Search results for "InformationSystems_DATABASEMANAGEMENT"

showing 10 items of 50 documents

Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing

2018

International audience; Bayesian networks (BNs) represent a promising approach for the aggregation of multiple uncertainty sources in manufacturing networks and other engineering systems for the purposes of uncertainty quantification, risk analysis, and quality control. A standardized representation for BN models will aid in their communication and exchange across the web. This article presents an extension to the predictive model markup language (PMML) standard for the representation of a BN, which may consist of discrete variables, continuous variables, or their combination. The PMML standard is based on extensible markup language (XML) and used for the representation of analytical models…

0209 industrial biotechnologyDesignComputer sciencecomputer.internet_protocol02 engineering and technologycomputer.software_genreBayesian inferenceIndustrial and Manufacturing EngineeringArticle[SPI]Engineering Sciences [physics]020901 industrial engineering & automationPMML0202 electrical engineering electronic engineering information engineeringanalyticsUncertainty quantificationMonte-Carlouncertaintycomputer.programming_languageParsingBayesian networkInformationSystems_DATABASEMANAGEMENTstandardPython (programming language)XMLComputer Science ApplicationsmanufacturingComputingMethodologies_PATTERNRECOGNITIONBayesian networksControl and Systems EngineeringSurface-RoughnessData analysisPredictive Model Markup Language020201 artificial intelligence & image processingData miningcomputerXML
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STEP 0: Initial Scenario

2011

The main purpose of this video-tutorial is to show you how to clone a DB2 subsystem to other subsystem running in the same Z/OS LPAR. Each step is explained with a video (screen cast). It is supossed that you have two DB2 subsystems, in this case DB8G (source) and DBF (target), both up & running.The procedure is automated in three main steps contained in library SYSADM.ODBCLONE. First step is related with the physical COPY from SOURCE datasets to TARGET DB2 datasets. While the remaining, are related with making target DB2 BSDS & CATALOG consistent with the location of the DATASETS copied from source subsystem

33 Ciències TecnológiquesInformationSystems_DATABASEMANAGEMENT
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STEP 3: Restart Target DB2

2011

Finally, we RESTART TARGET DB2 and it's stopped databases.

33 Ciències TecnológiquesMathematicsofComputing_NUMERICALANALYSISInformationSystems_DATABASEMANAGEMENTComputingMethodologies_GENERAL
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Data for: Impact of pre-hospital renal function on the detection of acute kidney injury in acute decompensated heart failure

2021

Database THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

Acute Heart FailureInformationSystems_DATABASEMANAGEMENTOtherInterdisciplinary sciencesAcute Kidney Injury
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Handling Evolving Data Warehouse Requirements

2015

A data warehouse is a dynamic environment and its business requirements tend to evolve over time, therefore, it is necessary not only to handle changes in data warehouse data, but also to adjust a data warehouse schema in accordance with changes in requirements. In this paper, we propose an approach to propagate modified data warehouse requirements in data warehouse schemata. The approach supports versions of data warehouse schemata and employs the requirements formalization metamodel and multiversion data warehouse metamodel to identify necessary changes in a data warehouse.

Business requirementsDatabaseComputer scienceInformationSystems_INFORMATIONSYSTEMSAPPLICATIONSSchema (psychology)InformationSystems_DATABASEMANAGEMENTcomputer.software_genrecomputerData warehouseMetamodeling
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Evolution-Oriented User-Centric Data Warehouse

2011

Data warehouses tend to evolve, because of changes in data sources and business requirements of users. All these kinds of changes must be properly handled, therefore, data warehouse development is never-ending process. In this paper we propose the evolution-oriented user-centric data warehouse design, which on the one hand allows to manage data warehouse evolution automatically or semi-automatically, and on the other hand it provides users with the understandable, easy and transparent data analysis possibilities. The proposed approach supports versions of data warehouse schemata and data semantics.

Business requirementsDatabaseComputer scienceProcess (engineering)Data transformationInformationSystems_DATABASEMANAGEMENTDimensional modelingcomputer.software_genrecomputerData warehouseUser-centered designData semantics
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ViziQuer: A Visual Notation for RDF Data Analysis Queries

2019

Visual SPARQL query notations aim at easing the RDF data querying task. At the current state of the art there is still no generally accepted visual graph-based notation suitable to describe RDF data analysis queries that involve aggregation and subqueries. In this paper we present a visual diagram-centered notation for SPARQL select query formulation, capable to handle aggregate/statistics queries and hierarchic queries with subquery structure. The notation is supported by a web-based prototype tool. We present the notation examples, describe its syntax and semantics and describe studies with possible end users, involving both IT and medicine students.

Computer scienceEnd userProgramming languageInformationSystems_INFORMATIONSTORAGEANDRETRIEVAL010401 analytical chemistry05 social sciencesQuery formulationInformationSystems_DATABASEMANAGEMENTcomputer.file_formatNotationcomputer.software_genre01 natural sciences0104 chemical sciencesSPARQLGraph (abstract data type)0501 psychology and cognitive sciencesVisual notationRDFcomputer050107 human factors
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Metadata to Support Data Warehouse Evolution

2009

The focus of this chapter is metadata necessary to support data warehouse evolution. We present the data warehouse framework that is able to track evolution process and adapt data warehouse schemata and data extraction, transformation, and loading (ETL) processes. We discuss the significant part of the framework, the metadata repository that stores information about the data warehouse, logical and physical schemata and their versions. We propose the physical implementation of multiversion data warehouse in a relational DBMS. For each modification of a data warehouse schema, we outline the changes that need to be made to the repository metadata and in the database.

Data elementInformation retrievalDatabaseComputer scienceInformationSystems_DATABASEMANAGEMENTcomputer.software_genreData warehouseMetadata repositorySchema evolutionMetadataRelational database management systemData extractionSchema (psychology)computer
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Spatio-temporal Schema Integration with Validation: A Practical Approach

2005

We propose to enhance a schema integration process with a validation phase employing logic-based data models. In our methodology, we validate the source schemas against the data model; the inter-schema mappings are validated against the semantics of the data model and the syntax of the correspondence language. In this paper, we focus on how to employ a reasoning engine to validate spatio-temporal schemas and describe where the reasoning engine is plugged into our integration methodology. The validation phase distinguishes our integration methodology from other approaches. We shift the emphasis on automation from the a priori discovery to the a posteriori checking of the inter-schema mapping…

Data modelDescription logicComputer scienceData integritySchema (psychology)InformationSystems_DATABASEMANAGEMENTSemantic reasonerData miningLogic modelcomputer.software_genrecomputerComputer Science::DatabasesData modeling
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Additional file 1 of Ethnobotany of dye plants in Southern Italy, Mediterranean Basin: floristic catalog and two centuries of analysis of traditional…

2020

Additional file 1: Supplementary File 1. A wider and more complete database and the currently available data.

Data_FILESInformationSystems_DATABASEMANAGEMENT
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