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AbstractThis paper discusses navigation issues in large-scaledatabases and proposes hypermap visualizations aseffective navigational views. We describe the ZTree, atechnique that allows users to explore both hierarchicaland relational aspects of the information space. TheZTree uses a fisheye map layout that aids the user incurrent navigational decisions and provides a history ofprevious information retrieval paths.Key words: Information visualization, interactivetechniques.

1 IntroductionComplex information systems demand differentinterface approaches from the usual desktop paradigm.People increasingly get lost in electronic space.Navigation is a familiar activity in the real world but abewildering process in abstract information spaceswhich lack analogous cues and tools to situate and guidethe user along desired routes. The challenge facing us ishow to facilitate navigation in such systems withoutimposing extra cognitive overhead.

Complexity in these spaces is a function of size,scope and organisation. One taxonomy definesinformat ion s t ruc tures as anarch ic (a rb i t ra ryorganization) vs. moderated (imposed organization) andknown (fully defined) vs. unknown (constantlychanging) [16]. However, even well-structured andfully-defined spaces can be “unknowable”. In manapplications it is the relationships between elementsinformation that are as interesting as the informatiitself, and these relationships may be both fixe(imposed by the information model) and dynam(created by the user as part of the informatioassimilation task). Further, in many complex spaces ithe user ’s path through them (the trace of thinformation retrieval “dialogue”) that is of interesrather than individual nodes. We use Tweedie’s concof derived [24] rather than fully known structure toexpress this accumulated set of retrieval paths. A umay der ive di fferent structures from the sam

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underlying space in the context of different searches aproblem-solving tasks.

While much work in information navigation hasbeen in the well-known domain of the World Wide We([6,9,10]), many other information spaces preseequally challenging problems. The project describedthis paper concerns large-scale databases engineering applications in which the informatiosubmits to a defined structure but is of such a size ascope that it cannot be fully known by any one usSimple query-based approaches are insufficient: usneed to understand the information in given contexand quickly find relevant relationships to other succontexts. We are interested in discovering whicelements of information visualization techniques appwell to navigational support in such systems.

Navigational approaches in large information spavisualization tend to fall into two categories. Multiscalinterfaces such as Pad++ [5] and EPS [7] operate oninformation space directly and allow variable levels exploration. More common are structure views:abstractions of the underlying structure which providthe user with selectable points to deliver detaileinformation in another display area (such as file systeviews). Our research focuses on the use of explicit mbased representations as structure views.

This paper describes the ZTree, a fisheye viewbased on the Continuous Zoom [3] which provides a 2navigational map. A major issue in applying suctechniques as mapping tools is the layout of thstructure. An automatic layout algorithm was developto accommodate dynamic structure accumulated asuser moves through the information space.

The paper is organized as follows. We discugeneral issues of navigation and define the hypermap,an effective navigational tool for complex spaces. overview of related work follows. We then brieflydescribe a large-scale engineering logistics applicat(SiLog) and the issues in navigating the informatiospace as context for the description of the ZTree

Navigating Complex Information with the ZTree

Lyn Bartram Axel Uhl Tom CalvertSchool of Computing Science

Simon Fraser UniversityBurnaby, BC, V5A 1S6

CANADA

ABB Corporate Research†

Heidelberg

GERMANY

Technical University of British ColumbiaSurrey, BC, V3R 7P8

CANADAlyn@cs.sfu.ca axel.uhl@io-software.com calvert@techbc.ca

† Current address: InterActive Software Objects GMBH, Freiburg, GERMANY

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interface based on the CZ and the Grid automaticlayout algorithm. We conclude the paper with someopen research questions and suggestions for furtherinvestigation and development.

2 From Structure Views To HypermapsHow do people find information in a complex space?Furnas def ines navigation as “moving oneselfsequentially around an environment, deciding wherego next based on the task and the parts of tenvironment seen so far” [16] . Effect ive viewnavigation requires small v iews and short andiscoverable paths [13]. Users need support constructing an overview of the information space [16This helps them construct a mental map of thinformation structure and topology. In addition, theneed history: where they’ve been and the set connections and traverses they have made. A studyWWW navigation found that visualizing the entirstructure was less important than being able to re-fiinteresting pages one had previously visited and retrassociations [10]. Finally, because navigation iscognitive activity that requires context, users needpersistent representation of the overall space.

Humans rely on maps to navigate in the physicwor ld. We bel ieve that graphical maps of thinformation structure which elicit the familiar process omap-based navigation will be most useful in oucomplex environment. We extend the definition of GIS hypermap [18] to be any 2D or 3D structure viewwhich evokes three key aspects of paper-based mapersistent overview of navigation possibil it iesidentification and retrieval of key features; anidentification of how features relate to one another.

A 2D fisheye map of a WWW space was found to buseful because it evoked two key characteristicspaper-based maps: identification of key features, aidentification of relationships between those featur[10]. Map layouts exploit human spatial cognitioabilities, and it appears that substantial distortion canapplied as long as relative positional constraints arespected [11].

3 Related WorkMany approaches use t ree models to organiinformation space hierarchically and variants of trestructures to display them. TreeMaps [2] used a 2space-filling approach to lay out a tree-structure, which higher nodes in the tree got more space in the tmap. The resulting display gave effective contextuinformation and elicited the immediate perception distribution in the tree, but individual features werdifficult to discriminate once the tree was reasonabpopulated. Cone Trees [21] are a 3D extensiontraditional 2D tree layouts, allowing a much greatamount of information to be concurrently shown at thcost of some occlusion. The user may have to rotatnode to get access to a subtree, but smooth anima

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greatly reduces the cognitive transition of re-orientinthe tree. Several database visualization systeincorporate Cone Tree approaches. WINONA modeclass-object structure in a Cone Tree view [20LyberWorld, a hypertext document database, uses Ctrees to represent the information retrieval history building up a view of query paths [14]. Of particulainterest is PadPrints [5], a 2D multiscale hypermap ouser’s path through the WWW based on Pad++[6PadPrints dynamically builds up a tree structure nodes corresponding to pages accessed which allowsuser to maintain as much context as desired (zooming in and out of the structure view) while viewindetail in the standard browser window.

All of the above are constrained by an inherelimitation of tree views: relational information that doenot correspond to hierarchical structure cannot be eashown. Thus there is great interest in exploring tflexibility and extensibility of graph visualizations. BothOFDAV [9] and NicheWorks [25] are examples of graplayouts for WWW navigation that manage arbitrarillarge graphs. OFDAV does so by only rendering a sugraph around a current focus node and giving cues tlead “off-screen”: the user never sees the entire conteNicheWorks employs specialized layout algorithms cluster related elements in a graph closely togetherperceptual clumps, highlighting only a few elements interest. Design Gallery Browser [1] uses 2D and 3graphs to layout semantically organized clusters similar images. Thumbnails of the images surround tgraph and are connected to their relevant node by linUsers navigate to a cluster by panning and zoomingthe space to select full size image views, so overcontext can be lost.

The lack of hierarchical structure in such graphbased approaches makes them harder to navigate trees. Moreover, moving around the graph and addnodes results in often disorienting reconfiguration of tlayout which seriously perturbs any perceptual map tuser may have had of the space, complicating featretrieval.

A detail-in-context approach which models bothierarchical and relational structure is the ContinuoZoom [3] (CZ). Parts of the information space arsummarized by being contained in closed cluster nodwhile the user can open other clusters to successivexamine f iner leve ls o f de ta i l . Such f i sheyapproaches[12] have been generally shown to haadvantages whenever users find themselves ininformation space which is too complex to easily rendon one small display or window [23]. However, thetend to suffer from problems of distortion [15,22]. Ouintuition is that such techniques may hold morpotential as hypermaps than for their previousexp lo red app l ica t ion s o f d i rec t in fo rmat ionvisualization.

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visualization of the underlying information space inwhich detail was mapped onto the leaf nodes. Theresulting screen space issues limited the technique.However, recent approaches which use a CZ basis asstructure views for network management and the WWW[10,11] have proven successful. A sample visualprogramming application built with the Hyper Mochiinterface [27] (a CZ-based technique) suggests that thecombination of hyperlinks and hierarchy provides anintuitive navigational model.

4 The Silog Project: Large-scale Logistics Building turnkey power generation installations is anenormously complex logistical undertaking, requiringthe coordination of hundreds of suppliers, thousands ofshipments and millions of parts within tight time andquality constraints. An important aspect is the flow ofinformation between all participants along the supplychain. The ABB SiLog (Site Logistics) application isdesigned to streamline processes on-site. These includehandling on-site material delivery and reception,purchase requests, providing feedback on missing andbroken parts and communicating changes in due dates.

Visualization and navigation of the complexlogistical data must support different informationretrieval and management strategies for people indifferent roles. For example, the project manager puts ina request like “by when do we have all parts foassembly group #123?”, whereas the material handlmanager might ask “where in our outdoor storage is crate with ID #987?”. Role needs are modeled as usecases, each with differing requirements for how the dais visualized, grouped and arranged. Not only is thereenormous amount of object data, there are also a varof relationships between the different entities likdelivery items, purchase orders or transport items. A of them are one-to-many relations where, for example, apurchase order knows the set of all delivery itemssubsumes. At the same time, each delivery item pertato a particular shipping bill of materials, which, in turnbelongs to a purchase order. Thus entities canreached in a variety of ways. This makes visualizing tavailable information and streamlining the navigationchallenging task for two reasons. First, the scope asize of the information space exceeds the scope of individual user (complexity), so the space is essentia“unknowable”. Second, users need to see only thsubset of the information in context to their roles. Thstandard query-filter-requery approach provides detbut quickly strands them in space. Thus they neegraph of both entity and structural information; morprecisely, they need the derived structure [24] of tinformation space that fits their roles and expandsaccommodate information retrieval activities.

Because large graphs are inherently difficult fohumans to navigate [13,25], SiLog maps the objegraph onto a hierarchy, interpreting certain edges a

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containment relation. Hierarchical views tailored foparticular use cases are defined for the object graph. hierarchies are arranged so that the user gets a viewcorresponds with the use case’s terminology and logThis lets us present the graph as a familiar tree view agives the user a simplified representation of the spaTree views allow the user to drill down and roll uspecific branches of the tree (by expanding nodes acollapsing subtrees) and are generally considered toan effective navigational tool when the underlyinstructure is moderately balanced [13]. However, the trmodel is problematic because the structure is nexclusively hierarchical, but is also a graph with linkthat are not modeled by containment in the hierarchy.

To handle this problem SiLog models canonicalpaths. Each object contained in a hierarchy view hexactly one canonical path associated with it leadifrom the root of the hierarchy to that object. Other nocanonical paths may exist in the hierarchy leading to same object. This conceptualizes the fact that there way to reach an object that is more usual to a user igiven context than any other way, but that there aother potentially relevant paths to that information. Wuse this concept to handle links that run across thierarchy. Tracing a selected node in the tree view bato the root of the tree results in the navigation paleading to that node. Whenever a node in the hierarcoffers a link to an object whose canonical path does notstart with the current navigation path, traversing thlink will expand the canonical path for the reacheobject in the tree view and display the object there. Tway, each object is represented at most once in whole tree, but can be related to and accessed frmany other parts of the tree.

While this approach provides a more tractable modof the underlying information space, it is problematic the interface. The original SiLog interface comprisetwo views: a detailed content view, usually tables andlists of data at each node, and a tree-based strucview. The structure view supports “coarse” navigatioto the node required: the user can see appropriattributes and details in the content view. Selectingitems in the content view is analogous to exploring arelationship (a cross-hierarchy link) and may result new paths being opened in the structure view. Figureshows the initial design of the structure view: aindented scrolling list akin to familiar file systemviewers, called the JTree after the Java widget used toimplement it.

The JTree is inadequate for visualizing andnavigating even small prototype projects. The lirapidly gets large and is awkward as soon as the uneeds to manipulate or discover entries that may hascrolled off the page. The user quickly loses conteMoreover, there is no clear way to emphasize or evdetect re lat ions between ent i t ies that are nhierarchical. In the prototype of Figure 1, the user

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explored the Shipments subtree to reach TransportItem 803257983. Selecting a delivery item from theaccompanying content view resulted in an abruptchange to the structure view: a path was opened in thePurchase Order subtree to the related element and thefocus “jumped” to it with no accompanying visual cuto indicate the relationship and explain the suddchange to the structure view.

Our goal was the design of better navigation suppfor the SiLog application by combining the power ohypermapping techniques with the flexibility of detailin-context offered by multiscale approaches. Widentified the following criteria for a SiLog structureview.• Effective view traversal and navigation: users didn

want to expend too much effort tracking down infomation.

• Preservation of context: information can be reachedby several paths.

• Explicit visualization of relations between informa-tion elements. Derived structure must involve bothpaths (tree descents) and relationships (cross-hierar-chy traverses).

• Automatic layout. Users do not need to see the entirespace but only the subset of interest. Thus the struc-ture view gets populated “on the fly” by user querieand needs to be dynamically reconfigured in suchway that the transitions are easily followed. Sincthe user’s task is to find information and not to reorganize the view, this reconfiguration should nrequire user intervention.Because it supports simultaneous detai l a

contextual views, hierarchical and associative structuand spatial cognition [11], these criteria led us to selethe Continuous Zoom as a basis for a hypermap.

Figure 1. The Jtree SiLog interface

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5 The Ztree: A Continuous Zoom HypermapCZ models a hierarchical data structure with add

links between arbitrary nodes in the hierarchy. Nodrepresent discrete points in the information space: linrepresent relationships. Therefore CZ can effectiverepresent combinations of trees and graphs. Parts ofinformation space are summarized by being containin closed cluster nodes while other clusters can bopened to successively examine finer levels of detail.

As described in [3], CZ manages a rectangular 2display space by recursively breaking it up into smallrectangular areas, creating a hierarchy of nestrectangles. The user controls the amount of detaildifferent areas of the display by opening and closiclusters. The contents of an open cluster are visiballowing one to see the deeper (more detailed) levelsthe hierarchy. Closing a cluster effectively prunesportion of the tree from the display, reducing the detshown for that part of the system. Open clusters aallocated more space than closed clusters. In additionthis automatic resizing of cluster nodes, the user cenlarge or reduce any node on the display. Throuopening and closing clusters, and resizing nodes, user has complete control over the amount of detail sin each part of the display. Since the entire hierarchyvisible at all times, the detailed portions always appein con tex t . Mu l t i p le a reas can be zoomesimultaneously.

Our previous experience with the CZ in networvisualization [4] indicated it was effective for navigatinlarge, hierarchically structured graphs. The Ceffect ively supports both the hierarchical anassociative (topological) thinking which are essenticomponents of information searching [17,19]. Anothreason is the explicit support the CZ provides the usfor recognizing and understanding her present locatin the information space, a feature targeted as a maneed in other complex, multiply-linked informationspaces such as hypertext [26]. Finally, CZ layouappear to exploit the spatial cognition aspects graphical maps [11] without suffering from theexcessive distortion drawbacks of other 2D fisheyes. Whave hypothesized two reasons for this. First, Clayouts do not violate relative layouts: that is, essent“left of”, “inside”, “on top of” relationships which arefundamental to cognitive consistency [11]. Seconsmooth animations perceptually guide the user throuthe view transition.

5.1 ZTree Description

The ZTree is a general-purpose widget responsible fspecifying an in it ial CZ layout, responding toapplication-specific events and defining how the Cview controller interacts with other application view(in the SiLog application, the content view.) Itrenders some subset of the data model up to aincluding the entire data model based on the user's a

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or programmer’s specification. When the informationspace is too large, application-defined heuristics cancontrol what subset of the space is initially presented. Inthe SiLog case, the initial structure view only containsnodes with less than 10 children to reduce the startingcomplexity. This prunes large branches of the tree.Users build up a richer derived structure view throughsubsequent queries and browsing in the detailed view.

When the SiLog application is launched twowindows appear: one with the standard SiLog view, andone with the initial ZTree view. Using the ZTree issomewhat similar to using the JTree. The user canopen and collapse parts of the tree view; can select whatto display in the Content pane from the tree view; andcan select something in the content view which willadd an open path to the tree view (perhaps dynamicallychanging the structure of the tree itself). The ZTreeexpands in 2D rather than in 1D, and zooming andshrinking interactions can be applied to make individualnodes larger and smaller. However, when the usercauses a node to be opened, a degree-of-interestalgorithm (DOI) tracks the user’s attention and devotmore size to the most recent node. Thus manual sizinpossible but not necessary: desirable behaviour from user’s point of view as indicated in the Hyper Mochstudy [27].

Items can be selected in the ZTree to view in moredetail in the Content Pane. The ZTree node whosecontents are currently displayed in the content viewis considered to be the current focus node and its titlebar is highlighted. Items selected in the content viewaffect the ZTree view in different ways. If the item hasalready been rendered (i.e., it has been laid out aspecified), then the ZTree is opened along the relevanpath in the tree structure to that item. However, if thappropriate item has not been specified and renderewill be dynamically added to the ZTree structure andthe ZTree layout will change to reflect the new node. Ithe item’s canonical path is different from the path bwhich it was selected, the canonical path will be openas well and a link drawn between the two relate

Figure 2. Expanding the ZTree structure through detail selections.

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elements. This behaviour causes an incrementaderived structure view to be accumulated over the usesession as she explores other parts of the informatspace. In the current example, when the user seleitem di0203 in the content view, the ZTree will openup the associated path for that item and indicate relationship between the two with a link (Figure 2).

It is important to note that this behaviour isupported by the ZTree but must be programmaticallyinvoked: that is, the ZTree has no concept of a“canonical path”, but it does have methods to define arender links based on related path criteria. The resultview both explicitly renders the relation and reduces tnavigational overhead required to explore the relatcontext ( in this example, the Purchase OrderOccasionally a diagnostic message will indicate ththere is not enough space to actually open the nodesuser can then close some other nodes to free up spacan resize the ZTree window.

Previous versions of the CZ supported only simplinks, which could cross levels of the hierarchy but wealways rendered in detail. The resulting web quickgrew too cluttered. In the ZTree, links are themselvhierarchical and selectable objects.When a cluster nis closed, any links to its children will also be “closedand rendered as a single virtual link. Just as one can drilldown into the space by successively opening clusnodes, one can also explore relations in successivmore detail by opening the virtual links. Figure 3shows a a ZTree view with virtual links: opening thelinks results in the more detailed view in Figure 3b.

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(b)Figure 3. Virtual links in the ZTree.

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the interface these links are rendered in magenta andblue respectively. Finally, links also have a DOI whichinfluences how much space they can have. This ensuresthat important links never get “squashed” betweeadjacent nodes.

The ZTree and CZ libraries support saving anrestoring views. The derived structure displayed in tgraphical map represents a composited history of user’s information forays (a graphical map of theinformation retrieval and problem-solving strategiesUsers can thus recall their contexts over sessions, can in fact share the maps with other users to highligaspects of the information.

Layout: The Grid algorithm

The CZ algorithm has two inputs: the initial layout othe space, or normal geometry, and a set of scale factors(one for each node). The normal geometry and the scfactors are combined to produce the zoomed geometry,which is then displayed. In previous applications thnormal geometry has remained constant (i.e., the sphas been fully defined at runtime.) However, ihypermap applications such as the ZTree and CzWeb[11], the information structure can change over time the user builds up successive paths through tinformation space. One approach would be to model entire space and only render subsets of it. Indeed, original CZ approach required the layout of the 2Dspace to be defined in an external map file. Thisgenerally undesirable, as it restricts flexibility anintroduces computational overhead. Instead wrecalculate the normal geometry at each reconfiguratusing an automatic grid-based layout algorithm.

2D automatic layout is an open research areagraph visualization (see [8] for an review). Forcedirected (spring) layouts are common. However, in tZTree, nodes are not necessarily fully connected edges, necessi ta t ing a la t t ice s t ruc ture to superimposed to use a spring approach. Moreovforce-directed layouts cannot avoid overlap in all casand do not distribute nodes aligned well with the axesa bounding box. Instead, the ZTree layout problem canbe seen as a variant of the bin-packing problem whthe sizes of the bins are not known until the children alaid out.

We separate logical layout (topology and locatiofrom pixel (x,y) layout. The CZ normal space consisof hierarchically organized clusters of nodes. We brethis problem down into sub-problems of automaticallaying out each group of siblings within a larger parenOur logical layout approach (the Grid algorithm)partitions space into a rectangular grid of cells inwhich each cell is one logical unit. This grid is initiallyas close to square as possible, as we have observedmost reasonable layouts are achieved in grids that areeither or units.

Grids can contain other Grids and have a 2D arrayx x× x x 1+( )×

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of cells, a capacity (how many cells can be occupiedtotal), a weight (where )and an orientation, since a Grid may need to be rotatedto fit in the parent’s available space. Each grid isoptionally associated with some external (domaiobject.

Child Grids can be added to the parent at any timChildren are sorted by size and inserted into the parin a modified first-fit algorithm. Adding a child Grid toa parent may cause a “refit” of the parent: if there is nenough space in the parent, it will resize itself accommodate the child and so on recursively up thierarchy of Grids. If there is enough space in theparent to incorporate a rotated child, the child Grid willbe rotated, and so will its children recursively down thtree. Nodes can be deleted in a similar fashion. Therno restriction on the size or number of Grids in a tree.Although theoretically this algorithm is potentially vercostly, in practice the “close-to-squareness” constraand the hierarchical partitioning of the solution sparender it tractable, producing automatic layoucalculat ion of ZTree spaces with no apparenperformance lag.

We apply this in the ZTree by giving each node inthe tree a Grid, laying out the space logically byrecursively adding the child Grids associated with theZTree node’s children to that Grid, and then mappingthe logical layout to pixel space (to account for bordegaps between nodes, and other rendering issues.) resulting layouts seem extremely workable. Overlapimpossible. All the layouts in the screen images in thpaper were generated by this approach.

One potential disadvantage of this approach relato the sorting of child nodes on size, since it can resulchanged relative locations as new nodes are added. can violate the consistency principle of maps discussearlier. We are investigating variants which maintarelative layouts wherever possible.

6 Discussion, Issues and Future WorkAt first inspection the ZTree seems to address the issuewe ident i f ied for th is appl icat ion.I t supportsnavigational maps which include both hierarchical anrelational information (Figure 5). It allows users to dridown in the information space without sacrificincontext using both node-centric (entity) and link-centr(relation) access. View traversal and navigation aaided both by persistent context and an automatic laywhich preserves relative positioning necessary feffective perceptual processing. User intervention resizing views or in re-arranging the automatic layoutsupported but unnecessary. Composite graphs are bup of user information retrieval allowing the user trecall higher-level problem solving contexts and share them with other users.

However, this is very preliminary work, and manquestions remain. The obvious one is: Do the users l

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it? Is it useful? We have added the ZTree as analternative user interface technique to the existing SiLogapplication based on initial user feedback. We arehoping to arrange more detailed studies with field-basedIT managers in the future. In the interim, we raise someopen questions about the extent of ZTree utility.

Node representation and DOI: Each object (nodeor link) has an interest measure associated with it whichis altered by user attention or by application specificfactors. The DOI affects an object’s chances of gettingscreen space. In previous CZ applications that has beenuseful since we use the node i tself to containinformation. However, as a structure view, it may makemore sense to tune the DOI to other measures such ashow many elements it contains, or how important it iswith respect to the application, or how many times theuser has visited it. Moreover, it introduces the potentialfor using the object space to convey information aboutthe detailed view. How can we exploit this in a database

Figure 4. JTree and ZTree maps for the same paths.

application, and would it be useful?Space requirements: As Figure 5 shows, the Ztree

can accommodate more contextual information than ascrolling list. Eventually, however, it requires morescreen space than is available.Thus previously accessednodes and links may have to be pruned from the view.There are issues to be decided in how we go about this.Do we automatically prune the view based on factorslike age (least recently accessed), DOI, or distance fromcurrent focus point? Do we prompt the user to free upspace and let her make the decision? This will have tobe tested.

Representing links : While we feel that linkmanipulation and representation has great potential forfacilitating database navigation and comprehension,there is as yet little knowledge and experience on howbest to approach this. We hypothesize that in manycomplex information worlds, relations between entitiesare more interesting than the entities themselves. Weanticipate much interesting research in this area.

7 Acknowledgments

This work was supported by a research grant from ABBCorporate Research, Heidelberg. We are indebted to ourcolleagues Anne Tissen at ABB and Dr. ThomasStrothotte at the Technical University of Magdeburg fortheir interest and feedback.

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