Visual analytics

Visual analytics is a multidisciplinary science and technology field that emerged from information visualization and scientific visualization. It focuses on how analytical reasoning can be facilitated by interactive visual interfaces.

Visual analytics is a multidisciplinary science and technology field that emerged from information visualization and scientific visualization. It focuses on how analytical reasoning can be facilitated by interactive visual interfaces. Visual representations translate data into a visible form that highlights important features, including commonalities and anomalies. These visual representations make it easy for users to perceive salient aspects of their data quickly. Augmenting the cognitive reasoning process with perceptual reasoning through visual representations permits the analytical reasoning process to become faster and more focused. File:Nlsy97_jobsatis.jpg|Bar chart representing the relative distribution of job satisfaction File:NLSYpie.jpg|Pie chart representing the relative distribution of job satisfaction according to the National Longitudinal Survey of Youth, 1997 cohort, survey year 2008 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 = S 1 , ..., S m are chosen, whereas each S i , i ∈ (1, ..., m) consists of attributes A i1 , ..., A ik . 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 formally the visual analytics process is a transformation F: S → I, whereas F is a concatenation of functions f ∈ {D W , V X , H Y , U Z } defined as follows: D W describes the basic data pre-processing functionality with D W : S → S and W ∈ {T, C, SL, I} including data transformation functions D T , data cleaning functions D C , data selection functions D SL and data integration functions D I that are needed to make analysis functions applicable to the data set. V W , W ∈ {S, H} symbolizes the visualization functions, which are either functions visualizing data V S : S → V or functions visualizing hypotheses V H : H → V. H Y , Y ∈ {S, V} represents the hypotheses generation process. We distinguish between functions that generate hypotheses from data H S : S → H and functions that generate hypotheses from visualizations H V : V → H. Moreover, user interactions U Z , Z ∈ {V, H, CV, CH} are an integral part of the visual analytics process. User interactions can either affect only visualizations U V : V → V (i.e., selecting or zooming), or can affect only hypotheses U H : H → H by generating a new hypothesis from given ones. Furthermore, insight can be concluded from visualizations U CV : V → I or from hypotheses U CH : H → I. The typical data pre-processing applying data cleaning, data integration and data transformation functions is defined as D P = D T (D I (D C (S 1 , ..., S n ))). After the pre-processing step either automated analysis methods H S = {f s1 , ..., f sq } (i.e., statistics, data mining, etc.) or visualization methods V S : S → V, V S = {f v1 , ..., f vs } 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

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