An Event-Based Approach to Vsualzaton Chrstan Tomns Hedrun Schumann Insttute for Computer Scence Unversty of Rostoc, Germany {ct,schumann}@nformat.un-rostoc.de Abstract Vsualzaton of large data sets s a demandng tas, especally f dfferent users are nterested n dfferent aspects of the same data set. Today s vsualzaton technques often do not dstngush dfferent aspects and thus vsualze all aspects of the data, whch leads to overcrowded and cluttered representatons. Moreover, users are provded wth nformaton rrelevant to them. In ths paper we descrbe an event-based approach for vsualzng large data sets. The basc dea s to let users descrbe nterestng aspects of the data by means of events and then adapt the vsual representaton of the data on occurrence of events. We present a formal descrpton of events, assumng that the data are gven n relatonal form. Furthermore, a model for eventbased vsualzaton s developed. A bref overvew of an early realzaton of the model along wth frst vsual results s part of ths paper.. Introducton Nowadays modern database technology enables scentfc nsttutons, governmental authortes, and companes to collect large amounts of data. These data are for nstance expermental datasets, health statstcs, or customer surveys. Vsualzaton has been proven to be an effcent method for analyzng such data. Therefore, (abstract) data are mapped to vsual representatons n order to beneft form the capabltes of the human vsual system. North, Conln, and San state n [] that modern relatonal database technologes allow effcent and flexble data management, but today s vsualzaton technques do not reach ths level of flexblty. Developed by a sngle programmer, vsualzaton technques are often specfc to only one certan problem. Moreover, n the case of large datasets t s common that dfferent users explore the data wth respect to dfferent aspects. Ths means that a vsualzaton technque must be able to represent any of these dfferent aspects. However, most vsualzatons do not dstngush between dfferent aspects and thus, users are oblged to create a dataset consstng of only relevant data aspects pror to vsualzaton. Otherwse, users are provded wth a lot of nformaton they are not nterested n at all. Wth respect to modern vsualzaton methods for large datasets and dfferent users, ths leads to the followng two demands: Increase of flexblty,.e. allow users to specfy ther nterests and then adapt the vsualzaton automatcally. Increases of effcency,.e. emphaszng portons of the data users are nterested n and reduce nformaton rrelevant to them. In ths paper we present a model for event-based nformaton vsualzaton, whch can help to reach the descrbed goals. The basc dea of the event-based vsualzaton s to allow users to specfy pattern they are nterested n. Once a pattern s detected wthn the data (ether a statc or a dynamc dataset) an event s trggered. Accordng to the event and ts propertes the vsualzaton s adapted automatcally. The followng sectons wll ntroduce our deas for event-based vsualzaton. Related wor and a state-ofthe-art overvew are gven n Secton 2. The descrpton of events (Secton 3) s the bass for the development of our model n Secton 4. Furthermore, n Secton 5 we show frst results of a realzaton of our model n the axes-based vsualzaton framewor VsAxes. 2. Related wor Events and event-based approaches are wdely used n a varety of nformaton technology related problems. Events are a prerequste for reactve
behavor and thus a requrement for automatcally adaptng software systems. Wth respect to vsualzaton, only events lmted to specfc problems have been used n the past years. Matowc et.al. [2] descrbe how smple events can be used n order to adapt vrtual nstruments n process vsualzaton. In fact, the vsual representaton and thus the level of detal of vrtual nstruments s changed f certan thresholds are reached. In ths way the vsualzaton supports an observer n fndng crtcal ponts n a real tme process. From the area of flow vsualzaton the approach from Renders et al. [3] s nown to mae use of events. Based on feature extracton relevant portons (.e. features) of tme-dependent flow data are vsualzed. Addtonally, events descrbed as any development n the evoluton of a feature that s sgnfcant ([4] p.07) are detected and presented n an event graph. Brth and death, entry and ext, as well as splt and merge of features are consdered sgnfcant and thus relevant for the vsualzaton of features n the event graph. By representng relevant events n combnaton wth con-based flow vsualzaton t s easer for users to comprehend the characterstcs of the flow data. The vsual representaton of algorthms [5] as well as dstrbuted program debuggng and vsualzaton [6], [7] can also be enhanced by means of event-based technques. The transton from one state of a program or algorthm to another s consdered an event. There s an ongong dscusson [5] whether state-based or eventbased approaches are more expressve for the purpose of specfyng program vsualzaton. Intruson and msuse detecton [8] n IT-networs s an evolvng area of vsualzaton. In [9] an event-based archtecture for such vsualzatons s descrbed. Based on events read from server log fles n combnaton wth events generated by the user nterface, the vsualzaton s refreshed automatcally at runtme. By such means, events le port scans, fle transfers, or arbtrary net connectons are represented vsually. Ths helps admnstrators to fnd wea spots n a networ, heavy load tmes, or msuse of resources. These examples for vsualzatons show that eventbased approaches can be used to ease the analyss of arbtrary data. However, all descrbed systems developed ther own propretary events and are lmted to a specfc vsualzaton problem. Our am s to defne a more general event-based approach n order to allow an effectve and flexble analyss of large statc or dynamc data sets. We assume that these data sets are gven n relatonal form. 3. Events The essental requrement for a general event-based vsualzaton model s the descrpton of the term event. In our context we consder events wth respect to relatonal data sets, attrbutes, and data records (.e. tuples). By dong so, we want to close the gap n flexblty (cp. []) between relatonal data management and data representaton. 3.. Event descrpton Events n our words are specal portons of a data set complyng wth certan condtons and constrants regardng attrbutes and records. Specal about these portons s that a user s nterested n them. Once detected, events may have assocated a varety of parameters, le attrbutes nvolved, data records nvolved, relatons to other events, mportance, and so forth. Concrete values for these parameters are determned ether by means of the event detecton (e.g. for nvolved records) or by users themselves (e.g. for mportance). An example of an elementary event s an exceedng of a certan threshold of an attrbute (e.g. cases of nfluenza > 300). Snce elementary events are elgble for smple vsualzaton tass only, t s necessary to allow for composte events created from elementary events and certan operators (for nstance logcal operators le and, or, not). By dong so, certan combnatons of values n a data record (e.g. sex = male and professon = manager and dagnoss = S.A.R.S.) can be detected. 3.2. Event formalsm Requrements: Suppose, = { A,, An } = { R R } A and R,, m are fnte and non-empty sets of attrbutes and value ranges, respectvely, and a functon range : A R assgnng each attrbute a range. We call U = m = R the data unverse. Then, a data set can be modeled as a fnte set of tupels { : and ( ) ( )} T = t t A U t A range A where a tuple s a functon that assocates each attrbute wth a value from the attrbute s value range. Event defnton: We now want to gve a general defnton of events. An event can be descrbed by means of frst order predcate logc (PL-I) formulas. The tuple relatonal calculus nown form relatonal
database theory [0] defnes formulas whch can be adapted to our needs. Symbols that mght appear n a formula are: tuple varables (elements of a countable nfnte set V dsjont from the unverse U ), aggregate functons over attrbutes h : A U, -ary functons of the form = then g s g : U U U (f 0 a constant), and -ary logcal predcates P U U. These symbols except the latter one are used to defne atoms, whch are then used to construct formulas. Atoms are defned as follows:. If x V s a tuple varable and A A an attrbute, then xa. s an atom denotng the value of attrbute A n the tuple represented by x. 2. If h s an aggregate functon and 3. If A A an attrbute, then ha ( ) s an atom denotng the value of the functon for A. g s a -ary functon and α,, α are atoms, then g ( α,, α ) s an atom denotng the value of the functon. We now can defne an elementary formula usng the defned atoms and predcates as follows. If P s an α α are atoms (as -ary logcal predcate and,, elem defned n.-3.), then f = P( α,, α ) s an elementary formula. In order to evaluate a formula we requre a substtuton of tuple varables wth tuples of the data set gven by a complete functon s: V T. We denote as ( ) tuple varable n α. s α the atom after applyng s to each elem Now we can state that a formula f evaluates to true ff there exsts a substtuton s so that (( s α ),, s( α )) P. Elementary formulas (wth predcates le <=,, ), common logcal connectors ( ),,, and quantfers ( ), can now be used to construct general event formulas f as usual. Followng [0], we defne the general form of an event as { x f}, where f s a formula (the event formula) and ẋ (the target) s a tuple varable n f. We call et { x f s} { x f} = an nstance of an event for a concrete tuple t T ff f s true under. substtuton s and sx ( ) = t Examples: These defntons provde us wth a powerful formalsm for descrbng a varety of events wth respect to the relatonal data model. Some formal examples of event types shall be gven. The exceedng of a threshold can be descrbed as { x x.nfluenza 300} detected wth { x x.temp x.temp 7}. A certan range of values s. An unusual networ load per user event can be defned. If a user s as { x (.load/ x x.users) >.024} nterested n maxmum values of the pollutant carbonmonoxde (CO) a correspondng event can be defned ether by usng more than one tuple varable (.e. xy, ) or by means of an aggregate n { x yy (.co x.co) } functon { x x.co = max(co) }. An ncrease of the pollutant wth respect to the prevous measurement can be descrbed by usng two tuple varables n { x x.co > y.co x.date = ( y.date+ ) }. 4. Processng events for vsualzaton Havng defned our nterpretaton of events, we now wll descrbe how these events can be ncorporated to vsualzaton. 4.. Event specfcaton The frst queston arsng s how events can be specfed by users. For that we dentfed the followng requrements: Intutve event specfcaton s requred to allow dfferent user groups (e.g. decson maers or data analysts) worng wth events effcently. Events must be storable wth respect to the dataset addressed and events must be edtable to allow an easy adaptaton to changes n the worng envronment. The specfed events should be tested for satsfablty.
In order to reach these goals we ntend to use event templates n combnaton wth a vsual nterface. The templates are descrbed by means of XML grammar. They provde parameters, whch are set by users. Instances of parameterzed templates can then be used for event detecton. Fgure gves an example of an event template and ts parameterzed vsual representaton. In order to create a vsual representaton of an event, symbols occurrng n event formulas are mapped to vsual prmtves, whch are lned regardng the symbol s poston n the formula. Ths allows for event specfcaton by means of draggng and lnng the prmtves n a vsual edtor. <event name= threshold > <param name= attrbute type= strng /> <param name= relaton type= choce data= <,=,> /> <param name= value type= strng /> </event> nfluenza > 300 Fgure. Exemplary threshold event template and ts parameterzed vsual representaton. 4.2. Event detecton Gven a set of events, t s the tas of the event detecton to determne, for whch data records these events evaluate to concrete event nstances. Ths means, that each event formula has to be evaluated regardng each data record of a data set. The result of the event detecton s a set of event nstances bound to data records. Wth respect to statc (.e. nvarant) data sets, event detecton can be easly realzed n a pre-process pror to vsualzaton. On the other hand, for dynamc data sets, whose content could be changng durng vsualzaton (e.g. records are nserted, altered, or removed) a pre-process can only ntally detect events. If a change to the data occurs, event detecton must be re-performed for each data record. Ths s necessary snce a change to a sngle data record may nfluence the detecton of events regardng other records. The event detecton can be realzed by mplementng the descrbed formalsm. In fact, the event formulas can be mapped to SQL-queres. After query executon a non-empty result-set ndcates an evaluaton of a formula to true (.e an event s detected). Technques requred for mplementng event detecton (e.g. SQL connectvty, XML handlng or table data structures) are nherent to modern programmng envronments le.net or Java. 4.3. Event representaton The man tas of event-based vsualzaton s to represent detected events. Dependng on the actual applcaton a varety of events may occur. Therefore, t s mportant that: occurrences of events are hghlghted for easy recognton and that dfferent event types are vsually dfferentated so that they can be easly dstngushed. Current vsualzatons represent events by means of some sort of a temporal axs (e.g. a tmelne [6], [7] or an event graph [4]) or by adaptng a gven vsual representaton (e.g. dfferent levels of detal n [2]). Therefore, we dfferentate: explct event representaton (.e. usng separate representatons for events and data) and mplct event representaton (.e. adaptng the data vsualzaton). Though our man nterest regards mplct representaton, n most applcatons t s useful to addtonally provde explct event vsualzaton upon user request (e.g. by hghlghtng rows n a table vew). In order to allow an adaptaton of a vsualzaton technque t s necessary to fnd expressve parameters that can be altered. Furthermore, t must be descrbed how these parameters change. Instantaneous changes can be expressed by actons. Moreover, n dynamc vsualzaton envronments parameter changes can be dynamc as well. Therefore, processes are defned, whch descrbe such changes. Frst results of how vsualzaton can be adapted by means of actons are gven n Secton 5. 4.4. Model of event-based vsualzaton We use the classc vsualzaton model as bass for our model. The three man steps of the classc model are flterng (.e. preprocessng the raw data), mappng (.e. mappng the data to geometrc prmtves and ther attrbutes), and renderng (.e. renderng geometrc data to mages). We extend ths model wth our event-based concepts. Therefore, parameters nfluencng each step of the classc model are ntroduced. Furthermore, events and actons/processes as well as the event detecton are ntegrated n the model (cp. Fgure 2). The presented model of event-based vsualzaton can now be used as bass for an actual realzaton presented n Secton 5.
Events Event detecton Actons / Processes Vsualzaton Parameters Data Flterng Mappng Renderng Fgure 2. Model of event-based vsualzaton. 5. Realzaton and prelmnary results Images Especally when data sets are large, representng all nformaton vsually s a demandng tas. Parallel Coordnates [] are commonly used for multvarate data vsualzaton. They consttute a projecton of n- dmensonal data space onto 2-dmensonal screen by means of parallel axes representng attrbutes. The data records are then represented by lne segments crossng the axes regardng the attrbute values. Wth extensons le hstograms and brushng (cp. [2], [3]) Parallel Coordnates are a powerful tool, whch s applcable for ntegraton of a general event-based model for vsualzaton. Therefore, we decded to realze our event-based model n an axes-based vsualzaton framewor called VsAxes. Ths led to the followng desgn goals: ntegraton of the event-based vsualzaton model, expressve vsual representaton of relatonal data, hgh degree of adaptablty of the vsual representaton by means of events, and hgh degree of nteractvty for easy data exploraton. In order to reach these goals a modular objectorented class desgn was chosen, whereas the eventbased model s combned wth axes-based vsualzaton by means of well defned nterfaces. Wth respect to axes-based vsual representatons, conceptually t s mportant to separate the desgn of an axs, whch s used to represent the values of an attrbute, from the arrangement of all axes on the screen. Besdes a smple statc axs, we have ntegrated three nteractve axes for easy data exploraton [4]: scroll axs, whch allows for a selecton of a value range of nterest, focus+context axs, whch allows for an emphass of a certan pont of nterest on an axs, and herarchcal axs, whch s useful for herarchcally structured value ranges. These axes can be adapted by nteractons of a user or automatcally as a result to the detecton of a certan event. Dependng on the actual data set several axes of certan types are arranged as Parallel Coordnates or as a Coordnates Wheel. The Coordnates Wheel s a novel radal arrangement for axes-based vsualzaton (and was ntroduced n [4] as TmeWheel). It s used f an attrbute of a data set provdes an order of the records of the data set (e.g. for tme-dependent data) and furthermore, the user ntends to explore the data wth respect to ths attrbute (.e. the attrbute of reference). The basc dea of the Coordnates Wheel s to place one axs representng the attrbute of reference exposed n the center of the dsplay and radally arrange further axes representng the remanng attrbutes. Just le for axes, t s possble to adapt arrangements ether nteractvely or on occurrence of events. Some examples for adaptaton shall be gven realzed by nstantaneous actons. For a frst example we vsualze a dynamc health data set consstng of the daly reported number of cases for a varety of dseases. Suppose an event { x x.nfluenza 300} occurs f the number of cases of nfluenza exceeds a threshold ndcatng an epdemc. Automatc brushng can be used n order to emphasze such crtcal data records. Fgure 3 clarfes that brushng s an effectve means for adaptng the vsual representaton n an event-based envronment. Fgure 3. Brushng data records wth respect to crtcal events. In a second example we use the Coordnates Wheel to represent a multvarate tme seres of clmate related attrbutes. The Coordnates Wheel n Fgure 4 shows sx of these tme-dependent attrbutes, each of whch mapped to one crcular axs. The central axs represents tme steps. In our case the relaton between tme and temperature represented by blue lnes s of specal nterest. If we assume a scentst analyzng the data wth respect to a specal range of temperatures crtcal regardng a certan chemcal process the event { x x.temp x.temp 7} can be defned. On occurrence of such an event t s useful to de-clutter the
vsual representaton by omttng rrelevant nformaton. In Fgure 4 the usefulness of such operaton becomes obvous. It can be seen, that the reducton of blue lnes n the Coordnates Wheel allows a better nsght to the sub-range of nterest. The same operaton could be performed for more than one attrbute to further de-clutter the vsual representaton. Moreover, other scentsts may defne dfferent events of nterest, whch results n adapted vsualzatons for each of them. -6.00 22.20-0.82 6.34 Fgure 4. Usng a scroll axs wth respect to an event reduces rrelevant nformaton and thus, clutterng n the mage. A more global adaptaton than n the prevous examples s realzable f the arrangement of the axes s calculated accordng to events. Fgure 5 (left) depcts far outlers for the attrbute precptaton from the clmate data mapped onto the upper left axs connected wth blue lnes to the central tme axs. An aggregate functon outler : A R can be used to determne outlers n the event { x x.prec > outler(prec)}. Once ths event s detected the arrangement can be adapted automatcally by means of rotatng the Coordnates Wheel and scalng ts axes. Ths leads to an arrangement, where the axs representng outlers s centered above the central tme axs and furthermore, s provded wth more drawng space. These examples show that a varety of vsual adaptatons s possble and useful. Further vsual event clues have to be examned wth respect to ther expressveness and effcency. Especally for dynamc vsualzaton enhanced technques le anmaton or color fadng are promsng. 6. Concluson and future wor In ths paper we addressed problems that arse f large data sets are analyzed vsually by dfferent users regardng dfferent aspects of the data. An event-based approach s suggested as elgble means to solve these problems. We presented a formal descrpton of events that allows for a varety of events to be specfed by users. Furthermore, the model of event-based vsualzaton descrbes how events can be ncorporated to vsualzaton. Our approach has been evaluated n frst user tests consderng human health data and clmate data n combnaton wth smple event templates. In general, our test users gave us postve feedbac regardng the adaptaton of the vsual representaton. However, event specfcaton turned out to be dffcult. Therefore, we thn our event-based approach ncreases the flexblty (user specfed events) as well as effcency (relevant nformaton emphaszed) of vsualzaton. However, more wor has to be done n the future. As yet, events are formally descrbed wth respect to tuples. On the other hand, events regardng attrbutes could be of nterest as well. Therefore, a formal descrpton of attrbute events s requred. Furthermore, n a varety of applcatons tme plays an mportant role. Although tme-dependent events can be expressed by our formalsm, the event specfcaton can be mproved by a vsual nterface whch taes tme nto account (e.g. for specfyng sequences of events [5]). The development of such a vsual edtor allowng users to specfy events n a drag and drop manner and map them to acton/processes s stll n progress. Addtonally, further parameters of vsualzaton must be revealed to be able to defne expressve actons and processes for adaptaton of the vsualzaton. Regardng ths, t s necessary to nvestgate solutons for concurrent actons and processes. Fnally, after ncorporatng the mentoned extensons, our approach has to undergo an extensve evaluaton n order to fully prove ts elgblty. References [] C. North, N. Conln, and V. San, Vsualzaton Schemas for Flexble Informaton Vsualzaton, Proceedngs of Symposum on Informaton Vsualzaton 2002, Boston, October, 28-29, 2002, pp. 7-4. [2] K. Matovc, H. Hauser, R. Santzer, and M.E. Gröller, Process Vsualzaton wth Levels of Detal, Proceedngs of Symposum on Informaton Vsualzaton 2002, Boston, October, 28-29, 2002, pp. 67-70. [3] F. Renders, F.H. Post, and H.J.W. Spoelder, Vsualzaton of tme-dependent data wth feature tracng and event detecton, The Vsual Computer, Vol. 7, No., Sprnger Verlag, Hedelberg, 200, pp. 55-7.
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