Open Idea: an intelligent platform for managing innovative ideas

La finalidad del proyecto OPEN IDEA es el desarrollo de una herramienta que permita gestionar de manera eficiente las ideas innovadoras dentro de una organización, mediante el uso de tecnologías semánticas y del procesamiento del lenguaje natural. El objetivo central del sistema es fomentar el concepto de innovación abierta facilitando, durante todo el proceso de gestión de ideas, la interacción entre usuarios de la organización con las ideas innovadoras aportadas. Este proyecto está siendo desarrollado conjuntamente por la empresa QualityObjects y el grupo TECNOMOD de la Universidad de Murcia y ha sido financiado por el Ministerio de Industria, Energía y Turismo a través de la convocatoria de Avanza Competitividad I+D de 2012.The main goal of the OPEN IDEA Project is the development of a platform which efficiently manages the innovative ideas within an organization by using semantic technologies and natural language processing. The main challenge of this system is to promote the concept of Open Innovation in the enterprise by making easier the interaction between the organization users and the innovative ideas proposed during the whole management process. This project is being jointly developed by the Quality Objects Enterprise and the TECNOMOD research group from the University of Murcia, and it is funded by the Ministry of Industry, Energy and Tourism (Research and Development programme Avanza Competitividad 2012).Este trabajo ha sido financiado por el Ministerio de Industria, Energía y Turismo a través del proyecto OPEN IDEA (TSI-020603-2012-219)

Natural Language Interfaces for Querying and Retrieving Information from Ontology-based Knowledge Bases

Tesis doctoral titulada “Interfaces de lenguaje natural para la consulta y recuperación de información de bases de conocimiento basadas en ontologías ", defendida por Mario Andrés Paredes Valverde en la Universidad de Murcia y elaborada bajo la dirección de los doctores Rafael Valencia García (Universidad de Murcia) y Miguel Ángel Rodríguez García (King Abdullah University of Science & Technology). La defensa tuvo lugar el 23 de mayo de 2017 ante el tribunal formado por los doctores Juan Miguel Gómez Berbís (Presidente, Universidad Carlos III de Madrid), Francisco García Sánchez (Secretario, Universidad de Murcia) y la doctora Catalina Martínez Costa (Vocal, Medical University of Graz) y la tesis obtuvo la mención Cum Laude y Doctor Internacional.Ph.D. thesis entitled “Natural language interfaces for querying and retrieving information from ontology-based knowledge bases” written by Mario Andrés Paredes Valverde at the University of Murcia under the supervision of the Ph.D. Rafael Valencia García (University of Murcia) and Ph.D. Miguel Ángel Rodríguez García (King Abdullah University of Science & Technology). The viva voice was held on the 23rd May 2017 and the members of the commission were the Ph.D. Juan Miguel Gómez Berbís (President, University Carlos III of Madrid), Ph.D. Francisco García Sánchez (Secretary, University of Murcia) and Ph.D. Catalina Martínez Costa (Vocal, University of Graz) and the thesis obtained the mention Cum Laude and International Doctor

Ontology Based Semantic Web Information Retrieval Enhancing Search Significance

The web contain huge amount of structured as well as unstructured data/information. This varying nature of data may yield a retrieval response that is expected to contain relevant response that is expected to contain relevant as well as irrelevant data while directing search. In order to filter out irrelevance in the search result, numerous methodologies have been used to extract more and more relevant search responses in retrieval. This work has adopted semantic search dealing directly with the knowledge base. The approach incorporates Query pattern evolution and semantic keyword matching with final detail to enhance significance of relevant data retrieval. The proposed method is implemented in open source computing tool environment and the result obtained thereof are compared with that of earlier used methodologies

An Anomaly Detection Algorithm of Cloud Platform Based on Self-Organizing Maps

Virtual machines (VM) on a Cloud platform can be influenced by a variety of factors which can lead to decreased performance and downtime, affecting the reliability of the Cloud platform. Traditional anomaly detection algorithms and strategies for Cloud platforms have some flaws in their accuracy of detection, detection speed, and adaptability. In this paper, a dynamic and adaptive anomaly detection algorithm based on Self-Organizing Maps (SOM) for virtual machines is proposed. A unified modeling method based on SOM to detect the machine performance within the detection region is presented, which avoids the cost of modeling a single virtual machine and enhances the detection speed and reliability of large-scale virtual machines in Cloud platform. The important parameters that affect the modeling speed are optimized in the SOM process to significantly improve the accuracy of the SOM modeling and therefore the anomaly detection accuracy of the virtual machine

Research on Methods for Discovering and Selecting Cloud Infrastructure Services Based on Feature Modeling

Nowadays more and more cloud infrastructure service providers are providing large numbers of service instances which are a combination of diversified resources, such as computing, storage, and network. However, for cloud infrastructure services, the lack of a description standard and the inadequate research of systematic discovery and selection methods have exposed difficulties in discovering and choosing services for users. First, considering the highly configurable properties of a cloud infrastructure service, the feature model method is used to describe such a service. Second, based on the description of the cloud infrastructure service, a systematic discovery and selection method for cloud infrastructure services are proposed. The automatic analysis techniques of the feature model are introduced to verify the model’s validity and to perform the matching of the service and demand models. Finally, we determine the critical decision metrics and their corresponding measurement methods for cloud infrastructure services, where the subjective and objective weighting results are combined to determine the weights of the decision metrics. The best matching instances from various providers are then ranked by their comprehensive evaluations. Experimental results show that the proposed methods can effectively improve the accuracy and efficiency of cloud infrastructure service discovery and selection

An agility-oriented and fuzziness-embedded semantic model for collaborative cloud service search, retrieval and recommendation

Cloud computing enables a revolutionary paradigm of consuming ICT services. However, due to the inadequately described service information, users often feel confused while trying to find the optimal services. Although some approaches are proposed to deal with cloud service semantic modelling and recommendation issues, they would only work for certain restricted scenarios in dealing with basic service specifications. Indeed, the missing extent is that most cloud services are "agile" whilst there are many vague service terms and descriptions. This paper proposes an agility-oriented and fuzziness-embedded ontology model, which adopts agility-centric design along with OWL2 (Web Ontology Language) fuzzy extensions. The captured cloud service specifications are maintained in an open and collaborative manner, as the fuzziness in the model accepts rating updates from users on the fly. The model enables comprehensive service specification by capturing cloud concept details and their interactions, even across multiple service categories and abstraction levels. Utilizing the model as a knowledge base, a service recommendation system prototype is developed. Case studies demonstrate that the approach can outperform existing practices by achieving effective service search, retrieval and recommendation outcomes

Leveraging cloud computing for the semantic web: review and trends

Semantic and cloud computing technologies have become vital elements for developing and deploying solutions across diverse fields in computing. While they are independent of each other, they can be integrated in diverse ways for developing solutions and this has been significantly explored in recent times. With the migration of web-based data and applications to cloud platforms and the evolution of the web itself from a social, web 2.0 to a semantic, web 3.0 comes as the convergence of both technologies. While several concepts and implementations have been provided regarding interactions between the two technologies from existing research, without an explicit classification of the modes of interaction, it can be quite challenging to articulate the interaction modes; hence, building upon them can be a very daunting task. Hence, this research identifies and describes the modes of interaction between them. Furthermore, a “cloud-driven” interaction mode which focuses on fully maximising cloud computing characteristics and benefits for driving the semantic web is described, providing an approach for evolving the semantic web and delivering automated semantic annotation on a large scale to web applications