The integration of lessons learned knowledge in Building Information Modelling (BIM)

Lessons learned systems are vital means for integrating construction knowledge into the various phases of the construction project life cycle. Many such systems are tailored towards the owner organisation’s specific needs and workflows to overcome challenges with information collection, documentation and retrieval. Previous works have relied on the development of conventional local and network/cloud-based database management systems to store and retrieve lessons gathered on projects. These lessons learned systems operate independently and have not been developed to take full advantage of the benefits of integration with emerging building information modelling (BIM) technology. As such construction professionals are faced with the shortcomings of the lack in efficient and speedy retrieval of context-focused information on lessons learned for appropriate utilization in projects. To tackle this challenge, we propose the integration of lesson learned knowledge management in BIM in addition to existing 2D-8D modelling of project information. The integration was implemented through the embedding of non –structured query system, NoSQL (MongoDB), in a BIM enabled environment to host lessons learned information linked to model items and 4D modelling project tasks of the digitised model. This is beyond existing conventional text-based queries and is novel. The system is implemented in .NET Frameworks and interfaced with a project management BIM tool, Navisworks Manage. The demonstration with a test case of a federated model from a pre-design school project suggests that lessons learned systems can become an integral part of BIM environments and contribute to enhancing knowledge reuse in projects

Optimal and probabilistic resource and capability analysis for network slice as a service

Network Slice as a Service is one of the key concepts of the fifth generation of mobile networks (5G). 5G supports new use cases, like the Internet of Things (IoT), massive Machine Type Communication (mMTC) and Ultra-Reliable and Low Latency Communication (URLLC) as well as significant improvements of the conventional Mobile Broadband (MBB) use case. In addition, safety and security critical use cases move into focus. These use cases involve diverging requirements, e.g. network reliability, latency and throughput. Network virtualization and end-to-end mobile network slicing are seen as key enablers to handle those differing requirements and providing mobile network services for the various 5G use cases and between different tenants. Network slices are isolated, virtualized, end-to-end networks optimized for specific use cases. But still they share a common physical network infrastructure. Through logical separation of the network slices on a common end-to-end mobile network infrastructure, an efficient usage of the underlying physical network infrastructure provided by multiple Mobile Service Providers (MSPs) in enabled. Due to the dynamic lifecycle of network slices there is a strong demand for efficient algorithms for the so-called Network Slice Embedding (NSE) problem. Efficient and reliable resource provisioning for Network Slicing as a Service, requires resource allocation based on a mapping of virtual network slice elements on the serving physical mobile network infrastructure. In this thesis, first of all, a formal Network Slice Instance Admission (NSIA) process is presented, based on the 3GPP standardization. This process allows to give fast feedback to a network operator or tenant on the feasibility of embedding incoming Network Slice Instance Requests (NSI-Rs). In addition, corresponding services for NSIA and feasibility checking services are defined in the context of the ETSI ZSM Reference Architecture Framework. In the main part of this work, a mathematical model for solving the NSE Problem formalized as a standardized Linear Program (LP) is presented. The presented solution provides a nearly optimal embedding. This includes the optimal subset of Network Slice Instances (NSIs) to be selected for embedding, in terms of network slice revenue and costs, and the optimal allocation of associated network slice applications, functions, services and communication links on the 5G end-to-end mobile network infrastructure. It can be used to solve the online as well as the offline NSIA problem automatically in different variants. In particular, low latency network slices require deployment of their services and applications, including Network Functions (NFs) close to the user, i.e., at the edge of the mobile network. Since the users of those services might be widely distributed and mobile, multiple instances of the same application are required to be available on numerous distributed edge clouds. A holistic approach for tackling the problem of NSE with edge computing is provided by our so-called Multiple Application Instantiation (MAI) variant of the NSE LP solution. It is capable of determining the optimal number of application instances and their optimal deployment locations on the edge clouds, even for multiple User Equipment (UE) connectivity scenarios. In addition to that multi-path, also referred to as path-splitting, scenarios with a latency sensitive objective function, which guarantees the optimal network utilization as well as minimum latency in the network slice communication, is included. Resource uncertainty, as well as reuse and overbooking of resources guaranteed by Service Level Agreements (SLAs) are discussed in this work. There is a consensus that over-provisioning of mobile communication bands is economically infeasible and certain risk of network overload is accepted for the majority of the 5G use cases. A probabilistic variant of the NSE problem with an uncertainty-aware objective function and a resource availability confidence analysis are presented. The evaluation shows the advantages and the suitability of the different variants of the NSE formalization, as well as its scalability and computational limits in a practical implementation

Near Field Communication: From theory to practice

This book provides the technical essentials, state-of-the-art knowledge, business ecosystem and standards of Near Field Communication (NFC)by NFC Lab - Istanbul research centre which conducts intense research on NFC technology. In this book, the authors present the contemporary research on all aspects of NFC, addressing related security aspects as well as information on various business models. In addition, the book provides comprehensive information a designer needs to design an NFC project, an analyzer needs to analyze requirements of a new NFC based system, and a programmer needs to implement an application. Furthermore, the authors introduce the technical and administrative issues related to NFC technology, standards, and global stakeholders. It also offers comprehensive information as well as use case studies for each NFC operating mode to give the usage idea behind each operating mode thoroughly. Examples of NFC application development are provided using Java technology, and security considerations are discussed in detail. Key Features: Offers a complete understanding of the NFC technology, including standards, technical essentials, operating modes, application development with Java, security and privacy, business ecosystem analysis Provides analysis, design as well as development guidance for professionals from administrative and technical perspectives Discusses methods, techniques and modelling support including UML are demonstrated with real cases Contains case studies such as payment, ticketing, social networking and remote shopping This book will be an invaluable guide for business and ecosystem analysts, project managers, mobile commerce consultants, system and application developers, mobile developers and practitioners. It will also be of interest to researchers, software engineers, computer scientists, information technology specialists including students and graduates.Publisher's Versio

Factors encouraging and hindering a wider acceptance and more frequent utilization of mobile payment systems: an empirical study among mobile phone subscribers in Turkey

Purpose- This research deals with determining the factors that affect adoption of mobile payment technology among consumers, in Turkey. It seeks to find any patterns and connections that may be of aid in framing an implementation strategy for facilitating further adoption. It has gathered different definitions of “mobile payment” in literature and used a consumer side definition. Methodology- A survey is conducted among mobile phone subscribers in Istanbul, Turkey for primary data collection phase of this research. Istanbul is the city that holds the biggest population and has the highest amount of mobile phone subscribers in the country. Istanbul’s current population is more than 15.6million and mobile phone subscriptions are more than 22million as of 2019. Survey responses have been analysed with structural equation modelling and results are presented in the corresponding sections. Findings- Empirical findings of the research show that factors such as usefulness, security, social influence, ease of use, enjoyment and innovativeness have positive effects on use of mobile payments among consumers. Factors such as attractiveness of alternatives and new technology anxiety have negative effects on use of mobile payments. Conclusion- This research has shown that mobile payments are a potential mainstream trend for the near future. Several benefits of the mobile payment value chain for both technology providers and the consumers have been identified. Other findings of this research can be stated as the challenges which the stakeholders are experiencing while trying to extend mobile payment technologies to a wider consumer base. Therefore, the results and the variables can be used by service providers who want to launch new mobile payment solutions for similar markets and they can take actions for getting more efficient results accordingly.Publisher's Versio

Unified communications: The search for ROI through tomorrow’s business communication solutions

The traditional workplace is evolving; the way in which businesses communicate today is different than it was in the past and yet is likely to change again in the future. The current state of the economy and globalization has forced every organization to review its future business plans and cut costs everywhere including communications. Organizations are seeking out technology in hopes of finding new ways to reduce their bottom-line communication costs. Today, many enterprise business infrastructures are comprised of separate networks - voice, data, and mobile - yet most of the time these networks never interact. The ability to link business applications from various networks with communications proves to be valuable and is known as convergence. Convergence is defined as the combining of one or more elements into one. Unified Communications is a concept that looks to build on convergence, although it is not a new technology. Unified Communications is the term coined by the communications industry that signifies the comprehensive integration of various communication networks for reasons of increased revenue and reduced costs. Unified Communications will fundamentally transform the way in which people work - from decreased carrier costs to increased response times, the benefits of Unified Communications greatly outweigh the investment. This thesis will analyze the adoption of the Unified Communications paradigm by examining the Unified Communications solutions of tomorrow and prove that establishing a cohesive Unified Communications strategy will indisputably have a return on investment. In doing so, solutions from four Unified Communications vendors (Microsoft, Cisco, IBM, and RIM) will be examined to expose the potential benefits available to any enterprise business. The end result will show the rate of return for reducing costs and increasing revenue to yield a positive ROI for each vendors\u27 UC solution

SenseBloom

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economic

PENGEMBANGAN MEDIA PEMBELAJARAN MIND-MAP AKUNTANSI BERBASIS ANDROID UNTUK SISWA KELAS XI IPS SMA

Penelitian ini bertujuan untuk (1) Mengembangkan mind-map akuntansi berbasis Android sebagai media pembelajaran siswa kelas XI IPS SMA. (2) Mengetahui kelayakan media pembelajaran Mind-map Akuntansi berbasis android kelas XI IPS SMA berdasarkan validasi oleh ahli materi, ahli media dan praktisi pembelajaran Akuntansi. (3) Mengetahui pendapat siswa mengenai penggunaan Mind-map Akuntansi berbasis Android sebagai media pembelajaran. Penelitian ini merupakan jenis penelitian dan pengembangan (Research and Development) dengan mengikuti model penelitian pengembangan ADDIE (Analysis, design, development, implementation, evaluation) yaitu tahap analisis, desain, pengembangan, implementasi, dan evaluasi, namun pada penelitian ini hanya dilaksanakan hingga tahap implementasi. Validasi media pembelajaran dilakukan oleh ahli materi, ahli media dan praktisi pembelajaran Akuntansi (guru Akuntansi SMA). Media yang dikembangkan diujicobakan pada 28 siswa kelas XI IPS 3 SMA Negeri 1 Pengasih. Hasil penelitian in menunjukkan bahwa media layak untuk digunakan, terbukti dengan validasi oleh ahli materi yang mendapat nilai rata-rata 4,75 dengan kategori “Sangat Layak”, ahli media yang mendapat nilai rata-rata 4,38 dengan kategori “Sangat Layak”, dan praktisi pembelajaran Akuntansi yang mendapat nilai rata-rata 4,10 dengan kategori “Layak”. Siswa berpendapat bahwa media dikemas dengan menarik dan praktis, tampilan Mind-map yang memetakan pikiran dapat memudahkan siswa dalam pembelajaran dan mendorong rasa ingin tahu untuk belajar Akuntansi, menambah pemahaman tentang Akuntansi, dan menambah minat siswa dalam belajar Akuntansi. Kata Kunci: Media Pembelajaran, Mind-map Accounting, Android

A Research Approach to Study Human Factors in Transportation Systems

This thesis proposes a new general-purpose methodology to conduct studies on Human Factors in Transportation Systems.A full-fledged setup and implementation of the methodology is provided for validation. This setup, which uses real data to perform the simulation, includes a traffic micro-simulator, a driving simulator, a traffic control centre and an Advanced Driver Assistance System, providing an experimentation laboratory, in which empirical research can be conducted. The communication between the simulation components is made interchangeably using both the European standard Datex II and the SUMO TraCI protocols.Several usage scenarios are implemented and indications on how to extend the methodology to accommodate different requirements are provided; as to prove its usability and feasibility. A simple Human Factors study was conducted using the implemented setup. This study uses naturalistc data and evaluates the network performance gain by using an Advanced Driver Assistance System that recommends new routes to drivers in congestion situations and provides a final validation of the methodology.In conclusion, the methodology has been proved usable to effectively conduct Human Factors research and also to develop Advanced Driver Assistance Systems applications in a controlled, yet realistic environment.This thesis proposes a new general-purpose methodology to conduct studies on Human Factors in Transportation Systems.A full-fledged setup and implementation of the methodology is provided for validation. This setup, which uses real data to perform the simulation, includes a traffic micro-simulator, a driving simulator, a traffic control centre and an Advanced Driver Assistance System, providing an experimentation laboratory, in which empirical research can be conducted. The communication between the simulation components is made interchangeably using both the European standard Datex II and the SUMO TraCI protocols.Several usage scenarios are implemented and indications on how to extend the methodology to accommodate different requirements are provided; as to prove its usability and feasibility. A simple Human Factors study was conducted using the implemented setup. This study uses naturalistc data and evaluates the network performance gain by using an Advanced Driver Assistance System that recommends new routes to drivers in congestion situations and provides a final validation of the methodology.In conclusion, the methodology has been proved usable to effectively conduct Human Factors research and also to develop Advanced Driver Assistance Systems applications in a controlled, yet realistic environment

Cooperative Clustering Model and Its Applications

Data clustering plays an important role in many disciplines, including data mining, machine learning, bioinformatics, pattern recognition, and other fields, where there is a need to learn the inherent grouping structure of data in an unsupervised manner. There are many clustering approaches proposed in the literature with different quality/complexity tradeoffs. Each clustering algorithm works on its domain space with no optimum solution to all datasets of different properties, sizes, structures, and distributions. Challenges in data clustering include, identifying proper number of clusters, scalability of the clustering approach, robustness to noise, tackling distributed datasets, and handling clusters of different configurations. This thesis addresses some of these challenges through cooperation between multiple clustering approaches. We introduce a Cooperative Clustering (CC) model that involves multiple clustering techniques; the goal of the cooperative model is to increase the homogeneity of objects within clusters through cooperation by developing two data structures, cooperative contingency graph and histogram representation of pair-wise similarities. The two data structures are designed to find the matching sub-clusters between different clusterings and to obtain the final set of cooperative clusters through a merging process. Obtaining the co-occurred objects from the different clusterings enables the cooperative model to group objects based on a multiple agreement between the invoked clustering techniques. In addition, merging this set of sub-clusters using histograms poses a new trend of grouping objects into more homogenous clusters. The cooperative model is consistent, reusable, and scalable in terms of the number of the adopted clustering approaches. In order to deal with noisy data, a novel Cooperative Clustering Outliers Detection (CCOD) algorithm is implemented through the implication of the cooperation methodology for better detection of outliers in data. The new detection approach is designed in four phases, (1) Global non-cooperative Clustering, (2) Cooperative Clustering, (3) Possible outlier’s Detection, and finally (4) Candidate Outliers Detection. The detection of outliers is established in a bottom-up scenario. The thesis also addresses cooperative clustering in distributed Peer-to-Peer (P2P) networks. Mining large and inherently distributed datasets poses many challenges, one of which is the extraction of a global model as a global summary of the clustering solutions generated from all nodes for the purpose of interpreting the clustering quality of the distributed dataset as if it was located at one node. We developed distributed cooperative model and architecture that work on a two-tier super-peer P2P network. The model is called Distributed Cooperative Clustering in Super-peer P2P Networks (DCCP2P). This model aims at producing one clustering solution across the whole network. It specifically addresses scalability of network size, and consequently the distributed clustering complexity, by modeling the distributed clustering problem as two layers of peer neighborhoods and super-peers. Summarization of the global distributed clusters is achieved through a distributed version of the cooperative clustering model. Three clustering algorithms, k-means (KM), Bisecting k-means (BKM) and Partitioning Around Medoids (PAM) are invoked in the cooperative model. Results on various gene expression and text documents datasets with different properties, configurations and different degree of outliers reveal that: (i) the cooperative clustering model achieves significant improvement in the quality of the clustering solutions compared to that of the non-cooperative individual approaches; (ii) the cooperative detection algorithm discovers the nonconforming objects in data with better accuracy than the contemporary approaches, and (iii) the distributed cooperative model attains the same quality or even better as the centralized approach and achieves decent speedup by increasing number of nodes. The distributed model offers high degree of flexibility, scalability, and interpretability of large distributed repositories. Achieving the same results using current methodologies requires polling the data first to one center location, which is sometimes not feasible