Data Mining Assignment Critiquing A Seminal Dmkd Paper
This part of the assignment is to produce, in a manner of a critical review, an 8 -10 page paper which discusses the importance of a research paper published in the last few years. As part of that review, you will need to canvas:
- The situation (ie, the open problem) before the paper was published,
- What it was that made this paper so important,
- A brief sketch of the solution proposed by the paper,
- What subsequent research and systems have flowed from this paper (both by the authors and by others)
- The open questions that remain unresolved,
- Anything else that you believe is interesting about the paper.
You can choose any major data mining paper which has more than 5000 citations on Google Scholar.
Papers should be ten pages long in the format specified (approximately) in the CRPIT Style available at http://crpit.scem.westernsydney.edu.au/CRPITTemplate.rtf. Don't worry about copyright notice!
This part of the assignment is to produce a bibliography (of about 15 papers) consistent with the paper chosen in part A. Select papers that have relevance to the paper you chose and write one paragraph on the significance of each.
This data mining assignment reviews the Advancement of technology has led to a rapid evolution of data. Multiple firms and government agencies have preferably shifted to a digital framework of data management. The data mining assignment selected for evaluation is “Application of conventional data mining techniques and web mining to aid disaster management. In Environmental Information Systems: Concepts, Methodologies, Tools, and Applications”. The ease of using technology has to lead to widespread access to technology, both in far off rural areas and with different age groups. The high volume of data is being generated from various sources. Such a high volume of data needs proper management of and handling, leading to the conversion of data to useful information will also be reviewed on this data mining assignment.
The data mining assignment deals with the conventional and the web mining technique for aiding disaster management.
Open problems before the publishing of the paper
Proper technique of data mining is crucial for solving the complexity associated with the data, associated with a particular event, and the generation of relevant and liable information. Proper data mining process can help in the investigation of natural phenomenon, as the data involved in the natural phenomenon is a complex process. The problem is effectively undermined through the fact that uncertainty involved in the natural phenomenon, other natural disasters has to be identified as the timely identification of the issues, and situations can help to save precious human life and avoid loss of life and property. The data mining assignment effectively discusses the lack of proper data mining technique may lead to a lack of information and awareness about such disasters. Various techniques for the assessment of hazards related to the various natural disasters and landslides in specific have to lead to the modelling of various aspects related to the landslide. In this relation, multiple data mining techniques have been used, as the Artificial Neural Network, Fuzzy Geometric mean techniques etc. for the handling and the management of data.
It is evident on the data mining assignment that proper analysis and the processing of data are not sufficient. There is an equal need for a proper decision-making system, that can ensure the precision and the authenticity of the decision making the process and the outcome of the information obtained from the data. Further, there is also the need for a proper mechanism of transforming data into a form that makes it fit for the purpose of analysis and decision-making. Such issues lead to the need for the development of proper data mining techniques. Data mining can be preferably defined as the process of extraction of data from the set of inputs. The extracted data have to be in relevance with the required objective, and there should be a clear relationship between the raw data obtained and the outcomes placed (Kumar et al. 2019).
Importance of the paper
The involvement of society with multi-dimensional and non-linear variables leads to the identification of complex characteristics associated with society. The interaction of humankind with society can be considered to be a dynamic and complex phenomenon and can lead to consequences that are unpredictable. The complexity of the identification and the miss prediction of natural events can lead to a huge loss of life and property caused by multiple natural disasters, like an earthquake, landslide, flash floods, rainfall and so on. Proper analysis of the aftermath after the occurrence of disasters can help in the instant, providing relief and help for the society and can help in the preservation of the sanity of the ecology.
In an empirical tone, geographical or natural phenomenon is associated with numerous geographical and natural variables. Identification of the variables can help in the identification of the changes and the factors that govern the triggering of the disasters. This data mining assignment also analyses complex variables include the assessment of numerous possibilities, and proper management and analysis of the variables can be crucial for the assessment of the exact possibility associated with the analysis of the data.
Effective management of disasters can help in the proper understanding of the influencing variables that can trigger the disaster. This has to be associated with the proper understanding of the integrating factors controlling the environment and the proper application of the control measures to eliminate or reduce the risk of disaster and proper management of science and technique for the extraction of useful information from the set of analysing the influencing factors (Kumar et al. 2019).
It is identified on this data mining assignment that proper methods of data mining can be effectively used for analysing the environmental variables and reduce the impact of natural disasters on society. This data mining technique can range from the conventional data mining process to the web data mining process and can be effective in aiding disaster management.
Solution Proposed on the data mining assignment
Importance of Disaster management: Disaster management can be identified as the readiness, prevention, recovery and the response of the individual for the disaster. The action of disaster management is based on the severity of the situation involving the disaster management. Proper analysis of data and the identification of the variables can be a crucial way of reporting instantly to disaster management. Based on the analysis done for the data mining assignment that data and the identification of the issues, various departments like Police, Fire extinguisher, army and public health can be made to be prepared for the predicted outcome. This can be a crucial step for providing instant support during times of disasters.
Delineation of Landslide Hazard Zone: The identification of the previous data regarding the landslide of a particular place can be an effective way of zone marking of the place based on the landslide history. Further, satellite mapping can be used for the purpose of providing a well-framed demographically segregated map of the landscape. Identification of the land areas prone to the landslide can help in the reduction of the hazards associated with landslides, and the individuals of the place prone to landslide can be effectively made to evacuate the place on the identification of the probable risk ascertained with the variables.
Methodology for Delineation: For the zone mapping, high quality of satellite images can be used. The high resolution of satellite data can be effective in the mapping of data. The geological survey of data can be used for the identification of the landslide zone. Further, the use of ASTER DEM is used for preparing the thematic layer on the slope, aspect and relief. The data can be preferably used for the generation of the geomorphologic map, road density map, drainage density map and various data for the purpose of demarcation of the zone based on the vulnerability of the areas based on the landslide (Kumar et al. 2019).
Subsequent research and systems followed
For the purpose of the data mining assignment and the analysis of the data related to the research, various methodologies were used. The preliminary work has been conducted for the purpose of studying the archival material, terrain reconnaissance, questionnaires involving local people, and the conceptual engineering of the geological model.
For the purpose of Landslide Investigation, engineering geological mapping, longitudinal and cross-sectional data analysis, sampling of the soil, including the boreholes, trial pits, sampling of rocks and soils and the laboratory testing has to be done.
Further, the depth of the rupturing surface has to be identified and analysed for the identification of the strength of the land and the looseness of the soil.
Further, the monitoring of deformations can be identified. Proper monitoring of the hydrogeological features, the groundwater level of the place and the fluctuation of the pore pressure have been identified to be an important factor. Geophysics can be effectively utilised for the purpose of the monitoring of the landmass and the soil.
Further, the prediction can be used for the identification of the probable hazard. The technology used to prepare this data mining assignment can be preferably be remotely censored and GIS technology, prediction of the mechanism and the dimensions related to the failure and the prediction of the time for the arrival of the disaster can be another crucial point for effective disaster management.
The Open Question 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.
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.
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)
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