VMEXT: A Visualization Tool for Mathematical Expression Trees

نویسندگان

  • Moritz Schubotz
  • Norman Meuschke
  • Thomas Hepp
  • Howard S. Cohl
  • Bela Gipp
چکیده

Mathematical expressions can be represented as a tree consisting of terminal symbols, such as identifiers or numbers (leaf nodes), and functions or operators (non-leaf nodes). Expression trees are an important mechanism for storing and processing mathematical expressions as well as the most frequently used visualization of the structure of mathematical expressions. Typically, researchers and practitioners manually visualize expression trees using general-purpose tools. This approach is laborious, redundant, and error-prone. Manual visualizations represents a user’s notion of what the markup of an expression should be, but not necessarily what the actual markup is. This paper presents VMEXT – a free and open source tool to directly visualize expression trees from parallel MathML. VMEXT simultaneously visualizes the presentation elements and the semantic structure of mathematical expressions to enable users to quickly spot deficiencies in the Content MathML markup that does not affect the presentation of the expression. Identifying such discrepancies previously required reading the verbose and complex MathML markup. VMEXT also allows one to visualize similar and identical elements of two expressions. Visualizing expression similarity can support support developers in designing retrieval approaches and enable improved interaction concepts for users of mathematical information retrieval systems. We demonstrate VMEXT’s visualizations in two web-based applications. The first application presents the visualizations alone. The second application shows a possible integration of the visualizations in systems for mathematical knowledge management and mathematical information retrieval. The application converts LTEX input to parallel MathML, computes basic similarity measures for mathematical expressions, and visualizes the results using VMEXT.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

John Von Neumann Y El Computador Moderno

Highly-available models and congestion control have garnered improbable interest from both analysts and analysts in the last several years. After years of robust research into suffix trees, we disprove the visualization of the partition table. We present a Bayesian tool for visualizing von Neumann machines, which we call Gid.

متن کامل

A NOTE ON THE EQUISEPARABLE TREES

Let T be a tree and n_{l}(eIT) and n_{2}(eIT) denote the number of vertices of T, lying on the two sides of the edge e. Suppose T_{l} and T_{2} are two trees with equal number of vertices, e in T_{1} and f in T_{2}. The edges e and f are said to be equiseparable if either n_{l}(eIT_{I}) = n_{l}(fIT_{2}) or n_{l}(eIT_{I}) = n_{2}(fIT_{2}). If there is an one-to-one correspondence between the ver...

متن کامل

A new approach for data visualization problem

Data visualization is the process of transforming data, information, and knowledge into visual form, making use of humans’ natural visual capabilities which reveals relationships in data sets that are not evident from the raw data, by using mathematical techniques to reduce the number of dimensions in the data set while preserving the relevant inherent properties. In this paper, we formulated d...

متن کامل

The Hyper-Zagreb Index of Trees and Unicyclic Graphs

Topological indices are widely used as mathematical tools to analyze different types of graphs emerged in a broad range of applications. The Hyper-Zagreb index (HM) is an important tool because it integrates the first two Zagreb indices. In this paper, we characterize the trees and unicyclic graphs with the first four and first eight greatest HM-value, respectively.

متن کامل

A visualization tool for interactive learning of large decision trees

Decision tree induction is certainly among the most applicable learning techniques due to its power and simplicity. Howevel; learning decision trees from large datasets, particularly in data mining, is quite different from learning from small or moderately sized datasets. When learning from large datasets, decision tree induction programs often produce very large trees. How to visualize efficie...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017