84 research outputs found

    Multidimensional modeling and analysis of large and complex watercourse data: an OLAP-based solution

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    International audienceThis paper presents the application of Data Warehouse (DW) and On-Line Analytical Processing (OLAP) technologies to the field of water quality assessment. The European Water Framework Directive (DCE, 2000) underlined the necessity of having operational tools to help in the interpretation of the complex and abundant information regarding running waters and their functioning. Several studies have exemplified the interest in DWs for integrating large volumes of data and in OLAP tools for data exploration and analysis. Based on free software tools, we propose an extensible relational OLAP system for the analysis of physicochemical and hydrobiological watercourse data. This system includes: (i) two data cubes; (ii) an Extract, Transform and Load (ETL) tool for data integration; and (iii) tools for OLAP exploration. Many examples of OLAP analysis (thematic, temporal, spatiotemporal, and multiscale) are provided. We have extended an existing framework with complex aggregate functions that are used to define complex analysis indicators. Additional analysis dimensions are also introduced to allow their calculation and also for purposes of rendering information. Finally, we propose two strategies to address the problem of summarizing heterogeneous measurement units by: (i) transforming source data at the ETL tier, and (ii) introducing an additional analysis dimension at the OLAP server tier

    Flexible Integration and Efficient Analysis of Multidimensional Datasets from the Web

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    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

    ROOMS:ROlap based Occupation Measurement System

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    Flexible Integration and Efficient Analysis of Multidimensional Datasets from the Web

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    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

    Energy4People: Sistema de soporte para la toma de decisiones en el dominio de las energías renovables orientado al usuario no experto

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    Este proyecto propone un sistema de soporte a la toma de decisiones, en el dominio de las energías renovables y orientado a usuarios no expertos. El sistema utiliza de manera transparente para el usuario técnicas de BI como la integración de datos, que ha sido aplicada a datos procedentes de satélites de la NASA, estructuras multidimensionales como cubos OLAP y modelos de datos orientados al análisis como el esquema de estrella de un data warehouse. Con este sistema, por ejemplo, un usuario no experto puede visualizar y entender dónde es mejor realizar una inversión en paneles solares

    On the mismatch between multidimensionality and SQL

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    ROLAP tools are intended to ease information analysis and navigation through the whole Data Warehouse. These tools automatically generate a query according to the multidimensional operations performed by the end-user, using the relational database technology to implement multidimensionality and consequently, automatically translating multidimensional operations to SQL. In this paper, we consider this automatic translation process in detail and to do so, we present an exhaustive comparative (both theoretical and practical) between the multidimensional algebra and the relational one. Firstly, we discuss about the necessity of a multidimensional algebra with regard to the relational one and later, we thoroughly study those considerations to be made to guarantee the correctness of a cube-query (an SQL query making multidimensional sense). With this aim, we analyze the multidimensional algebra expressiveness with regard to SQL pointing out the features a query must satisfy to make multidimensional sense and we also focus on those problems that can arise in a cube-query due to SQL intrinsic restrictions. The SQL translation of an isolated operation does not represent a problem, but when mixing up the modifications brought about by a set of operations in a single cube-query, some conflicts derived from SQL could emerge depending on the operations involved. Therefore, if these problems are not detected and treated appropriately, the automatic translation can retrieve unexpected results.Postprint (published version

    The Use of Olap Reporting Technology to Improve Patient Care Services Decision Making Within the University Health Center

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    The purpose of this paper is to demonstrate that it is feasible for the student health center to leverage existing clinical data in a data warehouse by using OLAP reporting in order to improve patient care and health care services decision making. Historically, University health care centers have relied mainly on operational data sources for critical health care decision making. These sources of data do not contain enough information to allow health officials to recognize trends or predict how future changes in health care services might vastly improve overall heath care. Four proof of concept artifacts are constructed through design science research methodology, and a feasibility study is presented to build the case for the adoption of OLAP reporting technology. The study concludes that it is feasible to implement an OLAP reporting infrastructure at the student health center if physicians, clinical staff, and administration clearly define the need for the new technology, develop proper data extraction, loading, and transformation strategy, and adequately provide project management and data warehouse design towards the implementation of the solution

    Automating the multidimensional design of data warehouses

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    Les experiències prèvies en l'àmbit dels magatzems de dades (o data warehouse), mostren que l'esquema multidimensional del data warehouse ha de ser fruit d'un enfocament híbrid; això és, una proposta que consideri tant els requeriments d'usuari com les fonts de dades durant el procés de disseny.Com a qualsevol altre sistema, els requeriments són necessaris per garantir que el sistema desenvolupat satisfà les necessitats de l'usuari. A més, essent aquest un procés de reenginyeria, les fonts de dades s'han de tenir en compte per: (i) garantir que el magatzem de dades resultant pot ésser poblat amb dades de l'organització, i, a més, (ii) descobrir capacitats d'anàlisis no evidents o no conegudes per l'usuari.Actualment, a la literatura s'han presentat diversos mètodes per donar suport al procés de modelatge del magatzem de dades. No obstant això, les propostes basades en un anàlisi dels requeriments assumeixen que aquestos són exhaustius, i no consideren que pot haver-hi informació rellevant amagada a les fonts de dades. Contràriament, les propostes basades en un anàlisi exhaustiu de les fonts de dades maximitzen aquest enfocament, i proposen tot el coneixement multidimensional que es pot derivar des de les fonts de dades i, conseqüentment, generen massa resultats. En aquest escenari, l'automatització del disseny del magatzem de dades és essencial per evitar que tot el pes de la tasca recaigui en el dissenyador (d'aquesta forma, no hem de confiar únicament en la seva habilitat i coneixement per aplicar el mètode de disseny elegit). A més, l'automatització de la tasca allibera al dissenyador del sempre complex i costós anàlisi de les fonts de dades (que pot arribar a ser inviable per grans fonts de dades).Avui dia, els mètodes automatitzables analitzen en detall les fonts de dades i passen per alt els requeriments. En canvi, els mètodes basats en l'anàlisi dels requeriments no consideren l'automatització del procés, ja que treballen amb requeriments expressats en llenguatges d'alt nivell que un ordenador no pot manegar. Aquesta mateixa situació es dona en els mètodes híbrids actual, que proposen un enfocament seqüencial, on l'anàlisi de les dades es complementa amb l'anàlisi dels requeriments, ja que totes dues tasques pateixen els mateixos problemes que els enfocament purs.En aquesta tesi proposem dos mètodes per donar suport a la tasca de modelatge del magatzem de dades: MDBE (Multidimensional Design Based on Examples) and AMDO (Automating the Multidimensional Design from Ontologies). Totes dues consideren els requeriments i les fonts de dades per portar a terme la tasca de modelatge i a més, van ser pensades per superar les limitacions dels enfocaments actuals.1. MDBE segueix un enfocament clàssic, en el que els requeriments d'usuari són coneguts d'avantmà. Aquest mètode es beneficia del coneixement capturat a les fonts de dades, però guia el procés des dels requeriments i, conseqüentment, és capaç de treballar sobre fonts de dades semànticament pobres. És a dir, explotant el fet que amb uns requeriments de qualitat, podem superar els inconvenients de disposar de fonts de dades que no capturen apropiadament el nostre domini de treball.2. A diferència d'MDBE, AMDO assumeix un escenari on es disposa de fonts de dades semànticament riques. Per aquest motiu, dirigeix el procés de modelatge des de les fonts de dades, i empra els requeriments per donar forma i adaptar els resultats generats a les necessitats de l'usuari. En aquest context, a diferència de l'anterior, unes fonts de dades semànticament riques esmorteeixen el fet de no tenir clars els requeriments d'usuari d'avantmà.Cal notar que els nostres mètodes estableixen un marc de treball combinat que es pot emprar per decidir, donat un escenari concret, quin enfocament és més adient. Per exemple, no es pot seguir el mateix enfocament en un escenari on els requeriments són ben coneguts d'avantmà i en un escenari on aquestos encara no estan clars (un cas recorrent d'aquesta situació és quan l'usuari no té clares les capacitats d'anàlisi del seu propi sistema). De fet, disposar d'uns bons requeriments d'avantmà esmorteeix la necessitat de disposar de fonts de dades semànticament riques, mentre que a l'inversa, si disposem de fonts de dades que capturen adequadament el nostre domini de treball, els requeriments no són necessaris d'avantmà. Per aquests motius, en aquesta tesi aportem un marc de treball combinat que cobreix tots els possibles escenaris que podem trobar durant la tasca de modelatge del magatzem de dades.Previous experiences in the data warehouse field have shown that the data warehouse multidimensional conceptual schema must be derived from a hybrid approach: i.e., by considering both the end-user requirements and the data sources, as first-class citizens. Like in any other system, requirements guarantee that the system devised meets the end-user necessities. In addition, since the data warehouse design task is a reengineering process, it must consider the underlying data sources of the organization: (i) to guarantee that the data warehouse must be populated from data available within the organization, and (ii) to allow the end-user discover unknown additional analysis capabilities.Currently, several methods for supporting the data warehouse modeling task have been provided. However, they suffer from some significant drawbacks. In short, requirement-driven approaches assume that requirements are exhaustive (and therefore, do not consider the data sources to contain alternative interesting evidences of analysis), whereas data-driven approaches (i.e., those leading the design task from a thorough analysis of the data sources) rely on discovering as much multidimensional knowledge as possible from the data sources. As a consequence, data-driven approaches generate too many results, which mislead the user. Furthermore, the design task automation is essential in this scenario, as it removes the dependency on an expert's ability to properly apply the method chosen, and the need to analyze the data sources, which is a tedious and timeconsuming task (which can be unfeasible when working with large databases). In this sense, current automatable methods follow a data-driven approach, whereas current requirement-driven approaches overlook the process automation, since they tend to work with requirements at a high level of abstraction. Indeed, this scenario is repeated regarding data-driven and requirement-driven stages within current hybrid approaches, which suffer from the same drawbacks than pure data-driven or requirement-driven approaches.In this thesis we introduce two different approaches for automating the multidimensional design of the data warehouse: MDBE (Multidimensional Design Based on Examples) and AMDO (Automating the Multidimensional Design from Ontologies). Both approaches were devised to overcome the limitations from which current approaches suffer. Importantly, our approaches consider opposite initial assumptions, but both consider the end-user requirements and the data sources as first-class citizens.1. MDBE follows a classical approach, in which the end-user requirements are well-known beforehand. This approach benefits from the knowledge captured in the data sources, but guides the design task according to requirements and consequently, it is able to work and handle semantically poorer data sources. In other words, providing high-quality end-user requirements, we can guide the process from the knowledge they contain, and overcome the fact of disposing of bad quality (from a semantical point of view) data sources.2. AMDO, as counterpart, assumes a scenario in which the data sources available are semantically richer. Thus, the approach proposed is guided by a thorough analysis of the data sources, which is properly adapted to shape the output result according to the end-user requirements. In this context, disposing of high-quality data sources, we can overcome the fact of lacking of expressive end-user requirements.Importantly, our methods establish a combined and comprehensive framework that can be used to decide, according to the inputs provided in each scenario, which is the best approach to follow. For example, we cannot follow the same approach in a scenario where the end-user requirements are clear and well-known, and in a scenario in which the end-user requirements are not evident or cannot be easily elicited (e.g., this may happen when the users are not aware of the analysis capabilities of their own sources). Interestingly, the need to dispose of requirements beforehand is smoothed by the fact of having semantically rich data sources. In lack of that, requirements gain relevance to extract the multidimensional knowledge from the sources.So that, we claim to provide two approaches whose combination turns up to be exhaustive with regard to the scenarios discussed in the literaturePostprint (published version

    A UML framework for OLAP conceptual modeling

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    Data warehouses are used by organizations around the world to store huge volumes of historical data. Ultimately, the purpose of the warehouse is to allow decision makers to assess both the history and, more importantly, the future of the organization. In practice, the capacity to make meaningful decisions is further supported through the use of Online Analytical Processing (OLAP) applications that provide more sophisticated representations of the warehouse data. In order to do this, OLAP systems rely on a multidimensional conceptual data model that represents the core elements of the data warehouse, as well as the relationships between them. Currently, there is no definitive conceptual model for this kind of environment. It is therefore quite difficult for data warehouse designers to express the kinds of complex analytical requirements which arise in real-world situations. In this thesis, we propose a robust and flexible conceptual model that can be used to represent multi-dimensional OLAP domains. Specifically, we present a profile extension of the Unified Modeling Language (UML) that consists of a set of stereotypes, constraints and tagged values that elegantly represent multi-dimensional properties at the conceptual level. We also make use of the Object Constraint Language (OCL) to ensure the correctness and completeness of the specification, thereby avoiding an arbitrary use of the basic components. Furthermore, we demonstrate how the new OLAP profile is utilized in MagicDraw, one of the leading UML development tools. The end result is an OLAP Modeling Environment (OME) that should significantly reduce development time, as well as improving the quality of the analytical interface for the end user

    Real market concentration through partial acquisitions

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    We study horizontal partial acquisitions in an oligopolistic industry in the absence of synergies. Contrary to existing results, we find that a dominant shareholder may choose to acquire shares in a competitor although the aggregate profit of the group of firms under his control, and even the greater group of firms in which he has a stake, is reduced. This is due to a “favorite” effect: after the acquisition, the dominant shareholder will favor the firm in which he eventually holds the relatively higher share to the detriment of shareholders of the other firms. For this reason, a block of shares can be bought at a discount when the value of the firm of the initiator decreases post acquisition. Moreover, we show that the existence of initial silent toeholds in rivals enhances the incentive for a dominant shareholder to buy shares in other firms in the industry, whereas controlling ones may discourage them.Horizontal partial acquisitions; Real market concentration; Dominant shareholder; Minority shareholders; Silent interests
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