Abstract

Everyday huge amount of data is being captured and stored. This can either be due to several social initiatives, technological advancement or by smart devices. This involves the release of data which differs in format, language, schema and standards from various types of user communities and organizations. The main challenge in this scenario lies in the integration of such diverse data and on the generator of knowledge from the existing sources. Various methodology for data modeling has been proposed by different research groups, under different approaches and based on the scenarios of the different domain of application. However, a few methodology elaborates the proceeding steps. As a result, there is lack of clarification how to handle different issues which occurs in the different phases of domain modeling. The aim of this research is to presents a scalable, interoperable, effective framework and a methodology for data modeling. The backbone of the framework is composed of a two-layer, schema and language, to tackle diversity. An entity-centric approach has been followed as a main notion of the methodology. A few aspects which have especially been emphasized are: modeling a flexible data integration schema, dealing with the messy data source, alignment with an upper ontology and implementation. We evaluated our methodology from the user perspective to check its practicability.