An efficient Search Tool for an Anti-Money Laundering Application of an Multi-national Bank's Dataset

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1 An efficient Serch Tool for n Anti-Money Lundering Appliction of n Multi-ntionl Bnk's Dtset Nhien-An Le Khc 1, Smmer Mrkos 1, Michel O'Neill 1, Anthony Brbzon 2 nd M-Thr Kechdi 1 1 School of Computer Science & Informtics, University College Dublin, Dublin, Irelnd 2 School of Business, University College Dublin, Dublin, Irelnd Abstrct - Tody, money lundering (ML) poses serious thret not only to finncil institutions but lso to the ntions. This criminl ctivity is becoming more nd more sophisticted nd seems to hve moved from the cliché of drug trfficking to finncing terrorism nd surely not forgetting personl gin. Most of the finncil institutions interntionlly hve been implementing nti-money lundering solutions (AML) to fight frud ctivities. However, the AML systems re so complicted tht simple query tools provided by current DBMS my produce incorrect nd mbiguous results nd they re lso very time-consuming due to the complexity of the dtbse system rchitecture. In this pper, we present new pproch for identifying customers quickly nd esily s prt of n AML ppliction. This will help AML experts to identify quickly customers who re mnged independently cross seprte dtbses of the orgniztion. This pproch is tested on lrge nd rel-world finncil dtsets. Some preliminry experimentl results show tht this new pproch is efficient nd effective. Keywords: Anti-Money lundering, customer identifiction, serch lgorithms, tree topology, inverted list. 1 Introduction Money lundering (ML) is process of disguising the illicit origin of "dirty" money nd mkes them pper legitimte. It hs been defined by Genzmn s n ctivity tht "knowingly engge in finncil trnsction with the proceeds of some unlwful ctivity with the intent of promoting or crrying on tht unlwful ctivity or to concel or disguise the nture loction, source, ownership, or control of these proceeds" [7]. Through money lundering, criminls try to convert monetry proceeds derived from illicit ctivities into clen funds using legl medium such s lrge investment or pension funds hosted in retil or investment bnks. This type of criminl ctivity is getting more nd more sophisticted nd seems to hve moved from the cliché of drug trfficking to finncing terrorism nd surely not forgetting personl gin. Tody, ML is the third lrgest Business in the world fter Currency Exchnge nd Auto Industry. According to the United Ntions Office on Drug nd Crime, worldwide vlue of lundered money in yer rnges from $500 billion to $1 trillion [1] nd from this pproximtely $ Billion is ssocited with drug trfficking. These figures re t times modest nd re prtilly fbricted using sttisticl models, s no one exctly knows the true vlue of money lundering, one cn only forecst ccording to the frud tht hs lredy been exposed. Nowdys, it poses serious thret not only to finncil institutions but lso to the ntions. Some risks fced by finncil institutions cn be listed s reputtion risk, opertionl risk, concentrtion risk nd legl risk. At the society level, ML could provide the fuel for drug delers, terrorists, rms delers nd other criminls to operte nd expnd their criminl enterprises. Hence, the governments, finncil regultors require finncil institutions to implement processes nd procedures to prevent/detect money lundering s well s the finncing of terrorism nd other illicit ctivities tht money lunderers re involved in. Therefore, nti-money lundering (AML) is of criticl significnce to ntionl finncil stbility nd interntionl security. Typiclly, n AML system is composed of some components such s customer identifiction, trnsction monitoring, cse mngement, reporting system, etc. Among them, customer identifiction is one of the most importnt tsks s it ssists AML experts in monitoring customer behviours; trnsctions tht they re involved in, their frequencies, vlues, etc. Fundmentlly, customer is identified by serching customer dtbses using query tools provided by DBMS. However, in the cse where specific customer is stored in seprte dtbses tht re mnged independently, this will require very lrge processing time due the serch opertions initited over ll the dtbses. Users need firstly to login to different dtbses, run the sme query repetedly, get the results seprtely, nd displyed independently. Furthermore, in lrge finncil institutions, these dtbses re heterogeneous nd hve very complex designs. This sort of pproch llows gret flexibility, however it hs poor performnce. In ddition, dt qulity is lso nother fctor tht mkes this nïve pproch becoming unfesible.

2 In this pper, we present new pproch for identifying customers in n interntionl investment bnk BEP 1. This pproch provides globl view of customer informtion nd it is developed s tool tht llows the users to quickly nd efficiently identify customers who re mnged independently cross seprte dtbses. This tool is component of n AML solution developed for BEP. The rest of this pper is orgnised s follows: the section 2 presents bckground highlighting the current sttus of BEP s dtsets nd their customer serch problems within the AML context. Some indexing pproches re lso discussed in this section. We present our new pproch tht is globl indexing bsed on word-ordered grouping nd inverted list in the Section 3. We describe the implementtion of this pproch in Section 4. Section 5 presents preliminry experimentl results. Finlly, we conclude in section 6. 2 Bckgrounds We strt this section with brief presenttion of n AML project t BEP nd then we will discuss on customer serch problems in its current environment. We finish this section by reviewing some indexing pproches for dt serch in the literture. 2.1 AML in BEP Similr to ny bnking institution, BEP is required by lw to conduct strict AML governnce on ll trnsctions. The BEP AML Unit does not hve n utomted solution to support pttern recognition nd detection of suspicious ctivities. The purpose of this project is to pply new principles nd methodologies to build n AML frmework in order to detect suspicious customer trnsctions nd behviour for the AML Unit. In this frmework, one of the importnt components is customer identifiction. Before lunching ny customer trnsctionl investigtion, the customer should be identified in ll customer dtbses of BEP. The structure of the BEP dtbses is complicted nd there re mny problems with dt qulity tht will cn be extrcted nd nlysed, which re discussed in the following prgrphs. BEP dtsets re divided into different environments corresponding to sixteen clients with multiple funds per client nd mnged hence by sixteen independent dtbses. When new customer or n investor X wnt to invest into specific fund (client specific), the AML tem would request certin documenttion nd will lwys tret him s new customer even though he could lredy invest into one/more of the other fifteen clients, i.e. lredy exist in nother dtbses. The purpose of the customer serch is to verify nd identify customer s profile in ll invested funds. The AML Unit is 1 Rel nme of the bnk cn not be disclosed becuse of confidentil greement of the project. currently pplying mnul serch bsed on DBMS queries. However, this is time-consuming tsk becuse users should login seprtely to ech dtbse nd crry out repeted queries. Moreover, ech dtbse contins not only dt but lso its met-dt, so mny joint opertions re needed to retrieve the informtion required. Menwhile, the dt qulity is lso nother impct tht ffects the serching tsk. BEP s input GUI is not efficient nd its dtbses design is cumbersome. Ech customer dtbse is identicl, i.e. the customer identifiction (CID) is only unique in this dtbse but the CID is not unique in ll dtbses. For instnce, we cn hve (nme= John Smith, CID= ) in dtbse A vs. (nme= Peter Chng, CID= ) in dtbse B. Briefly; there is uniqueness violtion t the globl level. Furthermore, ech dtbse hs different set of qulity problems t the instnce level. Some problems cn be listed s: Missing vlues, dummy vlues or null. These pper in most of the dt fields in ll dtbses except the CID, the customer type (corporte, individul nd joint) nd the fund nme. Misspellings; usully typos nd phonetic errors. For instnce, we hve MACAO vs. MACAU, vs , Bloggs Corportion A/C 001 vs. Bloggs Corportion 001, etc. Abbrevitions; e.g. A/C vs. AC nd Account Word trnspositions; e.g. John Smith vs. Smith John Duplicted records e.g. John Smith vs. J. Smith Moreover, the nmes of some corporte customers re normlly not identicl even though they re the sme compny. For instnce, First Commercil Bnk Ltd 2, First Commercil Bnk Ltd OBB Account, First Commercil Bnk Ltd Trust Account TA , First Commercil Bnk Ltd Trust Account TA , etc. We cll this compny nme group property. Besides, some customer dtbses lso hve the problem of incoherent dt in ddress dt fields. The ddress informtion includes the following dt fields: Street, Town, Zip, Country Code nd Country. And then, for exmple, the Zip field contins informtion bout the street, house number nd/or town, city insted of its zip code. 2 Agin, due to the confidentil greement, ll exmples presented in this pper do not use the rel customer nmes, compny nmes nd ddress

3 Becuse of the customer dtsets qulity s well s its complicted design, the mnul customer serch tsk by DBMS queries currently tkes more thn two hours to identify customer. 2.2 Indexing Fundmentlly, serch engines index the dt in order to fcilitte fst nd ccurte informtion retrievl. Some indexing methods in literture re tree-bsed, suffix tree, inverted list, cittion index, ngrm index, term document mtrix, etc. The tree-bsed index would be the most populr method where the serch opertions re linked with tree nodes. The tree topology cn be vried from binry [10] to B-Tree fmily such s B/B*/ B+Tree [2] [3] [12]. For instnce, some DBMS implement n index structure bsed on B-Tree such s MySQL, SQL Server [11]. Nevertheless, this topology is not efficient enough for indexing complex, heterogeneous, nd bd qulity dt fields. 5 $ n 3 $ n $ 1 suffix link b n n $ 0 n $ n $ 4 2 internl node lef node 1 strting position Figure 1. A suffix tree for bnn$ Suffix tree [8], so-clled PAT tree or position tree, is dt structure tht presents given string in suffix wy (Figure 1). The suffix tree for string S is tree whose edges re lbelled with strings such tht ech suffix of S corresponds exctly to one pth from the tree s root to lef. The dvntge of suffix tree is tht opertions on S nd its substring cn be performed quickly. However, the constructing suffix tree tkes time nd storge spce liner in the length of S. Inverted list [13] is nother kind of index where ech entry in the index tble includes two elements: n tomic serch item nd list of occurrences of this item in the whole serch spce. For exmple, the index of book lists every pge on which certin importnt words pper. This pproch is normlly implemented by the hsh tble [4] or the binry tree [10]. Inverted list is one of the most efficient index structures [14]. Cittion index pproch [6] stores the cittion or hyperlinks between documents to support cittion nlysis. This pproch is normlly pplied in the Bibliogrphy domin. Ngrm index [9] stores sequences of length of dt nd term document mtrix stores the occurrences of words in the documents in two-dimensionl sprse mtrix. The lst two index methods re minly used in informtion retrievl or text mining [5]. 3 Customer Serch pproches As mentioned bove, the current techniques bsed DBMS queries re not suitble for the BEP s AML system, s they depend strongly on the qulity of the dt sets. Therefore, the current qulity of BEP s customer dt sets should be improved before running ny query. For instnce, in order to correct the misspelling problem, spelling module should be implemented to del with typos nd phonetic errors. Similrly, bbrevited words must be uniform cross ll seprte dtbses; e.g. A.C, Account, AC. re trnsformed to A/C. Menwhile, dt mining techniques such s decision tree induction, regression, nd inferencebsed tools cn be pplied to fill missing vlues (tuples tht contin missing vlue fields cnnot be ignored becuse ll customer informtion re importnt). Indeed, in some cses, we should fill the missing vlue mnully. Similrity, the word trnsposition nd duplicted records often need mnul intervention. Besides, the incoherent dt problem in ddress dt fields (ref. Section 2.1) cn only be mnully corrected but it is n unfesible tsk with lrge dtsets. Briefly, generl solution for improving efficiently the qulity of BEP s customer dt sets is still n open question. Finlly yet importntly, the execution of DBMS queries on sixteen independent BEP s customer dtbses is lso very timeconsuming tsk. Next, we present our pproch, which cn overcome the qulity nd design problems of BEP s customer dtbses 3.1 Bsic concepts In this new pproch, we im to provide globl view of informtion bout ll customers mnged independently cross the BEP s customer dtbses. Concretely, we build globl index of these customers nd provide serch engine for AML users. Firstly, by nlysing BEP s customer dtsets, some importnt fetures cn be summrised s: There re two min types of customer: individul nd corporte. The individul customer hs two nme fields: First Nme nd Lst Nme. In some records, First Nme (resp. Lst Nme ) field stores ll prts of customer nme; e.g. in record X, First Nme field stores John Smith nd its Lst Nme is empty. This is specil kind of missing vlue. The corporte customer hs only one nme field: Compny Nme nd most of them hve compny nme group property s mentioned in the Section 2.1 bove.

4 The Country field is the most populr, i.e. its missing vlue is less thn 1%. In ddition, we lso build summry of ll bbrevition cses fter this pre-processing process. We build our solution bsed on these fetures nd ssuming tht ll bbrevition words re expressed in uniform wy s well s ll minor dt preprtion is performed. In order to del with the incoherence of the ddress dt (Section 2.1), we merge ll of the ddress dt fields into one field clled Address. Similrity, we merge the First Nme nd Lst Nme of individul customer into one field nmed Customer Nme to solve the missing vlue problem. For ech dt record, word order in Address nd Customer Nme cn vry. For instnce, we cn hve John Smith vs. Smith John or 123, Min Street vs. Min Street, 123 (word trnsposition problem). Therefore, the inverted list technique cn be used in this cse to index Address nd Customer Nme. Menwhile, the word order in the Compny Nme field is importnt becuse of the compny nme group property. Therefore, we need nother type of index in this cse. We cn ddress the compny nme group s kind of suffix problem nd we cn then use the suffix-tree topology. Nevertheless, the implementtion of this topology is complicted. Hence, we rely on this topology to build n index tree, which is simpler thn the suffix tree for the Compny Nme field of BEP s customer dtsets. So, our globl index is composed of three min prts: Compny Nme index, Customer Nme nd Address index nd they re bsed on tree topology (the first index) nd inverted list (the lst two ones), which re detiled in the following section. by C i, CN tree T of N from customer dtsets is defined by the following: The height of T is h, h =mx(c i ), i =1,2...n node p k T, p k set of elements {em}: Crd{em}>0, em w j. The level 0 hs t most one node p0. Ech element em0 i p0, em0 i contins the first word of ech compny nme cn i. element em T t the level l (l > 0), there is link, so-clled node-link, between em nd node p em T t the level l+1. The node p em contins the first word of ll suffixes of the word w j stored in em. A pth from the root to the lef by following nodelinks will crete specific compny nme. Ech element t the lef level does not hve node-link but list of {client identifiction (FID), customer identifiction (CID)} of the compny nme tht cretes this pth. 3.2 Index rchitecture The min rchitecture of our index consists of Compny Nme tree, Customer Nme nd Address inverted lists (Figure 2, 3 nd 4). Furthermore, the whole customer dtsets re grouped by the customer type (corporte or individul) nd by the country. Compny Nme Tree (CN tree) design. This is suffix tree bsed topology. Generlly, the first word of compny nme ppers t the root level (level 0) nd its lst word is t the lef level (Figure 2). The CN tree includes set of nodes. Ech node contins one or mny elements nd ech element t level l links to only one node t level l+1 or to NULL if l is t the lef level. Ech element hs key, which is word from the compny nme string. Hence, ech node p t the level l (l>0) contins ll words derived from their prefix word t the level l-1 nd so on. Formlly, supposing tht customer dtsets includes set of n compny nmes N nd ech compny nme cn i N (i=1, 2... n) is string composed of set of words w j nd the number of words in cn i is noted Figure 2. An exmple of CN tree index For instnce, s shown in Figure 2, we hve the following compny nme: FIRST COMMERCIAL BANK LTD, FIRST BANK LTD OBB ACCOUNT, FIRST AMERICA BANK LTD TRUST ACCOUNT TA , FIRST AMERICA BANK LTD TRUST ACCOUNT TA , ABC CAPITAL GROUP, ABC CAPITAL NEW YORK BRANCH, BANK OF UBUBA nd INTERNATIONAL DDD INVEST CORP. Hence, the root node hs four elements: em0 0 = ABC, em0 1 = BANK, em0 2 = FIRST nd em0 3 = INTERNATIONAL. The element em0 0 links to

5 node tht hs one element CAPITAL t the level 1. Then, this element links to nother node tht hs two elements: GROUP nd NEW. The element GROUP is t the lef level, it then contins { Skd, B123 } (list of {FID, CID} hs only one element). Otherwise, the element NEW links to nother node t the level 2, etc. The pth from the root node with the element ABC following its node-links cretes two compny nmes ABC CAPITAL GROUP nd ABC CAPITAL NEW YORK BRANCH. Similrity, the element em0 2 links to node with three elements t the level 1: AMERICA, BANK nd COMMECIAL. The element AMERICA links to nother node t the level 2 nd so on, t the level 7, the element is t the lef level nd contins [{Merlu, 1024}, {Abb, 392}] (list of {FID, CID} hs two elements). list per item (Figure 4). An item is word from the Address i.e. ech ddress in customer dtsets is lso prsed into set of seprte words. For instnce, we hve n ddress index tble s shown in Figure 4. The whole index structure is shown in Figure 5. In order to limit the serch spce, these dtsets re lso grouped by country (the most populr dt field, ref. section 3.1). Therefore, our serch engine llows users to lunch requests on customer informtion mnged in different dtbses only through their nme nd ddress. If customer is corporte then the serch process will scn the CN Tree nd Address Index tble. Menwhile, Customer Index Tble nd Address Tble re used for n individul customer. Advntges nd problems of this pproch will be discussed in section 5. Bsed on this CN Tree, the serch engine cn find ll {FID, CID} of requested compny nme. For instnce, if the query is ABC CAPITAL GROUP then the result is { Skd, B123 }, etc. Indeed, this index lso supports n pproximte serch i.e. users might not know the sufficient nme of compny so they just input its first few worlds, e.g. the query is ABC CAPITAL then the result list will be { Skd, B123 } nd { Abb, 566 }. Next, we cn retrieve ll detils of customers whose { FID, CID } re in the result list. Figure 4. An exmple of Address Index Figure 3. An exmple of Customer Nme Index Country Afghnistn... Belrus... Zimbbwe Index Customer Nme Index is n index tble bsed on the inverted list technique. This index tble consists of two prts: items nd collection of lists; one list per item (Figure 3). An item is word from the Customer Nme i.e. ech customer nme in customer dtsets is prsed into set of seprte words. For instnce, the customer nme John Smith is split into two words: John nd Smith. A list L i of word w i records tupes of {FID, CID} of customers whose nmes contin the word w i. We hve, for exmple, the customer John Smith with {FID= ABBA, CID= 1234 } nd Murphy John with {FID= MERLU, CID= 112 }. Hence, the index tble hs three elements: [ John : { ABBA, 1234 }, { MERLU, 112 }], [ Murphy : { MERLU, 112 }] nd [ Smith : { ABBA, 1234 }]. Address Index is lso n index tble bsed on inverted list technique. Similr to the Customer Nme Index, its index tble consists of two prts: items nd collection of lists, one 4 Implementtion Figure 5. Index structure We developed our pproch s serch tool bsed on distributed prdigm nd this tool is implemented s web services tht cn support 2-tier or 3-tier ppliction model

6 (Figure 6). We implemented services for two kinds of users: end-users nd dministrtive users. There re indexing, updting services for dministrtors. End-users exploit this system through serching nd extrcting services. 4.1 Indexing service This service scns ll the BEP s dtbses once nd builds indexes of Compny Nme, Customer Nme nd Address. Elements in ech node of Compny Nme index s well s items in Customer Nme nd Address index tble re sorted by lexicl order. Indexing service lso builds country list nd ech element in this list stores informtion (hsh code) bout pproprite entries of three indexes bove. These indexes re orgnised is min memory nd this service llows them to be sved in secondry memory. Hence, the dministrtor only needs to crete indexes once nd stores them in dtbses (index dtbses) nd then ech time she/he relods it to the min memory on ppliction lunch. In the rel world of bnking ppliction, these indexes re loded permnently in the min memory of the servers nd re synchronised periodiclly with their dtbses (index dtbses). The customer informtion is not rel-time dt processing i.e. when customer opens n ccount s/he lwys hs to wit for certin period of time for ll the security checks to be crried out (7 dys, for exmple) before the ccount is ctivted to perform her/his first trnsction (or Subscription in term of the investment bnking). Therefore, if indexes in the min memory re dmged due to system hlt, the electric cut, etc., the dministrtors cn relod them from index dtbses without losing ny informtion. Figure 6. Appliction models 4.2 Updting service When indexes re being exploited, new customer profiles re dded in the customer dtsets. Therefore, this service llows updting new customer informtion into indexes. It updtes firstly in the min memory of the servers nd it then synchronises this informtion with the index dtbses. The updte service is utomticlly performed t predefined time by scnning ll the dtbses (ll updte informtion of customer is lwys stored for uditing). 4.3 Serching nd extrcting service A serch request submitted by the users includes customer/compny nme nd its ddress. Serching service uses this informtion to look for set of relted {CID, FID} on Compny Nme / Address indexes (corporte customer) or on Customer Nme / Address (individul customer). This is followed by performing queries on dtbses to retrieve relted customer informtion. The users cn choose which informtion to extrct or perform further investigtions. 5 Evlution nd discussion We implemented nd tested our pproch on rel-world customer dtsets. The dtbse rchitecture is similr to BEP s dtbses. The hrdwre pltform for testing includes 1 Pentium Dul Core 3.4Ghz 2Gb RAM Windows Server 2003 (dtbse server), 1 Pentium 4 Hyper Threding 3.4Ghz 1Gb Windows XP SP2 (ppliction server), 1 Pentium 4 2.7Ghz Windows XP SP2 512Mb (front-end user). This web service-bsed tool is developed in C#/Visul Studio 2005 nd we use SQL Server All services re implemented t the ppliction server nd the dtbse server mnges the dtsets (3-tier model, Figure 6). The number of records is pproximtely for ll the dtbses. We rn different tests on this pltform nd took the verge results. The indexing time I is bout 17 seconds. The totl serch time S is bout 15 seconds (15s 40ms) for one request. The serch time S is composed of locl serch on the indexes, query process by SQL server nd communiction overheds; mong them, the locl serch on the indexes only tkes bout 2 milliseconds on the ppliction server. We lso lunched customer serch by SQL queries with exct customer/compny nme nd ddress utomticlly on ll the dtbses nd it tkes bout 3 minutes for one request. We cn see tht our technique is much more efficient. The pproch presented in this pper hs mny dvntges. First, it solves the problem of ccess nd querying independent nd seprte dtbses by providing globl view of ll customer informtion without chnging the current rchitecture of BEP s dtbses system. Then, this pproch lso overcomes the dt qulity problems tht normlly tke n importnt time to pre-process, especilly the mnul correction of incoherent ddress dt. The preliminry tests show tht it is efficient. It is bout 10 times fster thn the norml pproch by DBMS queries nd exhibits better ccurcy thn the trditionl pproch.

7 Moreover, our pproch lso supports prllel processing where two threds cn be lunched to serch independently on Compny/Customer Nme index nd Address index. It cn benefit from the multi-core rchitecture of BEP s servers. Besides, two min spects of this pproch need to be improved. The first one is memory consumption becuse ll index structures re stored in the min memory. However, ech item stored is not word but its hsh code nd it uses smll mount of memory in our experiments. Furthermore, in the rel BEP s servers, the min memory spce is greter thn 100 Gb nd the whole indexes tke less thn 0.2%. Besides, loding index in the min memory is norml prctice of mny current DBMSs to exploit it efficiently. Another spect is the repliction of items in the Compny Nme tree, e.g. the word BANK exists in mny nodes (Figure 2). This problem cn be improved by replcing this tree with grph where ech item ppers once s node nd the edges linking these items represent the pths. 6 Conclusions nd Future Works In this pper, we hve presented n pproch for identifying specific customer ptterns in n investment bnk. This pproch hs been developed s tool, which is set of web services on distributed pltform. The contribution of this reserch is to provide set of indexes combined with suffix-tree bsed on n inverted list in order to overcome the problem of dtbse design nd dt qulity of BEP s customer dtsets. Our experimentl results obtined on prts of the BEP s customer dtsets, we cn conclude tht pproch is very efficient tool nd it stisfies the needs of n AML tsk. Experimentl results on rel-pltforms of BEP re lso being produced nd these will llow us to test nd evlute the tool robustness. We re currently working on the grph index pproch tht tkes in ccount the memory consumption issue to tckle huge dtsets. A multithreding version is lso under development. 7 References [1] R. Bker, The biggest loophole in the free-mrket system, Wshington Qurterly, 1999, 22, pp [4] T. H. Cormen, C.E Leiserson, R.L. Rivest, C. Stein, Clifford Introduction to Algorithms MIT Press nd McGrw-Hill 2 nd Ed pp [5] R. Feldmn nd J. Snger The Text mining hndbook: Advnced Approches in Anlyzing Unstructed Dt, Cmbridge University Press, [6] E. Grfield, A. I. Pudovkin, V. S. Istomin. Algorithmic Cittion-Linked Historiogrphy-Mpping the Literture of Science. ASIS&T 2002: Informtion, Connections nd Community. 65th Annul Meeting of ASIST, Phildelphi, PA. November 18-21, [7] L. Genzmn, Responding to orgnized crime: Lws nd lw enforcement, Orgnized crime, In H.Abdinsky (Ed.) Belmont, CA: Wdsworth, pp [8] R. Giegerich nd S. Kurtz "From Ukkonen to McCreight nd Weiner: A Unifying View of Liner-Time Suffix Tree Construction". Algorithmic 19 (3): , 1997 [9] ctlogid =LDC2006T13 [10] D. E. Knuth The Art of Computer Progrmming: Fundmentl Algorithms, Addison Wesley, 3 rd Ed. Volume 1, Chpter 2, 1997 [11] [12] A.A. Toptsis. "B**-tree: dt orgniztion method for high storge utiliztion". Computing nd Informtion, Proceedings ICCI '9, 1993.: pges [13] J. Zobel, A. Mofft, R. Scks-Dvis. An efficient indexing technique for full-text dtbse systems Proceeding of the 18th VLDB Conference Vncouver, British Columbi, Cnd, 1992, [14] J. Zobel, A. Mofft, R. Scks-Dvis. Serching Lrge Lexicons for Prtilly Specified Terms using Compressed Inverted Files, Proceeding of the 19th VLDB Conference Dublin, Irelnd, 1993, [2] R. Byer, Binry B-Trees for Virtul Memory, Proceedings of 1971 ACM-SIGFIDET Workshop on Dt Description, Access nd Control, Sn Diego, Cliforni, November 11-12, [3] R. Byer nd E.M. McCreight Orgniztion nd Mintennce of Lrge Ordered Indices. Act Informtic 1, 1972:

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