Preprocessing of Web Usage Data for Application in Prefetching to Reduce Web Latency

Transcription

1 Internatonal Journal of Electrcal& Computer Scences IJECS-IJENS Vol:14 No:04 1 Preprocessng of Web Usage Data for Applcaton n Prefetchng to Reduce Web Latency G T Raju Professor, Department of CSE, RNS Insttute of Technology, Bangalore Vsvesvaraya Technologcal Unversty, Karnataka, INDIA gtraju1990@yahoo.com Nandn N Research Scholar, Bharathyar Unversty Lecturer, Department of CSE RNS Insttute of Technology, BangaloreVsvesvaraya Technologcal Unversty,Karnataka, INDIA nandu_8449@redffmal.com Abstract--The Popularty of Web resulted n heavy traffc n the Internet. Ths ntense ncrease n nternet traffc has caused sgnfcant ncrease n the user perceved latency. In order to reduce ths effectvely. Prefetchng technques are found to be best sutable. Prefetchng technque s motvated by the fact that, n general, once a user goes to a Web ste; he/she generally browses around for several pages before leavng for another ste. Snce the user follows hyperlnks upon hs/her nterests, t s lkely that lnks are not followed unformly. It s possble to ether predct each user s nterest usng cookes or mne a consensus of nterests (.e., generally what pages wll be requested after the current page) wth some confdence from access log fles recorded by the Web server. Ths nformaton not only s valuable for the Web admnstrator to elmnate unnterested pages, or balance load among the servers, but also can help to mprove Web-browsng tme. In ths paper, we propose a comprehensve preprocessng methodology as a prerequste and frst stage for Prefetchng applcaton, whch has four steps: Data Cleanng, Identfcaton of users & Sessons, and fnally the Data Formattng and Summarzaton. An attempt s made to reduce the quantty of the WUD and thereby mprove the qualty of WUD for effectve use n Prefetchng applcaton. Several heurstcs have been proposed for cleanng the WUD whch s then aggregated and recorded n the relatonal data model. To valdate the effcency of our preprocessng methodology, several experments were conducted log fles on three dfferent web stes: Academc, Research, Commercal and the results shows that our methodology reduces the Web access log fles sze down to 72-83% of the ntal sze and offer rcher logs that are structured for applcaton n Prefetchng. Index Term-- Preprocessng, Prefetchng, Web Usage Data, Web Usage Mnng. 1. INTRODUCTION The exponental growth of the Web n terms of Web stes and ther users durng the last decade has generated huge amount of data related to the user s nteractons wth the Web stes. Ths data s recorded n the Web access log fles of Web servers and usually referred as Web Usage Data (WUD). Web Usage Mnng (WUM) deals wth the applcaton of data mnng technques to extract nterestng knowledge from the WUD. Data preprocessng has a fundamental role n Web Usage Mnng (WUM) applcatons. A sgnfcant problem wth most of the pattern dscovery methods s that, ther dffculty n handlng very large scales of WUD. Despte the fact that, most of the WUM processes done off-lne, the sze of WUD s n the orders of magntude larger than those met n common applcatons of machne learnng. Rushng to analyze usage data wthout a proper preprocessng method wll lead to poor results or even to falure. Preprocessng methodology has not receved enough analyss efforts. Hence managng the quantty of data that s contnuously ncreasng, and the great dversty of pages on Web ste has become crtcal for WUM applcatons. Prefetchng means fetchng of Web pages before the users request them. Prefetchng technque s lke a Web cachng technque to reduce the user perceved latency. In Web usage mnng s the area of data mnng whch deals wth the dscovery and analyss of usage patterns from web logs, n order to mprove web based applcatons. Web usage mnng conssts of three phases, preprocessng, pattern dscovery, and pattern analyss. Web Usage Mnng [22,23] data can be collected n server logs, applcaton server logs. A complete preprocessng technque s beng proposed to preprocess the web log for extracton of user patterns. In ths paper, we propose a comprehensve preprocessng methodology that allows the analyst to transform any collecton of web server log fles nto structured collecton of tables n relatonal database model for further use n Prefetchng applcaton. The log fles from dfferent web stes of the same organzaton are merged to apprehend the behavors of the users that navgate n a transparent way. Afterwards, ths fle s cleaned by removng all unnecessary requests, such as mplct requests for the objects embedded n the Web pages and the requests generated by non-human clents of the Web ste (.e. Web robots). Then, the remanng requests are grouped by user, user sessons, page vews, and vsts. Fnally, the cleaned and transformed collectons of requests are saved onto a relatonal database model. We have provded flters to flter the unwanted, rrelevant, and unused data. Analyst can select the log fles from dfferent web servers and decde what entres he/she s nterested (HTML, PDF, and TXT). The objectve of ths research work s to consderably reduce the large quantty of Web usage data avalable and, at the same tme, to ncrease ts qualty by structurng t and provdng addtonal aggregated varables for the data mnng analyss that follow. The rest of the paper s organzed as follows. We formalze the problem of data preprocessng n secton 2. An overvew of the data preprocessng process s gven n secton 3. We

2 Internatonal Journal of Electrcal& Computer Scences IJECS-IJENS Vol:14 No:04 2 present the four steps System Archtecture: Data cleanng (secton 3.1), Identfcaton (secton 3.2) of Users, Sessons, Page vews and data formattng & summarzaton (Secton 3.3). The expermental results and observatons are provded n secton 4. Related work s gven n secton 5. Fnally, n secton 6, we conclude and provde some perspectves n Prefetchng applcatons. 2. PROBLEM FORMALIZATION Consder the set R = {r 1, r 2, r nr } of all Web resources from a Web ste. If U = {u 1, u 2, u nu } s the set of all the users who have accessed that ste, we can defne a log entry as l =<u, t, s, r, [ref ] >, where u U; r R; t represents the access tme, and s represents the request status. ref represents the referrng page and s optonal as n some Web log formats, lke the CLF format[1]where the referrng page s not recorded. s s a threedgt code ndcatng the request's success or falure. In the latter case, t also ndcates the cause of the falure. A status wth a value of 200 represents a succeeded request; whle a status of 404 shows that the requested fle was not found at the expected locaton. L = {l 1, l 2, l nl }, ordered ascendng by the tme value of l, consttutes a Web server log. The preprocessng frst groups the log entres nto page vews by usng the log. A page vew p conssts of p = {r 1, r 2, r np }, where r j R. The compacted log entry s lp =<u, t, p, ref >. Consderng a tme nterval t, we can defne a vst v for a user u as v =<u, t, pv >, where pv =<(t 1,p 1 ); (t 2,p 2, (t n,p n ) >; t +1 t and t +1 - t <t; =1:n-1.Usng these notatons, the preprocessng problem s formalzed as follows: Gven a thatlog fle for gven Web ste L. The objectve s to extract the Users, User sessons, vsts, and page vews of the Web stes' users wth a gven t. The sesson dentfcaton process Varable w R D N r j t rj S S L S j j n Table I Summary of Varables and Notatons Descrpton The total number of users The ordered lst of th user access records n the web log fle The set of all R The total number of access records n R The j th access record of user The tme when r j was logged n the web log Sessons for all the users Sessons for the user Total number of sessons for user j th sesson for user The length for sesson S j 3. SYSTEM ARCHITECTURE System Archtecture of the proposed work s shown n fgure 1. It conssts of Pre processng, Sequental Pattern Mnng and Prefetchng Modules. In ths paper we focus on Web usage data pre processng, whch s an essental step for Sequental Pattern Mnng and n turn for prefetchng applcatons. can be formulated as follows: LetR ={r 1,r 2, r N } be the ordered lst of th user access records n the log (sorted by the ascendng order of the access frequency), and t rj (0 < j N ) be the tme when r j was logged n the web log fle. Let A= { A 1, A 2,.A v } be the lst of categores and V the total number of categores. We assume that each r j s represented by one of these categores and the user generates L sessons (possbly unequal length ordered sequences of pages). Assumng that w s the total number of users n the web log fle, let D = {R 1, R 2,...,R w } be the observed access records for all the users n the log. The sessons for ndvdual user are defned as a lst of subsets: S 1 = { r 1, r 2,.r n 1 }, S 2 ={ r ( n 1 +1),, r ( S 1 = {r ( n 1 nl ).. r ( Where t r( n 1 +1) t r( n 1 ) 30 mnutes, N= S 2 S L S n 1 n L + + n 1 + n 2 ) }, S L n )} n L and S1 Then the set S={S 1, S 2,..S w } represents the sessons for all the users n our data set. A summary of all the above varables and notatons s gven n the Table I. Fg. 1. System Archtecture Data preprocessng of web logs s usually complex and tme demandng. It comprses of four steps: Data cleanng, User Identfcaton, Sesson dentfcaton, and Data formattng &Summarzaton. At the begnnng of the data preprocessng, we have the Log contanng the Web server log fles collected by several Web servers. When sharng logs fles or publshng results, for prvacy reasons, we need to remove the host names or the IP addresses. Therefore, we replace the orgnal host name wth an dentfer that keeps the nformaton about the doman extenson (.e. the country code or organzaton type, such as.com,.edu, and.org). We can also use these parameters later n the analyss, and the log fles can be shared wthout revealng senstve nformaton. 3.1 Data Cleanng The frst step of data preprocessng conssts of removng useless requests from the log fle. Snce all the log entres are not vald, we need to elmnate the rrelevant entres. Usually, ths process removes requests concernng non-analyzed resources such as mages, multmeda fles, and page style fles. For example, requests for graphcal page content (*.jpg & *.gf mages) and requests for any other fle whch mght be ncluded nto a web page or even navgaton sessons performed by robots and web spders. By flterng out useless

3 Internatonal Journal of Electrcal& Computer Scences IJECS-IJENS Vol:14 No:04 3 data, we can reduce the log fle sze to use less storage space and to facltate upcomng tasks For example, by flterng out mage requests, the sze of Web server log fles reduced to less than 50% of ther orgnal sze. Thus, data cleanng ncludes the elmnaton of rrelevant entres lke: Requests executed by automated programs, such as web robots, spders and crawlers; these programs generate the traffc to web stes, can dramatcally bas the ste statstcs, and are also not the desred category whch WUM nvestgates. Requests for mage fles assocated wth requests for partcular pages; an user s request to vew a partcular page often results n several log entres because that page ncludes other graphcs, whle we are only nterested n what the users explctly request, whch are usually text fles. Entres wth unsuccessful HTTP status codes; HTTP status codes are used to ndcate the success or falure of a requested event, and we only consder successful entres wth codes between 200 and 299. Entres wth request methods except GET and POST. 3.2 Identfcaton Ths step groups the unstructured requests of a log fle by user, user sesson, page vew and vsts. At the end of ths step, the log fle wll be a set of transactons, where by transacton we refer to a user sesson or a vst User In most cases, the log fle provdes only the computer address (name or IP) and the user agent (for the ECLF[2] log fles). For Web stes requrng user regstraton, the log fle also contans the user logn (as the thrd record n a log entry). In ths case, we use ths nformaton for the user dentfcaton. When the user logn s not avalable, we consder (f necessary) each IP as a user, although we know that an IP address can be used by several users. In ths paper, we approxmate users n terms of IP address, type of OS and browsng software User Sessons Identfyng the user sessons from the log fle s not a smple task due to proxy servers, dynamc addresses, and cases where multple users access the same computer (at a lbrary, Internet cafe, etc.) or one user uses multple browsers or computers. A user sesson s defned as a sequence of requests made by a sngle user over a certan navgaton perod and a user may have a sngle (or multple) sesson(s) durng a perod of tme. Sesson dentfcaton s the process of segmentng the access log of each user nto ndvdual access sessons [5]. Two tmeorented heurstc methods: sesson-duraton based method and page-stay-tme based method have been specfcally proposed by [6, 7, and 8] for sesson dentfcaton. In ths paper, we use the tmeout threshold n order to defne the users sessons Page Vews We dentfy the page vews by usng the tme of the request. For requests made at the same tme (.e. the same second), we keep only the frst request (as ordered n the log fle) and dscard the followng ones. After the page vew dentfcaton, the log fle wll contan, normally, only one request for each user acton. The page vew dentfcaton step determnes whch page fle requests are part of the same page vew and what content was served. Ths s necessary to provde meanngful results n the pattern analyss phase. If ths step s not performed, the dscovered patterns can be domnated by page fles that make up a sngle popular page vew. Each sesson must start wth a seed page vew.e., an ntal page fle or set of page fles from whch all subsequent page vews wll be derved. In the vast majorty cases, the seed page vew s made up of a sngle fle, or starts wth a sngle fle that defnes the frame structure and mmedately causes addtonal page fles to be requested. It s very rare for an unrelated ste to lnk to more than one page fle of a dfferent ste n a sngle hypertext lnk. However, t s possble, and for these cases all of the page fles contrbutng to the seed page vew would have to be explctly entered nto the algorthm. Once the seed page vew s specfed for a sesson, each subsequent request s checked aganst the ste structure to determne whch frame s beng replaced. The new page vew conssts of the requested fle plus all of the page fles that are remanng n the browser dsplay from the prevous page vew. Each dstnct page vew dscovered n a log s gven an dentfer. 3.3 Data Formattng and Summarzaton Ths s the last step of data preprocessng. In ths step, frst, we transfer the structured fle contanng sessons and vsts to a relatonal database. Afterwards, we apply the data generalzaton at the request level (for URLs) and the aggregated data computaton for vsts and user sessons to completely fll n the database. The data summarzaton step concerns wth the computaton of aggregated varables at dfferent abstracton levels (e.g. request, vst, and user sesson). These aggregated varables are later used n the data mnng step. They represent statstcal values that characterze the objects analyzed. For nstance, f the object analyzed s a user sesson, n the aggregated data computaton process, we propose to calculate the followng varables: The number of vsts for that sesson; The length of the sesson n seconds (the dfference between the last and the frst date of the vst) or n pages vewed (the total number of page vews); 4. EXPERIMENTAL RESULTS Several experments have been conducted on log fles collected from NASA Web ste, Academc Web ste and MSNBC Web ste. Results show that our preprocessng methodology reduces sgnfcantly the sze of the ntal log fles by elmnatng unnecessary requests and ncreases ther qualty through better structurng. It s observed from the Table II that, the sze for s reduced to 72-83% of the ntal sze NASA log fle. Unque mage/css related statstcs are

4 Internatonal Journal of Electrcal& Computer Scences IJECS-IJENS Vol:14 No:04 4 presented n the fgures Fgure 2. Day wse unque vstors statstcs are presented n the Fgure3, and Page vews statstcs are presented n the Fgure4. The user sessons for Academc web ste are shown n the Table III.Smlarly for Academc web ste these statstcs are shown n Fgure 5,Fgure 6, and Fgure 7. The user sessons for NASA web ste are shown n the Table IV. Web transacton data for Academc web ste of50 pages s shown n the Table V. Sequence data for Academc web ste of 50 pages s shown n the Table VI. 5. RELATED WORK In the recent years, we have seen much research on Web usage mnng [3,4,5,6,7,8, 9,10,11,12,13,14,15,16]. However, as descrbed below, data preprocessng n WUM has receved far less attenton than t deserves. Methods for user dentfcaton, sessonzng, page vew dentfcaton, path completon, and epsode dentfcaton are presented n [3]. However, some of the heurstcs proposed are not approprate for larger and more complex Web stes. For example, they propose to use the ste topology n conjuncton wth the ECLF [2] fle for what they call the user dentfcaton. The proposed heurstc ams to dstngush between users wth the same IP address, OS and Browser by checkng every page requested n a chronologcal order. If a page requested s not referred by any prevous page requested, then t belongs to a new user sesson. The drawback of ths approach s that t consders only one way of navgatng n a Web ste, by followng lnks. However, n order to change the current page, the users can, for nstance, type the new URL n the address bar (most browsers have an auto completon feature that facltates ths functon) or they can select t from ther bookmarks. In another work [15], the authors compared tme-based and referrer-based heurstcs for vst reconstructon. They found out that a heurstc's approprateness depends on the desgn of the Web ste (.e. whether the ste s frame-based or frame-free) and on the length of the vsts (the referrer-based heurstc performs better for shorter vsts). In [16], Marquardt et al. addressed the applcaton of WUM n the e-learnng area wth a focus on the preprocessng phase. In ths context, they redefned the noton of vst from the e-learnng pont of vew. Frequent access patterns are determned from reconstructed sessons. There are several algorthms n the lterature for the sequental pattern mnng,the dffculty of mnng sequental patterns from a large fxed database has been wdely addressed n [24]. Baoyao Zhou [17] proposed a smple algorthm that to access sesson s created as a par of URL and the requested tme n a sequence of requests wth a tmestamp. The duraton of an URL s estmated as the dfference of request tme of successor entry and current entry. Ths algorthm s sutable when there are more number of URL s n a sesson. The default tme set by author s 30 mnutes per sesson. Murat Al et.al., [18] proposed, Smart Mner: A New framework for Mnng Large Scale Web Usage Data. Ths frame work s a part of Web Analytcs Software. Smart Mner sessons are set of paths traversed n the Web graph that corresponds to user navgatons among web pages. The Author developed an effcent verson of the Apror-All Technque whch uses the structure of web graph to ncrease the performance. The sessons constructed by SMART-SRA contans sequental pages accessed from server-sde works n two stages. 1. Tmestamp Orderng Rule: the tme stamp orderng condton smply represents the standard user sesson defnton. 2. Topology rule: The topology condton s ntroduced to force each user navgaton path to correspond to a path n the web ste graph. In Smart SRA there are two phases. In the frst phase, the access data stream of web users are parttoned nto shorter page request sequences called canddate sessons. In the second phase canddate sessons are dvded nto maxmal sub sessons such that for each consecutve page par n the sequence there exsts a lnk from prevous one to latter one. At the same tme, page stay tme rule for consecutve pages s also satsfed. An agent smulator s developed by authors to smulate an actual web user. It randomly generates a typcal web ste topology and a user agent to accesses the same from ts clent sde and acts lke a real user. An mportant feature of the agent smulator s ts ablty to model dynamc behavors of a web agent. Tme constrant s also consdered as the dfference between two consecutve pages s smaller than 10 mnutes. Robert F.Dell et.al [19] proposed, web user sesson reconstructon usng nteger programmng. The man advantage of ths method s constructon of all sesson s smultaneously Author approach s groups log regsters from same IP address and agent ensurng the lnk structure of the ste s followed n any constructed sesson. Each constructed sesson from a web log s an ordered lst of log regsters where each regster can only be used once n only one sesson. Experment s focused wth IP address wth hgh dversty and a hgher number of regsters. Sessons produced better match an expected emprcal dstrbuton. Navn Kumar Tyag et al., [21] proposed An Algorthmc approach to Data Pre processng n Web Usage Mnng. The author survey some data preprocessng actvtes lke data cleanng and data reducton and. It s mportant that before applyng data mnng technques to dscover user access patterns from web log, data must be processed to mantan qualty of results s based on data to be mned. An Effcent Algorthm for Data Cleanng of Log Fle usng Fle Extensons proposed [25], algorthm tested on the log fles for data cleanng. The approach show a somewhat salent reducton n the number of records and n the log fles sze and ncreases the qualty of the avalable data.

5 Internatonal Journal of Electrcal& Computer Scences IJECS-IJENS Vol:14 No:04 5 Webste NASA July 95 ACADE MIC Dec MSNBC Aug 95 Orgnal Sze (20.6M B) (2.9MB ) (7.6MB ) Table II Results after Preprocessng Sze after Preproce ssng % Reduct on n Sze No. of Sesson s % No. of Use rs % % Fg. 2.Unque Image/CSS related statstcs (NASA Log fle, July 1995)(Academc Log fle, Dec. 2013) Fg. 5. Unque Image/CSS related statstcs Fg. 3.Unque Vstors (NASA Log fle, July 1995) Fg. 6. Unque Image/CSS related statstcs (Academc Log fle, Dec. 2013)

6 Internatonal Journal of Electrcal& Computer Scences IJECS-IJENS Vol:14 No:04 6 Fg. 4. Page Vews statstcs (NASA Log fle, July 1995) Fg. 7. Unque Image/CSS related statstcs (Academc Log fle, Dec. 2013) Table III Sample User Sessons for Academc Web ste Sesson Id IP Address Date &Tme URL Accessed :37: :36: :37: :37:22 /ece-dep.html :22:24 /mages/placements.jpg :20:06 /mages/placements.jpg :30:25 /Alumn.html :31:06 /Alumn.html :30:34 /bestteachers.html :30:35 /bestteachers.html Table IV Sample User Sessons for NASA Web ste Sesson Id IP Address Date &Tme URL Accessed :16:58 /shuttle/mssons/sts-73/msson-sts-73.html :17:25 /shuttle/mssons/sts-74/msson-sts-74.html :26:33 /dsclamer.html :09:32 /dsclamer.html :27:49 /shuttle/countdown/countdown.html :28:11 /shuttle/technology/sts-newsref/stsref-toc.html :05:11 /shuttle/countdown/lftoff.html :06:33 /shuttle/countdown/lftoff.html

7 Internatonal Journal of Electrcal& Computer Scences IJECS-IJENS Vol:14 No:04 7 Table V Web Transacton Data for Academc Web ste of 50 pages User Id Page Id User Id Page Id U32 P38 U199 P47 U32 P15 U199 P24 U32 P35 U199 P36 U57 P29 U199 P37 U57 P26 U93 P39 U57 P15 U93 P9 U30 P38 U93 P4 U30 P26 U100 P43 U30 P15 U100 P33 U80 P28 U9 P32 U80 P21 U9 P29 U80 P30 U393 P34 U80 P36 U393 P18 U14 P23 U393 P8 U14 P14 U379 P28 U14 P46 U379 P28 U101 P9 U379 P20 U101 P4 U153 P10 U101 P15 U153 P12 U163 P15 U491 P1 U163 P44 U491 P9 U163 P13 U491 P4 In ther approach, a learnng sesson (LS), vst n our case, can span over several days f ths perod corresponds to a gven learnng perod. As shown above, the preprocessng step s mportant and should be present n all WUM analyss. Therefore, we compared our preprocessng methodology wth the preprocessng descrbed n other general WUM research works [3, 9, 10, 11, 12, 15 and16]. 6. CONCLUSION In ths paper, we have presented Pre-processng as a pre requste for Prefetchng applcaton. The expermental results presented n secton 4, llustrates the mportance of the data preprocessng step and the effectveness of our methodology, by reducng not only the sze of the log fle but also by ncreasng the qualty of the data avalable through the new data structures that we obtaned. Although the preprocessng methodology presented allows us to reassemble most of the ntal vsts, the process tself does not fully guarantee that we dentfy correctly all the transactons (.e. user sessons & vsts). Ths s due to the poor qualty of the ntal log fle ths may affect the data mnng, resultng n erroneous Web access patterns. Therefore, we need a sold procedure that guarantees the qualty and the accuracy of the data obtaned at the end of data preprocessng. Future work n ths regard n the applcaton of Frequent Pattern Mnng algorthms on the preprocessed web transacton data to get nterestng Frequent Sequental Patterns, further these patterns would be used as gudng rules for prefetchng applcatons. Table VI Sequence Data for Academc Web ste of 50 pages Use Id U32 Page Id P38, P15, P35 U57 P29, P23,P40 U30 U80 U14 U101 U163 U199 U93 U100 U9 U393 U379 U153 U491 P38, P26, P15 P28, P21, P30, P36 P23, P14, P46 P9, P4, P15 P15, P44,P13 P47, P24, P36, P37 P39, P9, P4 P43, P33 P32, P29 P34, P18, P8 P28, P28, P20 P10, P12 P1, P9, P4 REFERENCES [1] Confguraton fles of W3C httpd, (1995). [2] W3C Extended Log Fle Format, (1996). [3] J. Srvastava, R. Cooley, M. Deshpande, P.-N. Tan, Web usage mnng: dscovery and applcatons of usage patterns from web data, SIGKDD Exploratons, 1(2), 2000, [4] R. Kosala, H. Blockeel, Web mnng research: a survey, SIGKDD: SIGKDD exploratons: newsletter of the specal nterest group (SIG) on knowledge dscovery & data mnng, ACM 2 (1), 2000, 1 15 [5] R. Kohav, R. Parekh, Ten supplementary analyses to mprove e- commerce web stes, n: Proceedngs of the Ffth WEBKDD workshop, [6] B. Mobasher R. Cooley, and J. Srvastava, Creatng Adaptve Web Stes through usage based clusterng of URLs, n IEEE knowledge & Data Engg work shop (KDEX 99), 1999 [7] Bettna Berendt, Web usage mnng, ste semantcs, and the support of navgaton, n Proceedngs of the Workshop WEBKDD Web Mnng for E-Commerce - Challenges and Opportuntes, 6th ACM SIGKDD Int. Conf. on Knowledge Dscovery and Data Mnng, 2000, Boston, MA [8] B. Berendt and M. Splopoulou. Analyss of Navgaton Behavour n Web Stes Integratng Multple Informaton Systems. VLDB, 9(1), 2000, [9] A. Josh and R. Krshnapuram. On Mnng Web Access Logs. In ACM SIGMOD Workshop on Research Issues n Data Mnng and Knowledge Dscovery, pages 2000, [10] C. Shahab and F. B. Kashan. A Framework for Effcent and Anonymous Web Usage Mnng Based on Clent-Sde Trackng. In WEBKDD Mnng Web Log Data Across All Customers Touch Ponts, Thrd Internatonal Workshop, San Francsco, CA, USA, August 26, 2001, Revsed Papers, volume 2356 of LNCS, Sprnger, 2002, [11] Y. Fu, K. Sandhu, and M. Shh. A Generalzaton-Based Approach to Clusterng ofweb Usage Sessons. In Proceedngs of the 1999 KDD Workshop on Web Mnng, San Dego, CA, vol of LNAI,. Sprnger, 2000, [12] M. S. Chen, J. S. Park, and P. S. Yu. Effcent Data Mnng for Path Traversal Patterns. Knowledge and Data Engneerng, 10(2), 1998,

8 Internatonal Journal of Electrcal& Computer Scences IJECS-IJENS Vol:14 No:04 8 [13] B. Mobasher, H. Da, T. Luo, and M. Nakagawa. Dscovery and Evaluaton of Aggregate Usage Profles for Web Personalzaton. Data Mnng and Knowledge Dscovery, 6(1), 2002, 61-82, [14] M. El-Sayed, C. Ruz, and E. A. Rundenstener. FS-Mner: Effcent and Incremental Mnng of Frequent Sequence Patterns n Web Logs. In Proceedngs of the Sxth Annual ACM Internatonal Workshop on Web Informaton and Data Management (WIDM '04), ACM Press, 2004, [15] B. Berendt, B. Mobasher, M. Nakagawa, and M. Splopoulou. The Impactof Ste Structure and User Envronment on Sesson reconstructon n Web Usage Analyss. In Proceedngs of the Forth WebKDD 2002 Workshop, at the ACM-SIGKDD Conference on Knowledge Dscovery n Databases (KDD'2002), Edmonton, Alberta, Canada, 2002 [16] C. Marquardt, K. Becker, and D. Ruz. A Pre-Processng Tool for Web Usage Mnng n the Dstance Educaton Doman. In Proceedngs of the Internatonal Database Engneerng and Applcatons Symposum (IDEAS'04), 2004, [17] BaoyaoZhou,Su Cheung Hu and AlyssC.M.Fong, An Effectve Approach for Perodc Web Personalzaton,Proceedngs of the IEEE/ACM Internatonal Conference on Web Intellgence.IEEE,2006 [18] Murat Al Bayr,IsmalHakkToroslu, AhmetCosar and GveyFdan Dscoverng more accurate Frequent Web Usage Patterns., arxv v1,2008 [19] Lu, L., & Lu, J., Mnng maxmal sequental patterns wth layer coded Breadth-Frst lnked WAP-tree., Asa-Pacfc Conference on Computatonal Intellgence and Industral Applcatons (PACIIA), IEEE, pp , [20] Chetna Chand, Amt Thakkar, Amt Ganatra, Sequental Pattern Mnng: Survey and Current Research Challenges, Internatonal Journal of Soft Computng and Engneerng (IJSCE) ISSN: , Volume-2, Issue-1, March 2012 [21] Navn Kumar Tyag, A.K. Solank & Sanjay Tyag. An Algorthmc approach to data preprocessng n Web usage mnng, Internatonal Journal of Informaton Technology and Knowledge Management July- December 2010, Volume 2, No. 2, pp [22] Yogsh H K, Dr. G T Raju, Manjunath T N, The Descrptve Study of Knowledge Dscovery from Web Usage Mnng, IJCSI Internatonal Journal of Computer Scence Issues, Vol. 8, Issue 5, No 1, September 2011 [23] Vjayashr Losarwar, Dr. Madhur Josh, Data Preprocessng n Web Usage Mnng, Internatonal Conference on Artfcal Intellgence and Embedded Systems (ICAIES'2012) July 15-16, 2012 Sngapore [24] R. Agrawal and R. Srkant, Mnng sequental patterns, In 11 th Internatonal Conference of Data Engneerng (ICDE 95), pp. 3-14, 1995 [25] Surbh Anand, Rnkle Ran Aggarwal, An Effcent Algorthm for Data Cleanng of Log Fle usng Fle Extensons, Internatonal Journal of Computer Applcatons ( ),Volume 48 No.8, June 2012