A Framework for the Organization and Discovery of Information Resources in a WWW Environment Using Association, Classification and Deduction

The Semantic Web is envisioned as a next-generation WWW environment in which information is given well-defined meaning. Although the standards for the Semantic Web are being established, it is as yet unclear how the Semantic Web will allow information resources to be effectively organized and discovered in an automated fashion. This dissertation research explores the organization and discovery of resources for the Semantic Web. It assumes that resources on the Semantic Web will be retrieved based on metadata and ontologies that will provide an effective basis for automated deduction. An integrated deduction system based on the Resource Description Framework (RDF), the DARPA Agent Markup Language (DAML) and description logic (DL) was built. A case study was conducted to study the system effectiveness in retrieving resources in a large Web resource collection. The results showed that deduction has an overall positive impact on the retrieval of the collection over the defined queries. The greatest positive impact occurred when precision was perfect with no decrease in recall. The sensitivity analysis was conducted over properties of resources, subject categories, query expressions and relevance judgment in observing their relationships with the retrieval performance. The results highlight both the potentials and various issues in applying deduction over metadata and ontologies. Further investigation will be required for additional improvement. The factors that can contribute to degraded performance were identified and addressed. Some guidelines were developed based on the lessons learned from the case study for the development of Semantic Web data and systems

Investigations into Elasticity in Cloud Computing

The pay-as-you-go model supported by existing cloud infrastructure providers is appealing to most application service providers to deliver their applications in the cloud. Within this context, elasticity of applications has become one of the most important features in cloud computing. This elasticity enables real-time acquisition/release of compute resources to meet application performance demands. In this thesis we investigate the problem of delivering cost-effective elasticity services for cloud applications. Traditionally, the application level elasticity addresses the question of how to scale applications up and down to meet their performance requirements, but does not adequately address issues relating to minimising the costs of using the service. With this current limitation in mind, we propose a scaling approach that makes use of cost-aware criteria to detect the bottlenecks within multi-tier cloud applications, and scale these applications only at bottleneck tiers to reduce the costs incurred by consuming cloud infrastructure resources. Our approach is generic for a wide class of multi-tier applications, and we demonstrate its effectiveness by studying the behaviour of an example electronic commerce site application. Furthermore, we consider the characteristics of the algorithm for implementing the business logic of cloud applications, and investigate the elasticity at the algorithm level: when dealing with large-scale data under resource and time constraints, the algorithm's output should be elastic with respect to the resource consumed. We propose a novel framework to guide the development of elastic algorithms that adapt to the available budget while guaranteeing the quality of output result, e.g. prediction accuracy for classification tasks, improves monotonically with the used budget.Comment: 211 pages, 27 tables, 75 figure

Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence

Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an educational context such information may be deemed insufficient, it would be beneficial for both the student and the instructor to receive additional feedback about the overall development of the task. This work aims to tackle this limitation by considering the further exploitation of the information gathered by the OJ and automatically inferring feedback for both the student and the instructor. More precisely, we consider the use of learning-based schemes—particularly, Multi-Instance Learning and classical Machine Learning formulations—to model student behaviour. Besides, Explainable Artificial Intelligence is contemplated to provide human-understandable feedback. The proposal has been evaluated considering a case of study comprising 2,500 submissions from roughly 90 different students from a programming-related course in a Computer Science degree. The results obtained validate the proposal: the model is capable of significantly predicting the user outcome (either passing or failing the assignment) solely based on the behavioural pattern inferred by the submissions provided to the OJ. Moreover, the proposal is able to identify prone-to-fail student groups and profiles as well as other relevant information, which eventually serves as feedback to both the student and the instructor.This work has been partially funded by the “Programa Redes-I3CE de investigacion en docencia universitaria del Instituto de Ciencias de la Educacion (REDES-I3CE-2020-5069)” of the University of Alicante. The third author is supported by grant APOSTD/2020/256 from “Programa I+D+I de la Generalitat Valenciana”

Intents-based Service Discovery and Integration

With the proliferation of Web services, when developing a new application, it makes sense to seek and leverage existing Web services rather than implementing the corresponding components from scratch. Therefore, significant research efforts have been devoted to the techniques for service discovery and integration. However, most of the existing techniques are based on the ternary participant classification of the Web service architecture which only takes into consideration the involvement of service providers, service brokers, and application developers. The activities of application end users are usually ignored. This thesis presents an Intents-based service discovery and integration approach at the conceptual level inspired by two industrial protocols: Android Intents and Web Intents. The proposed approach is characterized by allowing application end users to participate in the process of service seeking. Instead of directly binding with remote services, application developers can set an intent which semantically represents their service goal. An Intents user agent can resolve the intent and generate a list of candidate services. Then application end users can choose a service as the ultimate working service. This thesis classifies intents into explicit intents, authoritative intents, and naïve intents, and examines in depth the issue of naïve intent resolution analytically and empirically. Based on the empirical analysis, an adaptive intent resolution approach is devised. This thesis also presents a design for the Intents user agent and demonstrates its proof-of-concept prototype. Finally, Intents and the Intents user agent are applied to integrate Web applications and native applications on mobile devices

Framework for data quality in knowledge discovery tasks

Actualmente la explosión de datos es tendencia en el universo digital debido a los avances en las tecnologías de la información. En este sentido, el descubrimiento de conocimiento y la minería de datos han ganado mayor importancia debido a la gran cantidad de datos disponibles. Para un exitoso proceso de descubrimiento de conocimiento, es necesario preparar los datos. Expertos afirman que la fase de preprocesamiento de datos toma entre un 50% a 70% del tiempo de un proceso de descubrimiento de conocimiento. Herramientas software basadas en populares metodologías para el descubrimiento de conocimiento ofrecen algoritmos para el preprocesamiento de los datos. Según el cuadrante mágico de Gartner de 2018 para ciencia de datos y plataformas de aprendizaje automático, KNIME, RapidMiner, SAS, Alteryx, y H20.ai son las mejores herramientas para el desucrimiento del conocimiento. Estas herramientas proporcionan diversas técnicas que facilitan la evaluación del conjunto de datos, sin embargo carecen de un proceso orientado al usuario que permita abordar los problemas en la calidad de datos. Adem´as, la selección de las técnicas adecuadas para la limpieza de datos es un problema para usuarios inexpertos, ya que estos no tienen claro cuales son los métodos más confiables. De esta forma, la presente tesis doctoral se enfoca en abordar los problemas antes mencionados mediante: (i) Un marco conceptual que ofrezca un proceso guiado para abordar los problemas de calidad en los datos en tareas de descubrimiento de conocimiento, (ii) un sistema de razonamiento basado en casos que recomiende los algoritmos adecuados para la limpieza de datos y (iii) una ontología que representa el conocimiento de los problemas de calidad en los datos y los algoritmos de limpieza de datos. Adicionalmente, esta ontología contribuye en la representacion formal de los casos y en la fase de adaptación, del sistema de razonamiento basado en casos.The creation and consumption of data continue to grow by leaps and bounds. Due to advances in Information and Communication Technologies (ICT), today the data explosion in the digital universe is a new trend. The Knowledge Discovery in Databases (KDD) gain importance due the abundance of data. For a successful process of knowledge discovery is necessary to make a data treatment. The experts affirm that preprocessing phase take the 50% to 70% of the total time of knowledge discovery process. Software tools based on Knowledge Discovery Methodologies offers algorithms for data preprocessing. According to Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms, KNIME, RapidMiner, SAS, Alteryx and H20.ai are the leader tools for knowledge discovery. These software tools provide different techniques and they facilitate the evaluation of data analysis, however, these software tools lack any kind of guidance as to which techniques can or should be used in which contexts. Consequently, the use of suitable data cleaning techniques is a headache for inexpert users. They have no idea which methods can be confidently used and often resort to trial and error. This thesis presents three contributions to address the mentioned problems: (i) A conceptual framework to provide the user a guidance to address data quality issues in knowledge discovery tasks, (ii) a Case-based reasoning system to recommend the suitable algorithms for data cleaning, and (iii) an Ontology that represent the knowledge in data quality issues and data cleaning methods. Also, this ontology supports the case-based reasoning system for case representation and reuse phase.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Fernando Fernández Rebollo.- Secretario: Gustavo Adolfo Ramírez.- Vocal: Juan Pedro Caraça-Valente Hernánde

Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Peer reviewe

Linked Data Supported Information Retrieval

Um Inhalte im World Wide Web ausfindig zu machen, sind Suchmaschienen nicht mehr wegzudenken. Semantic Web und Linked Data Technologien ermöglichen ein detaillierteres und eindeutiges Strukturieren der Inhalte und erlauben vollkommen neue Herangehensweisen an die Lösung von Information Retrieval Problemen. Diese Arbeit befasst sich mit den Möglichkeiten, wie Information Retrieval Anwendungen von der Einbeziehung von Linked Data profitieren können. Neue Methoden der computer-gestützten semantischen Textanalyse, semantischen Suche, Informationspriorisierung und -visualisierung werden vorgestellt und umfassend evaluiert. Dabei werden Linked Data Ressourcen und ihre Beziehungen in die Verfahren integriert, um eine Steigerung der Effektivität der Verfahren bzw. ihrer Benutzerfreundlichkeit zu erzielen. Zunächst wird eine Einführung in die Grundlagen des Information Retrieval und Linked Data gegeben. Anschließend werden neue manuelle und automatisierte Verfahren zum semantischen Annotieren von Dokumenten durch deren Verknüpfung mit Linked Data Ressourcen vorgestellt (Entity Linking). Eine umfassende Evaluation der Verfahren wird durchgeführt und das zu Grunde liegende Evaluationssystem umfangreich verbessert. Aufbauend auf den Annotationsverfahren werden zwei neue Retrievalmodelle zur semantischen Suche vorgestellt und evaluiert. Die Verfahren basieren auf dem generalisierten Vektorraummodell und beziehen die semantische Ähnlichkeit anhand von taxonomie-basierten Beziehungen der Linked Data Ressourcen in Dokumenten und Suchanfragen in die Berechnung der Suchergebnisrangfolge ein. Mit dem Ziel die Berechnung von semantischer Ähnlichkeit weiter zu verfeinern, wird ein Verfahren zur Priorisierung von Linked Data Ressourcen vorgestellt und evaluiert. Darauf aufbauend werden Visualisierungstechniken aufgezeigt mit dem Ziel, die Explorierbarkeit und Navigierbarkeit innerhalb eines semantisch annotierten Dokumentenkorpus zu verbessern. Hierfür werden zwei Anwendungen präsentiert. Zum einen eine Linked Data basierte explorative Erweiterung als Ergänzung zu einer traditionellen schlüsselwort-basierten Suchmaschine, zum anderen ein Linked Data basiertes Empfehlungssystem