Johnny Ryan
email: johnnyryan1@gmail.com

Dirk Heilmann
email: d.heilmann@vhb.de

Siegfried Handschuh
email: siegfried.handschuh@deri.org

SEDA_Lab: Towards a Laboratory for Socio-Economic Data Analysis

In the paper we present an idea for a 'Laboratory for Socio-Economic Data Analysis' aiming to explore the use of extremely large amounts of socio-economic data from several sources of various degrees of heterogeneity. Based on these data, and on the state-of-the-art techniques in knowledge extraction and processing, we intend to deploy robust and high performance data analytics processes. Our goal is to enable the two databearing business partners Irish Times and Handelsblatt to use the data they have in conjunction with bigger data from external sources, to increase the value of their products and services offered and to reposition themselves in the market. We focus on two use cases that can produce tangible results in the analysis of socio-economic trends (e.g. unemployment, poverty) and socio-economic events (e.g. election tracking, bankruptcy) enabling better reporting, as well as timely decision support in crisis situations.

Basic Notions and the Background

The large volume of textual data available for exploitation by global user communities is growing daily. The current blogosphere hosts an average of 133 million blogs, with a daily publishing rate of 0.9 million blog posts, and with 346 million people (approx. 77% of the Internet users) reading them. The situation is analogous for many other types of resources and fields (e.g., financial data, news articles, patient records, business reports or patents).

Making the knowledge locked in these vast amounts of available texts fully accessible and interconnected is still a challenge for today's search engines and/or content management systems. The main reasons can be summarized as follows.

Externalising the knowledge hidden in the individually authored texts (so that computers can efficiently process it and make it accessible) is virtually impossible when done manually. The automatic extraction of knowledge from texts has often too noisy and sparse results, which prevents their meaningful exploitation within the current knowledge representation frameworks. Hence, it is virtually impossible to integrate the results of automated knowledge extraction with legacy resources without significant (and expensive) human involvement and curation efforts.

We determine three broad types of data:

-Document corpora consisting of large amounts of structured information from metadata about the collections of documents, as well as large amounts of unstructured information represented by the textual contents of the documents themselves.

-Linked Open Data from the Web of Data -consisting of huge amounts of structured interconnected datasets, available online.

-Stream data -consisting also of online data, this time in unstructured form, captured from real-time information streams like Twitter, Facebook, blogs, news reporting agencies, stock market, financial services, etc. The data in this category is Big Data, through its volume, velocity and variety. Based on these data, and on the state-of-the-art techniques in knowledge extraction and processing, we intend to deploy robust and high performance data analytics processes. Our goal is to enable the two data-bearing business partners Irish Times and Handelsblatt to use the data they have in conjunction with bigger data from external sources, to increase the value of their products and services offered and to reposition themselves in the market. We plan to deliver innovative and robust solutions for the representation, refinement and augmentation of the extracted knowledge, as well as for its integration. We will realise this goal through the development of novel methodologies for real-time processing, visualization and analytics over large volumes of socio-economic data (both structured and unstructured).

We focus on two use cases that can produce tangible results in the analysis of socio-economic trends (e.g. unemployment, poverty) and socio-economic events (e.g. election tracking, bankruptcy) enabling better reporting, as well as timely decision support in crisis situations.

The Adopted Approach

Our approach, in a nutshell, is to extract large amounts of knowledge from various resources and integrate it using lightweight and robust techniques with native support for data imprecision and uncertainty. These techniques will allow for storing of possibly conflicting information, resolving inconsistencies and lack of precision later on, either by means of empirical data-driven refinement, or by explicit ad-hoc interventions of users interacting via pro-active user interfaces.

More specifically, we will first deliver means for the integration and co-evolution of knowledge coming from resources of varying expressivity, noise level and relevance. In order to support that, we will build on an initial version of our novel lightweight, uncertainty-aware knowledge representation with simple similarity-based semantics easily extensible by intuitive rules. Based on these achievements, we will pursue three additional research threads:

1. Scalable multi-source knowledge extraction -Incremental knowledge extraction from textual resources and legacy terminological repositories (i.e., existing ontologies, thesauri or databases), and integration of the extracted content into continuously refined, emergent knowledge bases, which are in turn further augmented by means of lightweight inference and used in order to boost the initial extraction results. 2. Exploiting expert communities -In addition to large-scale automatic knowledge extraction from "inanimate" resources, we will also glean much more precise content from human expert communities, their actions and contributions, integrating the gleaned content into the emergent knowledge bases in order to improve them. The integrated knowledge will serve back to the domain experts, allowing for automated detection of possibly important hidden trends and other dynamic features. 3. Pro-active user interfaces -Participation of individuals in collaborative community efforts, exploitation of the extracted knowledge and explicit individual addition or curation of the emergent knowledge will be enabled by intelligent user interfaces. These will allow for searching, visualising, browsing, but also for pro-active fetching of knowledge of interest, facilitated by profiles automatically created from the content particular users are working with. Apart from "reading" the content, the interfaces will also allow the users to "write" and thus augment that knowledge, i.e., to curate (modify) or add to the knowledge bases. The output of the research threads described above will be implemented as an integral suite of services, accessible and interoperable. Thus we will ensure both coherence and modularity of the anticipated system, as well as easy re-use, extension for and adaptation to a large variety of use cases where textual information overload is occurs. The primary functionalities of the tool suite will be:

search, browse and analyse knowledge extracted or inferred from textual resources of interest (either submitted manually to the system by users, or fetched automatically from external content repositories, like the information centre repositories, or the Irish Times service),

retrieve the resources of interest based on knowledge either contained in, or pointing to them (apart from services extending the traditional key-word search),

refine and augment the emergent knowledge bases either via one-click actions, or using intuitive machine-assisted input interfaces,

exploit results of advanced social network analysis aimed at communities particular users are interested or directly active in. To demonstrate and assess the applicability and usability of the results, we will be continually deploying them within two realistic complementary application scenarios in the socio-economic domain. This will be done in close cooperation with our business partners from the Irish Times and Handelsblatt, both information centres in control of huge datasets if publications in the chosen domains. The partners will continually express their case-specific requirements in an agile development process. They will also assist the technical partners and contribute with both empirical and user-based evaluation of the project deployment in the later stages.

The data that we will use in the realisation of the scenarios comes from several sources of various degrees of heterogeneity. The data is of three categories: Document corpora -consisting of large amounts of structured information from metadata about the collections of documents, as well as large amounts of unstructured information represented by the textual contents of the documents themselves. The Irish Times and Handelsblatt provide a corpus of documents and the necessary metadata about them in the areas of the economy and the society.

This qualifies the data that we plan to use as big data as it definitely exceeds the processing capacity of conventional database systems; the data is too big, moves too fast, or doesn't fit the structures of your database architectures. To gain value from this data, there is need to choose an alternative way to process it.

SEDA_Lab Overview

The general structure of both pilot use cases is depicted in the Fig. 1. There is a distinction between the producers of data and the consumers of the output knowledge. However, the same entity can be at the same time a producer and a consumer. The producers of data can be of two types, centralised and distributed. Our two business partners The Irish Times and Handelsblatt are centralised producers of data. They provide more than just the document corpora, which are an important part, but also metadata about the documents, and other already extracted annotations. The directions for the use cases are complementary. One use case looks at determining long-term trends in the information processed, for example unemployment and poverty in the social domain, or stock trading values in the economic domain. The other use case aims to examine special events, which are time-critical, like bankruptcy announcements or election result tracking. The knowledge extracted, both in regards of long-term trends and time-critical events, will be used to augment the information provided by the two information centres to their users. Apart from working towards the accomplishment of our research agenda, we intend to ensure maximum impact and sustainability of the results of the project, from the very beginning. The main means for achieving sustainability can be summarised as follows:

We will deliver a modular, open source and generally applicable framework for the extraction, integration, processing and delivery of knowledge coming from disparate textual or legacy resources. This will maximise the ease of deployment, re-use and extension of the researched technologies.

Building on the generally applicable framework, we will provide two specifically targeted pilot solutions. Although tailored to the requirements of two particular stakeholders, the solutions will still be transferable to many other subjects in the same areas. We will use the deployment at the major information centres, not only for mediating the respective publication knowledge to the pilot use case data consumers, but also to showcase the content delivery technologies to the customers, advertising the project's results to a potentially very large number of people.

Contributions to the State-of-the-Art

SEDA Lab aims at the integral advancement of machine, as well as collective and individual human intelligence, by making knowledge locked in electronic textual resources more accessible and meaningful to users who seek it. Inherent to the overall goal, the project will make the knowledge being extracted and integrated from various disparate textual sources amenable not only to exploitation, by also to open-ended intuitive augmentation or curation by individual and collaborative actors. The particular research outputs delivered in order to accomplish the goals will be implemented as a generic modular tool suite with two specific social sciences and economics pilot applications. Automated Knowledge Extraction. Automated knowledge extraction is typically performed on two layers of text analysis. The first one deals with finding named entities and normalizing them relative to their respective database identifiers. The second layer of text analysis is concerned with the extraction of relations between named entities. Many different approaches have already been tried, among them pattern-based ones, rule-based ones, and machine learning-based ones. In the case of social sciences, an added difficulty in the task of relationship ex.

Building on various text-mining [START_REF] Feldman | The Text Mining Handbook[END_REF] approaches as those outlined above, ontology learning [START_REF] Maedche | Ontology learning[END_REF][START_REF] Buitelaar | Ontology Learning and Population[END_REF] currently presents the most advanced method for the extraction of expressive knowledge (in terms of formal statements about entities, i.e., concepts or individuals, and their mutual relations) from natural language texts. The ontology learning approaches have recently been extended by emergent semantics principles [START_REF] Maedche | Emergent semantics for ontologies[END_REF][START_REF] Ottens | Dynamic ontology co-evolution from texts: Principles and case study[END_REF] in order to construct more expressive, representative and precise ontologies (or knowledge bases, in general) in a bottom-up manner.

In addition to the text analytics, we plan to extract expressive semantic structures from traditional databases by means of data mining techniques [START_REF] Dzeroski | Relational Data Mining[END_REF][START_REF] Hastie | The Elements of Statistical Learning: Data Mining, Inference, and Prediction[END_REF] wherever such datasets are available. W3C's RDB2RDF Working Group standardizes languages for mapping relational data and relational database schemas into RDF and OWL, through its two published candidate recommendations [START_REF] Das | R2RML: RDB to RDF Mapping Language -W3C Recommendation 27[END_REF][START_REF] Arenas | A Direct Mapping of Relational Data to RDF -W3C Recommendation 27[END_REF].

Emergent Knowledge Representation and Processing. Emergent knowledge is essentially dynamic and often vague or inconsistent. Approaches generalising classical formal logics have been proposed to tackle these features [START_REF] Bobillo | An expressive fuzzy description logic reasoner[END_REF][START_REF] Haase | Ontology learning and reasoning -dealing with uncertainty and inconsistency[END_REF]. However, the simple structure of the emergent knowledge does not allow for many non-trivial logical conclusions, rendering formal logical knowledge representations insufficient without significant human post-processing [START_REF] Haase | Ontology learning and reasoning -dealing with uncertainty and inconsistency[END_REF][START_REF] Bechhofer | Tackling the ontology acquisition bottleneck: An experiment in ontology reengineering[END_REF]. Also, even for relatively simple but large domains with many instances, logics-based querying may quickly become intractable [START_REF] Hustadt | Data complexity of reasoning in very expressive description logics[END_REF]. Thus, more lightweight approaches are appropriate for emergent knowledge.

Regarding Semantic Web research, numerous extensions of the basic RDF standard towards features of emergent knowledge have been investigated. Contextual features (e.g., provenance, certainty) are handled by [START_REF] Schueler | Querying for meta knowledge[END_REF][START_REF] Hartig | Querying Trust in RDF Data with tSPARQL[END_REF]. Similarity-based query post-processing with imprecision support is proposed in [START_REF] Kiefer | The fundamentals of isparql: A virtual triple approach for similarity-based semantic web tasks[END_REF], while [START_REF] Mazzieri | A fuzzy RDF semantics to represent trust metadata[END_REF] directly extends RDF semantics to support uncertainty (fuzzy degrees). Dynamic merging of RDF graphs is looked into by [START_REF] Udrea | A graph theoretical foundation for integrating RDF ontologies[END_REF]. [START_REF] Oren | Anytime query answering in RDF through evolutionary algorithms[END_REF] researches robust approximate and scalable RDF query answering that can be used for practical exploitation of large emergent knowledge bases.

Big Data Processing. Big Data affects every sector and region of the economy and every aspect of society -providing insight into education (Dobbie, 2011), ecology (Economist, 2011) and financial risk management [START_REF] Flood | Using Data for Systemic Financial Risk Management[END_REF]. It has the potential to create valuable new opportunities for individuals, communities, organisations and societies, but it also introduces new challenges (data capture, management, and analytics), and risks (privacy, security etc.). Big Data requires a new set of technologies order to transform raw data into valuable knowledge. The sheer scale of the data is an obvious challenge, however, other characteristics are equally important: the variety and velocity of the data [START_REF] Russom | Big Data Analytics[END_REF]. Variety means heterogeneity of data types, representation, and semantic interpretation. Velocity means both the rate at which data arrives and the time in which it must be acted upon.

Alternatively, [START_REF] Jagadish | Challenges and Opportunities with Big Data[END_REF] presents a generic pipe-line for Big Data processing, with multiple distinct phases, each important in the overall process, and each introducing new challenges: (i) acquisition, recording, (ii) extraction, cleaning, annotation, (iii) integration, aggregation, representation, (iv) analysis, modelling, (v) interpretation. Similar to this, [START_REF] Fisher | Interactions with big data analytics[END_REF] describes a pipeline, focused on the architecture of the system used rather than on the process.

To tackle the challenges posed by each of the phases of the pipeline, we can make use of properties of the data, or of the characteristics of the process -the logical data independence of some databases or the inherent parallelism of some algorithms. A prominent example is Apache Mahout (mahout.apache.org) that provides implementations of a range of machine learning algorithms on Hadoop. Mahout has the drawback that it operates in batch mode and does not work effectively on stream data. Twitter's Storm (storm-project.net) is one of a number of frameworks that has emerged for scalable analytics on real-time data.

Event and Trend Detection.

There has been much research on data mining techniques for detecting interesting patterns in time series, one being event detection (a change in the values over time, which are considered quantitatively significant), but also repetitive patterns, or trends. In the statistics literature, the problem is known as "the change point detection problem" [START_REF] Cherkassky | Learning from Data[END_REF] and can be treated in two ways: batch, or offline, where all the points are known and inspected together, or incremental or online, when the time points are processed one at a time [START_REF] Keogh | A probabilistic approach to fast pattern matching in time series databases[END_REF]. However, in general, an event is not a single point, but a subgroup of points, which together represent the event.

Trend detection is complementary to event detection, as trends can lead to accurately predicting events even before they happen. In the project domain of socioeconomic data, detecting trends like crime rates or accident hot-spots can, with the proper analysis tools, result in positive societal effects. While Twitter has been also a popular choice for trend detection [START_REF] Mathioudakis | TwitterMonitor: trend detection over the twitter stream[END_REF][START_REF] Castellanos | Tapping social media for sentiments in real-time[END_REF], other equally rich sources are available to us.

Value Chain Thinking and the Underlying Business

Our idea for SEDA Lab builds on the principles of Value Chain Thinking (VCT) [START_REF] Fearne | Sustainable Food and Wine Value Chains, Adelaide Thinker in Residence, Department of the Premier and Cabinet[END_REF] and reflects the natural flow of processes as these are found in the real world business environment and in a way that can support sustainable and organic growth of business ventures amongst members of the consortium. Our approach in using VCT for building the consortium and organising the workflows aims to understand stakeholders/experts attitude which after an extensive study of the field we now see that it is something not trivial at all, uncover hidden expert groups and identify hidden associations between different drivers based on the results of the above two.

As such, Value Chain Thinking is expected to help tailoring of the scenarios we deploy in the context of the project in a number of different (and with a high contextual variability) cases while still making use of the basic structure of the SEDA Lab infrastructure.

In the context of the above, SEDA Lab services can be regarded as knowledgeintensive interactions; we assume that knowledge is both the key resource for the service operations (especially as the services we deal with tend to be intangible) and a basic type of benefit/outcome received by the customer and used for the co-creation of value "in context" and "in use". In other words, we assume that the value cocreation is based on the knowledge provided by the customer (e.g. a reader of The Irish Times) to the provider (The Irish Times) and vice versa, together with the knowledge of the customer how to use in context the service in order to exploit and enhance its benefit potential. In sum, the value of the service for the SEDA Lab customer is the result of a collaborative knowledge creation process between the customer and service providers, facilitated but not controlled or manipulated by our infrastructure. In this respect, the customer integrates all the benefits provided by the service provider, this incentivizing content owners and providers to invest from their side in building synergies amongst them and across all different steps of their data, information and knowledge value chains. In the picture above we try to visualise the layers that the adopted SEDA Lab Architecture affects:

Starting from the end user perspective, the upper layer is the one that s/he can have direct access to: these are the various Apps that a SEDA Lab user (actually: an Irish Times customer, or a Handelsblatt subscriber or registered user) may download to her/his smartphone or have access to from her/his computer at home or in office. Of course, access to these applications may happen through 3rd party applications or services through APIs. This layer is important for the project because it will be our front end to a general audience; the people will hate it or love it if the socio-economic trend-spotting, trend-blending or event detection Apps will be "cool" or have a "hot" interface.

One level down there are the Supporting services -without them no sense-making or useful App can be built. These services are built for the needs of the project by the consortium and while some of them are based on existing functionality, some others will be built for first time and will be critical for the success of the entire project. These are related to the management of socio-economic (S-E) trends and events.

The third layer deals with the Linked Open Data. Since their debut, the cloud of published data has grown considerably. Today there are 295 datasets in the form of a connected cloud, with about 31 billion RDF triples interlinked by around 504 million RDF links. Of this is only the "surfaced"/public data. More data will exists in intranets. Picking up on another example is the schema.org work. This activity from the major Web search operators will add further to the amount of machine processable.

Finally the fourth layer is the one that is provided by the Users and owners of big data in our consortium. With the trend of putting computing and networking capabilities into everyday objects and places, there are increasing numbers of data sources, producing increasing volumes of data that record what is happening in the world. Data is generated from everywhere: blogs, digital pictures, enterprise computing, feeds, social media (e.g. Twitter, Facebook), news streams and so on. This explosion of data is the result of many factors, increasing number of internet users, mobile phones, and increasing use of social networks and multimedia. Sectors such as news agencies, finance, social-economic tracking, and consumer-facing industries have also contributed significantly to the growth of Big Data.

Conclusions

Our idea for a laboratory for Socio-Economic Data Analysis builds on synergies with two established actors in the news publishing and news media world that will operate as big data owners and value added service providers involved in the dissemination, commercialisation and outreach activities.

SEDA Lab shall also offer a unique opportunity to both the Irish Times and Handelsblatt in terms of enabling both papers to offer a platform for comment in a variety of spheres including business, politics and public affairs, culture, the environment, health and education, to move from being newspapers of record to newspaper of reference.

Fig. 1 .

 1 Fig. 1. Conceptual overview of the SEDA Lab interactions amongst producers and consumers

Fig. 2

 2 Fig.2Presentation of the four layers that SEDA Lab addresses. Links with other entities such as ZBW (www.zbw.eu) and GESIS (www.gesis.org) are included as well as for any new entrants in the SEDA Lab scheme.


Eleni Kaldoudi
email: kaldoudi@med.duth.gr

Nikolas Dovrolis
email: ndovroli@alex.duth.gr

Semantic Social Networks for Integrated Healthcare

Keywords: Social networking, Semantic technologies, Heterogeneous social Networks, UMLS, Healthcare integration, Actor network theory

An important aspect of integrated care is the provision of personalized patient empowerment and decision support services, especially in case of the chronic patient with comorbidities. The paper discusses a blending of notions of social networks and semantic technologies in order to develop comprehensive personalized and dynamic models of the chronic comorbid patient, their environment and healthcare related issues, procedures, etc., so as to be able to support meaningful patient empowerment and decision support services.

Introduction

The first decades of applications of information technology in medicine have targeted the health care enterprise and services provided therein. A major technological challenge has been the integration of various information systems and services to support the healthcare enterprise with emphasis on the tertiary level (e.g. hospitals). Towards this goal, a number of standards and standard communication protocols have been developed and implemented, with variable, rather questionable, success [START_REF] Lenz | Semantic integration in healthcare networks[END_REF].

Along with this, the notion of integrated care has risen and now is a central issue in the domain of healthcare. Integrated care has many meanings [START_REF] Kodner | Integrated care: meaning, logic, applications, and implications -a discussion paper[END_REF]; the notion of "reunite parts of a whole" is an underlying commonality, whether integration refers to integration within different healthcare settings or in terms of integration of healthcare, social care, long-term and self-care or even integration of patient management for different conditions. In any case, a patient-centered bottom-up approach is favored [START_REF] Kodner | Integrated care: meaning, logic, applications, and implications -a discussion paper[END_REF], [START_REF] Gröne | Trends in Integrated Care -Reflections on Conceptual Issues[END_REF]. Closely related is the concept of patient empowerment, which has emerged as a new paradigm that can help improve medical outcomes while lowering costs of treatment by facilitating self-directed behavior change. The concept seems particularly promising in the management of chronic diseases [START_REF] Chatzimarkakis | Why patients should be more empowered[END_REF], [START_REF] Anderson | Patient empowerment: myths and misconceptions[END_REF] and it is directly connected with personalized patient services and preventive measures. An important aspect of integrated care is the provision of personalized patient empowerment and decision support services, especially in case of the chronic patient with comorbidities. Comorbidity refers to the presence of one or more disorders in addition to a primary disease or disorder (either independently, or as a consequence of the primary condition or otherwise related) [START_REF] Akker | Comorbidity or multimorbidity: what's in a name?[END_REF]. Comorbidity management is a hot topic in current medical literature [START_REF] Barnett | Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study[END_REF], [START_REF] Tinetti | Designing healthcare for the most common chronic condition: multimorbidity[END_REF]. When addressing disease in the presence of comorbidities, each different medical condition the patient presents should not be viewed independently, but a "patient as a whole" view approach should be followed [START_REF] Nagarajan | Management of comorbid conditions in heart failure: a review[END_REF]. This places an emphasis on and extra burden of dealing successfully with all associations, interactions, co-dependencies, implications, adverse events, etc. that occur between different conditions co-presenting at the same patient at the same time, as well as between the different treatment regimens these conditions involve.

The goal of our work is to address integrated care for the chronic patient with comorbidities. Although a lot of work has been conducted towards a common understanding of the healthcare enterprise, even in the special case of the provision of home care for the chronic patient, e.g. [START_REF] Valss | Using ontologies for structuring organizational knowledge in home care assistance[END_REF], an analysis and definition of the personal environment of the healthy citizen and the patient is still missing. In our approach we propose the blending of notions of social networks and semantic technologies in order to develop comprehensive personalized and dynamic models of the chronic comorbid patient, their environment and healthcare related issues, procedures, etc., so as to be able to support meaningful patient empowerment and decision support services. Section 2 of this paper gives a brief overview of the current state of the art in social network technologies highlighting a novel approach adopted by our work. Section 3 discusses semantic tools and technologies in healthcare that can be invoked for building social network relationships. Section 4 presents the proposed model for the semantic social network and some preliminary implementation approaches while a discussion is given in Section 5.

Heterogeneous Social Networks

A conventional social network approach concentrates on the network of humans, presumably based on some common social or professional interest -any implied artefacts or concepts are of no interest and are not represented or accounted for in such networks. Here the focus is to establish relationships and connections among humans. These are based on some commonly shared interest, object and/or concept; however, this is only implied and not really accounted for in the network. Such an example from the (of the many) in the field of healthcare is the CarePages (http://www.carepages.com/) a social network of people collaborating together to share the challenges, hopes and victories of anyone facing a life-altering health event. Social networking in this sense is good at realizing and representing links between people, but it doesn't explain what connects those particular people together and what connects those and not others [START_REF] Knorr-Cetina | Sociality with objects: social relations in postsocial knowledge societies[END_REF].

One way to provide meaning to social networks is to establish relationships and promote self-organization into communities based on shared interests, and even more on specific items of interests. Recently the term 'object-centered sociality' was introduced [START_REF] Engeström | Why some social network services work and others don't. The case for object-centered sociality[END_REF] to describe the fact that strong social relationships are built mainly when individuals are grouped together around a shared object that mediates the ties between them. This can be achieved by organizing the network around the content people create together, comment on, link to, annotate similarly etc. [START_REF] Breslin | The future of social networks on the Internet. The need for semantics[END_REF]. In this case emphasis is placed on the connections between the humans and the objects and social interactions are basically established on the basis of commonly shared objects. This new approach to sociality has drawn attention, and current state-of-the-art research in the area involves various ways to exploit object-oriented sociality to the benefit of the community. An indicative example from the field of healthcare is the PatientsLikeMe site (http://www.patientslikeme.com/), which connects people based on their health issues and related shared experiences.

In either case, the focus is on trying to establish connections based on human action, agency and perception. A recent work, introduced the notion of heterogeneous social networks, where humans and social objects are uniformly treated as equal actors [START_REF] Kaldoudi | Information Organization on the Internet based on Heterogeneous Social Networks[END_REF]. This paradigm follows the view and concepts of the actor-network theory (ANT). The basis of actor-network theory, a sociological theory developed in the 1980's [START_REF] Latour | Reassembling the Social: An Introduction to Actor-Network Theory[END_REF], is the concept of the heterogeneous network, that is, a network containing many dissimilar elements, all treated as inseparable. This is the so-called principle of generalized symmetry, whereby human and non-human (e.g. artifacts, organization structures) are assigned equal amounts of agency. The basis of actornetwork theory is the concept of the heterogeneous network, that is, a network containing many dissimilar elements, including both human and non-human entities. Using ANT one can show how things are attracted into or excluded from these networks, how some linkages work while others don't, and how connections are strengthened to make themselves stable and durable by linking to other networks and things. Some of these connections link together to form and identifiable entity of assemblage that can exert force -this is referred to as 'actor'.

Semantics for Building Social Network Connections

In implementing such a network, major challenges include a unified treatment and representation of all types of possible actors as well as the development of a social behavior for various nonhuman actors, and subsequently their own associations and networks. Both challenges can be addressed by concepts and technologies of the Semantic Web. In the conventional Web, a resource can be described via an XHTML/XML document, where various tags are used to annotate the document, mainly regarding its presentation, not conveying any semantics about the resource itself. In order to describe a resource the W3C Resource Description Framework (RDF) [16] is commonly used to represent metadata about a resource in the form of triples: subject, predicate, object. Generally, the subject can be the resource itself while the predicate can be any relationship as defined in any XML namespace published on the Web. The object can be an explicit value, but also a dereferencable URI. This way, an RDF triple can link the description of a resource with other sources of information on the Web, thus creating a worldwide graph-like linking of resources, what is currently termed Linked Open Data (LOD) cloud [START_REF] Bizer | Linked Data -The story so far[END_REF], [18. The building blocks of the Semantic Web are considered to be ontologies, i.e. formal descriptions of parts of the world [START_REF] Gruber | A translation approach to portable ontologies[END_REF] that guide the specification and generation of the triplets. There are numerous ontologies that are specific to domain, resource type and objective (also known as application ontologies), but there are only a few general ones (known as upper or foundation ontologies) that are used frequently to build the former. One of the basic requirements is a unified and rich description of all actors to form as a basis for their social presence and their interactions within the social environment. This description includes two main aspects. The first is a domain specific description of the profile for both humans and nonhuman entities. Such a social profile can be described with a variety of domain specific schemata or even ontologies. For example, humans' profile can be based on the FOAF (Friend Of A Friend) (http://www.foaf-project.org/) ontology, mainly used to describe people, the links between them and the things they create and do.

In the healthcare domain there is a plentitude of formal controlled vocabularies and ontologies, for example see the NCBO BioPortal [START_REF]The National Center for Biomedical Ontology[END_REF], [START_REF] Noy | BioPortal: ontologies and integrated data resources at the click of a mouse[END_REF] for an indicative list. Although a tremendous amount of effort has been devoted by the medical informatics community, a universal standard medical terminology and/or ontology has yet to be achieved [START_REF] Lenz | Semantic integration in healthcare networks[END_REF]. Thus current state of the art presents a wealth of numerous diverse and often overlapping vocabularies, terminologies and ontologies, e.g. more than 340 available only in NCBO Bioportal. The ontologies used include amongst else for prominent medical ontologies such as SNOMED-CT (Systematized Nomenclature of Medicine -Clinical Terms), ICD9/10 (International Statistical Classification Diseases and Related Health Problems), Body System (body system terms used in ICD11), MeSH (Medical Subject Headings), NCI (Meta)Thesaurus, Galen (the high level ontology for the medical domain), HL7 (the Normative RIM model v2), Biomedical Resource Ontology (BRO, a controlled terminology of resources to improve sensitivity and specificity of Web searches).

Research efforts have focused on producing mappings between different terminologies, probably the most notable contribution is the UMLS mapping between more than 100 of the most commonly used controlled vocabularies in healthcare [START_REF] Bodenreider | The Unified Medical Language System (UMLS): integrating biomedical terminology[END_REF]. Indeed, UMLS (The Unified Medical Language System, http://www.nlm.nih.gov/research/umls/) also provides a semantic network of concepts via sets of semantic relationships. This network has 135 semantic types and 54 relationships, and covers with these effectively all concepts and connections in the healthcare domain.

Semantic Social Networks for Integrated Healthcare

A recent thorough treatment of the domain of chronic comorbid patient [START_REF] Guthrie | Adapting clinical guidelines to take account of multimorbidity[END_REF] suggests that one way to improve care is to cross reference evidence and guidelines for each condition with guidelines on comorbidities. As it has already been argued in literature [START_REF] Guthrie | Adapting clinical guidelines to take account of multimorbidity[END_REF], simple cross referencing of existing medical evidence and guidelines for all possible combinations of conditions would quickly make it unreadable and thus inefficient. Thus, the goal of our work is to follow a personalized and semantically enriched approach to create dynamic cross referencing of semantically related evidence data. The effort lies in semantic interlinking of three types of data (a) medical ground knowledge (b) up-to-date medical evidence and (c) personal patient data in order to create a personalized model of the disease and comorbidities progression pathways and trajectories (Fig. 1). Following the line of thought presented above, the health environment for the patient and/or the healthy citizen comprises of various coexisting and strongly interlinked entities: (a) individuals, including patients, healthy citizens and healthcare professionals; (b) organizations, including any institutional or organizational entity involved in any way in the healthcare process, e.g. healthcare providers, social services, health insurances, medical research institutions, research projects, pharmaceutical companies, well-being and fitness clubs, etc.; (c) health conditions, i.e. any health or medical condition; and (d) health interventions, including diet, lifestyle, therapy and drugs, supporting devices, etc. The UMLS semantic network [START_REF]Chapter 5 -Semantic Networks[END_REF] concepts and relationships have been used a basis for developing the proposed model shown (in a simplified, partial view) in Fig. 2.

determine current status of patient

All entities presented in this model can be viewed as symmetric actors within a heterogeneous social structure which represents the health and healthcare environment of the healthy citizen and patient, thus building and representing all interactions and effects amongst different concepts, objects and humans. A variety of ANT concepts and constructs can then be employed to support development of patient centered services for integrated care like symmetry, translation, black boxes, immutable mobiles and obligatory points of passage.

Symmetry: all things (humans and non-humans, concepts or real world) are treated in the same way, and are assumed to be capable of exerting force and joining together, changing and being changed by each other.

Translation: what happens when human and nonhuman entities come together and connect, changing one another to form links. At each such connection, one entity is working upon another to translate or change it to become part of a network of coordinated things and actions. In our approach, the concept of translation can be used to enhance inherently weak health relationships (such as a "may_cause disease") via multiple connections especially based on evidence based medicine findings and published research papers (e.g. systematic reviews and meta-analyses). Black boxes: Dynamics among actors to translate each other can become stabilized and the network can settle into a stable process or object that maintains itself. This can be viewed as a black-box, concealing all negotiations that brought it into existence. In our approach, a chronic condition (together with all its inflicting factors, treatments, monitoring, existing co-morbidities, etc.) well established in a patient could be treated as a black-box.

Immutable mobiles: these are only visible within a particular framework of relations. They can be silent, ignored or overridden by other active objects, but they have developed enough solidity to move about and still hold their relations in place. For example, certain health and lifestyle behaviors, habits and/or conditions with solid relations to disease and risk of disease can eventually become immutable mobiles thus allowing a more generalized treatment of the pattern for groups of individuals/patients.

Obligatory points of passage: some immutable mobiles become obligatory points of passage, that is, central assemblages through which all relations in the network must flow at some time. This construct can be crucial for treating lifestyle and disease management, and more importantly for supporting clinical protocol alignment in cases of comorbidities.

In addition to this model and based on it we explore a system following the technologies and approaches described previously. More specifically, this work involves creating a subset of the UMLS Semantic Network and introducing some more fields specific to comorbidities, in order to address principles pertaining to clinical pathways, including temporal properties and organization of entities. The ultimate goal is the development of an ontology to describe the model of comorbidities and their management. This ontology will be the basis for creating the social semantic heterogeneous network discussed in the previous section. Entity interactions within this network can be presented using graph visualization tools enabling further analysis and user exploratory navigation through the graph.

The proposed approach due to its nature of handling detailed patient data and other "sensitive" information has to take consideration of the exposure risks and apply some kind of anonymity that is counter-intuitive to the social aspect of a patient empowerment network. In order to circumvent that an anonymity layer should be introduced protecting the real identity of patients but guaranteeing the validity of the medical data and allowing the social interactions to be formed. A simple approach to that could be a frontend-backend compartmentalization of patient data. In the backend the system knows everything about a patient and has all the data available for the expert in order to manage the patient but in the frontend the patient is known only as a unique ID provided by the system. This allows patients to interact with the network without revealing anything they don't intend to.

Discussion

The co-existence of multiple networks of individuals, organizations and health conditions/interventions can be exploited in order to create different views of the healthcare environment, thus creating variable impact. For example, an individualcentered linking visualization enhances integrated personal management of healthcare, collaboration and expert finding services. On the other hand, an organization-based linking visualization supports administrative, strategy and financial oriented goals, at an institutional, national and international level. Finally, resource oriented linking visualization/organization may serve a variety of goals. For example, visualization based on health conditions and interventions places focus on epidemiology and generation of new evidence on a large scale.

Following the approach of a heterogeneous network to organize information objects and humans alike and record their variable interactions one can further exploit notions and concepts of the actor-network theory to analyze the social structures and thus eventually gain more insights on the organization and communication of information.

Fig. 1 .

 1 Fig. 1. Shifting focus towards personalized comorbidity management.

Fig. 2 .

 2 Fig. 2. A simplified partial view of the proposed model which is based on the UMLS semantic network.

Acknowledgments. This work has been funded in part by the Project HELCOHOP: Development of Electronic Clinical Protocols, 2012-2015, MIS 375876, co-funded by European and National funds.


Bholanathsingh Surajbali
email: b.surajbali@cas.de

Markus Bauer
email: markus.bauer@cas.de

Holger Bär
email: holger.baer@cas.de

Spiros Alexakis
email: spiros.alexakis@cas.de

A Cloud-Based Approach for Collaborative Networks Supporting Serviced-Enhanced Products

Keywords: Collaborative networks, Virtual Organisation, Cloud Computing

Collaboration through sharing competencies and resources has been a key approach to both creating new competitive environments, as well as achieving the needed agility to rapidly answer to market demands. Establishing proper collaborative networks for service-enhanced products is challenging considering the wide diversity of, business operations and involved resources. The creation of software solutions to support such collaboration is an effortintensive task, especially when these solutions are designed to run in the cloud and when they are required to be highly customisable to different end-user scenarios. In this paper we describe how a cloud-based platform can support the creation of software solutions for the collaborative development and operation of highly customised and service-enhanced products. The platform has been designed based on numerous key requirements that have been generated from the analysis of different business scenarios from various use cases/domains, as well as general key requirements a cloud based platform should provide to support collaboration among organisations.

Introduction

There is a growing trend in manufacturing to move towards highly customised products, even one-of-a-kind, which is reflected in the term mass customisation. Mass customisation refers to a customer co-design process of products and services which meet the needs/choices of each individual customer with regard to the variety of different product features. The development of these products typically requires a variety of competencies and resources, hardly available in a single enterprise, which calls for collaboration among several companies and individuals. Collaboration through sharing competencies, resources and services has been a key approach to both creating new competitive environments, as well as achieving the needed agility to rapidly answer to market demands. Being able to rapidly react to a collaboration opportunity in fast changing market conditions is a key requirement for dynamic virtual organisations (VOs) [START_REF] Camarinha-Matos | Methods and Tools for Collaborative Networked Organisations[END_REF].

The notion of glocal enterprise emerged to represent the idea of thinking and acting globally, while being aware and responding adequately to local specificities, namely in collaboration with local stakeholders and customers for VO.

Software that runs in the cloud has gained significant attraction in the past few years representing the emerging paradigm for distributed value added services. With cloud computing, enterprises are given new opportunities to push virtual collaborative alliances to the next level, breaking down some barriers and enabling dynamic continuous collaboration that generates globally composed business value. In this paper, we propose a cloud based platform, developed in the GloNet project [START_REF] Camarinha-Matos | Collaborative Networks in Support of Service-Enhanced Products[END_REF] and providing support for the co-creation/ mass customisation of service-enhanced products. The rest of the paper is organised as follows: in Section 2 describes related work followed by Section 3 describing the main functionality required for collaborative networks. Then in Section 4, we describe the essential design principles for a cloud-based approach for collaborative networks. Next, in Section 5 we describe the main architecture of the GloNet platform. Finally we offer our conclusion in Section 6.

Related Work

A number of research and development work has been focusing on the collaboration platform problem. Despite the research and development work done in the last decade, the collaboration platform problem remains a practically unresolved issue, each addressing separate concerns of the collaboration domain. A number of research projects i-SURF [START_REF] Vieira | VALTE -Methodology for VALidation and TEsting of Supply Chain Software Components[END_REF], CONVERGE [START_REF] Rasoulifar | The need for a tool to exchange information in non-hierarchical network of the electronic industry: an European project[END_REF], COIN [START_REF] Kiauleikis | Collaboration and Interoperability Services vs. Traditional Communication Means[END_REF], SUDDEN [START_REF] Weichhart | Modelling of Complex Supply Networks[END_REF] focussed on the support of collaboration across the supply chain to enhance planning, decision making and coordinate supply networks. Other research works such as ATHENA [START_REF] Goldstein | Implementing clinical practice guidelines while taking account of changing evidence: ATHENA DSS, an easily modifiable decision-support system for managing hypertension in primary care[END_REF], SODIUM [START_REF] Pautasso | Flexible Binding for Reusable Composition of Web Services In[END_REF], COMMIUS [START_REF] Melchiorre | A community based interoperability utility for SMEs[END_REF], DIP [START_REF] Oberle | Semantic Management of Distributed Web Applications[END_REF] focused into development of web-based tools to enable interoperability of tools and applications. In addition to the web-based tools, ECOSPACE [START_REF] Prinz | ECOSPACE --Towards an Integrated Collaboration Space for eProfessionals[END_REF] offers service-oriented tools to manage complex daily work tasks in collaboration spaces. Significant research works such as LABORANOVA [START_REF] Mentzas | Semantic Enterprise Application Integration for Business Processes: Service-Oriented Frameworks[END_REF], SYNERGY [START_REF] Popplewell | Supporting Adaptive Enterprise Collaboration through Semantic Knowledge Services[END_REF], COIN [START_REF] Kiauleikis | Collaboration and Interoperability Services vs. Traditional Communication Means[END_REF], ECOLNET [START_REF] Witczynski | Virtual Organizations Enabling Net-based Engineering[END_REF] have also looked into creating tools to enable collaboration in teams, companies, organisations, networks and social communities in general. GloNet follows a top-down research approach and build on existing results from completed and on-going research projects for a best practice in Virtual Enterprise management for complex industrial units, supply-chain management, knowledge sharing, tracking complex processes and products as well as privacy issues.

Functionality Required by Collaborative Networks Supporting Serviced-Enhanced Products

The driving use case in GloNet is in the Solar Park construction sector. The different stakeholders range from customers, project developers, construction and commissioning firms, equipment suppliers, service provision companies and many others, are considered to be either a member of a long term alliance of stakeholders involved in the area of the product, that is a VBE (Virtual organisation Breeding Environments) [START_REF] Noran | Towards a Meta-Methodology for Collaborative Networked Organisations[END_REF] member or a partner in a goal-oriented collaborative network which is established to perform specific task for the product that is a VO partner.

Based on numerous business scenarios identified in the GloNet project, focusing on product characteristics, stakeholders' characteristics, and organisational structures and collaborative tools needs an shortcomings (as described in Section 2), the following five functionalities [START_REF] Camarinha-Matos | Collaborative Business Scenarios in a Service-Enhanced Products Ecosystem[END_REF] have been identified as key for collaborative networks (CN) in service-enhanced products contexts. They are:

1. Support for setting up a collaborative space for project among different stakeholders in terms of goal-oriented network. Platform functionalities shall facilitate setting-up a goal-oriented networks (i.e. VOs), whereby individuals and/or organisations can strategically join their competencies to rapidly respond to a business or collaboration opportunity. It should be noted that different VOs, with very different time durations, can be needed during the life cycle of a complex product. Each VO must be able to plan and schedule the collaborative work as well as the option to allow new partners to join the VO. It is also important to have a robust and reliable negotiation mechanism that supports the potential VO partners in achieving agreements during the VO formation process, reducing the amount of time spent in this process. These VO agreements will then be the basis for the governing principles of the VO during its operation phase.

Supporting different roles in a CN and permissions for accessing information and knowledge resources.

The platform must ensure resources protection in terms of permissions of members accessing shared resources as well as privacy of the shared resources. Appropriate mechanisms need to be in place through the identification of the property rights per resource and provision of authorised access for network members, according to their roles.

Sharing knowledge, data among stakeholders in the CN.

The platform must provide functionalities for sharing of knowledge among CN members (for example standardised product definitions and processes), software tools, and lessons learned. As such, the system must support organisations in the process of sharing their resources, allowing that each organisation inside the CN can search, retrieve and update information and knowledge about e.g. templates, standard processes, ontologies, etc. The system should also include mechanisms to implement incentive policies to increase resources sharing.

Groupware related tasks (appointments, task management).

The platform must provide a collaborative environment for groupware related tasks of members within the product/service development project. This can be reflected, in terms of sharing appointments, tasks to meet the project goals within a VO.

Defining and tracking workplans/processes.

VOs involved in service-enhanced products must support and promote the collaborative design of products and services as well as the emergence of innovative solutions. It includes two main aspects: i) mass customisation combined with offering value added services that improve the perceived product quality and prolong the product life cycle; and providing services based on innovation, knowledge and customer orientation; ii) emergence of new products identifying future needs, through collaboration among manufacturers and the customer and members of the customer's community.

A Cloud-Based Approach for Collaborative Networks

Cloud computing can provide a centralised platform [START_REF]Cloud Computing Use Cases Version 1.0[END_REF], which can be accessed from anywhere, by any (authorised) collaborator, and at any time over the Internet. Cloud solutions are typically presented as exhibiting a number of advantages for the involved stakeholders in terms of:

• Reduced initial costs. Cloud computing provides cost reduction that particularly SME organisations can benefit from whereby they do not own the resources (server, storage) that produce the computing capabilities they require and pay for.

• Ubiquitous, simple access through Internet. With cloud computing, organisations are given new opportunities to push virtual collaborative alliances to the next level, breaking down geographical barriers and enabling dynamic continuous collaboration that generates globally composed business value.

• Centralised platform for collaborative scenarios, integration hub. Cloud computing provides a centralised platform, which can be accessed from anywhere, by any (authorised) collaborator, and at any time over the internet.

• Flexible cost models. In addition, customers benefit from the fact that cloud computing employs a pay-per-use model. Customers do not have to do a high initial investment in hardware and software, instead they are typically paying monthly or yearly fees depending on the number of user licenses they need and/or the resources they consume.

It is essential for a cloud-based software solution to be designed considering the fundamental requirements of a collaborative workspace environment. As such we believe the following five key design principles [START_REF]GloNet Platform Design Specification[END_REF] are essential in building a collaborative environment support platform:

1. Modularity and extensibility. In cloud-based software solutions, software subsystems are typically consolidated having a code-base that works for all customers. However in collaborative networks, this approach is not feasible, since customer specific configurations, data models, business rules and workflows, need to be tailored to each individual user need. As such, software solutions need to support programmatic extensible and customisable features, namely allowing the addition and removal of modules for specific needs.

Multi-tenancy.

Multi-tenancy is the capability of a software system to serve multiple customers or tenants (which in turn comprises multiple users). In essence, having a cloud-based approach, two potentially conflicting requirements need to be addressed. That is on one hand the cloud-based approach needs to leverage the economy of scale principle by employing a consolidated architecture that handles all customers uniformly, and on the other hand customers demand that the software they use can be tailored to meet their specific requirements and match with their highly-individual business and the processes they work with. This implies that both data and customisations have to be isolated on a tenant-based level by the cloud-based platform.

Scalability and Availability.

For any collaborative environment, coming up with a scalable software architecture is a major concern. For cloud-based systems, this is even more critical. The software will be used by thousands of users in parallel, namely the expected average number of concurrent users per customer, multiplied by the number of customers having licensed the software. There are different ways to achieve scalability. One way is to scale-up the system, that is to move the software to more powerful servers (for example having more processing power, more RAM, more and faster storage) when the need arises. For a cloud software approach, a scale-out strategy is much more suitable. This means that the workload of the system can be distributed among several servers. A scaling-out architecture offers two main advantages. First, standard and cheap off-the-shelf servers can be used instead of expensive high-end servers. Second, it offers higher availability, since many instances of the same application server are deployed.

Security and

Trust. An important issue in adopting a cloud approach is ensuring data security. For collaborative solutions security mechanisms need be designed in a way that they still allow for data sharing among users that want to collaborate.

Network access.

Cloud architectures need to be designed for network access from the ground up. This concerns the end-user interfaces (web-based UIs) as well as programmatic access (web services).

The GloNet Platform

The GloNet platform is built according to functionality required by collaborative networks as discussed in Section 3 and the five key principles a cloud-based approach should take into consideration. It uses a framework-based approach: it defines an application architecture and provides a number of components that implement the basic building blocks of this architecture. In addition, the platform is extensible, providing customisation mechanisms as well as extension and integration mechanisms for additional modules and services that may complement the basic features of the platform.

GloNet Platform Architecture

The GloNet platform follows a layered architecture [START_REF] Buschmann | Pattern-Oriented Software Architecture[END_REF], whereby a system is decomposed into several distinct layers or tiers that can be developed, maintained and (often) deployed independently from each other. Figure 1 shows the GloNet three layers, and zoom into the Cloud-based platform, which is also composed of 3 layers: the presentation layer, the application layer and data layer. The presentation layer focuses on interacting with the user through a graphical user interface. It displays data and collects user input and commands. The application layer or business logic layer provides operations that implement the processes and operations that the software solution provides. The data layer encapsulates the storage and provides access to the persistent data of the solution. In most cases this layer makes use of a database management system.

Cloud-based platform

Collaborative networks management system

Service-enhanced product support

GloNet System

Fig. 1 -GloNet system overview

The most important modules of the GloNet cloud-based platform are the following:

• The data access module that encapsulates database specifics. It enforces tenant isolation and implements a permission system, thereby strictly controlling the access to the data stored in the databases. As a clean interface, it provides a database and platform neutral simplified SQL-like language for data access including a number of helper functions to simplify the access to more complex data structures.

• The server core module, which builds the backbone of the backend implementation of the GloNet platform. It provides mechanisms to register and unregister additional modules, which in turn may provide additional operations. It is also responsible for enforcing a strict security policy by providing and verifying security contexts (for example, based on user and tenant credentials) when executing any kind of operation. Furthermore, it makes use of the data access module to manage generic (extensible) data objects.

• A number of business operation modules provide implementations for the basic services of the GloNet platform. Such operations include infrastructure-related operations (user and tenant management, permission management), but also operations concerning more end-user related features (e.g. document management, calendar management). New modules can be registered using mechanisms provided by the server core module, creating an extensible platform.

• Any operation from the business operation modules or the server core with a publically defined interface is exposed via a number of external interfaces, namely in-process method calls, RMI and SOAP-style web services. A subset of those is also accessible as REST-style web services. This way, the GloNet platform provides a service-oriented application programming interface (API) suitable for a large variety of use cases and implementation technologies.

• The presentation layer of the GloNet platform provides a framework for creating and presenting a web-based user interface. The presentation logic module provides generic user interface functionality like reusable controls, the application frame, and configurable list and form views.

The other layers of the GloNet system are not addressed in this paper. The platform supports customisation facilities on all layers of the software system:

• User interface. The GloNet platform allows users to define and customise their user interface to match with their corporate branding, that is adapt rather simple things like logos, colours and fonts, or to better reflect their internal nomenclature. Users can also modify the layout and ordering of the presented information in order to put an emphasis on business critical information.

• Business logic. The platform allows users to implement their daily business needs, by supporting customisation of business logic layer rules. For instance, a VO member in could define rules that are used to evaluate the rating or potential of a customer. In such situation, one CN member's rating rule might be based on the yearly turnover with that customer, whereas another CN member might prefer to make that rating dependent on the number of sold licenses for a software company or the overall value of profitable insurance contracts for an insurance company.

• Data model extensions. The platform allows CN member users to store additional information in the system. In these cases, the platform supports extensions of the data model using custom objects or custom fields for each user.

GloNet Users Roles and Permissions

The GloNet platform provides a framework for the authentication and authorisation of its users. These are classified in three main categories:

• Users, groups and resources. These are called permission owners. Users can authenticate themselves with the platform's authentication methods. Users can be organised into groups. For each user, the platform maintains some basic attributes.

• Security contexts. After authenticating a user login, the platform creates a security context. Any operation runs within this security context. The platform provides different classes of security contexts, with user contexts always associated with an authenticated user and a user can only perform privileged operations based on the , privileged contexts set to the user.

• Permissions. Most data elements and operations are associated with permissions.

Permissions are used for authorising the access to data or the invocation of operations within the platform. For example, for permissions related to financial details, such as an employee's salary, the platform ensures only people from the HR department can be granted the permission to change the salary; however the platform ensures administration members are able to read the value in order to give out the pay checks.

GloNet Platform Implementation

The GloNet platform implementation employs a number of industry standards and off-the-shelf components which form the technical infrastructure of the GloNet platform. Fig. 2

Mechanisms for Integrating Existing Services in GloNet

The GloNet platform provides two key approaches to integrate external components or systems: proxy-based and mashup-based. In the proxy-based mechanism each external service has a proxy within the GloNet platform that performs the mapping between the GloNet platform and the external service. The mash-based approach, external services makes use of their own user interface that can be plugged-in to the GloNet platform. To exchange data with the GloNet platform, the provided web service API can be used. In order to interact with the GloNet application, basic user interface context information is exposed using a RESTful interface [START_REF] Hoyer | Enterprise Mashups: Design Principles towards the Long Tail of User Needs[END_REF]. This approach is used to build the upper layers of the GloNet system, namely the Collaborative networks management system and the Service-enhanced product support (see Fig. 1).

Conclusions

In this paper we have described the required functionality for a collaborative networks environment and proposed the GloNet platform, a cloud based framework for creating applications for the collaborative design and operation of complex service-enhanced products. The platform is built according to a tailored cloud based principle, taking into consideration specific requirements of collaborative networks. Additionally, the platform provides mechanisms for the integration of external systems and services. We envisage enhancing the platform customisation part. In this perspective we want to investigate on meta-data based configuration that can be used to provide customisation of services across all the three layers (User interface, business logic and data model extensions) of the GloNet platform architecture. The current platform has been designed on the needs identified in the context of solar plants, and we envisage validating the approach to other context industries involved in service provisioning.

Fig. 2 -

 2 Fig. 2 -GloNet platform deployment architecture

  illustrates the basic infrastructure for the deployment of GloNet. It consists of a farm of standard Linux-based PC-Servers that operate either as Java application servers based on Apache Tomcat or as database servers using MySQL. The presentation layer is currently implemented using server-side frameworks for AJAXstyle user interfaces[START_REF] Lange | Eclipse Rich Ajax Platform: Bringing Rich Client to the Web[END_REF], Vaadin and Eclipse RAP. These components are standard building blocks of a Java based technology stack. Therefore, an instance of the GloNet platform can be deployed on almost any cloud infrastructure provider offering that allows for standard VM images as units of deployment. Adaptations to specific cloud infrastructure provider offerings are only necessary regarding the load balancing and VM administration interface. Alternatively the GloNet platform can be hosted in almost any computing centre that has some capability of operating Javabased application servers.

	Mobile Apps iPhone, iPad,	Mobile Device	Browser	Browser	GloNet Browser-Client
	Android,...				IE, Chrome, Firefox
					Safari,…
			Load Balancer
	Administration	Presentation	Presentation	GloNet -
	Interface	Layer	Layer		Server
	e.g. Account Mgmt, Billing	Server Backend	Server Backend	Linux + Apache Tomcat
					DB-Server
		Global Data	Tenant B Tenant A	Tenant Y Tenant X	Linux + MySQL
			…	…

Acknowledgments

. The work on this paper is funded in part by the European Commission through the GloNet project (FP7 programme-GLONET [FP7 / Project No. 285273, 2011-2014]). The authors would also like to thank the contributions of the different partners of the GloNet project.


Maik Herfurth
email: maik.herfurth@hilti.com

Thomas Schuster
email: schuster@fzi.de

Peter Weiß

Simulation-based Service Process Benchmarking in Product-Service Ecosystems

Keywords: service processes, service procurement, benchmarking, business process simulation

Increasing market competition leads to a differentiation of service providers by offering hybrid services as value added services. In productservice ecosystems the effectiveness of cross-company business processes constitutes a major competitive factor. In this paper, we present a procedure model for simulation-based process benchmarking in the domain of industrial service procurement. In focus are cross-company business processes in service networks in bilateral collaboration scenarios of service providers and service consumers. A systematic analysis of performance indicators measures reveals weaknesses and potential for improvement. A set of hands on uses cases realizes validation and evaluation of yielded benchmarking results.

Introduction

Current surveys highlight that services will constitute an integral element of many products creating value offerings to customers [1]. Industrial services being a part of product-service systems (PSS) gain importance. These value added services are delivered through external service providers granting availability and reliability of industrial facilities and infrastructures. Industrial services make a significant share of companies total spending and ensure required operational levels and availability of systems and facilities. For the procurement of industrial services, organizations consisting of service providers and service consumers interact in product-service ecosystems.

The paper is focusing on how the collaboration and interaction of organizations can be measured and improved. Our presented research approach is based on case studies of a research and standardization project [START_REF] Smeltzer | Purchasing Professionals' Perceived Differences between Purchasing materials and Purchasing Services[END_REF][3].

Product-Service Ecosystems for Service Procurement

Today digitalization is omnipresent and increases competition between companies.

New developments in ICT (information and communication technologies) require companies to create new innovative value offerings for their customers combining products and services in order to sustain on the market. Hence, the ability to integrate and share products and services offerings of external business partners turns out to be a major competitive factor in future. In consequence, service suppliers take focus on supporting customer's processes or even offering to take offer whole or parts of its value creation processes (through outsourcing). Service suppliers and service consumers are collaborating in so-called industrial service networks. These networks are part of product-service ecosystems. In these service networks, the production of services is in focus. Since service consumers request different service types from different service suppliers, new ways of flexible collaborations are emerging. The internal and cross-company business processes describing the administrative processes for the procurement of services are called service processes. The collaborations are always confronted with similar challenges: The missing harmonization, integration and standardization of cross-company service processes. Therefore creation of new collaborations suffer from low quality of business interactions caused by integration and transaction costs, manual exception handling, offline communication (media breaks) and procurement times; also resulting in less transparency and low quality of processes and data. In focus of industrial service procurement is an integrated perspective of goods and services [4][5].

Service E-Procurement and E-Business-Standards. The use of e-business solutions supports strategic and operative procurement activities, resulting in lower costs and improved competitiveness. Service e-procurement constitutes an important segment of e-business activities. It compasses extensive use of ICT to improve productivity and business processes. Effective service procurement requires looking at distinct phases (encompassing planning, negotiation, delivery to payment). Respective automation of a business process, in whole or in part results in an electronic process.

In electronic business documents, information or tasks are passed from one participant to another for action, according to a set of procedural rules [START_REF] Schönthaler | Business Processes for Business Communities -Modeling Languages, Methods, Tools[END_REF]. Electronic processes support business interactions reducing interfaces, process and throughput times and support harmonization of meta data, activities, procedures and integration of resources. E-business standards support a shared process understanding and increase process transparency amongst business partners by harmonization and structuring of exchanged business data. In this way, e-business standards facilitate enhanced interoperability. For the domain of service e-procurement, various ebusiness standards do exist, however, do not adequately support service procurement operations, lacking appropriate business objects (documents, information) and required service logic of respective procedural rules and control flows.

Challenges. Business processes and respective exchange of documents and information lack harmonization. Business processes must support procedural rules and service logic of required business interactions and communication between service suppliers and requesters [4] [5]. Industrial service procurement is still source of high cost because underlying business processes are error prone. Errors and failures occur foremost through the absence of coherent e-business standards and reference frameworks offering meta models of processes, data objects and interaction patterns taking into account the service logic. Although, a variety of e-business standards and frameworks is available, they typically cover solely parts and phases of business processes, and are cause of numerous media breaks and inefficiencies such as manual error and exception handling, decreasing productivity of service operations. Due to these challenges, collaborations in service procurement networks seek for standardized, optimized and key performance indicator based service processes.

Quantitative Evaluation of Service Processes

To meet the challenges described above, a quantitative evaluation of service processes identifies further improvement opportunities. A quantitative analysis of service processes validates pre-defined service process targets based on best practices and leading companies. The result is the improvement of the efficiency (performance) and productivity of the business processes. The process efficiency does have several impacts, hereto especially the following improvements are of interest like the reduction of process costs, reduction of process times, reduction of process throughput time, the improvement of process quantity, improvement of process transparency and increase of process flexibility. Service processes of administrative order processing in productservice ecosystems in the domain of service procurement weren`t drew much attention. These service processes need many resources, long process times and throughput times. Inefficiencies result from the internal and especially cross-company handling und coordination of transactions und non-harmonized and non-integrated electronic service processes.

Business Process Simulation. Business process simulation (BPS) [7][8]

[9] can used to support business process benchmarking and furthermore BPS can be seen as a part of business process improvement (BPI) [START_REF] Van Der Aalst | Workflow Management: Models, Methods, and Systems[END_REF]: the continuous progression of market competition by a continuous comparison of market competitions and adaption of business processes. In the domain of service procurement, different service process variants, internal and company-wide service processes in product-service ecosystems can be compared. The efficiency of service processes in service collaborations can be measured by defining different service process simulation models. Those simulation model variants of simulation models can be simulated. For the execution of simulation runs, relevant process inputs and process parameters have to be defined. Possible scenario variants are order volumes as process input or the amount of human resources. The execution of simulation experiments can be used to gain insights about a real system at build time [START_REF] Page | The Java Simulation Handbook: Simulating Discrete Event Systems with UML and Java[END_REF]. The advantages of business process simulation experiments are [START_REF] Von Haaren | Konzeption, Modellierung und Simulation eines Supply-Chain-Risikomanagements[END_REF][12][9]:

• Most real systems cannot be analyzed by analytical methods • Simulation models allow for analysis in different environments and under different conditions and lower risks and can easily be changed and adapted • The behavior of business processes can be analyzed on a much broader scale and alternative developments can be syntactically and semantically analyzed and visualized

• Simulation experiments are repeatable and can be designed easily and executed in a cost-efficient way • Systems can be simulated in very short or very long time periods. A simulation experiment takes less time than real experiments and no experiments are executed on real systems so they are not endangered • Different action alternatives and scenarios can be simulated to gain important statement prospectively Business Process Benchmarking. Camp [3] defines benchmarking as "the search for solutions, based on the best methods and procedures of the industry, the best practices, to lead a company to top performance". Benchmarking is a method to compare products, services and business processes. A systematic analysis of performance differences and their cause helps to detect possible improvement potentials. Benchmarking is a continuous process to compare business processes across different companies. Therefore differences and causes as well as possibilities for further improvements will be detected. Target dimensions of benchmarking are quality, costs and times and customer orientation. Types of benchmarking of companies are internal and external benchmarking. An internal benchmarking is company-related (internal benchmarking). An external benchmarking is marketrelated (competitive benchmarking of best practices) [START_REF] Bodmer | Benchmarking im Einkauf[END_REF]. The process benchmarking serves as an instrument to expose existing deficits like productivity and performance gaps, lacking process transparency and lacking link between strategic and operative targets. The analysis of service processes serves as identification and provision of mission critical data for a performance comparison to identify potentials for improvement. In the domain of service procurement inefficiencies of service processes result from sequential process flows, multistage processes, poor data quality of information flows, insufficient customer-supplier-orientation, poor information transparency and non-value creating activities. In product-service ecosystems in the domain of service procurement, service customers and service supplier are benchmarking partners. Different combinations of service customers and service supplier can be benchmarked pairwise to compare and improve the business process performance.

Procedure Model for Simulation-based Service Process Benchmarking

For the execution of simulation-based service process benchmarking, a procedure model in dependence on Neumann [START_REF] Neumann | Simulation von Geschäftsprozessen[END_REF], Schuster [START_REF] Schuster | Modellierung, Integration und Analyse von Ressourcen in Geschäftsprozessen[END_REF] and Wildemann [START_REF] Wildemann | Prozess-Benchmarking -Leitfaden zur Erreichung von Quantensprüngen in Geschäftsprozessen[END_REF] is presented. The procedure model defines a structured approach for the quantitative evaluation of service processes. In order to meet the requirements in line with business process benchmarking, an adequately structured procedure model needs to be defined. The described approach extends and details a phase oriented procedure model for the analysis, modeling, simulation and benchmarking of business processes in productservice ecosystems. The procedure model combines the advantages of business process simulation and business process benchmarking in a common approach. Bilateral collaborations of service consumers and service suppliers can be benchmarked. In Fig. 1, the procedure model is shown with its cyclic different phases. 1. Project Initialization: The organizational determining factors are defined. The fundamental target of the project and the project scope as well as the involved people and organization units are defined. Data and information suppliers are defined and the project specific framework like resources, budget and timeline are determined. Based on strategic planning, targets are refined to derive tactical and operational targets.

Benchmarking Planning:

The target definitions of service processes have to be reviewed continuously. In the benchmarking planning phase, benchmarking targets are defined. Benchmarking objects are defined like the analysis area, the choice of structured business process models, needed resources, benchmarking-partners, key performance indicators and methods. Conditions are defined for the execution of benchmarks with the benchmarking partners. For the modeling and simulation of business processes, a modeling language and a modeling and simulation tool has to be chosen. 3. Data Acquisition: Process inputs and process outputs of the business processes are defined. The existing process flows are analyzed and comparative data of potential benchmarking partners are collected. Secondary data sources are also used. 4. Functional and technical Modeling: Technical framework requirements are defined for the technical modeling. Requirements for resources are determined. During the functional modeling, service processes and service objects are modeled. In the context of a hierarchical process modeling, service processes are modeled on different abstraction layers. During a successive refinement, service processes are modeled on a high modeling layer and complex activities are consecutively refined. Hierarchical service process models combined with a modularization of service processes support the decomposition and application of service processes.

Bench mark ing Plann ing D a t a A c q u is it io n P ro ce ss S im u la ti o n fo r P e rf o rm a n ce

A n a ly si s [START_REF] Weiß | Leverage Productivity Potentials in Serviceoriented Procurement Transactions: E-Standards in Service Procurement[END_REF]. Process Simulation for Performance Analysis: For the business process evaluation, key performance indicators are selected and defined. Critical factors and main influence factors are specified. The service processes are simulated. Based on the simulation evaluation of service processes, possible patterns can be identified. The simulation of service processes is executed for a longer time period. After the simulation, the simulation model has to be verified and validated. For the verification, the transformation of the process model into a simulation model is analyzed. For the validation, the reproduction fidelity of the process model and the fidelity of the simulation parameters based on the simulation results are analyzed. 6. Process Improvement: Targets of process improvements are defined and possible process alternatives are generated. Based on alternative process flows, improved process structures can be identified and realized. The process model is configured with alternative parameters in order to enable a comparison between different configurations und their implications on the process model. 7. Process Simulation of revised business Processes: Improved service process models or different service process model variants are simulated. The phases of process improvement and process simulation of improved business process are iterative: improved process models and variants can be simulated until the target definitions are met. The process simulation of the revised business processes ends with the preparation of the simulation results. 8. Business Process Implementation and Execution: The improved service process models can be implemented to operate a business. Change management has to be executed for practical training and mind change of personal resources.

Evaluation of the Procedure Model

The procedure model for simulation-based service process benchmarking serves as an approach to strengthen the competition position of a company. Business process simulation complements business process benchmarking and serves as an adequate method. The advantages of business process simulation increase the value of process benchmarking. The execution of a process benchmarking identifies differences of relevant factors like throughput times, resource assignments or cost items. The causes of performance gaps can be analyzed. Based on benchmarks, performance gaps can be quantified. Redundant service processes and non-value creating activities can be identified. Possible automation potentials for service processes can be identified and the error data is reduced. Also the cost-effectiveness of service processes can be ensured. Problems of the simulation-based service process benchmarking evolve due the comparability of service processes and the effort for the information search of competitive company data. However, the information retrieval of other companies can be difficult as a result of losing competitive advantages. For the simulation models, a realistic model calibration has to be found. The identification of benchmarking objects, the modeling of service processes and process evaluation acquires some effort.

Benchmarking of Service Processes in bilateral Service Procurement Networks.

The method of simulation-based service process benchmarking has been successfully

applied in a research projects in the domain of service procurement [START_REF]eBusInstand -Einsatz von Standards in der industriellen Instandhaltung[END_REF]. The service process models of 18 use cases between six service suppliers and four service customers were analyzed, modeled, simulated and benchmarked. The evaluated uses cases were compared pairwise for benchmarking applying the presented procedure model. Service process models were modeled with service nets [START_REF] Herfurth | Simulationsbasierte Analyse von Lösungskonzepten im Service E-Procurement[END_REF], a higher petri net variant of XML nets. The modeling of service nets was based on a reference process model to structure and align the individual service processes. For the modeling and simulation of service process models, the software tool Horus [START_REF] Schönthaler | Business Processes for Business Communities -Modeling Languages, Methods, Tools[END_REF] was used for a software based simulation. As key performance indicators for obtaining relevant conclusions about the efficiency and performance of the analyzed service process models, we were concentrating on the process time, the process throughput time, used human resources and media breaks. For the benchmarking of several service procurement use cases in bilateral collaboration scenarios, historical data from real business process evaluations were used to calibrate the simulation process models [START_REF] Weiß | Leverage Productivity Potentials in Serviceoriented Procurement Transactions: E-Standards in Service Procurement[END_REF].

As process input, the data amount of service orders per day, the time period of a year, the average amount of resources, the amount of failure checks and repeating activities like double work and the process and process throughput times were taken. As business process simulation method, a discrete event driven business process oriented simulation was used [START_REF] Banks | Discrete-Event System Simulation[END_REF]. The strengths of this approach are the independent simulative analysis of the real system and the possibility of a "playground" by simulating different process alternatives. Evaluation of simulation results can shed light on correlations of system parameters at build time and can be used to develop action strategies [START_REF] Neumann | Simulation von Geschäftsprozessen[END_REF]. Therefore alternative resource configurations in business processes and their allocations can be simulated and possible impacts on process times and process throughput times at run time can be identified. Unlike analytical procedures, the simulation can be used for the analysis of large systems. Weaknesses are the abstraction of the simulation models since with an increasing abstraction level, the granularity of model descriptions of the real system decreases. Also the dependency of data input for the benchmarking approach can be an obstacle. As a main implication of our analysis results, we can state that the benchmarking approach based on process simulation can serve as an adequate analysis method for a long-term process evaluation. Limitations exist due to the characteristics of the simulation and the derivation of the appropriate conclusions and measures.

Conclusions and Outlook

In this paper, a procedure model for the simulation-based service process benchmarking was presented. The procedure model offers an adequate proposition of an efficient way for the quantitative evaluation of service processes for benchmarking of bilateral collaborations in product-service ecosystems. The procedure model combines two efficient approaches for the quantitative evaluation of business processes: business process simulation and business process benchmarking. The integration of business process simulation for benchmarking allows for cost and effort efficient way to analyze and improve service processes in product-service ecosystems. This approach was evaluated from real-life case studies [START_REF] Weiß | Leverage Productivity Potentials in Serviceoriented Procurement Transactions: E-Standards in Service Procurement[END_REF]. Next steps foresee to elaborate further de-

Fig. 1 .

 1 Fig. 1. Simulation-based business process benchmarking procedure model Project Phases. The procedure model consists out of eight process phases:

tailed steps for this approach. Further benchmarkings of business process models in different domains are planned.