Data module of the Visualisation Ontology (VISO)

http://purl.org/viso/data/
Current version:
1.0
Authors:
http://purl.org/viso/JanPolowinski
http://purl.org/viso/MartinVoigt
Contact:
jan.polowinski at tu-dresden.de
Imported Ontologies:
http://purl.org/viso/anno/
http://purl.org/viso/bibliography/
Download / Source view:
Ontology source code
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More on http://purl.org/viso/ can be found in the Wiki.

Table of Content

  1. Introduction
  2. Classes
  3. Data Properties
  4. Annotation Properties
  5. Namespace Declarations

Abstract

The data module of VISO describes characteristics of data, as far as needed for visualization purposes.

Classes

Characteristicc

http://purl.org/viso/data/Characteristic

  • »The value of an attribute«
has super-classes

Continuous Variablec

http://purl.org/viso/data/Continuous_Variable

  • [General note on the topic, no concrete quotation given]
    (Comment: The authors state that continuity and discreteness are properties of a variable with respect to some independent variable (n-ary-relation).)
has super-classes

Datac

http://purl.org/viso/data/Data

  • »###Data Type### «
    (Comment: We consider "Data type" the same as the class "Data" in ontological modeling. Card uses data types to distinguish a) spatial axes and b) nodes and links in network visual structures: **unstructured (unlabeled) **nominal (labeled) **ordinal (labeled with an ordinal quantity) **quantitative (weighted links))
  • »###Data Type###«
  • »###Data Type###«
    (Comment: Good summary on "Data Type")
  • »Regularities (geometric and/or topological) have long been used to structure and organize data, and these all underpin fundamental disinctions, e.g. between structured and unstructured grids. Several taxonomies of data and its organization have been been developed, in particular work by Butler, Pendley, and Bergeron and Kao; other approaches are also notable, for example the lattice model of Hibbard et.al. is significant in addressing error and uncertainty. A classification scheme for data proposed by Brodlie emphasises the importance of the underlying field, i.e. the phenomenon that is captured within the data. Building on this, Tory and Möller have developed a visualization taxonomy based on data models rather than data. These two groups of taxonomies (e.g. Bergeron and Kao, and Brodlie), are complimentary; the former concentrate on how data is structured, while the latter the link between data and representation. A synthesis of these two contributions should help to develop the ‘data’ branch of the top-level ontology.«
  • [General note on the topic, no concrete quotation given]
    (Comment: Bertin separates data into values with attributes and structures that define the data as a whole.)
  • »###Type of Information### Type of information that is expressed by different spatial structures. See section 3.4 for a very brief discussion of types of information.«
  • »###Domain Set### Each domain set can be one of the following three types [...]: A domain set is nominal when it is a collection of unordered items, such as {Jay, Eagle, Robin). A domain set is ordinal when it is an ordered tuple, such as (Monday, Tuesday, Wednesday). A domain set is quantitative when it is a range, such as [ 0, 273].«
has super-classes
  • owl:Thing
has sub-classes
Meta Datac, Nominal Datac, One-Dimensional Datac, Ordinal Datac, Physical Datac, Quantitative Datac, Raw Datac, Structured Datac, Unstructured Datac

Data Objectc

http://purl.org/viso/data/Data_Object

has super-classes
  • owl:Thing

Data Schemac

http://purl.org/viso/data/Data_Schema

Prescribes a data structure. Data schema is similar to data structure, however a data structure may exist without a schema that prescribes it.

has super-classes
  • owl:Thing

Data Structurec

http://purl.org/viso/data/Data_Structure

  • [General note on the topic, no concrete quotation given]
    (Comment: Bertin separates data into values with attributes and structures that define the data as a whole.)
has super-classes
  • owl:Thing
has sub-classes
Linked Data Structurec, Tabular Data Structurec, Triplesc

Data Variablec

http://purl.org/viso/data/Data_Variable

In contrast to a relation that may exist also without measuring something, a data variable stands for a variable in a measurement.

has super-classes
  • owl:Thing

Dependent Variablec

http://purl.org/viso/data/Dependent_Variable

has super-classes

Dimensionc

http://purl.org/viso/data/Dimension

  • »###Related Referer and Attributes### *Similar: dependent and independent dimensions«
  • »###Attribute###«
    (Comment: These authors use "Attribute" as a synonym for "Dimension".)
  • [General note on the topic, no concrete quotation given]
    (Comment: Bertin separates data into values with attributes and structures that define the data as a whole.)
  • »###Attribute### attributes or variables«
    (Comment: Card uses "attribute" and "variable" as synonyms.)
has super-classes

Directed Acyclic Graph (DAG)c

http://purl.org/viso/data/DAG

has super-classes
has sub-classes
Treec

Discrete Variablec

http://purl.org/viso/data/Discrete_Variable

  • [General note on the topic, no concrete quotation given]
    (Comment: The authors state that continuity and discreteness are properties of a variable with respect to some independent variable (n-ary-relation).)
has super-classes

Domainc

http://purl.org/viso/data/Domain

A topic area of real life.

has super-classes
  • owl:Thing

Geographical Datac

http://purl.org/viso/data/Geographical_Data

has super-classes
is disjoint with

Graphc

http://purl.org/viso/data/Graph

  • »###network### *Context: a datatype«
has super-classes
has sub-classes
Directed Acyclic Graph (DAG)c, Polyarchyc

Independent Variablec

http://purl.org/viso/data/Independent_Variable

  • [General note on the topic, no concrete quotation given]
has super-classes

Interval Scale of Measurementc

http://purl.org/viso/data/Interval_Scale_of_Measurement

  • »Interval (can do subtraction on values, but no natural zero and can't compute ratios). Example: |10. Dec. 1978-4 Jun. 1982|«
  • »With the interval scale we come to a form that is "quantitative" in the ordinary sense of the word. Almost all the usual statistical measures are applicable here, unless they are the kinds that imply a knowledge of a 'true' zero point. The zero point on an interval scale is a matter of convention or convenience, as is shown by the fact that the scale form remains invariant when a constant is added.«
has super-classes

Linked Data Structurec

http://purl.org/viso/data/Linked_Data_Structure

We distinguish linked data structures from other structures such as tabular structures.

has super-classes
has sub-classes
Graphc

Listc

http://purl.org/viso/data/List

has super-classes

Meta Datac

http://purl.org/viso/data/Meta_Data

  • [General note on the topic, no concrete quotation given]
has super-classes
is disjoint with

Multidimensional Datac

http://purl.org/viso/data/Multidimensional_Data

  • »Most relational and statistical databases are conveniently manipulated as multi-dimensional data in which items with n attributes become points in a n-dimensional space. The interface representation can be 2-dimensional scattergrams with each additional dimension controlled by a slider.«

There could be a property-restriction on has_dimensionality_of_independent_variables, however restricting datatype ranges is not yet possible.

has super-classes
is disjoint with

Multivariate Datac

http://purl.org/viso/data/Multivariate_Data

There could be a property-restriction on has_dimensionality_of_dependent_variables, however restricting datatype ranges is not yet possible.

has super-classes
is disjoint with

Nominal Datac

http://purl.org/viso/data/Nominal_Data

  • »###Nominal type of information### Nominal relations between elements (categories of elements)«
has super-classes

Nominal Scale of Measurementc

http://purl.org/viso/data/Nominal_Scale_of_Measurement

  • »Nominal (can only distinguish whether two values are equal). Example: {Goldfinger, Ben Hur, Star Wars}«
has super-classes

One-Dimensional Datac

http://purl.org/viso/data/One-Dimensional_Data

  • »Linear data types include textual documents, program source code, and alphabetical lists of names which are all organized in a sequential manner.«
has super-classes

Ordinal Datac

http://purl.org/viso/data/Ordinal_Data

  • »###Ordinal type of information### Ordinal relations between categories of elements (ordered categories of elements)«
has super-classes

Ordinal Scale of Measurementc

http://purl.org/viso/data/Ordinal_Scale_of_Measurement

  • »Ordinal (can distinguish whether one value is less or greater but not difference or ratio). Example: <Small, Medium, Large>«
has super-classes

Physical Datac

http://purl.org/viso/data/Physical_Data

has super-classes
has sub-classes
Spatial Datac, Temporal Datac

Polyarchyc

http://purl.org/viso/data/Polyarchy

  • »Polyarchies are structures composed of multiple intersecting hierarchies and in Robertson et al. [2002] a Web-based visualization technique called Visual Pivot is proposed for the representation of polyarchies.«
has super-classes

Quantitative Datac

http://purl.org/viso/data/Quantitative_Data

  • »###Quantitative type of information### quantitative relations between elements, concerning a single attribute«
has super-classes

Quantitative Scale of Measurementc

http://purl.org/viso/data/Quantitative_Scale_of_Measurement

  • »Quantitative (can do arithmetic on values). Example: |0-100| kg«
has super-classes
has sub-classes
Interval Scale of Measurementc, Ratio Scale of Measurementc

Ratio Scale of Measurementc

http://purl.org/viso/data/Ratio_Scale_of_Measurement

  • »Ratio scales are those most commonly encountered in physics and are possible only when there exist operations for determining all four relations: equality, rank-order, equality of intervals, and equality of ratios. Once such a scale is erected, its numerical values can be transformed (as from inches to feet) only by multiplying each value by a constant. An absolute zero is always implied, even though the zero value on some scales (e.g. Absolute Temperature) may never be produced. All types of statistical measures are applicable to ratio scales, and only with these scales may we properly indulge in logarithmic transformations such as are involved in the use of decibels. Foremost among the ratio scales is the scale of number itself-cardinal number-the scale we use when we count such things as eggs, pennies, and apples. This scale of the numerosity of aggregates is so basic and so common that it is ordinarily not even mentioned in discussions of measurement.«
has super-classes

Raw Datac

http://purl.org/viso/data/Raw_Data

  • Card Stuart K Jock D. Mackinlay Ben Schneiderman in »Readings in information visualization: using vision to think«
has super-classes

Referencec

http://purl.org/viso/data/Reference

  • »A value of a referrer, or a set of values from multiple referrers.«
has super-classes

Relationc

http://purl.org/viso/data/Relation

  • »Relations define the structures and patterns that relate entities to one another. Sometimes the relationships are provided explicitly; sometimes discovering relationships is the very purpose of visualization.«
has super-classes
  • owl:Thing

Scale of Measurementc

http://purl.org/viso/data/Scale_of_Measurement

  • »Variables imply a scale of measurement, and it is important to keep these straight. The most important to distinguish are N= Nominal (are only = or # to other values) O = Ordinal(obeys a < relation) Q = Quantitative (can do arithmetic on them) A nominal variable N is an unordered set, such as film titles {Goldfinger, Ben Hur, Star Wars}. An ordinal variable 0 is a tuple (ordered set), such as film ratings (G, PG, PG-13, R). A quantitative variable Q is a numeric range, such as film length [O,360]. In addition to the three basic types of variables, subtypes represent important properties of the world associated with specialized visual conventions. We sometimes distinguish the subtype QuantitativeSpatial(Qg) for intrinsically spatial variables common in scientificvisualization and the subtype Quantitative Geographical (Qp) for spatial variables that are specifically geophysical coordinates. Other important subtypes are similarity metrics Quantitative Similarity (Q,,), and the temporal variablesQuantitative Time (Q) and Ordinal Time (0,) We can also distinguish Interval Scales (I) (like Quantitative Scales, but since there is not a natural zero point, it is not meaningful to take ratios). An example would be dates. It is meaningful to subtract two dates (June 5, 2002 - June 3, 2002 = 2 days), but it does not make sense to divide them (June 5,2002 + June 23, 2002 = Undefined). Finally, we can define an Unstructured Scale (4, whose only value is present or absent (e.g., an error flag).«
  • »We can distinguish the same types of nodes and links in network Visual Structures that we did for spatial axes: (a) Unstructured (unlabeled), (b) Nominal (labeled), (c) Ordinal (labeled with an ordinal quantity), or (d) Quantitative (weighted links).«
  • »###Type of Information### Type of information that is expressed by different spatial structures.«
    (Comment: Table on spatial structures and the type of information they can express. However - are these all Spatial Structures? They are partially already classified as Spaces. What does this imply?)
has super-classes
  • owl:Thing
has sub-classes
Nominal Scale of Measurementc, Ordinal Scale of Measurementc, Quantitative Scale of Measurementc

Spatial Datac

http://purl.org/viso/data/Spatial_Data

has super-classes
has sub-classes
Geographical Datac

Structured Datac

http://purl.org/viso/data/Structured_Data

has super-classes
has sub-classes
Multidimensional Datac, Multivariate Datac
is disjoint with

Tabular Data Structurec

http://purl.org/viso/data/Tabular_Data_Structure

has super-classes

Temporal Datac

http://purl.org/viso/data/Temporal_Data

  • [General note on the topic, no concrete quotation given]
has super-classes

Treec

http://purl.org/viso/data/Tree

  • Ben Schneiderman in »The eyes have it: a task by data type taxonomy for information visualizations« (p. 336-343)
has super-classes
has sub-classes
Listc

Triplesc

http://purl.org/viso/data/Triples

Subject, Predicat, Object Structures such as RDF.

has super-classes

Unstructured Datac

http://purl.org/viso/data/Unstructured_Data

  • »###No information###«
    (Comment: Uses the type of information "no information".)
has super-classes

Value Rolec

http://purl.org/viso/data/Value_Role

has super-classes
  • owl:Thing
has sub-classes
Characteristicc, Referencec

Variable Rolec

http://purl.org/viso/data/Variable_Role

has super-classes
has sub-classes
Continuous Variablec, Dependent Variablec, Dimensionc, Discrete Variablec, Independent Variablec

Data Properties

has fractional valuedp

http://purl.org/viso/data/has_fractional_value

has super-properties

Assigns a ratio value between 0 and 1.

has interval valuedp

http://purl.org/viso/data/has_interval_value

has super-properties
has sub-properties
has ratio valuedp

Assigns an interval value.

has numeral valuedp

http://purl.org/viso/data/has_numeral_value

has super-properties
  • owl:topDataProperty
Example

Assign "1" to "small", "2" to "medium" and "3" to big. This creates an ordinal scale. By using subproperties, it can be further defined, if even more than an ordinal scale applies (interval, ratio).

This property should be used to assign numeral values to individuals or strings that actually are nominal and thereby put them on an ordinal or quantitative scale. The assignment of ordinal values is an alternative to placing the resources in an (ordered) list.

has ordinal valuedp

http://purl.org/viso/data/has_ordinal_value

has super-properties
  • owl:topDataProperty

Assigns an ordinal value.

Alternatively to the definition of a new order relation between ObjectProperty values and connecting them by this means, a numeral value can be attached, which is able to provide the ordinal information and additionally also quantitative (interval, ratio) information.

has quantitative valuedp

http://purl.org/viso/data/has_quantitative_value

has super-properties
  • owl:topDataProperty
has sub-properties
has interval valuedp

has ratio valuedp

http://purl.org/viso/data/has_ratio_value

has super-properties
has sub-properties
has fractional valuedp

Assigns a ratio value.

heterogeneousdp

http://purl.org/viso/data/heterogeneous

has super-properties
  • owl:topDataProperty

Heterogeneous means that various data structures and distributed sources may exist. The opposite is homegeneous.

planardp

http://purl.org/viso/data/planar

has super-properties
  • owl:topDataProperty

sparsedp

http://purl.org/viso/data/sparse

has super-properties
  • owl:topDataProperty

A data set is sparse with respect to a type of relation if only a few items take part in relationships of that type. A data set could be called sparse in general when this is true for many of its relation types.

weighteddp

http://purl.org/viso/data/is_weighted

has super-properties
  • owl:topDataProperty

Annotation Properties

Namespace Declarations

default namespace
http://purl.org/viso/data/
basic
http://prismstandard.org/namespaces/1.2/basic/
bibo
http://purl.org/ontology/bibo/
bibo-extension
http://purl.org/viso/bibo-extension/
data
http://purl.org/viso/data/
event
http://purl.org/NET/c4dm/event.owl#
foaf
http://xmlns.com/foaf/0.1/
ns
http://www.w3.org/2003/06/sw-vocab-status/ns#
owl
http://www.w3.org/2002/07/owl#
rdf
http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs
http://www.w3.org/2000/01/rdf-schema#
schema
http://schemas.talis.com/2005/address/schema#
skos
http://www.w3.org/2004/02/skos/core#
terms
http://purl.org/dc/terms/
xsd
http://www.w3.org/2001/XMLSchema#

Work on this project received financial support from the European Union and the Free State of Saxony.
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