A Semantic Peer to Peer Network to Support e-Science

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support the management of scientific experiments in a peer-to- peer network. This architecture, named e-ScienceNet[12], is responsible for providing data and ...
2015 IEEE 11th International Conference on eScience

A Semantic Peer to Peer Network to Support e-Science Tadeu Classe1, Regina Braga2, Fernanda Campos2, José Maria N. David2 Federal University of Juiz de Fora Master Program in Computer Science Juiz de Fora, Brazil 1 [email protected] 2 regina.braga, fernanda.campos, jose.david{@ufjf.edu.br} In this context, the e-ScienceNet allows scientists that are geographically dispersed to be synchronously connected via nodes (peers). As a result, scientists can manage, share, analyze and discuss their works in a more efficient way and include new web service applications in order to create new experiments.

Abstract - In scientific scenario, there is an increasing collaboration among research groups, which leads to the growth in the use of information processing techniques, and the creation of new experiments as well as the sharing of their results. In this article, we present an architecture named e-ScienceNet, that creates an access point to distributed scientific applications, where scientists can work with heterogeneous data, form groups based on their interests, and create new experiments based on service compositions. Therefore, e-ScienceNet, a semantic peerto-peer network system, aims to provide support to scientist in the e-Science context, enabling the use and sharing of scientific resources.

Therefore, this work has as main objective to propose an architecture to support distributed processing of scientific experiments, as an implementation of so-called collaborative laboratories [30]. According to Olson et al. [30], collaborative laboratories are an important and emerging computing infrastructure which enable the geographic distribution of research projects. Unlike traditional laboratories, collaborative labs allow scientists to share information, exchange results and use tools in a distributed manner, creating new possibilities for scientific work. E-ScienceNet contributes somehow to these ideas, using semantic resources to enable automatic service composition in a collaborative environment, with the help of peer to peer technologies.

Keywords - e-Science; Semantic Web; Peer-to-Peer; Ontology; Service Composition.

I.

INTRODUCTION

The use of computational resources in scientific research is not trivial, because, in general, scientists from others areas than Computer Science have trouble dealing with low-level computer technology. Besides that, there is also the difficulty to find software artifacts that perform specific tasks that are necessary for the experiment being carried out [2].

Besides that, considering the importance of scientific service composition in order to execute a collaborative experiment, this paper emphasizes the specification of two eScienceNet modules, namely Composition Manager and Service Manager. These modules were specified in order to help scientists in workflow composition, preventing them from worry about technical issues. Thus, using the e-ScienceNet, peers can search for services and create workflows (service compositions) according to their demand. Furthermore, these compositions can be shared with others, saving time during the experiments, because they do not need to be developed from scratch. The paper is organized as follows. Section 2 presents the basic principles and related work. Section 3 presents the eScienceNet overview. Section 4 presents a proof of concept about the e-ScienceNet use and Section 5 concludes the paper.

Despite the growing interest observed in scientific research, workflow design remains a challenging, tedious, and often error-prone process, that slows down the adoption of scientific workflows [26]. Although, recently, catalogs of scientific services are common, the low-level interface representations used (e.g. WSDL for Web Services) usually only provides information suited to assess the technical details of different services connected within a workflow. There is absolutely no guarantee regarding the coherency of the process composed, nor its validity from an application point of view [26]. Considering technologies, such as Semantic Web [3] , web services and peer-to-peer networks (P2P)[11], this work aims to describe the design and development of a platform to support the management of scientific experiments in a peer-topeer network. This architecture, named e-ScienceNet[12], is responsible for providing data and services in a scientific collaboration network, which is defined as public to all users (scientists) connected to the network, and, when necessary, is defined as private to only those users previously registered. All information provided by a component can be shared with other components into e-ScienceNet.

978-1-4673-9325-6/15 $31.00 © 2015 IEEE DOI 10.1109/eScience.2015.25

II.

BASIC PRINCIPLES

Hendler [13] defines e-Science as concepts related to integration of computing in scientific research in various areas. The context of composition of scientific applications and the increasing interest in developing scientific services, raise the necessity of composition of smaller services into complex ones in order to carry out a scientific experiment. To help the services composition task, technologies such as scientific workflows and Semantic Web services should be considered [2]. Since services could be connected to create (from simple 536 503

services. Other interesting research based on semantic peer-topeer is the work presented in [24]. They highlight the use of these technologies for e-Government, allowing more dynamicity in society, economy and politics. The use of web services in order to execute applications in the bioinformatics domain was highlighted in [25]. These web services were applied to models to provide scientific results.

services) complex services, the discovery of new scientific services has been a very important process involving technologies such as Semantic Web, web services and distributed computation, among others [14]. Web services provide the loose coupling of implementation details, using simple calls to its operations. The Semantic Web services are similar to simple web service applications, but they use semantic mechanisms, such as ontological mappings, based on OWL-S [15], WSDL-S [16] or SAWSDL [17] [18] languages.

Some of the characteristics and ideas presented by these works contribute to the e-ScienceNet proposal, such as ontology mapping [7], the search for annotated services [8], and the use of semantic groups of peers [9]. However, our approach presents different aspects. We can share all types of files and documents among peers. Information about these resources (name, extension, path, size, date, type, services descriptions (WSDL or other description language) etc.) are obtained and mapped to existing ontologies on each node (peer). The creation of semantic groups is based on the researcher´s interest and is controlled by super nodes. Based on these semantic groups, the system performs searches considering the groups in which the peer is included, finding adequate resources (services or documents) and including them as semantically annotated web services (in the case of services). The search for web services also enables the latter composition of services, and the result of this composition is executed, mapped into ontologies and made available as reusable resources for further compositions.

A peer-to-peer network is a decentralized network architecture that uses distributed resources of multiple computers. A "peer" is a distributed computer that works to perform a common task, often sharing files on the Internet [20]. Peer-to-peer networks are by their nature a distributed system without any hierarchical organization or centralized control over the data, where a node on the network can act both as a client and as a server [11]. The design philosophy of a peer-topeer network system is similar to e-Science domain, which aims to provide its users with the flexibility in cooperation among them [21]. In most peer-to-peer networks, the distribution of requests to other nodes is randomly done. By transmitting messages randomly to other nodes, there is a decrease in the efficiency of communication and an accuracy loss. To solve this problem, it is recommended to perform the peer grouping. If we use research areas and interests of scientists connected to the network to create these groups, we can prevent information from being propagated through the network randomly, sending the requests only to nodes that have semantic similarity, considering research areas and user's interest.

Differently of other scientific workflow management systems such as Taverna [17], Kepler [28] or Vistrails [29], our approach is based on semantic composition of web services, using domain ontologies to help the user in the modelling of experiments. Thus, the scientist can use semantic resources for the development of workflow models, based on rules and on inference mechanism.

In this case, in a peer-to-peer network, we can use ontologies [3] related to one or more network nodes. With ontologies, it is possible to perform different types of tasks, such as routing between peers, searching for resources, importing data from files, inferring common interest groups, mapping between peers and composition of scientific services, among other tasks [7].

Considering a broader context, Belloum et al. [31] propose the Virtual Laboratory for e-Science (VL-e) to assist scientists in developing scientific applications, using a complex distributed computing infrastructure, allowing the sharing of scientific resources in multidisciplinary scientific fields. One of the tools used by the project (to share resources) uses FTP (Grid-FTP and SSH-FTP), while in our work is used a peer to peer network, in order to decentralize file sharing. Another difference of Belloum´s work is the absence of a multi-user environment.

Research proposals have been presented in some papers, considering the use of peer-to-peer systems together with the use of Semantic Web in scientific communities. One of the classic works that can be highlighted is the Bibster tool [7], a Semantic Web system that was designed based on P2P networks. It focuses on the sharing of bibliographic information based on ontology mapping. Bianchini et al. [8] provide an approach using peer-to-peer networks with Semantic Web services based on a dynamic model of peers that provide similar services. In this proposal, each peer has a local knowledge structure, which includes service publication units and content-based ontologies.

Zhang, Kuc and Lu [32] propose an approach called Confucius to the collaboratively develop of scientific workflows, extending the Taverna SWMS. The proposal involves the development of a cooperation protocol to assist scientists in the composition of scientific workflows. The infrastructure has a service-oriented approach coupled with a collaborative ontology. However, the approach has focused only on the composition activity, not dealing with the earlier and later stages of the scientific experimentation process. Similarly, the Co-Taverna approach is proposed by Zhang [33], as an extension of the Taverna project. An initial version was developed enabling that several scientists can share workflow composition workspace. However, the proposal does not address the remaining steps of a scientific experiment nor uses

The creation of a P2P approach for the discovery of web services, based on the use of Semantic Web technologies is also shown in [22] and [23]. In these works, they create web services composition based on three basic operations, defined as AND, OR and SEQUENCE. Di Modica et al. [9] propose an architecture combining the P2P paradigm with Semantic Web technologies, grouping peers that have semantically related

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semantic resources, whereas contributing to the collaborative experimentation process. We can highlight as the main contribution of e-ScienceNet the possibility of automatic service composition, based on semantic and syntactic information, in order to specify and execute a scientific experiment. Considering that scientists are not experts in Computer Science and may have difficulty in composing different services in order to specify an experiment, our proposal can provide an easy way to do it. III.

E-SCIENCENET ARCHITECTURE

As previously mentioned, e-ScienceNet is a peer-to-peer semantic network that supports science. E-scienceNet was developed based on requirements specified by a group of users from our partners research institutions (EMBRAPA and FioCruz) and also considering the literature and some gaps that was identified in collaboration between distributed research groups. The e-ScienceNet architecture [12] encompasses a group of modules (managers), each one with its specific features. This paper focuses on services composition related modules. The architecture and the main e-ScienceNet modules are detailed in "Fig. 1", and discussed below. •

Tool Manager: is responsible for the peer-to-peer network configuration, including its resources, search interfaces and the chat;

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Semantic Manager: is responsible for the semantic processing on e-ScienceNet. It processes semantic information related to peer’s data, services, semantic grouping and others (this information is processed during the connection with the peer and is done once). This module is also responsible for the PeerOntology ("Fig. 2") (considered the core of each peer, because it contains information such as the name of the peer, groups that are connected to the peer, all available resource (services or files)) and also for domain ontologies (PeerOntology is stored in its original format, i.e. OWL files);

Fig. 1. E-ScienceNet Architecture.

•

Resource Manager: is composed by the Data Manager and Service Manager; o

Data Manager: is responsible for providing information about files to the Search Manager. This manager is also responsible for the management of data files in e-ScienceNet;

o

Service Manager: similar to Data Manager, it provides information about available web services to the Search Manager.

Web services are included into e-ScienceNet using a graphical user interface where scientists, with the help of a software engineer, can select each operation of a web service and annotate its parameter in order to create an OWL-S file. The inclusion of a new service follows the steps: 1) process the WSDL file (we are planning to support REST services and other lightweight solutions) in order to obtain the web service information; 2) select a task to be annotated; 3) associate a service with a domain term of the domain ontology connected to the scientist´s peer; 4) annotate each parameter with terms of the domain ontology; 5) create and validate the OWL-S files; 6) store WDSL and OWL-S files into the repositories.

Fig. 2. A) PeerOntology Asserted. B) PeerOntology Infered.

There is also a functionality provided by the Service Manager that is important in order to compose an experiment.

It is the possibility to include a connector in the composition. A connector in this context is a web service that can be linked

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into the inputs or outputs of a service, converting the parameter types. The inclusion of a new connector is similar to the inclusion of a new service. Besides that, in order to improve the e-ScienceNet approach, we had to make some changes on the OWL-S native structure. As a result, we extended the OWL-S specification, creating the OWL-Se ("Fig. 3"). The extension is related to semantic service annotation about domain terms that a service must be linked. So, we created a Data Property named domainTerm ("Fig. 3(C)") to include this information on the OWL-S file. Another improvement done using OWL-Se was to fix a problem with OWL-S that captures and annotates only simple parameters directly on the WSDL file. In other words, there are many services with complex parameters (parameters that have a set of sub parameters) and the native OWL-S structure does not capture the sub parameters. To solve this, in OWL-Se, we created a new SubParameter ("Fig. 3(A)") class and some Object Properties, such as hasSubParameter ("Fig. 3(B)") and isSubParameterOf, in order to make possible the annotation of all services parameter information. •

•

using the main interface through the inclusion of information in a text field and the selection of which field will be searched (name, size, data, extension). The other, the service search, is more complex. This search is based on finding web services in e-ScienceNet. For this purpose, each search needs to formulate SPARQL queries considering the related ontologies. This process can be summarized as follows (omitting some details for space reasons): a scientist needs to create a new experiment, so he finds a first service that he needs, but to connect the first service to the second, the Search Manager must make queries to domain ontologies to obtain all similar terms, and then query OWL-S files to obtain service information based on similar terms. Then, the Semantic Manager needs to compare the outputs parameters of previous service with the inputs of the next service to verify if they are compatible. If not, a connector can be used. After these steps, a result can be recovered for the user; •

Interest Manager: each scientist has his/her specific area of interest and this manager configures the e-ScienceNet to recover information related to the user´s current interest. This concept is related to semantic grouping, which consists in grouping scientists from similar areas of interest in one group. Other important feature of semantic grouping is to decrease the traffic between peers that could slow down the network. In addition, the interest manager is responsible for controlling the domain ontologies. Domain ontologies are knowledge representation about areas of interest. This control is done using "Super Nodes". A super node is a peer in a semantic group that is responsible to store and share domain ontologies. For example, a scientist is connected to e-ScienceNet by a bioinformatics group, and needs to create an experiment or find out a resource. In order to connect to the network, he must be linked to a domain ontology already available in the super node, or, include a new domain ontology for his peer. All new domain ontology included by a peer will be uploaded to group’s super node which will verify its validity and version and will put it available to other nodes;

Composition Manager: this is the main module of the eScienceNet, which is responsible for the creation of new scientific experiments through service composition. The service composition is done in three main steps: i) the creation of an abstract workflow; ii) the search for compatible services, considering the tasks of the abstract workflow; and iii) the connections creation and workflow execution. The Composition Manager acts together with the Search Manager coordinating the search rules and the services connections. This module will be detailed in the following sections. IV.

THE USE OF E-SCIENCENET FOR SCIENTIFIC EXPERIMENT COMPOSITION

As stated before, the semantic service composition is an important contribution of our approach. The domain ontology use is crucial to this service composition, because it provides the knowledge about the services. Based on rules, restrictions, relations and terms provided by the ontology, it is possible to create an abstract workflow that represents a given experiment in a specific domain. In previous author’s work, [2], [26], experiments related to genetic sequencing/aligning subdomain were presented. In this context, [26] worked on the specification of an abstract workflow to create a bioinformatics experiment ("Fig. 4"). This abstract workflow provides information about the

Search Manager: the Search Manager is responsible for the resource search in e-ScienceNet. All searches are done together with the Semantic Manager. There are two search methods. The first is the file search, which can be done

Fig. 3. A) OWL-Se showing the new class SubParameter and all parameters of BlastP service. B) All sub parameters of a main parameter. C) The data property domainTerm annotating the BlastP service with a term of the domain ontology.

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behavior of the workflow, the connections between task and services execution sequence. E-ScienceNet provides mechanisms to develop experiments, through the creation of these abstract workflows, together with the search of web services related to ontological terms and the execution of experiments, in order to generate results. Fig. 4 Abstract workflow [26]

To create an abstract workflow the scientist must follow these steps:

Fig. 5. A) Task selection based on ontology restrictions. B) Part of Sanger platform workflows possible tasks restriction.

1. Based on a domain ontology, the scientist can select related terms to specify the workflow. "Fig. 5(A)" presents the first part of the abstract workflow creation where the scientist should, loading the SequenceAlignOntology 1 , select a sequence alignment platform such as Sanger for example. Then, the e-ScienceNet, based on restrictions, shows the properties (workflow_possible_tasks), which infer the possible tasks (vector_masking, sequence_grouping, viewing_and_editing, aligning, assembly and base_calling) in order to create the service composition. An example of a domain ontology restriction related to this step is shown in "Fig. 5(B)". 2. Considering the possible tasks provided by e-ScienceNet, the scientists can select them and activate the function “Insert in previous WF”, which provides the selected tasks, so the scientist can choose the services that are related to the tasks. This connection is done based on task order restrictions that exist in the domain ontology. In this specific example, the SequenceAligmentOntology, available in:

1

http://plscience.superdignus.com/e_ScienceNet/SequenceA ligningOntology.owl. For example, to create complete workflows to execute the Sanger platform it is necessary to load a valid task sequence. This sequence is provided based on the restrictions specified in the SequenceAlignOntology, which provides the correct task sequence, not allowing tasks sequence mismatches. "Fig. 6(A)" presents the last part of the abstract workflow creation responsible for the connections and one example of restrictions on the ontology. In the example ,presented in "Fig. 6(B)", a message that explains it to the scientist is shown. He intends to insert the aligning task in the composition. Before he can insert this task, it is necessary to insert the vector_masking or sequence_grouping task. Also in "Fig. 6(C)", it is presented an example of the domain ontology restrictions which is responsible to control the previous and next task to be included. 3. Finally, to connect a service to a task, it is necessary to select the operation that connects one to another, which can be a SPLIT, JOIN or SEQUENCE operations. Comparing the abstract workflow in "Fig. 4" with "Fig. 6(A)" it is possible to see that the Composition Manager is capable of specifing the same workflow presented in [26],

This ontology is related to the Sequence / Alignment domain, used in the Proof of Concept.

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considering the semantic support provided by e-ScienceNet for its specification and service composition. Thus, we consider this work an evolution of [26], providing a mechanism that effectively allows service connection and execution of an experiment. It is important to emphazise that

this specification and connection is done with a semantic support, which allows scientits to specify workflows in an easy way. Besides that, this service composition is stored on eScienceNet repositories and can be shared and reused by other scientists.

Fig. 6. A) Abstract Workflow Creation. B) Restriction Message. C) Task Aligning Restrictions

1.

Load Abstract Workflow: this step is related to the asbtract workflow previously created or shared on the eScienceNet. The scientist must load the model and then the Search Manager considers, for each task of the workflow, its connections restrictions and the semantic of the task, searching for services. The e-ScienceNet controls what task will be searched before, relying on the rules specified in the domain ontology;

workflow, it is necessary to find out services that perform the functionality required by the selected task and also that has inputs parameters that match with the output parameters of the previous task´s selected service (if the task has a previous related task on the abstract workflow). Before the search request be sent to other peers, the Semantic Manager searches in the domain ontology for variations and related terms of the selected task. Then, considering these ontology terms, the search is based on semantic and syntatic search on OWL-S files that are related to available services. With the services found, an algorithm ("Fig. 7") makes the semantic match between services. Then, if both services are semantically compatible, they are sent to syntatic match, verifying the data types of each parameter. If the services are completely compatible, they are returned as search result and available for composition. Finally, the scientist can choose the compatible services.

2.

Service Search: this is the most complex step of the service composition. For each task of the abstract

2.1. Connector Search: if the services are not completely compatible, it is necessary to provide connectors in

The next steps on service composition are detailed below. Based on the abstract workflow creation, there is a service search functionality, where users can load the abstract workflow to create an effective composition that specifies their experiment. Composition Manager and Search Manager, work togheter to find the best matching between the tasks of the abstract workflow. This service search process involves the following steps:

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order to make them compatible. In the service search screen, the scientist can search for connectors to services included in the composition. The default is the search for connector to outputs, but the scientist can select the option Input Connector in order to search for connectors to input parameters. There is also the option of searching only connectors previously associated with a service. For example, a service BlastP has, at a first moment, inputs such as email, title, database and sequence. However, the scientist knows only the code of the sequence to execute this. Therefore, it is necessary a connector that converts this code into a sequence. As connectors are web services too, it is possible to get services, which must be connected to a certain service in the workflow, and send these services to the Search Manager in order to search and perform the match between the services and the available connectors. These connectors are presented to the scientist, and he can select one of them and link it into the service. For example, one of the available connectors for the BlastP service is the fetchBatch, which converts a fasta code in a sequence. When this connector is connected with the BlastP service, it is suggested by the system in which parameter the connectors will be linked. The BlastP also needs a sequence input, so the connector will be linked with these parameters, because its output needs a sequence format. Therefore, with this connection, the BlastP input parameters will be changed. This provides new possibilities of connections for this service, changing its default parameters. "Fig. 8" shows this connector with BlastP service.

3.

Parameters Connection: when the service search process is finished, the next step links the output parameters of a previous service to the input parameters of a next service. The e-ScienceNet provides a graphical user interface ("Fig. 9(A)") that shows all parameters of a service. The scientist can then easily connect the parameter, since this module suggests what parameter must be linked, validating it semantically and syntactically again, suggesting connectors or asking the user to specify a manual value. In this example ("Fig. 9(A)") it is shown a workflow that specifies a bioinformatics experiment, considering the protein sequence code. The fetchBatch service is responsible for searching the protein fasta code, so the result of this task is passed to the BlastP service, which is responsible to process the sequence and align the results with other sequences. However, BlastP service does not return the sequence results. Then, it is necessary to specify a connector (that is a service too) to allow the connection between these services. The getRestulBlastP was used in this case, recovering all sequences aligned by the blast.

4.

Workflow Execution: when the services parameters are connected, the e-ScienceNet can run the service composition ("Fig. 9(B)"). So, beyond creating a new experiment in this platform, this system gives the opportunity to execute their experiments, simulating a collaborative laboratory, considering that the services are available at distinct peers and the e-ScienceNet will run, remotely, each service. Besides that, all results can be shared as resources and any composition can be reused in other experiments.

Thus, we consider that the use of e-ScienceNet can save time to scientists because it can offer reusable knowledge and resources according to their interest and research areas. V.

PROOF OF CONCEPT

This proof of concept was specified in order to demonstrate the use of e-ScienceNet in a Bioinformatics scenario. In this context, it is possible to detail a scenario where a biologist process biological samples in a genetic sequencer, using crossing mechanisms. This scientist needs to know if other scientists have information about his sequences. In order to obtain this data, he uses some sequence alignment algorithm to compare the samples. Considering the composition model proposed in Silva et al. [2], it is possible to do the alignment process ("Fig. 10").

Fig. 7. Part of semantic and syntactic service comparison algorithm (partial view).

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Fig. 8. Service connector search

(B)

(A) Fig. 9. A) Connect parameters Screen. B) Workflow execution screen

Relying that these services were previously semantically annotated by the e-ScienceNet Service Manager and shared with other peers, it is possible to perform the service discovery, using the Search Manager in order to create and execute the experiment. However, before service search, it is necessary that the scientist creates the abstract workflow model. This model is a conceptual model connected by logic operations (sequences, joins and splits), which is built using rules and restrictions provided by domain ontologies. The eScienceNet provides a graphical interface to generate this model ("Fig. 11").

This model ("Fig. 10") presents the following tasks: 1) recover the sequences, using a biological sequence database; 2) format the sequences recovered; 3) process the alignment algorithm and; 4) present the results. For executing these tasks, it is necessary services, such as: FetchBatch, to recover sequences; Read and Format Sequences, to format all sequences and; Clustal Omega, to align multiple sequences.

Considering this abstract model, the Search Manager together with the Composition Manager, using tasks sequence, performs the search for services. Each task has a correspondent operation associated to it. The search process consists in selecting a task in the model and using this task, the Semantic Manager infers new related terms. With these new terms, the Search Manager search for related services in other peers, processing semantic and syntactic matches. In "Fig. 13", it is possible to see the e-ScienceNet search interface related to this composition.

Fig. 10. Sequences aligning proposed by [2].

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Fig. 11. Workflow abstract model to genetic aligning

Fig. 12. Result of sequences aligning composition(partial view)

Fig. 13 Service search to sequences alignment composition

After the services discovery step, the scientist needs to connect the web services using its parameters (inputs and outputs). E-ScienceNet provides an interface which suggests, based on each service, parameters connections.

With all connected services, e-ScienceNet transforms the abstract workflow into a functional workflow. This functional workflow allows the execution of the experiment ("Fig. 12"). Besides that, the scientists can share this

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[9]

workflow with other peers in the semantic group, and thus, this service composition can be reused by other scientists. The experiment execution results can also be shared with other scientists. With this proof of concept it was possible to obtain evidences of the viability of the use of e-ScienceNet to create and execute scientific services compostions, allowing the sharing of resources and the reuse of experiments.

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VI.

CONCLUSION AND FUTURE REMARKS

The development of scientific experiments is a complex process where different methods and services can be used together for their construction. With e-ScienceNet, services compositions can be created and shared, helping scientists and enabling reusability and automation of the experiment process. This composition is possible by the use of two managers: Composition Manager and Service Manager, which are aimed at facilitating the automation of service composition for the specification and execution of distributed scientific experiments. Besides the Composition and Service Managers, some other modules related to the semantic functionalities of e-ScienceNet were also detailed, emphasizing the Semantic Manager and Interest Manager, which have functionalities that enable semantic services discovery and creation of semantic groups in the network, facilitating the connection between scientists according to their research interests. In addition, a proof of concept was described in order to verify the viability of the proposed solution. However, eScienceNet is still a prototype and some points still need to be improved and provided. It is necessary to improve its validation. In this context, the development of real validated scenarios is needed and new scientific ontological domains must be established. Currently, we are using e-ScieneNet in a real scenario, an educational one, that connects some peers to develop a collaborative research. This new scenario is part of a huge project that involves a scientific ecosystem (http://pgcc.github.io/plscience/)

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