Modeling Big Data-based Systems through Ontological Trading

TL;DR

This paper introduces a formal ontological trading framework for designing large-scale, heterogeneous data management systems, demonstrated through an environmental knowledge system case study.

Contribution

It presents a novel ontological web-trading model (OntoTrader) that facilitates the development of Big Data systems across various domains using model-driven and ontology-driven engineering.

Findings

Framework effectively manages heterogeneous Big Data sources.

Case study demonstrates practical application in environmental knowledge systems.

Model supports scalable and flexible system design.

Abstract

One of the great challenges the information society faces is dealing with the huge amount of information generated and handled daily on the Internet. Today, progress in Big data proposals attempts to solve this problem, but there are certain limitations to information search and retrieval due basically to the large volumes handled the heterogeneity of the information, and its dispersion among a multitude of sources. In this article, a formal framework is defined to facilitate the design and development of an environmental management information system, which works with a heterogeneous and large amount of data. Nevertheless, this framework can be applied to other information systems that work with Big data, because it does not depend on the type of data and can be utilized in other domains. The framework is based on an ontological web-trading model (OntoTrader), which follows model-driven engineering and ontology-driven engineering guidelines to separate the system architecture from its implementation. The proposal is accompanied by a case study, SOLERES-KRS, an environmental knowledge representation system designed and developed using software agents and multi-agent systems.