of the technology clusters and therefore different pre-sets of characteristics. Nevertheless, two of the technology clusters are essential for every use-case-type because of the definition of a cyber-physical system: the transmission technologies to transfer the data of the data sources to a central or decentral data store, which is part of the IT infrastructure.The first use-case-type Quality requires e.g. all kinds of proving and checking processes as well as measurement processes. Problems of quality management in the intralogistics are e.g. producing lots of waste or certify critical products. Stage-Gate processes are known in the quality management to decide the status of a product. The second use-case-type Time requires support systems and automation systems. The pre-set questions focus on Human-Machine-Interfaces and the possibilities to assist

Acknowledgement

This work has been funded by the Leitmarkt.NRW program of the European Federation R E cooperating with the federation of North-Rhine-Westphalia (NRW) while the project "cyberKMU²". The authors wish to acknowledge the EFRE and NRW for their support.


Mohamed Adel Rezk
email: mohamed.adel@insight-centre.org

Adegboyega Ojo
email: adegboyega.ojo@insight-centre.org

Islam A Hassan
email: islam.hassan@insight-centre.org

Mining Governmental Collaboration through Semantic Profiling of Open Data Catalogues and Publishers

Keywords: unstructured data analysis, data mining, collaborative network; open data, e-government

Due to the increasing adoption of open data among governments worldwide especially in the European Union area, a deeper analysis of the newly published data is becoming a mandate. Apart from analyzing the published dataset itself we aimed on analyzing published dataset catalogues. A dataset catalogue or a dataset metadata contains features that describe what the data is about in a textual representation. So, we first acquire data from open data portals, choose descriptive dataset catalogue features, and then construct an aggregated textual representation of the datasets. Afterwards we enrich those textual representations using Natural Language Processing (NLP) methods to create a new comparable data feature "Named Entities". By mining the new data feature we are able to produce datasets and publishers relatedness network. Those networks are used to point similarities between the published data across multiple open data portals. Pointing all possible collaborations for integrating and standardizing data features and types would increase the value of da1ta and ease its analysis process.

Introduction

Despite the availability of data loaded into open data portals worldwide 1 [START_REF] Shadbolt | Linked open government data: Lessons from data[END_REF][START_REF] Breitman | Open Government Data in Brazil[END_REF], methods to maximize stakeholders' engagement and ease data integration still not complete [START_REF] Mutuku | Increasing Kenyan open data consumption[END_REF][START_REF] Artigas | Visual analytics for open government data[END_REF][START_REF] Ribeiro | An Urban Data Profiler[END_REF]. We believe that a proper mining of collaboration channels within a single data portal internally as well as between multiple open data portals are not introduced yet. Our work is aiming to develop an open data portals collaboration channels mining framework as shown in Fig. 1. To achieve this, we start with data acquisition by harvesting metadata of datasets published on the portal then restructure and store them in MongoDB 2 . Afterwards we construct textual representation from the dataset metadata's unstructured features, apply DBpedia [START_REF] Auer | Dbpedia: A nucleus for a web of open data[END_REF] Named Entity Recognition pipeline called DBpedia Spotlight [START_REF] Mendes | DBpedia spotlight: shedding light on the web of documents[END_REF] to extract information that represent those dataset and their publishers as well. After that we end up with a semantically enriched dataset upon which we can apply our profiling [START_REF] Ribeiro | An Urban Data Profiler[END_REF] and collaboration opportunities analysis. To illustrate our work, we organized the paper as follows: Section 2 presents a background on Open Government Data, NLP and Collaboration Mining. Section 3 discusses our approach to tackle the research question. Section 4. Discussing our research findings, conclusions and future plan.

Background and Related Work

Following concepts definitions and a literature review of correlated research areas Open Government Data, NLP and Collaboration Mining:

Open Government Data

Open Government Data referred to the datasets generated and published by governmental departments "without any restrictions on its usage or distribution" and it doesn't contain any personal or undisclosed data [START_REF] Janssen | Benefits, Adoption Barriers and Myths of Open Data and Open Government[END_REF]. OGD vary by multiple aspects for example: a) OGD publishing department or agency domain e.g. Agriculture Data, Transport Data, Environmental Data, Financial Data and Telecommunication Data. b) Data format e.g. Excel, Text, PDF, CSV, Theoretically, Government Open Data is operational or administrative governmental data available to use, redistribute, and analyze "in any form without any copyright restrictions" [START_REF] Kassen | A promising phenomenon of open data : A case study of the Chicago open data project[END_REF]. Regarding the open government working group draft in 2007 3 they generated initial open data principles: data must be complete, primary, timely, accessible, machine-processable, nondiscriminatory, nonproprietary, and license-free. Then they generated further open data principles, data must be online and free, permanent, trusted, assumed to be open, documented, safe to open, and designed with public input. Fig. 2 shows the Irish government's open data portal which we used for our experiments 4 . Fig. 2. Irish Government's Open Data Portal.

Natural Language Processing

Following we discuss the correlated features of Natural Language Processing to our research. Specifically, Named Entity Recognition applications:

Named Entity Recognition

Named Entity Recognition is the process of discovering Named Entities (NE) laying within a given text, a common definition of NE is as follows [START_REF] Nadeau | A survey of named entity recognition and classification[END_REF], "an information unit described by the name of a person or an organization, a location, a brand, a product, a numeric expression including time, date, money and percent found in a sentence." [START_REF] Grishman | Message Understanding Conference-6: A Brief History[END_REF]. NER applications are implemented using multiple methodologies:

The Supervised Learning techniques use a big manually categorized dataset. Then this dataset is used for training the recognition algorithm. Supervised Learning techniques apply Conditional Random Fields [START_REF] Mccallum | Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons[END_REF], Hidden Markov Models [START_REF] Bikel | Nymble[END_REF], Decision Trees [START_REF] Borthwick | NYU: Description of the MENE named entity system as used in MUC-7[END_REF], Support Vector Machines [START_REF] Asahara | Japanese Named Entity extraction with redundant morphological analysis[END_REF] and Maximum Entropy Models [START_REF] Hoffart | Robust disambiguation of named entities in text[END_REF] The objective of these methods is to identify and categorize related key-words. The unavailability of manually categorized datasets and the high cost of generating them, represent a challenging obstacle against Supervised Learning Techniques.

The Semi-Supervised Learning and Unsupervised Learning techniques use either a small categorized dataset for training the algorithm [START_REF] Ji | Data selection in semi-supervised learning for name tagging[END_REF], or a clustering based algorithm. Further Unsupervised Learning techniques depend on lingual resources e.g. WordNet, and statistics to solve the NER task as a prediction problem [START_REF] Alfonseca | An unsupervised method for general named entity recognition and automated concept discovery[END_REF].

Natural Language Processing in E-Government

There are few implementations of NLP technologies in the e-government area. Examples from the works found: A proposed application for gathering crime data from police departments and eyewitness stories and apply NLP technologies with GATE [START_REF] Ku | Natural Language Processing and e-Government : Crime Information Extraction from Heterogeneous Data Sources[END_REF]. A system that imitate email answering process automatically or semiautomatically using NLP technologies [START_REF] Dalianis | Clustering E-Mails for the Swedish Social Insurance Agency -What Part of the E-Mail Thread Gives the Best Quality[END_REF]. Another application presents an original model for incorporating multimedia data to assist e-government tasks [START_REF] Amato | Semantic Management of Multimedia Documents for E-Government Activity[END_REF].

Mining for Collaboration

In general, due to the great benefits and possibilities of collaboration opportunities mining and discovery research e.g. Process speed enhancing, Standardization and Integration. The detection of possible collaboration opportunities within an organization or across multiple organizations and platforms is targeted in multiple domains. Following the few existing work digging into mining for collaboration area: Mining for collaboration in library domain, the research is harnessing the detection of possible collaboration opportunities with academic professional based on their publications to increase the benefits of students [START_REF] Williams | Prospecting for new collaborations: Mining syllabi for library service opportunities[END_REF]. Collaboration mining between governmental levels and departments based on their objectives, resources and services to increase the government efficiency regarding public policy development and implementation, crisis management, etc. [START_REF] Basanya | Mining collaboration opportunities to support Joined-Up Government[END_REF]. Collaboration mining tool using agent technology to analyze the collaboration between information on the web to help the tool users to get their desired materials more accurately and faster [START_REF] Wan | An Information Mining Model of Intelligent Collaboration Based on Agent Technology[END_REF]. Collaboration mining of team members using summaries of successful past projects to increase moderator efficiency to promote project partner's awareness of best way to formulate a proposal for a European research project [START_REF] Palmer | Generating rules from data mining for collaboration moderator services[END_REF].

Semantic Profiling for Collaboration Mining

As shown in Fig. 1 and zoomed in Fig. 3 we have designed a solution pipeline that incorporates Data Acquisition, Data Modeling, Data Analysis, and Data visualization technologies to enable the existence of a collaboration mining tool. We start with inputting the targeted open data portal(s) in which we seek mining for collaborations then we start acquiring metadata (catalogue) of the datasets. Then we restructure the catalogue to fit into the predesigned storage model (semantic profile), within this model we enhance, filter and exclude less important catalogue features -regarding our use case -and we add new features that are corresponding to our collaboration mining requirements e.g. we add "textual representation" feature by merging original textual features of the data catalogue, we add "Entities" feature to the new catalogue storage model by applying NER over the new "textual representation" of the catalogue, we filter features like "author" and "creator" to end up with only "publisher ID" feature, and we exclude "groups" and "tracking summary" features. After constructing and storing the new data model (semantic profile) we start the unstructured data analysis (text mining) pipeline by applying NER algorithm. At the end of that process we generate a comparable feature "Entities" and add it to the new data model to be used for collaboration mining. After that we construct dataset's publisher data model (semantic profiles) which contains aggregated features' values from their published datasets. Finally, we compute relation strengths between dataset publishers based on comparing their semantic profiles that we built using the aggregation of unique entities they publish datasets about and store it as shown in Fig. 4 for later visualization and web service usages as shown in Fig. 10 and11. Following we discuss and represent the results of our Semantic Profiling for Collaboration Mining approach.

Profiling the Catalogues

By querying the stored enriched metadata of open data portal we are able to generate charts that are profiling the underlying open data catalogue. As an example of those queries we are able to retrieve the named entities detected from mining unstructured textual representations of data catalogues generated by our tool. Those named entities which are originally derivate from dataset metadata are -same as their origin -able to demonstrate a description of the contents of the data portals see Fig. 5 and6.

Publishers Profiles

Open data publishers are an interesting open data analysis feature; publishers could be governmental departments, councils, etc. which make their profiles a key component of governmental data integration and standardization. An open data publisher's profile is the aggregation of the information extracted from its published dataset metadata. One of the usages of a publisher's profile is to understand more about the domain of the publisher see Fig. 7 for an example.

Interlinking Publishers

The resulted publisher profiles are used to mine possible collaboration channels between data publishers at data portal level and among portals level by using the added comparable feature "Entities" see Fig. 8910.

According to our results "marine-institute (129) datasets" and "geological-surveyof-ireland (67) datasets" have the highest relation strength score of (82) which means that they share 82 entities/topics in common. We examined the datasets published by both publishers and we found that for pollution concept/topic there are (7) datasets published by "marine-institute" and (7) dataset published by "geological-survey-ofireland" and similarly for hydrography concept/topic there are (4) datasets published by "marine-institute" and (18) datasets published by "geological-survey-of-ireland" as shown in Fig. 11 and12.

Limitations

Named Entity Recognition area of the work is tightly coupled with the training and the quality of the Named Entity Recognition algorithm. Through this research we have experimented Natural Language Tool Kit (NLTK), Stanford NER and Stanford NER with nGram of (3) enhancement, then we ended up using DBpedia Spotlight as the NE source as through our manual examination of the text analysis phase results DBpedia out performed the other methods in its NE detection quality. DBpedia spotlihght still have its limitations though and we reported one of the issues we faced to their github repository 9 .

Applications

Standardization and Collaboration Analysis

Despite most of governments already publishing their data via their open data portals, when a government decides to integrate their data sources over its variant departments and councils, this heterogeneous domain dependent data will consume huge analysis resources and a considerably extended period of time to be fitted into an integrated data repository. Our profiling service will lead the way for data analysts to define integration channels, and necessary concepts standardizations between governmental departments and councils, using the available data published on open data portals. Same example would fit a multinational enterprise as well.

For example "marine-institute" and "geological-survey-of-ireland" share the named entity (pollution), this concept shall be standardized regarding its code and its measurement unit to ease integration and comparability or analysis in general among multiple datasets.

Intelligent Open Data Portals Exploration

Open data portals are meant to be facing the public in other words the citizens, but citizens can't directly comprehend, and consume this row data [START_REF] Artigas | Visual analytics for open government data[END_REF]. Open data portals profiling service will help citizens to easily and intelligently explore the open data portal using visualized semantic profiles of publishers and datasets.

Conclusion and Future Work

Regarding our approach results we believe that we are on the right track to tackle the collaboration mining problem in open governmental data domain, as we are getting interested collaboration recommendations out of our pipeline in a visualized way that is easy to comprehend by general public users of open governmental data.

Our future plan is to overcome the NE limitation by developing a new text analysis pipeline that integrates statistical text analysis, babel.net 10 , and DBpedia11 as our NE source. Also we are planning to replace the string comparison module with semantic relatedness comparison module as the way of calculating relation strength between open governmental data publishers.

Fig. 1 .

 1 Fig. 1. Open Data Catalogues and Publishers' Semantic Profiling Conceptual Framework.

Fig. 3 .

 3 Fig. 3. Unstructured Data Analysis (Text Mining).

Fig. 4 .

 4 Fig. 4. Publisher Collaboration Network.

Fig. 5 .

 5 Fig. 5. Top Named Entities Describing the Open Data Portal "data.gov.ie".

Fig. 6 .

 6 Fig. 6. Top Named Entities Types Describing the Open Data Portal "data.gov.ie".

Fig. 7 .

 7 Fig. 7. Top Named Entities Describing Data Posted by Top Publishers to the Open Data Portal "data.gov.ie".

Fig. 8 .

 8 Fig. 8. Publishers Collaboration Network of Open Data Portal "data.gov.ie" with relation strength > 20.

Fig. 9 .

 9 Fig. 9. Publishers Collaboration Network of Open Data Portal "data.gov.ie" -Showing highest relation strength score between "marine-institute" and "geological-survey-of-ireland".

Fig. 10 .

 10 Fig. 10. Publishers Mined Relations of Open Data Portal "data.gov.ie".

Fig. 11 .

 11 Fig. 11. Datasets shared between Marine Institute and Geological Survey of Ireland around the concept pollution5 6

Fig. 12 .

 12 Fig. 12. Datasets shared between Marine Institute and Geological Survey of Ireland around the concept hydrography 7 8 .

http://opendatabarometer.org/

https://www.mongodb.com

http://public.resource.org/8_principles.html

http://data.gov.ie

https://data.gov.ie/data/search?q=pollution&publisher=marine-institute

https://data.gov.ie/data/search?q= pollution &publisher=geological-survey-of-ireland

https://data.gov.ie/data/search?q=hydrography&publisher=marine-institute

https://data.gov.ie/data/search?q=hydrography&publisher=geological-survey-of-ireland

https://github.com/dbpedia-spotlight/dbpedia-spotlight/issues/407

http://babelnet.org/

http://wiki.dbpedia.org/

Acknowledgments

This paper is partially supported by European Union's Horizon 2020 research and innovation programme under grant agreement No 645860, project ROUTE-TO-PA (Raising Open and User-friendly Transparency-Enabling Technologies for Public Administrations).


Tapani Ryynänen
email: tapani.ryynanen@vtt.fi

Iris Karvonen
email: iris.karvonen@vtt.fi

Heidi Korhonen
email: heidi.korhonen@vtt.fi

Kim Jansson

Supporting Product-Service Development through Customer Feedback

Keywords: Platform for collaboration, Co-creation, Product-Service engineering, Customer feedback, Manufacturing intelligence, Use cases

When developing product-services (P-S) it is important to take a collaborative and user-centric perspective to ensure that the P-S fits to customer needs. There are different approaches for the user involvement from intensive co-creation to the collection of customer feedback about the P-S design or P-S behavior. The feedback again can be collected using different methods and tools. The paper discusses different methods for collecting customer feedback for product-service innovation and design. The context of the study is the Manutelligence project in which a P-S collaboration platform is developed, also to support interaction with customer. In the paper four Manutelligence use cases from different industrial fields are analysed to identify the different customer types, lifecycle stages of the feedback, feedback forms and how the platform can support the feedback collection and use. The benefits of the feedback collection in different cases are also described.

Introduction

Manufacturing companies are interested to better engage their customers and to deliver more benefits through offering product-services (P-S) or services related to their products. In this way they can also obtain additional business, which is often not as dependent on economic cycles as pure manufacturing. There are several definitions for a product-service (P-S). For example, it is defined as "a mix of tangible products and intangible service designed and combined so that they are jointly capable of integrated, final customer needs" [START_REF]New business for old Europe. Product-service development as a means to enhance competitiveness and eco-efficiency[END_REF]. Another definition of a service in general (not only P-S) reveals the special nature of services -the intangibility and the importance of interaction with the customer: "A service is an activity or series of activities of more or less intangible nature that normally, but not necessarily, take place in interactions between the customer and service employees and / or physical resources or goods and / or systems of the service provider, which are provided as solutions to customer problems." [START_REF] Grönroos | Service Management and marketing[END_REF].

Collaboration with the customer is a vital part of any business network and moving into the P-S offering changes co-creation roles and requires different data. Relational coping strategies, including role clarification, role redefinition and role adaption, may be especially important during the co-creation process [START_REF] Sjödin | Value co-creation process of integrated productservices: Effect of role ambiguities and relational coping strategies[END_REF]. The data collected from the relationship for different roles has wide impact in the whole network. Therefore the quality and management of this data has a domino effect throughout the collaborative network.

The special characteristics address that developing P-Ss needs close collaboration with the customer. This has been discussed for example by the GloNet project which developed support platforms for effective operation of collaborative networks for service-enhanced products [START_REF] Camarinha-Matos | Collaborative Networks in Support of Service-Enhanced Products[END_REF]. In principle the feedback from customers may come from different customer/user levels and the P-S providers would be interested to get the feedback as early as possible in the lifecycle. However, typically it is not as easy to get feedback from the user level (if not the first customer) in the lifecycle phase when the P-S is still in the design or manufacturing/ implementation phase. On the other hand, in the usage phase it is often more simple to get the feedback from the user.

This paper is focused on a specific subtype of co-creation: customer feedback. The paper context is the development of a P-S engineering platform in project Manutelligence ("Product Service Design and Manufacturing Intelligence Engineering Platform", H2020 636951). The objective of Manutelligence platform is to manage manufacturing intelligence; all data, information and knowledge related to the P-S and its lifecycle. In the project, product information is divided into definition data and feedback data [5]. The feedback information can be created in different life cycle stages, like P-S design and P-S operation/use and using different methods. The idea is that the Manutelligence platform supports the collection of customer feedback, for example by visualizing the designed P-S and offering the channels for customer feedback. The platform is based on the integration of existing tools and platforms.

It is clear that extracting customer feedback can make the P-S more attractive to the customers but that it is always not easy to get the feedback. Thus the objective of this paper is to analyze the approaches for customer feedback based on the Manutelligence industrial cases: four industrial pilots from different industrial fields (automotive, ship, smart house, fablab/3D-printing). From the point of the customer two of the pilot products are highly complex (automotive, ship), one is medium complex (fablab) and one is low in complexity (smart house). Respectively the datarichness of the feedback follows the complexity.

The following research questions are discussed:

 What kind of methods can be used to collect customer feedback of Product-Services?  How can a digital Product-Service engineering platform support the feedback collection and utilization?

Approaches for P-S Feedback

Product-service customer feedback is typically linked to the specific, offered, often also customized Product-Service and its behavior. This may happen in the P-S design or manufacturing phase in which case the feedback may be used to modify the P-S according to customer comments. The importance of customer feedback is emphasized due to the co-created nature of value of P-S. Co-creation refers to the customer participating in value creation and value being jointly created by the company, the customer, and often other [START_REF] Vargo | Evolving to a new dominant logic for marketing[END_REF][START_REF] Prahalad | The Future of Competition[END_REF].

It is also possible to collect the feedback once the product is produced, i.e. once a product instance exists. From that moment on, the product instance is affected by loads caused by lifecycle processes (e.g. production, transportation and usage) [START_REF] Sjödin | Value co-creation process of integrated productservices: Effect of role ambiguities and relational coping strategies[END_REF]. These loads affect the degradation and the operation behavior of a product. Feedback information from the use/operation phase allows the producers to learn more about the users, the usage of products and the product behavior [START_REF] Wellsandt | Approach to describe knowledge sharing between producer and user[END_REF]. In principle, information about services can also be collected, but the channels may vary and the instantiation of a service is different compared to products. In Manutelligence, a service is instantiated once information is created about it (e.g. a contract or service request) [START_REF] Sjödin | Value co-creation process of integrated productservices: Effect of role ambiguities and relational coping strategies[END_REF]. This is different from the commonly mentioned characteristic that a service is produced and consumed at the same time-technically, the service is instantiated in computer systems before it is consumed.

Often both users and sensors create data about the system. A possibility to manage and compare all data creates new possibilities to understand and verify the system behavior. Zolkiewski et.al. [START_REF] Zolkiewski | Strategic B2B customer experience management: the importance of outcomes-based measures[END_REF] say that "In support of this focus on outcomes-based measures, we contend that other data, beyond user's perceptual data, should be employed to enhance customer experience measurement and management." and they mention sources like Big Data, IoT, Cloud and smart assets

Methodology

The platform development is based on existing platforms and their adaptation according to the needs of four use case pilots (automotive, ship, smart house, fablab). The needs were collected and analysed in a requirement engineering process including phases: elicitation, structuration, analysis and validation. In the elicitation phase each of the pilots specified the use scenarios, that is: descriptions of processes or process parts which could be supported by a P-S engineering platform. The description included the objectives, challenges, lifecycle stage and the use cases included. Each scenario could have more than one use case. Each use case again was described with a common format including for example actors, precondition, postcondition, systems involved, diagram and the main steps. The number of scenarios and use cases defined in the first phase was the following:

-Automotive pilot: 3 scenarios and altogether 6 use cases -Ship pilot: 3 scenarios and 6 use cases -Smart house pilot: 3 scenarios and 5 use cases -Fablab pilot: 5 scenarios and 17 use cases.

Thus altogether 14 scenarios and 34 use cases were defined in the first phase of the project. At this time point the companies had not yet decided which use cases will be implemented during the Manutelligence project but the focus was on the identification of future opportunities. Also during the project some new use cases emerged. The scenario descriptions were used in the elicitation of requirements for the Manutelligence platform.

Currently the project is in its final phase. Most of the use cases have developed into pilot demonstrations. Some of the defined use cases have been left out of the current project and some new use cases have emerged. Validation of the pilots against requirements will take place during the last six months of the project.

This paper describes the analysis of the use case scenarios from the viewpoint of customer feedback. The analysis included the following steps:

1. Identification of the use scenarios and use cases in which customer is involved as an active or passive source of feedback. As a whole in 7 of the 14 scenarios and 9 of the 17 use cases some kind of feedback was included. 2. Analysis of the identified scenarios and use cases using a common approach and template. The main points of the template are presented in table 1 and as a whole in [10]. It was not possible to find all the information for all the cases in the basic source documents. In some cases prototype presentations and discussions with the use case representatives were needed. 3. Consolidation of the analysis results. This was performed for the items considered most important, like the customer / user type, objectives and benefits, lifecycle stage, feedback type and channels or tools, how the feedback is used and what could be the role of the Manutelligence platform. 4. Identification of similarities and differences; developing a mapping framework and conclusions.

Results -Feedback Case Analysis

Even if the Manutelligence cases significantly differ from each other in size and complexity, they all express, in different ways, one common objective for the collection of the customer feedback. It is the improvement of the P-S, either of the P-S instant (a specific P-S) or the future P-Ss. The improved P-S is expected to influence the customer satisfaction and thus to improve the competitiveness of the company. Additionally it is expected that the interaction including the feedback and the potential to influence the P-S design strengthens the customer relationship and enables better understanding about the future needs.

The availability of life cycle analysis (LCA) and life cycle cost analysis (LCC) on the platform allows the user to give feedback if the P-S performance is sufficient. The end user can make the decision based on sustainability assessment and long term costs. For example, the customer can predict future energy consumption and adapt.

In addition to high quality P-S, one benefit expected is to speed up the design, P-S specification and implementation processes, and to decrease the costs. Efficient feedback tools enabled by the platform allow faster fixing of the design decisions, but also avoiding errors in the design. Thus there is decreased need to waste time for the correction of errors and the subsequent manufacturing / implementation phases may be more efficient.

In one specific case, fablab, the community of customers (users) is seen important for the manufacturing activity as a whole. The feedback supports the optimization of the use of production machinery for additive manufacturing, sharing knowledge amongst users, design improvement and design reuse.

The feedback or data coming from the P-S use phase also supports the failure management and reduction of repair time as well as activities through predictive maintenance. All this can contribute to the keeping the products in a good shape and thus achieving a longer life for the products.

When analysing the customer feedback, two main distinctive features could be identified:

-The P-S lifecycle phase in which the feedback is collected and analysed. Main phases identified are P-S design/ implementation and P-S operation/ use phases.

The end-of-life phase was not visible in the scenarios. -The type and method of feedback: type meaning the information type (unstructured information, structured information, data) and method how the feedback is given (customer manual input, customer selection from predefined options, automatic (for example) sensor data). It seems clear that in most cases these are not independent but interlinked: The unstructured information requires some customer activity while the bigger amounts of data are coming automatically from sensors, for example via IoT. The structured feedback (for example selection between options) can be derived either from the customer or from automatic devices.

Thus the main dimensions against which the use cases can be compared and analyzed are: P-S lifecycle phase and the source of feedback: customer activity/ automatic retrieval. The customer activity may mean feedback given through platform, email or discussions in a meeting etc. In Manutelligence the focus is on feedback which could be supported by the platform; this may be automatic or action-based. In table 2 the Manutelligence use cases including feedback in some form were analyzed in relation to these dimensions. The table shows that the different use cases had a different focus in their scenarios and collaboration with customers. The main focus of the ship case was feedback from the ship owner in the design phase, based on virtual models, gamification and augmented reality. As this kind of feedback cannot be automated, the idea was to utilize the virtual models for the easy issue of the observations and again use these as the basis for a managed change management. The smart house scenario included some configuration ideas for the design phase but the main objective was to collect automatically data from sensors to monitor the product behavior and to develop new services. In the automotive case the main point was automatic data collection from testing even if also customer opinions were collected. The fablab case was a specific case where all the lab users (manufacturers) are customers. Thus, analysis of the Manutelligence case scenarios brings out that there is a need for retrieving customer feedback in all lifecycle phases even if no end of life scenarios were available in the current project. It is clear that an IT platform utilizing IoT (Internet of Things) is needed to collect and manage the large amounts of data given by automatic sensors. The data can be used for use phase services and for further design. Often there is also a need to compare the real data against designed performance (for example energy consumption models).

To receive feedback from the customer through customer actions the P-S provider needs to make the feedback action attractive for the customer. This means that it should be easy and interesting for the customer, for example to understand the current P-S version in the design phase, and also easy to give the feedback. In the use cases this was implemented through visualization, even experimenting through gamification, supported by the platform. The feedback could be directly appointed to the visual models or given by more traditional means, like in meetings. Thus also here the platform is needed both to present the P-S for which feedback is needed but also to save and analyse the collected, often heterogeneous information.

In the fablab case there is a user community which is interested to interact and give feedback to the P-S provider but also to share experiences. Thus the platform needs to offer tools also for this communication and collaboration.

As an important function of the platform in relation to customer feedback is to take care that the customer feedback is handled and used for the current P-S instant or for future P-Ss. Thus systematic change management process, supported by the platform, is needed. The change process also takes care about informing the customer about the results of the feedback process: what changes were made.

Based on the findings in the project a P-S platform may have different roles in the customer feedback process:

-The platform should manage the rich P-S data and information throughout the

Summary

This paper is focused on supporting P-S design through customer feedback. The paper is based on Manutelligence pilots (H2020 no. 636951) and their needs and experimentation of the Manutelligence platform. The aim of Manutelligence platform is to manage manufacturing intelligence; all data, information and knowledge related to the P-S and its lifecycle. Even if the main requirements elicited in the beginning of the project were focused on other needs, the customer feedback and collaboration with the customer were visible in 7 out of 21 aggregated requirements [START_REF] Jansson | Processing Requirements for a Product-Service Engineering Platform -A Multiple Use Case Approach[END_REF]. Customer feedback is expected to improve the P-S, customer satisfaction, process efficiency and overall company competitiveness. New methods and channels for customer feedback require that the platform is able to integrate and manage the rich feedback data.

  lifecycle. -The platform should offer the P-S information to the customer in an understandable interface. -The platform should offer the customer a possibility to give different types of comments. -The platform should integrate to IoT to collect data from different types of sensors. -It should be possible to analyse the feedback data and information using the platform, for example to compare real and designed data. -The platform should support the change management process. -The platform should allow communication between different users or different actors. -The platform should support organizational change management by enabling dynamic changes required by changes in roles and tasks.

Table 1 .

 1 Use case analysis framework

	Use case name, Scenario, Date, completed by
	Product-Service (P-S) delivered
	When is the feedback collected? In what P-S lifecycle phase? How often is the
	feedback collected?
	Who is customer or use of the P-S?
	Objectives. Why is the feedback from customers needed?
	Type of customer interaction? (E.g. B2B, B2C, C2C/P2P, P-S production)
	Dimensions of feedback. What does the feedback concern?
	What does the feedback include? What is the feedback content? E.g. textual,
	visual, audio, data, proposition/ requirement, free/ specified or other format
	How is the feedback given? What are current (and future Manutelligence)
	cannels for delivering feedback? E.g. Listening and asking customers, build with
	customers, in a meeting/ e-mail / platform / IoT
	What happens after the feedback is given?
	What is the Manutelligence Platform role in customer feedback? How it could
	help and support in enhancing the use of customer feedback?

Table 2 .

 2 Use case feedback analysis

			Life cycle phase
		Sales	Design	Manu-	Use/	End
				facturing	Operation	of life
	Action-based	Ship1 Smart-Ship2	Ship1 Smart-house1 Ship2	Ship2	Automotive1 Automotive2
		house1
	Automatic		Automotive2	Fablab1 Automotive1 Automotive2
					Fablab2
					Fablab3
					Smart-
					house2

Acknowledgements

This work has been partly funded by the European Union's Horizon 2020 research and innovation program under grant agreement no. 636951 (Manutelligence).