The DSD Schema Language and its Applications

XML (eXtensible Markup Language), a linear syntax for trees, has gathered a remarkable amount of interest in industry. The acceptance of XML opens new venues for the application of formal methods such as specification of abstract syntax tree sets and tree transformations. A user domain may be specified as a set of trees. For example, XHTML is a user domain corresponding to the set of XML documents that make sense asHTML. A notation for defining such a set of XML trees is called a schema language. We believe that a useful schema notation must identify most of the syntacticrequirements that the documents in the user domain follow; allow efficient parsing; be readable to the user; allow a declarative default notation `a la CSS; and bemodular and extensible to support evolving classes of XML documents. In the present paper, we give a tutorial introduction to the DSD (Document Structure Description) notation as our bid on how to meet these requirements. TheDSD notation was inspired by industrial needs, and we show how DSDs help manage aspects of complex XML software through a case study about interactive voiceresponse systems (automated telephone answering systems, where input is through the telephone keypad or speech recognition). The expressiveness of DSDs goes beyond the DTD schema concept that is alreadypart of XML. We advocate the use of nonterminals in a top-down manner, coupled with boolean logic and regular expressions to describe how constraints on tree nodes depend on their context. We also support a general, declarative mechanism for inserting default elements and attributes that is reminiscent of CascadingStyle Sheets (CSS), a way of manipulating formatting instructions in HTML that is built into all modern browsers. Finally, we include a simple technique for evolving DSDs through selective redefinitions. DSDs are in many ways much more expressive than XML Schema (the schema language proposed by the W3C), but their syntactic and semantic definition in English is only 1/8th the size. Also, the DSD notation is self-describable: the syntax of legal DSD documents and all static semantic requirements can be captured in a DSD document, called the meta-DSD

Dimensional enrichment of statistical linked open data

On-Line Analytical Processing (OLAP) is a data analysis technique typically used for local and well-prepared data. However, initiatives like Open Data and Open Government bring new and publicly available data on the web that are to be analyzed in the same way. The use of semantic web technologies for this context is especially encouraged by the Linked Data initiative. There is already a considerable amount of statistical linked open data sets published using the RDF Data Cube Vocabulary (QB) which is designed for these purposes. However, QB lacks some essential schema constructs (e.g., dimension levels) to support OLAP. Thus, the QB4OLAP vocabulary has been proposed to extend QB with the necessary constructs and be fully compliant with OLAP. In this paper, we focus on the enrichment of an existing QB data set with QB4OLAP semantics. We first thoroughly compare the two vocabularies and outline the benefits of QB4OLAP. Then, we propose a series of steps to automate the enrichment of QB data sets with specific QB4OLAP semantics; being the most important, the definition of aggregate functions and the detection of new concepts in the dimension hierarchy construction. The proposed steps are defined to form a semi-automatic enrichment method, which is implemented in a tool that enables the enrichment in an interactive and iterative fashion. The user can enrich the QB data set with QB4OLAP concepts (e.g., full-fledged dimension hierarchies) by choosing among the candidate concepts automatically discovered with the steps proposed. Finally, we conduct experiments with 25 users and use three real-world QB data sets to evaluate our approach. The evaluation demonstrates the feasibility of our approach and shows that, in practice, our tool facilitates, speeds up, and guarantees the correct results of the enrichment process.Peer ReviewedPostprint (author's final draft

QB2OLAP : enabling OLAP on statistical linked open data

Publication and sharing of multidimensional (MD) data on the Semantic Web (SW) opens new opportunities for the use of On-Line Analytical Processing (OLAP). The RDF Data Cube (QB) vocabulary, the current standard for statistical data publishing, however, lacks key MD concepts such as dimension hierarchies and aggregate functions. QB4OLAP was proposed to remedy this. However, QB4OLAP requires extensive manual annotation and users must still write queries in SPARQL, the standard query language for RDF, which typical OLAP users are not familiar with. In this demo, we present QB2OLAP, a tool for enabling OLAP on existing QB data. Without requiring any RDF, QB(4OLAP), or SPARQL skills, it allows semi-automatic transformation of a QB data set into a QB4OLAP one via enrichment with QB4OLAP semantics, exploration of the enriched schema, and querying with the high-level OLAP language QL that exploits the QB4OLAP semantics and is automatically translated to SPARQL.Peer ReviewedPostprint (author's final draft

Direct NN-body code on low-power embedded ARM GPUs

This work arises on the environment of the ExaNeSt project aiming at design and development of an exascale ready supercomputer with low energy consumption profile but able to support the most demanding scientific and technical applications. The ExaNeSt compute unit consists of densely-packed low-power 64-bit ARM processors, embedded within Xilinx FPGA SoCs. SoC boards are heterogeneous architecture where computing power is supplied both by CPUs and GPUs, and are emerging as a possible low-power and low-cost alternative to clusters based on traditional CPUs. A state-of-the-art direct $N$-body code suitable for astrophysical simulations has been re-engineered in order to exploit SoC heterogeneous platforms based on ARM CPUs and embedded GPUs. Performance tests show that embedded GPUs can be effectively used to accelerate real-life scientific calculations, and that are promising also because of their energy efficiency, which is a crucial design in future exascale platforms.Comment: 16 pages, 7 figures, 1 table, accepted for publication in the Computing Conference 2019 proceeding

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

A new approach to the semantics of model diagrams

Sometimes, a diagram can say more than a thousand lines of code. But, sadly, most of the time, software engineers give up on diagrams after the design phase, and all real work is done in code. The supremacy of code over diagrams would be leveled if diagrams were code. This paper suggests that model and instance diagrams, or, which amounts to the same, class and object diagrams, become first level entities in a suitably expressive programming language, viz., type theory. The proposed semantics of diagrams is compositional and self-describing, i.e., reflexive, or metacircular. Moreover, it is well suited for metamodelling and model driven engineering, as it is possible to prove model transformations correct in type theory. The encoding into type theory has the additional benefit of making diagrams immediately useful, given an implementation of type theory

A quantitative dendritic analysis of four functionally distinct areas of human cerebral cortex

Recent investigations have begun to elucidate the function of dendritic arbors, revealing that the geometry of dendrites and the presence of dendritic spines play important roles in both simulated and actual dendritic function (Midtgaard, 1994). The present study attempts to correlate dendritic complexity with cortical function under the assumption that connectivity in a local cortical area may determine its more holistic functional properties. Two human brains (ages: 23 and 69 years) were used for the present study. Four cortical areas (Brodmann\u27s [1908] areas 3, 1, and 2; area 22; area 44; and area 10), respectively representing Benson\u27s four levels of cortical function (primary, unimodal, heteromodal, and supramodal; 1993, 1994), were stained with the modified rapid Golgi technique (Scheibel & Scheibel, 1978). Twenty supragranular pyramidal cells per cortical level were selected (N = 80). Basilar dendritic systems were analyzed using the Neurolucida computer/microscope interface system (Microbrightfield, Inc.). Dendritic measures, which included total dendritic length (TDL), mean dendritic length (MDL), dendritic segment count (DSC), dendritic spine number (DSN), and dendritic spine density (DSD), were used to determine dendritic complexity. All dendritic measures, with the exception of MDL, increased from primary to supramodal cortex. The present findings are consistent with previous research correlating dendritic measures and cortical function (Larsen, Wainwright, Swanson, & Jacobs, 1994; Scheibel, Conrad, Perdue, Tomiyasu, & Wechsler, 1990; Scheibel et al., 1985) and suggest that dendritic extent is related to the functional capabilities of human cerebral cortex