Marco Della Gala

Matthew Reed

Marco Della

The Role of ICTs in Supporting Collaborative Networks in the Agro-Food Sector: Two Case Studies from South West England

Keywords: Food Hubs, Alternative Food Networks, Local Food System

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

Introduction

Over the last three decades, in the academic and policy discourses observe a common concern has arisen about the sustainability of the agro-food system. The mainstream procurement system is vertically and horizontally integrated, global in scale, and aimed at maximizing efficiency and profit [START_REF] Cleveland | Local food hubs for alternative food systems: A case study from Santa Barbara County, California[END_REF]. This model, based on economic efficiency and profit maximization, has resulted in negative environmental and social externalities [START_REF] Jablonski | Assessing the economic impacts of food hubs on regional economies: A framework that includes opportunity cost[END_REF] [3]. The increasing concentration of large corporations operating in the processing and retail sectors has led both to the crisis of trust in placeless and uniform products among consumers, and to power asymmetries along the food supply chain [START_REF] Jablonski | Assessing the economic impacts of food hubs on regional economies: A framework that includes opportunity cost[END_REF] [START_REF] Volpentesta | Classifying short agrifood supply chains under a knowledge and social learning perspective[END_REF]. The loss of bargaining power and constant cost-price pressure on commodity production has resulted in a decline in farm numbers, with consequences for rural areas [START_REF] Berti | Competitiveness of Small Farms and Innovative Food Supply Chains: The Role of Food Hubs in Creating Sustainable Regional and Local Food Systems[END_REF]. In this context, driven by consumers' renewed interest in locally and sustainably produced foods, grassroots initiatives based on new forms of collaboration between producers and consumers have emerged [START_REF] Stroink | Complexity and food hubs: five case studies from Northern Ontario[END_REF] [6] [START_REF] Matson | The role of food hubs in food supply chains[END_REF] [START_REF] Krejci | Analysis of Food Hub Commerce and Participation Using Agent-Based Modeling: Integrating Financial and Social Drivers[END_REF]. These initiatives include direct-toconsumer sales, pick your own, farmers' markets, box schemes, community-supported agriculture, collective buying groups, and community gardens. Typically grouped under the umbrella of 'alternative food networks' (AFNs) [START_REF] Renting | Understanding alternative food network: exploring the role of short food supply chains in rural development[END_REF], they represent collaborative networks allowing for the development of new forms of relationship and governance, enabling a re-distribution of value to primary producers [START_REF] Berti | Competitiveness of Small Farms and Innovative Food Supply Chains: The Role of Food Hubs in Creating Sustainable Regional and Local Food Systems[END_REF] [START_REF] Volpentesta | Classifying short agrifood supply chains under a knowledge and social learning perspective[END_REF]. Even if there is a common agreement that AFNs could offer the opportunity to revitalize local rural communities, they face many difficulties attempting to minimize the number of intermediaries in the food supply chain. The direct marketing activities in the AFNs are highly labour-intensive, profitable levels are low, due to a low volume of sales, high logistic and marketing costs [START_REF] Krejci | Analysis of Food Hub Commerce and Participation Using Agent-Based Modeling: Integrating Financial and Social Drivers[END_REF]. Food Hubs (FHs) have been emerging as a way to face these challenges of coordination of marketing functions and efficient management distribution of local foods [START_REF] Cleveland | Local food hubs for alternative food systems: A case study from Santa Barbara County, California[END_REF], [START_REF] Berti | Competitiveness of Small Farms and Innovative Food Supply Chains: The Role of Food Hubs in Creating Sustainable Regional and Local Food Systems[END_REF], [START_REF] Krejci | Analysis of Food Hub Commerce and Participation Using Agent-Based Modeling: Integrating Financial and Social Drivers[END_REF].

In this paper, we aim to explore the role of ICTs in fostering the development of FHs in the establishment of local food networks. In particular, the paper will addresses the following research questions (RQs):

-RQ1: Does the adoption of ICTs support the rise of new collaborative networks within the "alternative" food supply chains? -RQ2: How do the adopted ICTs tools enable collaboration and coordination among all the networked actors in the identified case studies? The paper is organized as follows: the section 2 introduces the theoretical background about Food Hubs initiatives, while in the section 3 and 4 the two case studies are presented and discussed. In the final section, we propose our conclusions.

Food Hubs (FHs): Theoretical Background

The concept of a Food Hub is a recent addition in the literature on AFNs. Scholars and public institutions have worked to elaborate a comprehensive definition of FH: Morely et al. [START_REF] Morley | Food hubs: the'missing middle'of the local food infrastructure?[END_REF], Horrel et al. [START_REF] Horrell | An Investigation into the Workings of Small Scale Food Hubs[END_REF], Horst et al. [START_REF] Horst | Toward a more expansive understanding of food hubs[END_REF] and USDA [START_REF] Barham | Regional food hub resource guide (No. 145227[END_REF] focused their attention on the functional aspects of co-ordination, looking at FHs as an intermediary between many local producers and suppliers and customers, including institutions, food service firms, retail outlets, and consumers. All these authors specified that the main functions characterizing FHs are information management, aggregation, distribution, and marketing of source-identified food products. Blay-Palmer et al. [START_REF] Blay-Palmer | Constructing resilient, transformative communities through sustainable "food hubs[END_REF], in their definition of FHs, highlighted the sustainability aspect of FHs, while other authors try to balance the functional aspects with the civic and sustainable ones. In their study Fisher et al. [START_REF] Fischer | Food Hubs: Definitions, Expectations, and Realities[END_REF] stated: "Food hubs are, or intend to be, financially viable businesses that demonstrate a significant commitment to place through aggregation and marketing of regional food." In the same vein, Franklin et al. [START_REF] Franklin | Moving beyond the alternative: sustainable communities, rural resilience and the mainstreaming of local food[END_REF] and Franklin and Morgan [START_REF] Franklin | Exploring the new rural-urban interface: Community food practice, land access and farmer entrepreneurialism[END_REF] pointed out that community food hubs provide to local producers and consumers the coordination of activities such as sourcing, supply, and/or marketing and distribution of products. According to these authors, the primary goals of FHs are environmental protection goals, and the economic viability of local farmers. With social motivations such as community cohesion, social gain, healthy eating, and local food access, as secondary goals. With a similar perspective, Berti and Mulligan [START_REF] Berti | Competitiveness of Small Farms and Innovative Food Supply Chains: The Role of Food Hubs in Creating Sustainable Regional and Local Food Systems[END_REF], in their definition of FHs, highlighted both their role as intermediary organization, and capacity to operate as "activator" and "animator" of a strategic network involving all the actors in the short food supply chain. In such a network, producers preserve their identity, their organizational independence and their strategies, but, at the same time, all the actors collaborate to achieve mutual convenience. Together they co-produce shared economic and socio-environmental values to be distributed within the network and positively affect the entire local community.

Scholars proposed different perspective in FHs' definition, as well as noting the variety of organizational structures, target customers, ownership and control, function and operation [START_REF] Volpentesta | A collaborative network model for agrifood transactions on regional base[END_REF], [START_REF] Morley | Food hubs: the'missing middle'of the local food infrastructure?[END_REF], [START_REF] Horst | Toward a more expansive understanding of food hubs[END_REF], [START_REF] Barham | Regional food hub resource guide (No. 145227[END_REF],. Despite this diversity it is possible to observe a common agreement about the potential of FHs to overcome the distributional limitations of small-scale AFNs and to help initiatives to 'scale-up' and deliver benefits for local communities [START_REF] Leblanc | Building resilience in nonprofit food hubs[END_REF], [START_REF] Krejci | Analysis of Food Hub Commerce and Participation Using Agent-Based Modeling: Integrating Financial and Social Drivers[END_REF], [START_REF] Cleveland | Local food hubs for alternative food systems: A case study from Santa Barbara County, California[END_REF], [START_REF] Berti | Competitiveness of Small Farms and Innovative Food Supply Chains: The Role of Food Hubs in Creating Sustainable Regional and Local Food Systems[END_REF], [START_REF] Jablonski | Assessing the economic impacts of food hubs on regional economies: A framework that includes opportunity cost[END_REF], [START_REF] Fischer | Food Hubs: Definitions, Expectations, and Realities[END_REF], [START_REF] Barham | Regional food hub resource guide (No. 145227[END_REF].

The main services provided by the FH organization to coordinate the activities of its networked partners and manage the goods and information flows are: -Logistics. Aggregation (on-farm pick up and on-FH drop off), product storage, repackaging for distribution (customer location delivery and/or delivery management at the FH's drop off points) and relative information management with order consolidation for each producer and customer order management; -Marketing. Recruitment of producers and consumers, internal communication management, product identity and branding activities, as well as sales management;

-Community engagement. Communication with farmers and consumers to identify needs for the following season, collaboratively planning cropping. Organising social events to allow meetings and engender trust between farmers and consumers.

Creating and promoting educational programmes and training events. One of the main role played by Food Hubs is building an effective information flow and transparency among the actors adhering to this form of AFN [START_REF] Barham | Regional food hub resource guide (No. 145227[END_REF].

The advancement of the ICTs and the rapid growth of e-business have offered the space for new business models that, in line with FHs' objectives, allow for novel ways to create, co-produce and distribute value. The adoption of ICTs and its support to the supply chain management represent a great opportunity to assist the operations of Food Hubs [START_REF] Kurnia | Open Food Network: the Role of ICT to Support Regional Food Supply Chains in Australia[END_REF]. FHs can take advantage of services and functionalities that allow them to manage inventory and online catalogues, allowing consumers to place online orders and carry out payments, supported by order processing and logistic activities. ICTs enable FHs to establish connections between buyers and sellers and to share data among all the parties in the network. As Barham et al. stated, "it is not coincidental that the emergence of the regional food hub concept is occurring at a time when technology is increasingly accessible and portable" [START_REF] Barham | Regional food hub resource guide (No. 145227[END_REF].

The overall online FHs' value proposition relies in reducing search, transaction and coordination costs for buyers and sellers adopting an online food hub model that has been defined by Horst et al. [START_REF] Horst | Toward a more expansive understanding of food hubs[END_REF] as an "internet-based online directory and marketplace that fosters efficient connections between local and regional food producers and consumers"

Two Case Studies from the South West England

The case studies were two FHs initiatives both located in Gloucestershire, a predominantly rural County in the South West England. The two FHs are respectively Stroudco CIC and Dean Forest Food Hub Ltd. On February 2017 by the means of semistructured interviews to FHs key members; we collected data on the FHs' structure, their processes, the volume of their trading activities and on how the adoption of the ICTs affected the operations and coordination management activities in both the case studies. The interviews were oriented to investigate the main actors, and processes, as well as the impact of the ICTs in terms of enabling a better information flow management both for decision-making and for operational processes.

Stroudco

The Stroudco Food Hub is a member-owned Community Interest Company (CIC) jointly controlled by all its producers and consumers members. It started its trading activities in 2009 in Stroud by launching its online food hub system. The town of Stroud has a population of 13,260 inhabitants (2011 census) and displays socio-economic characteristics typical of English semi-rural communities. The area is well known for its long established local food movement which includes one of the most popular farmers' market in the UK, as well as a community-supported agriculture (CSA) project and a co-operative allotment project of national importance [START_REF] Franklin | Moving beyond the alternative: sustainable communities, rural resilience and the mainstreaming of local food[END_REF]. In this context, in 2008, two local food activists set up a community-led FH. The Stroudco's value proposition was to provide, locally produced food to low-income households living in a specific area of Stroud who were not able to afford the products sold in the Stroud's farmers market. At the same time, the business model was oriented to give to local producers (located within 15 miles of Stroud), who could not be engaged in the other AFNs in Stroud, to access a local market at higher than wholesale prices. The aims were to build connection between local consumers and producers, to develop a more sustainable local food culture and resilient community. Central to the Stroudco were the adoption of a web-based ordering system, to manage supply and demand of local food, on fixed order cycle, combined with the use of a school hall, located in a deprived area, as the drop off point for producers and pick up point for consumers.

The initiative received a grant of approximately £70,000 for the development of FH's web-based platform and for the set-up costs.

To guarantee the economic sustainability of the initiative, the FH has adopted a revenue model based on two sources of income: consumer households pay a membership fee of either £2 per month or £1 per order, plus £2 if they choose for the home delivery service, and producers pay 8% of gross sales made through Stroudco.

In their first year of trading 8 producers and 20 consumers were engaged in the FH as members to run monthly trading activities. After few years, the marketing activities were oriented to recruit new members from across Stroud to reach a critical mass of orders. Moreover, the range of available products was broadened with the inclusion of non-local foods provided by a wholefood wholesaler, with a mark-up at 28% above wholesale. Stroudco also employed a manager to carry on the day-to-day running of the hub with the support of some volunteer. Currently, the number of Stroudco member has The Role of ICTs in Supporting Collaborative Networks in the Agro-food 691 reached almost 500 consumers and 60 producers, with the number of collected orders ranging from 20 to 50 per week.

Central to Stroudco is an internet-based administration system. Stroudco started its activities adopting a self-designed system, developed as an open source project, free and easy to copy. On May 2016, the FH switched to a new platform named Open Food Network (OFN) UK, a software platform supporting food hubs management and operations, provided by the global not-for-profit foundation Open Food Foundation and customized by some of the FH's members. The new platform is enabling the food hub to manage efficiently their main processes and operations. It offers functionalities to the FH's suppliers, who are trained by the FH's members involved in the platform customization in managing their own description and contacts, as well as their catalogue by controlling products descriptions, prices, and their stocks. Consumers can join Stroudco and, through an online shop front, get information about producer, products, the full price breakdown, as well as order and paying. The platform offers to the FH's staff tools to set the order cycles, to add mark-ups and shipping fees, orders consolidation and procurement as well as order fulfilment. The platform also provides tools for Customer Relationships Management and for reporting. The Stroudco allows the direct communication between consumers and producers through a blog. Furthermore, through the web portal and the newsletter, Stroudco advertises the social events organized to foster the relationships and connections among its members.

Dean Forest Food Hub

The Dean Forest Food Hub is a workers' co-operative and a social enterprise. It started its operations on July 2013 thanks to the initiative of two of the eight key members who currently manage and control the FH. The project started without any founding grant, but with the support of Stroudco that helped by providing advice on the FH model and the Stroudco software. The software was then set up and customized by one of the founders who has a background in software engineering.

The area of Forest of Dean surrounds the second largest Crown Forest in England with 110 Km 2 of mixed woodland. It has not traditionally been an agricultural area and, in contrast to Stroud it is not characterized by the presence of many AFNs schemes and people in the area found difficult to access to fresh local produce. Spreads around the forest are a large number of un-connected market gardens, orchards, cheese makers, and meat producers. The objective of the Dean Forest Food Hub is to provide everyone in the Forest of Dean to access affordable, quality local food that is fair for the producer. The FH's value proposition is to provide a web-based platform representing a single place to source food from over 20 local producers, whose sustainability is checked by the FH's staff, in an easy and convenient way. The FH provides a network of 11 accessible pick-up points located around the forest. At the same time, the FH provide to producers a platform to access the local market in an efficient way aggregating orders coming from several consumers on a weekly order cycle and providing a single dropoff point. The model adopted by the FH is a low margin model in which producers set the sale price. The FH works to link farmers with each other, with farm tours for exchanging ideas on best practice, as well as to connect consumers and producers with social events like the "Forest Food Hunt". The FH also uses a forum and social medias to foster interaction among people engaged in the initiative.

The revenue model is based on the income provided by the margin of 10% on the sale price for the local produce and a mark-up of 40% for non-local products. In addition, the home delivery service entails a £2.50 charge.

In July 2013, the Dean Forest Food Hub started its trading activities with just one producer and a few consumers ordering 3-4 boxes per week, with volunteers running it. Now the number of producers has grown to 20, with 600 registered consumers and 3 people have been employed. The FH is collecting an average of 50 orders per week for an average value of £4,400 per week and these numbers are growing every years especially after the Dean Forest Food Festival held every autumn.

At operational level, similarly to the Stroudco FH, the Dean Forest Food Hub is opening and closing at a fixed time each week. Producers register to the online platform and get a producer agreement electronic survey to fill in. Then, they decide whether they prefer to be trained to manage their own page where they can describe their production methods, list their produce for sale with stock levels and prices, or if they prefer, allow the FH's staff to do that for them. Consumers can buy from the list of produce available up to three days before the food drop, as well as directly contact producers to ask any questions. FH's staff then process consumers' orders collected and registered through the web system and each producer receives its own order. In this phase, FH's staff communicates with producers located close to one another and involved in the same order to facilitate a joint delivery to the FH's drop-off point. Producers have two days to deliver their products to the drop-off point provided by the producer who was the first to be engaged in the FH initiative. Then a team of roughly 30 volunteers and the FH's staff pack everything into the right boxes according to the picking lists generated by the software. When all the boxes are ready, the FH's staff, according to a shipment roster, distribute them to the network of pick-up points, or, if consumers opted for the service, they provide home delivery. All the sales data are stored and every season analysed by the FH's staff, identify any gaps in supply or demand, to carry out with produces a pre-season crop planning for following season.

Discussion

This study let us to positively confirm that ICTs represent a useful tool for supporting sustainability initiatives within local food supply chains supporting the rise of new business models such as the online FHs adopted by the initiatives we investigated (RQ1). The adoption of online FH models helped FHs to reduce their operational costs. We observed that the web platforms adopted by the two case studies, with different levels of sophistication, allowed the FH initiatives to be more efficient as well as automating and standardising processes, supporting volunteers whose help in carrying out FHs activities. The adoption of ICTs enabled the two FHs to face the challenges imposed by the complexity of activities typical of FHs and including the management of seasonality and perishability of products, the inconsistency and unpredictability of supply, working with multiple suppliers and having multiple incoming and outgoing logistics (RQ2). With the timely provision of salient information, as in the case of the The Role of ICTs in Supporting Collaborative Networks in the Agro-food 693 Dean Forest Food Hub, networked actors are enabled to optimize their logistical activities. In terms of social benefits, ICT solutions help foster better relations between consumers and producers. By improving the flows of information, good practices are shared and the isolation of small business overcome. While forums and social media foster online communities linking consumers to one another and to producers,

Conclusions

From the case studies above, we make the following observations:

Firstly, this type of FH is often dependent on donations, grant funding and voluntary work, which limits the model's overall resilience. ICTs could play an important role in lowering costs and making it easier to establish a FH. Simultaneously, leveraging on their capacity to aggregate local productions, the FHs could differentiate their business model by wholesaling food to institutional buyers such as schools, in addition to retailing food to households. In this case, the adoption of an online auction tool allowing buyers to get information to attain the produce they desire could be beneficial.

Second, confirming Matson & Thayer's studies [START_REF] Matson | The role of food hubs in food supply chains[END_REF], FH seem to be a successful business model when it addresses the needs of local food deserts. In the Dean Forest Food Hub, consumers perceive the FH as the most convenient way to source local fresh food, in the absence of other AFNs, and the initiative has more opportunities. While the Stroudco FH showed some limits in the scaling up of the initiative that can be due to the presence of other popular AFNs in Stroud. The argument could be made that there is currently a degree of saturation in the alternative food market of the region and people prefer to be engaged in face-to-face interactions occurring in other forms of AFNs.

The study is limited by the lack of data on how consumers and producers perceive online FHs and a deeper analysis of what are the main factors limiting the scaling up of such initiatives. Confronting this question, observed by others in initiatives in UK, and in other countries, would help us to investigate if the online FH model is sustainable and if people could rely on it as the sole source of their food.

This paper is an exploratory study aimed to deepen the knowledge on FHs and the role ICTs play in fostering the rise of new forms of AFNs, we are conducting further research aimed at overcoming limits of the present work as well as to explore the potential of mobile applications in the context of local food. These technologies might offer the exchange of contextualized knowledge, which might overcome limits of the current model with sporadic direct meetings, and persuade more people in to choose the online FH, thus helping the initiatives to scale up.

Acknowledgments. A Marie Skłodowska-Curie IF of the EU's Horizon 2020 program, SOFIA (G.A. 702884), has supported this work. This paper only reflects the authors' views and funding agencies are not liable for any use that may be made of the information contained herein.


Ikram El Asri
email: ikram.asri@um5s.net.ma

Noureddine Kerzazi
email: n.kerzazi@um5s.net.ma

Lamia Benhiba
email: lamia.benhiba@um5s.net.ma

Mohammed Janati
email: a.janati]@um5s.net.ma

From Periphery to Core: A Temporal Analysis of GitHub Contributors' Collaboration Network

Keywords: Collaboration, Core-Periphery, Socio-Technical Relationships, SNA

Open-source projects in GitHub exhibit rich temporal dynamics, and diverse contributors' social interactions further intensify this process. In this paper, we analyze temporal patterns associated with Open Source Software (OSS) projects and how the contributor's notoriety grows and fades over time in a core-periphery structure. In order to explore the temporal dynamics of GitHub communities we formulate a time series clustering model using both Social Network Analysis (SNA) and technical metrics. By applying an adaptive time frame incremental approach to clustering, we locate contributors in different temporal networks. We demonstrate our approach on five long-lived OSS projects involving more than 700 contributors and found that there are three main temporal shapes of attention when contributors shift from periphery to core. Our analyses provide insights into common temporal patterns of the growing OSS communities on GitHub and broaden the understanding of the dynamics and motivation of open source contributors.

Introduction

Open source communities grow and fade over time. Understanding how to maintain, sustain, and grow a community of contributors is crucial for the survival and success of any open source project [START_REF] Zhou | What make long term contributors: willingness and opportunity in OSS community[END_REF]. That said, more than 12,000 individuals have contributed to Linux since 2005, from which more than 4,000 contributed in just the last 15 months (50% are first-time contributors); 3000 for Rails; and 1403 for AngularJs. Scaling from one to thousands highly distributed developers in an interesting challenge of collaboration [START_REF] Hinds | Structures that work: social structure, work structure and coordination ease in geographically distributed teams[END_REF]. However, there is very little evidence as to how those virtual communities grow? And more interestingly, how newcomers can navigate from the periphery of a given project (i.e., first-time contribution) to the core contributors of the project (i.e., constantly committing, commenting, and participating in important decision-making)?

Recent studies have shown that only small portion of contributors leads an OSS project making a large proportion of technical contributions [START_REF] Koch | Effort, cooperation and coordination in an open source software project: Gnome[END_REF][START_REF] Goeminne | Evidence for the pareto principle in open source software activity[END_REF][START_REF] Geldenhuys | Finding the Core Developers[END_REF][START_REF] Dinh-Trong | The FreeBSD project: a replication case study of open source development[END_REF][START_REF] Robles | Remote analysis and measurement of libre software systems by means of the cvsanaly tool[END_REF][START_REF] Mockus | Two Case Studies of Open Source Software Development: Apache and Mozilla[END_REF]. For instance, Mockus et al. [START_REF] Mockus | Two Case Studies of Open Source Software Development: Apache and Mozilla[END_REF] studied two open source projects: Apache and Mozilla and revealed that only 10 to 15 developers collaborate to carried out 80% of the contributions. Similarly, Dinh-Trong and Bieman [START_REF] Dinh-Trong | The freebsd project: a replication case study of open source development[END_REF] stated that only 28 to 42 contributors performed 80% of the development activity. Koch and Shneider [START_REF] Koch | Effort, cooperation and coordination in an open source software project: Gnome[END_REF] showed that 17% (51 out of 301 developers) provides core functionalities to the GNOME project. However, again these previous works do not help to understand how these small portions of contributors shift from the periphery, or even stick, to the core.

The picture that emerged from this evidence-contribution of developers in OSS projects-has been taken to shape OSS organization structures. OSS communities can be seen such as a Core-Peripheral structure [START_REF] Geldenhuys | Finding the Core Developers[END_REF]. At the core, there are those contributors who have been involved with the project for a relatively long time, are leading the project, and making significant contributions (80%) to the evolution of OSS projects. In the other side, at the periphery there are newcomers or people interested in the project and making few contributions with much less notoriety.

The key idea in our work is to analyze temporal patterns by which newcomers to OSS project shift from the periphery to the core teams. This shift remains largely uncovered even in organizational theories. Understanding this phenomenon within open source project can help gain insights on how to maintain virtual communities and how to attract new world wild contributors in order to accelerate software development projects in both OSS and traditional commercial organizations [START_REF] Lussier | New Tricks: How Open Souce Changed the Way My Team Works[END_REF].

In this paper, we have undertaken a socio-technical analysis of five OSS collaborative communities aiming at uncovering the dynamics of growing and fading of those communities over time. Particular attention has been paid to the migration of newcomers from the periphery to core team.

Paper organization. The remainder of the paper is organized as follows. Section 2 presents related work. Section 3 provides our reasoning about core-periphery structure for the open source context. Section 4 describes our methodology including description of studied systems and data collection, for which the results are presented in Section 5. Section 6 discusses our finding and highlights some limitations. Finally, section 7 draws conclusions and enlightens future work.

Related Work

Open source software is built by teams of volunteers. A series of efforts in recent years have focused on the OSS development organization [START_REF] Zhou | What make long term contributors: willingness and opportunity in OSS community[END_REF]. Newcomers are explorers who must orient themselves within an unfamiliar landscape. As they gain experience, they eventually settle in and create their own places within the landscape [START_REF] Dagenais | Moving into a new software project landscape[END_REF].

Understanding Motivation-Members of OSS communities are volunteers whose motivation to participate and contribute is a necessary condition to the success of open source projects. Ye and Kishida [START_REF] Ye | Toward an understanding of the motivation open source software developers[END_REF] argued that learning is one of the major driving forces that motivate people to get involved in OSS communities. Hars and Ou [START_REF] Hars | Working for free? Motivations of participating in open source projects[END_REF] categorized open source participants' motivations into two broad categories: internal factors meaning that open source programmers are not motivated by monetary rewards but by their own hobbies and preferences. External rewards, when contributors are interested in receiving indirect rewards by increasing their marketability and skills or demonstrating their capabilities in programming. Whatever the motivation behind the contribution the most interesting is a contributor level of activity and engagement within a project. In this paper, we are interested in that engagement in the project and how contributors gain notoriety and fade over time from core-periphery structure throw investigating technical and social collaboration activities of developers.

Detecting the Core-Periphery Structure-A series of efforts in recent years have focused on detecting the core-periphery structure in OSS projects. For example, from Social Perspective-Dabbish et al. [START_REF] Dabbish | Social coding in GitHub: transparency and collaboration in an open software repository[END_REF] performed a series of in-depth interviews with central as well as peripheral GitHub users. Authors found that people make a rich set of social inferences from the networked activity information within GitHub and then combine these inferences into effective strategies for coordinating their work, advancing technical skills and managing their reputation. More concretely, Bosu and Carver [START_REF] Bosu | Impact of developer reputation on code review outcomes in OSS projects: An Empirical Investigation[END_REF] proposed a K-means classifier based on SNA metrics. for detecting coreperiphery structure. We build upon these previous works to develop a further social perspective of collaboration. Our approach is based on k-means classifier using three classes (core, gray area, and peripheral) instead of two. We use three classes to include transitional states where nodes are neither core nor peripheral, which gives us a higher accuracy (80%) in identifying and dealing with peripheral vs core contributors.

Amrit and van Hillegersberg [START_REF] Amrit | Hillegersberg Exploring the impact of socio-technical core-periphery structures in open source software development[END_REF] examined core-periphery movement in open source projects and concluded that a steady movement toward the core is beneficial to a project, while a shift away from the core is not. Toral and al. [START_REF] Toral | Analysis of virtual communities supporting OSS projects using social network analysis[END_REF] found that a few core members post the majority of messages and act as middlemen or brokers among other peripheral members.

Our study, by contrast is a field study of the migration of contributors from the periphery to core team. We, therefore, aim to analyze and understand interactions and contributors' evolvement per month. Specifically, we would like to address a practical question: can the activities of newcomers reveal who would be part of the core team leading the project? Predicting Who Will Stay-Zhou et al. [START_REF] Zhou | Who Will Stay in the FLOSS Community? Modeling Participant's Initial Behavior[END_REF] attempted to predict who will stay in OSS communities. The authors proposed nine measures of involvement and environment based on events recorded in the issue tracking system. One of their funding stipulates that newcomers who are able to get at least one issue reported in the first month to be fixed are doubling their odds of becoming a long-term contributor. Gamalielsson et al. [START_REF] Gamalielsson | Sustainability of Open Source software communities beyond a fork: How and why has the LibreOffice project evolved[END_REF] studied the sustainability of Open Source software communities beyond a fork. Forking an OSS means that a sub set of contributors take another direction of the project because they are not in line with decisions made by notorious contributors.

Core-Peripheral Contributors

The existing literature provides a number of theories and approaches that may help identifying core-peripheral structures in OSS projects [START_REF] Geldenhuys | Finding the Core Developers[END_REF]. We could classify the OSS structure, as most of these previous works, in two classes core/peripheral. However, found that classification into three groups provides a more accurate model as reported in Table 1.

More specifically, we start from discovering and retrieving SNA metrics for coedition files networks. Then we run a k-means classifier to identify three classes (core, gray area, and peripheral). Finally, we evaluate our classification using O(m) Algorithm for Cores Decomposition of Networks [START_REF] Batagelj | An O(m) Algorithm for Cores Decomposition of Networks[END_REF].

Step 1: The first step in this analysis was to compute the social networks (SNA) metrics for each project. We use an SNA package implemented in python, named Networkx 1 , to compute automatically SNA metrics for each graph. As described in section 4, historical collaboration data have been processed to create one Network per month. Table 1 shows, in column 1, the number of generated sub-graphs for each project.

Step 2: The second step was to identify core/periphery contributors. To identify the core/peripheral structure in our OSS projects, we use K_means classifiers (k=3). The underlying idea is to classify contributors in three groups according to their SNA metrics and see whether they belong to Core -Peripheral -Gray area groups. Table 1, presents the size of each cluster (k=2 and k=3). Step 3: We cross validate the resulting k-means clusters for core-periphery using two methods. First, we compare k-means classification against the result of O(m) Algorithm for Cores Decomposition of Networks [START_REF] Batagelj | An O(m) Algorithm for Cores Decomposition of Networks[END_REF]. The algorithm takes as input a graph and provides partitions as an output. For randomly selected networks, we computed the k_core function provided by Networkx implementing the O(m) algorithm.

Table 1. k-means classifier precision

The matching between the two algorithms was 100% agreement for small network.

For large networks, we obtained a 68% agreement matching for core nodes( for example 24 node of 35 identified as core were in the partition with max k_core score).This can be due to the evolution of the collaboration network structure into an onion shape [START_REF] Louzada | Smart rewiring for network robustness[END_REF] with multiple layers of peripheral nodes and thus the increasing fuzziness of the gray-area-periphery border as the network grows in size.

We then manually inspect the visualization of a random sample of graphs using Cytoscape. We validated that identified core contributors effectively belong to a dense and cohesive bloc showing core members physically centered in the network, as depicted in Figure 1.

Methodology

Study Subjects

In order to understand how a newcomer makes a shift from the periphery to being part of the core team within OSS projects, we studied socio-technical interactions for five long-lived and highly stared projects from GitHub. Hence, we have carefully chosen projects with approximately a thousand of contributors, with different lifespan, programming languages, and different domain in order to have diverse histories. Table 2 shows general information about the chosen OSS projects. In total, we have analyzed 850 stories of contributors spanning five different projects.

Data Collection

We used the REST (Representational state transfer) API2 provided by Github in order to get access to all the available information about hosted projects. The API provides access to a lot of information in JSON (JavaScript Object Notation) format. For each of the five studied projects we retrieved data history including: (1) information on commits [author, date, code churn, count of comments on commits, reviews, and edited files]; (2) and then for each edited file we were interested to investigate the collaboration between contributors with respect to co-edition files (Two contributors collaborate if they modify the same file). It is worth noting that the collaboration in our context is asynchronous (timeless) because a contributor can edit files years after another contributor.

Collaboration Over Files Co-edition-OSS projects such as GitHub are collaborative repositories hosting services including social features brought a new transparency to the development project [START_REF] Mcdonald | Petakovic Modeling Distributed Collaboration on Github[END_REF]. Collaboration among GitHub users can be seen in different ways and forms several kinds of social networks. The most intuitive one is the network of collaboration between developers over projects. On the other hand, collaboration within the repository is of great importance and gained interests in OSS collaboration analysis [START_REF] Dabbish | Social coding in GitHub: transparency and collaboration in an open software repository[END_REF]. In our case, coediting the same file is the dependent variable indicating whether or not the collaboration between two developers happened. We leverage on information of co-edition files to construct our collaborative networks.

Data Processing: Building Networks

The data sets have been processed and sliced by month to provide time frames (TF) for dynamic data analysis. For each time frame (for instance, 86 TF for Angular), we constructed three undirected, weighted cumulative networks: The first network is re-lated to Files co-edition Network (FCN) where nodes represent contributors and edge weights represent the quantity of interactions between those contributors based on the amount of files they both edited. The second is Committed Comments Network (CCN) where nodes are commenters and edge weights are the amount of commits they interacted together. Finally, the Review's comments Network (RCN) based on comments interactions on reviews. Figure 1 illustrates the three examples of generated graphs.

We performed a dynamic network analysis on monthly sequences of collaboration networks based on files co-edition. By progressively adding one month activity after another, we obtained a sequence of cumulative collaboration networks that allows us to study the evolvement of social structures of each community as well as its contributor's evolution according to the core-periphery perspective.

Results

RQ1.

Are some activities more prevalent than others for a newcomer to become a core contributor?

Motivation: Few newcomers in OSS end up being into the core of the project suggesting that somehow, they are gaining notoriety due to their participation in some collaborative activities such as changing source code, reviewing contributions from others, and commenting on commits and reviews. We aim at discovering what kind of contribution or collaboration are more relevant. We are interested in detecting existing correlations between social position and technical participation. Our primary goal is to equip the community of OSS with a better understanding of collaborative activities and potential guidelines for newcomers to play an efficient role.

Approach. We first identify the ascension of the top 10 core contributors for each. Next, we trace back the history of contributions aiming at quantifying collaboration activities. Then we considered contributors' activities under three types of contributions (#commits, #comments on commits, #comments on reviews, #editedFiles, and #changed Line of Code. We use a k-mean clustering approach to identify core contributors (see section 3) in monthly networks. This task requires us to identify different stages through which sequences of activities progress and then segment individual sequences according to the discovered stages. Finally, we measured the most correlated collaborative activity with respect to the contributors' social network metrics.

Results. Figure 2 shows the monthly evolution of core contributors for each studied project. It also illustrates how attractive is the project in terms of its capacity to build a large community of contributors. We were also interested to quantify the amount of contributors' transition from a status to another. Figure 3 illustrates a dynamic movement from periphery to the core and vice versa. For instance, we can track only the evolvement of core developer over time (CC) as well as the contributors that quit the core for a gray area (CG).

Our monthly analysis of OSS structure networks reveals a relatively stable evolution of the core teams. An average of 19.1 contributors per month for Docker, 11.6 for Angular, 16.46 for Symfony, and 16.2 for Tenserflow. We observed, for all projects, a monthly evolution of core contributors ranging between [.4 and 10.4].

In terms of featured activities, we found that newcomers spend significant portions of their time committing, editing source code files obviously adding and deleting lines of code more than commenting on commits and reviews. 3 shows the correlation between SNA centrality metric and the measure of each activity feature. One can notice that activities related to source code changes are more correlated to the positon of contributors within the structure core/periphery. For instance, we found for AngularJs project a correlation factor of .76 between staying in the core team and the number of contributor's commits.

RQ2.What collaboration activities can influence how long it takes for a contributor to be a core team member?

Motivation. We aim at identifying those collaborative activities that are more supporting newcomers gaining notoriety and being part of the core team.

Approach. Knowing the most correlated metric from RQ1, we calculated the number of commits for core contributors of each project as well as the amount of Line of code add to the project.

Results. Figure 4 shows the medians for four projects, (52 , 5465) for AngularJS, (109, 21787) for Docker, (154, 7975) for Rails, (145 , 6421) for Symfony, and (80, 36155) for Tenserflow. The results show that the number of commits and the amount of line of code add are both a statistically significant characteristics of core contributors. RQ3. Does the extent of involvement and environment predict whether a core contributor will churn from the project?

Motivation. Enormous effort over the past decades was spent in attempts to understand factors that affect involvement and sustainability of OSS communities. We contribute to that body of knowledge through our predicting model.

Approach. To answer RQ3 and predict who will leave the project, given the collaborative metrics, we applied the J48 decision tree algorithm on the data clustered previously using k-means for all projects in WEKA3 . To the purpose of this analysis, we filtered out only contributors that shift from the core to the gray area (CG) before the final transition to the periphery (GP), meaning leaving the project.

Results. Table 4 reports the results of our supervised machine learning approach regarding the four projects. For instance, we have 27 contributors within Angular project shift from the core to the gray area (CG). With a decision tree approach, we are able de predict 74.07% (.93 recall) the shift from C to G with only 7 out of 27 misclassified case. Interestingly, we found the root node of the decision tree to be the "EditedFiles" <= 871.39. This means that the amount of edited file is the most closely related metric to contributors' churn. The tree showed that if the number of edited files keep dropping then the contributor is likely to leave the core. However, the root activity of decision tree is not always the same for each project. We hypothesis that this difference is due to the progression stage of each project. For instance, it's easier to be part of the core team of Tenserflow, a relatively new project on Github, by just committing new changes.

Discussion

Practical Implications

Understanding the involvement of contributors and their gain of notoriety can help the OSS communities to attract more valuable and highly motivated individuals. Moreover, analyzing the history of contributors 'activity may help to build a sustainable community of contributors around OSS.

Providing guidelines for whom want to lead future decisions of an OSS. We found the ascension of a newcomer becoming a core contributor to be associated mainly with technical contribution, especially the amount of code changes and interactions with existing source code. Most importantly, the number of commits and the amount of Line of code added rather than other activities such as commenting and reviewing others work. This may reflect some inherent differences between OSS projects and industrial ones in which we have other collaborative contributions such as requirement analysis, testing, etc.

Predicting who will churn along and who will stay is important. Allows project owners to find potential long-term contributors earlier and helps newcomers to improve their behaviors. Figure 5 illustrates tracking one contributor from Docker project. This contributor has belonged to the core team and then churned from the project at the end of 2013. If we could predict contributors 'turnover according to some aspects of behavior that we are able to model and quantify then we have the ingredients to build a sustainable long-lived community of contributors.

Fig 5. Contributor Turnover

In summary, understanding the shift from peripheral to core contributors and then sustainable core team in OSS projects requires an understanding of "Hidden" collaborative activities as well as the motivation behind observable developers' commitment. Therefore, while our results may be statistically valid, more care must be taken in interpreting their meaning to draw, for instance, a recipe to guide newcomer's behavior within OSS projects. Much work remains to be done in studying sustainable collaboration in open source projects.

From C to G

Threats to Validity

We recognize few threats to our reported results. First, we did not check the bug database to assess the quality of contributions; instead we rely on crowd comments and code reviews that OSS communities use to enhance the software quality. Our choice was deliberate since we assume that core teams have gained their notoriety by performing in the quality also. However, such choice possesses threats of over estimation the quality of contributions. To mitigate the threat, we evaluate our classification against manual annotation. Second, we make certain assumption based on how we slice and build our co-edition, comments, and reviews graphs. For instance, we consider cumulative data for the co-edition network per month, and thus, building networks from the beginning of the projects up to the studied month. We consider source code collaboration as a sustainable activity in the sense that contributors leverage on the previous work of each other. Finally, our study is the subject of statistical conclusion validity which refers to the ability to make an accurate assessment of the strength of the relationship between our independent and dependent variables. For instance, in section 3, we used k-means to cluster and segregate contributors in three categories (core, peripheral, and gray area) according to a series of metrics. To gain more confidence on our classification approach, we triangulate our results using different methods such as SNA metrics, O(m) Algorithm, GitHub information, and visual inspection of collaborative networks.

Conclusion

In this paper, we study the fine-grained evolution of structural collaborative networks of five OSS projects. We analyzed the evolution of communication patterns over time, we presented a dynamic visualization based on time series analysis by dividing the long period of the project into several consecutive time frames (one month). We were also interested to quantify the amount of contributors' transition from the periphery to the core crossing what we call a gray area. We have observed, for all projects, a monthly evolution of core contributors ranging between [0.4 and 10.4].

Information on developer roles is crucial to understanding the collaborative dynamics of software projects. Ultimately, the most important collaborative activity to join and stay in the core team of an OSS is the amount of submitted changes (#commits). The more source code changes a new contributor submits, the faster and more likely he will make it the shift to the core team. Finally, depending on progression stage of the project, a drop in certain collaborative activity predicts who will leave the core. Our future work will focus further on community building and how to build a sustainable OSS ecosystem.

Fig 1 .

 1 Fig 1. Illustrative Examples of Networks Visualization.

Fig 2 .

 2 Fig 2. Monthly Evolution of Core Contributors.

Fig 3 .

 3 Fig 3. Monthly Evolution of contributors' status shifting for DockerTable3shows the correlation between SNA centrality metric and the measure of each activity feature. One can notice that activities related to source code changes are more correlated to the positon of contributors within the structure core/periphery. For instance, we found for AngularJs project a correlation factor of .76 between staying in the core team and the number of contributor's commits.

Fig 4 .

 4 Fig 4. Number of commits (left) and LOC add from core contributors

Table 2 .

 2 Overview of the Studied Systems

		Language	Contribu-	Com-	Commits	Reviews	Reviews	Line of
			tors	mits	comments		Comments	Code
	AngularJs	JavaScript	1,430	8,403	1,292	497	3,013	543,246
	Docker	Go	1,633	31,291	298	4,754	23,153	1,039,309
	Rails	Ruby	3,273	61,782	9,986	302	5,028	413,393
	Symfony	PHP	1,424	30,106	2,309	3,226	17,014	744,619
	TensorFlow	C++	700	15,221	147	111	872	1,349,495

Table 3 .

 3 Average correlation between centrality metric and activity features

			Source Code Changes		Commenting
		Commits	Edited	Lines	Lines	Commits	Reviews
			Files	Additions	Deletions	comments	comments
	AngularJs	0.76	0.77	0.70	0.72	0.28	0.60

Jan-14 Sept-14 May-15 Jan-16 Sept-16 May-17 Jan-18 Sept-18 May-19 Jan-20 Sept-20

Table 4 .

 4 Machine Learning: Decision Tree Results (J48)

		Correctly	Incorrectly	Precision	Recall	Root Activity
		Classified	Classified
	AngularJs	20	7	74.07	.93	# Edited Files
	Docker	43	0	100	1	# Commits
	Rails	39	14	73.58	.84	CommentsOnReview
	Symfony	19	13	59.37	.94	CommentsOnReview
	TensorFlow	23	3	88.46	.70	# Commits

https://networkx.github.io/

https://developer.github.com/v3/

http://www.cs.waikato.ac.nz/ml/weka/downloading.html


Daniele Spoladore
email: daniele.spoladore@itia.cnr.it

Ontology-based Decision Support Systems for Health Data Management to Support Collaboration in Ambient Assisted Living and Work Reintegration

Keywords: ontology, semantic web, decision support system, data integration, health

The modern evolution of healthcare systems towards even more complex networks has highlighted the emerging need of a standardized and interoperable model for the management of health data. Several studies in the past years have underlined how the adoption of Semantic Web technologies can provide a valuable solution for both of the aforementioned needs. Semantic modelling of health-data can indeed provide a sound and sharable conceptualization of a patient's health condition and can leverage automatic generation of new knowledge related to the clinical contexts. In fact, thanks to reasoning processes these technologies can be used as part of decision-support systems in a variety of domains. In this paper two examples of ontologies are presented. Both models rely on a worldwide-known classification and are the basis for two decision-support tools related to the Ambient Assisted Living and Work Reintegration domains, which enable cooperation among different clinical and non-clinical stakeholders.

Introduction

The evolution of healthcare systems towards more complex networks has highlighted the need of a standardized model for health-data management. Clinical personnel, rehabilitators, care-providers and non-clinical professionals need to access and interpret many heterogeneous sets of data related to the health condition of patients to provide them with optimal and customized solutions. In details, specialized personnel can exploit these data at two different levels. The first one consists in the so-called primary use of health information: in this case, data are used to deliver direct healthcare services, which are care, therapy and drug administration. The latter is the secondary use, which involves the usage of heterogeneous data for applications placed outside the process of direct healthcare delivery as, for instance, clinical research, public health surveillance and quality measurement. It consists in the analysis of aggregated data to study a variety of issues related to the health. In this second scenario, it is even more difficult for researchers and non-clinical personnel retrieving patients' data, since they are often inconsistent in formats and models. One of the most promising answers to this issue is the adoption of solutions based on Semantic Web (SW), which allows the integration of different types of data collected from different kind of patients and provides a standard set of languages to model knowledge -namely Resource Description Framework (RDF) [START_REF] Pan | Resource description framework[END_REF] and Ontology Web Language (OWL) [START_REF] Heflin | others: An Introduction to the OWL Web Ontology Language[END_REF]. Domain knowledge is modeled into ontologies, explicit, shared and Description Logic-based conceptualizations of the knowledge and the relationships of the concepts composing a domain [START_REF] Gruber | others: A translation approach to portable ontology specifications[END_REF]. In addition, with the use of ontologies it is possible to derive new facts -which are not explicitly expressedthrough reasoning, thus discovering new chunks of knowledge and adding value to several knowledge-based businesses and fields [START_REF] Aiello | Emergent semantics in distributed knowledge management[END_REF]. This feature is particularly interesting and enriches the scenario of the secondary uses of health data with the possibility to infer new information related to a patient's health condition in several domains.

This paper describes two ontologies that can be framed among the secondary uses of health data, since they deal with two health-related domains, the Ambient Assisted Living (AAL) and the Work Reintegration (WR) and involve both clinical and nonclinical professionals. The presented ontologies model the health condition of patients through the World Health Organization's International Classification of Functioning, Disability and Health (ICF) [START_REF] Organization | International Classification of Functioning, Disability and Health: ICF[END_REF], an international standard acting as common language that allows diverse clinical and non-clinical professionals to exchange and use the patient's health data. In fact, the use of ontologies permits to support the collection and use of a vast amount of patients' data by making them interoperable, thus enhancing the collaboration among different stakeholders (e.g. rehabilitators, physicians, lab technicians, therapists, caregivers).

In the AAL scenario, the ontology is used to model the patient's health condition, from which it is possible to infer the most suitable sets of appliances to help him/her to cope with his/her impairments in everyday-life. The model serves as a Decision Support System (DSS) for the smart home designers, to support them in the design of a living environment for impaired users. In the WR scenario, the ontology is used to identify possible alternative jobs for novice wheelchair users who need to be reintegrated in the workplace after a trauma, providing a list of suitable jobs tailored on specific users. This ontology acts therefore as a DSS for vocational and rehabilitation therapists and employers, enabling them to choose the most suitable options for the to-be-reintegrated wheelchair user.

This paper is organized as follow: Section 2 presents the existing works in the fields of semantic health-data modelling for secondary uses and DSSs. Section 3 introduces the ICF as a common background for the two ontologies developed. Section 4 focuses on the description of the AAL ontology and the use of its derived knowledge in a design application. Section 5 describes the ontological model for WR and the possible inferences to support a DSS. Finally, Section 6 summarizes the main outcomes.

Related Works

The topic of modeling the health-data is one of the main issues when tackling the interoperability among various data sources. Data integration is not an easy task: the possibility to link different health-data is complicated by the heterogeneity of data formats and different naming conventions [START_REF] Cheung | Semantic Web for Health Care and Life Sciences: a review of the state of the art[END_REF]. The importance of this topic gave birth to the Semantic Web for Health Care and Life Sciences Interest Group [START_REF]Semantic Web for Health Care and Life Sciences Interest Group[END_REF], whose mission is to advocate, support and develop the adoption of SW technologies across clinical research, healthcare and life sciences. These matters have been addressed in [START_REF] Moner | Archetype-based semantic integration and standardization of clinical data[END_REF], where the authors used semantic archetypes based on ontologies to provide a formal representation of knowledge deriving from health domains and integrating this model with clinical data. A similar solution is explained in [START_REF] Tao | A semantic-web oriented representation of the clinical element model for secondary use of electronic health records data[END_REF], where the interoperability of electronic health records (EHR) is approached by turning the attention to SW technologies, providing a representation of the health-data using OWL. The issue of EHR reusability is also addressed in the European project EHR4CR [START_REF] Hussain | EHR4CR: A Semantic Web Based Interoperability Approach for Reusing Electronic Healthcare Records in Protocol Feasibility Studies[END_REF], where the authors overcame the interoperability issues adopting a platform able to provide semantic interoperability services based on standard terminologies. In [START_REF] Kashyap | others: Clinical observations interoperability: A semantic Web approach[END_REF], the need to provide services oriented to the patients encompassing an holistic vision brought to the adoption of a SW-based approach.

The benefits deriving from the adoption of semantic-based technologies in healthcare also concern the possibility to discover new knowledge through reasoning processes. In [START_REF] Abidi | Adaptable personalized care planning via a semantic web framework[END_REF], these features are used to develop a knowledge-mediated personalized care planning. The authors highlighted how the application of the SW technologies can lead to the identification of a planning system that automatically generates ad adaptive care plan, tailored on the patient's profile. In [START_REF] Artikis | others: Supporting telehealth and AI-based clinical decision making with sensor data fusion and semantic interpretation: The USEFIL case study[END_REF], the authors presented a semantic-and SWRL [START_REF] Horrocks | others: SWRL: A semantic web rule language combining OWL and RuleML[END_REF]-based DSS for clinical decision making. Doucette et al. [START_REF] Doucette | A Framework for AI-Based Clinical Decision Support that is Patient-Centric and Evidence-Based[END_REF] adopted a similar approach in their work, which develops a semantic framework for AI-based clinical DSS, which also supports data interoperability. Subirats et al. [START_REF] Subirats | On semantic, rule-based reasoning in the management of functional rehabilitation processes[END_REF] developed an SWRL-based DSS providing a personalization of therapies in a rehabilitation scenario, using ICF as a framework.

However, the DSSs presented in these works, although leveraging on the SW interoperable capabilities, are all focused on clinical aspects and are not suitable to enhance and support the cooperation between clinical and non-clinical professionals.

ICF: A Sharable and Standard Language for Disability

The International Classification of Functioning, Disability and Health (ICF) [START_REF] Aiello | Emergent semantics in distributed knowledge management[END_REF] is a framework aimed at providing a standard tool for the description of health and its related states. It conceptualizes the functioning of an individual as a "dynamic interaction between a person's health condition, environmental factors and personal factors" [START_REF] Organization | How to use the ICF: A practical manual for using the International Classification of Functioning, Disability and Health (ICF)[END_REF]. ICF acts as a tool able to ease the communication among the healthstakeholders (therapists, clinicians) providing a standard and worldwide comparable description of the functional experiences of the individuals. Due to its vocabulary, which is easily interpretable also by non-clinical personnel, ICF can also be used in health-related domains, such as AAL and WR, as a common means to support and facilitate the collaboration of different actors. The classification is organized in two main parts: the first, "Functioning and Disability", provides a description of the components Body functions, Body structures and Activities and participation; and the second, "Contextual Factors", provides the means to describe the impact of the components Environmental factors and Personal factors. Each component is further deepened into Chapters, which identify the addressed domain. The functioning of a person is then described through the interaction between his/her health condition and the context where he/she acts. Each component is identified by a letter (b for Body functions, s for Body structures, e for Environmental factors, d for Activities and participation) and can be deepened by adding digits (Fig. 1). According to the number of digits following the letter, it is possible to get a code, whose length indicates the level of granularity -up to five digits. The functioning or disability of an individual can be assessed selecting the suitable category and its corresponding code and then adding a qualifier (from 0-no impairment to 4-complete impairment). ICF has also been represented into an ontological model in RDF/OWL [START_REF]International Classification of Functioning, Disability and Health NCBIO BioPortal[END_REF], which can be used as reference in the modelling of more complex ontologies. However, it has to be underlined that this model inherits some shortcomings belonging to the whole classification, such as problems regarding incongruent classification of some concepts, a lack of clarity between activities and their qualities, incorrect parent-child relationships and overemphasis on subsumption [START_REF] Della Mea | An ontology-based exploration of the concepts and relationships in the activities and participation component of the international classification of functioning, disability and health[END_REF][20] [START_REF] Andronache | Semantic aspects of the International Classification of Functioning, Disability and Health: towards sharing knowledge and unifying information[END_REF].

DSS for Collaborative Configuration of Living Environments

The AAL-DSS is developed within Design For All, an Italian research project aimed at providing elderly or impaired people with a set of tools able to cope their physiological status. AAL should assure them the ability to perform several daily life activities during their domestic lives that would otherwise be precluded or strenuous. In this context, the semantic-based DSS is composed of five sets of domain ontologies.

The ontologies were developed following the NeOn methodology [START_REF] Suárez-Figueroa | The NeOn methodology for ontology engineering[END_REF], a methodology requiring several domain experts to collaborate. An Ontology Requirements Specification Document [START_REF] Suárez-Figueroa | How to write and use the ontology requirements specification document[END_REF] has been drafted and then completed with a set of Competency Questions [START_REF] Grüninger | The role of competency questions in enterprise engineering[END_REF] -a list of the questions that an ontology should be able to answer. The related answers provided the terminology for a pre-glossary of terms, which were later formally represented into the set of ontologies using the Protégé ontology editor [START_REF] Gennari | The evolution of Protégé: an environment for knowledge-based systems development[END_REF].

The first set is the User Model, which gathers a dweller's registry records (full name, date of birth, birthplace, full address, telephone number, Tax Identification Number) and describes a person's physiological status using a partially re-engineered version of the ICF ontology [START_REF]International Classification of Functioning, Disability and Health NCBIO BioPortal[END_REF]. In this particular module, the specific ICF codes originally modelled as individuals are converted into datatype properties, in order to enable to model several health conditions using the same ICF codes. The result of this process consists of a simplified TBox, containing the datatype properties describing the four main components of the classification (Body functions, Activities and participation, Body structures and Environmental factors). For each of the components, the Chapters are also specified, providing a grade of detail with subdatatype properties for the second and third level categories (Fig. 2). These datatype properties, linked to a Health Condition individual, allow to specify the generic qualifier (with a range of acceptable int value restricted to 0, 1, 2, 3 or 4) to be associated to a specific category. Following this modelling expedients, it is possible to keep the inferences strictly related to the Health Conditions and to build general SWRL rules regarding specific codes and impairments. Every person living in the house is associated with his/her Health Conditionsmodelled in a separate module -, according to an already tested Ontology Design Pattern (ODP) [START_REF] Sojic | Modularising ontology and designing inference patterns to personalise health condition assessment: the case of obesity[END_REF]. For each dweller, several Health Conditions can be assessed in different moments in time by clinicians. Health Conditions described by means of the ICF module can then be classified through reasoning process. In fact, the reasoning performed on the Health Condition module allows to understand if a person is characterized by a mild, moderate, severe or complete impairment and the quality of the impairment (visual, hearing, motor or cognitive).

The second set of ontologies is the Smart Object Model, which formally describes the appliances and sensors that can be potentially placed in the house and their related functionalities. This set allows to classify each appliance according to its typology (dishwasher, refrigerator, oven, etc.) and to the benefits it provides to specific categories of impaired dwellers. The description of the Smart Objects takes advantage of the HicMO [START_REF] Peruzzini | A Service-oriented Architecture for Ambient-assisted Living[END_REF] "grammar", a set of XML properties able to describe the features of any appliance. The description of a Smart Object is integrated with a module describing the list of programs available for each appliance. Reasoning process on this module allows to determine the most suitable appliance for each of the dwellers modelled in the User Module.

The third set of ontologies deals with the Measurements Description and allows to model the measurements performed by a Smart Object. Each measurement is classified according to its typology (environmental, physiological machinery if they refer respectively to rooms, people or Smart Objects) and described with a set of objects and datatype properties (Fig. 3). This model serves as a completion for the description of the appliances and provide a formal way to describe the domestic comfort metrics. Another relevant advantage offered by this module, is the possibility to get semantic interoperability among the various measurements performed by the different Smart Objects, thanks to their formal description and the use of reference ontology for the description of the physiological measurements -Vital Sign Ontology (VSO) [START_REF] Goldfain | Vital sign ontology[END_REF] -and comfort-related measurements -Units Ontology [START_REF] Gkoutos | The Units Ontology: a tool for integrating units of measurement in science[END_REF]. The fourth set of domain ontologies is the Domestic Environment Model, whose goal is to provide a description of the dwellers' house and its structures, together with the comfort metrics that must be effective in providing a comfortable and safe permanence inside the house. The description of a person's house is performed with a simple taxonomy, which is composed of a set of classes categorizing the different rooms of a domestic environment (living room, kitchen, bathroom, etc.). Comfort dimensions are expressed as environmental measurements and allow to describe air quality (in terms of CO2 concentration in a room), amount of illuminance and internal temperature during winter and summer. According to the typology and grade of impairment it is possible to set specific comfort dimensions for a particular dweller; for instance, a visual-impaired user characterized by hyposensitivity to light may need a customized (higher) level of illuminance to perform daily life activities. Finally, the fifth set of ontology provides a description of the customization work provided by the designers. In this model, a description of the projects for the configuration of a living environment is provided; thanks to the entailments performed on the User Model and on the Smart Object Model, this ontology allows to formalize the choices performed by a designer (Fig. 4). Fig. 4. An example of modelling of a Project for a moderately visual-impaired user; the designer has chosen the appliances and their programs (dashed lines represent object properties, dotted lines represent datatype properties).

The aggregated data regarding the user's health condition, the list of suitable Smart Objects, the user's house and the entailments performed by the AAL-DSS are retrieved via SPARQL queries [START_REF]SPARQL Query Language for RDF[END_REF] and fed to a Virtual Reality-based application that enables designers to reproduce a virtual model of the user's domestic environments. Inside this virtual environment, the designer can place the Smart Objects by choosing from the list of most suitable options provided by the reasoning process on the Smart Object Model. As already described, the entailments are performed taking into account the user's current Health Condition: in this way, it is possible to update the list of suitable Smart Objects whenever an enhancement or deterioration of the health condition is modelled in the User Model.

To validate the ontology-based DSS implemented within the Design For All project, two specific use cases were developed. The first one foresees the configuration of a kitchen for a 68-year-old moderately visual-impaired male user, while the second foresees the configuration of a bedroom for a 70-year-old female user characterized by frailty, who needs to perform physical activity on a cycleergometer on daily basis. The framework allowed physicians to assess the users' Health Condition with the use of ICF: these data were later formalized in the ontology; clinical personnel, in collaboration with ergonomists, also provided specific comfort metrics thresholds for illuminance and air quality. Reasoning processes were effective in retrieving the most suitable appliances and sensors to configure both the environments, allowing the designer to make the best choices to respond to the users' needs.

Due to all the above-mentioned aspects and characteristics, this ontology acts as a cooperation environment that allows clinical professionals to provide and ICF-based assessment of the dweller's health condition. The designer can exploit this health condition to provide the users with a suitable home configuration, while rehabilitators and caregivers can monitor the improvements of the dwellers. Each clinician can deepen the data of his/her interest, just as the designer can access to global health condition data to configure the living environment. In addition, the use of ICF and VSO provides clinical personnel with descriptions and measurements able to ease the communication among physicians, domestic caregivers, rehabilitation therapists and dwellers. As a result, this semantic DSS enhances the collaboration between clinical and non-clinical professionals through the exchange of health-related data in an interoperable way, providing non-clinical personnel with aggregated and holistic data and clinical personnel with in-depth health data.

Collaborative DSS for Novice Wheelchair Users' Rehabilitation and Work Reintegration

The second DSS here presented is developed within an ongoing research project financed by the Italian National Institute for Insurance against Accidents at Work (INAIL) and aimed at supporting the return to work and, more in general, at enhancing the quality of life of novice wheelchair users (WUs). Moreover, the whole framework provides the WU with a new awareness in facing the new physical condition, making him/her able to deal with the challenges related to the return to work by training in safe virtual environments. On the other hand, it supports the vocational personnel and employers with technological means able to discriminate the still suitable jobs for that specific user. In this framework, the role of Semantic Web technologies is in fact to assess, with the help of clinicians, the WU's health conditions and, basing on this assessment, (1) to provide a list of eligible jobs for the WU's new health condition and (2) to set the difficulty of the training exercises in the virtual environments (Fig. 6). To this purpose, a set of three domain ontologies is developed. Also for this DSS, the NeOn methodology for ontology development has been chosen, since it provides a framework able to assess the purpose of the semantic models through the Ontology Specification Requirements Document and the list of Competency questions.

The first set of ontologies foresees the modelling of four ICF Core Sets [START_REF] Bickenbach | ICF core sets: Manual for clinical practice for the ICF research branch[END_REF] corresponding to the main causes that force a person on a wheelchair: Spinal Cord Injury, Traumatic Brain Injury, Stroke; Vocational Rehabilitation Core Set is added as a fourth set to better address work reintegration. Together with this module, following the same ODP characterizing the AAL-DSS, the set describes the WU's registry record and Health Condition. Therefore, this model allows to provide a holistic view on the WU's health condition and to assess his/her residual functional capability. Since the focus of the project is very specific, it was preferred to develop the ICF Core Sets ontologies from scratch, instead of reusing the already existing ontologywhich would have been too wide for the WU's Health Conditions description. Since some of the categories belonging to different ICF Core Sets are the same, a metamodel is implemented. In this meta-model it is possible to state the equivalency between the same categories and to classify them according to the four ICF components. For instance, the category "b126 -Temperament and personality functions" is used in all the four considered Core Sets. It is modelled as datatype property inside all the respective Core Sets models and then, in the meta-model, it is set as equivalent among the different tokens of this same category; in this way, it is possible to model exhaustively the ICF Core Sets domains while avoiding redundancy of the information.

The second set of ontologies provides an ICF-based description of the jobs and the tasks composing them. Its goal is to assess the impairment typology and the maximum grade of disability that is acceptable to perform a certain job. Each job is thus analyzed assigning it ICF-based employment factors, with the aim of identifying the acceptable conditions that a worker must fulfill to perform that job. For instance, a specific job like Receptionist can be suitable for WUs with a grade of impairment in the codes "b2300 -Sound detection", "b2304 -Speech discrimination", "s250 -Structure of the middle ear" and "s260 -Structure of the inner ear" lower or equal to 1.

Assessment of WU's health condition, conducted periodically, is also necessary to set the training difficulty in the virtual environment: to achieve this goal, a third set of ontologies describing the training sets is developed. Following the same design pattern used for the jobs' modelling, each training set is described in the terms of the typology of impairment and the maximum grades of disability for a WU to perform the training set. It is indeed fundamental to provide the patients with challenging tasks, which are neither too difficult nor too easy [START_REF] Maclean | A critical review of the concept of patient motivation in the literature on physical rehabilitation[END_REF] to avoid the onset of negative feelings such as frustration or boredom. The matching between the WU's Health Condition, the list of eligible jobs and the assessment of the training difficulty is performed through two sets of SWRLs.

According to this approach, the DSS allows clinical personnel (physicians and rehabilitation therapists) and different health care structures (different hospital wards, rehabilitation structures) to periodically assess the WU's progresses and provides vocational therapists and rehabilitators with an up-to-date tool to evaluate (workrelated) residual capabilities. The model then provides employers with reasoned suggestions regarding WU's suitable jobs and with a set of environmental modifications that can be implemented to ease the WU's reintegration in the working place. The DSS is therefore able to enhance the cooperation between clinicians, rehabilitators and non-clinical stakeholders. This DSS is part of a framework which will be validated and later integrated into the return to work process adopted by INAIL as a further development of this work.

Conclusions

This work addresses the secondary use of health-related data with SW technologies, describing two DSSs. Both of them are based on a worldwide-known standard (International Classification of Functioning, Disability and Health) and allow to model into ontologies the patient's health condition. Thanks to reasoning processes, it is possible from these formalized models to infer several new information regarding different health-related domains -such as Ambient Assisted Living and work reintegration. This information can be enriched and used by a variety of clinical and non-clinical professionals who may intervene to improve several aspects of impaired patients' lives. Relying on ICF and its structured vocabulary provides a common language for clinicians, rehabilitators, vocational personnel and non-clinical personnel (designers and employers), offering a standard for the description of a patient's functioning, thus enhancing cooperation among different actors both from health and not health related fields. In addition, ICF's structure represents a suitable means to allow the development of ontologies with Semantic Web standard technologies. The developed ontologies can also be aligned with the upper-level ontologies [START_REF] Hoehndorf | What is an upper level ontology? Ontogenesis[END_REF], thus providing a common semantic framework for data interoperability and granting horizontal scalability.

Fig. 1 .

 1 Fig. 1. An example of ICF code.

Fig. 2 .

 2 Fig. 2. An excerpt of the ICF TBox, illustrating the second Chapter of the "Body functions" component and the degree of specification with the use of datatype properties.

Fig. 3 .

 3 Fig. 3. An example of modelling of a Smart Object performing a measurement; dashed lines represent object properties, while dotted lines represent datatype properties.

Fig. 5 .

 5 Fig. 5. An example of support to collaboration among different professionals provided by the application of ontology.

Fig. 6 .

 6 Fig. 6. A representation of how the health data flow among the three ontologies eases the cooperation among different stakeholders.