A Rule Based System for Semantical Enrichment of Building Information Exchange

International audienceIn the area of building construction and management, the dematerial-ization of data and processes has been a global issue for the past 10 years. Go-ing beyond the geometric representation of a building, Building Information Modeling (BIM) is an approach that aims at integrating into one single system heterogeneous data and processes from different actors. Such integration is a complex and fastidious task. The implementation of the related processes for data querying, retrieval or modification is not less difficult. To tackle this prob-lem, we have developed a novel approach based on Semantic Web technolo-gies. In doing so, we have defined an ontology inspired on IFC standard for rep-resenting building information. On top of this ontology, we have defined and implemented a set of SWRL rules. The paper at hand describes these rules and their application in the context of building information handling (notably by means of IFC files

On systematic approaches for interpreted information transfer of inspection data from bridge models to structural analysis

In conjunction with the improved methods of monitoring damage and degradation processes, the interest in reliability assessment of reinforced concrete bridges is increasing in recent years. Automated imagebased inspections of the structural surface provide valuable data to extract quantitative information about deteriorations, such as crack patterns. However, the knowledge gain results from processing this information in a structural context, i.e. relating the damage artifacts to building components. This way, transformation to structural analysis is enabled. This approach sets two further requirements: availability of structural bridge information and a standardized storage for interoperability with subsequent analysis tools. Since the involved large datasets are only efficiently processed in an automated manner, the implementation of the complete workflow from damage and building data to structural analysis is targeted in this work. First, domain concepts are derived from the back-end tasks: structural analysis, damage modeling, and life-cycle assessment. The common interoperability format, the Industry Foundation Class (IFC), and processes in these domains are further assessed. The need for usercontrolled interpretation steps is identified and the developed prototype thus allows interaction at subsequent model stages. The latter has the advantage that interpretation steps can be individually separated into either a structural analysis or a damage information model or a combination of both. This approach to damage information processing from the perspective of structural analysis is then validated in different case studies

IfcWoD, Semantically Adapting IFC Model Relations into OWL Properties

In the context of Building Information Modelling, ontologies have been identified as interesting in achieving information interoperability. Regarding the construction and facility management domains, several IFC (Industry Foundation Classes) based ontologies have been developed, such as IfcOWL. In the context of ontology modelling, the constraint of optimizing the size of IFC STEP-based files can be leveraged. In this paper, we propose an adaptation of the IFC model into OWL which leverages from all modelling constraints required by the object-oriented structure of IFC schema. Therefore, we do not only present a syntactic but also a semantic adaptation of the IFC model. Our model takes into consideration the meaning of entities, relationships, properties and attributes defined by the IFC standard. Our approach presents several advantages compared to other initiatives such as the optimization of query execution time. Every advantage is defended by means of practical examples and benchmarks.Comment: In proceedings of the 32nd CIB W78 Conference on Information Technology in Construction, Oct 2015, Eindhoven, Netherland

Semantic Enrichment for Building Information Modeling: Procedure for Compiling Inference Rules and Operators for Complex Geometry

Semantic enrichment of building models adds meaningful domain-specific or application-specific information to a digital building model. It is applicable to solving interoperability problems and to compilation of models from point cloud data. The SeeBIM (Semantic Enrichment Engine for BIM) prototype software encapsulates domain expert knowledge in computer readable rules for inference of object types, identity and aggregation of systems. However, it is limited to axis-aligned bounding box geometry and the adequacy of its rule-sets cannot be guaranteed. This paper solves these drawbacks by (1) devising a new procedure for compiling inference rule sets that are known a priori to be adequate for complete and thorough classification of model objects, and (2) enhancing the operators to compute complex geometry and enable precise topological rule processing. The procedure for compiling adequate rule sets is illustrated using a synthetic concrete highway bridge model. A real-world highway bridge model, with 333 components of 13 different types and compiled from a laser scanned point cloud, is used to validate the approach and test the enhanced SeeBIM system. All of the elements are classified correctly, demonstrating the efficacy of the approach to semantic enrichment

Semantic medical care in smart cities

Medical care is a vitally important part of successful smart cities further development. High quality medical treatment has always been a challenging task for administrative departments of cities government. The key reason is that the treatment of patients significantly depends on the skills of medical stuff that can hardly be controlled and estimated. Semantic technologies by now have showed capabilities to solve highly complicated badly formalized problems in conditions of uncertainty. It makes reasonable to apply them in medical domain. In the paper a real example of information system for semantic medical care is presented. The system is being developed for Federal Almazov North-West Medical Research Centre in St-Petersburg, Russia (http://www.almazovcentre.ru/?lang=en). The main attention is paid to the proposed solution for the problem of medical treatment estimation in administrative and managerial departments. We focus on medical treatment examinations matching, trend analysis and administrative analytical and prediction task solving making use of semantic technologies, statistical analysis and deep learning applied to huge amounts of diverse data. Semantic medical data analysis project is an attempt to proceed to semantic medicine - an interoperable approach to medical domain area

Introduction to the ISO specification language LOTOS

LOTOS is a specification language that has been specifically developed for the formal description of the OSI (Open Systems Interconnection) architecture, although it is applicable to distributed, concurrent systems in general. In LOTOS a system is seen as a set of processes which interact and exchange data with each other and with their environment. LOTOS is expected to become an ISO international standard by 1988

On a Connection between Procedural and Applicative Languages

This paper reports on the connection between procedural and applicative languages. It presents features, notions and methods derived from abstract data type theory that in our judgement are helpful and necessary for multi-level software engineering environments in general, and especially for the treatment of verification issues there. Reference is made to an existing software engineering system and exemplary languages of it. A denotational semantics based on algebraic structures is introduced and employed. Since object-orientedness is looked at as one of the most important properties of such environments the notion of correctness is applied to objects and object relations. Finally a realistic semi-automatic method for the check of correctness criteria is given, accompanied by remarks on our existing implementation

Outdoor-Indoor tracking systems through geomatic techniques: data analysis for marketing and safety management

Negli ultimi decenni, l'utilizzo di sistemi di gestione delle informazioni nel trattamento dei dati edilizi ha portato a cambiamenti radicali nei metodi di produzione, documentazione e archiviazione dei dati. Dato il crescente interesse per i dati e la loro gestione, l'obiettivo di questa tesi è quello di creare un flusso di lavoro efficace e chiaro a partire dai rilievi geomatici in un'ottica di miglioramento dei dati raccolti sul territorio, sugli edifici circostanti e su quelli relativi al comportamento umano, in modo che possano essere meglio sfruttati e integrati in modelli di gestione intelligenti. Come primo passo, questa tesi mira a comprendere i limiti dell'interoperabilità e dell'integrazione dei dati nei GIS. Per promuovere l'interoperabilità dei dati GIS, è necessario analizzare i metodi di conversione nei diversi modelli di archiviazione dei dati, come CityGML e IndoorGML, definendo un dominio ontologico. Questo ha portato alla creazione di un nuovo modello arricchito, basato sulle connessioni tra i diversi elementi del modello urbano in GIS. Il secondo passo consiste nel raccogliere tutti i dati tradotti in un database a grafo sfruttando il web semantico. Il risultato offrirà vantaggi sostanziali durante l'intero ciclo di vita del progetto. Questa metodologia può essere applicata anche al patrimonio culturale, dove la gestione delle informazioni gioca un ruolo fondamentale. Un altro lavoro di ricerca è stato quello di sviluppare un sistema di gestione SMART per le attività di conservazione dei borghi storici attraverso la gestione di tipologie eterogenee di dati, dal rilievo alla documentazione tecnica. Il flusso di lavoro è stato strutturato come segue: (i) acquisizione dei dati; (ii) modellazione 3D; (iii) modellazione della conoscenza; (iv) modellazione della gestione SMART. Questa ricerca apre la strada allo sviluppo di una piattaforma web in cui importare i dati GIS per un approccio di digital twin. Tutte le ricerche svolte fino a questo punto sono state finalizzate a comprendere la capacità di creare modelli e sistemi informativi intelligenti per capire la fattibilità di ospitare dati eterogenei che potrebbero essere inclusi in futuro. Il passo successivo consiste nel comprendere il comportamento umano in uno spazio. Finora sono pochi i lavori di ricerca che si occupano di sistemi di mappatura e posizionamento che tengono conto sia degli spazi esterni che di quelli interni. Questo argomento, anche se ha pochi articoli di ricerca, rappresenta un aspetto cruciale per molte ragioni, soprattutto quando si tratta di gestire la sicurezza degli edifici danneggiati. Angelats e il suo gruppo di ricerca al CTTC hanno lavorato su questo aspetto, fornendo un sistema in grado di seguire in tempo reale le persone dall'esterno all'interno di spazi chiusi e viceversa. L'uso di sensori GNSS combinato con l'odometria inerziale visiva fornisce una traiettoria continua senza perdere il percorso seguito dall'utente monitorato. Una parte di questa tesi si è concentrata sul miglioramento della traiettoria finale ottenuta con il sistema appena descritto, effettuando test sulla traiettoria esterna del GNSS per capire il comportamento della traiettoria quando si avvicina agli edifici o quando l'utente si sposta in indoor. L'ultimo aspetto su cui si concentrerà la tesi è il tracciamento delle persone in ambienti chiusi. Il comportamento umano è al centro di numerosi studi in diversi campi, come quello scientifico, sociale ed economico. A differenza del precedente caso di studio sul tracciamento delle persone in aree esterne/interne, l'obiettivo è stato quello di raccogliere informazioni sul posizionamento dinamico delle persone in ambienti indoor, sulla base del segnale WiFi. Verrà effettuata una breve analisi dei dati per dimostrare il corretto funzionamento del sistema, per sottolineare l'importanza della conoscenza dei dati e l'uso che se ne può fare.In the last decades, the use of information management systems in the building data processing led to radical changes to the methods of data production, documentation and archiving. Given the ever-increasing interest in data and their management, the aim of this thesis is to create an effective and clear workflow starting from geomatic surveys in a perspective of improving the collected data on the territory, surrounding buildings and those related to human behaviour so they can be better exploited and integrated into smart management models As first step this thesis aims to understand the limits of data interoperability and integration in GIS filed. Before that, the data must be collected as raw data, then processed and interpret in order to obtain information. At the end of this first stage, when the information is well organized and can be well understanded and used it becomes knowledge. To promote the interoperability of GIS data, it is necessary at first to analyse methods of conversion in different data storage models such as CityGML and IndoorGML, defining an ontological domain. This has led to the creation of a new enriched model, based on connections among the different elements of the urban model in GIS environment, and to the possibility to formulate queries based on these relations. The second step consists in collecting all data translated into a specific format that fill a graph database in a semantic web environment, while maintaining those relationships. The outcome will offer substantial benefits during the entire project life cycle. This methodology can also be applied to cultural heritage where the information management plays a key role. Another research work, was to develop a SMART management system for preservation activities of historical villages through the management of heterogeneous types of data, from the survey to the technical documentation. The workflow was structured as follows: (i) Data acquisition; (ii) 3D modelling; (iii) Knowledge modelling; (iv) SMART management modelling. This research paves the way to develop a web platform where GIS data would be imported for a digital twin approach. All the research done up to this point was to understand the capability of creating smart information models and systems in order to understand the feasibility to host heterogeneous data that may be included in the future. The next step consist of understanding human behaviour in a space. So far only a few research papers are addressed towards mapping and positioning systems taking into account both outdoor and indoor spaces. This topic, even though it has few research articles, represents a crucial aspect for many reasons, especially when it comes to safety management of damaged building. Angelats and his research team at CTTC have been working on this aspect providing a system able to track in real time people from outdoor to indoor areas and vice-versa. The use of GNSS sensors combined with Visual Inertial Odometry provide a continuous trajectory without losing the path followed by the monitored user. A part of this thesis focused on enhancing the final trajectory obtained with the described system above, carrying out tests on the outdoor trajectory of GNSS in order to understand behaviour of the trajectory when it gets close to buildings or when the user moves indoor. The last aspect this thesis will focus on is the tracking of people indoor. Human behaviour is at the centre of several studies in different fields such as scientific subjects, social and economics. Differently from the previous case study of tracking people in outdoor/indoor areas, the scope was to collect information about the dynamic indoor positioning of people, based on the WiFi signal. A brief analysis of the data will be made to demonstrate the correct functioning of the system, to emphasise the importance of data knowledge and the use that can be made of it

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results