Exchange Format Research Articles

Building Information Modeling Using Constraint Logic Programming

AbstractBuilding Information Modeling (BIM) produces three-dimensional object-oriented models of buildings combining the geometrical information with a wide range of properties about materials, products, safety, to name just a few. BIM is slowly but inevitably revolutionizing the architecture, engineering, and construction industry. Buildings need to be compliant with regulations about stability, safety, and environmental impact. Manual compliance checking is tedious and error-prone, and amending flaws discovered only at construction time causes huge additional costs and delays. Several tools can check BIM models for conformance with rules/guidelines. For example, Singapore’s CORENET e-Submission System checks fire safety. But since the current BIM exchange format only contains basic information about building objects, a separate, ad-hoc model pre-processing is required to determine, for example, evacuation routes. Moreover, they face difficulties in adapting existing built-in rules and/or adding new ones (to cater for building regulations, that can vary not only among countries but also among parts of the same city), if at all possible. We propose the use of logic-based executable formalisms (CLP and Constraint ASP) to couple BIM models with advanced knowledge representation and reasoning capabilities. Previous experience shows that such formalisms can be used to uniformly capture and reason with knowledge (including ambiguity) in a large variety of domains. Additionally, incorporating checking within design tools makes it possible to ensure that models are rule-compliant at every step. This also prevents erroneous designs from having to be (partially) redone, which is also costly and burdensome. To validate our proposal, we implemented a preliminary reasoner under CLP(Q/R) and ASP with constraints and evaluated it with several BIM models.

Virtual reality content creation based on self-contained components in the e-learning domain: Re-using pattern-based vr content in different authoring toolkits

In the context of e-learning, it is challenging to incorporate emerging technologies, such as alternate reality games or Virtual Reality (VR), within current learning trends. Microlearning is such a current trend. It divides large and complex chunks of content into small and elementary learning nuggets. These single self-contained nuggets are then composed to overarching lessons or courses. The concept of VR nuggets dovetails this educational trend. VR nuggets are standalone, self-contained, and rather short VR experiences that can be combined with other learning nuggets. By using initial implementations of VR nuggets, they can be used to let authors create VR earning content, for example, to let learners experience alternate realities. In this paper, we further investigate the VR nugget authoring concept and extent it. We introduce two novel authoring toolkits that rely on VR nuggets – one based on context-related module interaction (CoNMoD) and one based on visual scripting (ViNS Tiles). In two separate user studies, we examine the acceptance of the toolkits and compare them to existing authoring environments that also rely on VR nuggets but utilize different interface techniques. These studies’ results emphasize the importance of exchanging content between different established tools and indicate the acceptance of our tools regarding their hedonic and pragmatic qualities, also compared to existing tools from related work. As a conclusion, we propose an exchange format for VR nuggets that supports their reusability. It enables authors that use different toolkits to work together. They can utilize VR nuggets created with other toolkits and still use their own preferred toolkit. By means of an expert survey, we draw conclusions on technical aspects and a suitable platform to make VR nuggets available to the community. This survey indicates that potential authors would use such an exchange-approach for creating and presenting VR content and that they are willing to share their work and to contribute in a VR nugget authoring community.

Discrete modeling for integration and analysis of large-scale signaling networks.

Most biological processes are orchestrated by large-scale molecular networks which are described in large-scale model repositories and whose dynamics are extremely complex. An observed phenotype is a state of this system that results from control mechanisms whose identification is key to its understanding. The Biological Pathway Exchange (BioPAX) format is widely used to standardize the biological information relative to regulatory processes. However, few modeling approaches developed so far enable for computing the events that control a phenotype in large-scale networks.Here we developed an integrated approach to build large-scale dynamic networks from BioPAX knowledge databases in order to analyse trajectories and to identify sets of biological entities that control a phenotype. The Cadbiom approach relies on the guarded transitions formalism, a discrete modeling approach which models a system dynamics by taking into account competition and cooperation events in chains of reactions. The method can be applied to every BioPAX (large-scale) model thanks to a specific package which automatically generates Cadbiom models from BioPAX files.The Cadbiom framework was applied to the BioPAX version of two resources (PID, KEGG) of the Pathway Commons database and to the Atlas of Cancer Signalling Network (ACSN). As a case-study, it was used to characterize sets of biological entities implicated in the epithelial-mesenchymal transition. Our results highlight the similarities between the PID and ACSN resources in terms of biological content, and underline the heterogeneity of usage of the BioPAX semantics limiting the fusion of models that require curation. Causality analyses demonstrate the smart complementarity of the databases in terms of combinatorics of controllers that explain a phenotype. From a biological perspective, our results show the specificity of controllers for epithelial and mesenchymal phenotypes that are consistent with the literature and identify a novel signature for intermediate states.

UDM (Unified Data Model) for chemical reactions – past, present and future

Abstract The UDM (Unified Data Model) is an open, extendable and freely available data format for the exchange of experimental information about compound synthesis and testing. The UDM had been initially developed in a collaborative project between Elsevier and Roche, where chemical reactions data from a variety of disparate data sources existing at Roche was consolidated and integrated into the Roche in-house version of the Reaxys database. Elsevier adapted the UDM model to its needs and finally donated its pre-4.0 release to the Pistoia Alliance for further development together with the five project founders (Elsevier, Roche, BIOVIA, GSK and Novartis, joined later by BMS), who contributed with funding and expertise to the Pistoia Alliance UDM project between 2017 and 2020. The latest UDM version 6.0 has been made freely available for the community under the MIT license in January 2021. The past, present, and future of the UDM exchange format are discussed in this article and factors that contribute to the successful adoption of the UDM format.

Data-dependent visualization of biological networks in the web-browser with NDExEdit.

Networks are a common methodology used to capture increasingly complex associations between biological entities. They serve as a resource of biological knowledge for bioinformatics analyses, and also comprise the subsequent results. However, the interpretation of biological networks is challenging and requires suitable visualizations dependent on the contained information. The most prominent software in the field for the visualization of biological networks is Cytoscape, a desktop modeling environment also including many features for analysis. A further challenge when working with networks is their distribution. Within a typical collaborative workflow, even slight changes of the network data force one to repeat the visualization step as well. Also, just minor adjustments to the visual representation not only need the networks to be transferred back and forth. Collaboration on the same resources requires specific infrastructure to avoid redundancies, or worse, the corruption of the data. A well-established solution is provided by the NDEx platform where users can upload a network, share it with selected colleagues or make it publicly available. NDExEdit is a web-based application where simple changes can be made to biological networks within the browser, and which does not require installation. With our tool, plain networks can be enhanced easily for further usage in presentations and publications. Since the network data is only stored locally within the web browser, users can edit their private networks without concerns of unintentional publication. The web tool is designed to conform to the Cytoscape Exchange (CX) format as a data model, which is used for the data transmission by both tools, Cytoscape and NDEx. Therefore the modified network can be directly exported to the NDEx platform or saved as a compatible CX file, additionally to standard image formats like PNG and JPEG.

SpaRSE-BIM: Classification of IFC-based geometry via sparse convolutional neural networks

Information exchange between Building Information Modeling (BIM) tools is challenging, since many applications use their own native data formats. The Industry Foundation Classes (IFC) schema, an open data exchange format for BIM, does not capture the full semantic meaning needed for direct use by different BIM tools and can be prone to information loss due to reduction, simplification, translation and interpretation of the data. Current practice often treats the imported model as a reference and requires a user to remodel the building using the respective application’s native elements. Many BIM object properties are defined by its classification. Inconsistencies in the mapping between native BIM elements and IFC, e.g. due to unsupported export functionality or manual error, can lead to problems when using the model in a downstream application. Recent works demonstrate that neural networks offer a promising possibility to alleviate this issue via classification of the objects contained in a BIM model and suggesting those corrections to the user. However, the computational overhead of these deep learning models, either due to necessary pre-processing of the data or runtime performance of the model, makes it difficult for them to be used in plug-ins or middleware for BIM tools. This work proposes SpaRSE-BIM, a neural network model based on sparse convolutions for the classification of IFC-based geometry and semantic enrichment of BIM models. Experiments are performed on two IFC entity classification benchmark datasets. The results demonstrate that SpaRSE-BIM is significantly more efficient at inference time compared to previous approaches, while maintaining state-of-the-art accuracy. Further experiments explore the applicability of IFC entity classification datasets to the domain of Scan-to-BIM. It can be shown that the feature space of SpaRSE-BIM learns to discern objects in a semantically meaningful way, even in cases where fine-grained subtype information for IFC objects is not available during training.

FIG

This work presents version 1.3 of the Finite Improbability Generator (FIG): a statistical model checker to estimate transient and steady-state reachability properties in stochastic automata. Specialised in rare event simulation via importance splitting, FIG implements RESTART and Fixed Effort algorithms. Its distinctive feature is push-button automation: users need not define an importance function, because FIG can derive it from the property query and model specifications. The input models are Input/Output Stochastic Automata with Urgency, written either in the native IOSA syntax or in the JANI exchange format. The theory backing FIG has demonstrated good efficiency, comparable to optimal importance splitting implemented ad hoc for specific systems. Written in C++, FIG is FOSS released under the GPLv3 in https://git.cs.famaf.unc.edu.ar/dsg/fig.

Digitizing the ECG Workflow - A State-of-the-Art Analysis.

ECG is one of the most common examinations in hospitals, e.g. for diagnosing cardiovascular diseases - the most frequent cause of death worldwide. Goal of this paper was to identify and describe the typical digitized workflow, IT systems and data formats for ECG in hospitals: A survey on current ECG-data management practices was conducted with four German speaking hospitals. A generic model of ECG data management was drafted. Today, these hospitals do not use DICOM as exchange format nor do they implement IHE profiles such as REWF for the ECG. Reasons include missing IT infrastructure such as Master Patient Index or electronic archive. ECG data management could be improved at different levels, with the chance to reduce error sources and to improves in patient safety. Storage of ECG raw data promises better diagnoses based on big data and machine learning technologies.

Beacon v2 and Beacon networks: A "lingua franca" for federated data discovery in biomedical genomics, and beyond.

Beacon is a basic data discovery protocol issued by the Global Alliance for Genomics and Health (GA4GH). The main goal addressed by version 1 of the Beacon protocol was to test the feasibility of broadly sharing human genomic data, through providing simple “yes” or “no” responses to queries about the presence of a given variant in datasets hosted by Beacon providers. The popularity of this concept has fostered the design of a version 2, that better serves real‐world requirements and addresses the needs of clinical genomics research and healthcare, as assessed by several contributing projects and organizations. Particularly, rare disease genetics and cancer research will benefit from new case level and genomic variant level requests and the enabling of richer phenotype and clinical queries as well as support for fuzzy searches. Beacon is designed as a “lingua franca” to bridge data collections hosted in software solutions with different and rich interfaces. Beacon version 2 works alongside popular standards like Phenopackets, OMOP, or FHIR, allowing implementing consortia to return matches in beacon responses and provide a handover to their preferred data exchange format. The protocol is being explored by other research domains and is being tested in several international projects.

Potential applications of machine learning for BIM in tunnelling

AbstractMachine Learning (ML) and Building Information Modelling (BIM) are two topics that are part of a revolutionizing transformation in the construction industry – commonly referred to as digitalization. Being part of the research for artificial intelligence (AI), most of today's ML applications deal with computational processes that try to make sense of data. Automatic rockmass behaviour classification based on tunnel boring machine (TBM) data or tunnel construction site surveillance via closed‐circuit television (CCTV) analysis is an example for applications of ML in tunnelling. BIM describes a new type of planning, including model‐based collaboration and information exchange, which requires well‐organized storage and handling of data – a precondition and valuable source for any automated analysis method like ML. While other sectors of the construction industry have implemented BIM systems successfully, the development in underground engineering is currently at its beginning with multiple actors working towards common standards for semantics, data exchange formats, etc. This article seeks to combine the two fields by giving an overview of the two topics and then points out four potential fields of applications: semantic enrichment and labelling, automation of technical processes, knowledge derivation and online data analysis.

Construction of Artistic Design Patterns Based on Improved Distributed Data Parallel Computing of Heterogeneous Tasks

With the continuous upgrading of hardware in the terminal equipment, how to provide high-performance computing for low-tech threshold users has become a current research hotspot. In the era of green high-performance computing, the heterogeneous computing system can provide good versatility, performance, and efficiency and has broad development prospects. This article provides an in-depth analysis and research on the construction and application of improved models using the artistic design pattern of heterogeneous tasks and parallel computing. Based on the hardware resources in the existing desktop system, this article optimizes the original heterogeneous parallel technology from the aspects of task division and data transmission to reduce the complexity of data allocation and processing for users. Based on the analysis and study of the multicore CPU and GPU architectures in the desktop system, as well as the original CPU-GPU heterogeneous parallel technology, this article optimizes the solution of heterogeneous parallel computing, designs a heterogeneous parallel computing architecture, and deploys a heterogeneous parallel computing architecture. The nodes of the desktop system constitute the parallel computing system. In terms of task allocation, the computing system divides tasks according to the parallelism of tasks. According to the computing resources and bandwidth conditions of each heterogeneous node, starting from the parallel execution time, the task scheduling algorithm is optimized, and the load balancing scheduling scheme is designed to achieve the optimal allocation of resources. In terms of storage resources, the computing system adopts distributed storage as a whole. The CPU-GPU heterogeneous parallel in the desktop system adopts virtual unified storage. Global distributed storage and local shared storage are used to balance overall performance and programming complexity. This article introduces the design and implementation of JTangSync, a distributed heterogeneous data synchronization system. The system adopts a distributed architecture, and each node is organized by a data source module, a data transmission module, a processor module, etc. The data source module is responsible for extracting data, the data transmission module is mainly responsible for efficient data transmission, and the processor module is responsible for data processing. More importantly, each module is designed as a replaceable plug-in, which is convenient for secondary expansion. Each node relies on ZooKeeper to form a cluster, which realizes distributed functions such as centralized management of distributed resources, failover, and resumed transmission. Compared with the mainstream scheduling algorithms HEFT, CPOP, PEFT, and HSIP on heterogeneous systems participating in the experimental evaluation, the scheduling length ratio of DONF series algorithms is reduced by 36.3%–67.5% and the parallelism is increased by 17%–125% in terms of efficiency. Compared with the existing database synchronization system, the JTangSync system has built-in multiple heterogeneous database data sources and supports the synchronization of complex heterogeneous databases. The system supports users to develop and customize their own data sources and data processing programs, to promote secondary development. By adopting the custom compressed data exchange format and network optimization methods such as packet merging, caching, and adaptive compression algorithm, the system has high performance.

MTH5: An archive and exchangeable data format for magnetotelluric time series data

Magnetotellurics (MT) is a passive electromagnetic geophysical method that measures variations in subsurface electrical resistivity. MT data are collected in the time domain and processed in the frequency domain to produce estimates of a transfer function representing the Earth’s electrical structure. Unfortunately, the MT community lacks metadata and data standards for time series data. As the community grows and findability, accessibility, interoperability, and reuse of digital assets (FAIR) data principles are enforced by government and funding agencies, a standard is needed for time series data. Presented here is a hierarchical data format (MTH5) that is logically formatted to how MT data are collected. Open-source Python packages are also described to read, write, and manipulate MTH5 files. These include a package to deal with metadata (mt_metadata) based on standards developed by the Working Group for Magnetotelluric Data Handling and Software assembled by the Incorporated Research Institutions for Seismology (IRIS), and mth5: a package to interact with MTH5 files that uses mt_metadata. Example code and workflows are presented.

Геоинформационный мониторинг для решения экологических задач горнопромышленных территорий Среднего Урала

Geographic information monitoring is shown as the leading method of process control in old industrial territories. The main stages of geoinformation monitoring are outlined. The role of geographic information systems in the study of pollution components, systematization and accumulation of spatially distributed data, analysis and development of predictive solutions is considered. The solution of the problem of the conceptual organization of the geoinformation system of mining territories has been substantiated. The structure of software products and geographic information systems selection criteria are considered. The QGIS software product was chosen as the desktop geographic information systems. The functionality of JavaScript-libraries Leaflet and Highcharts, the possibility of their use as a web-based geographic information systems are described. The structure of the base layers of the geographic information systems, created for the organization of geoinformation monitoring of the old industrial territory of the Levikhinsky mine, is described. An algorithm for organizing spatially distributed data of the monitoring object, a fundamental algorithm for data processing are proposed. Considerable attention is paid to the typification and subsequent differentiation of the types of initial information, the storage of geographic information systems data in exchange formats. The author's algorithm of interaction between desktop and web-based geographic information systems is described. The provisions and technical solutions set out in the article allow using geographic information systems for operational monitoring, forecasting and comprehensive assessment, and management decisions.

Communizer: A collaborative cloud-based self-protecting software communities framework - Focus on the alert coordination system

Popular software has always been appealing to adversaries, as related vulnerabilities are synonymous with millions of exposed businesses. Collaborative intrusion detection, as well as software self-protection, try to alleviate this situation. However, they lack either autonomy and adaptation, or Internet-scale oversight and mitigation. In this work, we present Communizer: a collaborative cloud-based framework that creates communities of self-protecting software across organizations. It allows community members to turn their common weaknesses into collaborative and proactive self-protection, empowering them to detect intrusions, exchange alerts, and anticipate attacks. We start by integrating multiple autonomic MAPE-K loops through cloud-based coordination, and a novel hierarchical, regional coordination pattern (HRCP), optimizing scalability, resiliency, accuracy and privacy. Then, we design a trust-based multi-level alert coordination system (TMACS), as well as a lightweight alert coordination message exchange format (ACMEF). At its core, TMACS aggregates, validates, and shares security alerts among community members while fostering agreement and managing trust. It also addresses insider attacks by detecting and blacklisting rogue members. Moreover, TMACS identifies and neutralizes selfish members through a specifically designed probabilistic model. The analysis, optimization, and evaluation of TMACS show a good trade-off between the precision and recall of untrustworthy and selfish members detection. More importantly, we demonstrate a drastic reduction of monitoring loads on community members while ensuring a high collaborative attack detection and anticipation rate, even for small-scope attacks.

Ontology for Effective Security Incident Management

With the evolution of technologies like Internet of Things (IoTs), there will be more and more connected devices in use around the world. This is one of the reasons why cyber security is critical to contemporary society as it makes the large majority susceptible to cyber-attacks. Such cyber-attacks not only impact confidentiality, integrity, and availability but also can cause physical damage. This is evident from cyber-attacks like Stuxnet and German steel mill. Effective security incident management plays an important role in minimising negative impact of such attacks mainly in terms of the organizations’ finance, reputation, and personnel safety. Typically, the main phases of security incident management include: (i) preparation, (ii) mid-incident, and (iv) post-incident. There are diverse set of concepts like Structured Threat Information Expression (STIX) and Incident Object Description Exchange Format (IODEF) in the above-mentioned phases of security incident management. However, a comprehensive overview of different concepts and the relationships between such concepts in security incident management is missing. In this paper, we develop an ontology model with relevant concepts and their corresponding relationships between them especially in the mid-incident and post-incident phases of security incident management. Furthermore, we demonstrate the proposed ontology model using colonial pipeline example case study. The proposed model will help incident responders to operationalise concepts, by having a clear understanding on different concepts and their corresponding relationships, which in turn would also make the incident response more effective in practice.

FINANCIAL VIABILITY OF CRYPTOCURRENCIES IN PAKISTAN

The globally acknowledged accelerating crypto hype has put a lot of work on researchers and analysts. This new marvel of digital currencies requires a lot of attention, for it to become conventional worldwide. Virtual coins or cryptographic forms of money utilize encryption framework, so called the Block Chain technology, that manage the formation and supply of coins and exchanges must be recognized from a financial examination point of view. Subsequently, it is essential to inspect which social, money related & macroeconomic components decide its cost with a specific end goal to know the degree and outcomes of the economy. This paper aims to study different internal and external factors that affect cryptocurrencies’ prices. A sample of four digital coins with largest market capitalization has been selected. Daily price data from the years 2015 to 2020 of Bitcoin with other altcoins such as Ethereum, Ripple and Litecoin has been taken. Internal factors consist of demand and supply variables and also the attractiveness associated with its increasing hype. Other factors include KSE-100 Index (Karachi Stock Exchange), USD-PKR (Dollar to Pakistani Rupee) exchange rate and oil prices from PSO (Pakistan State Oil). ARDL analysis has been done to study the effect of these factors on the prices of crypto coins. Our analysis shows that circulating supply has a significant effect on Ethereum and Ripple prices in the long run. Attractiveness has been significant on the prices of Ethereum only.

Taiwan Stock Tape Reading Periodically Using Web Scraping Technology with GUI

Stock tape reading involves surveilling stock prices once in a while and recording stock prices. The method of observing stock prices may be television or stock exchange. The time step for recoding stock prices is every stock user’s experience and their theory, perhaps 3 min or 2 h and so on. As an example, the Taiwan stock market starts at 9:00 a.m. to 13:30 p.m. It will have a 4 h operating time. Splitting the 4 h operating time for tape reading is the skill of stock users. The stock price sequence generated by tape reading can be predicted by stock users, but finally, it is the stock user’s experience. Therefore, the meaning of tape reading is to record the stock price, but its concept should have no prediction purpose. This study used thread technology and proposed a tape-reading method with web scraping. This method can periodically scrape stock prices and generate a stock price sequence to Excel file. This application can satisfy the demand of these stock users, who are called day trading users. Because these day trading users want to gain stock price sequences minute by minute, rather than the stock exchange format day by day, and also ones which are better than the those provided by the stock website service, because its stock sequence format is limited and not normalized, these day trading users think that minute-by-minute stock price sequences are very clear to forecast. This study implemented the prior scheme and designed the GUI to query a company’s stock price and its stock news, even per second, etc., and how long it took to update the stock price, and the GUI also included a time-up feature to stop scraping stock prices if users just wanted to scrape stock prices periodically.

A new phylogenetic data standard for computable clade definitions: the Phyloreference Exchange Format (Phyx).

To be computationally reproducible and efficient, integration of disparate data depends on shared entities whose matching meaning (semantics) can be computationally assessed. For biodiversity data one of the most prevalent shared entities for linking data records is the associated taxon concept. Unlike Linnaean taxon names, the traditional way in which taxon concepts are provided, phylogenetic definitions are native to phylogenetic trees and offer well-defined semantics that can be transformed into formal, computationally evaluable logic expressions. These attributes make them highly suitable for phylogeny-driven comparative biology by allowing computationally verifiable and reproducible integration of taxon-linked data against Tree of Life-scale phylogenies. To achieve this, the first step is transforming phylogenetic definitions from the natural language text in which they are published to a structured interoperable data format that maintains strong ties to semantics and lends itself well to sharing, reuse, and long-term archival. To this end, we developed the Phyloreference Exchange Format (Phyx), a JSON-LD-based text format encompassing rich metadata for all elements of a phylogenetic definition, and we created a supporting software library, phyx.js, to streamline computational management of such files. Together they form a foundation layer for digitizing and computing with phylogenetic definitions of clades.

The formation of SCEs as an effect of occupational exposure to formaldehyde

Formaldehyde (FA) is a ubiquitous toxic chemical employed worldwide due to its disinfectant and preservative properties. Despite being classified as a human carcinogen, FA is still employed as formalin in pathology wards as standard fixative. We evaluated its relationship with the formation of sister-chromatid exchanges (SCEs) in cultured peripheral blood lymphocytes on 57 pathologists and 48 controls and the risk/protective role played by several genetic polymorphisms. All subjects were assessed for SCEs and genotyped for the most common cancer-associated gene polymorphisms: CYP1A1 exon 7 (A > G), CYP1A1*2A (T > C), CYP2C19*2 (G > A), GSTT1 (presence/absence), GSTM1 (presence/absence), GSTP1 (A > G), XRCC1 (G399A), XRCC1 (C194T), XRCC1 (A280G), XPC exon 15 (A939C), XPC exon 9 (C499T), TNFα − 308 G > A), IL10 − 1082 (G > A), and IL6 − 174 (G > C). Air-FA concentration was assessed through passive personal samplers. Pathologists, exposed to 55.2 μg/m3 of air-FA, showed a significantly higher SCEs frequency than controls, exposed, respectively, to 18.4 μg/m3. Air-FA was directly correlated with SCEs frequency and inversely with the replication index (RI). Regression models showed FA exposure as a significant predictor in developing SCEs, while did not highlight any role of the selected polymorphisms. Our study confirms the role of low air-FA levels as genotoxicity inductor, highlighting the importance to define exposure limits that could be safer for exposed workers.

IFC TO CITYGML CONVERSION ALGORITHM BASED ON GEOMETRY AND SEMANTIC MAPPING

Abstract. Geographic information system (GIS) is known traditionally for the modelling of two-dimensional (2D) geospatial analysis and therefore present information about the extensive spatial framework. On the other hand, building information modelling (BIM) is digital representation of building life cycle. The increasing use of both BIM and GIS simultaneously because of their mutual relationship, as well as their similarities, has resulted in more relationships between both worlds, therefore the need for their integration. A significant purpose of these similarities is importing BIM data into GIS to significantly assist in different design-related issues. However, currently this is challenging due to the diversity between the two worlds which includes diversity in coordinate systems, three-dimensional (3D) geometry representation, and semantic mismatch. This paper describes an algorithm for the conversion of IFC data to CityGML in order to achieve the set goal of sharing information between BIM and GIS domains. The implementation of the programme developed using python was validated using an IFC model (block HO2) of a student’s hostel, Kolej Tun Fatima (KTF). The conversion is based on geometric and semantic information mapping and the use of 3D affine transformation of IFC data from local coordinate system (LCS) to CityGML world coordinate system (WCS) (EPSG:4236). In order to bridge the gap between the two data exchange formats of BIM and GIS, we conducted geometry and semantic mapping. In this paper, we limited the conversion of the IFC model on level of details 2 (LOD2). The conversion will serve as a bridge toward the development of a software that will perform the conversion to create a strong synergy between the two domains for purpose of sharing information.