Big Data in Construction Organization Free-Answers -Myassignment

Question: Discuss about the Big Data in Construction Industry. Answer: Introduction The current assignment provides a critique review of the journal article that has been mentioned below. Research Aim The aim of the research paper is to present a detailed survey and review of the Literature that investigates application of big data techniques in construction industry. Evaluation Evaluation has been done by reviewing related works which has been published in data bases of Institute of electrical and electronics engineers (IEEE), American Association of Civil Engineers (AACE), Association of computing machinery (ACM) as well as Elsevier science direct digital library. Summary The current paper provides the gaps of literature present in the wide ranging statistics data mining, machine learning, warehousing as well as Big Data Analytics in context to the construction industry. The current state of adoption of big data in construction industry has been discussed as well as future potential of those technologies in domain specific sub areas of the industry has been provided. Open issues and direction for future works regarding big data adoption in construction industry has also been proposed. Research subject The research subject of this paper is to fill the gaps of literature which is present between wide-ranging study fields of data mining, statistics, warehousing, machine learning, big data and its application in the construction industry. Although data driven solutions have been proposed for the fields of the construction industry there is a lack of comprehensive literature survey the target stores application of big data in construction industry. Proposed technique and methodology Review of the extent literature on Big Data Engineering and Big Data Analytics in construction industry have been evaluated. Opportunities of big data in industrial sub domains are presented. Finally discussions about issues regarding the research and future work as well as pitfalls of big data in construction industry have been presented. It has been stated that Cloud can be used in processing BIM data in construction industry. The author has also been influenced by White (2012) in which it has been described that Hadoop distributed file system a design for managing large data sets as per requirement. The author has cited Das et al. (2014), in proposing social BIM for capturing social interactions of users along with the models of the buildings. Distributed BIM from work all the way in cloud is developed for storing the data through IFC. Further employment of data mining techniques for electrification of key factors that causes delay in construction projects has been evaluated citin g the papers of different authors in analysis of mashed up construction data sets. Uses of decision tree in construction research regarding structure related deficiencies introduced during the construction phases are discussed by reviewing BSA cycle papers. According to Chen et al. (2003) uses of FDA for development of integrated planning system focusing assignment of pre optimally on complex constants, its importance to workforce as well as resources are also discussed. Experimental analysis and results Experimental analysis and the results that has been obtained using construction waste simulation tools in which a minimization of construction waste could be presented by a rich application of BDA. For that the big data driven BIM system for construction monitoring progress could be done for preventing any kind of delay in project delivery. The design with data could be done using big data for collecting from the manufacturers (Bilal, 2016). It has been found that there are no tools for facilitating the designers for leveraging data during design activities. Assumptions The only assumption of this paper is attributed to independent consideration of name condition on Independence. The author also used assumption for evaluating cases taking into account prior information as well as likelihood of information incoming that constitutes posteriori probability model (Chen, 2003). For the base factor evaluation metric, the value is computed from Theorem of Bayes as well as Gaussian distribution identification (Fan, 2013). Response Few of the pitfalls of big data in construction industries are recognised which are privacy protection as well as data security, quality of data for construction industry data sets, connectivity of Internet for big data applications, exploitation of big data for its full potentials. The cost implications for Big Data in construction industry are also considered in the paper (Al Qady, 2014). The author have reviewed the literature thoroughly and highlighted gaps such as data security and privacy protection that occurs due to third party handling of the company data by cloud service providers. Moreover the data is highly susceptible to piracy and outer threats (White, 2012). Implications of cost for implementing Big data for modeling by architects and designers using BIM and other modeling information systems are also identified. Exploitation of full potentials of Big Data has been evaluated by the author as well (Jiao, 2013).. Conclusion It can be concluded that the paper has covered issues of construction industry in generating massive amounts of data throughout the building life cycle and that option of big data Technology for improvement and enhancement of those particular sectors. The author have reviewed latest research as well as relevant articles published over the few decades an explanation of big data Technology streams as well as its concepts for utilizing the technology across various domains of the construction industry. Important aspect of this paper is the identification of big data applicability in emerging Trends of construction Industries such as IOT, Cloud Computing, BIM, smart buildings and augmented reality. References Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinade, O. O., ... Pasha, M. (2016). Big Data in the construction industry: A review of present status, opportunities, and future trends.Advanced Engineering Informatics,30(3), 500-521. Chen, Q., Chen, Y., Worden, K. (2003). Structural fault diagnosis and isolation using neural networks based on response-only data. Comput. Struct, 81(22), 2165-2172. Das, M., Cheng, J.C. Kumar, S.S. (2014). BIMCloud: a distributed cloud-based social BIM framework for project collaboration, The 15th International Conference on Computing in Civil and Building Engineering (ICCCBE 2014), Florida, United States. Fan, H. Li. (2013). Retrieving similar cases for alternative dispute resolution in construction accidents using text mining techniques, Autom Construct, 34(1), 8591. Al Qady, A. Kandil (2014). Automatic clustering of construction project documents based on textual similarity, Autom. Construct. 42(2), 3649. White, T. (2012). Hadoop: The Definitive Guide. OReilly Media, Inc. Jiao, Y. Wang, S. Zhang, Y. Li, B. Yang L. Yuan. (2013). A cloud approach to unified lifecycle data management in architecture, engineering, construction and facilities management: integrating BIMs and SNS, Adv. Eng. Inform, 27(2), 173188. Y.-J. Chen, C.-W. Feng, Y.-R. Wang, H.-M. Wu, et al. (2011). Using BIM model and genetic algorithms to optimize the crew assignment for construction project, Int. J. 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