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Realization of Model-Driven Engineering for Big Data: A Baseball Analytics Use Case

Koseler, Kaan Tamer
http://orcid.org/0000-0001-6924-700X
http://rave.ohiolink.edu/etdc/view?acc_num=miami1524832924255132

Abstract Details

2018, Master of Science, Miami University, Computer Science and Software Engineering.
Data collection and analysis is widespread across all industries, leading to a glut of data and a dearth of specialists who can use this data to derive insights. Accompanying the new “Big Data” paradigm is a resurgence in interest in machine learning techniques. Using machine learning techniques to work with "Big Data” is a complex task, often requiring specialized knowledge of the problem space as well as appropriate computer algorithms and approaches. However, such specialists who also possess programming ability are difficult to find and expensive to train. The gap between the problem space and the software solution often includes developers who lack the requisite domain-specific knowledge. The Model-Driven Engineering (MDE) paradigm helps close this gap by allowing developers to implement quality software by modeling it using high-level domain specific concepts. In this thesis, we attempt to demonstrate the plausibility of applying MDE to big data by considering a use case of machine learning baseball analytics, specifically, prediction of the next pitch. We model and implement MDE solutions to this use case by employing and updating an existing, but untested, Domain-Specific Modeling Language (DSML). We implement model instances considering different prediction factors and a code generation scheme for this DSML that is targeted at a binary classification problem of fastball versus non-fastball. Our goal is to help demonstrate the viability of the MDE paradigm in the machine learning domain, make machine learning software development more accessible and formalized, and help facilitate future research in this area.
Matthew Stephan, PhD (Advisor)
James Kiper, PhD (Committee Member)
Michael Zmuda, PhD (Committee Member)
45 p.
Computer Science
Machine Learning, Model-Driven Engineering, Sports Analytics, Baseball Analytics, Probabilistic Graphical Models, Bayesian Inference, Bayes Point Machine, Binary Classification, Pitch Prediction, Code Generation, Papyrus, Epsilon Object Language

Recommended Citations

Citations

  • Koseler, K. T. (2018). Realization of Model-Driven Engineering for Big Data: A Baseball Analytics Use Case [Master's thesis, Miami University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=miami1524832924255132

    APA Style (7th edition)

  • Koseler, Kaan. Realization of Model-Driven Engineering for Big Data: A Baseball Analytics Use Case. 2018. Miami University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=miami1524832924255132.

    MLA Style (8th edition)

  • Koseler, Kaan. "Realization of Model-Driven Engineering for Big Data: A Baseball Analytics Use Case." Master's thesis, Miami University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=miami1524832924255132

    Chicago Manual of Style (17th edition)

miami1524832924255132
551
© 2018, all rights reserved.
This open access ETD is published by Miami University and OhioLINK.