334 research outputs found

    A first approach to the multipurpose relational database server

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    In this paper, an architecture and an implementation of a multipurpose relational database server are proposed. This architecture enables classical queries to be executed, deductions to be made, and data mining operations to be performed on fuzzy or classical data. The proposal of this integration is to combine several ways of querying different types of data. In order to achieve this, a combination of existing metaknowledge bases and new data catalog elements is presented. We also introduce a language for handling all these data coherently and uniformly on the basis of classical SQL sentences

    AsterixDB: A Scalable, Open Source BDMS

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    AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B+-tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store. Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements

    Fuzzy and uncertain spatio-temporal database models : a constraint-based approach

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    In this paper a constraint-based generalised object-oriented database model is adapted to manage spatiotemporal information. This adaptation is based on the definition of a new data type, which is suited to handle both temporal and spatial information. Generalised constraints are used to describe spatio-temporal data, to enforce integrity rules on databases, to specify the formal semantics of a database scheme and to impose selection criteria for information retrieval

    Proceedings of the first international VLDB workshop on Management of Uncertain Data

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    An extension of ontology based databases to handle preferences

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    1th International Conference on Enterprise Information Systems; Milan; Italy; 6 May 2009 through 10 May 2009Ontologies have been defined to make explicit the semantics of data. With the emergence of the SemanticWeb, the amount of ontological data (or instances) available has increased. To manage such data, Ontology Based DataBases (OBDBs), that store ontologies and their instance data in the same repository have been proposed. These databases are associated with exploitation languages supporting description, querying, etc. on both ontologies and data. However, usually queries return a big amount of data that may be sorted in order to find the relevant ones. Moreover, in the current, few approaches considering user preferences when querying have been developed. Yet this problem is fundamental for many applications especially in the e-commerce domain. In this paper, we first propose an extension of an existing OBDB, called OntoDB through extension of their ontology model in order to support semantic description of preferences. Secondly, an extension of an ontology based query language, called OntoQL defined on OntoDB for querying ontological data with preferences is presented. Finally, an implementation of the proposed extensions are described

    IoTility:Architectural Requirements for Enabling Health IoT Ecosystems

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    The increasing ubiquity of the Internet of Things (IoT) has the potential to drastically alter the way healthcare systems are utilized at home or in a care environment. Smart things offer new ways to assist in general patient wellness, such as promoting an active and healthy lifestyle and simplifying treatment management. We believe smart health things bring new requirements not typically addressed in traditional IoT systems, and that an architecture targeting these devices must address such requirements to fully utilize their potential and safe usage. We believe such an architecture will help improve adoption and efficacy, closing gaps between the variety of emerging health IoT systems. In this paper, we present a number of requirements we consider integral to the continued expansion of the digital health IoT ecosystem (Health IoT). We consider the current landscape of IoT in relation to these requirements and present solutions that address two pressing requirements: 1) democratizing mobile health apps (giving users control and ownership over their app and data), and 2) making mobile apps act and behave like any other thing in an IoT. We present an implementation and evaluation of these Health IoT requirements to show how health-specific solutions can drive and influence the design of more generalized IoT architectures

    Relative-fuzzy: a novel approach for handling complex ambiguity for software engineering of data mining models

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    There are two main defined classes of uncertainty namely: fuzziness and ambiguity, where ambiguity is ‘one-to-many’ relationship between syntax and semantic of a proposition. This definition seems that it ignores ‘many-to-many’ relationship ambiguity type of uncertainty. In this thesis, we shall use complex-uncertainty to term many-to-many relationship ambiguity type of uncertainty. This research proposes a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. The proposed approach is based on Relative-Fuzzy Logic (RFL), a novel type of fuzzy logic. RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic. To achieve the goal of proposing RFL, a question is needed to be answered, which is: how these two approaches; i.e. fuzzy logic and possible-world, can be mixed to produce a new membership value set (and later logic) that able to handle fuzziness and multiple viewpoints at the same time? Achieving such goal comes via providing possible world logic the ability to quantifying multiple viewpoints and also model fuzziness in each of these multiple viewpoints and expressing that in a new set of membership value. Furthermore, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net has been developed in this research, along with a new learning algorithm and new recalling algorithm. The architecture, learning algorithm and recalling algorithm of ML/RFL-Based Net follow the principles of RFL. This new type of HNN is considered to be a RFL computation machine. The ability of the Relative Fuzzy-based DM prediction model to tackle the problem of complex ambiguity type of uncertainty has been tested. Special-purpose Integrated Development Environment (IDE) software, which generates a DM prediction model for speech recognition, has been developed in this research too, which is called RFL4ASR. This special purpose IDE is an extension of the definition of the traditional IDE. Using multiple sets of TIMIT speech data, the prediction model of type ML/RFL-Based Net has classification accuracy of 69.2308%. This accuracy is higher than the best achievements of WEKA data mining machines given the same speech data

    GESTI 3N DE DATOS DIFUSOS: ATRIBUTOS TIPO 2 Y TIPO 3 EN BASES DE DATOS RELACIONALES

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    El proyecto "Desaf\uedo del Modelo Relacional Difuso" tiene como objetivo dar soluci\uf3n a problemas abiertos sobre el tratamiento de datos difusos en bases de datos relacionales. En el presente trabajo se reportan varios resultados de este proyecto. Algunos conciernen a datos difusos Tipo 2, los cuales son basados en distribuciones de posibilidad. Otros son sobre datos Tipo 3, basados en relaciones de similitud. Uno de los resultados m\ue1s emblem\ue1ticos es un prototipo de Gestor de Bases de Datos para manejo de datos difusos. Este prototipo es llamado fuzzydoDB y consiste en una extensi\uf3n a PostgreSQL. Esta extensi\uf3n soporta consultas con ordenamiento y agrupamiento sobre datos difusos Tipo 2 y Tipo 3. Adicionalmente, se muestra un portal web tambi\ue9n llamado fuzzydoDB, cuya finalidad es dar a conocer los resultados del proyecto. Palabras clave: modelo relacional difuso, SGBD, fuzzydoDB, datos difusos, consulta difusa. ABSTRACT The project "Challenges of the Fuzzy Relational Model" aims to solve open problems about the treatment of fuzzy data in relational databases. In the present work several results of this project are reported. Some concern with Type 2 fuzzy attributes, which are based on probability distributions. Others are on Type 3 fuzzy data, based on similarity relationships. One of the most emblematic results is a prototype of Database Management System for fuzzy data management. This prototype is called fuzzydoDB and consists of an extension to PostgreSQL. This extension supports queries with ordering and grouping on Type 2 and Type 3 fuzzy data. Additionally, a web portal also called fuzzydoDB is shown, whose purpose is to publicize the results of the project. Keywords: fuzzy relational model, DBMS, fuzzydoDB, fuzzy data, fuzzy query. <br

    Aspects of dealing with imperfect data in temporal databases

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    In reality, some objects or concepts have properties with a time-variant or time-related nature. Modelling these kinds of objects or concepts in a (relational) database schema is possible, but time-variant and time-related attributes have an impact on the consistency of the entire database. Therefore, temporal database models have been proposed to deal with this. Time itself can be at the source of imprecision, vagueness and uncertainty, since existing time measuring devices are inherently imperfect. Accordingly, human beings manage time using temporal indications and temporal notions, which may contain imprecision, vagueness and uncertainty. However, the imperfection in human-used temporal indications is supported by human interpretation, whereas information systems need extraordinary support for this. Several proposals for dealing with such imperfections when modelling temporal aspects exist. Some of these proposals consider the basis of the system to be the conversion of the specificity of temporal notions between used temporal expressions. Other proposals consider the temporal indications in the used temporal expressions to be the source of imperfection. In this chapter, an overview is given, concerning the basic concepts and issues related to the modelling of time as such or in (relational) database models and the imperfections that may arise during or as a result of this modelling. Next to this, a novel and currently researched technique for handling some of these imperfections is presented
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