Suhas Somnath


Lets clarify some nomenclature to avoid confusion.

Data schema

Data schema or model refers to the way the data is arranged. This does not depend on the implementation in a particular file format

File format

This corresponds to the kind of file, such as a spreadsheet (.CSV), an image (.PNG), a text file (.TXT) within which information is contained.

Data format

data format is actually a rather broad term. However, we have observed that people often refer to the combination of a data model implemented within a file format as a data format.


In all measurements, some quantity such as voltage, resistance, current, amplitude, or intensity is collected as a function of (typically all combinations of) one or more independent variables. For example, a gray-scale image represents the quantity - intensity being recorded for all combinations of the variables - row and column. A (simple) spectrum represents a quantity such as amplitude or phase recorded as a function of a reference variable such as wavelength or frequency.

Data collected from measurements result in N-dimensional datasets where each dimension corresponds to a variable that was varied. Going back to the above examples a gray-scale image would be represented by a 2 dimensional dataset whose dimensions are row and column. Similarly, a simple spectrum wold be a 1 dimensional dataset whose sole dimension would be frequency for example.


  • We consider data recorded for all combinations of 2 or more variables as multi-dimensional datasets or Nth order tensors:

    • For example, if a single value of current is recorded as a function of driving / excitation bias or voltage having B values, the dataset is said to be 1 dimensional and the dimension would be - Bias.

    • If the bias is cycled C times, the data is said to be two dimensional with dimensions - (Bias, Cycle).

    • If the bias is varied over B values over C cycles at X columns and Y rows in a 2D grid of positions, the resultant dataset would have 4 dimensions: (Rows, Columns, Cycle, Bias).

  • Multi-feature: As a different example, let us suppose that the petal width, length, and weight were measured for F different kinds of flowers. This would result in a 1 dimensional dataset with the kind of flower being the sole dimension. Such a dataset is not a 3 dimensional dataset because the petal width, length, and weight are only different features for each measurement. Some quantity needs to be measured for all combinations of petal width, length, and weight to make this dataset 3 dimensional. Most examples observed in data mining, simple machine learning actually fall into this category