João Rosas
email: jrosas@uninova.pt

Patrícia Macedo
email: patricia.macedo@estsetubal.ips.pt

Alexandra Tenera

António Abreu

Paula Urze

Risk Assessment in Open Innovation Networks

Keywords: Open Innovation, Risk Assessment, Collaborative Networks, and FMEA

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Nowadays, Innovation is crucial for the enterprises survival and current economic environment demands the best ways of achieving it. Open innovation is a useful strategy enterprises adopt to seek knowledge and value from outside of their boundaries. In such way, enterprises share technology and knowledge with their suppliers and customers to develop innovative and improved products, and leveraging value creation. Assuming this strategy, enterprises may even disclose secrete technology or free intellectual property, for the sake of obtaining such benefits. But this is not done without risks, as open innovation projects might fail for diverse reasons. For instance, the appropriation of others' know-how or trying to take unmerited benefits from a technology are considered disruptive behaviors in open innovation projects.

To effectively benefit from open innovation, it is recommended the utilization of adequate risk models. Open innovation is essentially a collaborative strategy. There is a significant number of risk-like assessment approaches for collaborative networks already, but few specifically addressing risk in open innovation.

Our aim is to develop research work towards developing a risk assessment approach for open innovation. As explained in section 3, this problem has had little attention by the research community. Given this scarcity, we considered a research question, which can be stated as: How to model open innovation risk assessment? Our effort is, therefore, devoted to contribute to provide an answer to this question.

Our research method starts by problem formulation. It is followed by the conceptualization of open innovation and risk and by the related research analysis. The next phase is devoted to work on risk modeling in open innovation. In this regard, our risk assessment approach is based on a widely used risk model, namely, FMEA. The obtained approach is illustrated through an application example, followed by result analysis.

In the next section, we provide the relevant concepts regarding open innovation and risk. We briefly describe a few existing risk assessment models. A research on the state of the art related to risk in open innovation is presented in section 3, aiming at identify and characterize risk assessment models in open innovation. In section 4, we illustrate how we could model risk assessment using the FMEA model. We also provide an illustrative application example. We finish the results analysis, conclusions and future work.

Base Concepts

Open Innovation

The concept of open innovation, coined by Chesbrough in 2003 [START_REF] Chesbrough | Open Innovation: The New Imperative for Creating and Profiting from Technology[END_REF], has become relevant in practice and in academia. Open innovation is now a mainstream research focus in innovation literature [START_REF] Elmquist | Exploring the field of open innovation[END_REF]. One of the reasons for the advance of the open innovation paradigm is based on the principle that today´s problems are often complex and require a wide range of expertise. To create and implement solutions it is required that collaboration occurs among different areas and people with a variety of experience and knowledge. It is very difficult for organizations to build solutions and create knowledge by themselves. The basic assumption of open innovation is opening up the innovation process. This means to treat research and development as an open system. Open innovation is usually put in contrast to closed innovation, supposedly its precursor, where companies generate their own innovation ideas, and then develop, build, market, distribute, service, finance, and support them on their own [START_REF] Chesbrough | Open Innovation: The New Imperative for Creating and Profiting from Technology[END_REF].

Open innovation suggests that valuable ideas can come from inside or outside the company as well. The mobility of competencies, the increasing presence of venturecapital, the emergence start-ups and the role of university research and its linkages give rise to a more open approach towards innovation. Collaboration is argued to facilitate the production of new knowledge more than just transferring it. Chesbrough [START_REF] Chesbrough | Open Services Innovation: Rethinking Your Business to Grow and Compete in a New Era[END_REF] has referred to open innovation not only as being a business model, but also as a way of promoting and sharing knowledge. This means that open innovation can go beyond the idea of knowledge transaction and of in/outsourcing ideas, but promote the creation of new knowledge.

Companies can develop and bring ideas to the market using channels outside of their current businesses, in order to generate value for the organization. A path for accomplishing this can involve new businesses and licensing agreements. These might be financed and staffed with the existing company's personnel. In addition, ideas can also be originated outside the company and be brought inside it, for commercialization [START_REF]Open innovation: Researching a new paradigm[END_REF]. In other words, the connection between a company and its environment is more permeable, enabling innovative ideas to transfer easily where boundaries once existed.

As argued by Felin & Zenger [START_REF] Felin | Open or Closed Innovation? Problem Solving and Governance Choice Research Policy[END_REF], the mechanisms for accessing external knowledge and promoting open innovation encompass a range of alternatives including alliances and joint ventures, corporate venture capital, licensing, open source platforms, and participation in various development communities. Each of the modalities brings distinct benefits, implies distinct levels of openness, and various risks. For instance, they vary in terms of intellectual property ownership, from Jointventures, created by formal-agreements, and "open-source models", in which IP is given away to a (large and open) software development community.

An open innovation strategy can generate a positive result, but, it also involves some risks. Therefore, companies should have a policy to secure open innovation within their organization, to create interfaces and make achievements measureable. The way employees manage their external partners is also very important and plays a central role.

Risk Models in Engineering

Proceeding as specified in our research method, we now address the concept of risk in an engineering context. To overcome global competition and rapid technological advances, in order to predict and positively respond to changes, the development of organizational capability to innovate has become one of the prime strategy in SMEs. This has been done despite the lack on practical models, metrics and tools to assist their risk management efforts [START_REF] Aleixo | New Product Development Process on High-Tech Innovation Life Cycle[END_REF].

For many years, organizations risk has been seen mainly as a combination of the probability of occurrence of harms and the severity of these harms. Nowadays, such in project management, risk is viewed as related to uncertain events or conditions that, when they occur, pose positive or negative effects on projects objectives. Similarly, as the uncertainty associated with innovative processes is bonded not only to inherent risk of failure, but also to inherent chance of success, these subsequently bring on the necessity of adequate risk management in innovative processes [START_REF] Vargas-Hernández | Management in the Innovation Project[END_REF]. Thereby, and since the purpose of an integrated risk management is to facilitate innovation rather than stifle it, innovating firms require a strategy not of risk avoidance, but of early risk diagnosis and management [START_REF] Keizer | From experience: Applying the Risk Diagnosing methodology[END_REF][START_REF] Pereira | Insights on Individual's Risk Perception for Risk Assessment in Web-based Risk Management Tools[END_REF], which should be spread through organizations and collaborative networks.

Based on general project management practices, several frequently used risk assessment models can be identified, such as the Balanced Scorecard (BSC), Failure Mode Effects Analysis (FMEA), Fault Tree Analysis (FTA), Analytic Hierarchy Process (AHP) and Risk Diagnosing Methodology (RDM).

The BSC model is typically used to facilitate the monitoring of the firm's success factors, which can be viewed as opportunities as well as risks, meaning that the BSC is by nature an instrument close to the risk's grounds function [START_REF] Henschel | Risk Management Practices of SMEs: Evaluating and Implementing Effective Risk Management Systems[END_REF].

Another commonly used risk assessment approach is the Failure Mode Effects Analysis (FMEA), which is a systemic approach suited to help identify and reduce critical aspects in early stages of products and processes conception. Due to its role in the context of our research work, this risk assessment technic is explained in more depth in the next section.

The Fault Tree Analysis (FTA) can be both used as a risk identification method or as a risk analysis instrument. In this approach, the probability of negative events can be estimated and their causes deducted from a named Fault Tree, in which the probabilities of alternative situations are assessed. These situations are organized using Boolean logic, in which lower-level events are fed into upper-level ones [START_REF] Henschel | Risk Management Practices of SMEs: Evaluating and Implementing Effective Risk Management Systems[END_REF].

Another used risk assessment approach is the Analytic Hierarchy Process (AHP). AHP is considered a multivariate analysis technique that aims to decrease the randomness of subjective assessments, by having in consideration different objectives grounded on distinct criteria [START_REF] Goodwin | Decision Analysis for Management Judgment[END_REF]. It is predominantly used in scenario selection and evaluation [START_REF] Hulle | Analytic network process -an overview of applications in research and practice[END_REF].

Another risk assessment approach is Risk Diagnosing Methodology (RDM) which main purpose is to help provide strategies that will support and improve the chance of projects success, by identifying and managing their potential risks [START_REF] Keizer | From experience: Applying the Risk Diagnosing methodology[END_REF][START_REF] Pereira | A Risk Diagnosing Methodology Web-based Platform for Micro, Small and Medium Businesses: Remarks and Enhancements[END_REF]. It is used to support the systematic diagnosis of companies, considering issues such as consumer and trade acceptance, commercial viability, competitive responses, external influential responses, product technology and manufacturing technology [START_REF] Keizer | From experience: Applying the Risk Diagnosing methodology[END_REF], in which the assessment of project risk is determined not only by risk likelihood and its effects, but also by the companies' ability to influence the course of the risk actions.

The FMEA Risk Assessment

FMEA is widely used risk assessment approach. It was original developed inside Aeronautic Industry in the 50's to guide the design process. But it has been widely used in a broader sense, not only to assess physical systems but also organizational ones, such as those in the areas of Knowledge Management [START_REF] Luoa | Application of Failure Mode and Effect Analysis (FMEA) for audit of HACCP system[END_REF] and supply-chain management [START_REF] Gary Teng | Implementing FMEA in a collaborative supply chain environment[END_REF]. The FMEA can be used as a design tool to systematically analyze postulated component failures and identify the resultant effects on system operations. A failure at a lower level may very well cause a larger failure on the higher level. Therefore, it is essential to find them as fast as possible [START_REF]IEC 60812, Analysis Techniques for System Reliability -Procedure for Failure Mode and Effects Analysis (FMEA)[END_REF].

With FMEA, we can quantify the risk level of each identified failure, whether known or potential. Then, an estimate of its likelihood of occurrence, severity, and detectability is made for each one. At this point, an evaluation of the necessary actions is performed, namely, they can be taken, planned or ignored. The emphasis is to minimize the probability of failures or to minimize their effects. The main constituents of the FMEA model are described as:

Failure -Event/process in which any part of the organizational system does not perform according to the prescribed behavior. Example: absence of knowledge sharing when it was expected.

Failure mode -The specific manner by which a failure occurs in terms of the failure of some part of the organization. It shall at least clearly describe the end state of the item under consideration. It is the result of the failure mechanism (see next). Example: An enterprise owing key knowledge left the project.

Failure cause and/or mechanism -Defects/problems detected in the elements of the organizational system which are the underlying cause or sequence of causes that initiate behaviors leading to a failure mode over a certain time. A failure mode may have several causes. Example: Inadequate organizational rules adoption may cause network weaknesses.

Failure effect: Immediate consequence of a failure. Example: Lack of knowledge sharing decreases the quality of a project and increases its duration.

Likelihood of occurrence (L) -The Likelihood of occurrence is the probability that a specific failure mode, which is the result of a specific cause under current openinnovation network, will happen. Failure Likelihood is a relative ranking within the scope of an individual FMEA. A suggested likelihood scale is given in Table 1.

Severity (S) -Severity is an assessment of the most serious effect for a given failure mode. Example: The severity of problematic partners can be reduced through their exchange. If such an exchange is attainable, the failure can be minimized eliminated. Severity is also a relative ranking within the scope of an individual FMEA.

Detectability (D) -Detectability is an assessment of the ability to identify any potential failure modes, case they occur. Detectability is a relative ranking within the scope of an individual FMEA.

The suggested risk guidelines for severity, occurrence and detection are given in Table 1.

Risk Priority Number (RPN) -The Risk Priority Number defines the priority of each failure. RPNs have no value or meaning by themselves. They are used only to rank (define) the potential open-innovation network deficiencies. The RPN is calculated by multiplying these three ratings: RPN = Failure Likelihood × Severity × Detectability.

(

) 1
In this equation, failure Likelihood, Severity and detectability must have a value greater than zero. A suggested RPN ranking is provided in Table 2.

Recommended Action -The recommended action may be specific action(s) or it may correspond to major changes in the operating process of an open innovation project or network. The idea of the recommended actions in FMEA is to reduce the severity, occurrence, detection or them all.

Depicting Existing Approaches of Risk Assessment in Open Innovation

During our research work, it was relatively difficult to find related research concerning risk assessment in open innovation. This difficulty led us to formulate the hypothesis that such models might be currently scarce. But this is an assumption that is difficult to demonstrate. Frequently in science, we can more easily prove that something exists than proving that it does not [START_REF] Alderson | Absence of evidence is not evidence of absence: We need to report uncertain results and do it clearly[END_REF][START_REF] Walton | Nonfallacious arguments from ignorance[END_REF]. In order to overcome this difficulty, we devised an approach to estimate the amount of research work concerning risk in open innovation. For such, a small content analysis based on the utilization of "Publish or Perish" search engine was performed.

"Publish or Perish" allows flexible searches of published research works. In our approach, we looked for papers with certain words in their titles. For instance, when a research intends to publish a paper related to risk modeling or assessment, the word "risk" or "uncertainty" is likely to appear in its title. Similarly, this is also true for researchers aiming to write papers on innovation. In this case, the word "innovation" or its synonymous would appear in the title.

The search was restricted to publications written since 2004, as the term "open innovation" was coined in 2003. After initial trials, we perceived that many results were unrelated to open innovation, so we tuned the search engine to filter out papers containing the words: "financial", "bank", "credit", "price", "climate" and "drug". We performed similar searches for words in areas that are considered consolidated, to serve as a comparison basis. The obtained results are summarized in Table 3. The mentioned table is split in three groups of results. The first group indicates there is a significant number of papers addressing risk in either closed and open innovation, and that some of them (could be up to 48) are more specific to risk models. The second group indicates there are few papers which consider risk in open innovation, about 7, and that only one has got the word "model" in title. Concerning risk, such areas of collaborative networks also present similar numbers. The last group of results serves as comparison basis, as it provides figures for areas that are more stabilized. The approach can be repeated and extended with more words. For instance, the number of papers with "uncertainty" and "innovation" in their titles was 308. But if we include the word "open", we only obtain 6 publications. A reader is right stating that the approach may not provide very rigorous figures. But the divergence of values between open innovation and other consolidated areas provides support to our claim, that such models for risk in open innovation might be scarce.

As to establish a theoretical bases for our research in open innovation risk assessment models, we performed an analysis of the more promising publications among the few ones which were obtained during our context analysis:

Research works addressing risks, whiteout risk assessment models: In [START_REF] Sleefe | Quantification of technology innovation using a risk-based framework[END_REF], a riskbased technology management approach was proposed. In this approach, risk is considered as an inherent aspect of the product development. It simultaneously considers both quantitative innovation objectives and quantitative product delivery objectives, such as cost, schedule and performance, enabling to establish formal quantitative technology innovation objectives and to track and monitoring them during a product development cycle. In [START_REF] Xiaoren | Interaction of open innovation and business ecosystem[END_REF], the external technology dependence, complex process management, difficulty in intellectual property protection, market information leakage, and mismatched resources capacity are identified as situations that pose risks in open innovation projects. In [START_REF] Alexy | Managing the business risks of open innovation[END_REF], several factors related to intellectual property competition in open innovation are highlighted. The study reveals the likelihood that open and proprietary competitors will clash, according to the industry type, e.g. radio and television, Medical, Electric motors, food and beverages, etc.

Identified risk models in open innovation:

The research work in [START_REF] Song | Risk evaluation of customer integration in new product development under uncertainty[END_REF] is focused on the risk evaluation of customer integration in new product development. A number of risk factors of customer integration in new product development were identified, namely, organization risk, capacity risk, knowledge risk, and market risk. Their goal was to develop a risk evaluation method. The approach is based on rough set theory to handle vagueness. In [START_REF] Rossi | Adoption of free/libre open source software in public organizations: factors of impact[END_REF], a research was conducted towards developing methodologies for managing risks of open-source software adoption in a context of an ecosystem of developers. The approach combined risk monitoring methods in order to provide early warnings of risks and their mitigation. The developed tools also included Bayesian networks and social network analysis. In [START_REF] Souza | Crowdsourcing Innovation: A Risk Management Approach[END_REF], a risk management approach for crowdsourcing innovation is proposed. It provided overall guidelines to managing risks associated with crowdsourcing strategy, and a risk model suited for small and medium enterprises.

Application of FMEA to Open Innovation

Factors of Risk in Open Innovation

As mentioned in previous section, there are many definitions of risk according to the respective contexts in consideration. For all that matters in this research work, we can assume one of these general descriptions, in which risk can be seen as a probability or threat of damage, injury, liability, loss, or any other negative occurrence that is caused by external or internal vulnerabilities, and that may be avoided through preemptive action [START_REF]Business dictionary[END_REF].

There are several innovation modalities, which as described before, are characterized by several aspects, like degree of openness to external partners. Each of these modalities pose distinct risk types. Envisaging a risk assessment instrument requires the identification of these risk types. Table 4 presents an initial characterization of these risks, which affect enterprises participating in open innovation.

Table 4. Situations posing risks in innovation, based on [START_REF] Xiaoren | Interaction of open innovation and business ecosystem[END_REF].

Situations Description External technology dependence

When enterprises rely excessively on their partners for technology and knowledge, they might get in weaker situation if cooperation with these partners fail.

Complex process management

The absence of organizational boundaries and the interaction with autonomous partners increases the difficulty of process control and management. Conflicts and uncooperative behavior might manifest.

Intellectual property protection

When a partner gives away its knowledge to its peers, core knowledge is difficult to protect. Enterprises risk lose control of knowledge ownership, and may not collect the desired benefits.

Market information leakage

When participating in open innovation, enterprises may need to disclosure knowledge and business secrets to peers. They risk product and user information being stolen by its peers.

Resources capacity mismatch

When resources and capacity of several partner are mismatched, it creates obstacles for innovation activity.

For each risk of a failure, we can identify its causes, its effects and corresponding degree of impact, and the likelihood of its occurrence, as illustrated in Table 5.

Assessing Risk Example

The information regarding the risk factors described in previous section can be used to illustrate how we could assess risks in open innovation using.

Let us suppose the existence of an open innovation project aiming at developing an Internet of Things application for health care. In an initial stage of the project, the set of potential failures where identified and analyzed. The corresponding values for the likelihood of occurrences, severity and detectability of each failure were estimated. The obtained risk assessment results are presented in Table 6. The values in the table were merely chosen for illustrative purposes. The obtained risk priority numbers provide indication of which failures must receive more attention. For instance, the RPN for "uncooperative behavior" is low, while the risk of abuse of IP by peers is extreme (see Table 2). Based on this assessment, a project manager can take preventive actions in order to minimize the likelihood of these failures and minimize the effects of their eventual occurrence. For instance, reducing the risk that some peers may abuse of intellectual property during the project implies establishing agreements with effective rules to protect IP, as well as the monitoring processes to ensure the compliance with these rules.

Conclusions

We described an approach for risk assessment towards providing an answer to the formulated research question, which is concerned on how to access risk in open innovation. Although existing research works address risk in open Innovation, specific works considering risk assessment models seem scarce.

After an initial conceptualization and related research analysis, we identified illustrative examples of failures in open innovation projects from literature. As a way to illustrate how to assess open innovation risks, we characterized these failures according to one widely used risk model, namely the FMEA. To our knowledge, the FMEA model had never been used in the concrete "open innovation" realm. An application example of FMEA illustrated the approach.

The study and development of our approach led us to conclude that it is necessary further research for an adequate open innovation risk assessment, including a more comprehensive characterization of open innovation risks. In this regards, we should assume a more holistic perspective. Furthermore, open innovation is inherently collaborative. Therefore, one line of research for future work may include the adaptation of existing collaborative risk models, so they could suit in open innovation. In the area of collaborative networks, there is a variety of collaboration-related key performance indicators already, as well as assessment models of more soft nature, such as value systems, benefits sharing methods, and collaboration preparedness. Encompassing these elements into a tailored and holistic approach to risk assessment in open innovation is planned for the next phase of this research work.

Table 1 .

 1 Risk guidelines Rank.

	Rank Failure	Severity of Effects	Detection Processes
		Likelihood
	5	Frequent	Maximum Severity -Failure leads to the	Extremely Unlikely -Management
			end of the open-innovation process	processes will almost certainly not
				detect the potential failure before
				it occurs.
	4	Reasonably	Very High Severity -open innovation	Remote Likelihood -Management
		Probable	network performance severely affected.	process more likely will not detect
			Members very dissatisfied. Network	the existence of a potential failure
			members will be able to correct the	before it occurs.
			failure/situation with some constraints.
	3	Occasional Moderate -Reduced performance with	Moderate -Management process
			gradual performance degradation. Network	may detect the potential failure
			members dissatisfied. Network members	before it occurs.
			will be able to correct the failure/situation.
	2	Remote	Minor -Network members will probably	High: Management processes have
			notice the effect, it is considered negligible.	a good chance of detecting the
				potential failure before it occurs.
	1	Extremely	Very Slight -Insignificant /negligible	Very high: Management processes
		unlikely	effect.	almost certainly will detect the
				potential failure before it occurs.

Table 2 .

 2 Risk Category guidelines.

	Heading level	Risk category
	90 -125	Extreme
	60 -89	Significant
	40 -59	Major
	18 -39	Moderate
	1 -17	Low

Table 3 .

 3 Publications regarding risk in open innovation, with comparison examples.

	Keywords	Papers found Number of citations
	Risk innovation	901	2446
	Risk innovation model	48	74
	Risk innovation modeling	5	12
	Risk innovation assessment	44	93
	Risk innovation management	182	461
	Open innovation Risk	7	4
	Open innovation Risk model	1	0
	Open innovation risk management	0	0
	Open innovation FMEA	0	0
	Virtual organizations risk	6	12
	Collaborative networks risk	6	22
	Collaborative FMEA	2	0
	Collaboration FMEA	2	0
	Business risk model	146	361
	Risk portfolio selection	280	1672
	Failure mode effects analysis	406	2829
	FMEA	above 1000	6602 (of these 1000)
	Risk management	above 1000	71809 (of these 1000)
	Organizational behavior	above 1000	44648 (of these 1000)

Table 6 .

 6 Open innovation risk assessment illustration with FMEA method

	Failure Mode	Likelihood of	Severity of	Detectability of	RPN
		occurrence (L)	consequence (S)	the failure (D)	(L × × × × S × × × × D)
	Partner with key technology left the project	5	3	4	60 (significant)
	Uncooperative behavior	2	2	3	12 (low)
	Abuse of IP by peers	4	5	5	100 (Extreme)
	Insecure
	disclosure of core	4	4	2	32 (moderate)
	knowledge
	Mismatched resource capacity	2	3	3	18 (moderate)

Acknowledgments. This work was funded in part by the Center of Technology and Systems and FCT-PEST program UID/EEA/00066/2013 (Impactor project), by DEMI/FCT/UNL and by CIUHCT: Interuniversity Center for the History of Science and Technology.


Cleber Maiara Heil Cancian
email: maiara.cancian@posgrad.ufsc.br

Pinelli Teixeira

Ricardo J Rabelo
email: ricardo.rabelo@ufsc.br

Supporting Business Processes for Collaborative Alliances of Software Service Providers

Keywords: Collaboration, Business Process, Software Services

Software sector has become a very important and increasing competitive sector, being mostly composed by SMEs. Enhancing partnerships at a more valuable business level can help companies to attend to wider markets in a more sustainable and agile way taking advantage of assets that they would not have if working alone. One strategy to reach this is via collaborative networks. However, companies should be prepared for that. One of the first issues is to understand more deeply what working collaboratively actually means in businesses and how to support it. This article presents the set of business processes that need to be handled in a collaboration among software service providers throughout its lifecycle. Final results are discussed at the end.

Introduction

The software industry has nowadays become a very important and increasing competitive sector. In Europe, for instance, there are more than fifty thousand SMEs within the ICT (Information and Communications Technology) sector [1], being mostly composed of small and medium-sized enterprises (SMEs). SMEs, however, usually have much difficulties to engage general conditions to be competitive, lacking more advanced and sustainable models [START_REF] Kramer | The Role of the Information and Communications Technology Sector in Expanding Economic Opportunity[END_REF].

In the software sector, SOA (Service Oriented Architecture) [START_REF] Papazoglou | Web Services & SOA, Principles and Technology[END_REF] has arisen as a prominent paradigm for wider and more sustainable business models. It has introduced a new outlook on system design, implementation, integration and agile partnerships, and has been increasingly adopted (as services-based applications) by software developers and customers in general [START_REF] Choi | Implementing Service-Oriented Architecture in Organizations[END_REF]. SOA and services-oriented market is already very representative and can reach up to US$22 billion in the next years [START_REF] Infoworld | Cloud Computing Deep Dive[END_REF].

In SOA, all system's features are regarded as independent and self-contained software modules -called software services or just services -that jointly form a virtual single logical unit to create software products and processes [START_REF] Papazoglou | Web Services & SOA, Principles and Technology[END_REF]. SOA is an architectural style that supports loosely coupled software services to enable business agility and flexibility. These services are made available by software services providers (SSP), which are independent organizations that own and provide software services' implementations and descriptions as well as the respective technical and business support throughout a given SOA solution's life cycle [START_REF] Papazoglou | Web Services & SOA, Principles and Technology[END_REF]. Web services are one of the most currently implementing technologies for SOA [START_REF] Papazoglou | Web Services & SOA, Principles and Technology[END_REF].

Regarding their intrinsic nature and goals, SOA projects are very complex, risky and costly. A big operational or strategic mistake in a given SOA project may even hazard the SME survival.

An alternative to decrease these obstacles but keeping companies' sustainability is to work collaboratively with other SSPs, as a network [START_REF] Perin-Souza | Services Discovery as a Means to Enhance Software Resources Sharing in Collaborative Networks[END_REF]. Collaborative Networks (CN) [START_REF] Camarinha-Matos | Collaborative Networks: A New Scientific Discipline[END_REF] leverages many competitive advantages. They allow its members to overcome individual limitations and to maximise the utilization and sharing of resources and assets (of many types) while risks and costs are shared, so as to better achieve common/compatible goals regarding the different members' culture [START_REF] Camarinha-Matos | Collaborative Networks: A New Scientific Discipline[END_REF] [START_REF] Perin-Souza | Services Discovery as a Means to Enhance Software Resources Sharing in Collaborative Networks[END_REF].

In order to support the envisaged collaborative scenario among disparate SSPs, two types of CN are of particular importance in this work: VO (Virtual Organization) and VBE (Virtual organization Breeding Environments). A VO is characterized as a temporary alliance formed by autonomous and heterogeneous organizations that join their complementary core-competences and resources to better attend to a given demand. VOs are originated from long-term alliances, the VBE. A VBE can be defined as a long-term association of organizations (companies, etc.) which have the willingness and enough pre-conditions to collaborate towards creating VOs with the most adequate partners in a more agile and trustful way [START_REF] Camarinha-Matos | Collaborative Networks: A New Scientific Discipline[END_REF].

In the CN scenario, working collaboratively means a practice to be introduced by companies in their daily routines. Focusing on the business process perspective, this work identifies and synthetizes which business processes are required to support the collaboration among SSPs that are members of VBEs. In general terms, it is assumed that a VO will be formed to represent every collaboration initiative among SSPs no matter its purpose (e.g. joint innovation, joint training, joint marketing, joint development of services-based software solutions, etc.).

Related works have proposed reference models and processes for creating and managing VBEs and VOs. However, they are (on purpose) generic and not devoted to any particular sector (although most of them have been based on manufacturing setor). Besides that, the scenario envisaged in this paper deal with "extended" VBEs, a "federation alliance", which logically embraces different VBEs (although can be applied to single VBEs), other alliances, individual companies and even independent professionals which develop and share their software services in a governed and collaborative cloud-like shared services repository [START_REF] Perin-Souza | Services Discovery as a Means to Enhance Software Resources Sharing in Collaborative Networks[END_REF] [START_REF] Rabelo | Advanced Collaborative Business ICT Infrastructures[END_REF].

Regarding the massive SME nature of SSPs, the relevance of knowing more precisely these processes are: i) processes are many and SMEs managers are usually not aware of them; ii) managers can more properly evaluate how prepared they are to indeed start collaborating more effectively and so which measures should be put in place for that; iii) processes have interdependencies and different levels of implementation complexity and practices; this process list helps SSPs' managers to plan their gradual introduction in this larger scale collaborate scenario regarding current maturity level and priorities.

This article is organized as follows: Section 1 has introduced the problem and intended contribution. Section 2 explains the adopted methodology. Section 3 presents the state-of-the-art review. Section 4 presents the set of collaborative processes. Section 5 discusses about the current main findings of this research.

General Methodology

In order to gather a coherent and comprehensive set of business processes to support collaboration among SSPs this research was conducted as action-research, qualitative and applied work, strongly grounded on literature revision.

The literature review was mainly conducted applying the SLR (Systematic Literature Review) methodology [START_REF] Kitchenham | Systematic literature reviews in software engineering -A tertiary study[END_REF] over the IEEExplore, ACM Digital Library, Compendex/Engineering Village and ScienceDirect scientific databases, collecting papers published in journals and conference proceedings in the period of 2002:2013 on long-term and more formal enterprising strategic alliances. 308 works were initially retrieved and a subset of that was considered as relevant for the purpose of this research was selected. A special attention was given on trying to identify the works which dealt with SMEs and software and services sectors.

Five steps were carried out to achieve this research's results. First, gaps and existing knowledge in the state-of-the-art about supporting processes for VBE-like alliances were identified (see next section). Second, applying an inductive approach, 28 papers were selected as the basis to generate an initial generalized list of business processes. Considering that there are too few VBE alliances already deployed over the world, more "classical" and very studied long-term alliances (namely clusters) were analyzed in terms of how (processes and actors) they have been created, managed and sustained. Due to space restrictions, these 28 papers are not shown in the references. Third, regarding that classic clusters do not handle some VBE process, a second study (upon other 14 selected papers) was executed to complement and adapt that list for the VBE context in a first step, and for the software services in a second step. Fourth, this list was compiled and refined by a working group, composed of some experts in the involved areas. Fifth, the list was finally evaluated by those experts and evaluated by some users, applying a questionnaire over the Internet (expert panel technique).

State-of-the-Art Review

The goal of this review is twofold: to identify gaps, and to gather and take advantage of existing models and processes to generalize and adapt to the envisaged scenario.

After evaluating related works it was observed that none of them have dealt with processes to the software services sector. On the other hand, several works have provided important related outcomes. For example, Afsarmanesh et al. [START_REF] Afsarmanesh | VBE management system, in Methods and Tools for collaborative networked organizations[END_REF] proposed a VBE reference model identifying a comprehensive list of required elements, but without identifying which business processes should be considered to support them. Romero et al. [START_REF] Romero | VO breeding environments & virtual organizations integral business process management framework[END_REF] identified a list of processes along the collaboration life cycle, but at a too generic level. Rabelo [START_REF] Rabelo | The ECOLEAD ICT Infrastructure for Collaborative Networked Organizations[END_REF], Krogstie [START_REF] Krogstie | Modeling of Digital Ecosystems -Challenges and Opportunities[END_REF], Franco et al. [START_REF] Franco | Open Ecosystems, Collaborative Networks and Service Entities Integrated Modeling Approach[END_REF] and Camarinha-Matos et al. [START_REF] Camarinha-Matos | Service Federation in Virtual Organizations[END_REF] [16] adopted the VBE concept as a wider and logical federation of providers to cope with that wider services-based digital business ecosystems, but also without identifying the required business processes. Cancian et al. [START_REF] Cancian | Supporting Processes for Collaborative SaaS[END_REF] have elicited the processes and practices for SSPs that want to develop a joint SOA solution, but just assuming that companies would come from a federation. Other works have proposed processes for dealing with collaboration but focused on single issues. For example, Danesh et al. [START_REF] Danesh | B: A Framework for Process Management in Service Oriented Virtual Organizations[END_REF] focused on services management; Haines et al. [START_REF] Haines | How a service-oriented architecture may change the software development process[END_REF] on how to glue different services in cohesive SOA solution; Svirska et al. [START_REF] Svirska | Agent-based service-oriented collaborative architecture for value chains of SMEs[END_REF] on the supporting services infrastructure; Santanna-Filho et al. [START_REF] Santanna-Filho | An innovation Model for Collaborative Networks of SOA-Based Software Providers[END_REF] on innovation among SSPs; BS 11000 British Standard only handles collaborative bidding processes [47]. In terms of EU funded projects, for instance, ECOLEAD [START_REF] Perin-Souza | Services Discovery as a Means to Enhance Software Resources Sharing in Collaborative Networks[END_REF] DBE [START_REF] Dbe Ecossystem | [END_REF], COIN [START_REF] Coin | [END_REF] and GLONET [START_REF] Glonet | [END_REF] have developed platforms and visions to support collaboration among (also) software providers, but without identifying which more concrete processes are necessary to support when creating and maintaining the alliance.

The Collaborative Processes

After the analysis and generalization (as described in section 2), a list of 22 business processes has been identified, reflecting the processes that are involved in a collaboration among SSPs throughout the VO life cycle. Therefore, it is not related to the processes involved in the creation, management and dissolution of VOs, but rather how collaboration activities span along this.

By business processes it is generally meant as the set of inter-related activities and resources involved in the accomplishment of organization's goals. Each process is expressed in a table form (Fig. 1) identified by its core objective and by an extended description, which was conceived after a refinement of similar definitions and semantic interpretations. Only the most relevant sources of supporting references are presented in the end of each process. Figure 1 shows an excerpt of the Trust Management process.

Objective

Management of inter-organizational trust (in terms of e.g. reputation, financial health, performance, competences, etc.) so that the Federation's members, customers and supporting institutions can be confident about the existing transparency, honesty and interpersonal relationship values.

Extended Description Considering that a federation is a long term alliance and independent of its aiming sector and size, one of the aspects to be discussed is the trust management. In order to provide the trust between partners in a Federation some elements should be managed. Those are transparency, honesty and interpersonal relationship values […]. The trust is defined as an expectation that others will behavior in a not opportunistic way […], or in a committed way not only with their tasks, but with the group […]. The trust management deals with the management of trust between organizations, including either a basic evaluation level of individual trust or between members of different organizations […]. Partners need to trust to each other enough in order to allow and/or to facilitate the collaboration. A low level of trust increases the "transactions costs", requiring an additional set of protection actions against unknown partners. In order to measure the level of trust a careful evaluation analysis criteria is necessary. (...) References

Process Objective

Trust Management [START_REF] Afsarmanesh | VBE management system, in Methods and Tools for collaborative networked organizations[END_REF][START_REF] Msanjila | Assessment and Creation of Trust in VBEs, in Network-Centric Collaboration and Supporting Frameworks[END_REF] Inter-organizational trust management of (in terms of e.g. reputation, financial health, performance, competences, etc.) so that the alliance's members, customers and supporting institutions can be confident about the existing transparency, honesty and interpersonal alliance's values Governance Management [START_REF] Franco | Open Ecosystems, Collaborative Networks and Service Entities Integrated Modeling Approach[END_REF][START_REF] Romero | Towards The Definition of Business Models and Governance Rules for Virtual Breeding Environments[END_REF][START_REF] Romero | Towards Governance Rules and Bylaws for Virtual Breeding Environments, in Establishing the Foundation of Collaborative Networks[END_REF] Definition of rules, decision making criteria, responsibilities and autonomy levels that should be set up upon the alliance's members, customers and other supporting institutions regarding current contracts and businesses. This process affects all the other ones directly, although with different degrees of intensity.

Quality Management [START_REF] Alonso | Enterprise Collaboration Maturity Model (ECMM) -Preliminary Definition and Future Challenges[END_REF] Management of quality aspects upon the alliance's members of their software services and supporting non-software services. This involves organizational-related aspects (e.g. members' reputation, services trustworthiness and QoS), software maturity models and certifications as means to selecting providers and SLA (Service Level Agreement) specifications, and general quality of supporting institutions. [START_REF] Romero | Value Co-creation & Co-Innovation Strategies in Collaborative Networks: Emergence in different Industrial Domains[END_REF] Management of all legal aspects related to the alliance establishment. It also provides legal support to all issues, conflicts, transactions and collaborative processes that are carried out among the alliance's members, customers, supporting entities and eventual external actors.

Legal Issues Management

Performance Management [START_REF] Romero | VO breeding environments & virtual organizations integral business process management framework[END_REF][START_REF] Ermilova | Competency Modeling Targeted on Promotion of Organizations towards VO Involvement, in Pervasive Collaborative Networks[END_REF] Management of the general performance of every alliance's member and supporting institutions by means of qualitative and quantitative indicators, following the specifications indicated in the governance process. It is a basis for some other processes, like membership, competence and knowledge management.

Membership Management [START_REF] Romero | VO breeding environments & virtual organizations integral business process management framework[END_REF][START_REF] Ermilova | Competency and Profiling Management in Virtual Organization Breeding Environments, in Network-Centric Collaboration and Supporting Frameworks[END_REF] Management of all issues related to the integration, accreditation, disintegration, rewarding, and categorization of members and supporting entities within the alliance.

Collaborative

Project Management [START_REF] Jansson | Governance and Management of Virtual Organizations, in Methods and Tools for Collaborative Networked Organizations[END_REF] Management of collaborative projects that can be done by and among the alliance's members or in connection with supporting entities and customers. Examples of collaborative projects include the creation of virtual organizations, collaborative innovation, collective purchasing, joint training, and shared inventory management. This also involves financial, human resources, project planning, risk management, among many other aspects typical in project management.

IPR Management [START_REF] Sebrae | What is Intellectual Property?[END_REF] Management of the rights, duties, rewarding, royalties, etc., related to intellectual property rights (IPR) associated to innovations (of any type), licenses, patents, etc., developed inside the alliance environment Competence Management [START_REF] Ermilova | Competency Modeling Targeted on Promotion of Organizations towards VO Involvement, in Pervasive Collaborative Networks[END_REF][START_REF] Ermilova | Competency and Profiling Management in Virtual Organization Breeding Environments, in Network-Centric Collaboration and Supporting Frameworks[END_REF][START_REF] Kai | Framework for SaaS Management Platform[END_REF] Management and permanent updating of information about technical and human capabilities and capacities of each member and supporting entities. It can also have an active role, feeding the strategic management process with such information for strategic plan feasibility analysis.

Financial Management [START_REF] Romero | Towards The Definition of Business Models and Governance Rules for Virtual Breeding Environments[END_REF][START_REF] Romero | Virtual organisation breeding environments value system and its elements[END_REF] Management of the activities to rise, allocate and use monetary resources over the alliance, regarding risk analysis and strategic plan. It also includes cash flow, accounting, tributary planning, general payments, invoicing and other financial related actions.

Contract Management [START_REF] Camarinha-Matos | Agreement Negotiation Wizard, in: Methods and Tools for Collaborative Networked[END_REF] Management of all contractual documents and legal issues to support the formal entrance and exiting of members, customers and supporting entities to/from the alliance. It also involves the establishing, reviewing and cancellation of all current SLAs (Service Level Agreement) associated to all members' software services as well as related negotiations among its members, customers and supporting entities.

Information Management [START_REF] Gartner | New Realities of IT[END_REF] Management of all information (and their life cycle) that is generated, stored and made available inside the alliance as a support to all other processes. This information can be used by the members, customers and other supporting institutions according to the governance process.

Knowledge Management [START_REF] Jain | Knowledge Management for 21st Century Information Professionals[END_REF] Management of all knowledge (and their life cycle) that is generated, stored, organized, combined, and made available inside the alliance as a support to all other processes. This knowledge can be used by the members, customers and other supporting institutions according to the governance process.

Inheritance Management [START_REF] Afsarmanesh | VBE management system, in Methods and Tools for collaborative networked organizations[END_REF][START_REF] Romero | On the management of virtual enterprise's inheritance between virtual manufacturing & service enterprises: supporting dynamic product-service business ecosystems[END_REF] Management of activities related to gathering, storing, refining, integrating and re-using information and knowledge from/about/along all actions and transactions among members, customers and supporting institutions for future usage and continuous improvement.

Strategic Management [START_REF] Sturm | Towards Strategic Management in Collaborative Network Structures[END_REF][START_REF] Serrano | Collaborative innovation in ubiquitous systems[END_REF] Management of the alliance's value system, bylaws, sustainability and competitiveness via e.g. SWOT analysis and BSC. It also comprises activities related to KPI and general performance indicators (seeing the alliance as a business); evaluation of members' alignment; strategic liaisons with supporting entities, new customers and markets; alliance's life cycle management, its metamorphosis and even its closure.

Innovation Management [START_REF] Santanna-Filho | An innovation Model for Collaborative Networks of SOA-Based Software Providers[END_REF][START_REF] Tidd | Innovation Management[END_REF][START_REF] Senano | Contribution of Pervasine Intelligence to Collaborative Innovation Processes, in Network-Centric Collaboration and Supporting Framework[END_REF] Management of activities, resources and results throughout the innovation life cycle developed in the scope of the alliance by its members and partnerships in terms products/services, processes, marketing and business.

Marketing and

Commercial Management [START_REF] Romero | VO breeding environments & virtual organizations integral business process management framework[END_REF] Management of the activities related to all commercial practices derived from the strategic and marketing plans considering the alliance as a business organization. Marketing acts as a process over the commercial activities to help the alliance to achieve its business goals. [START_REF] Romero | VO breeding environments & virtual organizations integral business process management framework[END_REF] Management of the activities responsible to handle the access and maintenance of all existing alliance's assets (e.g. information, knowledge, practices, partners' profiles, customers' information, software services and general tools, etc.). The access to it from certain members, supporting institutions, customers and other external actors depends on the governance process and bylaws.

Bag of Assets Management

Interoperability Management [START_REF] Coin | [END_REF][START_REF] Ermilova | Competency Modeling Targeted on Promotion of Organizations towards VO Involvement, in Pervasive Collaborative Networks[END_REF] Management of all levels of interoperability (data, applications, processes and models) required to support a proper communication among the members, customers and other supporting institutions so as to better conduct businesses, covering the many involved perspectives (organizational, legal, accounting, technological, etc.).

Infrastructure Management [START_REF] Rabelo | The ECOLEAD ICT Infrastructure for Collaborative Networked Organizations[END_REF][START_REF] Camarinha-Matos | Elements of a base VE infrastructure[END_REF] Management of the activities related to the ICT infrastructure, human staff, physical facilities and other general infrastructures to administrate the alliance.

Services Management [START_REF] Kai | Framework for SaaS Management Platform[END_REF][START_REF] Wu | Reference Models for Saas Oriented Business Workflow Management Systems[END_REF] Management of all kind of software services (at application, communication, infrastructure, integration, orchestration, security, etc., levels) provided by or under responsibility of the alliance, its members and supporting institutions. It includes services' life cycle and SOA governance management, and the management of the non-softwarebased services provided by supporting institutions.

Security Management [START_REF] Stihler | Integral Federated Identity Management for Cloud Computing[END_REF] Management of all access, communication and security policies involved in the general transactions among members, customers and supporting entities.

Processes were positioned along the alliance's life cycle (Fig. 2) so as to help managers in the planning of activities, resources allocation and priority processes, as well as to reason about impacts and implementation time. This placement considered the authors' experience as well as what the set of related papers and other general references on CN talked about. They were empirically categorized into four groups regarding their most intrinsic nature.

Conclusions

This paper has presented a list of business processes required to handle when more ample digital ecosystems of SSPs have the willingness to work collaboratively in a more intense, formal and systematic way so as to benefit from the sort of competitive advantages provided by Collaborative Networks.

Each business process was evaluated by a group of experts and some analyses can be made upon such processes. For example, none of the processes were considered as unnecessary by them. For most of the experts the processes related to trust, governance, legal aspects, project management and interoperability/integration are the first ones to be implemented or tackled from a more technical point of view. In terms of implementation complexity by companies, this was quite variable as it depends on local expertizes, "legacy" practices and culture, existing ICTs, and life cycle phase. About if SSPs tend to provide joint solutions in the near future in order to reduce costs and risks as well as to increase the chances of better addressing the market, around 75% agreed on that. About if more and more ICT companies can become part of larger IT ecosystems in the near future to take advantage of complementarities and additional scale, around 85% agreed on that. Another conclusion is that some processes are in fact similar to the ones found in other alliances, and there are other processes that are more related to software services and the federation scenario.

Although comprehensive and can be used by SSPs' managers as a useful guideline, the listed processes and life cycle positioning should however be taken as a reference and not as a definitive or mandatory view instead. Their deployment can vary depending on local factors, existing culture, already deployed processes, current business priorities, customers' needs, financial conditions, etc., as processes are different in terms of complexity, implementation costs and required human resources. One consequence of this is that processes could not be described at activities level as this can vary from one alliance to another. On the other hand, most of the identified processes deal with very known issues to which best practices can be used to more detailed define the usual activities to be supported within each process. For example, when dealing with strategic planning, BSC method can be used as a reference.

A business process-based perspective is only one among some others that should be dealt with by SSPs when working within networks. Therefore, perspectives like the socio-economic, the general business context, organizations' preparedness, among others, should complement the analysis that was done.

The proposed list of business processes was not yet validated in real or near-real alliances, although it has considered published material also about real cases. Anyway, SOA, software-services-based alliances and digital ecosystems are relatively new areas for SMEs, still having many open points and implementation challenges. Actually, there are too few real deployments of VBE-like long-term examples upon which more historical and consistent analysis could be taken as a reference.

Next short-term main steps of this research include a deeper validation activity by world-wide experts and in some real alliances, trying to also getting a more accurate notion of implementation complexity, importance and inter-relation among processes.

[Fig 1 .

 1 Fig 1. Partial example of a process' complete description

Fig 2 .

 2 Fig 2. Processes positioning along the alliance's life cycle

Table 1

 1

presents the 22 elicited business processes. Due to space restriction, only their short description (objective) is presented. Their complete and detailed descriptions can be accessed in https://sites.google.com/site/federationmanagement/.

Table 1 .

 1 List of processes

Acknowledgements

This work was partially supported by CNPq and Capes Brazilian research agencies.


Dario Antonelli
email: dario.antonelli@polito.it

Giulia Bruno
email: giulia.bruno@polito.it

Application of Process Mining and Semantic Structuring towards a Lean Healthcare Network

Keywords: Process mining, healthcare network, ontology

Modern healthcare systems are evolving towards a complex network of interconnected services. The increasing costs and the conversely increased expectations for high service levels leveraged the birth of healthcare monitoring activities and the proposition of numerous performance evaluation indicators. Generally, the adopted performance measures allow to draw a picture of quality, equity, appropriateness and efficiency of the medical care at different levels: caregiver, hospital, local health authority, region. The role of network organization and its impact on the performances is largely underestimated. It is difficult to build a Value Stream Mapping of the healthcare network because of the number and complexity of care and diseases followed. The study tries to overcome this issue. Starting from a database of the accesses to the services in a local health agency, the activity flow diagram is produced by using a process mining software, Disco. A knowledge structured by means of an ontology allows to describe the logic behind the health service provision. The resulting process flow chart is the base for the identification and amendment of redundant and non value added flows among services.

Introduction

Worldwide there is an increasing number of initiatives aimed at introducing a standardized and centralized information management in healthcare organizations (hospitals, medical centers, drugstores) through digitalization of medical data. It is proven that digital tools like the electronic health records provide benefits to both patients and physicians by improving health care efficiency [START_REF] Hillestad | Can electronic medical record systems transform health care? Potential health benefits, savings, and costs[END_REF]. The availability of the medical history of the patient's accesses to medical centers will allow both the physicians to express meaningful analyses at the patient level (e.g., searching for similar patients based on their medical history or predicting future events in care pathway) and the system manager to operate at the organizational level (e.g., discovering which are the most accessed resources or which are the anomalous managements of patients) [START_REF] Mans | Process Mining in Healthcare: Data Challenges when Answering Frequently Posed Questions[END_REF]. Therefore the analyses are intended to improve the quality of services offered to citizens while to reducing costs and wastes. Since the data volume is very high, and it is expected to grow dramatically in the years ahead, for healthcare organizations it is vitally important to acquire the available tools, infrastructure, and techniques to leverage big data effectively. The enormity and complexity of collected medical data present great challenges [START_REF] Sun | Big data analytics for healthcare[END_REF].

The format of medical data and the data base structure is a serious issue preventing their use for operation managements applications. Data describe the healing pathway of the patient and do not give direct evidence of the process flow in terms of process times, queues, unproductive times, etc. To have a better insight of the process flow, data should be elaborated by process mining.

Process mining aims at extracting process knowledge from event logs which may originate from different kinds of systems, e.g., enterprise information systems or hospital information systems [START_REF] Mans | Application of Process Mining in Healthcare -A Case Study in a Dutch Hospital[END_REF]. Typically, these event logs contain information about the start/completion of process steps together with related context data (e.g. actors and resources). Previous works addressed the problem of analyzing single entities (e.g., a hospital department) by applying techniques of data/process mining and simulation [START_REF] Mans | Application of Process Mining in Healthcare -A Case Study in a Dutch Hospital[END_REF][START_REF] Coelli | Computer simulation and discrete-event models in the analysis of a mammography clinic patient flow[END_REF][START_REF] Di Leva | The BP-M* Methodology for Process Analysis in the Health Sector[END_REF][START_REF] Cardoso | Aligning goal analysis and business process modelling: a case study in healthcare[END_REF]. In these cases, existing processes are compared with the medical knowledge to determine whether the pathway of a patient within the structure is correct. However, the analysis of a single entity is limitative, because it does not consider the previous history of the patient and thus it is not able to evaluate the quality of the healthcare system as a whole.

Before applying the process mining techniques, it is important to merge data of different nature to collect the patient movements inside the network. In order to merge heterogeneous data, we need a controlled vocabulary in term of set of ontologies to give data a meaning despite the different original data structure. After the merge of data, it is possible to apply a process mining tool, to automatically reconstruct the movements of patients inside the network. This analysis is useful both to analyse the changes in patient flows depending on age or gender of patients and to discover the bottleneck and waste of the system, toward a lean restructuring of the healthcare process.

The overall objective of this paper is to contribute in giving medical managers an accurate and deep understanding of the healthcare network functioning. There are several contributions: the first contribution is the definition of an ontology of the healthcare network: general concepts and relationships. Then, starting from the data organized in the model, the process mining analysis is used to extract information for the network evaluation. To make the methodology more concrete, the real data collected by an Italian Healthcare Territorial Agency (HTA) is exploited as a case study. The preliminary results we obtained proved the applicability and the usefulness of the proposed approach.

State of the Art

There is an evolving trend in recent years that has modified the healthcare system from a few nodes hospital based organization to a branched network of service centers spread on the territory [START_REF] Wan | Integrated Healthcare Networks Performance: A Growth Curve Modeling Approach[END_REF]. Furthermore the approach to disease treatment is now based on integration among the different agents of the care system. Therefore there is a convergent trend towards a network of centers that delivery integrated care [START_REF] Lenz | IT support for healthcare processes -premises, challenges, perspectives[END_REF].

The integration of different aspects of healthcare system has been a subject of study by many authors in the field of operations management [START_REF] Scott | The organization of medical care services: Toward an integrated theoretical model[END_REF][START_REF] Ahgren | Evaluating integrated health care: a model for measurement[END_REF][START_REF] Tjerbo | Coordinating health care: lessons from Norway[END_REF] and several authors highlighted the benefits of integration both in terms of quality of care as in terms of lean organization [START_REF] Axelsson | Integration and collaboration in public health: a conceptual framework[END_REF][START_REF] Bazzoli | The financial performance of hospitals belonging to health networks and systems[END_REF][START_REF] Provan | Do networks really work? A framework for evaluating publicsector organizational networks[END_REF]. Drawbacks are equally reported but are mainly due to lack of cooperation and commitment of the healthcare personnel [START_REF] Hurtado | Crossing the quality chasm: a new health system for the 21st century[END_REF][START_REF] Cesarini | ICT Management Issues in Healthcare Coopetitive Scenarios[END_REF].

The analysis of healthcare network by using the operations management models is not effective as in the industrial environment where the process activities and the flow of material are utterly defined. The production is substituted by care pathway, that is far less deterministic. Products are substituted by patients that are free to move along the process flow at their will (or even to abandon care and consequently interrupt the flow of activities). To extract performance variables to be a guidance in the process management, it is necessary to have recourse to other methods, like data mining techniques [START_REF] Lin | Mining Time Dependency Patterns in Clinical Pathways[END_REF][START_REF] Batal | Mining Recent Temporal Patterns for Event Detection in Multivariate Time Series Data[END_REF]. If time is not a monitored output, the focus is on pattern extraction in order to detect the most frequent medical treatments undergone by patients [START_REF] Baralis | Analysis of medical pathways by means of frequent closed sequences[END_REF][START_REF] Antonelli | Analysis of diagnostic pathways for colon cancer[END_REF][START_REF] Antonelli | Analysis of diabetic patients through their examination history[END_REF]. These techniques do not give a comprehensive view of the processes in act in the healthcare systems.

On the other hand, process mining can be applied to healthcare data to identify the processes and derive meaningful insights from the complex temporal relationships existing between activities and resources involved in processes [START_REF] Partington | Process Mining for Clinical Processes: A Comparative Analysis of Four Australian Hospitals[END_REF]. For example, process mining was applied to a hospital emergency service in a public hospital in Portugal to identify regular behavior, process variants, and exceptional medical cases in [START_REF] Rebuge | Business Process Analysis in Healthcare Environments: A Methodology Based on Process Mining[END_REF], and to a hospital in Belgium to model the activities related to breast cancer treatment in [START_REF] Poelmans | Combining business process and data discovery techniques for analyzing and improving integrated care pathways[END_REF]. It was also applied to perform a comparative analysis across four hospitals in Australia [START_REF] Partington | Process Mining for Clinical Processes: A Comparative Analysis of Four Australian Hospitals[END_REF]. We follow these previous works, but we applied process mining to extract the movements of patients among the different centers of a Healthcare Territorial Agency (HTA), in order to perform a comparative analysis among different patient segments.

Healthcare Network Ontology

The ontology used in our work has two aims: firstly to model the entities and relationships needed to collect data coming from a healthcare network, and secondly to provide the controlled vocabulary in order to merge data coming from heterogeneous sources. For the first aim, we define a model as a UML class diagram, after the carefully analysis of the data available in Italian HTA. For the second aim, we reused the controlled vocabularies previously developed relevant for our purpose, i.e., ICD10 [26] for disease classification, MDC [START_REF]MDC[END_REF] for the major diagnostic categories, DRG [28] for diagnosis related group and ATC [29] for drug classification.

The UML class diagram representing the healthcare data collected by the HTA is reported in Figure 2. A service is any kind of healthcare service provided to a citizen, form examinations to drugs to hospitalizations, while a user is any person who access the healthcare system. A prescriber is a physician who can do prescription of services to the users, while a provider is a structure that provides one or more services. For each provider, the list of services it provides is known. A prescription represents the information of the specific services that a prescriber prescribes to a user, and a provision stores the information of services provided by providers to the users. This is a very general model that is valid for all the healthcare territorial agency [START_REF] Antonelli | Healthcare network modeling and analysis, Collaborative Systems for Smart Networked Environments[END_REF]. This UML class diagram can be represented in the form of an ontology, where each entity is a class, with the corresponding relationships linking the classes. Then, for each entity, it is possible to specify the hierarchical tree of concepts at increasing detail levels which represent the knowledge related to the services, providers, users and prescribers of the HTA [START_REF] Antonelli | Evaluating Collaboration Effectiveness of Patient-to-Doctor Interaction in a Healthcare Territorial Network, Collaborative Networks in the Internet of Services[END_REF]. This information constitutes the HTA ontology that will be used to select the data of interest for the following analysis.

In a HTA, three types of providers can be identified: (i) the medical centers without hospitalization capacities, (ii) the hospitals and (iii) the drugstores. The existence of three providers determine the existence of three type of services, i.e., (i) the examinations, (ii) the hospitalizations and (iii) the drugs. For each kind of service, a further hierarchy can be defined. The examinations can be further specified based on the medical branch they belong, the hospitalizations based on the diagnosis-related group (DRG) and the major diagnostic category (MDC), the drugs based on the anatomical therapeutic chemical (ATC) classification. The prescribers can be divided in two categories, i.e., the general physician and the specialist physician. The users can be divided based on their pathology, represented by their exemptions code. These hierarchical structures are shown in Fig. 2.

The information stored in the ontology is used to extract from the database the subset of data relevant for the analysis.

Process Mining

The goal of process mining is to extract process models from event logs. It includes a family of a-posteriori analysis techniques exploiting the information recorded in the event logs. Typically, these approaches assume that it is possible to sequentially record events such that each event refers to an activity (i.e., a well-defined step in the process) and is related to a particular case (i.e., a process instance). Furthermore, some mining techniques use additional information such as the performer or originator of the event (i.e., the person/resource executing or initiating the activity), the timestamp of the event, or data elements recorded with the event (e.g., the size of an order).

Process mining was already applied in healthcare systems, trying to answer the following questions: (i) what are the most followed paths and what exceptional paths are followed? (ii) are there differences in care paths followed by different patient groups? Standard paths are the activities that are typically executed by patients and the order of them. Exceptional paths are anomalous activities due to the way of working of medical specialists or related to specific patient characteristics or not. The comparison of the behavior of different patient groups is another interesting issue. This comparison may not only be interesting for patient groups within a hospital but also for similar patient groups in different hospitals.

In this paper, we applied process mining techniques to reconstruct the actual movements of patients among the providers of a Healthcare Territorial Agency. To this aim, we exploit the software Disco (https://fluxicon.com/disco/), a commercial process mining tool, freely available under an academic license. It exploits the Fuzzy Miner algorithm for process mining [START_REF] Günther | Fuzzy Mining -Adaptive Process Simplification Based on Multi-perspective Metrics[END_REF], which uses significance/correlation metrics to interactively simplify the process model at desired level of abstraction.

The core functionality of this tool is the automated discovery of process maps by interpreting the sequences of activities in the imported log file. According to the process mining paradigm, at least the following three elements have to be identified in the file log: case id, activity, and timestamp. In our analysis, since we are interested in analyzing the movements of patients among the providers, the case id corresponds to the patient id. The activity is an event of the process, thus in our case it is the provider visited by the patient. The timestamp is the date in which the patient visits the provider.

Data Analysis

The database considered as a case study contains data collected by an Italian HTA in the 2007-2012 years. Indeed, the HTAs collect data about the supplied services for cost accounting. Supplied service data represent an essential resource in planning and monitoring the Regional Healthcare System. Data for the analysis of diagnostic pathway are selected from the data warehouse: Hospital Discharge Records, Ambulatory Care Records, Emergency Department Records, Ambulatory Care Records. They are composed of personal data and clinical data section, so that it was possible to merge anonymized personal and clinical data, to collect all databases in a single MySQL database by means of a PHP routine that automatically import data.

In order to focus our study on a specific pathology, we extracted from the database the log file of all the patients suffering from asthma for year 2007. The aim is to analyze the mobility of patients across the different medical centers placed on the territory. The data refer to a total of 451 asthma patients who accessed medical centers, divided in 207 males and 244 females. Regarding the age of patients, 155 are younger than 36 years, 333 are between 36 and 65 years old, and 63 are older than 65 years.

Before attempting any process mining it was necessary to preprocess the data in order to simplify them, by skipping unnecessary low level activities and by merging the significant low level activities in singular high level ones. We used the knowledge deriving from the ontology of section 3 to preprocess the data. An example is the log describing the accesses to a laboratory for executing analyses. Whether the analyses are executed on the same patient, the same day, for the sake of operations management, they can be safely converted in a singular access to one activity.

After preprocessing, we performed two alternative segmentations of the dataset, one based on gender and the other one based on age, in order to highlight the effect of both factors on the process flow diagram. Each segment was imported in Disco to perform the process mining. For readability reasons, only the 30% of activities involved and the 10% of the path between activities are shown in the results. These percentage grant to cover at least the 90% of the data, since there are many centers that are accessed only one or two times, and thus are not relevant for the analysis.

From the analysis of the first segmentation, it can be noticed that the some similarities exist between the two obtained graphs (Fig. 3 and Fig. 4). The first one is that in both cases there is a "hub center" which is the most accessed by both genders, since the majority of patients perform examinations only in this center. Another similarity is that the processes usually do not involve more than two different centers (the hub center and one of the other centers). Despite these similarities, the graphs also show some differences. First of all, the number of visited centers is different: male patients visit 12 different centers, while female patients visit 21 different centers, thus showing a higher mobility of female patients. Also the frequency of accesses is different. Fig. 5 reports the number of accesses to each center for the male segment and the female segment (the hub center is not reported since its accesses are significantly higher than the others: 1348 for male and 1820 for female). Furthermore, the majority of male processes follow the path "hub center -other center -hub center", while the female process follow various paths, starting for a center, then passing the hub center, followed by another center. From the analysis of the second segmentation (Fig. 6-Fig. 8), it can be noticed that the process extracted from the adult segment is the one which includes the higher number of different centers visited by patients and also the longest pathways involving different centers. The process extracted from the senior segment include less centers and shortest paths. This can be due to the low mobility of elder patients with respect to the others. The number of accesses to medical centers of each of these three segments is reported in Fig. 9.

Conclusion

The evaluation of a health network is a complex task because the management of patients is done by entities not controlled by a central unit, with the result that guidelines for the evaluation of the diagnostic and therapeutic pathways through the network are not available.

This paper aims at adapting tools and technologies derived from other research fields and using them firstly to obtain a model of a healthcare network, and secondly to perform a meaningful analysis of the mass of data produced by a healthcare network. The obtained results can be useful for healthcare managers to inform them clearly about the status of services under their responsibility, and to suggest improvements to system inefficiencies. It is also useful to evaluate the degree of collaboration among the different entities of the network.

We are currently working on extending the analysis to involve different kind of entities, and to explore different mining algorithms to refine the extracted model.

Fig. 1 .

 1 Fig. 1. Main entities of the healthcare network ontology.

Fig. 2 .

 2 Fig. 2. Hierarchical levels of the healthcare network ontology, built with the Protégé ontology editor (http://protege.stanford.edu).

Fig. 3 .

 3 Fig. 3. Process flow extracted by Disco on the male segment, all the ages considered.

Fig. 4 .

 4 Fig. 4. Process flow extracted by Disco on the female segment, all the ages considered.

Fig. 5 .

 5 Fig. 5. Different numbers of accesses to medical centers by male patients (blue bar at the left) and female patients (red bar at the right).

Fig. 6 .

 6 Fig. 6. Process flow extracted by Disco on the young segment for all the genders.

Fig. 7 .

 7 Fig. 7. Process extracted by Disco on the adult segment for all the genders.

Fig. 8 .

 8 Fig. 8. Process flow extracted by Disco on the senior segment for all the genders.

Fig. 9 .

 9 Fig. 9. Different numbers of accesses to the medical centers by young patients (first bar), adult patients (second bar) and senior patients (third bar).