Self-Organizable P2P Document Search Engine for Knowledge Management

for Knowledge Management

KazuhiroKojima

AIST,1-2-1Namiki,Tsukuba,Ibaraki,Japan

k.kojima@aist.go.jp

Abstract. Locatingcontentsisanessentialfunction, butit presentsa

verydiÆcultandchallengingproblemforlarge-scalePeer-to-Peer(P2P)

systems.ManyP2Psystems,protocols,architectures,andsearchstrate-

giesare proposed forthis problem.Inthis paper, we focus ontheself-

organizationofacommunitystructurebasedonuserpreferencesforP2P

systems.WeproposethesemethodstoimproveP2Psearchperformance:

1)ExtendedPong,2) PongProxy,3)QRP withFirework4)Backward

Learning, and 5) Community Self-Organization Algorithm. We evalu-

ate the performance of the self-organized community network through

simulations. Theseresultsshowthatthe self-organizedcommunitynet-

workmaintainsahigh queryhitratewithout overow.Asanexample

ofimplementation,weproposetheSelf-OrganizableP2PSearchEngine.

1 Introduction

Peer-to-Peer(P2P)protocolsandapplicationssuchasGnutella[1]andFreenet[2]

extendthepowerofInternetusers.ThecurrentmajorP2Papplicationisshar-

ingofillegalcontents.Inthefuture,P2Psystemswillbeappliedtodistributed

searchengines,knowledgemanagement,andmyriadInternetapplications.How-

ever,P2Psystemspresentmanyproblems,oneof whichisLocating Contents.

InGnutellaprotocolv0.4,aoodingalgorithm[1]isusedtolocatecontents.

Peer sends a Query tagged with a maximum Time-To-Live (TTL) to its all

neighborsontheoverlaynetwork.EachpeerforwardstheQuerytoitsneighbors.

Whenthe Querystringpartiallyorexactly matchesale namestored locally,

the peer replieswith the QueryHit. Thoughthis algorithm is verysimple and

robust even when peers are joining or leaving the system, it lacks scalability.

A systemcomprisingalargenumberofpeersisinundatedwith Queriesevenif

peersdonotforwardQueriesthattheyhaveforwardedpreviously.

Thispaperproposesanapproachtolocatingcontents,aself-organizationof

communities based onthe user's preferences. It is verynatural to introducea

structure of communityinto P2P systems.Communities in the real world and

cyberspace are formed by people who have similar preferences. For example,

they form mailing lists, chatrooms, and Bulletin BoardSystems (BBSs). The

membersofeachcommunitysharethecommonknowledgeswitheachother.We

proposeanewP2Pprotocol:TellaGatethatcanself-organizecommunitiesand

Browser

Document DB

Routing Engine Search

Engine

Connection Engine

Connection Engine

TCP/IP LAN or WAN Self-Organizable Search Engine

(a)ArchitectureofSelf-Organizable

P2PSearchEngine. (b)ScreenShotofTellaGateBrowser.

Fig.1.Self-OrganizableP2PSearchEngineandTellaGateBrowser.(a)Theproposed

Self-OrganizableP2PSearchEnginecomprises4modules:i.DocumentDB,ii.Search

Engine,iii.RoutingEngine,andiv.ConnectionEngine.

Theremainderof thispaperproceeds asfollows: Section2proposesaSelf-

OrganizableP2PSearchEngine;Section3givesafulldetailofTellaGateproto-

col; Section 4showsthroughsimulationsthat aself-organizedcommunitynet-

workmaintainsahighqueryhitratewithoutoverow;andSection5concludes

thediscussionandproposesfuture work.

2 Self-Organizable P2P Search Engine

Weshowthearchitecture ofthe Self-OrganizableP2PSearch Enginein Fig.1

(a).Theproposedsystemcomprisesthefollowing4modules.

i.DocumentDB

TheshareddocumentsarestoredontheDocumentDB.Eachdocumenthas

aGloballyUnique Identier(GUID).

ii.Search Engine

TheSearchEngineprovidesafulltextsearchservice.Keywordsareextracted

fromtheshareddocumentsusingtextminingtechniques.

iii.Routing Engine

TheRoutingEngineresolvesmessagesroutingaccordingtotheQueryRout-

ingProtocolwithFirework(Sec.3.2).

iv.Connection Engine

TheConnectionEngine controlsthe TCP/IPconnectionswith otherpeers

accordingtotheCommunitySelf-OrganizationAlgorithm(Sec.3.3).

InFig.1(a),useraccessestothesystembybrowserasshownin Fig.1(b).

TheTCPconnectionsbetweenpeersarereconguredbyTellaGateprotocol, so

that thecommunitystructuresareself-organizedonoverlaynetworks.

To implement the Self-Organizable P2P Search Engine, we propose a new

P2P protocol:TellaGate protocol. Inthe next section, wegivea full detail of

Descriptor ID Payload

Descriptor TTL Hop Payload Length

0 15 16 17 18 19 22

(b)Pong.

port IP Address Number of Files Shared

Number of Kilobytes Shared

0 1 2 5 6 10 11 13

(c)ExtendedPong.

port IP Address

0 1 2 5 6 32005

PeerDigest

Fig.2. Structure of message: (a), (b), and (c) are the Gnutella header, Pong, and

proposed extendedPong, respectively.(a)InPing,thePayloadLengthis 0.Thatis,

Pingcomprisesonlyaheader.(c)TheproposedextendedPongincludesaPeerDigest.

3 TellaGate Protocol

There areevidentcommunities in someconventionalclient-serverwebapplica-

tionssuchasmailinglists,chatrooms,andBBSs.Thesecommunitiesaremain-

tainedbycentralizedservers.Ontheotherhand,decentralizedanddistributed

P2Psystemshavenosuchservers.Inasocialnetwork,aso-calledacquaintance

network[3],communitiesarefrequentlyself-organizedbylocalinteractions.This

studyimplementslocalinteractionstoadecentralizedP2Psystemusing1)Ping-

Pongand2)Bloomlters.[4]Next,webrieyexplainthesekeytechnologies.

3.1 Key Technologies

Ping-Pong InGnutellaprotocolv0.4, apeer usesPing to probe thenetwork

activelyforotherpeers.Gnutellaprotocolmessagescompriseaheaderandpay-

load.APinghasonlyaheaderportion,asshownin Fig.2(a).Apeerreceiving

a Ping forwardsit to itsneighbors and responds with aPong, which contains

its own IPaddressand the Portnumber andthe amountof data it issharing

ontheGnutellanetwork.ThepayloadpartofaPongmessageisshowninFig.

2(b).Thenumberof Pingsincreasesexponentiallythroughreplicationandfor-

warding,therebyooding thenetworkwithPing-Pongs.

Bloom Filter and QRP ABloomlter[4]isaquickand space-eÆcientdata

structure for representingaset of N elements to support membership queries.

AnarrayofMbitsandKindependenthashfunctionsisusedforaBloomlter.

ABloomltermaygeneratefalsepositives.Accordingtotheanalysisin[5],the

falsepositiverateisgivenas

f =

1 e K

r

K

; (1)

whererrepresentsthenumberofbitsperelement,M=N.Someexemplaryvalues

M=N =12K=8f =0:00314 M=N =16K=11f =0:000458

WecanchooseareasonablevalueofM=N andK by consideringthe expected

falsepositiverate.

RohrsproposedaQueryRoutingProtocol(QRP)[6]for Gnutellanetworks.

InQRP,keywordsarehashedandembeddedinaBloomlter.TheBloomlter

of each peer is sentand forwardedto its neighbors so that the Bloom lter is

propagated throughoutthe network.Queries arerouted accordingto theQRP

withoutaooding algorithm.

3.2 Improvements

TheabovesubsectionbrieyexplainedPing-Pong,Bloomlters,andQRP.We

extendtheseimportantelementstoimprovetheP2Psystemperformance.

Extended Pong We extendaPongmessageso thatwecanuseBloomlters

to self-organizecommunities. The Extended Pong (ExPong) includes aBloom

lter as shown in Fig. 2(c). In web cache systems[7], a Bloom lter is called

a Summary Cache[8] or Cache Digests[9]. In this paper,a Bloom lter of the

ExPongiscalledaPeerDigest.ThePeerDigestofPeeriisrepresentedasdigest

i .

Werestrictthesharedcontentswithinsuchdocumentleshavingextensions

as .html, .ps, and .pdf. Shared .pdf or .ps les are transformed into text les

bypdftotextorps2text.Keywordsareextractedfromthesetextlesusingtext

miningtechniques.[10]It isconsidered that digest

i

showsthepreference ofthe

Peeribecausethese keywordsareembeddedintothePeerDigest.

PeerDigest Cache and Pong Proxy InGnutellaprotocol v0.6[1],ahierar-

chicalstructureisdenedasUltraPeerandLeafPeer.TheGnutellabackboneis

dominatedbyconnectionsofUltraPeersbecauseLeafPeerconnectsonlytoUl-

traPeers.UltraPeerservesasaproxyorserverlikeClip2ReectororNapster,

therebyreducingredundantPingsandQueries.

Inthispaper,weproposeaPongProxyinsteadofahierarchicalstructureto

reducePing-Pongs.EachpeerhastwoPeerDigestCaches:cache

l

(l=1;2).Pairs

(address

j

;digest

j

)arecachedintothecache

l

,whereaddress

j isip

j :port

j ofPeer

j. The rst and second neighbors of Peer i are representedas nbr

i;l

(l =1;2).

The index l of cache

l

and nbr

i;l

indicates the degree of neighbors. If Peer j

receivesaPingfrom Peeri, Peerj respondswithanExPongincludingdigest

j .

Moreover, Peer j responds with ExPongs including digest

k

(k 2 nbr

j;1 ). Peer

i caches digest

j

and digest

k

s into cache

i;1

and cache

i;2

, respectively. In this

manner,Peerj playstheroleofPongProxy.

If each peer sends Pings to all rst neighbor peers, as in Gnutella v0.4,

B A

C

D

E F

q

check(idx_C,q)=1 check(idx_E,q)=0 check(idx_F,q)=1 check(idx_D,q)=1

Peer B

qh

qh

qe

Fig.3.QRPwithFireworkandBackwardLearning.PeerAsendstheQuerytoPeer

B. Queryroutingisdecided bycheck(digest

j

;q)(j2nbr

B;1

) atPeerB.q,qh,andqe

showtheQuery,QueryHit,andQueryError,respectively.

size is usually very large. We also improve the Ping-ExPong protocol in the

following respect. Each peer sends Pings to MaxCon peers that are selected

randomly from cache

1

. The peer responds with MaxCon ExPongs that are

selected randomly from cache

1

. Hence, the upper bound for the total size of

ExPongsthatapeerwillreceiveisgivenas

upper bound=MaxCon(1+MaxCon)

Size

ExPong

: (2)

ItisfairtoaddthattheGnutellapeeralsosendsandforwardsPingstoMaxCon

peersinsimulationsofSection4.

QRPwithFirework AccordingtothePongProxy,eachpeerhasinformation

about the contents sharedby its neighbors. We usethe PeerDigestsfor query

routing.

InFig.3,Peer AsendsaQuery:q,includingsearchkeywords,toPeerB.If

PeerBhasnocontentfortheQuery,PeerBmust forwardtheQuery.Routing

of the Queryis implemented asfollows:Peer B checkswhether keywords of q

areindigest

j

(j2nbr

B;1

)ornot.Itisgivenas

check(digest

j

;q)=ftrue;falseg: (3)

If keywordsexist in digest

j

(j 2nbr

B;1

),PeerBforwardstheQuery toPeer j.

InFig.3,PeerBforwardstheQueryto Peers C,D, andF.Ifcheck(digest

j

;q)

is falseforall rstneighbors,theQueryis forwardedto Peerj(2nbr

1

),which

isselectedrandomly.ThisprocessisequivalenttoaRandomWalkSearch.

F. IfPeerF hasnocontentfortheQueryand isin Dead Lock, Peer Freturns

QueryError:qetoPeerB.AccordingtotheQueryError,PeerBmodiesdigest

F

as

digest (m)

F

=0; m=h

k

(q)(k=1;;K);

wheredigest (m)

isthem-thbitofdigestandhisahashfunction.Ontheother

hand,ifPeerChascontentsoughtbytheQuery,PeerCrepliesto PeerBwith

QueryHit:qh.TheQueryHit:qhisbackwardedtothePeerAbywayofPeerB.

AccordingtotheQueryHit,eachmediatepeerandoriginatorroutedbyrandom

walkstrategymodies digestas

digest (m)

j

=1; m=h

k

(q)(k=1;;K);

wherej(2nbr

i;1

)showsthenextorlast mediatepeer.

3.3 CommunitySelf-Organization Algorithm

Thissubsection presentstheCommunitySelf-OrganizationAlgorithm(CSOA).

The abovesection mentionedthat thePeerDigestshowsthe respectiveprefer-

ences of peers. Therefore, we can use PeerDigestto self-organizecommunities

basedonrespectiveusers'preferences.

Wemust denethe similarityof thepreference betweenPeer iand Peer j,

sim

ij

.Thatsimilarityisdenedas

sim

ij

count(digest

i

^digest

j

); (4)

where^isanANDoperatorandcount()isafunctionthatcountsbitswith1.

Self-organizationisimplementedasfollows.Firstly,Peericalculatesthesim-

ilarity sim

i;j

betweenitself and thesecond neighboring Peersj(2nbr

i;2 ).Sec-

ondly,PeeriselectsthePeerjthathasthelargestvalueofsim

i;j

.Thirdly,ifthe

numberof currentconnectionsCon

i

isgreaterthan themaximumconnections

MaxCon

i

,thenPeerirandomlyselectsPeerk(2nbr

i;1

)todisconnect.Finally,

Peeri disconnectsPeerkand connectstoPeerj. Thepseudo-codeofthis self-

organization algorithm is shown in Fig. 4. This algorithm is very simple and

requires onlylocal information,that is, thePeerDigestsof therstand second

neighborpeers.Moreover,thislocalinformationisobtainedbylocalinteractions:

Ping-ExPongandPongProxy.

4 Simulations

SimulatorofcomplexsystemssuchasGnutellaisnotaneasytask.Weprovided

cal cul atesim

ij

forallPeersj2nbr

i;2

nbr

i;2

=sortnbr

i;2

accordingtosim

ij

Peerj=topofnbri;2

connecttoPeerj

addPeerjtonbri;1andcachei;1

removePeerj fromnbr

i;2

andcache

i;2

if(Con

i

>MaxCon

i )

Peerk=sel ectfromnbri;1atrandom

disconnectPeerk

removePeerkfromnbr

i;1

andcache

i;1

Fig.4.PseudocodeoftheproposedCommunitySelf-OrganizationAlgorithm.Inthe

followingsimulationsofSection4,MaxCon=4.

{ InitialTopology

Theinitial topology of anetwork is given asarandom network,as shown

inFig.10(a).Thenetworkcomprises500peers.Eachpeerhasfourrandom

outgoinglinks;thereforeMaxCon

i

=4.Thenumberofpeersisconstantin

the following simulations. We also simulated cases wherein the network is

growing,butidenticalresultstothese wereobtained.

{ Community

Allpeersareclassiedintovecommunities.Eachpeerisassignedrandomly

tooneofthesecommunities.There arenoexplicitcommunitystructuresin

theinitialnetworktopologyasshownin Fig.10(a).

{ ContentsandKeywords

Eachkeywordisanintegervalueforsimplicity.[2]Thenumberofkeywords,

N,is500.Thekeywordsaredividedtoveclasses:KC

i

(i=0;;4).Class

KC

i

comprisestheinherent100keywordsand20keywordswhichbelongto

theclass KC

i+1 (KC

5

=KC

0

).The content associated with each keyword

isanintegerequaltothekeyword.

{ PeerDigest

Thehash functions are builtby rstcalculating theMD5 signatureof the

keywordstring, which yields 128bits, thendividing the 128bits into four

32-bitunsignedintegervalues,thennallytakingthemodulusofeach32-bit

unsignedintegervaluebythePeerDigestsizeM[8].MD5isacryptographic

messagedigestalgorithmthathashesarbitrarylengthstringsto128bits.The

bitsperelementM=N isset tofour in thefollowingsimulations;therefore,

thetheoreticalfalsepositiverateis0:160.

{ Local Storage

InrealP2Psystems,mostusershavenosharedlocalstorage;theyarecalled

LSC

NC

; (5)

whereLSC andNC arethelocal storagecapacityandthe numberof con-

tentsassignedtoacommunity,respectively.If1,eachpeercanstoreall

contentssharedbyitsowncommunityonthelocal storage.If <1,LRU

cachereplacementisused forstoredcontent.

{ Uploader andDownloader

Alllocalstoragelocationsareinitiallyempty.Contentsareprovidedbyspe-

cicpeers called Upload-Only Members(UOMs). Theremainingpeers are

Download-OnlyMembers(DOMs). Weassumedthat thenumberofUOMs

is 10%of allpeers. In Fig.10(a), UOM andDOM peers are shown asbig

andsmallcircles,respectively.

{ MessageSizeandBuer

TheTTLofPingandQueryaresettofive.Wedenethesizeofeachmes-

sageasshowninTable1.Accordingtotheaboveassumption,MaxCon

i

=4,

theupperboundeq.(2)is0:64M-byte.Themessagebuer,thatis,thenum-

berofmessagesthatPeeri canprocessinasingletimestep,isrepresented

asmb

i

.Eachpeerhasthesamesizemb.Ifthenumberofmessagesreceived

isgreaterthanthesizeofmb,theoverowedmessagesaredropped.

MessageTypeMessageSize[byte]

Ping 22

Pong 35

ExPong 32027

Query 50

QueryHit 100

QueryError 50

Table 1.Messagesize.

{ TimeStep

EachpeergeneratesaQueryandPingevery10and1000timesteps.CSOA

isappliedevery1000timesteps.

4.1 Performance

We compared the performance of TellaGate with Gnutella protocol v0.4. Re-

spective performances were evaluated by the success rate of search, network

load,averageHOP of thesuccessfulQuery, and theaveragenumberof Query-

Hit. Weimplementedseveralsimulationsunder various parametervalues, that

is, the local storage capacity factor (0 < 1:0) and the message buer

mb (10 mb 100).Due to limitation space, this paperdescribesresults of

0 0.2 0.4 0.6 0.8 1

0 10000 20000 30000

Success Rate

Time Steps Gnutella TellaGate

0 0.2 0.4 0.6 0.8 1

0 10000 20000 30000

Success Rate

Time Steps Gnutella TellaGate

0 0.2 0.4 0.6 0.8 1

0 10000 20000 30000

Success Rate

Time Steps Gnutella TellaGate

(a)=1:0,mb=100. (b)=0:25,mb=100. (c)=0:25,mb=20.

Fig.5.Performance:successrate.

0 1 2 3 4 5

0 10000 20000 30000

Messages [G byte]

Time Steps Gnutella TellaGate

0 1 2 3 4 5

0 10000 20000 30000

Messages [G byte]

Time Steps Gnutella TellaGate

0 1 2 3 4 5

0 10000 20000 30000

Messages [G byte]

Time Steps Gnutella TellaGate

(a)=1:0,mb=100. (b)=0:25,mb=100. (c)=0:25,mb=20.

Fig.6.Performance:networkload.

Resultsrepresentanaverageof 10simulations;theyareshownin Figs.5-8.

Inthosegures,resultsofGnutellav0.4andTellaGateareshownbythebroken

linewithopensquaresandthesolidlinewithlled squares,respectively.

Thesuccessratesconvergeto 1.0forGnutellav0.4andTellaGatewith=

1:0,mb=100,asshowninFig.5(a).Moreover,theaverageHOPconvergesto0,

asshowninFig.7(a)becauseeachpeerhaslocalstorageofsuÆcientcapacityto

storethecontentsandamessagebuerthatissuÆcienttoreceivethemessages.

However,Gnutellav0.4 induces aood of Ping-Pongs andQueries in an early

timestepasshowninFig.6(a).ThenetworkloadofGnutellav0.4convergesto

thelowervalueafterthepeak.Whenthesizeofthelocalstorageislimited,the

network loadof Gnutella v0.4 converges to the highervalue, as shown in Fig.

6(b).When thesize ofthe messagebueris alsolimited, asmanyQueries are

dropped out; the average number of QueryHit decreases and the success rate

convergestothelowervalueof0.68,asshownin Fig.8(c)andFig.5(c).

TellaGate doesnotengender arapid increaseofthe network load.Network

loads of TellaGate converge to the lower value, as shown in Fig. 6. In Fig.5,

eventhoughthesizeoflocalstorageandmessagebuerarelimited,thesuccess

rateofTellaGate convergesto1.0.

Wealso simulated TellaGate withoutthe CSOA to clarify the reasonwhy

the self-organized communitynetwork retains the high success rate value: the

0 1 2 3 4 5

0 10000 20000 30000

Average HOP

Time Steps Gnutella TellaGate

0 1 2 3 4 5

0 10000 20000 30000

Average HOP

Time Steps Gnutella TellaGate

0 1 2 3 4 5

0 10000 20000 30000

Average HOP

Time Steps Gnutella TellaGate

(a)=1:0,mb=100. (b)=0:25,mb=100. (c)=0:25,mb=20.

Fig.7.Performance:averagevalueofHOP.

0 5 10 15 20 25 30 35 40 45

0 10000 20000 30000

Average number of QUERYHIT

Time Steps Gnutella TellaGate

0 5 10 15 20 25 30 35 40 45

0 10000 20000 30000

Average number of QUERYHIT

Time Steps Gnutella TellaGate

0 5 10 15 20 25 30 35 40 45

0 10000 20000 30000

Average number of QUERYHIT

Time Steps Gnutella TellaGate

(a)=1:0,mb=100. (b)=0:25,mb=100. (c)=0:25,mb=20.

Fig.8. Performance:averagenumberofQueryHit.

of Queries isimplementedby QRPwith Firework.Resultsof thesuccess rate,

networkloadandaveragenumberofQueryHitareshowninFig.9.Thesethree

valuesofTellaGatewithouttheCSOAarelowerthanthevaluesoftheoriginal

TellaGate.

Accordingtothese results,weinferred thefollowinganswertothequestion

above.ForTellaGatewithouttheCSOA,theprobabilitythattherstneighbors

havethesamepreferenceisalowvalue;thereby,theroutingstrategyisequivalent

toaRandomWalkSearch.If1,thesearchstrategyisimprovedgraduallyto

QRPwithFireworkbybackwardlearningbasedonQueryErrorandQueryHit.

However,if<1,asthecontentsonthelocalstoragearereplacedbytheLRU

replacementpolicy,thePeerDigestwillbeinconsistentwiththecurrentcontents

sharedbythedistantpeers.ManyQueriesaredroppedoutaccordingtotheTTL

limitation.

Ontheother hand,in theself-organizedcommunitynetwork shownin Fig.

10(b), each peer has severalrst neighbors that haveidentical preferences. As

QRPwithFireworkreplicatesandforwardsaQuerybasedoneq.(3),theproba-

bilitythattheQueryisreplicatedishigherthanintherandomnetwork.Hence,

the probability of locating contents will be a high value. This eect that is

inducedbythecommunitystructureandreplicationofQueryiscalledtheCom-

0 0.2 0.4 0.6 0.8 1

0 10000 20000 30000

Success Rate

Time Steps with CSOA without CSOA

0 0.2 0.4 0.6 0.8 1

0 10000 20000 30000

Messages [G byte]

Time Steps with CSOA without CSOA

0 5 10 15

0 10000 20000 30000

Average number of QUERYHIT

Time Steps with CSOA without CSOA

(a)successrate. (b)networkload. (c)averagenumberofQueryHit.

Fig.9.Theeectofcommunitystructure:CSOAistheCommunitySelf-Organization

Algorithm.

5 Conclusion

This paper proposed the Self-Organizable P2P Document Search Engine and

anewP2Pprotocol:TellaGateprotocol.Simulationresultsshowthat theself-

organizedcommunitynetworkmaintainsahighqueryhitratewithoutoverow.

However,wedidnotconsiderthatpeersoftengooineintheabovesimulations.

Therefore,futureworkwilladdresssimulationsunder suchdynamicconditions.

TheproposedCommunitySelf-OrganizationAlgorithmisbasedonlocalin-

teractions.Recently,Davidsenandco-workersproposedamodelofacquaintance

networks.[19]In theirmodel, the network isself-organized from aninitial ran-

domnetworktoaSmallWorldnetwork[17][18]characterizedashaving:1)short

pathlength,2)highclustering[17],and3)scale-freeorexponentiallinkdistribu-

tions[18].TheproposedCSOAinthispaperandDavidsen'salgorithmarebased

onsimilarlocalinteractions;indeed,theSelf-OrganizedCommunityNetworkhas

shortpathlength(L'3:0)andahighclustering(C '0:14)property.However,

in Davidsen's model, not everynode has aninternal state such asPeerDigest.

HowdoSmallWorldpropertiesinuencetheself-organizationprocesses?Future

workswilladdressthisquestion.

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