KNOVA: INTRODUCING A REFERENCE MODEL FORKNOWLEDGE-BASED VISUAL ANALYTICS

Stefan FloringOFFIS Institute for Computer Science, Oldenburg, Germany

floering@offis.de

H.-Jurgen AppelrathDepartment for Database Systems, University of Oldenburg, Oldenburg, Germany

appelrath@informatik.uni-oldenburg.de

Keywords: Analytical reasoning, Data management and knowledge representation, Interface and interaction techniquesfor visualization, Knowledge-assisted visualization.

Abstract: When creating interactive applications for data exploration three major challenges can be identified: Theintegration of heterogeneous data sources at runtime, the integration of suitable visualization methods and theavailability of interaction methods which enable domain experts to (implicitly) apply their expert knowledge inthe knowledge driven exploration process. To address these challenges we introduce the KnoVA (Knowledge-Based Visual Analytics) reference model, which allows for generating a description of visualization methods,interaction methods and data sources. We then outline how this model can be useful to create knowledge basedvisual analytics systems in a model driven software development process.

1 INTRODUCTION

In public health, especially in the field of population-based epidemiology, data analysis has early beenidentified as important. As an example, the Epidemi-ological Cancer Registry Lower Saxony (Germany)(EKN) by now holds nearly two million data setsabout cancerous diseases. These data sets are hier-archically structured (patients, indications, tumor in-dications) and modeled highly dimensional. Periodi-cally the data collected at the cancer registry is inte-grated into a data-warehouse system and out of thisdata-warehouse pre-defined reports are being gener-ated (Meister et al., 2003).

Working closely with domain experts at the EKN,we have distinguished an increasing demand for ex-plorative analysis and for a more dynamic and inter-active ”ad-hoc” approach to the analysis than today’stools offer, in order to gain insight in diseases andpossible influence factors. The idea is to visualizethe collected data and then mingle data from othersources into the visualizations. For example the av-erage amount of certain tumor indications per regioncould be visualized on a thematic map, to possiblyfind regions with atypical high or low rates. Thendata from other sources could be integrated interac-

tively in the analysis process to find correlations ofpossible influence factors. (Floring and Hesselmann,2010).

2 MOTIVATION

Based upon this idea and upon feedback we receivedfrom the users at the EKN, we identified three impor-tant key factors that influence the effectiveness of vi-sual analytics applications in the epidemiological do-main. Firstly the suitable information to approach acertain analysis question has to be available. In theexample mentioned earlier, next to the epidemiolog-ical data it is vital to have the ability to integrate ad-ditional data sources, such as data about possible in-fluence factors. Secondly the analysis tools must pro-vide suitable graphical representations to visualize thedata. In typical analysis tasks the analysts will usevarious visualizations at once, each of which is bestsuitable for a specific kind of data. For the geograph-ically spread tumor indications thematic maps mightbe the best choice, while for timely oriented data ani-mated scatter plots might be advantageous. The thirdinfluence factor is expert knowledge. The choice ofthe right combination of data and suitable visualiza-

230

tions as well as the manipulation of data during the ex-plorative process are usually done by domain experts(in our example epidemiologists) based upon their do-main knowledge. According to these three key factorswe identified three major challenges to be addressedin order to design interactive explorative data analysistools:

Data Integration. The challenge in combining mul-tiple data sources is to create a suitable mappingthat allows for identification of similar entitiesacross these data sources. An example is the in-tegration of geo-spatial epidemiological data withgeo-spatial census data, aiming at normalizationbetween areas with a very high and areas with alow population density. In this scenario a trans-formation of possibly different geographic repre-sentations (e.g. postcodes or Gauss-Krueger coor-dinates) can be necessary and thus a transforma-tion between the different representations has tobe accomplished.

Visualization Integration. Likewise it is often notsufficient to only provide a single visualizationmethod for a certain analysis task. Multiple vi-sualization methods have to be integrated into thesystem so data can be linked across views andviewed from different perspectives and on differ-ent detail levels on co-located views. The chal-lenge here is to create a mapping between thedata format of the visualization and the data for-mat of the actual data. This is more challengingin dynamical systems with multiple data sources,which not necessarily share the same data model.

Application of Expert Knowledge. Any decisionsin the analysis process, e.g. which data sources tointegrate, which visualizations to use and whichoperations on the data to perform, depend on do-main expert knowledge. In the epidemiologicalexample only a medically and statistically trainedepidemiologist can make the right decision ofwhether population figures have to be normalizedor which additional data-sources can sense-fullybe integrated to create valuable new insight. It istherefore necessary to provide reasonable meansof interaction, suitable for domain experts to use.The challenge here is to find appropriate abstrac-tions to prevent the must of having knowledge ofthe underlying data manipulation operations (suchas SQL or OLAP) and which allow for a transla-tion and disposition of the operations throughoutintegrated data sources and across linked and co-located views.

To deal with these three challenges, we propose theuse of a reference model for visual analytics applica-

tions and the analysis process as the basis of a modeldriven software development process.

3 RELATED WORK

There have been several pervious efforts to createmodels for visualization and analytics applications. In(Tang et al., 2004) the usage of the relational model isproposed. One shortcoming of the relational model is,that it is data centric and the model itself does not sup-port the creation of suitable visual mappings. Haberand McNabb introduce a data-flow model to deal withthis problem (Haber and McNabb, 1990). The sys-tem DataMeadow (Elmqvist et al., 2008) uses a sim-ilar data-flow model to aid the user during the explo-ration process by visually presenting the transforma-tion pipeline. In a data-flow model the visual map-ping is defined as a pipeline where each node in thepipeline is a defining a data transformation. In oppo-sition to this data-state models define sates and tran-sitions and each transition can be seen as a data trans-formation. Lark (Tobias et al., 2009) is based on thedata-state model. This system is aimed at coordinatedinteraction for InfoVis systems on distributed work-stations. In (Chi, 2002) is shown that data-state mod-els and data-flow models are equally powerful and canbe transformed into the respective counterpart.

Classifications of visualization methods has beenapproached from different viewpoints: data centric(Chuah et al., 1995), task/goal centric (Wehrend andLewis, 1990) and (Valiati et al., 2006) and based uponstages (Pfitzner et al., 2003). In addition to that therewere efforts to combine different viewpoints (Wenzelet al., 2003). Keim has introduced a classification ofvisual analytics systems aiming for a description ofvisualization methods properties (Keim, 2001).

4 THE KnoVA APPROACH

It is our aim to create a method that, according tothe three challenges identified above, allows for a de-scription of data sources and visualizations for ad-hocintegration and which allows for a description of ap-plied expert knowledge. The goal of the KnoVA ap-proach is to facilitate the extraction of expert knowl-edge, which is applied by the user into the visualanalytics process and eventually apply this extractedknowledge to other analysis tasks. To achieve this wecreate a description, based upon existing classifica-tion approaches, which can be used as the basis fora domain specific language (DSL) in a model driven

KNOVA: INTRODUCING A REFERENCE MODEL FOR KNOWLEDGE-BASED VISUAL ANALYTICS

231

software development (MDSD) process. This is mo-tivated by the thought, that a MDSD process helpsto design and implement a broad variety of powerfulvisual analytics applications in various domains. TheKnoVA approach consists out of four distinctive parts:

1. A descriptive model for analysis applications, theKnoVA reference model.

2. A visualization state process model, based uponan adapted data-flow model (Tobias et al., 2009),where the KnoVA reference model is used to de-scribe the states.

3. A rule language, based upon the KnoVA referencemodel, for the expression of rules to derive knowl-edge.

4. A matching algorithm used to identify applicableknowledge in certain states of an analysis applica-tion.

This paper focuses on the development of the KnoVAreference model. In the following we firstly describethe considerations that lead to the development. Sub-sequently we outline how the reference model canbe used to create knowledge driven visual analyticsapplications in a model driven software developmentprocess. Finally we discuss new possibilities, whichare opened up by a knowledge based visual analyticsprocess.

4.1 The KnoVA Reference Model

According to (Keim et al., 2009) visual analytics is aniterative process with three distinctive steps: data se-lection and preprocessing, visualization, model build-ing. The iteration evidently leads to insight and there-fore to the generation of knowledge, that then can beapplied to the previous steps in a feedback loop untilthe process of analytical reasoning is finished. Henceknowledge generated by the users’ insight is appliedback to the process. Accordingly visual analytics isa knowledge driven process, in which expert knowl-edge is applied implicitly by the users interaction withthe visualization. The user interaction results in achange of the system state. Thus a description of thesystem state in combination with the interaction orprecisely the state changing operations triggered bythe interaction can be used to implicitly describe theapplied knowledge. Accordingly it is the intention ofthe KnoVA reference model to allow for a descriptionof system states and interactions.To approach this we examined five exemplary visualanalytics systems in order to derive a set of classi-fying properties: HD-Eye (Hinneburg et al., 2003),SellTrend (Liu et al., 2009), DataMeadow (Elmqvist

et al., 2008), MineSet (Brunk et al., 1997) and Ad-vizor (Eick, 2009). This bottom-up approach, inwhich existing visual analytics systems are examinedto build a reference model according to their prop-erties, was chosen to derive a larger set of commonproperties and then presumably create a potentiallygeneric model. Contrasting to this, a top-down ap-proach to create the reference model would have beento collect the requirements for a new analysis applica-tion and then use these to create the model. This goesalong with the risk to create a very specific model,which will only fit for a limited set of possible newvisual analytics applications.

From the five exemplary visual analytics systemsHD-Eye was chosen because it targets cluster analy-sis, which are very important in the health care do-main. SellTrend was chosen because it features alarge variety of visualizations in multi-coordinatedviews and supports multi-variant data. The same rea-sons lead to the investigation of Advizor, as the inte-gration of a broad variety of visualizations is one ofthe key challenges we identified. DataMeadow waschosen because it supports operation-based linkagebetween views. MineSet was used because of its in-tegrated knowledge model. The features of the sys-tems that we examined are influenced by the key chal-lenges: their integrated visualizations, their supportfor data integration and the means of interaction theyprovide. In addition to these five systems we exam-ined a selection of visualization methods such as par-allel coordinate views, different kinds of charts, scat-ter plots etc. to further improve the universal validityof the reference model.

So far we identified 32 distinctive descriptiveproperties. We ordered the properties according totheir similarities and identified six classes with a num-ber of subclasses, which are sufficient to subsume allof the identified properties. The result of this pro-cess can be seen in figure 1 where the six classes andsubclasses are visualized following the style of UMLpackage diagrams. Within the classes and subclassesthe properties are displayed in an iconographic no-tation, which is inspired by the notation introducedin (Aigner et al., 2007). We identified the followingclasses:

Data to be Visualized. This class is used to catego-rize the data supported by the visual analytics sys-tem. We identified three distinct subclasses herein which data can be categorized: data type, datastructure and data scale.

Analysis Goal. Most of the visual analysis applica-tions we investigated are optimized for a specificanalysis goal, like most visualization methods.Even though many visual methods can be used in

IVAPP 2011 - International Conference on Information Visualization Theory and Applications

232

continuous discrete

Dynamic RepresentationData to be visualized

multi dimensional

one dimensional

Text & Hypertext

Software & Algorithms

Auto

Cabrio Sportwagen

complex

tstart

tend

arithmetical

9020 60

Data Type

Data Scale

quantitative0.781.303.906.789.98

ordinal

lowmedium

highvery high

nominalAmerika

EuropaAsien

AfrikaAustralien

Antartika

Standard 2-D / 3-D Geometry based Icon and Glyph

Pixel based Nested

Vizualization technique

Association ClusteringClassification

Analysis goal

functional dependent multi dependent1:1

1:n

1:*...

Filtering

Distortion Zoom/Pan

Linking und Brushing

Visual Projection

Visual Transformation

Selection

Projection

Zoom/Exploration

hierarchical

Xerox

Cut PucBun

Data StructureAnimated / Dynamic

static

Interaction Technique

geo-spatial

Figure 1: Iconographic description of classes and properties of the KnoVA reference model of visual analytics systems.

tasks with varying goals, it is still useful to iden-tify all analysis goals to which methods are ap-plicable. Therefore we consider it valuable to useanalysis goal as category for classification. Clus-tering for example is a very common analysis goalin the visual analytics systems we investigated,scatter plots are a common visualization methodto reach this goal.

Visual Transformation. This class subsumes trans-formations on the visualization which modify thevisual representation but do not change the stateof the underlying data. Fish-eye lenses, which vi-sually enlarge or diminish parts of the visualiza-tion and local zooms, which enlarge the currentvisual representation without changing the under-lying data section, fall into this category. Typi-cally user interaction is necessary to apply thesetechniques. However, all interaction methods inthis class are stateless and therefore do not changethe mapping between the visualization and the un-derlying data.

Interaction Technique. In this class we group inter-action techniques, which result in (possibly per-sistant) changes to the underlying data. Thesetechniques vary from visual transformations, as

they change not only the visual representation butalso the current system state. For example in avisualization method for hierarchical data a zoomoperation can trigger a data operation that leadsto a switch in the mapping between the visualiza-tion and the underlying data. By doing that a morespecialized or generalized hierarchical level of thedata is displayed.

Dynamic Representation. Visualization methodscan be distinguished into those which, unlessthere is user interaction, offer a static represen-tation of the data and those where the data isanimated. Animations are either continuous, withsmooth transitions between frames or discretelike slide shows.

Visualization Technique. In this class finally wesum up the visualization techniques. Every visu-alization method has a specific visual representa-tion of the data. This representation can be pixelbased (e.g. each data point is mapped to a colorvalue and then visualized as a pixel or a group ofpixel), geometry based with a mathematical func-tion defining the visual representation and so on.

Based upon this work, we created the KnoVA refer-ence model of visual analytics systems, a language

KNOVA: INTRODUCING A REFERENCE MODEL FOR KNOWLEDGE-BASED VISUAL ANALYTICS

233

to describe the properties of visual analytics applica-tions.

4.2 Model Driven Realization

To demonstrate the possibilities that emerge out of theKnoVA reference model, we created the Visual Ana-lytics Transform System (VAT-System). It aims at theanalysis of epidemiological data combining variousdata sources and visualizations. A screenshot of thissystem can be seen in figure 2.

EKNDatabase

ExternalData Source

SelectionMenu

MenuWork Area

Data Sources Data TransformerVisualizations Path

Figure 2: Screenshot of the VAT-System.

In the screenshot the two main parts of the applica-tion are shown, the menu and the work area. Themenu gives access to so called system elements (datasources, data transformers and visualizations), whichcan interactively be connected to each other in thework area. System elements are connected by pathdrawn in between. Data transformers are used tomake further selections, e.g. a simple data trans-former would be a selection menu that lets the userspecify a partition of the data to be analyzed.

To create the VAT-system we translated theKnoVA reference model into a DSL using the VMTSmodeling framework (Levendovszky et al., 2005).Based upon the DSL new system elements (visualiza-tion methods or data sources) can be integrated intothe system by the definition of appropriate mappingsof their properties to the properties of the referencemodel. At runtime the linking of the selected systemelements is done transparently for the user by an au-tomatic evaluation of the mappings of the model in-stances of different system elements against the refer-ence model. Thus, when a path connects two systemelements, a matching component translates the map-pings of the different system elements to their repre-sentation in the reference model DSL and then com-pares those system elements based upon the referencemodel. As an example, when two data sources areconnected to the same data transformer (as shown infigure 2 for the selection menu), the mapping iden-tifies similar properties of the system elements ontheir DSL based representation. Given that both datasources contain geo-spatial information and this is be-ing identified on the level of the reference model DSL,

the pre-defined mappings can be evaluated to identifysimilar instances (in this case geographic coordinates)on the instance level, to create a link between the datasources.

5 SUMMARYAND CONTRIBUTION

The KnoVA reference model is based upon the workof Keim (Keim, 2001) where a classification sys-tem for visualization applications was proposed con-taining three orthogonal axis: Data to be visual-ized, visualization technique and interaction tech-nique. Our contribution here is a substantial enhance-ment of this classification by the introduction of ad-ditional classes (visual transformation, analysis goaland dynamic representation), the introduction of sub-classes (data type, data structure, data scale and an-imated/dynamic) and by the identification of addi-tional classifying properties (arithmetical, complex,functional dependent, multi dependent, continuous,discrete, static, panning zoom, explorative zoom, se-lection, projection, geo-spatial, association, classifi-cation and clustering) to create the KnoVA referencemodel.

Opposing to Keims classification the classes andclassifying properties of the KnoVA reference modelare not orthogonal; they are rather used in a descrip-tive way. The new concept of subclasses was intro-duced mainly because this hierarchical structure sim-plifies the language definition in the VMTS modelingenvironment, which is done by using a subset of UMLclass diagrams.

As shown exemplarily on the VAT-system themodel driven approach supports the development ofpowerful visual analytics applications where the in-tegration of new system elements is carried out andperformed as definition of a mapping between thenew system element to integrate and the DSL pre-senting the reference model. This addresses two ofthe key challenges we identified above as it simplifiesdata integration and visualization integration. Withthe iconographic language for the description of theKnoVA reference model, we substantially extendedthe graphical notation introduced in (Aigner et al.,2007). We believe using an iconographic languageadds value for communication in the scientific worldand as a side effect might be used in future to aid userswhen comparing different visualization methods bygiving them direct visual feedback about the proper-ties a certain visualization method has.

IVAPP 2011 - International Conference on Information Visualization Theory and Applications

234

6 FUTURE RESEARCH

The definition of the KnoVA reference model andthe DSL based implementation is the first step in theKnoVA approach. Currently we are working on a for-mal description of the DSL based system states, tocreate a visualization state process model. The ba-sic idea is, that knowledge is applied implicitly bythe users interaction, which leads to a change in thesystem states. In addition to this, a rule language forknowledge extraction has to be defined. This workbeing done, the complete KnoVA approach for knowl-edge based visual analytics applications can be usedto create visual analytics applications that allow foran extraction of implicit expert knowledge.

An open research questions is whether the knowl-edge extraction can take place automatically orwhether user interaction is necessary. Another openquestion is how the knowledge can be applied to othertasks. One possibility to use the knowledge in a dif-ferent context can be the automatic generation of pos-sible next step suggestions or the generation of sug-gestions for other suitable visualizations. This willaddress the third challenge identified above.

REFERENCES

Aigner, W., Bertone, A., Miksch, S., Tominski, C., andSchumann, H. (2007). Towards a conceptual frame-work for visual analytics of time and time-orienteddata. In WSC ’07: Proceedings of the 39th conferenceon Winter simulation, pages 721–729, Piscataway, NJ,USA. IEEE Press.

Brunk, C., Kelly, J., and Kohavi, R. (1997). Mineset: An in-tegrated system for data mining. In KDD, pages 135–138.

Chi, E. H. (2002). Expressiveness of the data flow and datastate models in visualization systems. In AVI ’02: Pro-ceedings of the Working Conference on Advanced Vi-sual Interfaces, pages 375–378, New York, NY, USA.ACM.

Chuah, M. C., Roth, S. F., Mattis, J., and Kolojejchick, J.(1995). Sdm: Selective dynamic manipulation of vi-sualizations. In ACM Symposium on User InterfaceSoftware and Technology, pages 61–70.

Eick, S. G. (2009). Data visualization software — advizorsolutions. ”‘Website”’.

Elmqvist, N., Stasko, J., and Tsigas, P. (2008).Datameadow: a visual canvas for analysis of large-scale multivariate data. Information Visualization,7(1):18–33.

Floring, S. and Hesselmann, T. (2010). Tap: Towards vi-sual analytics on interactive surfaces. In Collabora-tive Visualization on Interactive Surfaces - CoVIS ’09,number 2010-2, pages 9–12, Munich, Germany. LMUMedia Informatics. Technical Report.

Haber, R. and McNabb, D. A. (1990). Visualization id-ioms: A conceptual model for scientific visualizationsystems. In Visualization in Scientific Computing.

Hinneburg, A., Keim, D. A., and Wawryniuk, M. (2003).Hd-eye - visual clustering of high dimensional data: ademonstration. IEEE Computer Graphics and Appli-cations, 19(5):735–755.

Keim, D. A. (2001). Visual exploration of large data sets.Commun. ACM, 44(8):38–44.

Keim, D. A., Mansmann, F., Stoffel, A., and Ziegler, H.(2009). Visual analytics. In Encyclopedia of DatabaseSystems. Springer.

Levendovszky, T., Lengyel, L., Mezei, G., and Charaf, H.(2005). A systematic approach to metamodeling envi-ronments and model transformation systems in vmts.In Electronic Notes in Theoretical Computer Science,pages 65–75.

Liu, Z., Stasko, J., and Sullivan, T. (2009). Selltrend:Inter-attribute visual analysis of temporal transactiondata. IEEE Transactions on Visualization and Com-puter Graphics, 15(6):1025–1032.

Meister, J., Rohde, M., Appelrath, H.-J., and Kamp, V.(2003). Data-warehousing im gesundheitswesen. it- Information Technology, 45(4):179–185.

Pfitzner, D., Hobbs, V., and Powers, D. M. W. (2003). Aunified taxonomic framework for information visual-ization. In Pattison, T. and Thomas, B. H., editors, In-Vis.au, volume 24 of CRPIT, pages 57–66. AustralianComputer Society.

Tang, D., Stolte, C., and Bosch, R. (2004). Design choiceswhen architecting visualizations. Information Visual-ization, 3(2):65–79.

Tobias, M., Isenberg, P., and Carpendale, S. (2009). Lark:Coordinating co-located collaboration with informa-tion visualization. IEEE Transactions on Visualizationand Computer Graphics, 15(6):1065–1072.

Valiati, E. R. A., Pimenta, M. S., and Freitas, C. M. D. S.(2006). A taxonomy of tasks for guiding the evalu-ation of multidimensional visualizations. In BELIV’06: Proceedings of the 2006 AVI workshop on BE-yond time and errors, pages 1–6, New York, NY,USA. ACM.

Wehrend, S. and Lewis, C. (1990). A problem-orientedclassification of visualization techniques. In VIS ’90:Proceedings of the 1st conference on Visualization’90, pages 139–143, Los Alamitos, CA, USA. IEEEComputer Society Press.

Wenzel, S., Bernhard, J., and Jessen, U. (2003). Visual-ization for modeling and simulation: a taxonomy ofvisualization techniques for simulation in productionand logistics. In Chick, S. E., Sanchez, P. J., Ferrin,D. M., and Morrice, D. J., editors, Winter SimulationConference, pages 729–736. ACM.

KNOVA: INTRODUCING A REFERENCE MODEL FOR KNOWLEDGE-BASED VISUAL ANALYTICS

235