The Experts below are selected from a list of 6633 Experts worldwide ranked by ideXlab platform

Colby T Manwaring - One of the best experts on this subject based on the ideXlab platform.

  • Managing Temporal Data in a comprehensive modeling environment
    Journal of Hydroinformatics, 2000
    Co-Authors: Norman Jones, James E Nelson, Colby T Manwaring
    Abstract:

    Numerous graphical pre- and post-processors have been developed for numerical modeling. Many of these systems support numerical models that can be used for both steady state and transient simulations. Dealing with transient Data can be particularly difficult in such an environment due to the differing time scales and units associated with transient field Data and boundary conditions. A Data model and a set of functions are presented in this paper as a simple, yet powerful strategy for Managing Temporal Data in a comprehensive modeling environment. The strategy makes it possible to seamlessly integrate multiple types of transient Data and convert the Data to the appropriate values at the desired computational time intervals. In addition to simplifying the creation of transient models, the strategy makes it possible to automate much of the Data manipulation required for model calibration.

Norman Jones - One of the best experts on this subject based on the ideXlab platform.

  • Managing Temporal Data in a comprehensive modeling environment
    Journal of Hydroinformatics, 2000
    Co-Authors: Norman Jones, James E Nelson, Colby T Manwaring
    Abstract:

    Numerous graphical pre- and post-processors have been developed for numerical modeling. Many of these systems support numerical models that can be used for both steady state and transient simulations. Dealing with transient Data can be particularly difficult in such an environment due to the differing time scales and units associated with transient field Data and boundary conditions. A Data model and a set of functions are presented in this paper as a simple, yet powerful strategy for Managing Temporal Data in a comprehensive modeling environment. The strategy makes it possible to seamlessly integrate multiple types of transient Data and convert the Data to the appropriate values at the desired computational time intervals. In addition to simplifying the creation of transient models, the strategy makes it possible to automate much of the Data manipulation required for model calibration.

Kristian Torp - One of the best experts on this subject based on the ideXlab platform.

  • Managing Temporal Data a five part series
    1998
    Co-Authors: Richard T Snodgrass, Michael H Bohlen, Renato Busatto, Curtis E Dyreson, Heidi Gregersen, Dieter Pfoser, Simonas Saltenis, Janne Skyt, Giedrius Slivinskas, Kristian Torp
    Abstract:

    Temporal Data is pervasive, and challenging to manage in SQL. The June through October issues of Database Programming and Design (volume 11, issues 6–10) included a special series on Temporal Databases; the five articles in that series are reproduced here. Three separate case studies: a neonatal intensive care unit, a commercial cattle feed yard, and astronomical star catalogs, were used to illustrate how Temporal applications can be implemented in SQL. The concepts of valid time versus transaction time and of current, sequenced and nonsequenced integrity constraints, queries, and modifications were emphasized. 1 Of Duplicates and Septuplets This special series explores the many issues that arise when attempting to define and manage time-varying Data. Such Data is pervasive. It has been estimated that one of every 50 lines of Database application code involves a date or time value. Data warehouses are by definition time-varying: Ralph Kimball states that every Data warehouse has a time dimension. Often the time-oriented nature of the Data is what lends it value. DBAs and application programmers constantly wrestle with the vagaries of such Data. They find that overlaying simple concepts, such as duplicate prevention, on time-varying Data can be surprisingly subtle and complex. And they are perplexed that trade publications and books do not provide guidance and techniques for handling such Data. The five articles in this series will address this need by presenting specific, easily applied ways to manage timevarying Data, generally in SQL. Each will include concrete examples of code that can be immediately used in ongoing development efforts. Equally important, we will introduce and illustrate new ways to think about Temporal Data, imposing structure on a messy topic. In honor of the McCaughey children, the world’s only known set of living septuplets, this first article will consider duplicates, of which septuplets are just a novel special case. Specifically, we examine the ostensibly simple task of preventing duplicate rows, via a constraint in a table definition. Preventing duplicates using SQL is thought to be trivial, and truly is, when the Data is not time-varying. But when history is retained, things get much dicier. In fact, over such Data several interesting kinds of duplicates can be defined. And, as is so often the case, the most relevant kind is the hardest to prevent, and requires an aggregate or a complex trigger! We’ll first use standard SQL-92, then delve into the machinations required when using DB2, Oracle and Sybase.

Y. Kanamori - One of the best experts on this subject based on the ideXlab platform.

  • Implementing time-interval class for Managing Temporal Data
    Proceedings Ninth International Workshop on Database and Expert Systems Applications (Cat. No.98EX130), 1998
    Co-Authors: T. Amagasa, M. Aritsugi, Y. Kanamori
    Abstract:

    This paper describes a way of implementing a conceptual model for Temporal Data on an object Database system. In fact, the model which we have proposed is implemented as a class library. Any application can employ the library, because it does not depend on specific applications. Furthermore, we propose an enhanced version of Time Index. Temporal queries including a lot of events are effectively processed with the enhanced Time Index.

Hussain Saleem - One of the best experts on this subject based on the ideXlab platform.

  • conceptual fuzzy Temporal relational model ftrm for patient Data
    WSEAS Transactions on Information Science and Applications archive, 2010
    Co-Authors: Aqil Burney, Nadeem Mahmood, Tahseen Ahmed Jilani, Hussain Saleem
    Abstract:

    Classical relational model offer very little built-in support for Managing time varying Data. Relational model mainly deal with crisp Data and not with imprecise Data. Managing Temporal Data in the relational environment keeping the imprecise and fuzzy nature of the Data requires the extension of the relational model. Small numbers of extended models at conceptual level are proposed and there is no significant implementation of such model exists. Importance of developing ontological framework for imprecise Data and Temporal Data is required in many applications such as health and patient Databases. In this paper we propose a Temporal fuzzy ontology for patient Data in a hospital environment. On the basis of the ontology defined we propose a conceptual fuzzy Temporal relational model (FTRM) for handling time varying attributes and fuzzy attributes in the relational Database environment. The proposed model is easy to define, manage and incorporates the important and relevant features in the target fuzzy Temporal relational model.