Visual Analytics - Process

Process

The input for the data sets used in the visual analytics process are heterogeneous data sources (i.e., the internet, newspapers, books, scientific experiments, expert systems). From these rich sources, the data sets S = S1, ..., Sm are chosen, whereas each Si, i ∈ (1, ..., m) consists of attributes Ai1, ..., Aik. The goal or output of the process is insight I. Insight is either directly obtained from the set of created visualizations V or through confirmation of hypotheses H as the results of automated analysis methods. This formalization of the visual analytics process is illustrated in the following figure. Arrows represent the transitions from one set to another one.

More formal the visual analytics process is a transformation F: S → I, whereas F is a concatenation of functions f ∈ {DW, VX, HY, UZ} defined as follows:

DW describes the basic data pre-processing functionality with DW : S → S and W ∈ {T, C, SL, I} including data transformation functions DT, data cleaning functions DC, data selection functions DSL and data integration functions DI that are needed to make analysis functions applicable to the data set.

VW, W ∈ {S, H} symbolizes the visualization functions, which are either functions visualizing data VS : S → V or functions visualizing hypotheses VH : H → V.

HY, Y ∈ {S, V} represents the hypotheses generation process. We distinguish between functions that generate hyphotheses from data HS : S → H and functions that generate hypotheses from visualizations HV : V → H.

Moreover, user interactions UZ, Z ∈ {V, H, CV, CH} are an integral part of the visual analytics process. User interactions can either effect only visualizations UV : V → V (i.e., selecting or zooming), or can effect only hypotheses UH : H → H by generating a new hypotheses from given ones. Furthermore, insight can be concluded from visualizations UCV : V → I or from hypotheses UCH : H → I.

The typical data pre-processing applying data cleaning, data integration and data transformation functions is defined as DP = DT(DI(DC(S1, ..., Sn))). After the pre-processing step either automated analysis methods HS = {fs1, ..., fsq} (i.e., statistics, data mining, etc.) or visualization methods VS : S → V, VS = {fv1, ..., fvs} are applied to the data, in order to reveal patterns as shown in the figure above.

In general the following paradigm is used to process the data:

Analyse First – Show the Important – Zoom, Filter and Analyse Further – Details on Demand

Read more about this topic:  Visual Analytics

Famous quotes containing the word process:

    Science is a dynamic undertaking directed to lowering the degree of the empiricism involved in solving problems; or, if you prefer, science is a process of fabricating a web of interconnected concepts and conceptual schemes arising from experiments and observations and fruitful of further experiments and observations.
    James Conant (1893–1978)

    The toddler’s wish to please ... is a powerful aid in helping the child to develop a social awareness and, eventually, a moral conscience. The child’s love for the parent is so strong that it causes him to change his behavior: to refrain from hitting and biting, to share toys with a peer, to become toilet trained. This wish for approval is the parent’s most reliable ally in the process of socializing the child.
    Alicia F. Lieberman (20th century)

    Experiences in order to be educative must lead out into an expanding world of subject matter, a subject matter of facts or information and of ideas. This condition is satisfied only as the educator views teaching and learning as a continuous process of reconstruction of experience.
    John Dewey (1859–1952)