An expert system for environmental data management

1995 ◽  
Vol 34 (2) ◽  
pp. 185-195
Author(s):  
Petr Berka ◽  
Petr Jirku
Keyword(s):  
Expert System ◽  
Data Management ◽  
Environmental Data

Environmental Data Management

1976 ◽  
Vol 102 (2) ◽  
pp. 255-264
Author(s):  
George K. Young ◽  
G.F. Tierney
Keyword(s):  
Data Management ◽  
Environmental Data

scholarly journals BEXIS2: A FAIR-aligned data management system for biodiversity, ecology and environmental data

2021 ◽  
Vol 9 ◽  
Author(s):  
Javad Chamanara ◽  
Jitendra Gaikwad ◽  
Roman Gerlach ◽  
Alsayed Algergawy ◽  
Andreas Ostrowski ◽  
...  
Keyword(s):  
Data Management ◽  
Management System ◽  
Research Data ◽  
Environmental Data ◽  
The Self ◽  
Data Management System ◽  
Maturity Model ◽  
Ecological Data ◽  
Self Assessment ◽  
Research Data Management

Obtaining fit-to-use data associated with diverse aspects of biodiversity, ecology and environment is challenging since often it is fragmented, sub-optimally managed and available in heterogeneous formats. Recently, with the universal acceptance of the FAIR data principles, the requirements and standards of data publications have changed substantially. Researchers are encouraged to manage the data as per the FAIR data principles and ensure that the raw data, metadata, processed data, software, codes and associated material are securely stored and the data be made available with the completion of the research. We have developed BEXIS2 as an open-source community-driven web-based research data management system to support research data management needs of mid to large-scale research projects with multiple sub-projects and up to several hundred researchers. BEXIS2 is a modular and extensible system providing a range of functions to realise the complete data lifecycle from data structure design to data collection, data discovery, dissemination, integration, quality assurance and research planning. It is an extensible and customisable system that allows for the development of new functions and customisation of its various components from database schemas to the user interface layout, elements and look and feel. During the development of BEXIS2, we aimed to incorporate key aspects of what is encoded in FAIR data principles. To investigate the extent to which BEXIS2 conforms to these principles, we conducted the self-assessment using the FAIR indicators, definitions and criteria provided in the FAIR Data Maturity Model. Even though the FAIR data maturity model is developed initially to judge the conformance of datasets, the self-assessment results indicated that BEXIS2 remarkably conforms and supports FAIR indicators. BEXIS2 strongly conforms to the indicators Findability and Accessibility. The indicator Interoperability is moderately supported as of now; however, for many of the lesssupported facets, we have concrete plans for improvement. Reusability (as defined by the FAIR data principles) is partially achieved. This paper also illustrates community deployment examples of the BEXIS2 instances as success stories to exemplify its capacity to meet the biodiversity and ecological data management needs of differently sized projects and serve as an organisational research data management system.

WATER QUALITY MODELLING WITHIN THE RAISON EXPERT SYSTEM

1994 ◽  
Vol 02 (04) ◽  
pp. 453-466 ◽  
Author(s):  
W.G. BOOTY ◽  
I.W.S. WONG
Keyword(s):  
Water Quality ◽  
Expert System ◽  
Intelligent Systems ◽  
Regional Analysis ◽  
Auxiliary Information ◽  
Database Management System ◽  
Environmental Data ◽  
Microcomputer System ◽  
Analysis Tool ◽  
Fully Integrated

The RAISON (Regional Analysis by Intelligent Systems ON a microcomputer) system is a multimedia environmental data analysis tool-kit that contains a fully integrated database management system, spreadsheet, G.I.S., graphics, statistics, modelling and expert system modules as well as a programming language that allows the user to create specialized applications. This paper presents case studies of modelling applications which illustrate the utility of the system in assisting the users of water quality models to make the models more user friendly. This is accomplished through the use of added visualization of inputs and auxiliary information as well as on-line knowledge added. This system also enables the user to represent model results in numerous graphical forms as well as animated results presented on maps. In addition, it has the ability to interface models with expert systems to aid in the selection and use of models and in the interpretation of results.

Differential Learning Expert System in Data Management

2009 ◽  
pp. 597-604
Author(s):  
R. Manjunath
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Management ◽  
Knowledge Acquisition ◽  
User Interfaces ◽  
High Performance ◽  
Evolutionary Model ◽  
Human Expert ◽  
Incremental Development ◽  
Differential Learning

Expert systems have been applied to many areas of research to handle problems effectively. Designing and implementing an expert system is a difficult job, and it usually takes experimentation and experience to achieve high performance. The important feature of an expert system is that it should be easy to modify. They evolve gradually. This evolutionary or incremental development technique has to be noticed as the dominant methodology in the expert-system area. The simple evolutionary model of an expert system is provided in B. Tomic, J. Jovanovic, & V. Devedzic, 2006. Knowledge acquisition for expert systems poses many problems. Expert systems depend on a human expert to formulate knowledge in symbolic rules. The user can handle the expert systems by updating the rules through user interfaces (J. Jovanovic, D. Gasevic, V. Devedzic, 2004). However, it is almost impossible for an expert to describe knowledge entirely in the form of rules. An expert system may therefore not be able to diagnose a case that the expert is able to. The question is how to extract experience from a set of examples for the use of expert systems.

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