Using Semantic Web technologies in the development of data warehouses: A systematic mapping

The exploration and use of Semantic Web technologies have attracted considerable attention from researchers examining data warehouse (DW) development. However, the impact of this research and the maturity level of its results are still unclear. The objective of this study is to examine recently published research articles that take into account the use of Semantic Web technologies in the DW arena with the intention of summarizing their results, classifying their contributions to the field according to publication type, evaluating the maturity level of the results, and identifying future research challenges. Three main conclusions were derived from this study: (a) there is a major technological gap that inhibits the wide adoption of Semantic Web technologies in the business domain;(b) there is limited evidence that the results of the analyzed studies are applicable and transferable to industrial use; and (c) interest in researching the relationship between DWs and Semantic Web has decreased because new paradigms, such as linked open data, have attracted the interest of researchers.This study was supported by the Universidad de La Frontera, Chile, PROY. DI15-0020. Universidad de la Frontera, Chile, Grant Numbers: DI15-0020 and DI17-0043

A abordagem POESIA para a integração de dados e serviços na Web semantica

Orientador: Claudia Bauzer MedeirosTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: POESIA (Processes for Open-Ended Systems for lnformation Analysis), a abordagem proposta neste trabalho, visa a construção de processos complexos envolvendo integração e análise de dados de diversas fontes, particularmente em aplicações científicas. A abordagem é centrada em dois tipos de mecanismos da Web semântica: workflows científicos, para especificar e compor serviços Web; e ontologias de domínio, para viabilizar a interoperabilidade e o gerenciamento semânticos dos dados e processos. As principais contribuições desta tese são: (i) um arcabouço teórico para a descrição, localização e composição de dados e serviços na Web, com regras para verificar a consistência semântica de composições desses recursos; (ii) métodos baseados em ontologias de domínio para auxiliar a integração de dados e estimar a proveniência de dados em processos cooperativos na Web; (iii) implementação e validação parcial das propostas, em urna aplicação real no domínio de planejamento agrícola, analisando os benefícios e as limitações de eficiência e escalabilidade da tecnologia atual da Web semântica, face a grandes volumes de dadosAbstract: POESIA (Processes for Open-Ended Systems for Information Analysis), the approach proposed in this work, supports the construction of complex processes that involve the integration and analysis of data from several sources, particularly in scientific applications. This approach is centered in two types of semantic Web mechanisms: scientific workflows, to specify and compose Web services; and domain ontologies, to enable semantic interoperability and management of data and processes. The main contributions of this thesis are: (i) a theoretical framework to describe, discover and compose data and services on the Web, inc1uding mIes to check the semantic consistency of resource compositions; (ii) ontology-based methods to help data integration and estimate data provenance in cooperative processes on the Web; (iii) partial implementation and validation of the proposal, in a real application for the domain of agricultural planning, analyzing the benefits and scalability problems of the current semantic Web technology, when faced with large volumes of dataDoutoradoCiência da ComputaçãoDoutor em Ciência da Computaçã

Flexible Integration and Efficient Analysis of Multidimensional Datasets from the Web

If numeric data from the Web are brought together, natural scientists can compare climate measurements with estimations, financial analysts can evaluate companies based on balance sheets and daily stock market values, and citizens can explore the GDP per capita from several data sources. However, heterogeneities and size of data remain a problem. This work presents methods to query a uniform view - the Global Cube - of available datasets from the Web and builds on Linked Data query approaches

Flexible Integration and Efficient Analysis of Multidimensional Datasets from the Web

If numeric data from the Web are brought together, natural scientists can compare climate measurements with estimations, financial analysts can evaluate companies based on balance sheets and daily stock market values, and citizens can explore the GDP per capita from several data sources. However, heterogeneities and size of data remain a problem. This work presents methods to query a uniform view - the Global Cube - of available datasets from the Web and builds on Linked Data query approaches

Business Intelligence on Non-Conventional Data

The revolution in digital communications witnessed over the last decade had a significant impact on the world of Business Intelligence (BI). In the big data era, the amount and diversity of data that can be collected and analyzed for the decision-making process transcends the restricted and structured set of internal data that BI systems are conventionally limited to. This thesis investigates the unique challenges imposed by three specific categories of non-conventional data: social data, linked data and schemaless data. Social data comprises the user-generated contents published through websites and social media, which can provide a fresh and timely perception about people’s tastes and opinions. In Social BI (SBI), the analysis focuses on topics, meant as specific concepts of interest within the subject area. In this context, this thesis proposes meta-star, an alternative strategy to the traditional star-schema for modeling hierarchies of topics to enable OLAP analyses. The thesis also presents an architectural framework of a real SBI project and a cross-disciplinary benchmark for SBI. Linked data employ the Resource Description Framework (RDF) to provide a public network of interlinked, structured, cross-domain knowledge. In this context, this thesis proposes an interactive and collaborative approach to build aggregation hierarchies from linked data. Schemaless data refers to the storage of data in NoSQL databases that do not force a predefined schema, but let database instances embed their own local schemata. In this context, this thesis proposes an approach to determine the schema profile of a document-based database; the goal is to facilitate users in a schema-on-read analysis process by understanding the rules that drove the usage of the different schemata. A final and complementary contribution of this thesis is an innovative technique in the field of recommendation systems to overcome user disorientation in the analysis of a large and heterogeneous wealth of data

Metadata-driven data integration

Cotutela: Universitat Politècnica de Catalunya i Université Libre de Bruxelles, IT4BI-DC programme for the joint Ph.D. degree in computer science.Data has an undoubtable impact on society. Storing and processing large amounts of available data is currently one of the key success factors for an organization. Nonetheless, we are recently witnessing a change represented by huge and heterogeneous amounts of data. Indeed, 90% of the data in the world has been generated in the last two years. Thus, in order to carry on these data exploitation tasks, organizations must first perform data integration combining data from multiple sources to yield a unified view over them. Yet, the integration of massive and heterogeneous amounts of data requires revisiting the traditional integration assumptions to cope with the new requirements posed by such data-intensive settings. This PhD thesis aims to provide a novel framework for data integration in the context of data-intensive ecosystems, which entails dealing with vast amounts of heterogeneous data, from multiple sources and in their original format. To this end, we advocate for an integration process consisting of sequential activities governed by a semantic layer, implemented via a shared repository of metadata. From an stewardship perspective, this activities are the deployment of a data integration architecture, followed by the population of such shared metadata. From a data consumption perspective, the activities are virtual and materialized data integration, the former an exploratory task and the latter a consolidation one. Following the proposed framework, we focus on providing contributions to each of the four activities. We begin proposing a software reference architecture for semantic-aware data-intensive systems. Such architecture serves as a blueprint to deploy a stack of systems, its core being the metadata repository. Next, we propose a graph-based metadata model as formalism for metadata management. We focus on supporting schema and data source evolution, a predominant factor on the heterogeneous sources at hand. For virtual integration, we propose query rewriting algorithms that rely on the previously proposed metadata model. We additionally consider semantic heterogeneities in the data sources, which the proposed algorithms are capable of automatically resolving. Finally, the thesis focuses on the materialized integration activity, and to this end, proposes a method to select intermediate results to materialize in data-intensive flows. Overall, the results of this thesis serve as contribution to the field of data integration in contemporary data-intensive ecosystems.Les dades tenen un impacte indubtable en la societat. La capacitat d’emmagatzemar i processar grans quantitats de dades disponibles és avui en dia un dels factors claus per l’èxit d’una organització. No obstant, avui en dia estem presenciant un canvi representat per grans volums de dades heterogenis. En efecte, el 90% de les dades mundials han sigut generades en els últims dos anys. Per tal de dur a terme aquestes tasques d’explotació de dades, les organitzacions primer han de realitzar una integració de les dades, combinantles a partir de diferents fonts amb l’objectiu de tenir-ne una vista unificada d’elles. Per això, aquest fet requereix reconsiderar les assumpcions tradicionals en integració amb l’objectiu de lidiar amb els requisits imposats per aquests sistemes de tractament massiu de dades. Aquesta tesi doctoral té com a objectiu proporcional un nou marc de treball per a la integració de dades en el context de sistemes de tractament massiu de dades, el qual implica lidiar amb una gran quantitat de dades heterogènies, provinents de múltiples fonts i en el seu format original. Per això, proposem un procés d’integració compost d’una seqüència d’activitats governades per una capa semàntica, la qual és implementada a partir d’un repositori de metadades compartides. Des d’una perspectiva d’administració, aquestes activitats són el desplegament d’una arquitectura d’integració de dades, seguit per la inserció d’aquestes metadades compartides. Des d’una perspectiva de consum de dades, les activitats són la integració virtual i materialització de les dades, la primera sent una tasca exploratòria i la segona una de consolidació. Seguint el marc de treball proposat, ens centrem en proporcionar contribucions a cada una de les quatre activitats. La tesi inicia proposant una arquitectura de referència de software per a sistemes de tractament massiu de dades amb coneixement semàntic. Aquesta arquitectura serveix com a planell per a desplegar un conjunt de sistemes, sent el repositori de metadades al seu nucli. Posteriorment, proposem un model basat en grafs per a la gestió de metadades. Concretament, ens centrem en donar suport a l’evolució d’esquemes i fonts de dades, un dels factors predominants en les fonts de dades heterogènies considerades. Per a l’integració virtual, proposem algorismes de rescriptura de consultes que usen el model de metadades previament proposat. Com a afegitó, considerem heterogeneïtat semàntica en les fonts de dades, les quals els algorismes de rescriptura poden resoldre automàticament. Finalment, la tesi es centra en l’activitat d’integració materialitzada. Per això proposa un mètode per a seleccionar els resultats intermedis a materialitzar un fluxes de tractament intensiu de dades. En general, els resultats d’aquesta tesi serveixen com a contribució al camp d’integració de dades en els ecosistemes de tractament massiu de dades contemporanisLes données ont un impact indéniable sur la société. Le stockage et le traitement de grandes quantités de données disponibles constituent actuellement l’un des facteurs clés de succès d’une entreprise. Néanmoins, nous assistons récemment à un changement représenté par des quantités de données massives et hétérogènes. En effet, 90% des données dans le monde ont été générées au cours des deux dernières années. Ainsi, pour mener à bien ces tâches d’exploitation des données, les organisations doivent d’abord réaliser une intégration des données en combinant des données provenant de sources multiples pour obtenir une vue unifiée de ces dernières. Cependant, l’intégration de quantités de données massives et hétérogènes nécessite de revoir les hypothèses d’intégration traditionnelles afin de faire face aux nouvelles exigences posées par les systèmes de gestion de données massives. Cette thèse de doctorat a pour objectif de fournir un nouveau cadre pour l’intégration de données dans le contexte d’écosystèmes à forte intensité de données, ce qui implique de traiter de grandes quantités de données hétérogènes, provenant de sources multiples et dans leur format d’origine. À cette fin, nous préconisons un processus d’intégration constitué d’activités séquentielles régies par une couche sémantique, mise en oeuvre via un dépôt partagé de métadonnées. Du point de vue de la gestion, ces activités consistent à déployer une architecture d’intégration de données, suivies de la population de métadonnées partagées. Du point de vue de la consommation de données, les activités sont l’intégration de données virtuelle et matérialisée, la première étant une tâche exploratoire et la seconde, une tâche de consolidation. Conformément au cadre proposé, nous nous attachons à fournir des contributions à chacune des quatre activités. Nous commençons par proposer une architecture logicielle de référence pour les systèmes de gestion de données massives et à connaissance sémantique. Une telle architecture consiste en un schéma directeur pour le déploiement d’une pile de systèmes, le dépôt de métadonnées étant son composant principal. Ensuite, nous proposons un modèle de métadonnées basé sur des graphes comme formalisme pour la gestion des métadonnées. Nous mettons l’accent sur la prise en charge de l’évolution des schémas et des sources de données, facteur prédominant des sources hétérogènes sous-jacentes. Pour l’intégration virtuelle, nous proposons des algorithmes de réécriture de requêtes qui s’appuient sur le modèle de métadonnées proposé précédemment. Nous considérons en outre les hétérogénéités sémantiques dans les sources de données, que les algorithmes proposés sont capables de résoudre automatiquement. Enfin, la thèse se concentre sur l’activité d’intégration matérialisée et propose à cette fin une méthode de sélection de résultats intermédiaires à matérialiser dans des flux des données massives. Dans l’ensemble, les résultats de cette thèse constituent une contribution au domaine de l’intégration des données dans les écosystèmes contemporains de gestion de données massivesPostprint (published version

The use of TRAO to manage evolution risks in e-government

The need to develop and provide more efficient ways of providing Electronic Government Services to key stakeholders in government has brought about varying degrees of evolution in government. This evolution is seen in different ways like the merging of government departments, the merging of assets or its components with legacy assets etc. This has involved the incorporation of several practices that are geared towards the elimination of processes that are repetitive and manual while attempting to progressively encourage the interaction that exists between the different stakeholders. However, some of these practices have further complicated processes in government thus creating avenues for vulnerabilities which if exploited expose government and government assets to risks and threats. Focusing on ways to manage the issues accompanied with evolution can better prepare governments for manging the associated vulnerabilities, risks and threats. The basis of a conceptual framework is provided to establish the relationships that exist between the E-Government, asset and security domains. Thus, this thesis presents a design research project used in the management of evolution-related risks. The first part of the project focusses on the development of a generic ontology known as TRAO and a scenario ontology TRAOSc made up of different hypothetical scenarios. The resulting efficiency of the development of these ontologies have facilitated the development of an intelligent tool TRAOSearch that supports high-level semantically enriched queries. Results from the use of a case study prove that there are existing evolution-related issues which governments may not be fully prepared for. Furthermore, an ontological approach in the management of evolution-related risks showed that government stakeholders were interested in the use of intelligent processes that could improve government effectiveness while analysing the risks associated with doing this. Of more importance to this research was the ability to make inferences from the ontology on existing complex relationships that exist in the form of dependencies and interdependencies between Stakeholders and Assets. Thus, this thesis presents contributions in the aspect of advancing stakeholders understanding on the types of relationships that exist in government and the effect these relationships may have on service provisioning. Another novel contribution can be seen in the correction of the ambiguity associated with the terms Service, IT Service and E-Government. Furthermore, the feedback obtained from the use of an ontology-based tool during the evaluation phase of the project provides insights on whether governments must always be at par with technological evolution

Linked Open Data - Creating Knowledge Out of Interlinked Data: Results of the LOD2 Project

Database Management; Artificial Intelligence (incl. Robotics); Information Systems and Communication Servic

Semantic metadata for supporting exploratory OLAP

Cotutela Universitat Politècnica de Catalunya i Aalborg UniversitetOn-Line Analytical Processing (OLAP) is an approach widely used for data analysis. OLAP is based on the multidimensional (MD) data model where factual data are related to their analytical perspectives called dimensions and together they form an n-dimensional data space referred to as data cube. MD data are typically stored in a data warehouse, which integrates data from in-house data sources, and then analyzed by means of OLAP operations, e.g., sales data can be (dis)aggregated along the location dimension. As OLAP proved to be quite intuitive, it became broadly accepted by non-technical and business users. However, as users still encountered difficulties in their analysis, different approaches focused on providing user assistance. These approaches collect situational metadata about users and their actions and provide suggestions and recommendations that can help users' analysis. However, although extensively exploited and evidently needed, little attention is paid to metadata in this context. Furthermore, new emerging tendencies call for expanding the use of OLAP to consider external data sources and heterogeneous settings. This leads to the Exploratory OLAP approach that especially argues for the use of Semantic Web (SW) technologies to facilitate the description and integration of external sources. With data becoming publicly available on the (Semantic) Web, the number and diversity of non-technical users are also significantly increasing. Thus, the metadata to support their analysis become even more relevant. This PhD thesis focuses on metadata for supporting Exploratory OLAP. The study explores the kinds of metadata artifacts used for the user assistance purposes and how they are exploited to provide assistance. Based on these findings, the study then aims at providing theoretical and practical means such as models, algorithms, and tools to address the gaps and challenges identified. First, based on a survey of existing user assistance approaches related to OLAP, the thesis proposes the analytical metadata (AM) framework. The framework includes the definition of the assistance process, the AM artifacts that are classified in a taxonomy, and the artifacts organization and related types of processing to support the user assistance. Second, the thesis proposes a semantic metamodel for AM. Hence, Resource Description Framework (RDF) is used to represent the AM artifacts in a flexible and re-usable manner, while the metamodeling abstraction level is used to overcome the heterogeneity of (meta)data models in the Exploratory OLAP context. Third, focusing on the schema as a fundamental metadata artifact for enabling OLAP, the thesis addresses some important challenges on constructing an MD schema on the SW using RDF. It provides the algorithms, method, and tool to construct an MD schema over statistical linked open data sets. Especially, the focus is on enabling that even non-technical users can perform this task. Lastly, the thesis deals with queries as the second most relevant artifact for user assistance. In the spirit of Exploratory OLAP, the thesis proposes an RDF-based model for OLAP queries created by instantiating the previously proposed metamodel. This model supports the sharing and reuse of queries across the SW and facilitates the metadata preparation for the assistance exploitation purposes. Finally, the results of this thesis provide metadata foundations for supporting Exploratory OLAP and advocate for greater attention to the modeling and use of semantics related to metadata.El processament analític en línia (OLAP) és una tècnica àmpliament utilitzada per a l'anàlisi de dades. OLAP es basa en el model multi-dimensional (MD) de dades, on dades factuals es relacionen amb les seves perspectives analítiques, anomenades dimensions, i conjuntament formen un espai de dades n-dimensional anomenat cub de dades. Les dades MD s'emmagatzemen típicament en un data warehouse (magatzem de dades), el qual integra dades de fonts internes, les quals posteriorment s'analitzen mitjançant operacions OLAP, per exemple, dades de vendes poden ser (des)agregades a partir de la dimensió ubicació. Un cop OLAP va ser provat com a intuïtiu, va ser ampliament acceptat tant per usuaris no tècnics com de negoci. Tanmateix, donat que els usuaris encara trobaven dificultats per a realitzar el seu anàlisi, diferents tècniques s'han enfocat en la seva assistència. Aquestes tècniques recullen metadades situacionals sobre els usuaris i les seves accions, i proporcionen suggerències i recomanacions per tal d'ajudar en aquest anàlisi. Tot i ésser extensivament emprades i necessàries, poca atenció s'ha prestat a les metadades en aquest context. A més a més, les noves tendències demanden l'expansió d'ús d'OLAP per tal de considerar fonts de dades externes en escenaris heterogenis. Això ens porta a la tècnica d'OLAP exploratori, la qual es basa en l'ús de tecnologies en la web semàntica (SW) per tal de facilitar la descripció i integració d'aquestes fonts externes. Amb les dades essent públicament disponibles a la web (semàntica), el nombre i diversitat d'usuaris no tècnics també incrementa signifícativament. Així doncs, les metadades per suportar el seu anàlisi esdevenen més rellevants. Aquesta tesi doctoral s'enfoca en l'ús de metadades per suportar OLAP exploratori. L'estudi explora els tipus d'artefactes de metadades utilitzats per l'assistència a l'usuari, i com aquests són explotats per proporcionar assistència. Basat en aquestes troballes, l'estudi preté proporcionar mitjans teòrics i pràctics, com models, algorismes i eines, per abordar els reptes identificats. Primerament, basant-se en un estudi de tècniques per assistència a l'usuari en OLAP, la tesi proposa el marc de treball de metadades analítiques (AM). Aquest marc inclou la definició del procés d'assistència, on els artefactes d'AM són classificats en una taxonomia, i l'organització dels artefactes i tipus relacionats de processament pel suport d'assistència a l'usuari. En segon lloc, la tesi proposa un meta-model semàntic per AM. Així doncs, s'utilitza el Resource Description Framework (RDF) per representar els artefactes d'AM d'una forma flexible i reusable, mentre que el nivell d'abstracció de metamodel s'utilitza per superar l'heterogeneïtat dels models de (meta)dades en un context d'OLAP exploratori. En tercer lloc, centrant-se en l'esquema com a artefacte fonamental de metadades per a OLAP, la tesi adreça reptes importants en la construcció d'un esquema MD en la SW usant RDF. Proporciona els algorismes, mètodes i eines per construir un esquema MD sobre conjunts de dades estadístics oberts i relacionats. Especialment, el focus rau en permetre que usuaris no tècnics puguin realitzar aquesta tasca. Finalment, la tesi tracta amb consultes com el segon artefacte més rellevant per l'assistència a usuari. En l'esperit d'OLAP exploratori, la tesi proposa un model basat en RDF per consultes OLAP instanciant el meta-model prèviament proposat. Aquest model suporta el compartiment i reutilització de consultes sobre la SW i facilita la preparació de metadades per l'explotació de l'assistència. Finalment, els resultats d'aquesta tesi proporcionen els fonaments en metadades per suportar l'OLAP exploratori i propugnen la major atenció al model i ús de semàntica relacionada a metadades.On-Line Analytical Processing (OLAP) er en bredt anvendt tilgang til dataanalyse. OLAP er baseret på den multidimensionelle (MD) datamodel, hvor faktuelle data relateres til analytiske synsvinkler, såkaldte dimensioner. Tilsammen danner de et n-dimensionelt rum af data kaldet en data cube. Multidimensionelle data er typisk lagret i et data warehouse, der integrerer data fra forskellige interne datakilder, og kan analyseres ved hjælp af OLAPoperationer. For eksempel kan salgsdata disaggregeres langs sted-dimensionen. OLAP har vist sig at være intuitiv at forstå og er blevet taget i brug af ikketekniske og orretningsorienterede brugere. Nye tilgange er siden blevet udviklet i forsøget på at afhjælpe de problemer, som denne slags brugere dog stadig står over for. Disse tilgange indsamler metadata om brugerne og deres handlinger og kommer efterfølgende med forslag og anbefalinger, der kan bidrage til brugernes analyse. På trods af at der er en klar nytteværdi i metadata (givet deres udbredelse), har stadig ikke været meget opmærksomhed på metadata i denne kotekst. Desuden lægger nye fremspirende teknikker nu op til en udvidelse af brugen af OLAP til også at bruge eksterne og uensartede datakilder. Dette har ført til Exploratory OLAP, en tilgang til OLAP, der benytter teknologier fra Semantic Web til at understøtte beskrivelse og integration af eksterne kilder. Efterhånden som mere data gøres offentligt tilgængeligt via Semantic Web, kommer flere og mere forskelligartede ikketekniske brugere også til. Derfor er metadata til understøttelsen af deres dataanalyser endnu mere relevant. Denne ph.d.-afhandling omhandler metadata, der understøtter Exploratory OLAP. Der foretages en undersøgelse af de former for metadata, der benyttes til at hjælpe brugere, og af, hvordan sådanne metadata kan udnyttes. Med grundlag i disse fund søges der løsninger til de identificerede problemer igennem teoretiske såvel som praktiske midler. Det vil sige modeller, algoritmer og værktøjer. På baggrund af en afdækning af eksisterende tilgange til brugerassistance i forbindelse med OLAP præsenteres først rammeværket Analytical Metadata (AM). Det inkluderer definition af assistanceprocessen, en taksonomi over tilhørende artefakter og endelig relaterede processeringsformer til brugerunderstøttelsen. Dernæst præsenteres en semantisk metamodel for AM. Der benyttes Resource Description Framework (RDF) til at repræsentere AM-artefakterne på en genbrugelig og fleksibel facon, mens metamodellens abstraktionsniveau har til formål at nedbringe uensartetheden af (meta)data i Exploratory OLAPs kontekst. Så fokuseres der på skemaet som en fundamental metadata-artefakt i OLAP, og afhandlingen tager fat i vigtige udfordringer i forbindelse med konstruktionen af multidimensionelle skemaer i Semantic Web ved brug af RDF. Der præsenteres algoritmer, metoder og redskaber til at konstruere disse skemaer sammenkoblede åbne statistiske datasæt. Der lægges særlig vægt på, at denne proces skal kunne udføres af ikke-tekniske brugere. Til slut tager afhandlingen fat i forespørgsler som anden vigtig artefakt inden for bruger-assistance. I samme ånd som Exploratory OLAP foreslås en RDF-baseret model for OLAP-forespørgsler, hvor førnævnte metamodel benyttes. Modellen understøtter deling og genbrug af forespørgsler over Semantic Web og fordrer klargørelsen af metadata med øje for assistance-relaterede formål. Endelig leder resultaterne af afhandlingen til fundamenterne for metadata i støttet Exploratory OLAP og opfordrer til en øget opmærksomhed på modelleringen og brugen af semantik i forhold til metadataPostprint (published version