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

Improving Entity Retrieval on Structured Data

The increasing amount of data on the Web, in particular of Linked Data, has led to a diverse landscape of datasets, which make entity retrieval a challenging task. Explicit cross-dataset links, for instance to indicate co-references or related entities can significantly improve entity retrieval. However, only a small fraction of entities are interlinked through explicit statements. In this paper, we propose a two-fold entity retrieval approach. In a first, offline preprocessing step, we cluster entities based on the \emph{x--means} and \emph{spectral} clustering algorithms. In the second step, we propose an optimized retrieval model which takes advantage of our precomputed clusters. For a given set of entities retrieved by the BM25F retrieval approach and a given user query, we further expand the result set with relevant entities by considering features of the queries, entities and the precomputed clusters. Finally, we re-rank the expanded result set with respect to the relevance to the query. We perform a thorough experimental evaluation on the Billions Triple Challenge (BTC12) dataset. The proposed approach shows significant improvements compared to the baseline and state of the art approaches

QB4OLAP : Enabling business intelligence over semantic web data

Premio Primer puesto otorgado por la Academia Nacional de Ingeniería.The World-Wide Web was initially conceived as a repository of information tailored for human consumption. In the last decade, the idea of transforming the web into a machine-understandable web of data, has gained momentum. To this end, the World Wide Web Consortium (W3C) maintains a set of standards, referred to as the Semantic Web (SW), which allow to openly share data and metadata. Among these is the Resource Description Framework (RDF), which represents data as graphs, RDF-S and OWL to describe the data structure via ontologies or vocabularies, and SPARQL, the RDF query language. On top of the RDF data model, standards and recommendations can be built to represent data that adheres to other models. The multidimensional (MD) model views data in an n-dimensional space, usually called a data cube, composed of dimensions and facts. The former reflect the perspectives from which data are viewed, and the latter correspond to points in this space, associated with (usually) quantitative data (also known as measures). Facts can be aggregated, disaggregated, and filtered using the dimensions. This process is called Online Analytical Processing (OLAP). Despite the RDF Data Cube Vocabulary (QB) is the W3C standard to represent statistical data, which resembles MD data, it does not include key features needed for OLAP analysis, like dimension hierarchies, dimension level attributes, and aggregate functions. To enable this kind of analysis over SW data cubes, in this thesis we propose the QB4 OLAP vocabulary, an extension of QB. A problem remains, however: writing efficient analytical queries over SW data cubes requires a deep knowledge of RDF and SPARQL, unlikely to be found in typical OLAP users. We address this problem in this thesis. Our approach is based on allowing analytical users to write queries using what they know best: OLAP operations over data cubes, without dealing with SW technicalities. For this, we devised CQL, a simple, high-level query language over data cubes. Then we make use of the structural metadata provided by QB4 OLAP to translate CQL queries into SPARQL ones. We adapt general-purpose SPARQL query optimization techniques, and propose query improvement strategies to produce efficient SPARQL queries. We evaluate our implementation tailoring the well known Star-Schema benchmark, which allows us to compare our proposal against existing ones in a fair way. We show that our approach outperforms other ones. Finally, as another result, our experiments allow us to study which combinations of improvement strategies fits better to an analytical scenario.La World-Wide Web fue concebida como un repositorio de informa- ción a ser procesada y consumida por humanos. Pero en la última década ha ganado impulso la idea de transformar a la Web en una gran base de datos procesables por máquinas. Con este fin, el World Wide Web Consortium (W3C) ha establecido una serie de estándares también conocidos como estándares para la Web Semántica (WS), los cuales permiten compartir datos y metadatos en formatos abiertos. Entre estos estándares se destacan: el Resource Description Framework (RDF), un modelo de datos basado en grafos para representar datos y relaciones entre ellos, RDF-S y OWL que permiten describir la estructura y el significado de los datos por medio de ontologías o vocabu- larios, y el lenguaje de consultas SPARQL. Estos estándares pueden ser utilizados para construir representaciones de otros modelos de datos, por ejemplo datos tabulares o datos relacionales. El modelo de datos multidimensional (MD) representa a los datos dentro de un espacio n-dimensional, usualmente denominado cubo de datos, que se compone de dimensiones y hechos. Las primeras reflejan las perspectivas desde las cuales interesa analizar los datos, mientras que las segundas corresponden a puntos en este espacio n- dimensional, a los cuales se asocian valores usualmente numéricos, conocidos como medidas. Los hechos pueden ser agregados y resumidos, desagregados, y filtrados utilizando las dimensiones. Este pro- ceso es conocido como Online Analytical Processing (OLAP). Pese a que la W3C ha establecido un estándar que puede ser utilizado para publicación de datos multidimensionales, conocido como el RDF Data Cube Vocabulary (QB), éste no incluye algunos aspectos del modelo MD que son imprescindibles para realizar análisis tipo OLAP como son las jerarquías de dimensión, los atributos en los niveles de dimensión, y las funciones de agregaciónpara resumir valores de medidas. Para permitir este tipo de análisis sobre cubos en la SW, en esta tesis se propone un vocabulario que extiende el vocabulario QB denominado QB4OLAP. Sin embargo, para realizar análisis tipo OLAP en forma eficiente sobre cubos QB4OLAP es necesario un conocimiento profundo de RDF y SPARQL, los cuales distan de ser populares entre los usuarios OLAP típicos. Esta tesis también aborda este problema. Nuestro enfoque consiste en brindar un conjunto de operaciones clásicas para los usuarios OLAP, y luego realizar la traducción en forma automática de estas operaciones en consultas SPARQL. Comenzamos definiendo un lenguaje de consultas para cubos en alto nivel: Cube Query Language (CQL), y luego explotamos la metadata representada mediante QB4OLAP para realizar la traducción a SPARQL. Asimismo, mejoramos el rendimiento de las consultas obtenidas, adaptando y aplicando técnicas existentes de optimización de consultas SPARQL. Para evaluar nuestra propuesta adaptamos a los estándares de la SW el Star Schema benchmark, el cual es el estándar para la evaluación de sistemas tipo OLAP. Esto permite comparar nuestro enfoque con otras propuestas existentes, asi como evaluar el impacto de nuestras estrategias de mejoras de consultas SPARQL. De esta comparación podemos concluir que nuestro enfoque supera a otras propuestas existentes, y que nuestras técnicas de mejoras logran incrementar en 10 veces el rendimiento del sistema

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Structuring visual exploratory analysis of skill demand

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on

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

Exploiting Context-Dependent Quality Metadata for Linked Data Source Selection

The traditional Web is evolving into the Web of Data which consists of huge collections of structured data over poorly controlled distributed data sources. Live queries are needed to get current information out of this global data space. In live query processing, source selection deserves attention since it allows us to identify the sources which might likely contain the relevant data. The thesis proposes a source selection technique in the context of live query processing on Linked Open Data, which takes into account the context of the request and the quality of data contained in the sources to enhance the relevance (since the context enables a better interpretation of the request) and the quality of the answers (which will be obtained by processing the request on the selected sources). Specifically, the thesis proposes an extension of the QTree indexing structure that had been proposed as a data summary to support source selection based on source content, to take into account quality and contextual information. With reference to a specific case study, the thesis also contributes an approach, relying on the Luzzu framework, to assess the quality of a source with respect to for a given context (according to different quality dimensions). An experimental evaluation of the proposed techniques is also provide