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Data transformations and representations
for computation and visualization
At the core of successful visual analytics systems are computational techniques that transform data into concise, human comprehensible visualrepresentations. The general process often requires multiple transformation steps before a final visual representation is generated. This article characterizes the complex raw data to be analyzed and then describes two different sets oftransformations and representations. The first set transforms the raw data into more concise representations that improve the performance of sophisticatedcomputational methods. The second transforms internal representations into aThe Boeing Company, PO Box 3707, Seattle, visual representations that provide the most benefit to an interactive user. The end result is a computing system that enhances an end user's analytic process with effective visual representations and interactive techniques. While progress has been made on improving data transformations and representations, there cGeorgia, Institute of Technology, Atlanta, is substantial room for improvement.
Information Visualization (2009) 8,
visual metaphors; data characteristics; visual Introduction
Visual analytics systems must integrate a number of different computingcapabilities. In many ways, a visual analytics system is similar to othercomplex systems that people use daily. When abstracted, systems have user interface, algorithmic and data components. When dissected morecompletely, systems differ in terms of the tasks that a user must perform totransform data into more meaningful forms.
Because of certain data characteristics, a wide range of algorithmic approaches are needed to transform the raw data into increasingly conciserepresentations that are then transformed into visual representations thatusers examine to obtain insight.
This article examines specific types of raw data and the types of computa- tional and visualization transformations and representations that improve a user’s analytic ability. There are two different types of transformations and representations. The first is used to identify higher-order characteristics in Workshop attendees included representatives the data, such as relationships, trends, summaries, clusters and synopses.
from the visual analytics research communityacross government, industry and academia.
The second is responsible for transforming data into the visual represen- The goal of the workshop, and the resulting tations that help the user navigate the overall data space. Both types of article, was to reflect on the first 5 years of transformations and representations must cope with scale and complexity.
the visual analytics enterprise and proposeresearch challenges for the next 5 years.
The article incorporates input from work-shop attendees as well as from its authors.
Raw Data Characteristics
Computers store, move and analyze data that, on initial examination, are a simple collection of bits. Collections of bits are organized into different units (files, directories, databases, network packets and so on).
Information Visualization Vol. 8, 4, 275 – 285
These collections of bits form primitive data types1 evolution of value changes is often important. Examples that include text, numbers, still images, audio and video. Combinations of primitive data forms canbe: • A snapshot of a given data set (for example, a large set of documents) freezes geo-location and time at a specific • Structured (for example, relational tables, geometry).
Often contains numeric values. Some fields may contain A series of snapshots (for example, transaction-based relatively small amounts of free-form text.
systems, the web) that evolves over time.
Semi-structured (for example, e-mail that contains Streaming data (for example, real-time sensors, network header data, attachments and text; network packets data) are collected continuously, which increases data headers and payloads; scientific data resulting from Geospatial data gives an analyst critical understanding Unstructured (for example, a collection of text).
of the physical location of specific event occurrences.
When coupled with temporal data, significant patterns of Many of the challenges1, especially in dealing with activity may emerge using implicit methods (for example, textual data, still exist. This article examines a number kernel methods) and explicit methods (for example, of algorithmic approaches organized around key data feature combinations, supervised learning).
characteristics. The characteristics apply to all primitive Imperfect data. The data, regardless of volume, often data forms rather than algorithms that apply to specific contain noisy, missing, erroneous, incomplete or deliber- data types. The article adds the notion of inserting a ately misleading values. Text data are particularly difficult.
user-in-the-algorithmic-loop to help guide the raw data The values in a given text field or document range from transformation process. In addition, it introduces a set of cleanly edited to quick-and-dirty entries. Shorthand and transformations needed to produce effective visual repre- abbreviations are often present, especially in data that are sentations. Transforming data into an effective visual pertinent to a specific domain. For example, consider the representation is fundamentally different from trans- variation in language among medical records, airplane incident reports and cell phone text messages. Different When defining approaches for data transformations natural languages pose a problem because text can be and representations, algorithm designers must consider entered carelessly or erroneously by either native or non- that visual analytics systems are interactive in nature, native speakers. Analysts often gain insight from data which makes algorithms that are sufficiently fast enough anomalies, and the analyst is responsible for determining to interactive performance critical. Interactive users whether the unusual data are informative or extraneous.
expect a response for a simple task in a few seconds or less A significant amount of work is needed in this area, and are more patient when they realize that the computer although some techniques, such as those based on vector is performing a complex computation. Even so, tasks that take more than a few minutes can lead to user frustration Heterogeneous data. Analysts must often gain significant insight from multiple data sources. In some cases, inte- grating the schemas may be possible. Even if schema interactive analytics, characteristics of the data itself affect integration is possible, multiple data sources increase the transformations and representations for both compu- the raw data volume and increase the probability that tation and visualization. The key characteristics are: specific fields or values will have conflicting mean-ings. Furthermore, the methods needed to assemble Massive data. The amount of data that may be perti- the data in a heterogeneous data environment gener- nent to a specific analysis task is potentially unlimited.
ally differ from one another on a data store-by-data Even though the vast majority of data may be triv- store basis. Extracting, translating and loading (ETL) the ially rejected, the data volume can easily range from data into the visual analytics system may take longer megabytes to petabytes. Some analysts must make than the analysis itself. The long duration for ETL may decisions based on a relatively small amount of data even cause currency problems with the data and nega- (for example, a safety engineer looking at commer- tively affect temporal trend analyses. Two promising cial aircraft incident reports), while others require approaches are to use a joint probabilistic model for terabytes (for example, an administrator looking at event different attributes and to carefully build a dissimilarity logs for network intrusions). Massive data sets must often be transformed into a smaller number of dimen- User-in-the-loop. The data transformations and represen- sions or aggregated to allow users to cope with the tations that apply to basic analysis tasks are different from those that produce a visual display. In addition, Geospatial and temporal data. Significant amounts of data data volume differs from visual volume. For example, have location and temporal dependencies. Both geospatial consider a set of data that captures network traffic. The and temporal data are dynamic, and understanding the transformations and representations that produce various Information Visualization Vol. 8, 4, 275 – 285
summaries are fundamentally different from those that The need for scalable data representation and transforma- produce images of the network traffic.
tion methods forces the development of new paradigmsthat will enable major improvements in decision-making The visuals themselves can vary significantly as shown processes through better methods for understanding and in Figure 1. The image on the left offers a visual tech- predicting outcomes in complex situations and scenarios.
nique to show relationships between specific values in Achieving interactive performance adds further comple- a large table. The image on the right uses a traditional xity. Complex operations on large data sets today often histogram to show the numeric order of specific fields in require minutes and even hours to perform. Long response a relational table. Different transformations produced the times COPY
render such operations ineffective in highly interac- tive environments. Furthermore, data sets are becoming Adding a user-in-the-loop can help direct the analysis more massive and complex over time, necessitating devel- when the user has specific domain knowledge. Because of opment of scalable algorithms that are implementable on the breadth of visual analytics applicability, determining generalized methods for data and visual transformations Massiveness of data also refers to its high dimension- and representations is challenging. Domain knowledge ality. While humans are excellent at finding patterns in a often leads to simplifying assumptions and customiza- 2D- or 3D space, they have difficulty processing massive tions that improve both computational and visualization amounts of data in higher dimensions. Dimension reduc- transformation accuracy and performance.
tion generally is achieved through feature extractionthat creates new coordinate spaces through linear andnonlinear transformations or feature selection that iden- Transforming and Repr
esenting Data for
tifies an important subset of features from the original Computation
high-dimensional data set. Dimension reduction is oftenused to improve efficiency in computational cost and storage complexity, noise reduction, or noise removal.
methods combine mathematical, statistical and linguistic It can produce improved accuracy and is essential for analysis with hardware and software techniques to handle 2D and 3D visualization of data. Dimension reduction massive data, geospatial and temporal data, imperfect methods may differ when applied to visual analytics data, heterogeneous data, and users-in-the-loop. The combination poses significant research challenges. This For data sets for which there is no a priori knowledge, section discusses approaches and shortcomings in those dimension reduction methods such as Principal Compo- approaches that require additional work.
nent Analysis (PCA)3 and Latent Semantic Indexing (LSI)4provide theoretically well-justified projections of high- Massive data
dimensional data onto lower-dimensional spaces. BothPCA and LSI are based on the Singular Value Decompo- A major challenge arises from the sheer volume of data.2 sition (SVD).5 SVD, on which many methods are based, The size and complexity of the data sets appearing is a powerful mathematical tool in understanding the now and in the future are an impediment to the full space spanned by the data represented in a vector space.
exploitation of visual analytics. This section focuses on It provides a method to capture the rank, orthonormal the computational and algorithmic methods needed to bases and characteristics of the noise space associated distill information from ever-expanding data streams.
with the space spanned by the data. SVD has been used Information Visualization Vol. 8, 4, 275 – 285
extensively in numerous science and engineering prob- data are discarded. The applicability of feature selec- lems, including signal, image and text processing. When tion as a dimension reduction technique has not been additional information concerning characteristics of the extensively explored in visual analytics. Promising new data such as its cluster structure or the fact that data methods can be expected to arise from the development values are always non-negative is available, dimension of a comprehensive theory of automatic feature selec- reduction methods that reveal this fact can achieve better tion by sparse recovery. Such methods combine concepts results. Two examples are Linear Discriminant Analy- from learning theory and can yield insights into new sis (LDA)6 for clustered data and non-negative matrix algorithms (for example, boosting, kernel machines).
factorization7,8 for non-negative data. If the inter-data One example where the data sets are represented in relationship is not linear, nonlinear extensions such as high-dimensional space is text. Text documents are orig- Kernel PCA and Kernelized Discriminant Analysis may be inally represented as a sequence of words over a finite vocabulary V. This representation is problematic because To reveal nonlinear structure in the data, many documents of different lengths cannot be easily compared promising methods such as manifold learning have been to one another. Instead, the first step in text analysis is developed. In manifold learning, the goal is to find a to convert the documents into numeric vectors of fixed lower-dimensional (typically nonlinear) representation dimensionality. One option, leading to vector representa- of the data given in a high-dimensional space. A rich tion of dimensionality |V|, is to construct vectors whose literature exists in this area, and the most widely used components are the relative word frequency or normal- methods include multi-dimensional scaling ISOMAP, ized word counts in the document. A slight variation locally linear embedding, Laplacian eigenmap, and local represents a document as a binary vector of dimension- tangent space alignment. Typically, in these manifold ality |V| whose components represent presence or absence learning methods, the dimension-reducing nonlinear of words. Higher-dimensional representations may be transformations are not explicitly available. In other constructed by keeping track of appearances of short linear and nonlinear dimension-reducing transforma- tions such as PCA, LDA and their kernel counterparts, Promising new methods can be expected to arise transformations are explicitly computed and therefore from the development of a fundamental comprehensive make representation of unseen data points in the same theory COPY
of automatic feature selection by sparse recovery.
lower-dimensional space possible. Development of an Such methods link together many ideas from learning effective and general asymptotic theory for manifold theory and can yield insights into new algorithms such learning in terms of differential operators on manifolds can yield new algorithms for nonlinear dimension reduc- Many powerful new algorithms for dimension reduc- tion and address many practical questions.
tion pose even more difficult optimization problems than To make linear and nonlinear dimension reduction arise in current methods, leading to the need to solve methods more effective in handling massive data, the very large-scale, semi-definite programming problems.
basic characteristics of the dimension reduction methods Recent research has focused on the design of dimension- for 2D or 3D representation of high-dimensional data reduction methods that incorporate interpretability sets must be understood. In many dimension reduction constraints such as sparsity and non-negativity. The methods, the optimal reduced AUTHOR
resulting algorithms increase one’s understanding of smallest acceptable reduced dimension with respect to the transformations and further facilitate visual repre- the specific criterion of a dimension reduction method, sentation of very high-dimension data. In addition, is either unknown or much larger than 2 or 3. One may incorporating expert opinion and necessary constraints simply choose the leading two or three dimensions, but in the problem formulation of dimension reduction is this may result in loss of information. This loss hinders expected to produce more insightful representations of understanding because the true characteristics of the data sets (for example, cluster structure, relationships, oranomalies) are hidden. Substantial research effort needsto be made for progress in this direction, although there Geospatial and temporal data
Feature selection is another way to achieve dimension Complex geospatial and temporal data provide a wealth of reduction. Unlike feature extraction, feature selection information on complex phenomena that varies over time specifically selects a small number of relevant features.
and/or place. Such data streams are called spatio-temporal Feature selection algorithms typically perform feature multi-dimensional data (STMD). Geospatial and temporal ranking or subset selection. Feature ranking methods data include dynamically changing location and/or time determine relevant features by a certain scoring metric stamps as part of its metadata. STMD can be readily found and can be computationally expensive when the data in many real-world critical sources today, including dimension is very high. When feature selection is used toderive a 2D or 3D representation of the data, the results may not convey much information because too much Information Visualization Vol. 8, 4, 275 – 285
• human-activity logs that are becoming increasingly generalization performance if the dimensionality of the • less formal digital socializing (for example, web logs, RSS Another example of an explicit STMD transformation14 builds a graph-based data representation15, which consi-ders a given data set as a bipartite graph. This approach These applications and others like them reveal complex, increases the performance of supervised learning algo- time-series data that must be manually monitored for rithms while leaving the data space’s dimensionality near real-time analytic results. It is possible to apply tradi- unchanged. The latter aspect mitigates the exponential tional algorithms to these data, but doing so typically growth in dimensionality inherent in feature combina- pushes analytic results beyond near real-time applica- tion approaches. Vertices of one partition of the graph tion. Near real-time results can be accomplished through correspond to data instances. Vertices of the other parti- techniques such as sampling and aggregation. Such tion correspond to features. Two vertices u and v are techniques often remove or further mask the impor- connected by an edge (u,v) if feature v has non-zero tant underlying semantic information analysts seek to value in instance u. Unlike approaches that assume discover. New computational transformations are needed data instances are independent, this approach leverages to leverage such data in a near real-time visual analytics higher-order co-occurrence relations between feature values across different instances and enables virtually any Kernel methods10 have been applied as an implicit learning method to take advantage of this rich connec- data transformation for STMD. A kernel function can be tivity. Developing an unsupervised analogue will add viewed as an implicit (nonlinear) mapping of data objects from the original input space to a high-dimensionalfeature space. The application of learning methods subse-quently takes place in this feature space. The strength Imperfect data
of kernel methods lies in their ability to expose hiddendependencies between input features relevant to the Effectiveness and accuracy of a solution should not be learning task. This in turn leads to simplification of compromised in the name of achieving high efficiency the problem and improved performance of simple (for whether dealing with massive or small volumes of data.
example, hyperplane-based) learning methods. However, The fact that most real-life data sets are noisy, corrupt applying a kernel-based data transformation causes latent and have missing values presents a challenge. In some relationships among input features to be distributed cases, data may have been tampered with to be deliber- over a (sometimes infinite) number of dimensions of ately misleading. In addition, measures of accuracy are the feature space. A kernel only allows the computation not always known because of the high complexity of the of a certain aggregate quantity (the scalar product) in solution process in visual analytics.
the feature space. Therefore, it is not possible to analyze Methods for representing the noise level in data may the relations exposed by the kernel mapping between guide the analyst to ensure proper utilization of noisy input features. Even though a variety of kernel functions data. Ideally, methods for noise reduction and noise have been developed, these methods are only appro- removal can be applied. However, extreme caution must be taken because many existing practices are rather between objects can be estimated as some average of heuristic and often lack theoretical justification. Manu- (dis)similarities across all features. Finally, kernel methods ally entered data, in contrast to physical data that comes are critically dependent on domain experts for construc- from sensors, radio frequency identification devices, and tion of appropriate kernel functions. Extending kernel the like, contain noise characteristics that cannot be well methods to overcome their shortcomings as applied to STMD is a significant research challenge.
An even more difficult situation arises when the data In contrast to implicit STMD transformations, explicit set contains completely missing components. Many transformation approaches can explicitly access the analysis algorithms assume complete knowledge of the feature space and apply visualization and learning data points. Use of such algorithms in the presence of missing values requires imputation methods. Effective Mining12) that cannot be formulated in terms of vector information representation often comes from mathe- matical modeling of the problem and is constrained Explicit transformations can be applied to other prob- and driven by interactive visualization and analytical lematic data forms because explicit data transformations allow increased expressivity of features. One popular The choice of representation of noisy data should be example is feature combination, which may be used guided by close collaboration with domain experts and for expansion of the base set of features in natural an understanding of the users’ needs so that they can language.13 This work demonstrated that such feature be formulated in the model. Often these turn into large spaces allow for robust learning, whereas implicit kernel scale constrained optimization, matrix computation and expansion of the feature space may lead to degradation in graph theoretic problems. Robust algorithms that produce Information Visualization Vol. 8, 4, 275 – 285
solutions that are insensitive to perturbations in input or • A combination of quantitative and qualitative infor- conditions are needed, as are stable algorithms that reli- mation. This is the case when quantitative physical measurements are combined with qualitative human Another important challenge arises when there is the judgment that takes the form of text.
possibility of intentional disinformation or deception. In • Attributes from multiple, merged databases. Joining this case, the transformation and subsequent visualiza- databases for analysis is a difficult task that becomes tion should reflect the provenance and trustworthiness even harder when similar attributes have different of the data. Data provenance16 refers to the origin of the data and its movement and transformation from thepoint of origin to the visualization system. Source trust- Heterogeneity causes substantial difficulties in devel- worthiness refers to the probability that the information oping data transformation and dimensionality reduction source includes disinformation. Data trustworthiness techniques. Many techniques assume, either implicitly or refers to the probability that the received information was explicitly, that the attributes are normally distributed. For subjected to deception somewhere along the provenance example, PCA implicitly assumes a normal distribution because it is based on maximum likelihood estimation The trustworthiness of the source may be determined applied to a normal distribution. A similar observation from historical data or human judgment. The trustwor- applies to the k-means and Gaussian mixture clustering thiness of the received data may be computed from the models. It is not immediately clear why the normal distri- provenance path and the trustworthiness of the sources bution is an appropriate assumption in cases of heteroge- neous data. It is certainly a questionable assumption for There are some similarities between imperfect or noisy data and deception. In the former, noisy data may be A promising direction for deriving transformations removed or modified before selecting the computational for heterogeneous data is to first obtain a joint proba- and visualization transformations. In the latter, the poten- bilistic model for the heterogeneous attributes. Proba- tial for deception and the trustworthiness of the different bilistic models for heterogeneous data include loglinear information sources are important factors that need to be models and undirected graphical models17,18 considered. The suspected data may be removed or modi- Bayesian networks.19 Once the model parameters are fied before deriving the optimal transformation. However, estimated using a technique like maximum likelihood, an the data, their provenance, and trustworthiness need to appropriate transformation may be obtained by consid- be transformed and visualized along with the more reli- ering the model parameters. This approach can also be used to extend standard methods such as PCA. Exam- For anomaly cleaning and detection, formulations ples include probabilistic PCA and exponential family based on various vector norms, especially the L1 norm, can be extended to achieve practical robust methods.
An alternative approach is to forgo the modeling process Extensions to streaming, dynamic data and specific data and to rely instead on a carefully constructed distance types (for example, text, images) and data of mixed or dissimilarity measure. Such a measure may be used type need to be considered. Transforming imperfect data to derive an appropriate transformation in conjunction remains a continuing challenge AUTHOR
with multi-dimensional scaling.20 Avoiding the need to robust results for visual analytics.
construct a model for heterogeneous data and obtain themaximum likelihood parameters is a substantial advan-tage. A disadvantage is that the quality of the obtained Heterogeneous data
transformation is in direct relation to the quality of thedistance or dissimilarity measure. Constructing a sensible Heterogeneous data occur in a number of different forms, distance or dissimilarity for heterogeneous data may be a very challenging task. The use of domain knowledge orinteractive feedback is likely to play a key role in designing • Nominal attributes that possess different sets of possible effective distance or dissimilarity measures for heteroge- values. For example, medical records contain attributes neous data in visual analytics systems.
with substantially different ranges of values.
• A combination of numeric and nominal values.
For example, medical records may contain numeric User-in-the-loop
attributes such as weight, height and age, along withnominal attributes such as ethnicity, symptom appear- The goal of a visual analytics system is not to perform analysis automatically but to facilitate it. A user-in-the- • Multiple attributes possessing different noise character- loop is therefore a central and critical element of visual istics. For example, sensor network observations form analytics systems and must be in constant consideration a vector of measurements, where each component has throughout the design and implementation of such a Information Visualization Vol. 8, 4, 275 – 285
All of the above techniques take on an additional first published in 1955.22 The best clustering approach is burden when placed in the context of a person. Humans often very closely tied to the end goals of the intended have limited faculties (physical, mental and otherwise) users. For example, the task of binary clustering of a collec- that must be addressed by viable solutions if they are to tion of animals may produce two completely different be used in the context of visual analytics. For example, groups, such as mammals versus birds or predators versus while the winner of the InfoVis 2003 Contest21 could non-predators, depending on the features used to repre- computationally compare two trees of 100 000 elements each, it also provided several interface methods to support In visual analytics, experts can often provide addi- a human’s understanding and navigation. Data with tional information. This can be realized by designing high-order dimensionality must be reduced to two or clustering methods that use human-specified constraints.
three dimensions just to be displayed without losing key Semi-supervised clustering formulates the problem in information after dimension reduction is performed.
a way to satisfy cannot-link and must-link constraints.
People add a social dimension to visual analytics. Many Methods that can incorporate additional expert input organizations that perform large-scale analysis work in as constraints in the clustering problem formulation teams that may or may not be co-located. Some organi- will provide more accurate representations of data. New zations may address distributed analysis over an organi- approaches such as those based on multi-resolution data zational private network. Still other organizations, such approximation for scalable data transformation opera- as governmental agencies and public safety departments, tions using hierarchical data structures and multipole-like require alternative solutions because of the geographical, expansions provide promising directions.
legal and cultural boundaries that collaborative analyst The user-in-the-loop dimension of visual analytics sessions regularly cross. Therefore, there is a research need is being extensively studied in the later phases of the for systems that will facilitate multi-user collaborative analytical process. ‘Sensemaking’ systems and methods distributed analysis safely, securely and legally.
assist users in managing and making sense of their Users must be able to trust visual analytics results.
data.23,24 Enhanced visualization techniques25–27 are In line with the above comments regarding misleading being developed to display and navigate through the data, ‘trust’ in this sense refers to the user’s faith that the complex, dynamic and temporal data prevalent today.
analytics system is transforming data in ways that main- However, all of these techniques and systems involve tain the original data’s characteristics while foregoing the user interactively only when the data have been adding artificial biases. Establishing and maintaining this collected and transformed into their (final) analytical trust is especially important for analysts who may be representation. The possibility of including the user in called to explain their analytical process to another deci- the intermediate transformation and representation steps sion maker (for example, a chief scientist, a lawmaker, a is an interesting one. The effect of this compared to fully automated approaches and the effect of this interaction Users are dynamic and constantly change through anal- on the analytical process are all open areas of research.
ysis: their mental context, their model of the analyzed Recognizing and leveraging user dynamism provides phenomenon and their focus or trust in various regions significant benefit when done correctly. User modeling of data will often change through the course of analysis.
research28,29 is still exploring strong guidelines for devel- oping and maintaining an accurate model. With such of evidence, a new website discovered or a new laboratory a model, systems can adapt to the user’s context and result can quickly bring a new perspective on the current the machine’s processing capability.30,31 Systems could also use such modeling techniques to capture the user’s There are also physical constraints imposed by limited mental state in the analytical process32 and provide screen space with only two or three display dimensions.
support for following best analytical practices. Integrating Limitations in human cognition capacity to communi- user modeling with visual analytics systems is still in its cate high-volume and high-dimensional data also present important challenges. Even with today’s growing displaysize and resolution and the use of multiple monitors,display walls and CAVEs, the number of available pixels Challenge synopsis
remains a fundamental limiting factor. The small screenson mobile devices used by first responders exacerbate the Challenges in data transformations and representations Methods for judiciously approximating or down- weighting large regions as appropriate to the analysis of • Maintaining transformation performance to sustain interest will provide solutions to some of these demands.
interactive rates, even when handling huge volumes of Clustering can provide a simple starting point toward organizing data into related groups for improved under- • Because the same item may be interpreted differently standing and learning. Numerous clustering methods across heterogeneous data stores, reconciling semantic have been developed since the k-means algorithm was Information Visualization Vol. 8, 4, 275 – 285
• Uncertainty is caused by a number of different data effective analytic environments, visual metaphors are characteristics. Estimating this uncertainty and commu- needed for different data representations, including nicating it in a meaningful way is an important chal-lenge. Deriving value when the quality of the data varies significantly. For example, human language differences • data signatures and transformed data; change the meaning of words in text, video and audio; • metadata information including related data, transfor- noise in sensors affects numeric data.
mations and algorithms applied to generate the data • Developing provenance and context of data source(s) signatures, as well as data lineage.
• Computing with data in situ to minimize the impact of To be effective, these visual representations must accom- modate the users’ perceptual preferences and characteris- • Transforming information into knowledge.
tics (for example, color acuity, form dominance) and their • Keeping the user clearly involved in the analytic loop cognitive analysis style, the characteristics of the display to not only provide the results from various types of device (for example, cell phone versus display wall), and transformations but to also allow the user to guide the the characteristics of the task they are performing (for example, time frame for decision making, discovery task,analysis task, verification task, situational awareness task).
The key issues are centered on developing principles and Transformations and Representations for
techniques to enable cognition amplification.33 Creating Visualization
useful and appropriate cognitive artifacts enhances bothreflective and experiential cognition.33 The design task The first two sections described the raw data character- must use cognitive principles such as the appropriate- istics and the methods to transform the data to efficient ness principle the naturalness principle and the matching representations. The final step, described in this section, is to develop visual representations of the transformeddata that gives the end user an easy-to-analyze visualform.
Human adapted display of data to enhance
analysis -- The balance between automated
data processing and human reasoning
From data to visual display
Each data type (raw data, appropriately transformed The overall goal of creating visual representations is to data – using techniques from the previous section – use cognitive and perceptual principles that increase and metadata) offers the challenge of determining an the communication impact of the results of the data effective visual representation. Decision making is the transformation process to enable visual analysis and ultimate goal. The decision-making environment must knowledge synthesis. These techniques need to use visual allow visual cognition and analysis in a way that lets the representations that ease the user’s cognitive burden user guide additional data analysis and transformation to through the creation of effective AUTHOR
complete the task at hand. Over the past 10 years, this has facts, work across problems and data at multiple scales, become an active area of research, but many challenges and semi-automatically adapt to the task at hand. There- fore, a clear understanding of the principles of effective There have been some good systems that use data char- visual information depiction is needed.
acteristics to determine appropriate visual mappings.33 Incorporating these principles into visual analytics These are often based on low-level perceptual character- systems allows the creation of appropriate visual repre- istic mappings for the classes of data (for example, ordinal, sentations. The level of abstraction and choice of visual nominal, interval, ratio). Over the past several years, these representation are keys to success. The goal is to not techniques have begun appearing in commercial prod- only present the deluge of data that the analyst receives ucts to aid users in understanding their data (for example, but also extract the relevant information from these ShowMe in Tableau35). Several systems match task and data in a format that enables reasoning and analysis.
data characteristics to appropriate visualizations36,37 and Therefore, improved visual representation can be gener- there is new work in evaluations of their effectiveness.38 ated that incorporates both advanced techniques for Numerous systems provide abstract, illustrative render- showing complex 3D structures. In addition, techniques ings of data by attempting to harness the power and are needed for abstracting the representation, focusing conciseness of the representations developed by medical the user’s attention and providing contextual informa- and technical illustrators.39–41 A number of efforts have tion for reference. All of these techniques must adapt to been made to use design principles for visualization the large variety of types and kinds of information to over the past 10 years.42 All of these approaches have be simultaneously analyzed and scaled across both data been used on a limited basis and represent only initial size and display size (PDA to wall display). In creating steps at solving the problems of creating the most Information Visualization Vol. 8, 4, 275 – 285
effective visual representation for multi-source, multi- and missing data in geographical address information, variate, multi-modal, incomplete and temporal data.
as well as confidence in the values in the data fromself-reported illnesses. All of this must be numerically orcategorically represented in the transformations and then Purpose-driven visual representation
visually conveyed effectively to the user.
Just as challenging is creating visual representations As mentioned above, a key component in determining that enable the user to analyze data across multiple scales, effective visual representations is the purpose of the described in a previous article in this volume. Cross- visualization – what is the task the user is performing? scale reasoning is necessary in many systems that require Cognitive task analysis is a highly active research area, visual analytic solutions to manage the complexity of and many classifications of tasks have been developed.
the analysis task. Appropriate abstraction and aggrega- Two key factors in determining the appropriate visual tion of data to enable this cross-scale visual reasoning is representation are the type of task and time-scale of the task. Discovery, verification, inquiry and situationalawareness tasks all have different characteristics thatlead to different visual representations. For instance, in Visual representation solutions
situational awareness displays, the representation needsto succinctly convey all of the relevant situational vari- A large toolbox of visual representation techniques can ables at a summary level, while highlighting unusual be brought to bear on visual analytic problems with values/events. In contrast, in a verification or inquiry large and challenging data characteristics. Shape and task, detailed information presented clearly and enabling color have been well studied for representing data values.
comparative or correlative analysis is necessary.
Some less tested, more interesting techniques include the The time-scale of the task is equally important. For displays that users interact with for many hours per dayfor in-depth analysis, complex, rich visual representations • Transparency – potential for showing temporal data can be used. However, in real-time decision-making envi- (past/future), data certainty. Poor at showing defined ronments, pre-attentive or slightly longer visual informa- tion transfer may be necessary to convey the information • Texture patterns – potential for showing aggregation, quickly enough for effective decision making. In this clustering, categorical information, uncertainty with case, low-level perceptual cueing through simple visual representation such as size, course shape, color and trans- • Line style variation – heavily used in architec- parency may be the only viable choices. The frequency ture, technical and medical illustration for showing of system use also factors into the visual representa- certainty/uncertainty, known and missing information, tion that is appropriate if complex visual mappings are and temporal characteristics of data.
• Ghosting – great potential value for showing temporal Data characteristics for visual representations
The above are standard graphical techniques. The key to visual representations is the integration of graphics design adapted to visual display are critical to a visual analytics when building visual analytics systems to increase the environment’s success. Even with advanced data trans- formations, many data characteristics still make thevisual representation challenging to enable effectivevisual analysis. For instance, in multi-source data inte- Challenge synopsis
gration and fusion, it is vital that the data transforma-tions enable the fused data to be visually fused and Transforming data into effective visual representations compared – they need to have similar scales, magni- tudes of error and standard deviations, and they needto permit linear visual interpretation when mapped to • Classifying when the best visual representation can be 2D, 3D, and perceptual color spaces. Enabling visual comparison and integration of the resulting data signa- • Choosing effective visual representations for cross-scale tures is one key difference between automated data transformations and visual-analytic data transforma- • Defining visual representations classes that scale from tions. Linearizable transformations for uncertainty, confi- real-time to in-depth slow analysis.
dence, erroneous and missing43 data are also needed • Characterizing visual representation for confidence, to enable correct visual interpretation. For instance, in syndromic surveillance, there is uncertainty in • Developing effective visual representations for reasoning syndrome classification from free text, coarseness, errors Information Visualization Vol. 8, 4, 275 – 285
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14 Ganiz, M.C., Lytkin, N.I. and Pottenger, W.M. (2009) Leveraging Acknowledgements
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In: ECMLPKDD ’09: The European Conference on MachineLearning and Principles and Practice of Knowledge Discovery in Our thanks to Professor Richard Fujimoto, Computational Databases; 7–11 September, Bled, Slovenia.
Science and Engineering Division, Georgia Institute of Tech- 15 Ganiz, M.C., Kanitkar, S., Chuah, M.C. and Pottenger, W.M.
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PRESIDENT: HRH The Princess Royal KG, GCVO Royal Yachting Association Association CATEGORY C MEDICAL STORES . Category C Stores, are required by MSN 1768 (M + F) (numbering refers) on all coded vessels up to category 2. (Listed in annex 2 of MGN 280) (Quantities required.) See 1. CARDIO VASCULAR (b) ANTI - ANGINA PREPARATIONS Glycerol Trinitrite (GTN) (1 unit) GTN cau