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Modular construction is becoming more popular because of its efficiency, cost-saving, and environmental benefits, but its successful implementation necessitates detailed planning, scheduling, and coordination. Building information modeling (BIM) and fourdimensional (4D) simulation techniques have emerged as invaluable tools for visualizing and analyzing the construction process in order to meet these requirements. However, integrating distinctive data sources and developing comprehensive 4D BIM simulations tailored to modular construction projects present significant challenges. Case studies are used in this paper to define precise data needs and to design a robust data integration framework for improving 4D BIM simulations in modular construction. The validation of the framework in a real-world project demonstrates its efficacy in integrating data, promoting cooperation, detecting risks, and supporting informed decision-making, ultimately enhancing modular building results through more realistic simulations. By solving data integration difficulties, this research provides useful insights for industry practitioners and researchers, enabling informed decision-making and optimization of modular building projects. [ABSTRACT FROM AUTHOR]

Today, most information sources provide factual, objective knowledge, but they fail to capture personalized contextual knowledge which could be used to enrich the available factual data and contribute to their interpretation, in the context of the knowledge of the user who queries the system. This would require a knowledge framework which can accommodate both objective data and semantic enrichments that capture user provided knowledge associated to the factual data in the database. Unfortunately, most conventional DBMSs lack the flexibilities necessary (a) to prevent the data and metadata, evolve quickly with changing application requirements and (b) to capture user-provided and/or crowdsourced data and knowledge for more effective decision support. In this paper, we present CrowdSourced Semantic Enrichment (CroSSE) knowledge framework which allows traditional databases and semantic enrichment modules to coexist. CroSSE provides a novel Semantically Enriched SQL (SESQL) language to enrich SQL queries with information from a knowledge base containing semantic annotations. We describe CroSSE and SESQL with examples taken from our SmartGround EU project. [ABSTRACT FROM AUTHOR]

e-Navigation, a theme introduced but IMO to improve safety of navigation at sea, is starting to gain traction in various organizations. As the concept develops, the importance of data, both static as well as dynamic, is more and more realized. IALA and IHO, in their efforts to help making e-Navigation a reality, have agreed to propose the IHO GI Register (often known as S-100 Register) as the conceptual basis for the Common Maritime Data Structure (CMDS). Utilizing this common understanding of key players in e-Navigation arena, the IMO e-Navigation Correspondence Group has firmed up the overarching architecture, enabling a series to test beds proving the concept of integration of various data streams to enable improved communication between the stakeholders on shore or at sea. The integration of pre-composed, expert generated navigational data with real time and semi real-time data like Virtual AtoNs, Tide Gages Data or MSI transmission from VTS into a single e-Navigation display, if correctly developed, implemented and executed, will be the enabler of increased situational awareness.

Sensors are embedded nowadays in a growing number of everyday life objects. Smartphones, wearables, and sensor networks together play an important role in bridging the gap between physical and cyber worlds, a fundamental aspect of the Internet of Things vision. The ability to reuse sensor data integrated from multiple heterogeneous sources is a step towards building innovative applications and services. In this paper SIGHTED, a sensor data integration framework, is proposed exploiting semantic web technologies and linked data principles. It provides a layered structure as a guideline for integrating sensor data from various sources supporting accessibility and usability. DotThing, a demo platform, is implemented as an instantiation of SIGHTED framework and evaluated. Smartphones and sensor nodes are connected to DotThing showing the ability to query and reuse integrated sensor data from multiple sources to create more flexible horizontal applications. DotThing implementation also demonstrates the need for adding a semantic layer to existing IoT cloud-based platforms, like Xively, that generally lack such layer resulting in proprietary vertical solutions with limited data integration and discovery capabilities. DotThing makes use of vocabularies from existing ontologies on the linked data cloud providing a unified model to annotate data and link it to existing resources on the web. [ABSTRACT FROM AUTHOR]