Roger's Initiative: Using RapidMiner Software to Meet Community Needs - Essay Sample

Introduction

In order of meeting the community needs, Roger’s initiative gets used to enroll several groups in the city for them to work as a team. To accomplish our initiative, RapidMiner software for analyzing data get used. RapidMiner is the preferable software in our case due to several reasons: To begin with, budget gets not mentioned in our initiative. Our initiative lacks management group, stakeholders and board thus it can be said to be an endeavor operating at low budget. An enterprise operating on a high budget requires a management or stakeholder buy-in.

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The software offers integrated environs for text mining, deep and machine learning, predictive analytics as well as data preparation (Cho & Tallón-Ballesteros, 2017). RapidMiner contains features that are useful in exploration like graphs, visualization and descriptive statistics that enable the user to obtain valuable information. It too offers analytics based on the transformation of the real-life (Khan & Bhardwaj, 2019). It is an open-source tool which implies that less cost or no cost at all shall be spent on Roger initiative of analyzing the collected data. Thus, there is much advantage of using RapidMiner software over other proprietary systems due to cost comparison. Other proprietary software is costly (Kusuma et al., 2018).

Also, it is much simple to use the RapidMiner software tool than others. For instance, according to a research conducted by Gartner on the simplicity of using the tools in the year 2017, RapidMiner was highly ranked (Piatetsky, 2017). In our case of study, the software tool will be more applicable since it simplifies big data and permits staff that are non-technical to generate big data in the absence of custom scripting. The software tool also provides many procedures most specifically in the selection area and in the detection of outliers.

Additionally, RapidMiner offers maximum algorithm integration. It is so because it gets issued with several modelling abilities thus Roger does not center its attention on algorithms programming. Instead, Roger can run operators to create a set of items that are frequent or generate association rules that offer him the best model in achieving the initiative. For Roger to run the model of association rule and to ensure that he does not have to set the analysis manually every time he wants to run it after collecting additional or new data, Roger needs to set the operations and parameters to run the model rule. Once it gets set, no need of reviewing it regularly which simplifies his analysis work.

Tool Applicability to Data

RapidMiner is not only useful for Roger’s initiative but also it the best for analyzing data. Roger's data will be in the form of CSV file since he will have to export the information from the survey web. The mode in which the data is presented in fits well with the RapidMiner tool since the software tool has the ability to read files presented as CSV (Knape & Allen, 2016). Thus, Roger is required to do nothing extra for the data he presents to get analyzed. RapidMiner can analyze, load and access any data type, i.e. both unstructured and traditional structured. Thus, Roger does not have to worry of his unstructured data.

Data collected by Roger needs to be transformed and normalized to an optimized form used for analysis. For instance, some of the data that are in integer form need to be converted to binomial. RapidMiner is the best tool for handling this kind of transformations since it gets equipped well to handle such data transformation (Knape & Allen, 2016). Therefore, while using the tool, one can be able to generate an optimal set of data and format it for the predictive analytics. The tool also can combine unstructured with structured data and later grasp all data for the analysis.

Roger has an advantage of using the RapidMiner tool since even if the data becomes ambiguous, he needs not to use the manual methods since the tool provides him with the ease of transforming data. Last but not least, the tool provides Roger with web services abilities that he would use to merge with the third-party technology or tool (Knape & Allen, 2016). Roger gets also provided with the option of making a custom portal; thus he can easily skip the manual part into CSV file that allows him to integrate his real-life data.

Tool Recommendations

Various analytical tools could get applied to Roger's case aside from RapidMiner. R is one of them that too offers many possibilities for our initiative (Rajeswari et al., 2017). For Roger to use this tool, he has to learn programming language and scripting for him to be able to analyze the data. For instance in converting the data to binomial, a written script is needed. RapidMiner is thus the best tool since it has inbuilt wizards that aid Roger run his analysis without having to write a script. SQL is also another tool that can get used in place of RapidMiner (Venkatraman et al., 2016). A programming language is also needed for him to obtain any results. Thus offers the same analysis barriers as the R tool.

Conclusion

From the above argument, it is evident that the RapidMiner analyzing tool is the best for our case scenario. It is so because it is more flexible and its ease of use is incomparable with other tools. It limits not the user and offers a wide range of analyzing options due to the availability of the built-in wizards. R and SQL disadvantage the user in that one has to learn and understand the programming language before analyzing.

References

Cho, S. B., & Tallón-Ballesteros, A. J. (2017, June). Visual tools to lecture data analytics and engineering. In International Work-Conference on the Interplay Between Natural and Artificial Computation (pp. 551-558). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-319-59773-7_56

Khan, S., & Bhardwaj, S. (2019). Time Series Forecasting of Gold Prices. In Emerging Trends in Expert Applications and Security (pp. 63-71). Springer, Singapore. https://www.researchgate.net/profile/Saravanan_Govindarajan2/publication/329191563_Estimation_of_Microwave_Dielectric_Constant_Using_Artificial_Neural_Networks/links/5c0b85a14585157ac1b0b53f/Estimation-of-Microwave-Dielectric-Constant-Using-Artificial-Neural-Networks.pdf#page=79

Knape, T., & Allen, B. (2016). Report on RapidMiner. http://www.smartresilience.eu-vri.eu/sites/default/files/publications/SmartResD3.9.pdf

Kusuma, G. P., Hall, M., Gale, C. P., & Johnson, O. A. (2018). Process Mining in Cardiology: A Literature Review. International Journal of Bioscience, Biochemistry and Bioinformatics, 8(4), 226-236. https://pdfs.semanticscholar.org/e22d/7719ad895c3a4ebeea4ded99db9a0437fb65.pdf

Piatetsky, G. (2017). Gartner 2017 magic quadrant for data science platforms: Gainers and losers. https://rapidminer.com/resource/gartner-data-science-platforms-magic-quadrant/

Rajeswari, C., Basu, D., & Maurya, N. (2017, November). Comparative Study of Big data Analytics Tools: R and Tableau. In Materials Science and Engineering Conference Series (Vol. 263, No. 4, p. 042052). http://adsabs.harvard.edu/abs/2017MS%26E..263d2052R

Venkatraman, S., Fahd, K., Kaspi, S., & Venkatraman, R. (2016). SQL versus NoSQL movement with big data analytics. International Journal of Information Technology and Computer Science, 8(12), 59-66. http://j.mecs-press.net/ijitcs/ijitcs-v8-n12/IJITCS-V8-N12-7.pdf