Data Integration Patterns in the Context of Enterprise Data Management

2019 ◽  
pp. 235-244
Author(s):  
Roland Petrasch
Keyword(s):  
Data Integration ◽  
Data Management

scholarly journals DBE2 – Management of experimental data for the VANTED system

2011 ◽  
Vol 8 (2) ◽  
pp. 85-94
Author(s):  
Hendrik Mehlhorn ◽  
Falk Schreiber
Keyword(s):  
Experimental Data ◽  
Information System ◽  
Data Integration ◽  
Data Management ◽  
Data Storage ◽  
Controlled Vocabulary ◽  
Biological Experiment ◽  
Experiment Data ◽  
Measurement Units ◽  
Persistent Data

Summary DBE2 is an information system for the management of biological experiment data from different data domains in a unified and simple way. It provides persistent data storage, worldwide accessibility of the data and the opportunity to load, save, modify, and annotate the data. It is seamlessly integrated in the VANTED system as an add-on, thereby extending the VANTED platform towards data management. DBE2 also utilizes controlled vocabulary from the Ontology Lookup Service to allow the management of terms such as substance names, species names, and measurement units, aiming at an eased data integration.

Data Analysis and Integration in Healthcare

2019 ◽  
pp. 220-234
Author(s):  
Kara S. Evans ◽  
Elizabeth Baoying Wang
Keyword(s):  
Data Analysis ◽  
Data Integration ◽  
Drug Interactions ◽  
Data Management ◽  
Healthcare System ◽  
Human Body ◽  
Healthcare Providers ◽  
General Wellbeing ◽  
Missed Diagnoses

Healthcare providers treat a plethora of conditions associated with the human body for a patient to achieve optimal healthiness. However, aspects of a patients' entire wellbeing can often be overlooked, which leads to issues such as drug interactions, missed diagnoses, and other gaps in care. Healthcare can benefit from implementing better data management and integration to improve data analysis, which could bridge gaps in care. This chapter will explain data analysis and data integration, why they are pertinent in the healthcare system, and their associated rewards and challenges. After analyzing these healthcare facets, this chapter will conclude with a proposal for healthcare providers to leverage technology for patients' general wellbeing and a healthier population.

Research on the Expert System for Sweet Corn Standard Production Based on Heterologous Data Integration Technology

2013 ◽  
Vol 655-657 ◽  
pp. 1730-1733
Author(s):  
Lin Peng ◽  
Qiang Zheng ◽  
Zhao Rong Liu
Keyword(s):  
Expert System ◽  
Data Integration ◽  
Data Management ◽  
Sweet Corn ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Integration Technology ◽  
Shared Data ◽  
Heterogeneous Data Sources ◽  
Standard Production

To better share agricultural information in existed agricultural informatization condition, and to meet agro-departments new needs about local self-governed and global shared data management during standardized production of the sweet corn, this paper provides a method of integrated sharing of heterogeneous data sources to apply to standardized product of the sweet corn. This method solves the data integration and sharing problems during standardized production of the sweet corn. In this paper, the expert system for sweet corn standard production which is ability to combine heterogeneous data is constructed. This system is proved to be reliable, perform well and it is easy to operate.

scholarly journals Scientific Data Management Center Scientific Data Integration

2003 ◽  
Author(s):  
T J Critchlow ◽  
L Liu ◽  
C Pu ◽  
A Gupta ◽  
B Ludaescher ◽  
...  
Keyword(s):  
Data Integration ◽  
Data Management ◽  
Scientific Data ◽  
Scientific Data Management

Improving Model and Data Integration Using MOSAIC as Central Data Management Platform

2014 ◽  
Vol 86 (7) ◽  
pp. 1130-1136 ◽  
Author(s):  
Robert Kraus ◽  
Sandra Fillinger ◽  
Gregor Tolksdorf ◽  
Duc H. Minh ◽  
Victor A. Merchan-Restrepo ◽  
...  
Keyword(s):  
Data Integration ◽  
Data Management ◽  
Management Platform ◽  
Data Management Platform ◽  
Central Data

OnGIS

2019 ◽  
Vol 15 (1) ◽  
pp. 1-20
Author(s):  
Marek Smid
Keyword(s):  
Data Integration ◽  
Data Management ◽  
Geospatial Data ◽  
Data Sources ◽  
Query Execution ◽  
Spatial Query ◽  
Matching Algorithm ◽  
Query Containment ◽  
Data Source

Geospatial data sources are heterogeneous and backed by different data management technologies. This brings problems in data integration as well as their subsequent interpretation. This article proposes a technique for choosing the relevant data source out of many such sources, given a complex spatial query. Each source is described with a set of prototypical queries that are algorithmically arranged into a lattice. Upon query execution, the lattice is searched for an element matching best the input query. The matching algorithm makes use of the GeoSPARQL query containment enhanced with OWL 2 QL semantics. The technique is implemented in a prototypical system called OnGIS.

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