Semantic-Driven Visualized Model for Ecological Datasets
Large volume of datasets in ecological science requires an understanding of metadata standards and assessment techniques in ecological research findings among diverse research groups. It requires a sharing medium of technical assistance and comprehensive definition of interpretations about the modeling concepts. It can lead to adhoc solutions subjected to growth and change in course of new ecological improvements and methodologies. It urges the need of self-learning initiative to understand the terms with their semantic relationships. These requirements can be mapped to the creation of ontology schema of ecological datasets and visual-information driven models as an initiative. This strategy is clearly explained as follows:
• Collaboration of sharing and usage of datasets for analytical assessments by researchers and information managers make request/response to other research teams by queries and derive in a comprehensive description in their documentations for future references can be mapped in the form of observational metadata standards.
• These observational metadata standards can be stored as observational ontology schema.
• This schema can have technological intelligence of automated entity-relationship model creation for semantic identification and dependencies among the data standards.
• In extension to this semantic entity-relationship model (ERD), application of additional visualized models such as Unified Modeling Language (UML) can be used with the transformation of entities to different components constructs.
This concept can solve the information entropy problem of platform incompatibility of technological advancements and creates the impact of...
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...se recordings of observations in ontology schema along with the data model reduce the work effort of research groups to self-examine the behaviour of the datasets with more semantic relationships. Designing the data model can be automated and use as a visual-documentation standard throughout new findings.
FUTURE WORK The implementation of the data model through database schema information retrieval algorithms needed to be explored. In some situations, tracing package in the observed records is required to be the extension of this work.
Fig 4.Representation of ERD model in Observational Ontology Schema
ACKNOWLEDGEMENT
We thank the Department of Computer Science and Engineering of Velammal College of Engineering and Technology, Madurai to encourage and provide infrastructure for the preparation of this journal work.
The relational model consist of a relational structure, a set of integrity rules, and data manipulation operations. The relational structure is based on the representation of data in the form of tables. A table contains rows and columns, with each row representing an individual record, and each column representing a field for each record. Tables are related via indirect indexes of primary and foreign keys. The operations that are performed on these tables in order to store, manipulate and access this data include union, intersection, join, division, restriction, projection, assignment, difference, and product.
As defined by Kroenke Database is an integrated, self-describing collection of related data. Data is stored in a uniform way, typically all in one place- for example, a single physical computer. A database maintains a description of the data it contains and the data has some relationship to other data in the databa...
...el that's closely aligned with the software program’s object model. Obviously, an OODBMS may have a physical data model optimized for those types of logical data model it needs.
Key words and phrases (highlighted) were used to determine the appropriate entities and their attributes, and to help determine the kinds of queries that might be useful for key stakeholders.
meta data - data about the data itself, such as logical database design or data dictionary definitions
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The relational model, as implemented in most RDBMSs, can represent a lot of different models, but has difficulty representing inheritance hierarchies, and complex relationships (many many-to-many's) are costly to process
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Database management system (DBMS) is a collection of interrelated data and a set of programs to access the data. The collection of data, usually referred to as a database, contains the information related to the Company, the company's system at workplace, detailed information as an example employee personal information stored in the data. The goal of a database management system is to provide a way how to store and retrieve data information more efficiently. For examples, of the data, consider the name, telephone number and address are known, the recorded data is indexed address book, can be stored on a floppy disk, using a personal computer and software such as dBase IV or V, Microsoft Access or Excel
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
Mediators: controls the semantic, structural or conceptual divergence between different components. It can automatically handle interoperability problems.
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People have been relying for their daily needs and well-being on nature. The natural ecosystem provides varieties of goods and services to us, for instance, fresh water, fisheries, timber, water purification etc. The benefits that people directly get from the natural systems are called ecosystem services (ES).