pfilter: Global Information Filtering and Dissemination Using Structured Overlay Networks

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1 pfilte: Globl Infomtion Filteing nd issemintion Using Stutued Ovely Netwoks hunqing Tng ompute Siene eptment Univesity of oheste oheste, NY, Zhihen Xu H Lbotoies 5 ge Mill d., MLS 77 lo lto, 9434 zhihen@hpl.hp.om bstt The exponentil dt gowth te of the Intenet mkes it inesingly diffiult fo people to find desied infomtion in timely fshion. Infomtion filteing nd dissemintion systems llow uses to egiste pesistent queies lled use pofiles, nd notify uses when elevnt files beome vilble. xisting suh systems, howeve, eithe e not slble, o do not suppot mthing of unstutued douments (e.g., text, HTML, imge, udio o video files) tht ount fo signifint peentge of Intenet ontents. We popose pfilte, globl-sle, deentlized infomtion filteing nd dissemintion system fo unstutued douments. To hndle potentilly billions of douments fo millions of subsibes, pfilte onnets lge numbe of omputes into stutued pee-to-pee ovely netwok. omputes in the ovely olletively publish o ollet douments, build indies, egiste pofiles, filte nd disseminte douments. ofiles nd douments e distibuted though the netwok oding to thei semntis suh tht they n be mthed effiiently nd utely without exessive flooding. pfilte employs slble pplition-level multist to delive mthing douments to lge numbe of inteested pties effiiently.. Intodution The Intenet hs beome the single most impotnt infomtion soue tht ffets ou eveydy lives. With huge mount of infomtion t ou finge tips is etinly n dvntge, but it lso poses big hllenge with espet to getting desied infomtion in timely fshion. Seh engines suh s Google ptilly solve this poblem by wling douments nd using infomtion etievl (I) lgoithms [5] to nk them. Seh engines, howeve, nnot guntee timely ess to elevnt infomtion. euse of the size of the Intenet, it usully tkes seh en- This wok ws done when hunqing Tng woked s n inten t H Lbs duing. gines months to wl the web nd updte indies, nd they index only smll ftion of Intenet ontents []. Futhemoe, uses who onen bout ltest infomtion hve to fequently evisit seh engines fo possible updtes. Insted of equiing uses to fequently hek fo new ontents, infomtion filteing nd dissemintion systems [6] tke ove this esponsibility by llowing uses to egiste pesistent queies lled use pofiles. They detet new ontents, mth them ginst egisteed pofiles, nd notify uses when elevnt douments beomes vilble. We illustte the use of suh system using sientifi pojet s n exmple. t the beginning of the pojet, sientists use seh engines to find elted wok nd selet out the most elevnt ppes. They egiste pofiles to hve the system notify them when douments simil to those ppes show up. s esult, they e onstntly infomed bout elevnt ppes, disussions in news goups, et., lwys stying t the utting edge of the field. fte ompleting eseh ppe, they egiste nothe pofile to hve the system notify them when thei ppe gets ited. The bove senio, unfotuntely, is still f fom the elity. The mjoity of existing event-notifition systems suppot only subjet- o shem-bsed publish/subsibe model. They nnot file unstutued douments (e.g., text, HTML, imge, udio o video files) tht ount fo signifint peentge of Intenet ontents. few systems tht filte unstutued douments (e.g., SIFT [6]) un t entlized site, nd e unble to filte o disseminte douments t lge sle. To ddess these limittions, we e developing globlsle, deentlized infomtion filteing nd dissemintion system pfilte, with gol to poess potentilly billions of douments fo millions of uses. We identify sevel mjo hllenges fo pfilte: () slbility to keep up with the exponentil dt gowth te of the Intenet; () mthing to mth douments with pofiles utely without exessive flooding; nd (3) dissemintion to disseminte popul douments to lge numbe of subsibes effiiently.

2 To hieve good slbility, pfilte onnets lge numbe of omputes into stutued pee-to-pee () ovely netwok [8]. Nodes in the ovely olletively publish o ollet douments, build indies, egiste pofiles, filte nd disseminte douments. The dvntge of dopting the model is tht the pbility of the system sles utomtilly with the use popultion. pfilte distibutes pofiles nd douments though the ovely netwok oding to thei semntis suh tht simil pofiles nd douments e o-loted. s esult, they n be mthed effiiently nd utely without flooding eithe of them to evey ompute in the ovely. The doument semntis e deived fom I lgoithms suh s veto spe model (VSM) nd ltent semnti indexing (LSI) []. These lgoithms epesent the semntis of douments s vetos in tesin spe. Veto epesenttion of objets is not speifi to text. It is used in vitully ll uent multimedi etievl systems []. To effiiently delive popul douments to lge numbe of inteested pties, pfilte employs pplition-level multist with the following novel fetues. Fist, we genete n ute globl poximity mp of nodes in the system in deentlized fshion to guide the onstution nd mintenne of multist tees. Seond, simil subsiptions e fused to edue the numbe of individul multist tees. Lst, to suppot lge numbe multist tees fo dissimil subsiptions, esoues devoted to tee is tunble depending on the impotne of tht tee. The eminde of the ppe is ognized s follows. Setion povides bkgound infomtion bout netwok nd I. We desibe pofile mthing nd doument dissemintion in Setion 3 nd 4, espetively. elted wok is disussed in Setion 5. Setion 6 onludes the ppe.. kgound pfilte uses en [5] ( hiehil vesion of N []) to ognize lge numbe of nodes into ovely netwok, nd elies on extensions to VSM nd LSI [] to mth douments nd pofiles... ontent-ddessble Netwok (N) istibuted hsh tble (HT) systems (e.g., N, hod, sty nd Tpesty) build n dministtion-fee nd fult-tolent pplition-level ovely netwok to povide hsh tble intefe tht mps keys to vlues. N ptitions d-dimensionl tesin spe into zones nd ssigns eh zone to node. Two nodes e neighbos in the ovely if thei zones ovelp in ll but one dimension long whih they but eh othe. n objet key is point in the tesin spe nd the objet is stoed t the node whose zone ontins the point. Loting n objet is edued to outing to the node tht hosts the objet. outing fom soue node to destintion node is equivlent z o n e o o d i n t e s o b j e t k e y Figue. n exmple of -d N to outing fom one zone to nothe in the tesin spe. new node joins the ovely by ndomly piking point in the tesin spe, outing to the zone tht ontins the point, nd splitting the zone with its uent owne. n exmple of -dimensionl (-d) N is shown in Figue. Thee e five nodes to in the ovely. h node owns zone in the tesin spe. Initilly owns the entie zone t the uppe-ight one. When joins, the zone owned by splits nd pt of the zone is given to. When wnts to etieve the objet with key (.4,.), it sends the equest to beuse s oodintes e lose to the objet key. fowds the equest to. en [5] employs sevel tehniques to boost N s outing pefomne while leving N s tesin spe bsttion intt. We use en fo tul outing... Veto Spe Model (VSM) VSM epesents douments nd queies s tem vetos. h element of the veto oesponds to the impotne of wod (o tem) in the doument o quey. The weight of n element is often omputed using the sttistil tem fequeny * invese doument fequeny (TF*IF) sheme []. The intuition behind it is tht two ftos deide the impotne of tem in doument the fequeny of the tem in the doument nd the fequeny of the tem in othe douments. If tem ppes in doument with high fequeny, thee is good hne tht the tem ould be used to diffeentite the doument fom othes. Howeve, if tem lso ppes in mny othe douments, e.g., ompute, its impotne should be penlized. uing etievl opetion, douments e nked oding to the simility between the doument veto nd the quey veto, nd those with the highest simility e etuned. ommon mesue of simility is the osine of the ngle between vetos. VSM usully nomlizes tem vetos X to unit length ( X = ) in ode to ompenste fo diffeene in doument length. s esult, the osine of the ngle (simility) between two vetos is invesely popotionl to the uliden distne between them [].

3 .3. Ltent Semnti Indexing (LSI) Litel mthing shemes suh s VSM suffe fom synonyms nd noise in douments. LSI oveomes these poblems by using sttistilly deived oneptul indies insted of individul tems fo etievl. It uses singul vlue deomposition to tnsfom high-dimensionl tem veto into lowe-dimensionl semnti veto, by pojeting the fome into semnti subspe. h element of semnti veto oesponds to the impotne of n bstt onept in the doument o quey. In ddition to eduing the dimensionlity of the semnti subspe nd eliminting noise, LSI is pble of bing togethe douments tht e semntilly elted even if they do not she tems. Fo instne, seh bout my etun elevnt douments tht tully use utomobile in the text. s is in VSM, semnti vetos e nomlized nd thei simility is mesued s the osine of the ngle between vetos. 3. ofile Mthing pfilte onnets lge numbe of nodes into N. One dvntge of dopting the deentlized model is tht the pbility of the system sles utomtilly with the use popultion. pfilte stoes pofiles o outes douments in N using thei semnti vetos (geneted by LSI) s the HT keys. This hs the effet tht pofiles o douments stoed logilly lose in the ovely e lso lose in semntis, eduing the poblem of semnti-bsed seh to ovely outing. Figue illusttes this with n exmple. h zone is owned by node. Given pofile, it is outed in N using its semnti veto s the HT key (step ), nd egisteed t nodes within smll dius bsed on the simility theshold set by the use (step ). We efe to these nodes s the egisttion egion fo this pofile. Given new doument, it is gin outed using its semnti veto s the HT key (step 3). If mthes with the pofile (i.e., its HT key flls in the egisttion egion of the pofile), notifition is sent to the subsibe (step 4). Sine pofiles simil to bove etin theshold must hve been egisteed t the tget node whose oodintes ontin s semnti veto, pfilte will not miss pofiles tht mth with. In Figue, the sme pofile lso mthes with doument. The effiieny of this lgoithm to get extent depends on the size of the egisttion egion. If lge numbe of nodes e involved in egisteing single pofile, pfilte s pefomne would degde to simple system whee pofiles e flooded to evey node. Unfotuntely, the egisttion egion tends to be lge due to the high dimensionlity of the semnti spe (5-35). This poblem is known s the use of dimensionlity [6]. Two popeties of the semnti spe podued by LSI llows us to ttk this poblem t low ost. Fist, s p of i l e do 3 ' p of i l e 4 ' doument eg i s t ti on eg i on f o th e p of i l e Figue. ofile mthing in -d N. 4 do esult of the singul vlue deomposition used in LSI, the elements ppeing elie in semnti veto e muh moe impotnt thn those ppeing lte. Seond, douments nd pofiles e spsely populted in the semnti spe, foming tight lustes. We tke dvntge of these fts to edue the numbe of nodes involved in egisteing pofile while keeping LSI s good peision. ue to the spe limit, detiled disussion of this tehnique is povided elsewhee [4, 3]. 4. oument issemintion When new doument ives, pfilte needs to delive it to ll subsibes identified by the lgoithm desibed in Setion 3. n effiient wy to do this is to disseminte the doument though n pplition-level multist tee ompising ll elevnt subsibes. pfilte employs thee mjo tehniques to optimize doument multist: () utilizing deentlized globl view of the system to guide the onstution nd mintenne of multist tees; () eduing the numbe of multist tees by fusing simil pofiles; (3) eduing outing stte though disimintive tetment of multist tees. We desibe eh of them below. 4.. Tee onstution Using Globl Stte The effiieny of multist tee depends on how lose the tee stutue ppoximtes the undelying Intenet topology. One simple wy to onstut n effiient tee without oute suppot is to mesue the lteny between eh pi of nodes in the system, build gph with nodes s vetexes nd ltenies s edge weights, nd then un miniml spnning tee (MST) lgoithm ove the gph. Though simple, this lgoithm is not suitble fo pfilte due to its entlized ntue nd exessive lteny mesuement Tee onstution Using lose Neighbo One impotnt ide this nive lgoithm evels is tht, hving globl view of the system is uil fo building effiient multist tees. We popose moe elisti vint of the nive lgoithm, pst. With pst, new node 3

4 Steth smll-bkbone-itm lge-bkbone-itm teoute Numbe of nodes in the tee Steth S pneighbo Numbe of TT mesuements Figue 3. efomne of the pst lgoithm with optiml neighbo seletion. joining multist goup tthes to the node tht hs the lowest lteny to the new node mong ll nodes in the existing multist tee. The only globl infomtion pst equies is the knowledge of the losest neighbo. (We will desibe how to quie this infomtion in deentlized wy in Setion 4...) We evlute pst by omping it to the MST lgoithm. The meti we use is steth, defined s the tio of the ost fo the tee built by pst to tht of MST. Hee tee ost is the summtion of the weight of ll tee edges. Thee netwok topologies e used in ou expeiment. The fist two topologies e of, nodes eh, geneted fom GT-ITM [3]: one with lge bkbone (8 tnsit domins) nd the othe with smll bkbone (5 tnsit domins). The thid topology is onstuted fom el smples of the Intenet, using teoute to mesue ltenies mong 85,7 nodes. Fo eh topology, etin numbe of nodes e ndomly seleted to join multist goup. The esults e shown in Figue 3. In this figue, with s mny s,48 nodes in the tee, pst intodues less thn 5% ovehed fo the lge-bkbone-itm nd teoute topologies. The pefomne fo the smll-bkbone-itm topology degdes fste thn the othe two s the numbe of nodes ineses. Sine nodes in smll bkbone netwok e lose to eh othe, when new node N joins the tee, some nodes ledy in the tee e likely to benefit fom hoosing N s thei pent, s opposed to simply tthing N to n existing node (see Setion 4..4 fo moe disussions). On the othe hnd, steth only mesues the eltive ovehed. The bsolute ovehed fo pst in smll bkbone netwok is less signifint thn tht in big bkbone netwok Finding the losest Neighbo We use lndmk + TT mesuement lgoithm [5] lled pneighbo to find neby neighbos fo given node in deentlized fshion. The intuition behind We thnk Zhiheng Wng fom Univesity of Mihign fo poviding us with this topology. Figue 4. efomne of the pneighbo lgoithm. the lndmk tehnique is tht nodes lose in the Intenet e likely to hve simil ltenies to some seleted lndmk nodes. Suppose thee e l lndmk nodes L i, i =,..., l. h node hs lndmk veto (d, d,..., d l ) ssoited with it, whee d i is the node s ound tip time (TT) to lndmk L i. To find low-lteny neby neighbo, node nks othe nodes oding to the simility in lndmk veto, nd mesues TT to top k ndidtes to lote the tully losest one. We use N s lndmk odeing [] tehnique to nk simility in lndmk veto, lthough moe sophistited lgoithms ould be used [9]. We expeiment with the lge-bkbone-itm topology to evlute pneighbo. Given node, we use pneighbo to find its losest neighbo mong ll, nodes in the topology. The meti we use is gin steth, but defined hee s the tio of the lteny between nd its neest neighbo found by pneighbo to the lteny between nd its tul neest neighbo. We ndomly selet, nodes in the topology nd epot thei vege steth in Figue 4, whee S (expnding-ing seh) mesues lteny to k diet o indiet suounding nodes in the ovely to find low-lteny neby neighbo. Two onlusions n be dwn fom the figue. Fist, lgoithms tht utilize only lol infomtion (e.g., S) is not effetive t finding neby neighbo. Seond, pneighbo s pefomne impoves quikly s the numbe of TT mesuements ineses. It only needs to mesues TT to top -4 ndidtes to hieve steth lose to one ee-to-ee Tee onstution Setions 4.. nd 4.. pesent nd evlute the pst nd pneighbo lgoithm. In this setion, we desibe how to extend pst to wok in deentlized envionment. The bsi ide is to hve eh node independently disove neby nodes tht subsibe to the sme quey, using the lndmk veto of the node o the semnti veto of the quey s the key to ess N s HT. In the following, we fist give the steps of the deentlized pst lgoithm nd then illustte it with n exmple. When node N egistes pofile, it ies out the following steps of the pst lgoithm.

5 () (b ) ( ) u s i n g s e m n t i v e t o t o e g i s t e p o f i l e s u s i n g l n d m k v e t o t o e g i s t e p o f i l e s e g i s t t i o n e g i o n L e g i s t t i o n e g i o n S (d ) Figue 5. n exmple of the pst lgoithm: egisteing pofiles using both semnti veto nd lndmk veto s the HT key.. N mesues TT to lndmk nodes nd inlude its lndmk veto, U/netwok/stoge pity, nd uent lod in the pofile.. N uses the lndmk veto s the HT key to stoe the pofile in egisttion egion L of the ovely. 3 This poess is simil to step nd in Figue, but the outing is guided by the lndmk veto insted of the semnti veto. 3. On nodes within L, it sehes fo pofiles of neby nodes tht ledy subsibed to the sme quey. If suh nodes exist, it uses the pneighbo lgoithm to find the losest one, tth to it, nd the egisttion poess temintes. (In ptie, the neighbo seletion lso onsides othe ftos suh s netwok bndwidth nd poessing powe.) 4. If no neby nodes e found in step 3, N uses the semnti veto s the HT key to stoe the pofile in nothe egisttion egion S of the ovely. This poess is step nd in Figue. 5. On nodes within S, it gin uses the pneighbo lgoithm to find the losest neighbo tht ledy subsibed to the sme quey, nd tthes to it. If no suh node is found, N must be the fist node tht subsibes to this quey. It simply tthes to the endezvous nodes in S, whih e the oots of the multist tees. We illustte this lgoithm with n exmple in Figue 5. To mke the exmple intuitive, we dw the distne in 3 The numbe of lndmks my not mth with the dimensionlity of N. Solutions fo this poblem e desibed in [5]. U U Figue 5 ()-() popotionl to the netwok lteny mong nodes. In Figue 5 (d), the positions of nodes oespond to thei positions in N s logil tesin spe, nd the iles epesent the egisttion egions fo pofiles. In Figue 5 (), subsibe,, nd e ledy in multist tee. In Figue 5 (d), they use the semnti veto nd lndmk veto s the HT key to stoe thei pofile in egisttion egion L nd S, espetively. In Figue 5 (b), subsibe nd join the tee. In step 3 of pst, nd notie s pofile stoed in L nd dietly tth to, skipping step 4 nd 5 of pst. (The use of node U in Figue 5 will be explined in Setion 4.3.) omped to existing shemes, pst offes the following dvntges: () bette poximity ppoximtion it leveges the globl infomtion stoed in the ovely to find the losest neighbo fo new node; () fste tee onstution the pent seletion poess voids sehing the tee level by level, sequentilly; (3) no bottlenek t the oot of the multist tee, whih is typilly poblem fo existing publish/subsibe systems. It hs been shown tht smll numbe of popul queies ount fo signifint ftion of of ll queies submitted to seh engines [7]. Fo these hot queies, new subsibe is likely to find neby node tht ledy subsibed to the sme quey in step 3 of pst, voiding going to the oot in step 4. xept fo its diet hilden, the oot does not need to emembe evey egisteed pofile to funtion popely. In high lod situtions, fte dieting the owne of n inoming pofile to tth to n ppopite ple, the oot n disd the pofile if the lndmk veto in the pofile is simil to the lndmk veto in known pofile. Howeve, we expet it is not poblem fo the oot to stoe sevel thousnd pofiles. Nodes in the ovely eplite thei neighbos ontents. Should node s pent in multist tee fil, estts the pofile egisttion poess to eonnet to the tee. s desendnts ould be ignont of this epi. Should the oot of the tee fil, one of s neighbos Q will notie the filue nd tke ove the zone owned by nd the tee ooted t. Q uses the pofile egisttion poess to find the losest node in the tee, tthes to it, nd nnounes Q s the new oot. This nnounement is popgted thoughout the tee to hnge the dietion of edges popely Lol MST lgoithm fo Tee Mintenne In Figue 3, the pst tee is not s effiient s MST. The pefomne gp widens s the numbe of nodes in the tee ineses. This is beuse pst only tthes new node to its losest node in the tee without fixing existing tee edges. When netwok ondition fluxes, tee mintenne lso needs to be done to eflet these hnges. We popose lol MST lgoithm to ddess both poblems. It n be exeuted independently by nodes to impove the tee.

6 n N n N n N b b e e d d (, ) (, ) (, ) ( e, ) e ( d, ) d ( ) (b) ( ) () (b ) ( ) (d ) Figue 6. n exmple of the lol MST lgoithm. ithe t join o peiodilly ftewods, node N uses the egisttion poess to find set of nodes tht subsibe to the sme quey nd hve lndmk vetos simil to its own. N mesues TT to top k ndidtes nd identifies the losest l nodes. oth k nd l e tunble pmetes depending on the effot N is willing to spend. Let W denote the set of the losest l nodes, thei pents, N nd N s pent. N lolly builds gph onsisting of nodes in W nd un the MST lgoithm to find moe effiient tee stutue. We illustte the detils of the lgoithm using n exmple in Figue 6. t the beginning of the lgoithm, N finds ne neighbo, nd, nd builds the gph in Figue 6 (), whee x is X s pent nd is oneptul oot of the tee. Sine N does not know the stutue of the est of the tee, it n only dd thee vitul links between (, ), (, n ) nd (, ), inditing tht, n nd e somehow onneted though the oot. In Figue 6 (b), N dds link to evey node in W nd nnotte the links with el lteny tht is eithe mesued by N dietly o epoted by othe nodes upon N s equest. The lteny of the vitul links is set to. N uns the MST lgoithm ove the gph to find the most effiient tee stutue, nd infoms othe nodes to dpt odingly. The esult is shown in Figue 6 (). Note tht the vitul links e lwys inluded in the MST sine thei weights e. To guntees the esult of this lgoithm is still tee ompising ll nodes in the oiginl tee, vitul link is dded between nd node X if nd only if X is in W nd X is not desendnt of ny othe node in W. Fo instne, thee is no vitul link between nd beuse s pent is in W. To disove the nesto-desendnt eltionship, the oot peiodilly popgtes hello messge thoughout the tee nd eh node dds itself into the messge. The messge n lso be used to detet loops in the tee due to e e onditions duing tee onstution. 4.. ofile Fusion With gol to hndle potentilly millions of pofiles, it is imptil to mintin one multist tee fo eh unique pofile. pfilte builds single multist tee fo simil queies. Let (q, q ) denote pofile, whee q is the semnti veto of the quey, nd q is the simility theshold set by the use. ny doument whose semnti veto is within q distne to q is onsideed s mth to quey (q, q ). Figue 7. Two ses tht pofiles n be fused. Thee e two ses tht simil queies n be fused. We illustte them in Figue 7. In the fist se (Figue 7 ()), quey (, ) oves quey (b, b ). We simply dd subsibes fo (b, b ) to the (, ) multist tee with the guntee tht nobody will miss inteesting douments. In the seond se s is illustted in Figue 7 (b), quey (, ) nd (d, d ) do not ove eh othe, but we n build n tifiil quey (e, e ) to ove both. In Figue 7 (), the multist tee is initilly dedited fo quey (, ). In Figue 7 (d), when node egistes its quey (d, d ), it noties tht thee exists multist tee fo simil quey (, ). Insted of building new tee, tthes to ne neighbo tht is ledy in the tee fo quey (, ). then infoms to inese the quey egion oveed by this tee. in tun popgtes this notifition to its pent. (The multist tee fo quey (, ) nd nodes suh s n be found by node in step 3 o 5 of the pst lgoithm). In tee fo fused queies, node must ompe n inoming doument to its hilden s inteests to deide whethe to fowd the doument long etin links. Fo instne, in Figue 7 (d), when eeives doument, it my only fowd the doument to, ignoing isimintive Tetment of Tees The mount of esoues pfilte devotes fo multist tee is tunble ontinuum depending on the populity of the quey the tee seves. Thee e two extemes. Fo popul queies, pfilte euits mny nodes in the ovely to build the most effiient tees; whees fo unpopul queies, whih e the mjoity, douments e sent to subsibes using point-to-point ommunition. In Figue 5 (), s tffi in the tee ineses, suffes fom the low bndwidth link (, ) nd wnts to euit neby high-bndwidth node to impove pefomne. It will disove U beuse nd U hve simil lndmk veto nd thei egisttion egion intesets (see Figue 5 (d)). In se tht finds no suh node s U, it omputes the lndmk veto of n idel node V suh tht the summtion of the lteny between (, V ), (V, ) nd (V, ) is likely to be substntilly less thn the summtion of the lteny between (, ) nd (, ). then uses the lndmk veto fo the idel node s the key to onsult the HT to find suh node. Note tht, lthough U nd V sit in the tee, they do not subsibe to the quey tht the tee seves.

7 5. elted Wok Thee is n enomous body of elted wok f too muh to enumete in this ppe. Hee we give only efeenes to suveys of elted fields, nd fous on those most elevnt. Suveys ould be found on [8], infomtion etievl nd filteing [5] nd event notifition systems [4]. VSM nd LSI [] e mong the most suessful I lgoithms, nd e dopted by mjo seh engines [7, 7]. st studies minly fous on peision the thn effiieny o slbility. SIFT [6] does py ttention to effiieny but it uns t entlized site, only filtes messges in news goups, nd does not use multist fo doument delivey. xisting ontent-bsed publish/subsibe systems [4] only mth sevel well-stutued ttibutes ginst vlues; whees pfilte filtes unstutued text douments, udio o video files, using vetos of hundeds of dimensions. publish/subsibe systems suh s Sibe [] only suppot subjet-bsed subsiption. The multist tee in these systems is impliitly embedded in the HT ovely. We use HT only s bootstpping mehnism nd expliitly build the multist tee, whih is likely to bette ppoximte undelying Intenet topology nd hene be moe effiient thn the embedded tee. 6. onlusion We pesented pfilte, globl-sle, deentlized infomtion filteing nd dissemintion system fo unstutued douments suh s text, HTML, udio o video files. To ou knowledge, pfilte is the fist system of this kind. We mde the following ontibutions in this ppe. () void flooding eithe douments o pofiles though eful ombintion of pofile plement nd doument outing. () Utilize globl view of the system to build nd mintin effiient multist tee. (3) edue the numbe of multist tees by fusing simil pofiles. (4) edue the outing stte though disimintive tetment of multist tees. We ledy built pfilte s bsi omponents septely: LSI-bsed seh engine [4, 3], middlewe fo topology-we ovely [5], nd pst simulto. uently we e in the poess of integting them togethe to build pfilte pototype. knowledgments We thnk Wenui Zho, Sndhy wkds nd the nonymous eviewes fo thei omments on this ppe. t Univesity of oheste, hunqing Tng is suppoted in pt by NSF gnts , -9848, S- 543, nd I-84, nd by the U.S. eptment of negy Offie of Inetil onfinement Fusion unde oopetive geement No. -F3-9SF946. efeenes [] M. K. egmn. The deep web: Sufing hidden vlue. [] M. ey, Z. m, nd. Jessup. Mties, veto spes, nd infomtion etievl. SIM eview, 4():335 36, 999. [3] K. lvet, M. o, nd. W. Zegu. Modeling intenet topology. I ommunitions Mgzine, June 997. [4]. znig,. osenblum, nd. Wolf. Intefes nd lgoithms fo wide-e event notifition sevie. Tehnil epot U-S , eptment of ompute Siene, Univesity of olodo, Otobe 999. [5]. Floutsos nd. W. Od. Suvey of Infomtion etievl nd Filteing Methods. Tehnil epot S-T- 354, ompute Siene eptment, Univesity of Mylnd, 995. [6] V. Gede nd O. Gunthe. Suvey on multidimensionl ess methods. Tehnil epot ISS-6, Institut fu Witshftsinfomtik, Humboldt Univesitt zu elin, ugust 995. [7]. Lempel nd S. Mon. Optimizing esult pefething in web seh engines with segmented indies. In VL,. [8]. S. Milojii, V. Klogeki,. Lukose, K. Ngj, J. uyne,. ihd, S. ollins, nd Z. Xu. ee-to-pee omputing. Tehnil epot HL--57, H Lb,. [9] T. S.. Ng nd H. Zhng. editing intenet netwok distne with oodintes-bsed ppohes. In INFOOM,. []. ghvn. Infomtion etievl lgoithms: suvey. In o. of the 8th SIM Symposium on isete lgoithms (SO), 997. [] S. tnsmy,. Fnis, M. Hndley,. Kp, nd S. Shenke. slble ontent-ddessble netwok. In M SIGOMM, ugust. []. I. T. owston,.-m. Keme, M. sto, nd. ushel. SI: The design of lge-sle event notifition infstutue. In o. of the Thid Intl. Wokshop on Netwoked Goup ommunition,. [3]. Tng, Z. Xu, nd S. wkds. ee-to-ee Infomtion etievl Using Self-Ognizing Semnti Ovely Netwoks. Tehnil epot, H Lbs, Novembe. [4]. Tng, Z. Xu, nd M. Mhlingm. pseh: Infomtion etievl in Stutued Ovelys. In HotNets-I, ineton, NJ, Otobe. [5] Z. Xu,. Tng, nd Z. Zhng. uilding Topology-we Ovelys using Globl Soft-Stte. In IS, My 3. [6] T. Yn nd H. Gi-Molin. SIFT tool fo wide-e infomtion dissemintion. In USNIX Tehnil onfeene, 995. [7] J.. Zkis nd Z. J. udlowski. The wold wide web s univesl medium fo sholly publition, infomtion etievl nd intehnge. Globl Jounl of ngineeing dution, (3),