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Graph-based databases are concerned with performance and flexibility. Most of the existing approaches used to design secure NoSQL databases are limited to the final implementation stage, and do not involve the design of security and access control issues at higher abstraction levels. Ensuring security and access control for Graph-based databases is difficult, as each approach differs significantly depending on the technology employed. In this paper, we propose the first technology-ascetic framework with which to design secure Graph-based databases. Our proposal raises the abstraction level by using ontologies to simultaneously model database and security requirements together. This is supported by the TITAN framework, which facilitates the way in which both aspects are dealt with. The great advantages of our approach are, therefore, that it: allows database designers to focus on the simultaneous protection of security and data while ignoring the implementation details; facilitates the secure design and rapid migration of security rules by deriving specific security measures for each underlying technology, and enables database designers to employ ontology reasoning in order to verify whether the security rules are consistent. We show the applicability of our proposal by applying it to a case study based on a hospital data access control.

A key challenge in current Business Analytics (BA) is the selection of suitable indicators for business objectives. This requires the exploration of business data through data-driven approaches, while modelling business strategies together with domain experts in order to represent domain knowledge. In particular, Key Performance Indicators (KPIs) allow human experts to properly model ambiguous enterprise goals by means of quantitative variables with numeric ranges and clear thresholds. Besides business-related domains, the usefulness of KPIs has been shown in multiple domains, such as: Education, Healthcare and Agriculture. However, finding accurate KPIs for a given strategic goal still remains a complex task, specially due to the discrepancy between domain as-sumptions and data facts. In this regard, the semantic web emerges as a powerful technology for knowledge representation and data modeling through explicit representation formats and standards such as RDF(S) and OWL. By using this technology, the semantic annotation of indicators of business objectives would enrich the strategic model obtained. With this motivation, an ontology-driven approach is proposed to formally concep-tualize essential elements of indicators, covering: performance, results, measures, goals and relationships of a given business strategy. In this way, all the data involved in the selection and analysis of KPIs are then integrated and stored in common repositories, hence enabling sophisticated querying and reasoning for semantic validation. The proposed semantic model is evaluated on a real-world case study on water management. A series of data analysis and reasoning tasks are conducted to show how the ontological model is able to detect semantic conflicts in actual correlations of selected indicators.

A key challenge in current Business Analytics (BA) is the selection of suitable indicators for business objectives. This requires the exploration of business data through data-driven approaches, while modelling business strategies together with domain experts in order to represent domain knowledge. In particular, Key Performance Indicators (KPIs) allow human experts to properly model ambiguous enterprise goals by means of quantitative variables with numeric ranges and clear thresholds. Besides business-related domains, the usefulness of KPIs has been shown in multiple domains, such as: Education, Healthcare and Agriculture. However, finding accurate KPIs for a given strategic goal still remains a complex task, specially due to the discrepancy between domain assumptions and data facts. In this regard, the semantic web emerges as a powerful technology for knowledge representation and data modeling through explicit representation formats and standards such as RDF(S) and OWL. By using this technology, the semantic annotation of indicators of business objectives would enrich the strategic model obtained. With this motivation, an ontology-driven approach is proposed to formally conceptualize essential elements of indicators, covering: performance, results, measures, goals and relationships of a given business strategy. In this way, all the data involved in the selection and analysis of KPIs are then integrated and stored in common repositories, hence enabling sophisticated querying and reasoning for semantic validation. The proposed semantic model is evaluated on a real-world case study on water management. A series of data analysis and reasoning tasks are conducted to show how the ontological model is able to detect semantic conflicts in actual correlations of selected indicators.

Nowadays, the increasing demand of water for electricity production, agricultural and industrial uses are directly affecting the reduction of available quality water for human consumption in the world. Efficient and sustainable maintenance of water reservoirs and supply networks implies a holistic strategy that takes into account, as much as possible, information from the stages of water usage. Next,-generation decision-making software tools, for supporting water management, require the integration of multiple and heterogeneous data sources of different knowledge domains. In this regard, Linked Data and SemanticWeb technologies enable harmonization of different data sources, as well as the efficient querying for feeding upper-level Business Intelligence processes. This work investigates the design, implementation and usage of a semantic approach driven by ontology to capture, store, integrate and exploit real-world data concerning water supply networks management. As a main contribution, the proposal helps with obtaining semantically enriched linked data, enhancing the analysis of water network performance. For validation purposes, in the use case, a series of data sources from different measures have been considered, in the scope of an actual water management system of the Mediterranean region of Valencia (Spain), throughout several years of activity. The obtained experience shows the benefits of using the proposed approach to identify possible correlations between the measures such as the supplied water, the water leaks or the population.

Nowadays, the increasing demand of water for electricity production, agricultural and industrial uses are directly affecting the reduction of available quality water for human consumption in the world. Efficient and sustainable maintenance of water reservoirs and supply networks implies a holistic strategy that takes into account, as much as possible, information from the stages of water usage. Next,-generation decision-making software tools, for supporting water management, require the integration of multiple and heterogeneous data sources of different knowledge domains. In this regard, Linked Data and Semantic Web technologies enable harmonization of different data sources, as well as the efficient querying for feeding upper-level Business Intelligence processes. This work investigates the design, implementation and usage of a semantic approach driven by ontology to capture, store, integrate and exploit real-world data concerning water supply networks management. As a main contribution, the proposal helps with obtaining semantically enriched linked data, enhancing the analysis of water network performance. For validation purposes, in the use case, a series of data sources from different measures have been considered, in the scope of an actual water management system of the Mediterranean region of Valencia (Spain), throughout several years of activity. The obtained experience shows the benefits of using the proposed approach to identify possible correlations between the measures such as the supplied water, the water leaks or the population.

Knowledge extraction and incorporation is currently considered to be beneficial for efficient Big Data an- alytics. Knowledge can take part in workflow design, constraint definition, parameter selection and con- figuration, human interactive and decision-making strategies. This paper proposes BIGOWL, an ontologyto support knowledge management in Big Data analytics. BIGOWL is designed to cover a wide vocab- ulary of terms concerning Big Data analytics workflows, including their components and how they areconnected, from data sources to the analytics visualization. It also takes into consideration aspects suchas parameters, restrictions and formats. This ontology defines not only the taxonomic relationships be- tween the different concepts, but also instances representing specific individuals to guide the users inthe design of Big Data analytics workflows. For testing purposes, two case studies are developed, whichconsists in: first, real-world streaming processing with Spark of traffic Open Data, for route optimizationin urban environment of New York city; and second, data mining classification of an academic dataset onlocal/cloud platforms. The analytics workflows resulting from the BIGOWL semantic model are validatedand successfully evaluated.

In this study, an ontology-driven approach is proposed for semantic conflict detection and classification inrule-based expert systems. It focuses on the critical case of anti-fraud rule repositories for the inspectionof Card Not Present (CNP) transactions in e-commerce environments. The main motivation is to examineand curate anti-fraud rule datasets to avoid semantic conflicts that could lead the underpinning expertsystem to incorrectly perform, e. g., by accepting fraudulent transactions and/or by discarding harmlessones. The proposed approach is based on Web Ontology Language (OWL) and Semantic Web Rule Lan- guage (SWRL) technologies to develop an anti-fraud rule ontology and reasoning tasks, respectively. Thethree main contributions of this work are: first, the creation of a conceptual knowledge model for de- scribing anti-fraud rules and their relationships; second, the development of semantic rules as conflict- resolution methods for anti-fraud expert systems; third, experimental facts are gathered to evaluate andvalidate the proposed model. A real-world use case in the e-commerce (e-Tourism) industry is used toexplain the ontological knowledge design and its use. The experiments show that ontological approachescan effectively discover and classify conflicts in rule-based expert systems in the field of anti-fraud ap- plications. The proposal is also applicable to other domains where knowledge rule bases are involved.

DisMatch is an experimental ontology matching system based on the use of corpus based distributional measure for approximating se- mantic relatedness. Through the use of a domain-related corpus, the measure can be applied to a problem focused on the domain of the cor- pus, here being the Disease and Phenotype track. In this paper, we aim to briefly present the proposed approach and the results obtained in the evaluation, as well as some early conclusions regarding the performance of DisMatch.

La Analítica Web supone hoy en día una tarea ineludible para las empresas de comercio electrónico, ya que les permite analizar el comportamiento de sus clientes. El proyecto Europeo SME-Ecompass tiene como objetivo desarrollar herramientas avanzadas de análitica web accesibles para las PYMES. Con esta motivación, proponemos servicio de integración de datos basado en ontologías para recopilar, integrar y almacenar información de traza web procedente de distintas fuentes. Estas se consolidan en un Repositorio RDF diseñado para proporcionar semántica común a los datos de análisis y dar servicio homogéneo a algoritmos de Minería de Datos. El servicio propuesto se ha validado mediante traza digital real (Google Analitics y Piwik) de 15 tiendas virtuales de diferentes sectores y países europeos (UK, España, Grecia y Alemania) durante varios meses de actividad.

Web analytics has emerged as one of the most important activities in e-commerce, since it allows com- panies and e-merchants to track the behavior of customers when visiting their web sites. There exist aseries of tools for web analytics that are used not only for tracking and measuring web traffic, but alsofor analyzing the commercial activity. However, most of these tools focus on low level web attributes andmetrics, making other sophisticated functionalities and analyses only available for commercial (non-free)versions.In this context, the SME-Ecompass European initiative aims at providing e-commerce SMEs with ac- cessible tools for high level web analytics. These software facilities should use different sources of datacoming from digital footprints allocated in e-shops, to fuse them together in a coherent way, and tomake them available for advanced data mining procedures. This motivated us to propose in this work anontology-based approach to collect, integrate and store web analytics data, from many sources of popularand commercial digital footprints. As article’s main impact, we obtain enriched and semantically anno- tated data that is used to properly train an intelligent system, involving data mining procedures, for theanalysis of customer behavior in real e-commerce sites. In concrete, for the validation of our semantic ap- proach, we have captured and integrated data from Google Analytics and Piwik digital footprints allocatedin 15 e-shops of different commercial sectors and countries (UK, Spain, Greece and Germany), through- out several months of activity. The obtained results show different perspectives in customer’s behavioranalysis that go one step beyond the most popular web analytics tools in the current market.