An ontology to represent geospatial data to support building renovation

Energy-efficient building renovation is an inter-disciplinary task and requires investigation about the building condition in the urban, environmental, and societal context. Existing literature implicitly mentions the effect of surrounding data in different stages of building renovation. Nevertheless, no conceptual framework is available for practitioners to realize the potential of such data in specific phases of the renovation.The main goal of this study is to understand: (1) based on what knowledge framework surrounding geospatial and environmental data can support building renovation projects, (2) if developing an ontology can help representing this knowledge framework, and (3) how experts and engineers involved in the renovation process can contribute to development of this knowledge framework. The results present an ontology that maps surrounding geospatial and environmental concepts for different renovation tasks and use cases within building renovation. The ontology is built upon knowledge captured from previous studies that implicitly mention the effect of these datasets in building renovation, as well as expert knowledge, brainstorming, and monitoring construction sites. Additionally, a semi-structured verification and validation workshop has been performed to incorporate insights from experts directly involved in different stages of building renovation process.This paper contributes to the body of knowledge by generating a common framework for the surrounding data required in building renovation. It has an implication in practice for engineers by providing a shared knowledge framework and for software developers by providing a basis for BIM (Building Information Modeling) and GIS (Geographic Information System) data integration for renovation purposes.     

An ontology change management approach for facility management

Facility management (FM) or technical property management is an approach to operate, maintain, improve and adapt buildings and infrastructures of organizations. A FM project requires the cooperation of many actors from different domains so it has to be automated in a constrained collaborative environment. This paper proposes a new approach for ontology change management applied on facility management of such projects. The industrial challenge is, firstly, to ensure consistency of a FM project knowledge from the construction phase to the technical property management phase (after delivery). Secondly, it has to provide to each actor of the project a personal up-to-date “view” of the building knowledge related to its business profile and allow its evolution. The scientific approach, called OntoVersionGraph, is a change management methodology for managing ontology life cycle including ontology evolution and versioning features, in conjunction with contextual view modeling. Its contribution is the impact management of changes between the ontology and its different views.     

Reno-Inst: An ontology to support renovation projects planning and renovation products installation

While the use of semantic models has been explored in different areas of the Architecture, Engineering and Construction (AEC) industry to map and formalize knowledge and support different tasks, building renovation seems to be a neglected area in this field. Therefore, this paper presents the Renovation-Installation (Reno-Inst) ontology, which maps knowledge from the renovation domain considering different requirements, constraints, and other elements related to the installation of common renovation products such as windows, thermal insulation panels, and heat radiators. The development of the Reno-Inst ontology relies on an approach combining input from experts and knowledge from related engineering documents. The verification and validation process includes a content evaluation workshop with experts, a verification design stage, and the analysis and implementation of a real case study. The paper provides another example of the power of ontologies as a method for mapping and representing knowledge, gathering, and retrieving relevant information. Particularly, renovation projects encounter diverse challenges that lead to cost and schedule overruns, making their performance typically low. Therefore, the proposed Reno-Inst ontology provides a basis from which new applications can be developed, tested, and deployed to support improvements in the building renovation field, especially in the planning and execution of renovation activities.     

A proposed ontology to support the hardware design of building inspection robot systems

Due to the growing number of architecturally complex buildings built in recent decades, mobile building inspection robot systems are required to operate in increasingly complex environments. This leads to higher requirements for their hardware design. The existing literature on the design of building inspection robots has implicitly mentioned the impact of building environments and building defects on defining robot hardware design requirements. However, the explicit representation of what information is required to define a specific hardware requirement is stimissing. To fill this gap, this paper presents an ontology that provides an overview of the building and inspection domain objects that affect the determination of robot hardware design requirements (RoboDesign). It also explores the relationship between specific robot hardware requirements and features of complex buildings and their defects. The RoboDesign ontology integrates two main domain ontology models including a Robot System Model and a Building and Defect Model. A content evaluation and an automated consistency checking are conducted for the internal evaluation of the ontology. Additionally, the proposed ontology is implemented in two wall-climbing inspection robot design cases to check if the investigation of the robot’s application environment is comprehensive. The validation results also demonstrate that the use of the proposed ontological model allows to efficiently retrieve information required to determine a particular hardware requirement.     

Temperature heterogeneity during travelling fire on experimental building

In order to follow modern trends in contemporary building architecture which is moving off the limits of current fire design models, assumption of homogeneous temperature conditions used for structural fire analysis needs to be validated. In this paper it is described, how temperature distribution in a medium-size fire compartment has been investigated experimentally by conducting fire test in two-storey experimental building in September 2011 in the Czech Republic. In the upper floor, a scenario of travelling fire was prepared. It has been observed that as flames were spreading across the compartment, considerable temperature gradients appeared. Numerical simulation of the travelling fire test conducted using FDS (Fire Dynamics Simulator) has been compared with simulation of compartment fire under uniform temperature conditions to highlight the potential impact of the gas temperature heterogeneity on structural behaviour. The temperature measurements from the fire test have been used for validation of the numerical simulation of travelling fire. The fire test has provided important data for design model of travelling fire and shown that its impact on structural behaviour is not in agreement with the assumption of homogenous temperature conditions.     

A software engineering approach to ontology building

Ontologies are the backbone of the Semantic Web, a semantic-aware version of the World Wide Web. The availability of large-scale high quality domain ontologies depends on effective and usable methodologies aimed at supporting the crucial process of ontology building. Ontology building exhibits a structural and logical complexity that is comparable to the production of software artefacts. This paper proposes an ontology building methodology that capitalizes the large experience drawn from a widely used standard in software engineering: the Unified Software Development Process or Unified Process (UP). In particular, we propose UP for ONtology (UPON) building, a methodology for ontology building derived from the UP. UPON is presented with the support of a practical example in the eBusiness domain. A comparative evaluation with other methodologies and the results of its adoption in the context of the Athena EU Integrated Project are also discussed.     

Building a global normalized ontology for integrating geographic data sources

Nowadays, the proliferation of geographic information systems has caused great interest in integration. However, an integration process is not as simple as joining several systems, since any effort at information sharing runs into the problem of semantic heterogeneity, which requires the identification and representation of all semantics useful in performing schema integration. On several research lines, including research on geographic information system integration, ontologies have been introduced to facilitate knowledge sharing among various agents. Particularly, one of the aspects of ontology sharing is performing some sort of mapping between ontology constructs. Further, some research suggests that we should also be able to combine ontologies where the product of this combination will be, at the very least, the intersection of the two given ontologies. However, few approaches built integrations upon standard and normalized information, which might improve accuracy of mappings and therefore commitment and understandability of the integration. In this work, we propose a novel system (called GeoMergeP) to integrate geographic sources by formalizing their information as normalized ontologies. Our integral merging process—including structural, syntactic and semantic aspects—assists users in finding the more suitable correspondences. The system has been empirically tested in the context of projects of the Italian Institute for Environmental Protection and Research (ISPRA, ex APAT), providing a consistent and complete integration of their sources.     

Bridging the gap between product lifecycle management and sustainability in manufacturing through ontology building

Green manufacturing has been a major concern in recent years. As product lifecycle management strategies embrace sustainability within its spectrum of multi-disciplinary efforts, it has become crucial that manufacturing companies have the ability to exchange product and process related data with emphasis on sustainability not only amongst its internal information systems like CAD, CAPP and ERP, but also throughout their supply chain and other stakeholders. Industry demands solutions for interoperability between heterogeneous systems that can account for the necessary semantics in order to establish seamless, unambiguous information sharing of data from a product's cradle to its grave. One of the most promising approaches to overcome these issues is the use of ontologies that serve as interlingua, for translating between local data structures. The present research proposes an ontology that relates sustainability terms to product and process data entities through semantic ties.     

An automatic key discovery approach for data linking

In the context of Linked Data, different kinds of semantic links can be established between data. However when data sources are huge, detecting such links manually is not feasible. One of the most important types of links, the identity link, expresses that different identifiers refer to the same real world entity. Some automatic data linking approaches use keys to infer identity links, nevertheless this kind of knowledge is rarely available. In this work we propose KD2R, an approach which allows the automatic discovery of composite keys in RDF data sources that may conform to different schemas. We only consider data sources for which the Unique Name Assumption is fulfilled. The obtained keys are correct with respect to the RDF data sources in which they are discovered. The proposed algorithm is scalable since it allows the key discovery without having to scan all the data. KD2R has been tested on real datasets of the international contest OAEI 2010 and on datasets available on the web of data, and has obtained promising results.     

An ontology based approach to organize multi-agent assisted supply chain negotiations

Effective supply chain management (SCM) comprises activities involving the demand and supply of resources and services. Negotiation is an essential approach to solve conflicting transaction and scheduling problems among supply chain members. The multi-agent system (MAS) technology has provided the potential of automating supply chain negotiations to alleviate human interactions. Software agents are supposed to perform on behalf of their human owners only when equipped with sophisticated negotiation knowledge. To better organize the negotiation knowledge utilized by agents and facilitate agents’ adaptive negotiation decision making ability, an ontology-based approach is proposed in this paper. Firstly, the multi-agent assisted supply chain negotiation scheme is presented to configure the general design components of the negotiation system, covering the agent intelligence modules, the knowledge organization method and the negotiation protocol. Then, the ontology-based negotiation knowledge organization method is specified. The negotiation knowledge is separated into shared negotiation ontology and private negotiation ontology to ensure both the agent communicative interoperability and the privacy of strategic knowledge. Inference rules are defined on top of the private negotiation ontology to guide agents’ reasoning ability. Through this method, agents’ negotiation behaviors will be more adaptive to various negotiation environments utilizing corresponding negotiation knowledge.     

Adding semantic modules to improve goal-oriented analysis of data warehouses using I-star

The success rate of data warehouse (DW) development is improved by performing a requirements elicitation stage in which the users’ needs are modeled. Currently, among the different proposals for modeling requirements, there is a special focus on goal-oriented models, and in particular on the i* framework. In order to adapt this framework for DW development, we previously developed a UML profile for DWs. However, as the general i* framework, the proposal lacks modularity. This has a specially negative impact for DW development, since DW requirement models tend to include a huge number of elements with crossed relationships between them. In turn, the readability of the models is decreased, harming their utility and increasing the error rate and development time. In this paper, we propose an extension of our i* profile for DWs considering the modularization of goals. We provide a set of guidelines in order to correctly apply our proposal. Furthermore, we have performed an experiment in order to assess the validity our proposal. The benefits of our proposal are an increase in the modularity and scalability of the models which, in turn, increases the error correction capability, and makes complex models easier to understand by DW developers and non expert users.     

An approach to information requirements specification for process control tasks

This paper discusses the need for designers of process plant supervisory systems to make greater effort in anticipating the information that operators require to carry out their duties. A method for dealing with this problem of information requirements specification in process plant design is proposed. This method translates a task analysis into a set of standard task elements from which standard sets of information, called ‘sub-goal templates’ can be derived. The resultant information requirements specification sets out the operators' information needs in the context of the operating goals that have to be attained. Early trials with the method indicate its promise, but highlight the need for its implementation in a computer tool to assist the designer. The features of such a tool and the further work necessary to develop and test the method are described.     

A trace metamodel proposal based on the model driven architecture framework for the traceability of user requirements in data warehouses

The complexity of the data warehouse (DW) development process requires to follow a methodological approach in order to be successful. A widely accepted approach for this development is the hybrid one, in which requirements and data sources must be accommodated to a new DW model. The main problem is that we lose the relationships between requirements, elements in the multidimensional (MD) conceptual models and data sources in the process, since no traceability is explicitly specified. Therefore, this hurts requirements validation capability and increases the complexity of Extraction, Transformation and Loading processes. In this paper, we propose a novel trace metamodel for DWs and focus on the relationships between requirements and MD conceptual models. We propose a set of Query/View/Transformation rules to include traceability in DWs in an automatic way, allowing us to obtain a MD conceptual model of the DW, as well as a trace model. Therefore, we are able to trace every requirement to the MD elements, further increasing user satisfaction. Finally, we show the implementation in our Lucentia BI tool.     

An ontology based approach to the integration of entity–relationship schemas

In schema integration, schematic discrepancies occur when data in one database correspond to metadata in another. We explicitly declare the context that is the meta information relating to the source, classification, property etc. of entities, relationships or attribute values in entity–relationship (ER) schemas. We present algorithms to resolve schematic discrepancies by transforming metadata into the attribute values of entity types, keeping the information and constraints of original schemas. Although focusing on the resolution of schematic discrepancies, our technique works seamlessly with the existing techniques resolving other semantic heterogeneities in schema integration.     

Semantic BMS: Allowing usage of building automation data in facility benchmarking

Facility benchmarking and evaluation of facility performance are the crucial tasks in reaching efficient, economical and sustainable facility operation. Modern buildings are equipped with building automation systems (BAS) that contain vast numbers of various sensors that can be utilised in performance assessment. However, such systems lack convenient tools for data inspection, which limits their use in building performance and efficiency analysis and benchmarking especially on large sites. The paper presents a middleware layer designed to enrich BAS data with additional semantic information. As a semantic model, an adaptation of the Semantic Sensor Network (SSN) ontology for the field of building operation analysis is used. The middleware provides convenient interfaces for querying the model. The proposed system provides the facility managers with a convenient way to use the BAS data for benchmarking and decision support.     

Cardiorespiratory responses of firefighters to a computerized fire strategies and tactics drill during physical activity

Firefighters are subjected to a combination of physical and mental challenges in the course of their occupational responsibilities. However, due to the ecological factors involved with firefighting, it makes it extremely difficult to examine physiological and psychological changes that occur as a result of these combined challenges. The purpose of this study was to examine the efficacy of a computer-based Fire Strategies and Tactics Drill (FSTD) in eliciting psychological and physiological measures of stress in professional firefighters. In one session, participants exercised at 60% VO_2max for 37 min (exercise alone condition, EAC), and in the other session the firefighter exercised for an equal amount of time and responded to the FSTD (dual challenge condition; DCC) while exercising. Cardiorespiratory (heart rate [HR], respiration rate [RR], minute ventilation [V_E], oxygen consumption [VO₂], ventilatory efficiency [V_E/VO₂], and respiratory exchange ratio [RER]) and psychometric measures (State Anxiety Inventory [SAI] and Ratings of Perceived Exertion [RPE]) were obtained throughout the experimental protocols. The NASA Task Load Index was used to assess perceived physical and mental load during each condition. The results demonstrated that the participants perceived overall workload to be higher in the DCC. Repeated measures ANOVAs revealed no differences between the EAC and DCC for VO₂ or RER, but the DCC did elicit significantly greater elevations in HR, RR, V_E, and V_E/VO₂ compared to the EAC. These results suggest that the FSTD utilized in this study provides an effective method for examining the physiological and psychological responses of firefighters in a research laboratory environment.     

An autonomous ground robot to support firefighters' interventions in indoor emergencies

Intervention teams act in hostile scenarios where reducing mission times and accident risks is critical. In these situations, the availability of accurate information about the environment plays a key role in ensuring the well-being of rescuers and victims. This information required to plan the interventions in indoor emergencies encompasses the location of fires and the presence of dangerous gases. Robotics and remote sensing technologies can help emergency teams to obtain this information in real-time without exposing themselves. Additionally, the accurate simulation of the environments allows the teams to plan their interventions, creating routes to safely access the affected areas and evacuate the victims. This article presents a robotic solution developed to satisfy the demands of intervention teams. More specifically, it describes an autonomous ground robot that can obtain real-time location and environmental data from indoor fires, as well as a simulator that reproduces these emergency scenarios and facilitates mission planning. In this way, emergency teams can know the conditions in the scenario before, during, and after the intervention. Thus, risks are minimized by improving their situational awareness and reducing their exposure times during the mission. The system has been developed and validated in collaboration with the end-users and under realistic harsh environments. During these experiments, the robot was used to detect fire sources and cold smoke and provide environmental information to firefighters. Additionally, the simulator provided alternative routes for accessing and exiting the scene faster and safer by dodging potentially dangerous areas.     

An IFC schema extension and binary serialization format to efficiently integrate point cloud data into building models

In this paper we suggest an extension to the Industry Foundation Classes (IFC) model to integrate point cloud datasets. The proposal includes a schema extension to the core model allowing the storage of points, either as Cartesian coordinates, points in parametric space of associated building element surfaces or as discrete height fields projected as grids onto building elements. To handle the considerable amounts of data generated in the process of scanning building structures, we present intelligent compression approaches combined with the Hierarchical Data Format (HDF) as an efficient serialization and an alternative to clear text encoded ISO 10303 part 21 files. Based on prototypical implementations we show results of various serialization options and their impacts on storage efficiency.In this proposal the deepened semantic relationships have been favoured over compression ratios. Nevertheless, with various near-lossless layers of compression and binary serialization applied, a compression ratio of up to 67.7% is obtained for a building model with integrated point clouds, compared to the raw source data. The binary serialization is able to handle hundreds of millions of points, out of which specific spatial and semantic subsets can rapidly be extracted due to the containerized hierarchical storage model and association to building elements. The authors advocate the use of binary storage for sizeable point cloud scans, but also show how especially the grid discretization can result into usable points cloud segments embedded into text-based IFC models.