Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Subject: search.filters.subject.Data integration

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Heterogeneous data format integration and conversion (HDFIC) using machine learning and IBM-DFDL for IoT
    (Springer Nature, 2024) Sandeep, S.; Chandavarkar, B.R.; Khatri, S.
    The future of the Internet of Things (IoT) demands the integration of synergetic applications to cater to societal needs. Examples of IoT-based confederated applications include Ambient Assisted Living with Active Healthy Ageing, CasAware with Smart Energy, Smart Gas Distribution Networks with GIS systems, and more. However, the data heterogeneity hinders integration, as these systems follow different standards, data formats, semantic models, and representations. Further, this leads to data interoperability issues in IoT. The major concern of academia and industry in the smooth integration of heterogeneous applications is interpreting different data formats and representing them in a common schema for further analysis. Existing solutions, such as message payload translation, middleware/cloud format, and Inter-IoT, are complex, time-consuming, and ineffective. Hence, this paper proposes the heterogeneous data format integration and conversion (HDFIC), a machine learning-based system to identify data formats using a Random Forest classifier and integrate them using the Data Format Description Language (DFDL). The content-based data format identification in the proposed HDFIC is trained with the standard features defined in RFC 7111, 8259, and 8996. Subsequently, the data is integrated into a single XML Schema Definition and converted into the required data format using the IBM App Connect Enterprise tool and DFDL. Finally, the performance of HDFIC is evaluated with the synergetic patient body vitals and room ambiance dataset for accuracy, data integration time, and conversion efficiency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
  • No Thumbnail Available
    Item
    AN ONTOLOGY-DRIVEN BI-DIRECTIONAL WORKFLOW FOR INTEGRATING PROJECT MANAGEMENT DATA INTO THE IFC STANDARD
    (International Council for Research and Innovation in Building and Construction, 2025) Kone, V.; Mahesh, G.
    The evolution of Building Information Modelling (BIM) towards a data-centric paradigm is often hindered by challenges in semantic interoperability, particularly when integrating project management data with the Industry Foundation Classes (IFC) standard. While IFC enables syntactic data exchange, a persistent gap exists dynamically linking building geometry with the complex, relational information of project schedules, resources, and costs in a semantically consistent, interoperable manner. This paper presents a novel, bi-directional methodology that leverages Semantic Web technologies (RDF, OWL, SPARQL) to address this challenge. The core of the methodology is an ontology-driven workflow that uses two purpose-built ontologies: BIMOnto, a lightweight representation of the building asset derived from if cOWL, and IproK (Integrated Project Knowledge Ontology), which formally structures project management information across schedule, resource, and cost domains. The workflow enables both directions: (1) transforming IFC models into queryable knowledge graphs, and (2) programmatically generating new, enriched IFC models from unified knowledge graphs. This reverse transformation creates native, standards-compliant IFC entities for tasks (IfcTask), resources (IfcResource), costs (IfcCostItem), and their standard relationships (IfcRelAssignsToProduct, etc.), moving beyond custom property sets. The feasibility and effectiveness of this approach are validated through a case study using a multi-story residential building model, demonstrating the successful generation of a verifiable, integrated BIM artifact. The findings show that this ontology-driven framework significantly enhances data integration, creating truly interoperable models where process data becomes a first-class citizen within the BIM environment, advancing the potential for more intelligent, data-centric BIM practices throughout the project lifecycle. © © 2025 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.