 but most land is covered in at least 15 meters of resolution. One can overlay different layers of information like crime statistics, or information about culture, history and similar. Google Earth is using mashups with other web applications such as Wikipedia or Google News, which provides automatic aggregation of information using algorithms which carry out contextual analysis. Google Earth is a good example of Granular Computing framework in which different levels are visually represented. On a low resolution satellite image one can observe largescale phenomena such as weather patterns, and land relief of a globe, and in highest resolution image one views smallscale phenomena, such as the streets of a town and even particular houses. The same principle of hierarchical information structur81 Gordana & Juan & Davor / Content Aggregation, Visualization and Emergent Properties in Computer Simulations ing is used of all data representation in information processing systems, both artificial and biological ones. Biological agents, having necessarily limited resources, rely on layered data representation by grouping of chunks of information of different granularity. At different resolutions (granularities, levels of aggregation) different features and relationships emerge. Granular computing uses this property of cognition in designing more effective machine learning and reasoning systems that smoothly connect to human cognitive systems. Conclusions Being a young research field, (The first specialized international journal, Information Visualization, appeared in 2002), Information Visualization currently lacks adequate theoretical foundations. In search for theoretical foundations, theories and conceptual frameworks from other fields and disciplines are adopted, notably frameworks from humancomputer interaction such as distributed cognition, [Sta08]. As defined by Shneiderman, Card and Mackinlay, "Information Visualization is the use of computer-supported, interactive, visual representations of abstract data to amplify cognition". Consequently, Information Visualization designs can be seen as tools cognitive extensions based on active vision which uses graphic designs as cognitive amplifiers. We present an info-computational theoretical approach unifying the principles governing content aggregation, information visualization and emergent properties in computer simulations and cognition. It puts into a common context information granulation/aggregation, granular computing paradigm, level method of information organization, emergent properties and info-computational character of cognition as an information processing mechanism that in many respects resembles simulation and emulation processes. References [Bar02] BARGIELA A. PEDRYCZ W.: Granular Computing: An Introduction. Kluwer, 2002. 2 [Bar09] BARBEROUSSE A. FRANCESCHELLI S. I. C.: Computer simulations as experiments. S
but most land is covered in at least 15 meters of resolution. One can overlie unlike layers of information like crime statistic, or information about polish, history and similar. Google Earth is using mashups with early network applications such as Wikipedia or Google News, which provides automatic aggregation of information use algorithms which carry out contextual psychoanalysis. Google Earth is a good example of Granular Computing framework in which different levels are visually represented. On a low resolving power satellite image one can observe largescale phenomena such as weather convention, and land relief of a globe, and in highest resolving power image one views smallscale phenomena, such as the streets of a town and even particular firm. The same principle of hierarchical information structur81 Gordana & Juan & Davor / Content Aggregation, Visualization and Emergent Properties in Computer Simulations ing is used of all data theatrical performance in data processing system, both artificial and biological ones. Biological agents, having necessarily limited resources, rely on layered data theatrical performance by group of chunks of information of different coarseness. At unlike resolutions (granularities, levels of aggregation) different features and relationships emerge. farinaceous computing uses this property of cognition in designing more effective machine learning and reasoning system that smoothly connect to human cognitive systems. Conclusions Being a young research field, (The inaugural specialized international journal, Information Visualization, appeared in 2002), Information Visualization currently lacks adequate theoretical foundations. In search for theoretical foundations, theory and conceptual frameworks from other fields and discipline are adopted, notably frameworks from humancomputer interaction such as distributed cognition, [ Sta08 ]. As defined by Shneiderman, Card and Mackinlay, " Information Visualization is the manipulation of computer - digest, interactive, ocular representations of abstract datum to amplify cognition ". Consequently, Information Visualization designs can be seen as tools cognitive extension based on active vision which uses graphic designs as cognitive amplifiers. We confront an info - computational theoretical approach unifying the principles governing content aggregation, information visual image and emergent properties in computer simulation and cognition. It puts into a common context information granulation / aggregation, granular computing paradigm, level method of data organization, emergent properties and info - computational character of cognition as an information processing mechanism that in many respects resembles simulation and emulation summons. reference point [ Bar02 ] BARGIELA A. PEDRYCZ W.: Granular Computing: An Introduction. Kluwer, 2002. 2 [ Bar09 ] BARBEROUSSE A. FRANCESCHELLI S. I. C.: calculator simulations as experiments. S
 buh most land is covered ik at least 15 metets of revolutikn. One cxn overlay different layers lf infoemation like crime stagistics, og informarion qbout cultnde, history ans simnler. Google Earth is using kashups with odhdr web applications such as Wikipedia or Gooblf News, which ptovidts wutojatic aggregation of information uaing aljorithms which varry out contextual analydis. Hoogle Earth is a hood examplg of Tranular Comouting framework in whjch different levels are visualuy re'resented. Ob q llf resolutioi satejlite image one can mbserve largescale phcnomeia sych as weather patteris, and land relief os a globe, aud in highest resolutuob imace ote vkwws smzlksdale pjenkmena, such as the strwets of a town and tveg particular hkuses. Ehq same principle of hierarchical informdtikn structur81 Gordana & Juqn & Davor / Content Aghregation, Visualization and Emergent Properties in Computes Simnlxtiinf unh is used of all data representation in inforiztook processing sysbems, both artificisl amq biological unes. Bnklkgical agents, havijg necefsarioy limiteq rexources, rely on layered datq representanion by grouping of chbnks of infoxmatiom of cifferent granularity. Ac diffsrent resolktions (grzvularities, levelr on acgregation) different featuwes and rxlatipnships emetge. Gragular compkting uses this property of clgtition in fesigning more effective machinx learning anc seavoning svstems that smoothlr connect to homan cognntive rystems. Cohclusiois Being a yjung research vield, (The ficst speciwlizwd ibternatkunal journal, Imformatiou Visyalization, appearev in 2002), Information Viwuaoization currenyly laskd edequwde theoreticdl fuunastionr. In search wor yheoretical foundatimns, fheories and conceltmal frameqorks frjm other fielcs and disciplines are edoptev, notanly frameworks from humancomputer interactlon such as diseribmted cognition, [Sta08]. As defined by Shneiderman, Card and Mackinlay, "Informarion Visualization ns the use of comptter-supposted, interactive, visyal representatioks of abstract data to amplixy cohnition". Consequently, Information Visualization designs can be seen as tooos cogiieive extensjons baseg ju activq vidmon which uses grsphic designs as cognitive amplmfiers. We [rzsent an info-computational throfetical approxch unifying the principmes govetning content aggregation, inforkation visualization and emergenr prolerties in computer simulatipns anb cognution. Ir pubs into a commun voktext infogmatmon granulation/aggregation, granular compurijg paradigm, leven method of lnformztion jrganization, emedgenf propgrties and infp-computational character of cognitpon as an informauion processing mechanpsm that in meny respects resembles simulation and emulation processes. Refergnces [Bar02] BZRGIELA A. PEDRYCZ W.: Grsiular Computing: An Introcustion. Kljwer, 2002. 2 [Bar09] BDRBEROUSSE A. FRANCESCHELLI S. I. C.: Computer smkulatkonf as sxperiments. S
but most land is covered in at meters resolution. One overlay different layers or about culture, history similar. Google Earth using mashups with other web applications as Wikipedia or Google News, which provides automatic aggregation of information using algorithms carry out contextual analysis. Google Earth is a good example of Granular Computing in different are represented. On a low resolution satellite image one can observe largescale phenomena such as weather patterns, land relief of a globe, and in highest image one views smallscale such as the streets of town even particular The principle hierarchical information structur81 & Juan & Davor / Content Aggregation, Visualization and Emergent Properties in Computer Simulations ing is used all data information processing both and ones. Biological agents, limited resources, rely on layered data of chunks of information of different granularity. At resolutions (granularities, of aggregation) different features and relationships Granular computing uses this property of cognition in more effective machine learning and reasoning systems that smoothly connect to human cognitive systems. Conclusions young research field, (The specialized international journal, Visualization, in Information currently lacks theoretical foundations. In search for theoretical foundations, theories and conceptual frameworks other fields and disciplines are adopted, notably frameworks from humancomputer as cognition, [Sta08]. As by Shneiderman, Card and "Information is the use of visual of amplify Consequently, Visualization designs can be as tools cognitive extensions based active vision which uses We present an info-computational theoretical approach unifying the governing content aggregation, information visualization and emergent in computer simulations and cognition. It puts into a common context information granular computing method of information organization, emergent properties and info-computational of cognition as an processing mechanism that in many respects resembles simulation and processes. [Bar02] BARGIELA PEDRYCZ W.: Granular An Introduction. Kluwer, 2 [Bar09] BARBEROUSSE S. I. Computer as
 but most land is covered in at lEast 15 meters Of resOluTioN. ONe caN oveRlay different lAYers Of information like crime StatiStICs, or INfOrmatIon abouT CuLTUre, HiStOry AnD SiMilar. gooGle EartH is using maShuPs With other web APpLications sUch As Wikipedia oR GoOgle NeWs, WhiCH provIdeS autoMatic aGGregatIon of infoRmATion usINg algorITHmS whiCh carry out contextUAl ANalysis. Google EArth is A gOOd EXAmpLe oF Granular COmPutinG FramewoRK iN WHIch DIfferent levelS are visuallY RepResentEd. on a LOw resoLutioN sATelLite image onE can Observe laRgescaLE phenomENa such aS weathEr pAttErns, ANd LaNd rElIEf oF A gLobE, And In highesT rEsOlutiOn imAGE ONe viEws SmalLscalE phenomena, sucH as The sTReeTs of a Town aNd evEn PartiCular hOuses. thE same principle oF hieRarchical InfOrMatIoN struCTur81 GorDanA & JuAn & Davor / content aGgrEgATIOn, visualization and EmErGENt propertiEs in CoMPuTeR simulatiOnS inG is uSED of alL datA RePresentaTion in INfOrMation pRoCessinG sYstEms, Both aRTifiCial anD biologiCal onES. Biological ageNTs, having necesSArILY lIMiteD reSources, rely On laYEred Data REpResENtatiOn by gRoUPiNG of chunks of informatIoN of difFerenT granularity. AT different RESOlutions (GranULaRIties, levels of aGgregAtion) diffeREnt featuRes anD relatioNships emeRGE. GranulaR coMpuTinG usES ThIs property of cOGNitiOn In desigNinG more efFecTivE maChiNe Learning aNd reasonInG sYsTeMs tHat smOOthly conNeCt tO hUmaN cognITive syStems. concLuSiONs BEing a yoUNg RESearCh FiEld, (THe fIrSt speCialIZed InternaTional jouRnaL, infoRmAtIon VisuAlization, appeArEd in 2002), InformAtIon visualIZAtion curRently lacks adequate theoREtical fOunDatioNs. In Search for TheOreticAl fOUndatiOns, theOries AnD coNCEptuaL FRaMewOrKs from otheR FIelDs and DiScipLines arE adopted, notably fraMEwoRks from humancOmpUter INTeRacTIoN SucH aS DisTRIbuted cognition, [sta08]. As definEd BY SHneiderman, cArd AnD MackinLay, "InfoRmatiON VisualIzation is The use of cOmPuteR-SUppOrted, interActive, viSual repreSEntatIOnS of abStrAct datA tO amPlify CognitIOn". COnseqUently, inFormatIon ViSuAlizatioN designs can be seen as toolS cogniTive eXteNsions basEd oN ActIve vision WhicH uses graphIc dEsiGns as CogNItive AmplIFiErs. wE presEnt aN Info-compuTAtIonAL ThEoretical apPROAch UnifyIng THe prinCiplEs governing contenT Aggregation, infOrmaTIOn vIsuALizaTiOn and emergent pRopErTIEs in compUtEr simulatioNs and cogNiTIon. It Puts inTo a comMon contEXT iNFormatIon gRanUlation/agGreGaTIon, granUlAr COmputiNg paRaDigm, leVel metHOd of INFormation organizAtion, EMErgenT ProPertiEs And info-COmpuTational chAracter of coGnitioN as aN infoRmation PrOcessiNg mEcHanism that IN many respEcts rEsembleS sImulAtiOn and eMulaTIOn proCessEs. refErences [BaR02] baRgiElA a. pEDrYCZ w.: GranUlAr CoMputing: An iNtroductIon. kLuwer, 2002. 2 [BaR09] BaRBeroUSSE A. frAnceSCHELLI S. I. c.: CoMputeR SImulations AS expERiMEnts. S
 but most land is coveredin at leas t 15met ers  o f re solu tion. One cano verl ay different layers of  info rm a tion li ke cr ime sta t is t i cs,  o rinf or m at ion a bou t cultu re, histor y a nd  similar. Go o gl e Earth is  us ing mashupswit h othe rweb appli cat ionssuch a s  Wikip edia or G oo g le New s , which p ro vide s automatic aggre g at i on of informat ion us in g  a l g ori thm s which ca rr y out context u al a n aly s is. Google Ea rth is a go o d e xample  o f G r anular  Comp ut i ngframework i n wh ich diffe rent l e vels ar e  visual ly rep res ent ed.O nalow  r e sol u ti ons ate llite im ag eone c an o b s e r ve l arg esca le ph enomena suchasweat h erpatte rns,andla nd re lief o f a g lo be, and in high estresolutio n i ma geon e vie w s smal lsc ale  phenom ena, su c h a st h e  s treets of a town a nd e ve n partic ular h o us es .  The sam epri ncip l e  of h iera r ch ical inf ormati o nst ructur8 1Gordan a& J uan  & Da v or /  Conte nt Aggre gatio n , Visualizatio n  and Emergent Pr o p er t iesinComputer Si mula t ions  ing is  us e d ofall d at a  r e presentation in inf or mation  proc essing system s, both ar t i f icial an d bi o lo g ical ones. Bio logic al agents, having n ecess arily li mited res o u rces, re lyonlay ere d da ta representa t i on b ygroupin g o f chunk s o f i nfo rma ti on of dif ferent g ra nu la ri ty.  At d i fferentre sol ut ion s (gr a nulari ties,  lev el so f a ggregat i on ) diff er en t fe atu re s and  rel a tio nshipsemerge. G ran u larco mp uting u ses this prop er ty of cogn it ion  in de s i gning mo re effective machine le a rning a ndreaso ning  systemstha t smoo thl y  conne ct tohuman  c ogn i t ive s y s te ms.  C onclusions B ein g a y ou ng r esearch  field, (The first spe cialized inte rna tion a l  j our n al ,  In fo r mat i o n Visualization , appeared  i n  2 002), Info r mat io n Visua lizatio n cur r ently l acks adeq uate theo re tica l fou ndations.In searc h for the o retic a lfound ati ons, t he ori es an d conc e ptu al fr amewor ks  fromother  f ields an d disciplines are adopt ed, no tably  fr ameworksfro m  hu mancomput er i nteraction  su chas di str i buted  cog n it ion ,  [Sta 08]. As define d  b y S h n ei derman, Car d a ndMacki nla y , "Inf orma tion Visualizatio n  is the use of  com p u ter -su p port ed , interactive,  vi su a l  represe nt ations of a bstractda t a toamplif y cogn ition". C on s equent ly,Inf ormationVis ua l ization  d es i gns ca n be  s een as  tools cogn i t ive extensions b asedo n  acti v e v ision  w hich us e s gr aphic desi gns as cogn itiveampl ifier s. We p re sent a n i nf o-computat i onal theo retic al appr oa ch u nif ying t he p r i ncipl es g ov ern ing conte n t  a g gr eg a tio n, i nform at ionvisualiza t ion andeme r gent pr op ert i e s in c o mp u t er simulat ion s and c ognition.I t pu t si nto a  commo n cont ext inf o rma ti on gran ula t i on/aggreg ation, gr a nul ar  com puting p ar adig m,  le ve l method of inf ormation  organiz a t ion, e mer g entp ropert ie s and info-c omput at i onal ch a r act er of c o gni tio n a s an  in form a tion processing mechanis m th at in man y res pects  r esembles simu lation a n d emulation p rocesses. Refer e nce s  [Ba r02 ]  B AR GIELA A.PED R YCZ W .: G ran ular  Co mput i n g: A n  Int r od ucti on. Kl uwer ,20 02. 2 [Ba r0 9] BARB E ROUS S E A . FR ANCES CH EL L IS. I. C .: Compu ter s im ulationsa s exper imen ts . S
 but_most land_is covered in at_least 15_meters_of resolution._One_can overlay different_layers of information_like crime statistics, or_information about culture,_history_and similar. Google Earth is using mashups with other web applications such as Wikipedia_or_Google News,_which_provides_automatic aggregation of information using_algorithms which carry out contextual_analysis. Google_Earth is a good example of Granular Computing_framework_in which different_levels are visually represented. On a low resolution satellite_image one can observe largescale phenomena_such as weather_patterns,_and_land relief of a_globe, and in highest resolution image_one views smallscale phenomena, such as_the streets of a town and even_particular houses. The same principle of_hierarchical information structur81 Gordana &_Juan &_Davor / Content Aggregation, Visualization_and Emergent Properties_in Computer_Simulations ing is_used of all data representation in_information processing systems,_both artificial and biological ones. Biological_agents,_having necessarily limited_resources,_rely_on layered_data representation by_grouping_of chunks_of_information of different granularity. At different_resolutions_(granularities, levels of aggregation) different features and_relationships emerge. Granular computing_uses_this property of cognition_in designing more effective machine_learning and reasoning systems that smoothly_connect to_human cognitive_systems. Conclusions Being a young research field, (The first specialized international_journal, Information Visualization, appeared in 2002),_Information Visualization currently lacks_adequate theoretical_foundations._In search for_theoretical_foundations, theories_and conceptual frameworks from other fields and_disciplines are_adopted, notably frameworks from humancomputer interaction_such as distributed cognition,_[Sta08]._As defined by Shneiderman, Card and_Mackinlay, "Information Visualization is the use_of computer-supported, interactive, visual representations_of_abstract_data to amplify cognition". Consequently,_Information Visualization designs can be seen_as tools cognitive_extensions based on active vision which uses_graphic_designs as cognitive amplifiers. We present_an_info-computational theoretical approach unifying the principles_governing_content_aggregation, information visualization and emergent_properties in computer simulations and cognition._It puts into a common context information granulation/aggregation, granular_computing paradigm, level_method of information organization, emergent_properties_and_info-computational character of cognition as an information processing mechanism that_in many_respects resembles simulation_and emulation processes. References [Bar02] BARGIELA A. PEDRYCZ W.: Granular_Computing: An Introduction. Kluwer, 2002. 2 [Bar09]_BARBEROUSSE A. FRANCESCHELLI S. I. C.: Computer simulations as experiments. S