Data Mining and Visualization for Business Intelligence -Sample

Question: Discuss about the Data Mining and Visualization for Business Intelligence. Answer: Data Mining in Business Importance of data mining in business Data mining is defined as a procedure of sorting with the help of large data sets for identifying various patterns and for establishing relationship for solving problems with the help of data analysis. Data mining tools mainly allows the enterprises to predict their future trends. The organization mainly utilizes the procedure of data mining for turning raw data into useful information (Witten et al., 2016). The procedure of data mining is mainly dependent on the procedure of appropriate method of data collection, computer processing as well as warehousing. In business organization, data mining is largely utilized in several applications such as understanding the consumer research marketing, demand and supply analysis, product analysis, business e-commerce, investment trends in various stocks and real estate (Wu et al., 2014). It is mainly dependent on mathematical algorithm as well as analytical skills for deriving the desired results from various collection of database. It is quite significant in highly competitive world of business environment. Recent article/news item relating to data mining in business The article that is reviewed on the topic of data mining in business is European Parliament divided as decision on text and data mining edges closer. It is identified that various publishers, researchers are competing as the European Parliament is edging towards decision of easing rules on data mining (Rokach Maimon, 2014). However, the lawmakers of the European Parliament have different views about different types of policy intricacies that mainly engage in opening up the utilization of technique so that they can be easily disagree about the problem and it purpose (European Parliament divided as decision on text and data mining edges closer, 2017). The policy fight is considered as one front on a huge battle for updating present EU related copyright rules. Text as well as data mining turbochargers research helps in enabling scientist to scour the article numbers. The main problem that mainly exists is because various researchers is that they want to control the journal publishers i n Europe, which mainly helps in owning the copyright. It is identified that a scientist who wants to mine various papers about malaria requires seeking permission for 1024 science journals. It is analyzed by the researcher that many grounds is lost due to the mining procedure as well as number of limitations that is imposed by the various European rules (Freitas, 2013). In the month of September, researcher identified the entire ban of default will be scraped on various computers scanning in order to pass legislation before the month of October. However, they generally consider the entire timetable by consulting various member states as well as parliament (Lin, Yao Zadeh, 2013). It is analyzed that proposal might improve the situation of mining in overall Europe. The various universities and research institutes are operating mainly for gaining public interest in the new rule. It mainly limits the scope of various mining activity for proposing various scientific research. Security, Privacy and Ethics Big data security problems threaten consumersprivacy The article reflects on the security problems of big data that threaten the privacy of the consumers. It is identified in this article that because of the involvement within the entire incidents of big data security, the stakes are getting higher as compared to earlier. In the year 2014, when the professional development system within the University of Arkansas is breached, then it is found that more than 50,000 people are affected (Big data security problems threaten consumers' privacy, 2017). From the security professional perspective, it is identified that securing big data is known as daunting. This is generally because of the underlying technologies that are utilized for storing as well as processing information. The companies of Big data like Amazon is dependent on the technique of distributed computing which mainly helps in involving various data centers that are mainly dispersed across the entire world. It is found that Amazon mainly helps in dividing the entire operations in to 12 regions that mainly contain different centers of data for potentially being the subject of physical attacks against the thousand servers that are housed inside (Baker Inventado, 2014). The most important strategy that is related with controlling both the information as well as physical space is very much secure. It is found that the vulnerability of big data is much higher due to its size as well as broad range of access (Zaki, Meira Meira, 2014). In addition to this, sophisticated components do not take security very much seriously enough and as a result, it helps in opening up further avenue that are related with potential attack. It is identified that it is quite critical from the perspective of the consumers to demand a heightened level of security with the help of vehicles including service level, terms and conditions, security trust seals and more. Appropriate countermeasures including access control, intrusion detection, and backup as well as auditing can be helpful in preventing data from being breached. In addition to this, security can be helpful in promoting privacy (Tang Zhang, 2013). It is found that heightened security helps in providing various types of legitimating excuses for collecting various types of private information. The fundamental principle of security helps in justifying the type of blanket surveillance (Romero Ventura, 2013). Once they are collected and join the rest of the information of the database then it is quite susceptible to abuse as well as breaches. It is identified that big data can be helpful in providing privacy by permitting more information on various potential attack s as well as attackers in cyberspace do. However, there are additional concerns that are mainly related with big data. It is identified that banning large scale of data collection is quite realistic option for solving the problem (Mukhopadhyay et al., 2014). For example, when big data is utilized securely as well as legitimately then it helps in improving the effectiveness of fraud detection, which reduces the chances of stealing identity. Thus, it is very much important for the companies of big data to earn appropriate public trust by providing appropriate explanation about various types of security controls procedures. Big Data, Human Rights and the Ethics of Scientific Research The article mainly focuses on human rights, big data as well as ethics of scientific research. It is identified that there are number of inter related approach of digital revolution and at the heart of it, there lie the enchanted ability to mass as well as store data as well as analytical models that are mainly applied for yielding knowledge. Big data is one of the advancing technologies that mainly pervade existing areas (Tasioulas, 2017). It is found that big data helps in generating number of hopes about potential good that it helps in brining important facets in human lives. The significant application related with big data is that it must be expected in various areas of public health as well as biomedical research (Freitas, 2013). It is found that early detection of diseases causes outbreaks that mainly help in identifying genomic underpinning of different types of diseases by recognizing unknown patterns. However, Snowden revelation about government surveillance mainly undersco res number growing fears about undermining not just the privacy but also trust, liberty as well as democracy (Baker Inventado, 2014). The various streams of report about hacking of database, kidnapping as well as cybercrime have increased the threat of vulnerability within the digital world. Big data mainly helps in pursuing the narrow self-interest in context to satisfaction of curiosity, career advancement and monetary enrichment. It is identified that electronic health record is considered as one of the significant practice in the sector of health care. The main objective of the EHRs is to collect as well as store different types of information about the patients. It is found that HER are mainly underutilized under the health research as well as public health practice that is relative to various types of benefits that they have appropriate ability of generating. The most significant cause behind the underutilization is privacy as well as security related concerns. The analysis of behavior, patterns as well as health risks are mainly related with different individual groups (Tang Zhang, 2013). The disclosure of that information generally creates number of challenges from business perspective. The most important way of resolving or mitigating the risk is synchronization of important data, which mainly helps in preventing data that is being associated with different individuals (Romero Ventura, 2013). The emer gence of big data is one of the drastic examples of technological innovation that helps in generating both benefits as well as risks. In responding to various types of challenges of securing benefits while minimizing different types of risks, it is very much important to engage in the procedure of ethical thinking. References Baker, R. S., Inventado, P. S. (2014). Educational data mining and learning analytics. InLearning analytics(pp. 61-75). Springer New York. Big Data, Human Rights and the Ethics of Scientific Research Opinion ABC Religion amp; Ethics (Australian Broadcasting Corporation). (2017).Abc.net.au. Retrieved 11 August 2017, from https://www.abc.net.au/religion/articles/2016/11/30/4584324.htm Big data security problems threaten consumers' privacy. (2017).The Conversation. Retrieved 11 August 2017, from https://theconversation.com/big-data-security-problems-threaten-consumers-privacy-54798 European Parliament divided as decision on text and data mining edges closer. (2017).Sciencebusiness.net. Retrieved 11 August 2017, from https://sciencebusiness.net/news/80380/European-Parliament-divided-as-decision-on-text-and-data-mining-edges-closer) Freitas, A. A. (2013).Data mining and knowledge discovery with evolutionary algorithms. Springer Science Business Media. Lin, T. Y., Yao, Y. Y., Zadeh, L. A. (Eds.). (2013).Data mining, rough sets and granular computing(Vol. 95). Physica. Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., Coello, C. A. C. (2014). A survey of multiobjective evolutionary algorithms for data mining: Part I.IEEE Transactions on Evolutionary Computation,18(1), 4-19. Rokach, L., Maimon, O. (2014).Data mining with decision trees: theory and applications. World scientific. Romero, C., Ventura, S. (2013). Data mining in education.Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,3(1), 12-27. Tang, Q. Y., Zhang, C. X. (2013). Data Processing System (DPS) software with experimental design, statistical analysis and data mining developed for use in entomological research.Insect Science,20(2), 254-260. Tasioulas, J. (2017).Big Data, Human Rights and the Ethics of Scientific Research Opinion ABC Religion Ethics (Australian Broadcasting Corporation).Abc.net.au. Retrieved 11 August 2017, from https://www.abc.net.au/religion/articles/2016/11/30/4584324.htm Witten, I. H., Frank, E., Hall, M. A., Pal, C. J. (2016).Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann. Wu, X., Zhu, X., Wu, G. Q., Ding, W. (2014). Data mining with big data.IEEE transactions on knowledge and data engineering,26(1), 97-107. Zaki, M. J., Meira Jr, W., Meira, W. (2014).Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press.