that remains Unresolved Understanding of an event based on the linear view of the event is a complex task, as the analysis process involves multiple complex variables. The use of data science can be effective for the identification of the data related to the remapping and the history data. The process of the identification of the data and conducting the relevant analysis involves the analysis of the historical data regarding the particular place. This historical data do not take into account the incorporation of the new data dynamically, and the system cannot be preferably updated for the most recent recordings. Further in this data mining assignment we identify the process of the backtracking of their data science process can be effective in the management of disasters but misses a proper process involved for the reaching of the target. The non-availability of the factors explaining the involved process leads to the vulnerability associated with the data handling and disaster management as the process gets more and more prone to outward influences. The uncertainty and unpredictability of the events involved in assessment is high and the system requires the proper mechanism for the gathering and the monitoring of the real-time data, so that the prediction and the management activity can be more and more efficient and precise and can lead to a massive saviour for the life and the property of mankind. Further, there has been a proper gap identified between the process of natural science and computational science. The natural science deals with the process related focus, while the computational data deals with the process involving the patterns. On this data mining assignment there is an urgent need for data science to develop framework fo0r the accommodation of the variables or the causal factors that can make data science capable of incorporating the defining process related to the disasters along with the unlocking of the hidden patterns. This can be effective in the quoting of the above problem and help in the identification of the probable scope of future work. Discussion The rising technology has led to the exponential rise in the quality and the quantity of data generated by the firms. The access of the technology has made the rural and the younger segments to be accustomed to the changing technology. The proper processing of data is equally important, along with the proper handling and proper storage of data. Data without proper analysing process and processing holds no importance. Further, it is equally important to convert the data in the form in which it can be directly fed to the system for generating the required result from the given set of raw data. The data mining assignment reaffirms that proper analysis of data is very crucial for disaster management. The natural interaction is a complex process involving numerous complex variables. The need of incorporating proper data management for the identification of the hazards and the prediction of the probable time of disaster and the intensity of the disaster can be beneficial for the process of providing instant support and reducing the loss of life and property. The process of disaster management can involve the analysis of the previous data related to the landslides of the place and the identification of the impact of a landslide on the particular area. For the same purpose, the geo-mapping of the demographic areas can be conducted for the identification of the vulnerability associated with a particular land area. Further, research can be conducted for the identification of the looseness of the soil, the sample of the soil and the study of the factors related to the land. This data mining assignment highlight it as being crucial in reducing the impact of disasters and can promote effective disaster management. Proper mining of data via traditional means or the digital data can be effective for the purpose of analysis and the prediction and can be very crucial for providing a proper approach to disaster management. Proper analysis of data from various sources can be used for the effective analysis of the complex variables related to nature, and the proper management of the land samples can provide an effective tool for the processing and the handling of the data. Various methods like proper geo-mapping or proper analysis of the historical data can be crucial for the identification of the risk associated. The data mining assignment has portrayed the importance of data mining technique for the purpose of disaster management. Part B A Survey on Trajectory Data Mining: Techniques and Applications The rapid advancement in the technologies related to the acquisition of locations can help in boosting the production of the trajectory data as explored on this data mining assignment. This would consequently help in the monitoring and finding the random moving objects. On a general basis, the trajectory is represented as through a time divisional geographical locations (Feng and Zhu, 2016). Through this, a broad range of spectrum can be successfully benefited from the trajectory techniques of data mining. The arrival of unprecedented opportunities also offers a substantial threat. In this data mining assignment, the focus is shed on the location prediction, path analysis which are the main applications of data mining. Along with this, existing techniques. Big Data Implementation of Natural Disaster Monitoring and Alerting System in Real-Time Social Network using Hadoop Technology The information used to compile this data mining assignment has been collected from the social platforms is quite high, and along with this, it also offers efficient systems which can yield productive results. The systems, which are in use, does not render relevant methodology for informing people regarding the disasters, which are happening at a rapid pace. Along with this, the rescue agencies are not notified because of which they fail to take any relevant steps. The existing approaches, namely the media, radio and television, are the armours of the people (Dhamodaran, Sachin and Kumar, 2015). On using a specific filter, the required keywords can be fetched, and ablest can be sent to the nearby location. The data mining assignment finds basically two data set representations, and along with this, certain improvements for the user is also provided. Implementation of data mining techniques for dealing with natural disasters Every time many people lose their lives across the globe besides the damage on the property, animal life, and so on. Because of natural disasters such as earthquakes, landslides, storms and such others. The data mining assignment focuses on the use of data mining techniques which are particularly designed for the detection, prediction as well as the creation of appropriate disaster management methods on the basis of data collected from disasters (Goswami et al. 2018). The data is loss available from the geological observatories, remote sensing and from the social media platforms. In this data mining assignment, in-depth analysis of the existing techniques for the prediction and, management et of the various kinds of disaster is done in an extensive manner. A UAV-Cloud System for Disaster Sensing Applications An application named (UAVs) has earned a significant response for sensing the disaster. However, due to the limitation in the computational abilities and lack of adequate resources of UAVs has posed a challenge to the data processing on a real-time basis which is considered as important for the applications related to the disaster (Luo et al. 2015). An application framework is proposed in the data mining assignment, which collaborates the data scheduling, processing and acquisition of data. The basic model of the framework consists of some elements hosted on the UAV. The servers offer data which is based on real-time and the information feedback applied to the control centre. Big data and disaster management: a systematic review and agenda for future research The recent advancement in Big Data and analytics are streaming up new opportunities for disaster management. Big Data helps users to visualise, examine and predicts the disaster, and with these abilities, it is completely transforming the manual operations and the management of the crisis in a dramatic way. However, the literature is quite diverse and split, which opens up for reviewing the uncertainties (Akter and Wamba, 2017). On the basis of systematic literature, the data mining assignment focuses on the Big Data in the disaster Management, which would represent the contributions, future scope and challenges. Data-driven techniques for information management of Disasters The different techniques associated with efficient management and the recovery concerning the disasters have been identified as a crucial point for nations for safeguarding the life and the property of the people. Various techniques of data analysis and management have been deployed for the purpose of management of data, and the relevant analysis helps in the identification of the variables on which the natural phenomenon depends. Proper disaster management requires the proper base of data and the identification of the importance of the data for understanding the relevance of providing instant actions and relevant future planning for reducing the impact of disasters. (Li et al. 2017). The potential of Big Data for adaptation to the changing climate Big Data has the potential to incorporate multiple changes and components and providing a competitive advantage for companies using big data. We explore on this data mining assignment how Big Data analysis can be crucial for the monitoring of environmental changes and the change of the weather of the placer. The changing climatic condition involves the identification of a lot of variables related to the changing environmental factors. The identification of such factors can provide an easy means of monitoring and predicting the climatic conditions of the place (Ford et al. 2016). Data mining technique for the flood susceptibility map Proper data mining is a crucial step for the processing of large volumes of data for the generation of useful output that can be analysed for the purpose of reducing the risk associated with the various natural disasters. The process can include the identification of historical data related to the place with vulnerability. The collection of the historical data related to the vulnerable places can lead to the identification of the flood-prone zones and thy probable time in which the areas remain flood-prone. This can be beneficial for taking pre measures and the conduction of the evacuation process that can help in the saving of life and prosperity of a large number of people (Hong et al. 2018). Data mining technique for the identification of the risk-prone area. Proper analysing of data has been a crucial factor for the identification of the risk-prone zones. The proper identification of the risk-prone zones can be effective in rapid response towards the emergency and an effective way of saving the lives of numerous people. Proper mining of data and the analysis of the data can result in the identification of the probable risk associated with the disaster management and the probable imp0act of the disaster. While preparing this data mining assignment we identify this as being crucial for the minimising of the impact of the disaster and the undertaking of the control measures (Traore et al. 2017) Big Data-drove crisis response The proper management of big data can be crucial for providing of instance response towards the environmental crisis. The big data analysis can be effective in the identification of the probable risk involved and the prediction of the probable time and the impact of the disaster. The prior identification of the data can be effective in the taking of spontaneous actions related to the crisis and creating awareness among the people to combat the probable alarming situation (Quadir et al. 2016). Social Media and Emergency Management Proper data mining can be effective in the management of the emergency. The advancement of technology has to lead to the far-reaching root of social media to a large number of people. The wide base of users can be crucial to spread awareness among the large masses of users. The proper spread of awareness can be effective in increasing awareness of the people and making them aware of the upcoming alarming situation and reduce the risk associated with the disaster (Luna and Pennock, 2018). Analysis and prediction of natural disaster using spatial data mining technique Proper data mining can be effective in the identification of the probable risk associated with natural disasters. Proper mining of data can be effective in the identification of the history which can help in the identification of the factors like the impact of the disaster, the probable escape route, the time of disaster, the areas badly affected, and can help the administration to be prepared for the confronting actions (Refonaa et al. 2015). Fog computing for Emergency Alert The fog has been identified as a major setback for the developed cities. The issues related to fog have not been provided with enough importance, but the combination of fog along with the pollutants leads to the creation of o0f smog, which reduced the visibility drastically. Proper management of the information can help in the identification of the time of smog and the impact that it causes on the population. This can enable the authority to take proper measures for the counteracting of the situation (Aazam and Huh, 2015). Disaster management using social media Social media have a far-reaching user absent and almost every individual has been identified to access or at least get impacted by social media. Proper spreading of awareness and the information related to the financial crisis can be crucial for enabling the public to be prepared for counteracting the disaster. The spreading of awareness about natural disasters can be effective with the involvement of social media, owing to the far-reaching impact associated with it (Huang et al. 2015). Impact of social media for enhancing the emergency management A large number of individuals is accessing social media globally. Spreading awareness on social media can be the most efficient way of reaching the masses. We conclude the data mining assignment highlighting that proper management of the data and sharing of the information and awareness on social media can be effective in the management of the emergency (Yin et al. 2015) Reference List Aazam, M. and Huh, E.N., 2015, March. E-HAMC: Leveraging Fog computing for emergency alert service. In 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) (pp. 518-523). IEEE. Akter, S. and Wamba, S.F., 2017. Big data and disaster management: a systematic review and agenda for future research. Annals of Operations Research, pp.1-21.Damodaran, S., Sachin, K.R. and Kumar, R., 2015. Big data implementation of natural disaster monitoring and alerting system in real-time social network using Hadoop technology. Indian Journal of Science and Technology, 8(22), p.1. Feng, Z. and Zhu, Y., 2016. A survey on trajectory data mining: Techniques and applications. IEEE Access, 4, pp.2056-2067. Ford, J.D., Tilleard, S.E., Berrang-Ford, L., Araos, M., Biesbroek, R., Lesnikowski, A.C., MacDonald, G.K., Hsu, A., Chen, C. and Bizikova, L., 2016. Opinion: Big data has big potential for applications to climate change adaptation — proceedings of the National Academy of Sciences, 113(39), pp.10729-10732. Goswami, S., Chakraborty, S., Ghosh, S., Chakrabarti, A. and Chakraborty, B., 2018. A review of the application of data mining techniques to combat natural disasters. Ain Shams Engineering Journal, 9(3), pp.365-378. Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A.X. and Chen, W., 2018. Application of fuzzy weight of evidence and data mining techniques in the construction of flood susceptibility map of Poyang County, China. Science of the total environment, 625, pp.575-588. Huang, Q., Cervone, G., Jing, D. and Chang, C., 2015, November. DisasterMapper: A CyberGIS framework for disaster management using social media data. data mining assignment. In Proceedings of the 4th International ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data (pp. 1-6). ACM. Kumar, A., Mukherjee, A.B. and Krishna, A.P., 2019. Application of conventional data mining techniques and web mining to aid disaster management. In Environmental Information Systems: Concepts, Methodologies, Tools, and Applications (pp. 369-398). IGI Global. Li, T., Xie, N., Zeng, C., Zhou, W., Zheng, L., Jiang, Y., Yang, Y., Ha, H.Y., Xue, W., Huang, Y. and Chen, S.C., 2017. Data-driven techniques in disaster information management. ACM Computing Surveys (CSUR), 50(1), p.1. Luna, S. and Pennock, M.J., 2018. Social media applications and emergency management: A literature review and research agenda. data mining assignment. International journal of disaster risk reduction, 28, pp.565-577. Luo, C., Nightingale, J., Asemota, E. and Grecos, C., 2015, May. A UAV-cloud system for disaster sensing applications. In 2015 IEEE 81st Vehicular Technology Conference (VTC Spring) (pp. 1-5). IEEE. Qadir, J., Ali, A., ur Rasool, R., Zwitter, A., Sathiaseelan, A. and Crowcroft, J., 2016. Crisis analytics: big data-driven crisis response. Journal of International Humanitarian Action, 1(1), p.12. Refonaa, J., Lakshmi, M. and Vivek, V., 2015, March. Analysis and prediction of natural disaster using spatial data mining technique. In 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015] (pp. 1-6). IEEE. Traore, B.B., Kamsu-Foguem, B. and Tangara, F., 2017. Data mining techniques on satellite images for the discovery of risk areas. Expert Systems with Applications, 72, pp.443-456.Yin, J., Karimi, S., Lampert, A., Cameron, M., Robinson, B. and Power, R., 2015, June. Using social media to enhance emergency situation awareness. data mining assignment In Twenty-fourth international joint conference on artificial intelligence.
Subject Name: Computer Science
Level: Undergraduate
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