Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Analysis of Building Information Modeling (bim) Performance Using Big Data From a Construction Project(01. Izmir Institute of Technology, 2023) Bostan, Berkay Batuhan; Çavka, Hasan BurakThis study aims to propose a systematical approach for evaluating BIM performance from a main contractor's perspective based on big data from a construction project. Retrospective case study is used as the research approach. Data is collected through interviews with the main contractor firm, and data from the logged project information in project databases including ACONEX and Microsoft Excel files. A framework containing performance metrics, specifically tailored to evaluate BIM performance based on big data, is developed from the combined analysis of literature review, interviews with main contractor, and overview of the project data. Collected project data and interview data are analyzed using the developed framework. Results of the data analysis are verified through follow-up interviews with the main contractor firm. Findings of the study suggest that it is possible to evaluate the BIM performance through analysis of collected BIM big data using the proposed systematical approach. Several performance problems were identified during the data analysis. Follow-up interviews revealed that identified performance problems from the data analysis largely coincided with the real-life experiences and accurate data entry is the key criterion for the analysis to yield correct results. The proposed framework should be tested in wider range of studies and may serve as a foundation for a future benchmarking system. Future work should focus on refining performance metrics, establishing a BIM big data database for benchmarking, exploring data's potential to be used for real-time performance assessment, and implementation of emerging Artificial Intelligence (AI) techniques for the analysis of big data.Master Thesis An Analysis of Information Spreading and Privacy Issues on Social Networks(Izmir Institute of Technology, 2017) Sayin, Burcu; Şahin, SerapWith Social Networks (SNs), being populated by a still increasing number of people, who take advantage of the communication and collaboration capabilities that they offer, density of the information, spread over SNs is increasing steadily. Furthermore, the probability of exposure of someone’s personal moments to a wider than expected crowd is also increasing. Hence, analyzing the spreading area and privacy level of any information through a SN is an important issue in social network analysis. By studying the functionalities and characteristics that modern SNs offer, along with the people’s habits and common behavior in them, it is easy to understand that several privacy risks may exist, for many of which people may be unaware of. We address this issue, focusing on interactions with posts in a SN, using Facebook as the research domain. As a novelty, we propose an application tool which visualizes the effect of potential privacy risks in Facebook and provides users to control their privacy. The proposed (and simulated) tool allows a Post Owner to observe the spreading area of his/her post, depending on the selected privacy settings of this post. Moreover, it provides preliminary feedback for all the Facebook users that have interacted with this post, to make them aware of the possible privacy changes, aiming to give them a chance to protect the privacy of their interaction on this post by deleting it when such a privacy change takes place.