David Romero
email: david.romero.diaz@gmail.com

Arturo Molina
email: armolina@itesm.mx

Green Virtual Enterprises and their Breeding Environments

Keywords: Collaborative Networked Organisations, Green Virtual Enterprises, Breeding Environments, Industrial Ecology, Industrial Symbiosis, Sustainable Industrial Development

The Industrial Ecology (IE) is a new interdisciplinary field focused on sustainable development. IE aims to the shifting of industrial processes from open-loop systems where different resources move through a system to become wastes to a closed-loop system where wastes become inputs for new processes. This paper introduces a Green Virtual Enterprise (GVE) model as an emerging sustainable manufacturing and logistics mode focused on offering, delivering and recovering green products to/from the market, under a lifecycle thinking and supported by its source network. GVEs creation is considered within a GVE breeding environment context, which acts as a long-term collaborative network aimed at offering the conditions to efficiently promote the sharing and recycling of resources such as: information, materials, water, energy and/or infrastructure with the intention of increasing economic gains and achieving sustainable development.

Introduction

The Industrial Ecology (IE) stands for an important strategy to promote sustainable industrial development for enterprises and their related support institutions by providing a unique collaboration opportunity, with a "business sense", to integrate ecological, economic and social considerations into the creation and operation/ evolution of new or existing industrial networks (e.g. industry clusters, industrial districts, eco-industrial parks). IE attempts to understand the potential improvement of industrial networks using an analogy between natural ecosystems and industrial systems. IE aims to the shifting of industrial production systems from open-loop systems to closed-loop systems, evolving from a linear to a cycle production approach, in order to reduce wastes and pollution and efficiently share and recycle resources within an industrial ecosystem towards increasing economic gains and achieving sustainable development [START_REF] Chertow | The Eco-Industrial Park Model Reconsidered[END_REF].

Collaborative Networked Organisations (CNOs) represent a promising paradigm in manufacturing and service industries to help organisations to cope with the challenges of turbulent market conditions in the context of ecological, economic and social pressures on the global industry. CNOs show a high potential as drivers of sustainable industrial development by joining of complementary capabilities and capacities for creating products that are non-polluting, conserve energy and natural resources, and that are economically viable and socially rewarding for all stakeholders involved [START_REF] Camarinha-Matos | Collaborative Networks: Value Creation in a Knowledge Society[END_REF].

Both scientific disciplines can provide together a new interdisciplinary approach for enterprises to improve their competitive and environmental performance in terms of better managing their skills or core-competencies and resources (e.g. information, materials, energy, waste, infrastructure) to realise sustainable industrial collaborative networks and pursue new green business opportunities. In this sense, collaboration is presented as a challenge and at the same time as an opportunity for enterprises to re-engineer their production processes and/or networks in order to eliminate/recycle their wastes to maximise returns per unit of resource consumed, share/reduce their costs over limited natural resources (e.g. raw materials) and supporting infrastructure, and increase their business opportunities and profit by establishing long-and shortterm strategic coalitions to develop new competitive advantages (e.g. green products and processes) without compromising critical resources for the future.

Green Virtual Enterprises and their Breeding Environments

Green Virtual Enterprise Breeding Environments (GVBEs), also known as virtual eco-industrial collaborative networks, are long-term strategic alliances of green enterprises 1 and their related support institutions aimed at offering the necessary conditions to support the rapid and fluid configuration of Green Virtual Enterprises. GVBEs, as source networks focus on traditional bases on creating an adequate environment for the establishment of cooperation agreements, common operation principles, common interoperable infrastructures, common ontologies, and mutual trust among others, with the objective of preparing their members to collaborate in potential GVEs that will be established when a green business opportunity arises or is identified by a GVBE member acting as a broker [adapted from 3] [see also [START_REF] Romero | Virtual Organisation Breeding Environments Toolkit: Reference Model, Management Framework and Instantiation Methodology[END_REF].

On more innovative bases, GVBEs focus on bringing together a variety of green enterprises into a business ecosystem that aims to mimic the relationships between different species in a natural ecosystem and establish symbiotic mechanisms to create synergies towards an optimal production level and ecological balance in a virtual eco-industrial collaborative network. GVBEs concentrate on bringing their business ecosystems as close as possible to being a closed-loop system by keeping a close interaction of material, energy, information and technology among their members towards a near complete recycle or sharing of resources for producing and delivering green products with sustainable manufacturing and logistics practices through GVEs creation, and by recruiting new GVBE members (green enterprises) that can enhance the network capabilities and capacities to grasp new green business opportunities in time and taking into account environmental impact and resources utility.

These last aims are what differentiate traditional VBEs from the novel GVBEs in where collaboration targets the creation of economies of scale in a recycling and shared sustainable engineering efficiency strategy. As a result, GVBEs aim to offer their members and to the market and society a sustainable industrial development model characterised by an economic growth together with a low environmental impact in comparison with other traditional industrial development models.

Green Virtual Enterprises (GVEs) are short-term and dynamic coalitions of green enterprises that may be tailored within a GVBE to respond to a single collaboration opportunity, through integrating the green technology (skills or core-competencies and resources) required to meet or exceed the quality, time and cost frames expected by the customer with a low ecological footprint, and that dissolve once their mission/ goal has been accomplished, and whose cooperation is supported through computer networks [adapted from 3].

Furthermore, GVEs as goal-oriented collaborative networks can be designed within a GVBE with two different aims, on the one hand to become dynamic forward supply networks for delivering new green products to the market, and on the other hand to become dynamic reverse supply networks for recovering the products sold under the GVBE brand (product stewardship) for direct-use, repair, re-manufacture, recycle or safe disposal (see Fig. 1).

Green Virtual Enterprise Breeding Environment

Green Virtual Enterprises Characterisation

GVEs represent an emerging sustainable manufacturing and logistics mode focused on offering, delivering and recovering green products to/from the market, under a lifecycle thinking and supported by their source networks. GVEs focus on adopting lean-agile manufacturing and other sustainable engineering and logistics principles in order to enhance production, reduce wastes and improve their management, decrease energy consumption, achieve logistics efficiency and consequently reduce production and logistics costs and environmental impact.

In this paper, two GVE modalities are introduced as a response to the difficulties of creating and managing integrated and stable forward & reverse supply networks, better known as closed-loop supply networks, to address the market dynamic changes (e.g. shortened product lifecycles) in a sustainable way, in where enterprises normally focus on their traditional forward supply networks and outsource their reverse supply networks to third-parties.

The two GVE modalities proposed will be crafted within a GVBE in where green enterprises will be prepared and ready to participate in dynamic forward and reverse supply networks created according to the needs and opportunities of the market and remain operational as long as these opportunities persist, offering in this way an assertive approach towards the market dynamicity and true sustainability.

Green Virtual Enterprises as Dynamic Forward Supply Networks

GVEs as dynamic forward supply networks (F-GVEs) are temporary alliances of green enterprises that come together in order to better respond the market demands through the most efficient use of their complementary skills or core-competences and shared resources, for developing and delivering in a sustainable way new products (goods and services) to the customer with a minimal environmental impact.

GVEs lifecycle in their dynamic forward supply networks modality will go through the following stages: During its creation, (a) a new green business opportunity will be identified (e.g. new product developments) and will trigger the F-GVE formation; (b) the green business opportunity will be characterised in order to detail its competency requirements in terms of green design, green materials, green processes, green manufacturing, green packaging and green logistics needed to later on look for the F-GVE partners that posses these green competencies 2 , and for drafting the F-GVE rough plan (e.g. work-breakdown-structure) to schedule, assign and locate activities, tasks and resources to be performed/used by the potential F-GVE partners during the F-GVE operation; (c) F-GVE partners will be searched, assessed and primarily selected from the pool of GVBE members available and in case there is a lack of competencies inside the GVBE, F-GVE partners can be recruited from outside, based on their green degree level 3 and other key performance indicators (e.g. price, delivery date, quality level, etc.) to respond to the competency requirements and constrains of the green business opportunity; (d) the selected F-GVE partners will negotiate to reach agreements and align needs with offers towards the F-GVE final configuration, following the triple bottom line sustainability principles (economicequitable, environmental-viable, social-bearable); (e) a detailed F-GVE planning (e.g. working structure) with roles and responsibilities assigned to the F-GVE partners will be defined; and lastly (f) the F-GVE collaboration will be formalised in a contract and the F-GVE will be launched [adapted from 5] (see Fig. 2).

Furthermore, during the operation stage, the F-GVE most perform in an agile, green and optimal way following sustainable engineering, manufacturing and logistics principles in order to offer and deliver green products with the required quality and within the required time and cost frame needed to meet or exceed the customer expectations and environmental regulations [adapted from 6] (see Fig. 2).

Finally, during the F-GVE dissolution, since the F-GVE was created within a GVBE, any surplus and/or abandoned resources/scrap should be placed in the GVBE bag of assets 4 for its direct-use (re-use), repair, re-manufacture, recycle and/or safe disposal as part of the GVBE sustainable strategy, and of course the GVE product will be delivered to the customer using optimised/green logistics [adapted from 7] (see Fig. 2 &3). 2 Green competencies are those environmentally conscious business practices and strategies such as: Design for the Environment (DFE), Product Lifecycle Management (PLM) and Lifecycle Analysis, Lean Manufacturing, Total Quality Environmental Management (TQEM), Environmental Management Systems (EMS), Green Supply Chain Management (G-SCM), Green Logistics, ISO14000 series' requirements, etc. [Focus on Forward Supply Networks]. 3 Green degree level is the outcome of using quantitative and qualitative metrics to scale and provide a meaningful evaluation of the green capabilities and capacities of an enterprise. 4 A GVBE bag of assets is a common virtual and physical warehouse to make easier the share of tangible and intangible assets between the GVBE members for different purposes.

GVE Modality: Dynamic Reverse Supply Networks

GVEs as dynamic reverse supply networks (R-GVEs) are temporary alliances of green enterprises that come together in order to better respond a business opportunity based on a sustainable reverse logistics and end-of-life manufacturing approach for recovering products, parts, subassemblies and/or scrap through the most efficient use of their complementary skills or core-competences and shared resources for their direct-use (re-use), repair, re-manufacture, recycle or safe disposal -within a GVBE. GVEs lifecycle in their dynamic reverse supply networks modality will go through the following stages: During its creation, (a) a new green business opportunity will be identified (e.g. products or scrap recovery) and will trigger the R-GVE formation; (b) the green business opportunity will be characterised in order to detail its competency requirements in terms of direct-use (re-use), repair, re-manufacturing, recycling and/or safe disposal approaches needed for managing certain products or scrap in their end-of-life to later on look for the R-GVE partners that posses these green competencies 5 , and for drafting the R-GVE rough plan to be carried-out by the potential R-GVE partners during the R-GVE operation; (c) R-GVE partners will be searched, assessed and selected based on their green degree level and other key performance indicators to respond to the competency requirements and constrains of the green business opportunity, (d) the selected R-GVE partners will negotiate to reach agreements towards the R-GVE final configuration, following the triple bottom line sustainability principles, (e) a detailed R-GVE planning with roles and responsibilities assigned to the R-GVE partners will be defined, and (f) lastly the R-GVE collaboration will be formalised in a contract and the R-GVE will be launched (see Fig. 2).

Furthermore, during the operation stage, the R-GVE most perform in an agile, green and optimal way following sustainable logistics, end-of-life manufacturing and safe disposal principles in order to re-use (e.g. deal with the direct-use of product components as spare parts), re-manufacture (e.g. restoring and rebuilding products), recycle (e.g. reclaim raw materials) and/or safe disposal (e.g. hazardous wastes management) of products and their scrap always respecting the environmental regulations (see Fig. 2).

Finally, during the R-GVE dissolution, outputs may take two paths: (a) the outputs of the end-of-life manufacturing processes that were selected for direct-use (re-use) re-manufacturing and/or recycling will be returned immediately, if possible, as inputs to active forward supply networks (F-GVEs) within the GVBE or will be placed in the GVBE bag of assets as inputs for future F-GVEs, and/or (b) those outputs selected for safe disposal may trigger a new GVE formation specialised in hazardous wastes disposal practices (see Fig. 2 &3).

In this paper, GVBEs are introduced as virtual eco-industrial collaborative networks with the capabilities and capacities to deploy innovative, cost-effective and green technologies and practices to promote sustainable industrial development through F-GVEs and R-GVEs creation, operation and dissolution. GVBEs have as their main goal becoming intelligent networks for resources management (GVBE bag of assets) in order to match GVEs inputs and outputs (match-making) to maximise resources utility towards achieving industrial symbiosis.

In Fig. 3 a generic GVBE industrial symbiosis scenario is presented, in this setting when a F-GVE is created: [a] some inputs can be collected from the GVBE bag of assets, [b] some others can be provided by running GVEs acting as green suppliers, and [c] if necessary, missing inputs within the GVBE bag of assets can be purchased outside of the virtual eco-industrial network. Furthermore, F-GVEs outputs such as surplus and/or abandoned resources: [d] will be placed in the GVBE bag of assets for its later use or [e] will be immediately incorporated as inputs for other active GVEs within the GVBE. On the other hand, in the R-GVE case, inputs may come from:

[f] products/scrap recovered from GVBE inside (bag of assets warehouse) as a result of an endogenous green business opportunity (e.g. 4R 6 or safe disposal strategy) that can be announced inside the GVBE by its manager and simply GVBE members will organise to respond to it (GVE creation), or [g] products/scrap can be recovered from GVBE outside (marketplace) as an exogenous green business opportunity that can be detected by a GVBE member playing the role of a broker and GVBE members will be selected to become the R-GVE partners. Moreover, R-GVEs outputs similar to F-GVEs outputs can be [h] placed in the GVBE bag of assets for its later use or [i] can be immediately incorporated as inputs for other active GVEs within the GVBE.

Fig. 3. GVBE Industrial Symbiosis

In general, GVBEs industrial symbiosis and other collaboration strategies can increase green enterprises' competitive and environmental performance by becoming GVBE members and participating in GVEs. Some industrial symbiosis benefits/ reasons for green enterprises to join a GVBE can be to reduce natural resources consumption, improve approaches for sustainable business operations, reduce (raw) materials costs, reduce treatment and disposal costs, etc. to meet economic gains by saving money and protecting the environment. While some collaboration benefits for green enterprises in a "business sense" can be to access/explore emerging green markets, increase activities/profit in a sustainable way, copying with market green trends and environmental regulations, joint purchasing (better negotiation power), joint promotion (eco-branding/marketing), social and environmental responsibility prestige/reputation, sustainable innovation as differentiator, among others; and in a more "technical sense", GVBEs collaboration benefits offer possibilities for shared commuting and shipping, alternative (green) packing, integrated (green) logistics, common environmental information systems, (green) production technology sharing and integration, etc. [see 2] [see 9].

Discussion & Reflexions

In the past, some eco-industrial collaborative models (e.g. eco-industry clusters/parks) bounded to their geographical proximity have improved their members competitive and environmental performance through different collaboration strategies (e.g. industrial symbiosis), which have resulted in economic benefits for the enterprises and their support institutions, and in environmental care and social welfare for their localities.

Nevertheless, with the advances in information and communication technologies and new environmental-friendly transportation vehicles/systems (e.g. hybrids), novel virtual eco-industrial collaborative models [START_REF] Ausubel | The Virtual Ecology of Industry[END_REF] such as the GVEs and their GVBEs are gradually emerging in the global industrial landscape, making (green) enterprises step beyond their geographical regions in order to access new complementary green competencies, and market and symbiosis opportunities. As such, nowadays ecoindustrial collaborative models do not have to be bounded anymore by their members' geographical closeness and can use collaborative business infrastructures [START_REF] Rabelo | The ECOLEAD Collaborative Business Infrastructure for Networked Organizations[END_REF] playing the intermediary role as the enablers of interoperation among enterprises to support their coordination and cooperation mechanisms (e.g. design, manufacturing, logistics, warehousing) towards sustainable operations and therefore development.

Geographical proximity vs. virtuality can be a debate for the eco-industry clusters establishment; however researchers and industrial practitioners have to consider that "it is possible", as proved by the Collaborative Networks scientific discipline, for not physically co-located enterprises to create economies of scale and cost effective support for information, materials, water and/or energy exchanges across distance using collaborative logistics networks [START_REF] Langley | Seven Immutable Laws of Collaborative Logistics[END_REF] and collaborative business ICT infrastructures [START_REF] Rabelo | The ECOLEAD Collaborative Business Infrastructure for Networked Organizations[END_REF] [see also [START_REF] Romero | VO Breeding Environments & Virtual Organizations Integral Business Process Management Framework[END_REF].

As any strategy for an eco-industry cluster establishment, traditional eco-industry clusters (e.g. eco-industrial parks) vs. virtual eco-industry clusters (e.g. GVBEs) have their advantages and disadvantages and these tradeoffs are briefly depicted in Table 1.

Table 1. Traditional Eco-Industry Clusters vs. Virtual Eco-Industry Clusters

Eco-Industrial Parks (EIPs) GVE Breeding Environments (GVBEs) Involve a strong investment and high environmental impact with a physical location construction and/or the re-development of an exiting one.

Do not involve an investment in a physical location construction, but require a moderate investment in a collaborative business ICT infrastructure, which represents a lower environmental impact. EIPs most suitable members need to be collocated in a certain geographic location in order to participate in the industrial symbiosis and other collaboration opportunities, incurring in the costs associated and environmental impact to re-setup a green enterprise in a new location. Also potential EIP members will be limited to geography proximity.

GVBEs most suitable members do not need to be collocated in the same place to participate in the industrial symbiosis and other collaboration opportunities, just need to create virtual linkages supported by computer networks without any geographic barriers that could limit the best green enterprises recruitment scope. Recruiting new EIP members in order to enhance the eco-industrial park capabilities and capacities can be extremely difficult because of the green enterprise reallocation.

Recruiting new GVBE members in order to enhance the breeding environment capabilities and capacities can be very easy thanks to a GVBE membership management system. In an EIP more materials, water, energy and/or other waste exchanges seem to be possible in comparison to GVBEs, but this exchanges as well as enterprises skills and resources sharing are limited to the ones available in the geographical proximity.

In a GVBE perhaps less materials, water, energy and/or other waste exchanges seem to be possible because of geographic distance, but more enterprises skills and resources sharing can take place by being able to have a large number of GVBE members.

Emerging Eco-Industry Clusters: Typology & Examples

As discussed in the previous section, geographical proximity vs. virtuality can offer different advantages and disadvantages towards traditional and/or virtual eco-industry clusters establishment. Nevertheless, as depicted in Fig. 4, different models for ecoindustry clusters exist, from traditional eco-industrial parks with enterprises located at the same site to geographically distributed enterprises creating virtual linkages, in opposition to co-location, to form virtual eco-industry clusters. Each of these sustainable industrial development models or combinations of them are characterised by different activities and uses, including green manufacturing (e.g. producing green products), closed-loop manufacturing (e.g. zero-emissions), by-product exchange (e.g. using by-products rather than disposing them as wastes), renewable energy, resource recovery (e.g. 4R strategy), green infrastructure (e.g. landscaping), green building design, cleaner production, pollution prevention, energy efficiency, and partnerships between green enterprises and their related support institutions [START_REF] Pcsd | Eco-Industrial Park Workshop Proceedings[END_REF]. Fig. 4. Eco-Industry Clusters Typology [START_REF] Pcsd | Eco-Industrial Park Workshop Proceedings[END_REF] Most famous and first eco-industrial park, which has been the reference model for today's eco-industry clusters, is Industrial Symbiosis at Kalundborg, Denmark, where different industrial firms have been working together for more than 30 years exchanging waste materials and energy, and sharing resources [START_REF] Chertow | Industrial Symbiosis: Literature and Taxonomy[END_REF]. Moreover, the first virtual eco-industry cluster was established at Brownsville, Texas (1996) as a regional approach to exchange materials and by-products across the USA-Mexican boarder [START_REF] Pcsd | Eco-Industrial Park Workshop Proceedings[END_REF], which has been followed by other virtual initiatives such as the Karlsruhe virtual eco-industrial park (1998), where different enterprises exchange organic and mineral by-products, share information and focus on dematerialisation chains, and the Bioenergie und Rohstoffzentrum Dormagen virtual eco-industrial park (1999), where companies focus on by-product exchanges and energy cascades, information sharing and extended collaboration with universities and public entities, both at Germany [START_REF] Fleig | ECO-Industrial Parks: A Strategy towards Industrial Ecology in Developing and Newly Industrialised Countries[END_REF].

Nowadays, different eco-industrial park projects can be found around the World, for some relevant case studies in America, Europe and Asia [see 15].

Conclusions & Further Research

The materialisation of a potential synergy between IE and CNOs scientific disciplines requires a further research on understanding these possible synergies and underlying principles for better GVEs and their GVBEs creation and management as sustainable industrial collaborative networks. Nevertheless, significant progress is already available in terms of conceptual frameworks and models [3] [4], methods and processes [START_REF] Romero | VO Breeding Environments & Virtual Organizations Integral Business Process Management Framework[END_REF], and software tools and systems [5] [6] in CNOs scientific discipline, and perhaps is just a matter of bringing from IE scientific discipline the sustainability element as a main driving force behind the future success of sustainable collaborative networks.

Future looks for more "green" enterprises and sustainable industrial development models, and the proposed approach based on GVEs and their GVBEs appears to be well-suited to cope with emerging socio-economic and environmental challenges of the global industrial landscape.

  of-Life Manufacturing→ 4R / Safe Disposal

Fig. 1 .

 1 Fig. 1. GVEs creation (both modalities) within GVBE lifecycle

A Green Enterprise is an enterprise that strives to meet the triple bottom line by ensuring that all products, processes and manufacturing activities in its business operation address the sustainable principles: economic-equitable, environmental-viable, and social-bearable.

Green competencies are those environmentally conscious business practices and strategies for dealing with the end-of-life of products such as: inspection, diagnostic and recondition techniques to obtain the most value from a recovered product through a re-use (e.g. spare parts), refurbish (e.g. repair or re-manufacturing), recycling (e.g. scrap) and/or safe disposal (e.g. hazardous wastes treatments) strategy [Focus on Reverse Supply Networks].

strategy stands for repair, re-manufacture, recycle and re-use green strategies.

Acknowledgments. The research presented in this document is a contribution for the ECOLEAD Project (FP6 IP 506958), for the S-MC-S Project (FP7 NMP-ICT-FoF 260090), and for the ITESM, Campus MTY & CCM, Research Chairs.

Green

GVE Breeding Environments: Industrial Symbiosis

Industrial Symbiosis (IS) can be defined as an industrial ecology strategy, based on collaboration and synergetic possibilities, aimed at sharing/exchanging information, materials, water, energy and/or infrastructure (e.g. services) among industrial actors in order to increase economic gains and achieve sustainable development in a ecoindustrial network [START_REF] Chertow | Industrial Symbiosis: Literature and Taxonomy[END_REF]. IS systems like GVBEs, offer their members the opportunity to collaboratively optimise resources utility at efficiencies beyond those achievable by any single enterprise and at the same time open new possibilities to access/explore green business opportunities in the global marketplace that would not be possible, or would have a higher cost, if attempted individually.


Tian Zhang
email: tian.zhang@univ-st-etienne.fr

Eric Marcon
email: marcon@univ-st-etienne.fr

Maria Di Mascolo
email: maria.di-mascolo@g-scop.grenoble-inp.fr

Care network Coordination for Chemotherapy at Home: A Case Study

Keywords: Care network coordination, Home Care Services, Cancer chemotherapy at home, Process modelling, Discrete event simulation

This paper deals with a system of chemotherapy at home which is managed by a Home Care Services (HCS) structure. The main role of this HCS structure is to coordinate care actors for a smooth organization of chemotherapy at home. In this work, we model a real system of chemotherapy at home managed by a HCS structure, and simulate its behaviour. The objective is to evaluate the relevance of such a system for current activities of the HCS structure, and to propose solutions for improving the optimal coordination of the care network for chemotherapy at home.

Introduction

The increased aging population is an important pressure to hospitals, which are heavily constrained by their resources to meet growing patient demands, especially for patients with severe acute illness and chronic conditions. The fact becomes a main motivation and explains the speed development of the Home Care Services (HCS) in France. The cancer chemotherapy at home occupies 15.6% of stays in HCS in 2006.

In this paper, we focus on cancer chemotherapy at home process by referring a real-word case study, which can be considered as a care network managed by a coordination center. The management performance depends greatly on quality and effectiveness of coordination. Therefore, we aim above all to understand the chemotherapy at home process and to identify the care actors' responsibility using process modelling method; and then to evaluate quality of coordination of the system using discrete event simulation. Based on previous results, we propose two improvement procedures, which are simulated. The simulation results are used for a comparative analysis.

The remainder of the article is organized as follows: In section 2, we give general information on the cancer chemotherapy at home. Section 3 describes the studied system of chemotherapy at home using process modelling method. Section 4 presents the simulation approach with three scenarios; the results of the study are also discussed. The article ends with a conclusion of our work.

Development of cancer chemotherapy at home

The French government encourages the development of chemotherapy at home in regarding it as one of the objectives of the Cancer Plan and the reorganization of oncology care [START_REF] Urban | Comité de coordination de Cancérologie et Pôle Thorax Vaisseaux, CHU Angers, La chimiothérapie au domicile en cancérologie thoracique[END_REF]. Some existing research works [START_REF] Dahan | Hospitalisation à domicile en onco-urologie : bilan des 5 premières années et enquête de satisfaction[END_REF]] [Boothroyd & al., 2004] show that chemotherapy at home is feasible, not only in terms of quality and safety of care, but also in terms of patients and their family satisfaction. [START_REF] Urban | Département de Pneumologie de CHU Angers, Réorganisation des Soins en cancérologie et chimiothérapie au domicile[END_REF] points that chemotherapy at home only suits valid patients, who are informed and integrated in a care program, and who are treated in a suitable family environment. Gorski & al. (2008) survey chemotherapy at home and other infusion therapies being realized at home; they indicate more clearly the advantages and disadvantages of these treatments, especially some critical success factors of their realization at home. [START_REF] Chahed | Sécurisation du processus chimiothérapie : quelles attentes vis-à-vis des nouvelles technologies[END_REF] are interested in a drugs delivery problem for a chemotherapy at home system. [START_REF] Chahed | Sécurisation du processus chimiothérapie : quelles attentes vis-à-vis des nouvelles technologies[END_REF] propose to use new technologies to facilitate the traceability management of products and care actors' activities in order to improve quality of care for chemotherapy at home. Some other researchers [START_REF] Has | Service évaluation medico-économique et santé publique[END_REF] [ [START_REF] Launois | Chimiothérapie ambulatoire : les économiques[END_REF] analyze the economic aspect of the chemotherapy at home and show that chemotherapy at home does not seem more expensive than its implementation in conventional hospitalization.

However practices of chemotherapy at home are not much in France, the same for research works surveying chemotherapy at home organization process. Our work helps to fill this gap.

Chemotherapy at home process modeling: a case study

In this work, we present a real world case study of the chemotherapy at home. The studied system is implemented by a HCS structure situated in the Rhône-Alpes province of France. The HCS structure exists for more than thirty years. Today, it treats about 250 patients per day, and its operation areas extend across the Rhône department and part of the Isere department. They began the chemotherapy at home activity in the early 90's. This activity represents nowadays 3% of the admissions of the studied HCS structure. Using process modelling method, we analyze the system in detail and divide the process into three phases: patient admission phase, home care preparation phase and cure realization phase. We present here only the realization phase of chemotherapy at home because that is the focus of the whole system. At this stage, we can see different internal and external actors really involved for delivering cares or services for patients.

The realization process of chemotherapy at home takes here three days; the most important activities are only concentrated in the first two days. The last day is used for the follow-up post chemotherapy that could be usually carried out by a coordinator nurse by phone, or occasionally by a private nurse with a home visit if necessary. A simplified model of the process is shown in Figure 1. We simulated the studied system based on this model for evaluating system performance. The simulation approach and results will be discussed in the next section.

Realization process for chemotherapy at home

The first day of the realization process is devoted to chemotherapy cure validation and drugs preparation. Above all, patient's blood sample has to be analyzed; blood sample could be taken by a private nurse or by a city biological laboratory, at patient's home or in the laboratory. And then, the city biological laboratory ensures blood analysis and transfers the results to the HCS structure, patient's family doctor and his hospital. The family doctor plays a role of evaluator by understanding the results and permitting or not chemotherapy cure realization. If results are normal, the family doctor visits the patient at his home and gives chemotherapy validation in signing a written order depending on patient's physical conditions. The validated written order is named "Clinical Green Light" (CGL) and will be transmitted to the HCS structure by fax. It is received by a coordinator nurse, especially allocated for the chemotherapy at home activity. The coordinator nurse shows the CGL to a coordinator doctor in order to obtain the final validation of the cure at home, named "Final Green Light" (FGL). The FGL is then transferred to the pharmacy of the HCS structure and verified by a pharmacist in terms of chemotherapy drugs dosage. The pharmacist gives his agreement for drugs preparation by fax with the signed FGL to a contracted hospital pharmacy which will prepare chemotherapy drugs corresponding to the defined dosage. Simultaneously, pharmacy of the HCS structure ensures other necessary medicines preparation. Ideally, chemotherapy drugs production should be finished in the first day in late afternoon; in cases of delays, this production must be performed in emergency on second day of the process in early morning.

During the second day of program, a staff delivery person of the HCS structure takes firstly the necessary medicines prepared by the HCS structure's pharmacy, then chemotherapy drugs at the hospital pharmacy, and transports them together to patient's home. Delivery verification will be done by an assigned private nurse after his arrival at patient's home. He hereafter prepares the patient for treatments and administrates chemotherapy drugs one by one. Treatment durations vary according to prescribed chemotherapy protocols. At the end of the cure, the private nurse stays with patient for a certain period of time for surveillance.

Coordination design presentation

By looking at the simplified model illustrated in Figure 1, we can observe that coordination is necessary all around the realization process. It is ensured by a coordinator doctor and a coordinator nurse belonging to the studied HCS structure.

The coordinator doctor plays here just a role of supervisor, by overseeing medical operations and chemotherapy protocols performance. However, the coordination tasks done by the coordinator nurse are very significant for the quality assurance of the realization process; she links the different actors (HCS structure, private nurse, city biological laboratory, family doctor, hospital pharmacy, …) delivering cares or services to a patient, and follows the accomplishment of their activity, accelerating them in case of forgetfulness, mistakes and delays. Obviously, the coordinator nurse is deemed as core of the chemotherapy at home system, especially for the first two days of the realization process, during which various activities must be achieved by different external actors, within a limited time. The coordinator nurse must ensure a good sequence for these activities, and an efficient communication.

In the studied system, the main activities are carried out by external actors, but coordination is principally guaranteed by a staff nurse of the HCS structure. We thus define this system as chemotherapy at home system managed by a coordination center.

Simulation approach and results

Discrete event simulation is a good method for resolving performance evaluation and coordination problems. Using this method, we evaluate in this section the studied system's performance, and propose improvement procedures.

The presented chemotherapy at home system, managed by a coordination center, seems flexible and simple to pilot, but the system performance depends hardly on the quality of coordination. Our aim here is to use simulation in order to evaluate firstly the quality of coordination for a real system; results are then compared to those of two proposed improved scenarios. We restrict our simulations to the first two days of the realization process, because they need more coordination tasks, due to the presence of concentrated activities.

Experiment configuration and validation

The proposed simulation model, built basing on the realization process of chemotherapy at home (Figure 1), includes not only the different necessary activities for chemotherapy at home realization, the coordination tasks being ensured by the coordinator nurse, but also the uncertainty on the accomplishment dates for some activities and decision-making fulfilled by external actors. Data used for building simulation model are collected and calculated from direct field observation and interviews with care actors.

The entities are created according to an analysis of a six months' patient treatment program. Seven chemotherapy protocols are considered. Two hospital pharmacies are available for chemotherapy drugs preparation, and one ensures more activities than the other. Patients' homes are dispersed in an area of 80 km around the studied HCS structure.

Performance indicators chosen for the evaluation of coordination quality are the coordination time spent by coordinator nurse and the effect of the coordination design on chemotherapy at home organization.

The experiment period is fixed to three months, and we use a 95% confidence interval to validate the relevance of the simulation model. Operation durations of key activities are seen as validation factors. Taking into account the stochastic aspects related to distribution laws, the simulation model is validated with 30 replications for offering reliable results.

Simulation scenarios description

The first simulation scenario concerns actual chemotherapy at home system of the studied HCS structure (Scenario 1), in which different external actors ensure only their activities for chemotherapy at home realization, but not the communication with the HCS structure. Coordination is initialized by coordinator nurse who follows activities' progress and accelerates external actors if necessary, usually by phone. The coordination design is supported by a manual way.

The first improvement solution we propose (Scenario 2) consists in modifying the coordination design by redefining actors' responsibility and eliminating superfluous tasks. On the one hand, all the external actors participate to coordination tasks; they should inform achievement of their activities to coordinator nurse on their own initiative. Instead of verifying, informing and accelerating external actors, the coordinator nurse ensures nothing more than information reception and transmission by phone or fax. On the other hand, we notice in the actual model (Figure 1), that some tasks are performed twice successively, by two competent actors: chemotherapy at home cure is validated by both, the family doctor and the coordinator doctor; chemotherapy drugs dosage is verified by the pharmacist of the HCS structure and then by hospital pharmacy. We propose here to keep only one actor for each task: family doctor for cure validation and hospital pharmacy for drugs dosage verification.

In the Scenario 3, we recommend the use of new information and communication technologies (NICT) to replace the manual way of coordination. Applications of NICT in the home care domain are already approved by the literature [START_REF] Koch | Home telehealth -Current state and future trends[END_REF][START_REF] Chahed | Sécurisation du processus chimiothérapie : quelles attentes vis-à-vis des nouvelles technologies[END_REF] [ [START_REF] Tan | Automating the drug scheduling of cancer chemotherapy via evolutionary computation[END_REF] [ [START_REF] Maglaveras | Home care delivery through the mobile telecommunications platforme : the Citizen Health System (CHS) perspective[END_REF]. In this second improvement procedure, we propose a framework of a traceability management of actors' activities, in which several actors are connected by cable network and others by mobile network. Patient records are stored in a central server and could be shared between different care actors. Information transfers become automatic, accompanied by an alert to recipient. The HCS structure, city biological laboratory and hospital pharmacy are connected to central server by cable network. A mini pocket pc should be assigned to each patient across mobile network. The validation tasks could be done by electronic signature that permits the automatic transfer of the necessary information to the predetermined recipient. For private nurse and family doctor, their electric signatures could be done using the pocket pc assigned to patient. Thus, patient has the responsibility to carry the pocket pc always with him for the treatments and to provide it to certain care actors (i.e. private nurse, family doctor) if necessary.

Results analysis

In this subsection, we present the results obtained thanks to the simulation of the three scenarios described above. A comparative analysis enables to determine the most optimal coordination design. The used comparison indicators are coordination time spent by coordinator nurse during experiment period for each patient, realization time used by each external actor for achieving his activity, and delay ratio depending on realization time constraint. For laboratory and family doctor, the realization time is composed by an operation time and a variable waiting time before activity execution.

Results of comparative analysis are showed in Table 1 and Table 2.

18 minutes

In Scenario 1, 60 patients are treated by the system during the experiment period; coordination nurse devotes 47 minutes per patient for coordination tasks. The realization time of each external actor is respectively 175 minutes for private nurse, 169 minutes for city biological laboratory, 253 minutes for family doctor and 22 minutes for hospital pharmacy. Ordinarily, family doctor should give the CGL before five o'clock p.m. in the first day of the process, but in 27% of cases, this deadline is not respected, which results in delay of chemotherapy drugs preparation in 28% of cases.

66 patients are received in Scenario 2. The coordination time per patient reduces to 32 minutes. There is no great difference compared with Scenario 1, in terms of realization time for most external actors, such as private nurse, city biological laboratory and hospital pharmacy. But family doctor uses less time: 187 minutes here against 253 minutes in Scenario 1, to fulfill his activity thanks to the first improvement solution. In the same way, the delay ratio drops from 27% to 5% for giving the CGL by family doctor, and from 28% to 5% for preparing chemotherapy drugs by hospital pharmacy. For scenario 3, the system treats 63 patients during the experiment period. The coordination time drops to 18 minutes per patient. Thanks to an automatic communication way, the waiting time for information transmission is almost eliminated; the quality of coordination is thus improved. This way, for two external actors mostly concerned by information transfers, namely the city biological laboratory and the family doctor, we can observe a reduction of time: 55 minutes only for the first, and 97 minutes for the second. The delays observed previously disappear here completely.

To sum up, the current system is not optimal, coordination time is long and delays are important. Responsibility redefinition, accompanied with redundant tasks elimination, causes quality improvement of coordination and delay reduction for certain external actors. As expected, the implementation of a traceability management system of actors' activities permits an optimal coordination and eliminates all the delays. Though decline of the delay ratio between Scenario 2 and Scenario 3 is not significant (5%), the latter could reduce potential incidents caused by human with automatic information transfers; the quality of coordination is more secure, especially against a growth of activity in the future. However, other aspects should be designed before the system implementation, such as economic aspect and technical aspect, which requires further studies.

Conclusion

In this work, we are interested in chemotherapy at home system implemented by a HCS structure in France. A staff nurse is assigned especially for coordination task to ensure a smooth organization. We model and simulate the system to evaluate its performance.

An optimal coordination design permits less coordination time, which is not actually the case for the studied system. We therefore propose to redefine its coordination design in the first place and secondly to implement a traceability management system of actors' activities. According to comparative analysis of results, the latter leads the most optimal coordination.

Fig. 1 .

 1 Fig. 1. Realization process of chemotherapy at home of the studied HCS structure

Table 1 .

 1 Comparative analysis in terms of coordination time between 3 scenarios

	Scenario	Number of patient	Coordination time per patient
	Scenario 1	60	47 minutes
	Scenario 2	66	32 minutes
	Scenario 3

Table 2 .

 2 Comparative analysis actors between 3 scenarios in terms of realization of external actors' activities

	Actors	Activities realization	Scenario 1	Scenario Scenario 2	Scenario 3
	Private	Realization time 175 minutes	136 minutes	135 minutes
	nurse	Delays (%)	0%	0%	0%
	Laborator	Realization time 169 minutes	165 minutes	55 minutes
	y	Delays (%)	0%	0%	0%
	Family	Realization time 253 minutes	187 minutes	97 minutes
	doctor	Delays (%)	27%	5%	0%
	Hospital	Realization time 22 minutes	21 minutes	21 minutes
	pharmacy	Delays (%)	28%	5%	0%


Elyes Lamine
email: elyes.lamine@mines-albi.fr

Sabrina Zefouni
email: sabrina.zefouni@univ-jfc.fr

Rémi Bastide
email: remi.bastide@univ-jfc.fr

Hervé Pingaud
email: herve.pingaud@mines-albi.fr

A System Architecture Supporting the Agile Coordination of Homecare Services

Keywords: Homecare ecosystem, collaborative process, workflow, information system, eHealth

Dependent people (especially the elderly) are nowadays surrounded by various health services, as well as by social and multi-technology monitoring devices. The PASPORD research project focuses on improving the management of processes involved in home care. These processes are known to be collaborative: their activities spread over different organizations, are carried out by diverse health or logistical actors. These activities must be coordinated by an integrated system, which is the purpose of the present project. However, the deployment of such a process takes place in a very dynamic and uncertain environment. In this paper, we seek to characterize and model this complex process. On the basis of its features, we define the principles of a system architecture to support the collaboration between the stakeholders and provide agility to overcome difficulties induced by its specificity.

Introduction

Given the increase of 'fragile' persons who require monitoring and care management on a long-term basis, and with the aim of reducing costs of health care, home care is becoming an increasingly common in healthcare delivery. However, in a current trend of increased numbers of elderly persons in need of home cares and shrinking resources within the healthcare sector, these home care providers are facing management challenges in terms of coordination and follow up of activities [START_REF] Fnehad | Livre blanc des systèmes d'information en hospitalisation à domicile[END_REF].

It is well-recognized that the establishment of an information system is a major asset to guaranty continuity of care and to enhance its quality. In addition, many reports have underlined that the use of information and communication technologies (ICT) in the performance and management of the health sector is essential for improving the global efficiency of healthcare systems. Therefore, the field of eHealth is currently the subject of intense research projects and commercial development, whose main objective is to design new products or new services to enrich the offer of ICT Health [START_REF] Boulos | Connectivity for Healthcare and Well-Being Management: Examples from Six European Projects[END_REF][START_REF] Lasbordes | La télésanté : un nouvel atout au service de notre bien-être[END_REF][START_REF] Gartner | eHealth for a Healthier Europe[END_REF][START_REF] Alexander | Information Technology Sophistication in Nursing Homes[END_REF].

However, as concluded in reports on the home healthcare field [START_REF] Fnehad | Livre blanc des systèmes d'information en hospitalisation à domicile[END_REF] [START_REF] Lasbordes | La télésanté : un nouvel atout au service de notre bien-être[END_REF][START_REF] Gartner | eHealth for a Healthier Europe[END_REF], today there is not, to our knowledge, a single "killer application" or work addressing the definition of a generic system architecture to support the home care process and allow easy integration of existing ICT health devices or services.

Within this context and in order to provide some answers to the definition of an open and interoperable technology architecture specific to the home care process, the research project PASPORD has been developed. This project, supported by the French region Midi-Pyrénées, aims to define a system architecture supporting the agile coordination of homecare services [START_REF] Bastide | The Homecare Digital Ecosystem: an Information System Support Architecture[END_REF]. It will be experimented across the French region Midi-Pyrénées.

The remaining of this paper is structured in three parts. The first one is focused on a description of the homecare process and its ecosystem. In the second part, the system architecture that we propose to improve is shown. We then present a case study on the use of the proposed system concerning the detection and monitoring of malnutrition in the elderly.

Analysis of Homecare process and its ecosystem 2.1 The organizational point of view

Home care includes all health services (medical and para-medical) and social services (cooking, cleaning) provided for the needs of the patient to allow him/her to continue living at home, regardless of age or disability. It is a complex process which represents the chain of various activities achieved by several stakeholder trades (nurse, physician, professional caregivers, etc.) which follow the patient's evolution and needs at home. Fig. 1 shows the ecosystem of the home care process which involves many stakeholders. As shown, multiple exchanges are required to mange and follow up this home care process.

The stakeholders in the homecare process differ widely in terms of their organizational structure, responsibility and technical ability [START_REF] Bastide | The Homecare Digital Ecosystem: an Information System Support Architecture[END_REF]:

-Most of the medical staff involved in homecare in France (doctors, nurses) is selfemployed (liberal profession): they have no hierarchic dependency on the organization that manages the overall homecare process. They usually possess their own information system support, albeit very limited (agenda management, paper or digital patient records, etc).

-Social service (house cleaning and meal delivery) is usually performed by staff that has little computer literacy and are often reluctant to use a computer-based system in their daily work.

-The elderly's relatives often live far away, paying him/her irregular visits. However, they wish to keep in touch with their parent via telephone and be kept informed on his/her health state.

-Finally, the homecare system is funded by regional public units, which are billed for the work of the various stakeholders. These units need to be reliably informed of the actual work that has been performed, time spent at home by the various stakeholders, etc. In France, homecare is mainly managed by regional associations that coordinate the activity of all the stakeholders involved (doctors, nurses, meal delivery, etc.).

Informational point of view

The coordination of these numerous stakeholders with heterogeneous profiles and trades is definitely required to assure the quality and the continuity of care. Two levels of coordination in the homecare are notable:

-Internal coordination between stakeholders from the same organization or the same service. They use a shared Information System (IS) and act towards the same goal.

-External coordination requires interacting and coordinating actors and heterogeneous IS belonging to the various organizations involved in the homecare.

Several upcoming services such as future electronic medical record will be required to interact with the systems already in place.

Currently, the activity of all these persons is mainly managed manually, using a socalled 'liaison logbook'. It is generally an unstructured, physical notebook that is kept at the elder's home and used for communication between the family and the various stakeholders, as well as between stakeholders. Each stakeholder notes the date of the visit, the operations performed, recommendations or any other form of comment. Fig. 1 illustrates the central role played by the liaison logbook in the various processes of care at home. The logbook is the primary means of communication between different stakeholders.

It is noteworthy that the liaison logbook contains very useful data, underused in the homecare process. Indeed, it stores information of different types: logistical information which helps for the coordination of activities, medical information, general information on the patient, any information which may help improve the quality of the homecare and possibly the detection of the patient's health change via an intelligent monitoring system [START_REF] Bastide | The Homecare Digital Ecosystem: an Information System Support Architecture[END_REF].

Proposed architecture system 3.1 Screening relevant elements to be considered

Currently, the home care ecosystem is still in a primary stage of maturity with a lack of adequate access to information and of communication between stakeholders which hampers the management and the follow up of the home care process.

This gap results in a weak use of the ICT tools and services and an insufficient interoperability between these services and the information system of the different stakeholders. The idea is thus not to develop new ICT products, but to think how to make them useful and integrate them in a consistent common scenario covering a wide range of home care situations. This is the main purpose of the research project PASPORD.

In order to target and facilitate the identification of components of our proposed architecture, two major findings are pointed out:

-Even though the patient in home care receives daily visits from the different stakeholders, it sometimes occurs that he/she spends a long time without being visited by any of them. This could be problematic especially in the case of elderly patients who are subject to falls, discomfort and many serious domestic incidents.

It is obvious that improving homecare implies a better detection and prevention of incidents or of the degradation of the elder's health.

-In order to assure the quality of care, relevant information such as alerts, personal to-do list, incident reports, organization requests, update of the patient's plan care or records, etc., should be accessible to the most appropriate actor of the home care circuit (doctor, nurse, emergency service, neighborhood, relatives, etc.) at the right time. This implies improved means of communication and coordination of stakeholders to overcome the limitations of the current liaison logbook.

Target architecture

In order to achieve the two objectives mentioned above, we propose as shown in Fig. 2, an architecture that includes a surveillance system in order to detect dangerous situations, combined with an agile system of automatic exchange of information to coordinate the different performances. The whole is supported by multi-modal communication systems, such as a combination between personal digital assistants (PDA) and tablet PCs). The architecture of choice that we propose here, is composed of: 1. PROSAFE which consists of a monitoring system for dependent people. It was designed in the laboratory LAAS in Toulouse, one of the project partners. This system has been tested on several sites such as a retirement home and an apartment. This system can warn of certain dangers in real time (fall, fugue, etc.), based on the learning habits of the person under surveillance [START_REF] Bonhomme | PROSAFE-extended, a telemedicine platform to contribute to medical diagnosis[END_REF]. 2. An intelligent system for detection of relevant events using Multi-Agent System (MAS), an organized set of agents [START_REF] Rammal | An Adaptive System for Home Monitoring Using a Multiagent Classification of Patterns[END_REF]. An agent is a software entity or person, which are assigned a certain task that they can perform independently and in cooperation with other agents. The purpose of this multi-agent system is to prevent, on the long-term, degradation of the patient's status. This is based on a classification made by agents regarding the parameters monitored. 3. The database saves patient information as it does at present, in addition to actimetry information provided by the monitoring equipment installed at home (e.g. stirring overnight, movement speed, etc.), and transmissions made between different stakeholders, in order to track them easily at any time.

The workflow engine [9][12] based on the WfMC (Workflow Management

Coalition Specification) is the automation process in which documents, information and tasks move from one part to another according to a set of rules. In other words, it consists of a system that allows the coordination of work among several participants following a predefined process model. Its role is to coordinate the work involved by transmitting the necessary information and tasks to different stakeholders, depending on the condition of the patient's treatment plan and the role of the interveners.

Case study

We propose a case study on the detection and monitoring of malnutrition for the elderly. According to a study conducted by the French HAS (Haute Autorité de Santé: High Health Authority) in 2007, cases of malnutrition were observed in 350 000 to 500 000 elderly people living at home and at least between 100 000 and 200 000 living in institutions [START_REF] David | Quality Indicators for the Care of Undernutrition in Vulnerable Elders[END_REF]. These numbers will tend to grow as the population continues to age. Malnutrition among the elderly has dramatic consequences -the risk of death is two to four times higher. Malnutrition exacerbates the health problems of the elderly person and increases the risk of falls, dehydration, mental disorders, and time required for healing. In addition, it generates enormous healthcare costs at the expense of the whole society. According to the causes of undernutrition, the treatment plan is different. This means that according to the patient's status the process should be adapted differently.

The best way to combat malnutrition is to detect it as early as possible. This can be done by early detection before it has negative consequences on the health of the elderly or even by avoiding people at risk. There are simple ways to detect malnutrition. Most studies and general practitioners [START_REF]HAS : Stratégie de prise en charge en cas de dénutrition protéino-énergétique chez la personne âgée[END_REF][START_REF] Pirlich | Nutrition in the elderly[END_REF][START_REF] David | Quality Indicators for the Care of Undernutrition in Vulnerable Elders[END_REF][START_REF] Guigoz | The Mini Nutrition Assessment (MNA) for grading the nutritional state of elderly patients: presentation of the MNA, history and validation[END_REF] agree on screening for malnutrition based on some criteria such as: the change in weight over time, body mass index, the MNA test result [START_REF] Guigoz | The Mini Nutrition Assessment (MNA) for grading the nutritional state of elderly patients: presentation of the MNA, history and validation[END_REF], etc.

In the table 1, largely inspired by that of the HAS [START_REF]HAS : Stratégie de prise en charge en cas de dénutrition protéino-énergétique chez la personne âgée[END_REF], We present the different thresholds for alerting undernutrition, distinguishing two types of malnutrition: undernutrition and the severe malnutrition that requiring medical intervention. The system proposed in the PASPORD project helps detecting malnutrition as soon as possible:

-Preventing measures can be taken by informing family members of a potential risk of malnutrition and by displaying the evolution of weight and body mass index to nurses visiting the patient.

-In other cases, through automatic monitoring of weight, an alert will be triggered whenever there would be an indication of malnutrition. This warning will trigger the appropriate process to the origins of the problem and how to treat it. The process of screening and treatment of malnutrition in the elderly is shown in Fig. 3. We chose to model this process with BPMN (Business Process Modeling Notation). Five pools represent five different categories of actors involved in the process: the monitoring system that triggers the alarm as the system environment. The nurses' aide, coordinator and family as business actors, and a central line (workflow) which represents the sequence of activities in the process of screening and treatment of malnutrition.

Conclusion

In this paper we present an overall architecture of a collaborative system to improve homecare. This architecture is a coupling system of monitoring and coordination system which helps to ensure the safety of the elderly and facilitates coordination among stakeholders. The innovative idea is the use of human actors as sensors that transmit information on the status of the elderly. This information feeds the progress of the workflow and helps prevent some deterioration in the elderly's condition. The next step in our work is the implementation of a flexible and adaptive inter-organizational workflow in which we will define connections between the business process of homecare and this workflow.

Fig. 1 .

 1 Fig. 1. Home care ecosystem

Fig. 2

 2 Fig. 2 Overall Architecture of the PASPORD project

Fig. 3 .

 3 Fig. 3. Overall process management of malnutritionIn summary, the process of screening and treatment of malnutrition follow these steps: 1. Detecting malnutrition: when the person is weighed, compare his current weight with his past weight record of the previous period. Trigger an alarm if malnutrition is suspected. 2. Preventing medical staff and family: alert stakeholders of the malnutrition problem. 3. Diagnose the cause of weight loss: diagnosis begins with a questionnaire that helps fill nursing and will be forwarded to the nurse coordinator. The family then decides to inform the treating physician. 4. After the intervention of a doctor and discussion between the family and the nurse coordinator, a new treatment plan will be implemented to allow the resumption of weight lost.

Table 1 .

 1 Characterization of under nutrition and malnutrition.

	Under nutrition	Severe malnutrition
	Weight loss of 5% in a month	Weight Loss: ≥ 10% in a month
	or 10% in 6 months	or ≥ 15% in 6 months
	Body Mass Index <21	BMI <18
	Albumin <35g / l	Albumin <30g / l
	Cholesterol <1.6 g / l
	17<MNA Global <23,5	MNA Global < 17

Acknowledgment

The work presented here, as well as the PhD. of Sabrina Zefouni, is mainly funded by French Region Midi-Pyrénées, with support from University J.F. Champollion and Castres Syndicate for Higher Education and Research.


Elisa Omodei
email: elisa.omodei@ens.fr

Jean-Philippe Cointet
email: jean-philippe.cointet@polytechnique.edu

Thierry Poibeau
email: thierry.poibeau@ens.fr

Mapping the Natural Language Processing Domain: Experiments using the ACL Anthology

Keywords: ACL Anthology, semantic network, social network

This paper investigates the evolution of the computational linguistics domain through a quantitative analysis of the ACL Anthology (containing around 12,000 papers published between 1985 and 2008). Our approach combines complex system methods with natural language processing techniques. We reconstruct the socio-semantic landscape of the domain by inferring a co-authorship and a semantic network from the analysis of the corpus. First, keywords are extracted using a hybrid approach mixing linguistic patterns with statistical information. Then, the semantic network is built using a co-occurrence analysis of these keywords within the corpus. Combining temporal and network analysis techniques, we are able to examine the main evolutions of the field and the more active subfields over time. Lastly we propose a model to explore the mutual influence of the social and the semantic network over time, leading to a socio-semantic co-evolutionary system.

Introduction

The statistical analysis of large scientific repositories of texts is a popular research theme since the 1960s with the first scientometric studies ("the science of analyzing and measuring science" as defined in Wikipedia). Initial research was mostly based on lists of authors and keywords provided with scientific papers. The growing availability of digital data has put new life into this research field, allowing analysts to build conceptual maps of different media, including scientific text archives. New methods are also being explored:

• Natural language engineering techniques are now available to analyse the text itself (and not only metadata),

• New methods from complex network analysis make it possible to better describe the relations between keywords, authors and their respective evolution over time.

We report here an experiment based on the ACL Anthology. How has the field evolved? What have been the more active research areas over time? Can we predict how it could evolve? These are some of the questions we would like to investigate.

We take the ACL Anthology as an example, but the method is of course reproducible for other domains as well. The availability of large archives containing documents spanning over large periods of time makes it possible to observe the dynamics of ideas over time. It is especially the case in the scientific field, where researchers produce a prolific literature: briefs, research reports, scientific papers, etc. For a growing number of domains, large scientific archives are now available over several decades. These scientific archives have already given birth to a large body of research. Collaboration networks have for example been automatically extracted so as to study the topology of the domain [START_REF] Girvan | Community structure in social and biological networks[END_REF] or its morphogenesis [START_REF] Guimera | Team Assembly Mechanisms Determine Collaboration Network Structure and Team Performance[END_REF]. Referencing has also been the subject of numerous studies on inter-citation [START_REF] Garfield | Citation Analysis as a Tool in Journal Evaluation[END_REF] and co-citation [START_REF] Henry | Co-citation in the scientific literature: A new measure of the relationship between two documents[END_REF]. Other variables can be taken into account like the nationality of the authors, the projects they are involved in or the research institutions they belong to, but it is the analysis of the textual content (mostly titles, abstracts and keywords provided with the papers) that have attracted the most part of the research in the area since the seminal work of Callon [START_REF] Michel Callon | Mapping the dynamics of science and technology[END_REF][START_REF] Michel Callon | Co-word analysis as a tool for describing the network of interaction between basic and technological research: The case of polymer chemistry[END_REF].

The ACL Anthology is a digital archive of conference and journal papers in natural language processing and computational linguistics. It has received recent attention thanks to the ACL Special Workshop "Rediscovering 50 Years of Discoveries" in 2012, that produced a few papers on the subject, among which a "history" of the field by Anderson and collaborators [START_REF] Anderson | Towards a computational history of the acl: 1980-2008[END_REF]. Since we would like to build on this work, we selected for our experiments the 12,000 articles published in the period 1985-2008 already used by Anderson and his colleagues.

Our study is complementary to the one of [START_REF] Anderson | Towards a computational history of the acl: 1980-2008[END_REF]. The similarities lie of course in the corpus but also in the method based on a joint study of the semantic network (keywords linked together in order to give a semantic description of the field) and the social network (authors publishing together). However The rest of the paper is structured as follows. We first propose a map of the domain based on the automatic extraction of relevant keywords. We then propose a technique to represent the evolution of the domain over time. Lastly, we propose some elements for the definition of a predictive model mixing the semantic and the social network under study.

Mapping the computational linguistics domain

We first need to define a "semantic map" of the computational linguistics domain, which means divide the domain into different homogeneous subfields and identify their most specific keywords. We do this thanks to an inductive method, extracting keywords (i.e. multiword expressions) from the papers and then clustering these keywords so as to obtain a semantic map of the domain.

Keyword extraction

The first step consisted in automatically extracting the terms that correspond to concepts and methods of the field from titles and abstracts of the papers. To achieve this we pre-process the text with POS-tagging, chunking, normalization and stemming. Noun phrases are automatically extracted and multi-terms that convey a certain semantic unit, i.e. those with the highest "unithood", are selected following the method defined by (Van Eck and Waltman, 2011).

To sort the list of candidate terms and select the most relevant ones we then combine together two statistical criteria: first we estimate the quality of each term using the c-value method [START_REF] Frantzi | Automatic recognition of multi-word terms:. the C-value/NC-value method[END_REF] and then we remove irrelevant multi-terms with low "termhood", i.e. terms that may occur very frequently but do not help characterize the content of the paper, such as "review of literature" or "past articles". All these techniques are implemented and available online through the Cortext platform (http://docs.cortext.net/lexical-extraction/).

Since the number of papers published every year increases, our dataset naturally contains more recent papers than older ones. In order to avoid excluding concepts that were popular a few decades ago but are not so much now (and would then be relatively infrequent in the dataset as a whole), we divided the papers set in three time slices, and extracted a list of 1000 terms from each subset (some more fine grained divides are of course possible). We then merged the three lists (since as expected the three lists have some terms in common) and eliminated all the terms that appeared in less than five papers, obtaining a list of about 1500 terms. We then showed the list to an expert of the field who validated it manually, eliminating all the terms that were too general (like "computational linguistics") or not relevant, and producing as a result a final list of 673 terms describing the concepts and methods of the field.

Mapping the domain

Starting from this list we then construct a semantic map of the field through a network-based approach. The nodes of the network are the extracted terms. Two terms are connected if they co-occur in the same title or abstract at least once. The links are weighted using Mutual Information, defined as the logarithm of the ratio between the number of joint occurrences of the two terms in the same document and a measure of the expected number of co-occurrences between them. In order to have the best readable clear-cut network, we then eliminate all the links whose weight is lower than a threshold defined so as to avoid the network to split into multiple connected components (consisting of more than three nodes).

The goal is to obtain a network consisting of several densely connected components of concepts co-occurring together because they belong to the same subfield of the discipline.

Through this analysis we expect to get a map in which the different subfields of natural language processing and computational linguistics naturally emerge. We thus apply an algorithm for community detection of graph: such algorithms are used to partition a network into groups of nodes which are densely connected among each other and loosely connected with the rest of the network (a technique also known as clustering). In this study we use Infomap [START_REF] Rosvall | Maps of random walks on complex networks reveal community structure[END_REF], which is found to be one of the best algorithm for the task (we also tried the algorithm from Louvain [START_REF] Vincent | Fast unfolding of communities in large networks[END_REF] that obtained slightly worse results to map the domain as a whole but can be interesting to analyze the evolution of the domain over time, see next section). The semantic network obtained is shown in Figure 1. Each circle surrounds a detected "community", each representing a thematic cluster, such as word sense disambiguation or POS tagging.

Evaluation

Different clustering techniques (Infomap and Louvain, cf. supra) and different settings have been tried and qualitatively evaluated by an expert of the field. Additionally, for each cluster we randomly selected 10 projected articles and the expert had to evaluate whether each article fitted well in the cluster. We then computed the precision of a cluster as the fraction of relevant articles. The average precision obtained is 0.84, which is judged acceptable for this kind of task.

Below are three examples of clusters automatically obtained with the method described:

Cluster 1: entity detection -coreference relation -Automatic Content Extraction -coreference resolution -coreference resolution system -coreference system

Cluster 2: Sentence Compression -text summarization system -term frequency -Document Understanding Conference -human judgments -sentence extraction -TIPSTER Text -topic identification -automatic text summarization -Automatic Summarization -multi-document summarizationextractive summaries -ranking algorithm -evaluation methods -text summarization -summarization method -summary generation -human evaluation -summarization evaluation -Text Summarization Challenge -document summarization -summarization system -summarization techniques -evaluation metrics -summarization task -Singular Value Decomposition -extractive summarization

Cluster 3: natural language understanding system -semantic lexicon -lexical knowledge base -Montague grammar -temporal expressions -lexical semantics -semantics

Mapping the evolution of the domain

We now want to describe the main evolutions of the domain of computational linguistics over time, which means representing the relative importance of the different subfields over time. We would like to know what subfield has attracted the most important part of the research effort, which subfields have merged or appeared during the period, etc.

Method for the analysis

The approach to perform this analysis can be divided into four different steps.

1. the corpus is divided over different periods of time;

2. all the papers related to a given period are put together and keywords are extracted as explained in the previous section;

3. clustering algorithms are applied over the set of keywords so as to obtain clusters of keywords representing the different subfields of the domain;

4. lastly, the different subfields identified for each period are mapped over time.

The clustering algorithms used are Infomap [START_REF] Rosvall | Maps of random walks on complex networks reveal community structure[END_REF] and Louvain (Blondel et al., 2008) as said in the previous section. The mapping of subfields over time is a challenging operation since all subfields evolve: keywords may disappear from a given cluster and new keywords may be added just because the techniques evolve. The issue is then to determine to what extent two clusters represent the same subfield or not.

Basically, two clusters are connected if they share enough common keywords. A threshold has to be defined so as to avoid connecting clusters sharing too few keywords over time. Note that this simple approach makes it possible to match one cluster c at a period of time t with one cluster c' at period t+1 but also to associate one cluster c with two clusters c' and c" at period t+1: this is typically the case when one subfield gives birth to two different subfields sharing themselves few keywords together (for example we observe that the cluster corresponding to message understanding gives birth to two subfields: named entity recognition and information extraction; these are considered as two different subfields since the automatic keyword analysis reveals that they contain few keywords in common). The reverse operation can be observed when two subfields give birth to a unique new subfield merging techniques from the two previous subfields (for example statistical parsing and dependency grammar merging to give birth to the field of statistical dependency parsing). Lastly, when no correspondence can be found, the subfield is supposed not to survive in itself.

An extensive description of the techniques used can be found in [START_REF] Chavalarias | Phylomemetic Patterns in Science Evolution?The Rise and Fall of Scientific Fields[END_REF], as well as all the implementation details. For our experiments, we used the Cortext platform which implements all the procedure and provides various choices for each step (the platform implements different techniques for keyword extraction, keyword clustering and cluster mapping over time). These alternative choices mean that various maps can be obtained for a same domain, providing different views over the evolution of the domain.

It must be noted that the different algorithms will provide different maps. These maps do not always show the same results, especially when looking at the details. There is no "good" or "bad" map but there are different maps, giving different views of the domain. Of course, the representation must be checked carefully and interpreted: for example if a cluster is not connected to any other cluster, it does not directly means that the subfield has disappeared. It may have largely evolve so that at period t+1 no cluster contains enough common keywords to be connected to the original cluster c. It may have merged with two different other subfields with few keywords in common overall, etc. The map should be considered as a way to kick-start the analysis, not as a definitive result per se.

Different maps should be produced to examine the "threshold effect". For example, two clusters may not be connected on one map but may be connected on another map generated with only a small variation in the parameter settings, which means that the change between the two observed periods of time is probably not as radical as one map may suggest. For example, it can be desirable to consider smaller or larger periods of time. The domain can also be divided into a smaller or larger number of subfields, depending on the granularity that one wants to observe in the end. Broad representations (maps considering fewer clusters and fewer periods of time) will highlight the main tendencies of one field while detailed descriptions will allow one to reconstruct the precise phylogeny of a domain.

Results

We provide here three maps showing the evolution of the computational domain from the late 1980s to nowadays. Figure 2 shows the major trends in the evolution of the domain. Each period consists in approximately 8-12 clusters showing the evolution of the main research subfields over time (but note that the number of clusters is the result of the parameter settings but one cannot directly define the number of clusters per time using the clustering techniques implemented for this study). Only clusters sharing a relatively large number of keywords are connected through grey tubes. We observe that the main field is now machine translation: this field has continuously increased since the late 1980s. We can also observe the development of the Question answering task since the late 1990s: this field has been especially popular at the time thanks to the QA evaluation tracks at TREC for example. Even if they have been reincarnated in a way or another,

Toward a predictive model mixing the semantic and the social network

The goal of our study is to understand how the social and the semantic structures of a research community are driving future research dynamics. Previous studies have mainly focused on one of these two dimensions, but we claim that both are fundamental in the evolution of scientific research communities. The goal of the following analysis is to try to quantify the contribution of each.

In order to answer such questions, we first need to characterize the "social features of the domain". We do this by introducing a second graph called "social network", in which the nodes are the researchers of the field, i.e. authors of the ACL Anthology papers. Two researchers are connected in this network if they co-authored at least one paper. We first begin by investigating how the social network evolves over time, i) depending only on its endogenous characteristics (which represent the social features), and then ii) depending on the semantic similarity between researchers. Then, using a similar approach, we study the evolution of the semantic network i) depending only on its endogenous characteristics and then ii) also depending on the social similarity between concepts. We first investigated to what extent the probability of a new collaboration (between two researchers who never coauthored a paper before) is affected by their social proximity, which is captured by the Jaccard index of common neighbors in the social (co-authorship) network:

J(i, j) = |Γ(i) ∩ Γ(j)| |Γ(i) ∪ Γ(j)| (1)
where Γ(i) denotes the set of neighbors of node i in the given network. The index takes values between zero and one, and is equal to zero when two authors have no collaborator in common, and one when they collaborated with the same set of researchers. The Jaccard index is a widely used statistics to measure the similarity of two sets, and for link prediction [START_REF] Lu | Link prediction in complex networks: A survey[END_REF].

To quantify the contribution of this variable to the evolution of the social network, we performed a logistic regression in which our input variable is the Jaccard index between two authors in the network (authors who are not directly connected for a given year), and the dependent variable is the presence or not of a link between them (i.e. whether they co-authored a paper or not the following year). The choice of logistic regression is based on the fact that the response variable is binary (a link is either created or not).

More precisely, the model takes the following form:

ln( p(x) 1 -p(x) ) = β 0 + β 1 x (2)
where x is the Jaccard index and p(x) the probability of link creation.

The obtained results indicate that the selected variable is significative (p-value < 2e -16) and the response is highly correlated with it. We find in fact that the coefficient is equal to β 1 = 10.85 ± 0.64, which means that for a tenthunit increase in the social proximity score, we expect to see the odds of the two authors becoming co-authors in the future increase of about three times. We also want to investigate whether these odds are correlated with the notion of semantic proximity, which we measure in the same way, i.e. as the Jaccard index between the set of concepts used by one author and the set of concepts used by the other one. Therefore we used logistic regression with multiple explanatory variables. The model in this case is defined in the following way:

ln( p(x 1 , x 2 ) 1 -p(x 1 , x 2 ) ) = β 0 + β 1 x 1 + β 2 x 2 (3)
where x 1 is the social proximity and x 2 the semantic one. We found a coefficient for this second variable equal to β 2 = 5.71 ± 0.53 (p-value < 2e -16). This means that, holding social proximity at a fixed value, for a tenth-unit increase in the semantic proximity score, we expect to see the odds of the two authors becoming co-authors in the future increase of almost twice. Moreover, we performed the likelihood ratio test to compare the first model (which considers only the social dimension) with the second one (which takes into account both the social and the semantic dimensions) and found a significant chi-square value of 67 (p-value = 2.78e -16), indicating that taking into account also the semantic dimension to study the social evolution of the system does improve significantely the prediction. In a completely symmetric way, we studied the probability of two concepts not connected before to become connected (i.e. found in the same paper), at first depending on their semantic proximity only, and then also of their social proximity. In this case the semantic proximity is given by the Jaccard index of common neighbors between two concepts in the previously defined semantic network, and the social proximity by the Jaccard index between the set of authors using one concept and the set of authors using the other one. Also in this case we found significative values for the coefficients (all p-values < 2e -16). The coefficient corresponding to the semantic proximity is 12.70±0.33, indicating that for a tenth-unit increase in the semantic proximity score, we expect to see the odds of the two concepts becoming connected in the future increase of about three and a half times. The coefficient for the social proximity in the second model is 9.99 ± 0.78, indicating an increase in the odds of more than two and half times. Also in this case the likelihood ratio test indicates a significant improvement in the prediction when considering also the second dimension: the chi-squared is 137 and the p-value < 1.17e -31. We can therefore conclude that also the semantic evolution of the system is driven by both networks.

The results of all the regression models are summarized in Table 1 and 2. We have shown that, as expected, two researchers are more likely to collaborate in the future if their previous work is in a related area or on the same topic. More precisely we showed that the social aspect has a stronger role, increasing the odds of a new link by three times, whereas the semantic similarity of about twice, which is a lower but still very significant value. Two con- cepts are more likely to become connected if they are already semantically similar, but also depending on the number of researchers who have already worked on both even if in separate publications. Taking these two symmetric results into account, we can suggest that the social and the semantic landscape of computational linguistics mutually influence each other over time so as to produce a kind of socio-semantic co-evolutionary system. Note that we also had recently obtained similar results for the domain of physics [START_REF] Omodei | A Symmetric Approach to Understand the Dynamics of Scientific Collaborations and Knowledge Production[END_REF].

Conclusions

In this paper, we have tried to analyse the evolution of the domain of computational linguistics between 1988 and 2012. We first extracted from the corpus a list of keywords characterizing the domain, thanks to a mixed approach combining linguistic and statistical information. We then built a "semantic map" of the domain using keyword co-occurrences and graph community detection methods to highlight the different "semantic communities" of the domain. The evaluation by domain experts has shown that we obtained a good representation of the different sub-domains of the field.

We have then explored how the domain evolves over time, through the creation of time-wise semantic maps that show the emergence and evolution of the different areas of research in the field. This analysis gives avenues for the exploration of the main trends in the history of computational linguistics (how new subfields have emerged, how some subfields have merged or nearly disappeared, etc.).

In the last section of the paper we added a social dimension to the semantic analysis of the domain. We showed that the emergence of new social links is strongly influenced by the proximity of the authors both in the social and in the semantic spaces. We observed a two way influence: the emergence of new semantic links is influenced by the proximity in the social network and conversely new social links are closely related to the proximity of the authors in the semantic space. We can thus observe the co-evolution of the social and of the semantic space.

Figure 1 :

 1 Figure 1: Semantic map obtained from the automatic analysis of the ACL Anthology.

Figure 2 :

 2 Figure 2: Observation of the evolution of the computational linguistics domain over time at the macro-scale.

Figure 3 :

 3 Figure 3: Observation of the evolution of the computational linguistics domain over time at the meso-scale.

Figure 4 :

 4 Figure 4: Observation of the evolution of the computational linguistics domain over time at the micro-scale

  Figure 3 and figure 4 are much more precise overviews of the domain. We can observe for example the fact that spoken dialogue merged with statistical machine translation to give birth to a new field of research combining the two approaches for task-oriented dialogue interfaces. Speech also merged with the discourse subfield at the end of the 1990s which shows a new interest in the management of dialogue structures, etc.

Table 1 :

 1 Social evolution logistic regression results.

	model	coefficient estimate std error p-value
	model I	β 0 β 1	-6.18425 0.04908 <2e-16 10.85418 0.63686 <2e-16
	model II	β 0 β 1	-6.32120 0.05272 <2e-16 10.45014 0.63149 <2e-16
		β 2	5.71085	0.53517 <2e-16
	Table 2: Semantic evolution logistic regression results.
	model	coefficient estimate std error p-value
	model I	β 0 β 1	-4.42195 0.02543 <2e-16 12.70563 0.33345 <2e-16
	model II	β 0 β 1	-4.45146 0.02562 <2e-16 11.82264 0.34216 <2e-16
		β 2	9.99627	0.78264 <2e-16