Pixel Bar Charts: A New Technique for Visualizing Large Multi-Attribute Data Sets without Aggregation

Size: px
Start display at page:

Download "Pixel Bar Charts: A New Technique for Visualizing Large Multi-Attribute Data Sets without Aggregation"

Transcription

1 Pxel Bar Chart: A New Technque or Vualzng Large Mult-Attrbute Data Set wthout Aggregaton Danel Kem*, Mng C. Hao, Julan Lach*, Mechun Hu, Umehwar Dayal Hewlett Packar Reearch Laboratore, Palo Alto, CA Abtract Smple preentaton graphc are ntutve an eay-to-ue, but how only hghly aggregate ata an preent only a very lmte number o ata value (a n the cae o bar chart). In aton, thee graphc may have a hgh egree o overlap whch may occlue a gncant porton o the ata value (a n the cae o the x-y plot). In th paper, we thereore propoe a generalzaton o tratonal bar chart an x-y-plot whch allow the vualzaton o large amount o ata. The bac ea to ue the pxel wthn the bar to preent the etale normaton o the ata recor. Our o-calle pxel bar chart retan the ntutvene o tratonal bar chart whle allowng very large ata et to be vualze n an eectve way. We how that, or an eectve pxel placement, we have to olve complex optmzaton problem, an preent an algorthm whch ecently olve the problem. Our applcaton ung real-worl e-commerce ata how the we applcablty an ueulne o our new ea.. Introucton Becaue o the at technologcal progre, the amount o ata whch tore n computer ncreae raply. Reearcher rom the Unverty o Berkeley etmate that every year about Exabyte o ata generate, wth % avalable only n gtal orm. Toay, computer typcally recor even mple tranacton o everyay le, uch a payng by cret car, ung the telephone an hoppng n e-commerce tore. Th ata collecte becaue bune people beleve that t a potental ource o valuable normaton an coul prove a compettve avantage. Fnng the valuable normaton hen n the ata, however, a cult tak. Vual ata exploraton technque are npenable to olvng th problem. In mot ata mnng ytem, however, only mple graphc, uch a bar chart, pe chart, x-y plot, etc., are ue to upport the ata mnng proce. Whle mple graphc are ntutve an eay-to-ue, they ether: - how hghly aggregate ata an actually preent only a very lmte number o ata value (a n the cae o bar chart or pe chart), or - have a hgh egree o overlap whch may occlue a gncant porton o the ata value (a n the cae o x- y plot). The ueulne o bar chart epecally lmte the uer nterete n relatonhp between erent attrbute uch a prouct type, prce, number o orer, an quantte. The reaon or th lmtaton that multple bar chart or erent attrbute o not upport the covery an correlaton o nteretng ubet, whch one o the man tak n mnng cutomer tranacton ata. For an analy o large volume o e-commerce tranacton [Ec 99], the vualzaton o hghly aggregate ata not ucent. What neee to preent an overvew o the ata but at the ame tme how the etale normaton or each ata tem. In th paper, we ecrbe a new vualzaton technque calle pxel bar chart. The bac ea o pxel bar chart to ue the ntutve an wely ue preentaton paragm o bar chart, but alo ue the avalable creen pace to preent more etale normaton. By colorng the pxel wthn the erent bar accorng to the value o the ata recor, very large amount o ata can be preente to the uer. To make the play more meanngul, two parameter o the ata recor are ue to mpoe an orerng on the pxel n the x- an y-recton. Pxel bar chart can be een a a generalzaton o bar chart. They combne the general ea o x-y plot an bar chart to allow an overlap-ree, nonaggregate play o mult-attrbute ata. Snce pxel bar chart ue each pxel to preent one ata value, they belong to pxel-orente technque. Other pxelorente technque nclue the pral technque [KK 94], the recurve pattern technque [KKA 95], an the crcle egment technque [AKK 96]. Other clae o normaton vualzaton technque nclue geometrc proecton technque (e.g. [In 85, ID 90]), con -bae technque (e.g., [PG 88, Be 90]), herarchcal technque (e.g., [LWW 90, RCM 9, Shn 9]), graph-bae technque (e.g., [EW 93, BEW 95]), whch n general are combne wth ome nteracton technque (e.g., [BMMS 9, AWS 9, ADLP 95]) an ometme alo ome torton technque [SB 94, LRP 95].. From Bar Chart to Pxel Bar Chart A common metho or vualzng large volume o ata to ue bar chart. Bar chart are wely ue an are very ntutve an eay to unertan. Fgure llutrate the ue o a regular bar chart to vualze cutomer trbuton n an e-commerce ale tranacton. The heght o the bar repreent the number o cutomer or erent prouct categore. Bar chart, however, requre a hgh egree o ata aggregaton an actually how only a rather mall number o ata value (only value are hown n Fgure ). Thereore, or ata exploraton o large multmenonal ata, they are o lmte value an are not able to how mportant normaton uch a: *Preently wth the Computer Scence Inttute, Unverty o Contance, Contance, Germany [email protected]; (mng_hao, mhu, ayal)@hpl.hp.com; [email protected]

2 - ata trbuton o multple attrbute - local pattern, correlaton, an tren - etale normaton, e.g., each cutomer prole. Bac Iea o Pxel Bar Chart Pxel bar chart are erve rom regular bar chart (ee Fgure a). The bac ea o a pxel bar chart to preent the ata value rectly ntea o aggregatng them nto a ew ata value. The approach to repreent each ata tem (e.g. a cutomer) by a ngle pxel n the bar chart. The etale normaton o one attrbute o each ata tem encoe nto the pxel color an can be accee an playe a neee. One mportant queton : how are the pxel arrange wthn each bar? Our ea to ue one or two attrbute to eparate the ata nto bar (vng attrbute) an then ue two atonal attrbute to mpoe an orerng wthn the bar (ee Fgure or the general ea). The pxel bar chart can thereore be een a a combnaton o the tratonal bar chart an the x-y agram. y - orerng attrbute x - orerng attrbute vng attrbute Fgure : A Pxel Bar Chart Now, we have a vualzaton n whch one pxel correpon to one cutomer. I the parttonng attrbute reunantly mappe to the color o the pxel, we obtan the regular bar chart hown n Fgure a (Fgure b how the equal-heght-bar-chart" whch we wll explan n the next ecton). Pxel bar chart, however, can be ue to preent large amount o etale normaton. The one-toone correponence between cutomer an pxel allow u to ue the color o the pxel to repreent atonal attrbute o the cutomer or example, ale amount, #o vt, or ale quantty. In Fgure 3a, a pxel bar chart ue to vualze thouan o e-commerce ale tranacton. Each pxel n the vualzaton repreent one cutomer. The number o cutomer can be a large a the creen ze (about.3 mllon). The pxel bar chart hown n Fgure 3a ue prouct type a the vng attrbute an number o vt an ollar amount a the x an y orerng attrbute. The color repreent the ollar amount pent by the correponng cutomer. Hgh ollar amount correpon to brght color, low ollar amount to ark color.. Space-Fllng Pxel Bar Chart One problem o tratonal bar chart that a large porton o the creen pace can not be ue ue to the erng heght o the bar. Wth very large ata et, we woul lke to ue more o the avalable creen pace to vualze the ata. One ea that ncreae the number o playable ata value to ue equal-heght ntea o equal-wth bar chart. In Fgure b, the regular bar chart o Fgure a hown a an equal-heght bar chart. The area (wth) o the bar correpon to the attrbute hown, namely the number o cutomer. I we now apply our pxel bar chart ea to the reultng bar chart, we obtan pace-llng pxel bar chart whch ue vrtually all pxel o the creen to play cutomer ata tem. In Fgure 3b, we how an example o a pace-llng pxel bar chart whch ue the ame vng, orerng, an colorng attrbute a the pxel bar chart n Fgure 3a. In th way, each cutomer repreente by one pxel. Note that pxel bar chart generalze the ea o regular bar chart. I the parttonng an colorng attrbute are entcal, both type o pxel bar chart become cale veron o ther regular bar chart counterpart. The pxel bar chart can thereore be een a a generalzaton o the regular bar chart but they contan gncantly more normaton an allow a etale analy o large orgnal ata et..3 Mult-Pxel Bar Chart In many cae, the ata to be analyze cont o multple attrbute. Wth pxel bar chart we can vualze attrbute value ung mult-pxel bar chart whch ue erent color mappng but the ame parttonng an orerng attrbute. Th mean that the arrangement o ata tem wthn the correponng bar o mult-pxel bar chart the ame,.e., the colore pxel correponng to the erent attrbute value o the ame ata tem have a unque poton n the bar. In Fgure 4, we how an example o three pxel bar chart wth prouct type a the vng attrbute an number o vt an ollar amount a the x an y orerng attrbute. The attrbute whch are mappe to color are ollar amount pent, number o vt, an ale quantty. Note that the pxel n correponng bar n multple bar chart are relate by ther poton,.e., the ame ata recor ha the ame relatve poton wthn each o the correponng bar. It thereore poble to relate the erent bar chart an etect correlaton. 3. Formal Denton o Pxel Bar Chart In th ecton we ormally ecrbe pxel bar chart an the problem that nee to be olve n orer to mplement an eectve pxel placement algorthm. 3. Denton o Pxel Bar Chart For a general enton o pxel bar chart, we nee to pecy the: - vng attrbute (or between-bar parttonng) - orerng attrbute (or wthn-bar orerng) - colorng attrbute (or pxel colorng). In tratonal bar chart there one vng attrbute whch partton the ata nto ont group correponng to the bar. In pace-llng bar chart, the bar correpon to a

3 y - orerng attrbute x - orerng attrbute a) Equal-Wth Bar Chart Fgure : Regular Bar Chart b) Equal-Heght Bar Chart a) Equal-Wth Pxel Bar Chart Fgure 3: Pxel Bar Chart b) Equal-Heght Pxel Bar Chart hgh $ A m o u n t low a) Color=ollar amount b) Color=number o vt c) Color=quantty Fgure 4: Mult-Pxel Bar Chart

4 parttonng o the creen accorng to the horzontal ax (x). 3 where D x, D y, O x, O y, C {A l,, A k, } an D x /D y are the vng attrbute n x-/y-recton, O x /O y are the orerng attrbute n x-/y-recton, an C the colorng attrbute. The mult-pxel bar chart o ale tranacton hown n Fgure 4, or example, are ene by the ve-tuple <prouct type,, no. o vt, ollar amount, C> where C correpon to erent attrbute,.e., number o vt, ollar amount, quantty. Fgure 5: Dvng attrbute on x-ax (e.g., Dx = ) We may generalze the enton o pace-llng pxel bar chart by allowng more than one vng attrbute,.e. one or the horzontal ax (D x ) an one or the vertcal ax (D y ). 3 Fgure 6: Dvng attrbute on x- an y-ax (e.g., Dx =, Dy= Regon) Next, we nee to pecy an attrbute or orerng the pxel n each pxel bar. Agan, we can o the orerng accorng to the x- an the y-ax,.e., along the horzontal (O x ) an vertcal (O y) axe ne each bar. 3 Fgure 7: Orerng attrbute on x- an y-ax (e.g., O x = Dollar Amount, O y=quantty) Fnally, we nee to pecy an attrbute or colorng the pxel. Note that n mult-bar chart we may agn erent attrbute to color n erent bar chart, whch enable the uer to relate the erent colorng attrbute an etect partal relatonhp among them. Note that the vng an orerng attrbute have to tay the ame n orer to o that. Let DB = {,, n } be the ata bae o n ata recor, each = K k, al Al, contng o k attrbute value { a,, a } where A l the attrbute name o value a l. Formally, a pxel bar chart ene by a ve tuple: <D x, D y, O x, O y, C > 3. Formalzaton o the Problem The bac ea o pxel bar chart to prouce ene pxel vualzaton whch are capable o howng large amount o ata on a value by value ba wthout aggregaton. The pecc requrement or pxel play are: - ene play,.e., bar are lle completely - non-overlappng,.e. no overlap o pxel n the play - localty,.e., mlar ata recor are place cloe to each other - orerng,.e., orerng o ata recor accorng to O x, O y. To ormalze thee requrement we rt have to ntrouce the creen potonng uncton : A K Ak Int Int, whch etermne the x-/y-creen poton o each ata recor,.e., ( ) = y) enote the poton o ata recor on the creen, an ( ). x enote the x- coornate an ( ). y the y-coornate. Wthout lo o generalty, we aume that O x = A an O y = A. The requrement can then be ormalze a:. Dene Dplay Contrant The ene play contrant requre that all pxel row (column) except the lat one are completely lle wth pxel. For equal-wth bar chart, the wth w o the bar xe. For a partton p contng o p pxel, we have to enure that p / w : í wth ( í ) = (, ) =.. w, =.. For equal-heght bar chart o heght h the correponng contrant p / h, =.. h : í wth ( í ) = (, ) =... No -Overlap Contrant The no-overlap contrant mean that a unque poton agne to each ata recor. Formally, we have to enure that two erent ata recor are place at erent poton,.e., DB : ( ) ( )., The element ue no attrbute pece.

5 3. Localty Contrant In ene pxel play the localty o pxel play an mportant role. Localty mean that mlar ata recor are place cloe to each other. The parttonng n pxel bar chart enure a bac mlarty o the ata recor wthn a ngle bar. In potonng the pxel wthn the bar, however, the localty property alo ha to be enure. For the ormalzaton, we nee a uncton m(, ) [0 ] whch etermne the mlarty o two ata recor an the nvere uncton o the pxel placement uncton -, whch etermne the ata recor or a gven (x,y)-poton on the creen. The localty contrant can then be expree a w h x= y= w h x= y= m( m( y), y), y + )) + ( x +, y)) mn Note that n general t not poble to place all mlar pxel cloe to each other whle repectng the ene play an no -overlap contrant. Th the reaon why the localty contrant ormalze a a global optmzaton problem. 4. Orerng Contrant The lat contrant whch cloely relate to the localty contrant the orerng contrant. The ea to enorce a one-menonal orerng n x- an y-recton accorng to the pece attrbute O x = A an O y =A. Formally, we have to enure,.. n : a > a ( ). x >,.. n : a > a ( ). y > ( ). x ( ). y Note that orerng the ata recor accorng to the attrbute an placng them n a row-by-row or column-by-column ahon may ealy ulll each one o the two contrant. Enurng both contrant at the ame tme may be mpoble n the general cae. We can ormalze the contrant a an optmzaton problem: w h ( (, ). (, ). + = x y a x y a x y= y). a y). a y + ). a w h ( (, ). (, ). + = x y a x y a x y= ( x +, y). a + ) + + ) mn Note that there may be a trae-o between the x- an the y- orerng contrant. In aton, the optma or the localty an the orerng contrant are n general not entcal. Th ue to the act that the mlarty uncton may nuce a erent optmzaton crteron than the x-/y-orerng contrant. For olvng the pxel placement problem, we thereore have to olve an optmzaton problem wth multple competng optmzaton goal. The problem a typcal complex optmzaton problem whch lkely to be NP-complete an can thereore only be olve ecently by a heurtc algorthm. 3.3 The Pxel Placement Algorthm For the generaton o pxel bar chart, we have to: () partton the ata et accorng to D x an D y ; () etermne the pxel color accorng to C ; an (3) place the pxel o each partton n the correponng regon accorng to O x, O y. The parttonng accorng to D x an D y an the color mappng are mple an traghtorwar to mplement, an thereore o not nee to be ecrbe n etal here. The pxel placement wthn one bar, however, a cult optmzaton problem becaue t requre a two -menonal ort. In the ollowng, we ecrbe our heurtc pxel placement algorthm whch prove an ecent oluton to the problem. The bac ea o the heurtc pxel placement algorthm to partton the ata et nto ubet accorng to O x an O y, an ue thoe ubet to place the bottom- an let-mot pxel. Th prove a goo tartng pont whch the ba or the teratve placement o the remanng pxel. The algorthm work a ollow:. For an ecent pxel placement wthn a ngle bar, we rt etermne the one-menonal htogram or O x an O y, whch are ue to etermne the α-quantle o O x an O y. I the bar uner coneraton ha extenon w x h pxel, we etermne the w, K, ( w) w- quantle or the parttonng o O x, an the h, K, ( h ) h - quantle or the parttonng o O y. The quantle are then ue to etermne the partton X,,X w o O x an Y,,Y h o O y. The partton X,,X w are orte accorng to O y an the partton Y,,Y h accorng to O x.. We can tart now to place the pxel n the lower-let corner,.e., poton (,), o the pxel bar: (,) = mn { } = mn. a { }. a X Y Next we place all pxel n the lower an let pxel row o the bar. Th one a mn (,) = {. a }.. w X = mn, ) =.. Y ( {. a } = h 3. The nal tep the teratve placement o all remanng pxel. Th one tartng rom the lower let to the upper rght. I pxel at poton (-, ) an (, -) are alreay place, the pxel at poton (, ) etermne a We ue a color map, whch map hgh ata value to brght color an low ata value to ark color.

6 (, ) = mn X Y {. a +. a } X Y Becaue we have place the ata n a ata tructure a ntrouce n tep, the pxel to be place at each poton can be etermne n O() tme X Y. I X Y =, we have to teratvely exten the partton X an Y an coner ( X X ) + Y. I th et tll empty, we have to coner ( X X + ) ( Y Y+ ) an o on, untl a ata pont to be place oun. Note that th proceure qute ecent ue to the ata tructure ue. 4. The Pxel Bar Chart Sytem To analyze large volume o tranacton ata wth multple attrbute, pxel bar chart have been ntegrate wth a ata mnng vualzaton ytem [HDHDB 99]. The ytem ue a web brower wth a Java actvator to allow real-tme nteractve vual ata mnng on the web. 4. Sytem Archtecture an Component The pxel bar chart ytem connect to a ata warehoue erver an ue the atabae to query or etale ata a neee. The ata to bul the pxel array kept n memory to upport real-tme manpulaton an correlaton. A llutrate n Fgure 8, the pxel bar chart ytem archtecture contan three bac component:. Pxel array orerng an groupng A pxel array contructe rom the pxel bar chart ve tuple peccaton. One pxel repreent one ata recor,.e., a cutomer. The parttonng algorthm agn each ata recor to the correponng bar accorng to the parttonng attrbute(). The pxel placement mplement a mple veron o the heurtc algorthm preente n ubecton Multple lnke pxel bar chart In mult-bar chart, the poton o the pxel belongng to the ame ata recor reman the ame acro multpxel bar chart or correlaton. The color o the pxel correpon to the value o the electe attrbute. 3. Interactve ata exploraton Th ytem prove multaneou browng an navgaton o multple attrbute. 4. Interactve Data Analy Interactvty an mportant apect o the pxel bar chart ytem. To make large volume o mult-attrbute ataet eay to explore an nterpret, the pxel bar chart ytem prove the ollowng nteracton capablte: () vual queryng; () layere rll-own; (3) multple lnke vualzaton; an (4) zoom. The attrbute ue or parttonng (Dx, Dy), orerng (Ox, Oy), an colorng (C) can be electe an change at executon tme. For entyng correlaton, a ubet o ata tem n a pxel bar chart can be electe to get the pxel correponng to relate attrbute value hghlghte wthn the ame play. A rll-own technque allow the vewng o all relate normaton ater electng a ngle ata tem. When mult-bar chart are preente, pxel ree at the ame locaton acro all the chart wth erent attrbute. In aton to coverng correlaton an pattern, the uer may elect a ngle ata tem to relate all t attrbute value. 5. Applcaton an Evaluaton The pxel bar chart technque ha been prototype n everal e-commerce applcaton at Hewlett Packar Laboratore. It ha been ue to vually mne large volume o ale tranacton an cutomer hoppng actvte at HP hoppng web te. 5. Cutomer Analy The pxel bar chart ytem ha been apple to cutomer buyng pattern an behavor. In Fgure 9, the pxel o the bar chart repreent cutomer makng tranacton on the web. In the reultng pxel bar chart, cutomer wth mlar purchang behavor (.e., prouct type, geographcal locaton, ollar amount, number o vt, an quantty) are place cloe to each other. A tore manager can ue the vualzaton to raply cover cutomer buyng pattern an ue thoe pattern to target marketng campagn. Fgure 9 how the our attrbute o 06,99 cutomer buyng recor. The our pxel bar chart o Fgure 9 are contructe a ollow: () Prouct type the vng attrbute D x; () Dollar amount the x-orerng attrbute O x, Regon y-orerng attrbute O y or 0 Unte State regon; an (3) Regon, ollar amount, number o vt an quantty are the our colorng attrbute C. The uer may oberve the ollowng act: a) Regon attrbute There are 0 erent color to repreent 0 erent regon (labele -0 n Fgure 9a) n the Unte State. The colore wave ncate the number o cutomer n each regon. Regon 9 (larget area) oun to have the larget number o cutomer. Regon 7 (mallet area) ha the leat number o cutomer acro all prouct type. b) Dollar amount attrbute Prouct type 5 ha the mot top ollar amount ale (blue & brown). Type 6 an 7 have a very mall varance acro all regon (ol blue/brown). c) Number o vt attrbute The blue color trbuton n prouct type 4 ncate that cutomer o th prouct type come back more oten than cutomer o other prouct type. ) Quantty attrbute The green color o prouct type 6 ncate that n th category all cutomer bought the ame number o tem acro all regon. It alo obvou that prouct type 4 cutomer have the larget quantte.

7 Clent Pxel Bar Chart Server A) Pxel Array B) Mult-pxel Bar Chart C) Interacton ortng lnkng explorng groupng colorng analyzng Fgure 8: Sytem Archtecture & Component hgh low a) Color: regon b) Color: ollar amount c) Color: no. o vt ) Color: quantty Fgure 9: Mult-Pxel Bar Chart or Mnng 06,99 Cutomer Buyng Tranacton (D x =, D y =, O x =ollar amount, O y =regon, C) hgh cutomer A $345,000 cutomer A 5 vt cutomer A 500 tem low a) Color: ollar amount b) Color: no. o vt c) Color: quantty Fgure 0: Mult-Pxel Bar Chart or Mnng 405,000 Sale Tranacton Recor (D x =, D y =, O x =no. o vt, O y = ollar amount, C)

8 By relatng the multple pxel bar chart o Fgure 9, the uer may oberve that the top ollar amount cutomer come back more requently an purchae larger quantte. 5. Sale Tranacton Analy One o the common queton electronc tore manager ak how to ue the cutomer purchae htory or mprovng prouct ale an promoton. Prouct manager want to unertan whch prouct have the top ale an who are ther top ollar amount cutomer. Whle regular bar chart prove aggregate normaton on the number o cutomer by prouct type (Fgure ), the correponng pxel bar chart nclue mportant atonal normaton uch a the ollar amount trbuton o the ale. Fgure 0 llutrate an example o a mult-pxel bar chart o 405,000 mult-attrbute web ale tranacton. The vng attrbute (D x) agan prouct type; the orerng attrbute are number o vt an ollar amount (O x an O y ). The color (C) n the erent bar chart repreent the attrbute ollar amount, number o vt, an quantty. The ollowng normaton can be obtane: a) Prouct type 7 an 0 have the top ollar amount cutomer (ark color o bar 7, 0 n Fgure 0a). b) The ollar amount pent an #o vt are clearly correlate,.e. or prouct type 4 (lnear ncreae o ark color at the top o bar 4 n Fgure 0b). c) Prouct type 4 an have the hghet quantte ol (ark color o bar 4 an n Fgure 0c). ) By clckng on a pecc pxel (A), we may n out that cutomer A vte 5 tme, bought 500 tem, an pent $345,000 on prouct type 5. It urther nteretng that there are cluter o arker color n bar 4 o Fgure 0c, whch mean that there are certan range o ollar amount ale or whch the quantty ten to be hgher than n other egment. Th obervaton unexpecte an may be ue to enty the cluter o ale tranacton an make ue o the normaton to urther ncreae the ale. Note that the normaton mentone above cannot be etecte by regular bar chart. 6. Concluon In th paper, we preente pxel bar chart, a new metho or vualz ng large amount o mult-attrbute ata. The approach a generalzaton o tratonal bar chart an x-y agram, whch avo the problem o long normaton by aggregaton or overplottng. Intea, pxel bar chart map each ata pont to one pxel o the play. For generatng the pxel bar chart vualzaton, we have to olve a complex optmzaton problem. The pxel placement algorthm an ecent an eectve oluton to the problem. We apply the pxel bar chart ea to real ata et rom an e-commerce applcaton an how that pxel bar chart prove gncantly more normaton than regular bar chart. Acknowlegement Thank to Sharon Beach o HP Laboratore or her encouragement an uggeton, Shu F. W. an Bran O. rom HP Shoppng or provng uggeton an ata. an Graham P. o Aglent Laboratore or h revew an comment. Reerence [ADLP 95] Anupam V., Dar S., Lebre T., Petaan E.: DataSpace: 3-D Vualzaton o Large Databae, Proc. Int. Symp. on Inormaton Vualzaton, Atlanta, GA, 995, pp [AKK 96] Anker M., Kem D. A., Kregel H.P.: Crcle Segment: A Technque or Vually Explorng Large Multmenonal Data Set, VISUALIZATION 96, HOT TOPIC SESSION, San Francco, CA, 996. [AWS 9] Ahlberg C., Wllamon C., Shneerman B.: Dynamc Quere or Inormaton Exploraton: An Implementaton an Evaluaton, Proc. ACM CHI Int. Con. on Human Factor n Computng, Monterey, CA, 99, pp [Be 90] Beow J.: Shape Cong o Multmenonal Data on a Mrcocomputer Dplay, Proc. Vualzaton 90, San Francco, CA, 990, pp [BEW 95] Becker R. A., Eck S. G., Wll G. J.: Vualzng Network Data, IEEE Tranacton on Vualzaton an Graphc, Vol., No., 995, pp [BMMS 9] Bua A., McDonal J. A., Mchalak J., Stuetzle W.: Interactve Data Vualzaton Ung Focung an Lnkng, Proc. Vualzaton 9, San Dego, CA, 99, pp [Ec 99] Stephen G. Eck: Vualzng Mult-menonal Data wth ADVISOR/000, Vualnght, 999. [EW 93] Eck S., Wll G. J.: Navgatng Large Network wth Herarche, Proc. Vualzaton 93, San Joe, CA, 993, pp [HDHDB 99] Hao M, Dayal Umeh.U, Hu M., D'eletto B., Becker J. A Java-bae Vual Mnng Inratructure an Applcaton, IEEE InoV99, San Francco, CA [ID 90] Inelberg A., Dmale B.: Parallel Coornate: A Tool or Vualzng Mult-Dmenonal Geometry, Proc. Vualzaton 90, San Francco, CA, 990, pp [In 85] Inelberg A.: The Plane wth Parallel Coornate, Specal Iue on Computatonal Geometry, The Vual Computer, Vol., 985, pp [KK 94] Kem D. A., Kregel H. P.: VDB: Databae Exploraton ung Multmenonal Vualzaton, Computer Graphc & Applcaton, Sept. 994, pp [KKA 95] Kem D. A., Kregel H. P., Ankert M.: Recurve Pattern: A Technque or Vualzng Very Large Amount o Data, Proc. Vualzaton 95, Atlanta, GA, 995, pp [LWW 90] LeBlanc J., War M. O., Wttel N.: Explorng N-Dmenonal Databae, Proc. Vualzaton 90, San Francco, CA, 990, pp [LRP 95] Lampng J., Rao R., Proll P.: A Focu + Context Technque Bae on Hyperbolc Geometry or Vualzng Large Herarche, Proc. ACM CHI Con. on Human Factor n Computng (CHI95), 995, pp [PG 88] Pckett R. M., Grnten G. G.: Iconographc Dplay or Vualzng Multmenonal Data, Proc. IEEE Con. on Sytem, Man an Cybernetc, IEEE Pre, Pcataway, NJ, 988, pp [RCM 9] Roberton G., Car S., Macknlay J.: Cone Tree: Anmate 3D Vualzaton o Herarchcal Inormaton, Proc. ACM CHI Int. Con. on Human Factor n Computng, 99, pp [SB 94] Sarkar M., Brown M.: Graphcal Fheye Vew, Communcaton o the ACM, Vol. 37, No., 994, pp [Shn 9] Shneerman B.: Tree Vualzaton wth Treemap: A D Space-Fllng Approach, ACM Tranacton on Graphc, Vol., No., 99, pp

Advances in Military Technology Vol. 10, No. 1, June 2015

Advances in Military Technology Vol. 10, No. 1, June 2015 AM Avance n Mltary echnology Vol., No., June 5 Mechancal an Computatonal Degn for Control of a -PUS Parallel Robot-bae Laer Cuttng Machne R. Zavala-Yoé *, R. Ramírez-Menoza an J. Ruz-García ecnológco e

More information

PERFORMANCE ANALYSIS OF PARALLEL ALGORITHMS

PERFORMANCE ANALYSIS OF PARALLEL ALGORITHMS Software Analye PERFORMANCE ANALYSIS OF PARALLEL ALGORIHMS Felcan ALECU PhD, Unverty Lecturer, Economc Informatc Deartment, Academy of Economc Stude, Bucharet, Romana E-mal: [email protected] Abtract:

More information

How To Understand Propect Theory And Mean Variance Analysis

How To Understand Propect Theory And Mean Variance Analysis Invetment Management and Fnancal Innovaton, Volume 6, Iue 1, 2009 Enrco De Gorg (Swtzerland ), Thorten Hen (Swtzerland) Propect theory and mean-varance analy: doe t make a dfference n wealth management?

More information

Mathematical Model for the Home Health Care Routing and Scheduling Problem with Multiple Treatments and Time Windows

Mathematical Model for the Home Health Care Routing and Scheduling Problem with Multiple Treatments and Time Windows Mathematcal Metho n Scence an Engneerng Mathematcal Moel for the Home Health Care Routng an Scheulng Problem wth Multple Treatment an Tme Wnow Anré Felpe Torre-Ramo, Egar Hernán Alfono-Lzarazo, Lorena

More information

An Efficient Recovery Algorithm for Coverage Hole in WSNs

An Efficient Recovery Algorithm for Coverage Hole in WSNs An Effcent Recover Algorthm for Coverage Hole n WSNs Song Ja 1,*, Wang Balng 1, Peng Xuan 1 School of Informaton an Electrcal Engneerng Harbn Insttute of Technolog at Weha, Shanong, Chna Automatc Test

More information

Cluster Analysis. Cluster Analysis

Cluster Analysis. Cluster Analysis Cluster Analyss Cluster Analyss What s Cluster Analyss? Types of Data n Cluster Analyss A Categorzaton of Maor Clusterng Methos Parttonng Methos Herarchcal Methos Densty-Base Methos Gr-Base Methos Moel-Base

More information

A Spam Message Filtering Method: focus on run time

A Spam Message Filtering Method: focus on run time , pp.29-33 http://dx.doi.org/10.14257/atl.2014.76.08 A Spam Meage Filtering Method: focu on run time Sin-Eon Kim 1, Jung-Tae Jo 2, Sang-Hyun Choi 3 1 Department of Information Security Management 2 Department

More information

ITS-90 FORMULATIONS FOR VAPOR PRESSURE, FROSTPOINT TEMPERATURE, DEWPOINT TEMPERATURE, AND ENHANCEMENT FACTORS IN THE RANGE 100 TO +100 C.

ITS-90 FORMULATIONS FOR VAPOR PRESSURE, FROSTPOINT TEMPERATURE, DEWPOINT TEMPERATURE, AND ENHANCEMENT FACTORS IN THE RANGE 100 TO +100 C. ITS-90 FORMULATIONS FOR VAPOR PRESSURE, FROSTPOINT TEMPERATURE, DEWPOINT TEMPERATURE, AND ENHANCEMENT FACTORS IN THE RANGE 100 TO +100 C Bob Hardy Thunder Scentfc Corporaton, Albuquerque, NM, USA Abtract:

More information

EXAMPLE PROBLEMS SOLVED USING THE SHARP EL-733A CALCULATOR

EXAMPLE PROBLEMS SOLVED USING THE SHARP EL-733A CALCULATOR EXAMPLE PROBLEMS SOLVED USING THE SHARP EL-733A CALCULATOR 8S CHAPTER 8 EXAMPLES EXAMPLE 8.4A THE INVESTMENT NEEDED TO REACH A PARTICULAR FUTURE VALUE What amount must you nvest now at 4% compoune monthly

More information

DEGREES OF EQUIVALENCE IN A KEY COMPARISON 1 Thang H. L., Nguyen D. D. Vietnam Metrology Institute, Address: 8 Hoang Quoc Viet, Hanoi, Vietnam

DEGREES OF EQUIVALENCE IN A KEY COMPARISON 1 Thang H. L., Nguyen D. D. Vietnam Metrology Institute, Address: 8 Hoang Quoc Viet, Hanoi, Vietnam DEGREES OF EQUIVALECE I A EY COMPARISO Thang H. L., guyen D. D. Vetnam Metrology Insttute, Aress: 8 Hoang Quoc Vet, Hano, Vetnam Abstract: In an nterlaboratory key comparson, a ata analyss proceure for

More information

ARTICLE IN PRESS. JID:COMAID AID:1153 /FLA [m3g; v 1.79; Prn:21/02/2009; 14:10] P.1 (1-13) Computer Aided Geometric Design ( )

ARTICLE IN PRESS. JID:COMAID AID:1153 /FLA [m3g; v 1.79; Prn:21/02/2009; 14:10] P.1 (1-13) Computer Aided Geometric Design ( ) COMAID:5 JID:COMAID AID:5 /FLA [mg; v 79; Prn:/0/009; 4:0] P -) Computer Aded Geometrc Degn ) Content lt avalable at ScenceDrect Computer Aded Geometrc Degn wwwelevercom/locate/cagd Fat approach for computng

More information

A Novel Architecture Design of Large-Scale Distributed Object Storage System

A Novel Architecture Design of Large-Scale Distributed Object Storage System Internatonal Journal of Grd Dtrbuton Computng Vol.8, No.1 (2015), pp.25-32 http://dx.do.org/10.14257/gdc.2015.8.1.03 A Novel Archtecture Degn of Large-Scale Dtrbuted Obect Storage Sytem Shan Yng 1 and

More information

Impact of the design method of permanent magnets synchronous generators for small direct drive wind turbines for battery operation

Impact of the design method of permanent magnets synchronous generators for small direct drive wind turbines for battery operation Impact o the degn method o permanent magnet ynchronou generator or mall drect drve wnd turbne or battery operaton Mha PREDESCU, Dr. eng.*, Aurelan CRǍCIUNESCU, Pro. Dr. **, Andre BEJINARIU, Eng.*, Octavan

More information

An Integrated Resource Management and Scheduling System for Grid Data Streaming Applications

An Integrated Resource Management and Scheduling System for Grid Data Streaming Applications An Integrated eource Management and Schedulng Sytem for Grd Data Streamng Applcaton Wen Zhang, Junwe Cao 2,3*, Yheng Zhong,3, Lanchen Lu,3, and Cheng Wu,3 Department of Automaton, Tnghua Unverty, Bejng

More information

Apigee Edge: Apigee Cloud vs. Private Cloud. Evaluating deployment models for API management

Apigee Edge: Apigee Cloud vs. Private Cloud. Evaluating deployment models for API management Apigee Edge: Apigee Cloud v. Private Cloud Evaluating deployment model for API management Table of Content Introduction 1 Time to ucce 2 Total cot of ownerhip 2 Performance 3 Security 4 Data privacy 4

More information

CASE STUDY ALLOCATE SOFTWARE

CASE STUDY ALLOCATE SOFTWARE CASE STUDY ALLOCATE SOFTWARE allocate caetud y TABLE OF CONTENTS #1 ABOUT THE CLIENT #2 OUR ROLE #3 EFFECTS OF OUR COOPERATION #4 BUSINESS PROBLEM THAT WE SOLVED #5 CHALLENGES #6 WORKING IN SCRUM #7 WHAT

More information

Vision Mouse. Saurabh Sarkar a* University of Cincinnati, Cincinnati, USA ABSTRACT 1. INTRODUCTION

Vision Mouse. Saurabh Sarkar a* University of Cincinnati, Cincinnati, USA ABSTRACT 1. INTRODUCTION Vson Mouse Saurabh Sarkar a* a Unversty of Cncnnat, Cncnnat, USA ABSTRACT The report dscusses a vson based approach towards trackng of eyes and fngers. The report descrbes the process of locatng the possble

More information

CASE STUDY BRIDGE. www.future-processing.com

CASE STUDY BRIDGE. www.future-processing.com CASE STUDY BRIDGE TABLE OF CONTENTS #1 ABOUT THE CLIENT 3 #2 ABOUT THE PROJECT 4 #3 OUR ROLE 5 #4 RESULT OF OUR COLLABORATION 6-7 #5 THE BUSINESS PROBLEM THAT WE SOLVED 8 #6 CHALLENGES 9 #7 VISUAL IDENTIFICATION

More information

Descriptive Models. Cluster Analysis. Example. General Applications of Clustering. Examples of Clustering Applications

Descriptive Models. Cluster Analysis. Example. General Applications of Clustering. Examples of Clustering Applications CMSC828G Prncples of Data Mnng Lecture #9 Today s Readng: HMS, chapter 9 Today s Lecture: Descrptve Modelng Clusterng Algorthms Descrptve Models model presents the man features of the data, a global summary

More information

Lecture #21. MOS Capacitor Structure

Lecture #21. MOS Capacitor Structure Lecture #21 OUTLINE The MOS apactor Electrotatc Readng: oure Reader EE130 Lecture 21, Slde 1 MOS apactor Structure MOS capactor (croectonal vew _ TE x EE130 Lecture 21, Slde 2 Typcal MOS capactor and trantor

More information

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna [email protected] Abstract.

More information

ESSAYS IN RENEWABLE ENERGY AND EMISSIONS TRADING

ESSAYS IN RENEWABLE ENERGY AND EMISSIONS TRADING ESSAYS IN RENEWABLE ENERGY AND EMISSIONS TRADING By JOSHUA D. KNEIFEL A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE

More information

The issue of whether the Internet will permanently destroy the news media is currently a

The issue of whether the Internet will permanently destroy the news media is currently a Wll the Internet etroy the New Meda? or Can Onlne Advertng Market Save the Meda? by Suan Athey, Emlo Calvano and Johua S. Gan * Frt raft: October, 009 Th Veron: November, 00 PRELIMINARY PLEASE O NOT QUOTE

More information

Development and use of prediction models in Building Acoustics as in EN 12354. 1 Introduction. 2 EN 12354, part 1 & 2. 2.2 Lightweight single elements

Development and use of prediction models in Building Acoustics as in EN 12354. 1 Introduction. 2 EN 12354, part 1 & 2. 2.2 Lightweight single elements evelopment and ue of predcton model n Buldng Acoutc a n EN 1354 Eddy TNO Scence and Indutry, P.O. Box 155, N-600 A elft, The Netherland, [email protected] Improvng the acoutc clmate n buldng an mportant

More information

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..

More information

Optical Illusion. Sara Bolouki, Roger Grosse, Honglak Lee, Andrew Ng

Optical Illusion. Sara Bolouki, Roger Grosse, Honglak Lee, Andrew Ng Optical Illuion Sara Bolouki, Roger Groe, Honglak Lee, Andrew Ng. Introduction The goal of thi proect i to explain ome of the illuory phenomena uing pare coding and whitening model. Intead of the pare

More information

Atkinson-Stiglitz and Ramsey reconciled: Pareto e cient taxation and pricing under a break-even constraint

Atkinson-Stiglitz and Ramsey reconciled: Pareto e cient taxation and pricing under a break-even constraint Abtract The Ramey tax problem examne the degn o lnear commodty taxe to collect a gven tax revenue Th approach ha been erouly challenged by Atknon and Stgltz (976) who how that (under ome condton) an optmal

More information

A Practical Study of Regenerating Codes for Peer-to-Peer Backup Systems

A Practical Study of Regenerating Codes for Peer-to-Peer Backup Systems A Practcal Stuy of Regeneratng Coes for Peer-to-Peer Backup Systems Alessanro Dumnuco an Ernst Bersack EURECOM Sopha Antpols, France {umnuco,bersack}@eurecom.fr Abstract In strbute storage systems, erasure

More information

Project Management Basics

Project Management Basics Project Management Baic A Guide to undertanding the baic component of effective project management and the key to ucce 1 Content 1.0 Who hould read thi Guide... 3 1.1 Overview... 3 1.2 Project Management

More information

APPLICATION OF BINARY DIVISION ALGORITHM FOR IMAGE ANALYSIS AND CHANGE DETECTION TO IDENTIFY THE HOTSPOTS IN MODIS IMAGES

APPLICATION OF BINARY DIVISION ALGORITHM FOR IMAGE ANALYSIS AND CHANGE DETECTION TO IDENTIFY THE HOTSPOTS IN MODIS IMAGES APPLICATION OF BINARY DIVISION ALGORITHM FOR IMAGE ANALYSIS AND CHANGE DETECTION TO IDENTIFY THE HOTSPOTS IN MODIS IMAGES Harsh Kumar G R * an Dharmenra Sngh ([email protected], [email protected]) Department

More information

CISCO SPA500G SERIES REFERENCE GUIDE

CISCO SPA500G SERIES REFERENCE GUIDE CISCO SPA500G SERIES REFERENCE GUIDE Part of the Csco Small Busness Pro Seres, the SIP based Csco SPA504G 4-Lne IP phone wth 2-port swtch has been tested to ensure comprehensve nteroperablty wth equpment

More information

DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENT-MATCHING INTRUSION DETECTION SYSTEMS. G. Chapman J. Cleese E. Idle

DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENT-MATCHING INTRUSION DETECTION SYSTEMS. G. Chapman J. Cleese E. Idle DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENT-MATCHING INTRUSION DETECTION SYSTEMS G. Chapman J. Cleee E. Idle ABSTRACT Content matching i a neceary component of any ignature-baed network Intruion Detection

More information

Two Dimensional FEM Simulation of Ultrasonic Wave Propagation in Isotropic Solid Media using COMSOL

Two Dimensional FEM Simulation of Ultrasonic Wave Propagation in Isotropic Solid Media using COMSOL Excerpt from the Proceeding of the COMSO Conference 0 India Two Dimenional FEM Simulation of Ultraonic Wave Propagation in Iotropic Solid Media uing COMSO Bikah Ghoe *, Krihnan Balaubramaniam *, C V Krihnamurthy

More information

DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENT-MATCHING INTRUSION DETECTION SYSTEMS

DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENT-MATCHING INTRUSION DETECTION SYSTEMS DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENT-MATCHING INTRUSION DETECTION SYSTEMS Chritopher V. Kopek Department of Computer Science Wake Foret Univerity Winton-Salem, NC, 2709 Email: [email protected]

More information

BUILT-IN DUAL FREQUENCY ANTENNA WITH AN EMBEDDED CAMERA AND A VERTICAL GROUND PLANE

BUILT-IN DUAL FREQUENCY ANTENNA WITH AN EMBEDDED CAMERA AND A VERTICAL GROUND PLANE Progre In Electromagnetic Reearch Letter, Vol. 3, 51, 08 BUILT-IN DUAL FREQUENCY ANTENNA WITH AN EMBEDDED CAMERA AND A VERTICAL GROUND PLANE S. H. Zainud-Deen Faculty of Electronic Engineering Menoufia

More information

On Secure Network Coding with Unequal Link Capacities and Restricted Wiretapping Sets

On Secure Network Coding with Unequal Link Capacities and Restricted Wiretapping Sets On Secure Network Coing with Unequal Link Capacitie an Retricte Wiretapping Set Tao Cui an Tracey Ho Department of Electrical Engineering California Intitute of Technology Paaena, CA 9115, USA Email: {taocui,

More information

The Impact of the Internet on Advertising Markets for News Media

The Impact of the Internet on Advertising Markets for News Media The Impact of the Internet on Advertng Market for New Meda by Suan Athey, Emlo Calvano and Johua S. Gan * Frt Draft: October, 009 Th Veron: October 0 In th paper, we explore the hypothe that an mportant

More information

DoSAM Domain-Specific Software Architecture Comparison Model *

DoSAM Domain-Specific Software Architecture Comparison Model * DoSAM Domain-Specific Software Architecture Comparion Moel * Klau Bergner 1, Anrea Rauch 2, Marc Sihling 1, Thoma Ternité 2 1 4Soft GmbH Mitterertraße 3 D-80336 Munich, Germany {bergner ihling}@4oft.e

More information

Trust Network and Trust Community Clustering based on Shortest Path Analysis for E-commerce

Trust Network and Trust Community Clustering based on Shortest Path Analysis for E-commerce Internatonal Journal of u- an e- Serce, Scence an Technology Trust Network an Trust Communty Clusterng base on Shortest Path Analyss for E-commerce Shaozhong Zhang 1, Jungan Chen 1, Haong Zhong 2, Zhaox

More information

How To Understand The Results Of The German Meris Cloud And Water Vapour Product

How To Understand The Results Of The German Meris Cloud And Water Vapour Product Ttel: Project: Doc. No.: MERIS level 3 cloud and water vapour products MAPP MAPP-ATBD-ClWVL3 Issue: 1 Revson: 0 Date: 9.12.1998 Functon Name Organsaton Sgnature Date Author: Bennartz FUB Preusker FUB Schüller

More information

Cluster-Aware Cache for Network Attached Storage *

Cluster-Aware Cache for Network Attached Storage * Cluter-Aware Cache for Network Attached Storage * Bin Cai, Changheng Xie, and Qiang Cao National Storage Sytem Laboratory, Department of Computer Science, Huazhong Univerity of Science and Technology,

More information

Medium and long term. Equilibrium models approach

Medium and long term. Equilibrium models approach Medum and long term electrcty prces forecastng Equlbrum models approach J. Vllar, A. Campos, C. íaz, Insttuto de Investgacón Tecnológca, Escuela Técnca Superor de Ingenería-ICAI Unversdad ontfca Comllas

More information

Report 4668-1b 30.10.2010. Measurement report. Sylomer - field test

Report 4668-1b 30.10.2010. Measurement report. Sylomer - field test Report 4668-1b Meaurement report Sylomer - field tet Report 4668-1b 2(16) Contet 1 Introduction... 3 1.1 Cutomer... 3 1.2 The ite and purpoe of the meaurement... 3 2 Meaurement... 6 2.1 Attenuation of

More information

How Enterprises Can Build Integrated Digital Marketing Experiences Using Drupal

How Enterprises Can Build Integrated Digital Marketing Experiences Using Drupal How Enterprie Can Build Integrated Digital Marketing Experience Uing Drupal acquia.com 888.922.7842 1.781.238.8600 25 Corporate Drive, Burlington, MA 01803 How Enterprie Can Build Integrated Digital Marketing

More information

reduce competition increase market power cost savings economies of scale and scope cost savings Oliver Williamson: the efficiency defense

reduce competition increase market power cost savings economies of scale and scope cost savings Oliver Williamson: the efficiency defense Mergers Why merge? reduce competton ncrease market power cost savngs economes of scale and scope Why allow mergers? cost savngs Olver Wllamson: the effcency defense Merger wthout cost savngs Before merger:

More information

How To Model A Multi-Home

How To Model A Multi-Home The Impact of the Internet on Advertng Market for New Meda by Suan Athey, Emlo Calvano and Johua S. Gan * Frt raft: October, 009 Th Veron: Aprl 03 In th paper, we explore the hypothe that an mportant force

More information

Enhancing the Visual Clustering of Query-dependent Database Visualization Techniques using Screen-Filling Curves

Enhancing the Visual Clustering of Query-dependent Database Visualization Techniques using Screen-Filling Curves Enhancing the Visual Clustering of Query-dependent Database Visualization Techniques using Screen-Filling Curves Extended Abstract Daniel A. Keim Institute for Computer Science, University of Munich Leopoldstr.

More information

Institut für Informatik der Technischen Universität München. MISTRAL: Processing Relational Queries using a Multidimensional Access Technique

Institut für Informatik der Technischen Universität München. MISTRAL: Processing Relational Queries using a Multidimensional Access Technique Insttut für Informatk er Technschen Unverstät München MISTRAL: Processng Relatonal Queres usng a Multmensonal Access Technque Volker Markl Preface Classcal one mensonal B-trees have been the stanar access

More information

THE ANALYSIS AND OPTIMIZATION OF SURVIVABILITY OF MPLS NETWORKS. Mohammadreza Mossavari, Yurii Zaychenko

THE ANALYSIS AND OPTIMIZATION OF SURVIVABILITY OF MPLS NETWORKS. Mohammadreza Mossavari, Yurii Zaychenko Internatonal Journal "Informaton Theore & Applcaton" Vol5 / 28 253 TE ANALYSIS AND OTIMIATION OF SURVIVABILITY OF MLS NETWORS Mohammadreza Moavar, Yur aychenko Abtract: The problem of MLS network urvvablty

More information

RSA Cryptography using Designed Processor and MicroBlaze Soft Processor in FPGAs

RSA Cryptography using Designed Processor and MicroBlaze Soft Processor in FPGAs RSA Cryptography usng Desgne Processor an McroBlaze Soft Processor n FPGAs M. Nazrul Islam Monal Dept. of CSE, Rajshah Unversty of Engneerng an Technology, Rajshah-6204, Banglaesh M. Al Mamun Dept. of

More information

Present Values and Accumulations

Present Values and Accumulations Present Values an Accumulatons ANGUS S. MACDONALD Volume 3, pp. 1331 1336 In Encyclopea Of Actuaral Scence (ISBN -47-84676-3) Ete by Jozef L. Teugels an Bjørn Sunt John Wley & Sons, Lt, Chchester, 24 Present

More information

Coordinate System for 3-D Model Used in Robotic End-Effector

Coordinate System for 3-D Model Used in Robotic End-Effector AU JT 8(: 8 (Apr Codnate Sytem f D Model Ued n Robot EndEffer ulfqar Al Soomro Shool of Advaned Stude, Aan Inttute of Tehnology Pathum Than, Thaland Abtrat Th paper reve the onept of odnate ytem on new

More information

CRIMINAL MAPPING BASED ON FORENSIC EVIDENCES USING GENERALIZED GAUSSIAN MIXTURE MODEL

CRIMINAL MAPPING BASED ON FORENSIC EVIDENCES USING GENERALIZED GAUSSIAN MIXTURE MODEL Voume No. 4 June 202 ISSN 2278-080 The Internatona Journa of Computer Scence & Appcaton TIJCSA RESEARCH AER Avaabe Onne at http://www.journaofcomputercence.com/ CRIMINAL MAING BASED ON FORENSIC EVIDENCES

More information

BERNSTEIN POLYNOMIALS

BERNSTEIN POLYNOMIALS On-Lne Geometrc Modelng Notes BERNSTEIN POLYNOMIALS Kenneth I. Joy Vsualzaton and Graphcs Research Group Department of Computer Scence Unversty of Calforna, Davs Overvew Polynomals are ncredbly useful

More information

SCM- integration: organiational, managerial and technological iue M. Caridi 1 and A. Sianei 2 Dipartimento di Economia e Produzione, Politecnico di Milano, Italy E-mail: [email protected] Itituto

More information

A Dynamic Load Balancing for Massive Multiplayer Online Game Server

A Dynamic Load Balancing for Massive Multiplayer Online Game Server A Dynamc Load Balancng for Massve Multplayer Onlne Game Server Jungyoul Lm, Jaeyong Chung, Jnryong Km and Kwanghyun Shm Dgtal Content Research Dvson Electroncs and Telecommuncatons Research Insttute Daejeon,

More information

Warehouse Security System based on Embedded System

Warehouse Security System based on Embedded System International Conference on Logitic Engineering, Management and Computer Science (LEMCS 2015) Warehoue Security Sytem baed on Embedded Sytem Gen Li Department of Electronic Engineering, Tianjin Univerity

More information

SELF-MANAGING PERFORMANCE IN APPLICATION SERVERS MODELLING AND DATA ARCHITECTURE

SELF-MANAGING PERFORMANCE IN APPLICATION SERVERS MODELLING AND DATA ARCHITECTURE SELF-MANAGING PERFORMANCE IN APPLICATION SERVERS MODELLING AND DATA ARCHITECTURE RAVI KUMAR G 1, C.MUTHUSAMY 2 & A.VINAYA BABU 3 1 HP Bangalore, Reearch Scholar JNTUH, Hyderabad, India, 2 Yahoo, Bangalore,

More information

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy 4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.

More information

Visual Mining of E-Customer Behavior Using Pixel Bar Charts

Visual Mining of E-Customer Behavior Using Pixel Bar Charts Visual Mining of E-Customer Behavior Using Pixel Bar Charts Ming C. Hao, Julian Ladisch*, Umeshwar Dayal, Meichun Hsu, Adrian Krug Hewlett Packard Research Laboratories, Palo Alto, CA. (ming_hao, dayal)@hpl.hp.com;

More information

The Greedy Method. Introduction. 0/1 Knapsack Problem

The Greedy Method. Introduction. 0/1 Knapsack Problem The Greedy Method Introducton We have completed data structures. We now are gong to look at algorthm desgn methods. Often we are lookng at optmzaton problems whose performance s exponental. For an optmzaton

More information

L10: Linear discriminants analysis

L10: Linear discriminants analysis L0: Lnear dscrmnants analyss Lnear dscrmnant analyss, two classes Lnear dscrmnant analyss, C classes LDA vs. PCA Lmtatons of LDA Varants of LDA Other dmensonalty reducton methods CSCE 666 Pattern Analyss

More information

Conversion between the vector and raster data structures using Fuzzy Geographical Entities

Conversion between the vector and raster data structures using Fuzzy Geographical Entities Converson between the vector and raster data structures usng Fuzzy Geographcal Enttes Cdála Fonte Department of Mathematcs Faculty of Scences and Technology Unversty of Combra, Apartado 38, 3 454 Combra,

More information

Queueing systems with scheduled arrivals, i.e., appointment systems, are typical for frontal service systems,

Queueing systems with scheduled arrivals, i.e., appointment systems, are typical for frontal service systems, MANAGEMENT SCIENCE Vol. 54, No. 3, March 28, pp. 565 572 in 25-199 ein 1526-551 8 543 565 inform doi 1.1287/mnc.17.82 28 INFORMS Scheduling Arrival to Queue: A Single-Server Model with No-Show INFORMS

More information

A hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm

A hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(7):1884-1889 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 A hybrd global optmzaton algorthm based on parallel

More information

Gaining Insights to the Tea Industry of Sri Lanka using Data Mining

Gaining Insights to the Tea Industry of Sri Lanka using Data Mining Proceedngs of the Internatonal MultConference of Engneers and Computer Scentsts 2008 Vol I Ganng Insghts to the Tea Industry of Sr Lanka usng Data Mnng H.C. Fernando, W. M. R Tssera, and R. I. Athauda

More information

CHARACTERISTICS OF WAITING LINE MODELS THE INDICATORS OF THE CUSTOMER FLOW MANAGEMENT SYSTEMS EFFICIENCY

CHARACTERISTICS OF WAITING LINE MODELS THE INDICATORS OF THE CUSTOMER FLOW MANAGEMENT SYSTEMS EFFICIENCY Annale Univeritati Apuleni Serie Oeconomica, 2(2), 200 CHARACTERISTICS OF WAITING LINE MODELS THE INDICATORS OF THE CUSTOMER FLOW MANAGEMENT SYSTEMS EFFICIENCY Sidonia Otilia Cernea Mihaela Jaradat 2 Mohammad

More information

Bio-Plex Analysis Software

Bio-Plex Analysis Software Multiplex Supenion Array Bio-Plex Analyi Software The Leader in Multiplex Immunoaay Analyi Bio-Plex Analyi Software If making ene of your multiplex data i your challenge, then Bio-Plex data analyi oftware

More information

Document Clustering Analysis Based on Hybrid PSO+K-means Algorithm

Document Clustering Analysis Based on Hybrid PSO+K-means Algorithm Document Clusterng Analyss Based on Hybrd PSO+K-means Algorthm Xaohu Cu, Thomas E. Potok Appled Software Engneerng Research Group, Computatonal Scences and Engneerng Dvson, Oak Rdge Natonal Laboratory,

More information

FORMAL ANALYSIS FOR REAL-TIME SCHEDULING

FORMAL ANALYSIS FOR REAL-TIME SCHEDULING FORMAL ANALYSIS FOR REAL-TIME SCHEDULING Bruno Dutertre and Vctora Stavrdou, SRI Internatonal, Menlo Park, CA Introducton In modern avoncs archtectures, applcaton software ncreasngly reles on servces provded

More information

Basic Principle of Buck-Boost

Basic Principle of Buck-Boost Bac Prncple of Buck-Boot he buck-boot a popular non-olated nvertng power tage topology, ometme called a tep-up/down power tage. Power upply degner chooe the buck-boot power tage becaue the requred output

More information

A Binary Particle Swarm Optimization Algorithm for Lot Sizing Problem

A Binary Particle Swarm Optimization Algorithm for Lot Sizing Problem Journal o Economc and Socal Research 5 (2), -2 A Bnary Partcle Swarm Optmzaton Algorthm or Lot Szng Problem M. Fath Taşgetren & Yun-Cha Lang Abstract. Ths paper presents a bnary partcle swarm optmzaton

More information

Mining Multiple Large Data Sources

Mining Multiple Large Data Sources The Internatonal Arab Journal of Informaton Technology, Vol. 7, No. 3, July 2 24 Mnng Multple Large Data Sources Anmesh Adhkar, Pralhad Ramachandrarao 2, Bhanu Prasad 3, and Jhml Adhkar 4 Department of

More information

Distributed Multi-Target Tracking In A Self-Configuring Camera Network

Distributed Multi-Target Tracking In A Self-Configuring Camera Network Dstrbuted Mult-Target Trackng In A Self-Confgurng Camera Network Crstan Soto, B Song, Amt K. Roy-Chowdhury Department of Electrcal Engneerng Unversty of Calforna, Rversde {cwlder,bsong,amtrc}@ee.ucr.edu

More information

Simple Interest Loans (Section 5.1) :

Simple Interest Loans (Section 5.1) : Chapter 5 Fnance The frst part of ths revew wll explan the dfferent nterest and nvestment equatons you learned n secton 5.1 through 5.4 of your textbook and go through several examples. The second part

More information

Small-Signal Analysis of BJT Differential Pairs

Small-Signal Analysis of BJT Differential Pairs 5/11/011 Dfferental Moe Sall Sgnal Analyss of BJT Dff Par 1/1 SallSgnal Analyss of BJT Dfferental Pars Now lets conser the case where each nput of the fferental par conssts of an entcal D bas ter B, an

More information

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy Fnancal Tme Seres Analyss Patrck McSharry [email protected] www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton

More information

Multifunction Phased Array Radar Resource Management: Real-Time Scheduling Algorithm

Multifunction Phased Array Radar Resource Management: Real-Time Scheduling Algorithm Journal of Computatonal Informaton Sytem 7:2 (211) 385-393 Avalable at http://www.jofc.com Multfuncton Phaed Array Radar Reource Management: Real-me Schedulng Algorm Janbn LU 1,, Hu XIAO 2, Zemn XI 1,

More information

NON-CONSTANT SUM RED-AND-BLACK GAMES WITH BET-DEPENDENT WIN PROBABILITY FUNCTION LAURA PONTIGGIA, University of the Sciences in Philadelphia

NON-CONSTANT SUM RED-AND-BLACK GAMES WITH BET-DEPENDENT WIN PROBABILITY FUNCTION LAURA PONTIGGIA, University of the Sciences in Philadelphia To appear n Journal o Appled Probablty June 2007 O-COSTAT SUM RED-AD-BLACK GAMES WITH BET-DEPEDET WI PROBABILITY FUCTIO LAURA POTIGGIA, Unversty o the Scences n Phladelpha Abstract In ths paper we nvestgate

More information

v = x t = x 2 x 1 t 2 t 1 The average speed of the particle is absolute value of the average velocity and is given Distance travelled t

v = x t = x 2 x 1 t 2 t 1 The average speed of the particle is absolute value of the average velocity and is given Distance travelled t Chapter 2 Motion in One Dimenion 2.1 The Important Stuff 2.1.1 Poition, Time and Diplacement We begin our tudy of motion by conidering object which are very mall in comparion to the ize of their movement

More information

320 The Internatonal Arab Journal of Informaton Technology, Vol. 5, No. 3, July 2008 Comparsons Between Data Clusterng Algorthms Osama Abu Abbas Computer Scence Department, Yarmouk Unversty, Jordan Abstract:

More information

Combining Vehicle Routing with Forwarding

Combining Vehicle Routing with Forwarding Combnng Vehcle Routng wth Fowang Extenon of the Vehcle Routng Poblem by Dffeent Type of Sub-contacton Hebet Kopfe Xn Wang Cha of Logtc, Unvety of Bemen, WHS 5, D-28359 Bemen, Gemany Coeponng autho: Pof.

More information

Measuring adverse selection in managed health care

Measuring adverse selection in managed health care Ž. Journal of Health Economc 19 2000 829 854 www.elever.nlrlocatereconbae Meaurng advere electon n managed health care Rchard G. Frank a,), Jacob Glazer b, Thoma G. McGure c a HarÕard UnÕerty, HarÕard

More information

A technical guide to 2014 key stage 2 to key stage 4 value added measures

A technical guide to 2014 key stage 2 to key stage 4 value added measures A technical guide to 2014 key tage 2 to key tage 4 value added meaure CONTENTS Introduction: PAGE NO. What i value added? 2 Change to value added methodology in 2014 4 Interpretation: Interpreting chool

More information

A Simple Approach to Clustering in Excel

A Simple Approach to Clustering in Excel A Smple Approach to Clusterng n Excel Aravnd H Center for Computatonal Engneerng and Networng Amrta Vshwa Vdyapeetham, Combatore, Inda C Rajgopal Center for Computatonal Engneerng and Networng Amrta Vshwa

More information

= i δ δ s n and PV = a n = 1 v n = 1 e nδ

= i δ δ s n and PV = a n = 1 v n = 1 e nδ Exam 2 s Th March 19 You are allowe 7 sheets of notes an a calculator 41) An mportant fact about smple nterest s that for smple nterest A(t) = K[1+t], the amount of nterest earne each year s constant =

More information

The Design of Efficiently-Encodable Rate-Compatible LDPC Codes

The Design of Efficiently-Encodable Rate-Compatible LDPC Codes The Desgn of Effcently-Encoable Rate-Compatble LDPC Coes Jaehong Km, Atya Ramamoorthy, Member, IEEE, an Steven W. McLaughln, Fellow, IEEE Abstract We present a new class of rregular low-ensty party-check

More information