Data Science Tools Application for Business Processes Modelling in Aviation

Data Science Tools Application for Business Processes Modelling in Aviation

Maryna Nehrey (National University of Life and Environmental Sciences of Ukraine, Ukraine) and Taras Hnot (National University of Life and Environmental Science of Ukraine, Ukraine)
Copyright: © 2019 |Pages: 15
DOI: 10.4018/978-1-5225-7588-7.ch006

Abstract

Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.
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Data Science: Essence, Principles, And Tools

Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of Data Science is the exploration of the complexities inherently trapped in data, business, and problem-solving systems.

Data Science is a science of learning from data. Data Science is a continuation of Data Mining and Predictive Analytics. This approach is multidisciplinary, it combines the methods and models of disciplines such as mathematics, statistics, probability theory, information technology, including signal processing, probabilistic models, machine learning, statistical training, data mining, databases, object recognition, visualization, uncertainty modeling, data warehousing, data compression, computer programming, and high-performance computing.

The essence of Data Science is the extraction of information based on the knowledge and skills from the various fields of activity necessary for gaining knowledge. The composition of such a set largely depends on the field of research. For specialists in this area of research - Data Scientist - generalized qualification requirements have been developed.

Data Science has a big list of tools: Linear Regression, Logistic Regression, Density Estimation, Confidence Interval, Test of Hypotheses, Pattern Recognition, Clustering, Supervised Learning, Time Series, Decision Trees, Monte-Carlo Simulation, Naive Bayes, Principal Component Analysis, Neural Networks, k-means, Recommendation Engine, Collaborative Filtering, Association Rules, Scoring Engine, Segmentation, Predictive Modeling, Graphs, Deep Learning, Game Theory, Arbitrage, Cross-Validation, Model Fitting, etc. Some of this tools were used in the next researches. Teaching data science, for example, were introduced (Brunner & Kim, 2016), Big data and Data Science methods presented in (Chen, Chiang & Storey, 2012), (George, Osinga, Lavie & Scott, 2016), (Kucherov, 2007), (Shoro, Soomro, 2015), (Xiong, Yu & Zhang, 2017), machine learning used (Parish & Duraisamy, 2016), Monte Carlo method presented (Patriarca, Di Gravio & Costantino, 2017), game theory and genetic algorithms combined (Periaux, Chen, Mantel, Sefrioui & Sui, 2001), Artificial Intelligence presented (Rizun & Shmelova, 2017). Data Science is fast developing. A large volume of information that grows with each passing year makes it possible to build high-precision models that simplify and partially automate the decision-making process. Models are being developed that implement the key data science algorithms for decision-making in business (Hnot, Nehrey, 2017).

Key Terms in this Chapter

Data Science: Is a broad field that refers to the collective processes, theories, concepts, tools and technologies that enable the review, analysis, and extraction of valuable knowledge and information from raw data. It is geared toward helping individuals and organizations make better decisions from stored, consumed and managed data.

Recommender systems: Is a system that identifies and provides recommended content or digital items for users. As mobile apps and other advances in technology continue to change the way users choose and utilize information, the recommendation engine is becoming an integral part of applications and software products.

Unsupervised Learning: Is a method used to enable machines to classify both tangible and intangible objects without providing the machines with any prior information about the objects. The things machines need to classify are varied, such as customer purchasing habits, behavioral patterns of bacteria and hacker attacks. The main idea behind unsupervised learning is to expose the machines to large volumes of varied data and allow it to learn and infer from the data. However, the machines must first be programmed to learn from data.

Data Filtering: In IT can refer to a wide range of strategies or solutions for refining data sets. This means the data sets are refined into simply what a user (or set of users) needs, without including other data that can be repetitive, irrelevant or even sensitive. Different types of data filters can be used to amend reports, query results, or other kinds of information results.

Supervised Learning: Is a method used to enable machines to classify objects, problems, or situations based on related data fed into the machines. Machines are fed with data such as characteristics, patterns, dimensions, color and height of objects, people or situations repetitively until the machines are able to perform accurate classifications. Supervised learning is a popular technology or concept that is applied to real-life scenarios. Supervised learning is used to provide product recommendations, segment customers based on customer data, diagnose disease based on previous symptoms and perform many other tasks.

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