Human performance on clustering web pages
Human Performance on Clustering Web Pages
Sofus A.Macskassy,Arunava Banerjee,Brian D.Davison,Haym Hirsh
Department of Computer Science
Rutgers,The State University of New Jersey
New Brunswick,NJ08903USA
sofmac,arunava,davison,hirsh@https://www.wendangku.net/doc/4f7062838.html,
August27,1998
Abstract
With the increase in information on the World Wide Web it has become dif?cult to?nd desired informa-
tion quickly without using multiple queries or using a topic-speci?c search engine.One way to help in
the search is by grouping HTML pages together that appear in some way to be related.In order to better
understand this task,we performed an initial study of human clustering of web pages,in the hope that it
would provide some insight into the dif?culty of automating this task.Our results show that subjects did
not cluster identically;in fact,on average,any two subjects had little similarity in their web-page clusters.
We also found that subjects generally created rather small clusters,and those with access only to URLs
created fewer clusters than those with access to the full text of each web page.Generally the overlap of
documents between clusters for any given subject increased when given the full text,as did the percentage
of documents clustered.When analyzing individual subjects,we found that each had different behavior
across queries,both in terms of overlap,size of clusters,and number of clusters.These results provide a
sobering note on any quest for a single clearly correct clustering method for web pages.1
1Introduction
Web pages are diverse,with an enormous number of ill-structured and uncoordinated data sources and a wide range of content,formats,ages,and authorships.New pages are being generated at such a rate that no individual or organization is capable of keeping track of all of them,let alone organizing them and presenting adequate tools for managing,manipulating,and accessing such information.For example,consider the last time you used a search engine.How many links to web pages did it produce?How many pages of suggested links did you have to go through before?nding what you wanted(if you did at all)?How many of the links you tried were unrelated to the topic of interest?
There is a wide range of approaches that have been taken to help people access and manipulate collections of on-line documents.Conventional document retrieval systems return a(usually long)list of ranked docu-ments based on a measure of each document’s similarity to the original query[9].Some work with general queries and general web-pages[8],while others are tailored to more focused tasks[10].A second class of tools provide graphical means for accessing data based,for example,on inter-document similarity[1,12,4], relationships to?xed attributes[11,6],and query term distribution patterns[5].A third approach is to clus-ter the collection of documents.For example,hierarchical agglomerative clustering(HAC)[13]?nds cluster centroids and clusters based on similarity to those centroids.A recent HAC-based method,Word-Intersection Clustering[15],clusters based on phrases and allows for overlapping clusters.Another interactive approach,
Scatter/Gather[3,2],lets the user navigate through the retrieved results and dynamically clusters based on this navigation.A K-means method[14]is used to cluster documents and?nd important words for each of those clusters.
Recently,we have been considering automated methods for clustering related web pages to simplify ac-cess to web-based information.To restrict ourselves to pages that are likely to be related,we have concen-trated our effort on web pages returned from a query to a search engine.Our results so far have not been encouraging,and so we must consider the question:Is it possible to meaningfully and effectively cluster web pages?Central to clustering methods are two assumptions;?rst that there are a set of coherent groups into which documents can be clustered,and second that there exist meaningful ways to cluster these docu-ment sets into coherent groups.It is these assumptions that we investigate in this paper.To this end we asked a group of subjects to cluster,by hand,the documents returned as the result of queries given a web search engine.This paper details the experimental process,presents our analysis of this initial survey,and raises issues regarding what it means to cluster in general.
2Data
Our experiments study how humans cluster collections of web pages returned from a web search engine.The ten subjects who participated in this study are members of the Rutgers Machine Learning Research Group —nine graduate students(including the?rst and third authors of this paper)and one faculty member(the fourth author of this paper).The search engine we used was the Rutgers webWatcher web search facility (https://www.wendangku.net/doc/4f7062838.html,),which indexes all pages at Rutgers University that are reachable by follow-ing links from the main Rutgers web page.This search engine was chosen for its inherent focus on Rutgers-speci?c information in the belief that by drawing on a narrower source of documents,there would be a greater likelihood of forming coherent clusters,as well as to exploit the background knowledge that all subjects had about Rutgers that could be brought to bear upon the clustering task.
Each subject was given the results of?ve queries to cluster.These?ve represent the most frequent queries involving disjoint sets of terms that were asked by users of the search engine as of7January1998.The?ve queries were:“accounting”,“career services”,“employment”,“library”,and“off campus housing”,with15, 16,16,11and10returned web links,respectively.To determine how important the actual text of the doc-ument was,for each query four subjects were given the complete text as well as the URLs and titles(when available)for each web link returned,while the other six subjects were given only the URLs and titles(again, when available).Subjects were assigned to queries randomly with the following restrictions:(a)no subject was asked to perform clustering with and without documents for the same query,and(b)each subject had access to the full text of each document for at least one query and had at least one query with access to only the URL and title of each returned web link.2Subjects were told to cluster the documents to the best of their ability and report for each query their clusters on a form provided them.The form had?ve spaces to re-port clusters on,but we speci?ed that additional clusters were acceptable.We also speci?ed that overlapping clusters were acceptable but not required.There was no time limit set on how long the subjects could spend on clustering the documents.Subjects spent between approximately45and90minutes to complete the en-tire task.See Appendix A for the subject response forms used.Appendix B contains,for each query,the clusterings that each subject created.
During our initial analysis of the data and post-experiment interviews with subjects,we found that some subjects placed in singleton clusters any document that they believed did not?t any of the other clusters, whereas other subjects had simply disregarded any such documents(and no subject who created singleton
clusters created one for a document that also appeared in a non-singleton cluster).To address this issue we deleted all singleton clusters from each subject’s results.We also found that some subjects listed documents in each cluster in the order in which they appeared in their original list of documents,while others did not. Furthermore,subjects listed their clusters in various orders.For example,most listed clusters neither in in-creasing nor decreasing size.Since the instructions to our subjects did not address these concerns,we did not study these issues.
3Results
Based on the results of the survey,we attempted to determine the degree of agreement between subjects and the extent to which the presence of the entire text affected the clusters.We also looked at the sizes of the clusters,the number of clusters,and the overlap between clusters.The latter would indicate whether it is appropriate to have disjoint clusters,as is often performed,or whether a more complex scheme of overlapping clusters is more appropriate.The questions we considered were:
1.How big were the generated clusters?Was there a correlation between the size of the clusters and the
total number of documents?Were there discernible patterns for individual subjects?
2.How similar were the generated clusters?Were there any subjects whose clusters were consistently
similar?Was there a pattern to the amount of agreement as we looked at bigger(sub)clusters?
3.How much overlap did subjects have between their clusters?In general was there a pattern to the
amount of overlap for any one query or subject?
4.How many clusters were generated?Was this the same for all subjects?Did it depend on the number
of documents or the subject?
We paid particular attention to how any of these results changed across subjects who had access to the full document and those that did not.
3.1Cluster Size
The?rst issue we study is what size clusters the subjects tend to form.For example,were bigger clusters pre-ferred over smaller ones or vice versa?Furthermore,did access to the complete text affect such preferences substantially?Finally,were such preferences universal or subject speci?c?
To address these questions we?rst compared the size of clusters for the full set of queries across all sub-jects who had access to the text to the size for those subjects who did not,and found little difference(Tables1 and2).Computing means and medians,we found that in both groups of subjects(those with access to the text and those without),the average cluster size was29.5%of the overall number of documents.The mean cluster size(in absolute terms)was found to be4.0(with the average number of documents per query being 13.6).A?nal interesting observation was that most subjects preferred to keep the average size of their largest clusters close to50%of the size of the entire document set.
To get a better idea of the behavior across all cluster sizes,we counted,for each set of documents clustered together by any subject,the number of times that set appeared across all subjects’clusters.Figures1(a)and 1(b)show,on average,how many times sets of documents of different sizes appeared per subject.It is clear that as the size grows bigger the number of times those groups appear decrease rapidly showing that the bigger size groups were only chosen by one subject.
However,looking at individual subjects we found that the range of sizes varied greatly both within and across queries.Table3depicts the absolute and proportional(with respect to the number of documents)range
Query (#docs):min mean min mean accounting (15)2
3.26
2
3.55
9 4.0
12
5.0
employment (16)2
4.94
2
3.69
6 2.0
6
3.0
off campus housing (10)2 4.002 3.50Overall(13.6):
2
3.992
3.98
Proportional (wrt #docs)Cluster Sizes without Documents with Documents max median max median 0.470.200.400.20career services (16)0.120.310.120.38
0.440.310.380.25library (11)
0.180.260.180.29
0.600.400.500.350.520.270.520.28
Table 2:Statistics on proportional cluster sizes per query,with and without documents.
00.511.522.533.5
40
2
468
10
12
n u m b e r o f o c c u r r e n c e s
Cluster Size
(a) Subjects without documents accounting career services employment
library
off campus housing
00.511.522.533.540
2
46810
12
n u m b e r o f o c c u r r e n c e s
Cluster Size
(b) Subjects with documents
accounting career services employment
library
off campus housing
Figure 1:Average number of occurrences per individual of each cluster size.
of cluster-size values for each subject.As a result,we conclude that there is a strong indication that users do not necessarily have a preference for a speci?c cluster size,and their cluster sizes are not signi?cantly affected by whether or not they have access to the full document texts.
Figure 2shows a graph of the probability that,for any query,a subject will generate a cluster of that size.
Subject Min Mean 12(.18)
5.33(.367)
7(.47)
4.0(.26)
32(.12)
3.89(.258)
9(.56)
2.5(.20)
52(.12)
3.71(.274)
7(.50)
3.0(.20)
72(.12)
3.08(.245)
9(.56)
3.0(.20)
9
2(.12)
5.15(.368)
6(.40)
3.0(.20)
Table 3:Absolute and proportional ranges of cluster-sizes per subject.
0.20.40.60.8124
681012
A v e r a g e f r e q u e n c y p e r i n d i v i d u a l
Cluster Size
only URL and title
using full text
Figure 2:The average frequency of occurrence of each cluster size.
This graph illustrates how the probability of bigger clusters being generated decreases rapidly,showing that in general the subjects preferred smaller clusters.
3.2Number of Clusters
A second issue we study is how many clusters are typically formed by subjects:overall,per query,and on an individual basis.An interesting question was whether the results changed signi?cantly between subjects who had access to the full text and those who did not.Table 4reports the average number of clusters generated.Both overall and per query values are reported.On average,those with access to the full text of a document seem to form more clusters than those without.
We found a comparatively large variance in the data when we compared the number of clusters generated by subjects who had access to the text to the number of clusters generated by those who did not (the only exception being the query “library’with 27%versus the 18-20%range from the others).We are not sure of the cause of this variance.
Interestingly,most subjects,while having great variance between each other,were relatively consistent
Num.of
Query Docs Ave.Proportion
0.2110.183
0.1350.188
0.1770.203
0.1970.273
0.1670.200
0.1760.206
for query number:
Subject24Average
531
433
221
231
543
121
322
342
352
222
0.050.10.150.20.250.30.350.40.450.5024
681012
P r o p o r t i o n o f p e o p l e a g r e e i n g
Cluster Size
Subjects without document Subjects with document
Figure 3:Proportion of subjects agreeing on a clustering of a particular subset of documents.
that subjects without documents on average across all ?ve queries had a similarity of 0.277,while those with documents had an average similarity of 0.162,with an overall average of 0.246.The amount of similarity varied greatly.Interestingly,this variation decreased when the documents were taken into account.
We also present a more ?ne-grained analysis of inter-subject similarity in Figure 4.Each cell contains
the preceding similarity calculation (on a scale of to
)for all pairs of subjects.Entries to the left of the diagonal represent cases in which both subjects performed clustering for a query when both had access to the full text of the documents for that query;those to the right correspond to the same when neither had access to the full text.Thus multiple entries in a cell indicate that the two subjects had more than one query in common,whereas an “xxx”indicates that they had no queries in common.Note that the similarity measures for pairs of subjects with multiple queries in common vary considerably,indicating that clustering is more query speci?c than subject speci?c.
As was mentioned above,when subjects were given the full text,more clusters were created.Because on average the size of the clusters stay constant,there is the potential for more pairings which should make the similarity between subjects higher.In actuality,given the text,the similarity dropped and the variance between similarities of subjects became much less.
3.4Amount of Cluster Overlap
We also studied the amount of overlap of clusters per subject.To quantify this measure we computed the average number of clusters to which each document belonged.This was done by calculating,for each sub-ject,the number of clusters in which a particular document occurred for each query.The average number of clusters for subjects without documents was 1.108,and 1.222for subjects with documents.
When the individual subjects were analyzed,the amount of overlap each subject had was extremely var-ied.The ranges across the ten subjects are shown in Table 6,with the measures of overlap being the average number of clusters in which each document appeared.Table 7shows the minimum and maximum proportion of clusters per subject,over all queries,that were disjoint from the rest.
These numbers are interesting in that they show that most subjects had a dissimilar way of clustering.The only exception to this general variation among subjects were seen here in that four of the ten people always chose to keep all the clusters disjoint.However,the high proportion of subjects forming overlapping clusters indicates that clustering methods allowing overlap may be more appropriate than those without overlap.
Figure4:Overall similarity between subjects with and without access to the text of the docu-
ments.Each entry in a cell corresponds to the two subjects compared with respect to one query.
Similarity is rated on a scale from1to1000.
3.5Documents not Clustered
In studying the data we observed that subjects did not place many of the documents in clusters at all.3In fact, when looking at the queries,it was found that on average more than a third of the documents were not placed in multi-document clusters.Table8shows the absolute ranges for the subjects,split by those who only had
the URL and title to work with and those who did not.Table9shows the proportional values of these same
Subject Max Subject Max
1.00 1.00
1.00 1.00
1.00 1.00
1.00 1.00
1.00 1.00
Min Min
1 1.006 1.00
20.257 1.00
3 1.008 1.00
4 1.009 1.00
5 1.0010 1.00
Table7:Proportion of clusters disjoint from the rest.
Absolute(per subject)
Query:Docs min mean max median
8 4.53 5.67
9 4.02 5.50
10 6.01 4.29
4 2.52 5.00
8 4.51 3.33
Overall13.60 4.5010 4.0 Table8:Absolute number of documents not clustered by subjects.
Proportion(per subject)
Query:min mean max median
.530.30.200.38
.560.25.130.34
.630.38.060.27
.360.23.180.46
.800.45.100.33 Overall.120.33.590.35 Table9:Proportional number of documents not clustered by subjects.
ranges.Given the data presented in the previous sections,it is surprising to?nd that the averages are almost the same for subjects with and without full text.
4Future Work
This work began as an off-shoot of our observation that it was dif?cult to build a system that clustered web pages when the subjective sense of the humans attempting to do so was that there was no obviously correct way to cluster them even by hand.Our goal in this work was to give more objective data supporting this pessimistic assessment by?nding a task in which the opportunity to cluster was hopefully increased by using a narrow range of web pages and using subjects very familiar with the domain of the documents.An obvious next step is to do a more elaborate experiment involving a larger number of people and documents.Ideally such experiments would explore queries for which a wider range of number of results occurs(especially cases where a query returns very large sets of documents).Given the small number of results for our queries, it is unreasonable to generalize our results too broadly,especially in relation to cluster sizes and number of clusters.Given more subjects and queries with more documents,we hope to be able to clarify these issues better.
A second issue to isolate in further experiments is whether the fact that subjects do not agree on clusters implies that there is no effective way to cluster the documents.For example,perhaps each subject’s way of clustering is a perfectly acceptable alternative,providing differing,but equally suitable ways to structure the results of a query.Our plan in subsequent work is to use a second disjoint subject group who would evaluate the merit of each cluster,to test the extent to which they are all acceptable.A negative result on this follow-up study would complement the results here—not only is there no way to uniquely cluster documents in all cases,but even when there are alternative ways to cluster,none are judged appropriate by all.The same subjective judgments that led us to perform this study also make us believe that this is true.A?nal piece of information that could be useful would be to?nd out exactly why subjects clustered the way they did.We plan to make more use of subject interviews in subsequent work.
5Summary
We had ten people cluster,by hand,?ve different sets of query-results from a fairly focused search domain. The data were analyzed to?nd generalities in the way the ten subjects clustered these results.Each subject tended to be diverse in his or her clustering across the?ve queries and little similarity was found between different subjects.It was found that subjects liked to create relatively small clusters,and that on average subjects tended to create fewer clusters with more overlap when given the full text as opposed to only the URL and title.These?ndings suggest that while there might be an acceptable overall clustering,people tend to be context speci?c and have little generality in the characteristics of the clustering,raising the question of whether effective clustering behavior can be achieved only through knowledge of the purpose of a query,if, indeed,a general-purpose clustering method is possible at all.
Acknowledgments
We would like to thank the members of the Rutgers Machine Learning Research Group for their participation in clustering the queries and for comments that helped the analysis of the data,and in particular Gary Weiss and Daniel Kudenko for comments on an earlier draft of this paper.This work was supported in part by NSF grant IRI-9509819and BSF grant96-00509.
A Subject Forms
This appendix includes the forms that were distributed to the subjects for our experiments.Each gives the query,lines for the clusters that the user forms,and the list of URLs that should be clustered.
A.1Query:Accounting
T hese are the results of your search for ‘‘"accounting".’’
Group-1:_______________________________
Group-2:_______________________________
Group-3:_______________________________
Group-4:_______________________________
Group-5:_______________________________
1.ftp://https://www.wendangku.net/doc/4f7062838.html,/pub/accounting
2.https://www.wendangku.net/doc/4f7062838.html,/~aldea/comments.html
3.https://www.wendangku.net/doc/4f7062838.html,/~jbragg/acct.html RUTGERS UNIVERSITY OFFICE OF BUSINESS
SERVICES-ACCOUNTING
4.https://www.wendangku.net/doc/4f7062838.html,/~lynneo/CAREER/cma.html AIP-Corporate
5.https://www.wendangku.net/doc/4f7062838.html,/~nwkmath/ac201.html
6.https://www.wendangku.net/doc/4f7062838.html,/~tectrain/account.html
7.https://www.wendangku.net/doc/4f7062838.html,/majors/accounting.html SBC Accounting Major
8.https://www.wendangku.net/doc/4f7062838.html,/meet/Alumni/Lewis_Juanita.html Juanita Lewis
9.https://www.wendangku.net/doc/4f7062838.html,/Academics/summer/business.html Summer Session School of
Business
10.https://www.wendangku.net/doc/4f7062838.html,/~cswebpg/OCR_Act.html EMPLOYER ON-CAMPUS INTERVIEWS
11.https://www.wendangku.net/doc/4f7062838.html,/~eclark/kli/m_accounting.html RECRUITMENT SOURCES - Accounting
12.https://www.wendangku.net/doc/4f7062838.html,/~gedajlov/1blinks.html Eric’s Home Page of Business Links
13.https://www.wendangku.net/doc/4f7062838.html,/~gillett/courses/fall96/AIS96.htm Accounting Information Systems 1996
14.https://www.wendangku.net/doc/4f7062838.html,/~ilc/ Division of Grant and Contract Accounting
15.https://www.wendangku.net/doc/4f7062838.html,/~ilc/vol1a.html
A.2Query:Employment
T hese are the results of your search for ‘‘"employment".’’
Group-1:_______________________________
Group-2:_______________________________
Group-3:_______________________________
Group-4:_______________________________
Group-5:_______________________________
1.https://www.wendangku.net/doc/4f7062838.html,/case/empop.htm
2."https://www.wendangku.net/doc/4f7062838.html,:80/~ispe/jobs.html
3. https://www.wendangku.net/doc/4f7062838.html,/~AGECON/brumfield.html
4.https://www.wendangku.net/doc/4f7062838.html,/~britto/off-campus.html Off-Campus Employment
5.https://www.wendangku.net/doc/4f7062838.html,/~dduncan/employment.htm 15-voting
6.https://www.wendangku.net/doc/4f7062838.html,/~lcrew/nwkprof.html Diocese of Newark: Privilege & Responsibility
7.https://www.wendangku.net/doc/4f7062838.html,/~lynneo/CAREER/ptjobs.html Part-time Employment
8.https://www.wendangku.net/doc/4f7062838.html,/~zahavy/cv.html Tzvee Zahavy
9.https://www.wendangku.net/doc/4f7062838.html,/~arnold/employ.html
10.https://www.wendangku.net/doc/4f7062838.html,/~ispe/jobs.html Employment-Related Information
11.https://www.wendangku.net/doc/4f7062838.html,/CPP/emp.html CPP Rutgers-Camden Employment Opportunities
12.https://www.wendangku.net/doc/4f7062838.html,/profcert/P6601.GIF
13.https://www.wendangku.net/doc/4f7062838.html,/~jtr/jtr3.htm
14.https://www.wendangku.net/doc/4f7062838.html,/~lienhong/ Lien Huong’s Home Page
15.https://www.wendangku.net/doc/4f7062838.html,/~mokhan/ Mir Moosa Khan’s Home Page.
16.https://www.wendangku.net/doc/4f7062838.html,/~steske/emphist.htm Employment History for Scott Teske
A.3Query:Library
T hese are the results of your search for ‘‘"library".’’
Group-1:_______________________________
Group-2:_______________________________
Group-3:_______________________________
Group-4:_______________________________
Group-5:_______________________________
1."https://www.wendangku.net/doc/4f7062838.html,:80/rulib/abtlib/alexlib/alexhome.html
2.13a.mls.co.html#17://https://www.wendangku.net/doc/4f7062838.html,:80/catalog/610:501
3.ftp://https://www.wendangku.net/doc/4f7062838.html,/pub/Macintosh/Dev/ErrorString.hqx
4.gopher://https://www.wendangku.net/doc/4f7062838.html,/11/Academics/Catalogs
5.gopher://https://www.wendangku.net/doc/4f7062838.html,:71
6.gopher://https://www.wendangku.net/doc/4f7062838.html,:72/00/Library_Services/Hours
7.https://www.wendangku.net/doc/4f7062838.html,/~drinkard/ger.htm
8.https://www.wendangku.net/doc/4f7062838.html,/~ehrlich/27feb97.html Heyward Ehrlich’s Bookmarks
9.https://www.wendangku.net/doc/4f7062838.html,/~rps/dana.html John Cotton Dana Library Information
10.https://www.wendangku.net/doc/4f7062838.html,/~wcjlen/About.WCJLN.text
11.https://www.wendangku.net/doc/4f7062838.html,/~wcjlen/Cambridge.Publications1.text
A.4Query:Career Services
T hese are the results of your search for ‘‘"career services".’’
Group-1:_______________________________
Group-2:_______________________________
Group-3:_______________________________
Group-4:_______________________________
Group-5:_______________________________
1.https://www.wendangku.net/doc/4f7062838.html,/~lynneo/NCAScdc.html
2.https://www.wendangku.net/doc/4f7062838.html,/placement.html SBC Job Placement
3.https://www.wendangku.net/doc/4f7062838.html,/cservices/ CAREER SERVICES - Rutgers School of Law - Camden
4.https://www.wendangku.net/doc/4f7062838.html,/~openhous/index.html Rutgers University- College of Engineering Open House 1997
5.https://www.wendangku.net/doc/4f7062838.html,/CPP/cpp.html Career Planning and Placement Rutgers-Camden
6.https://www.wendangku.net/doc/4f7062838.html,/~bsmcpher/aroundru/career.html Career Services
7.https://www.wendangku.net/doc/4f7062838.html,/~cifss/predeparture/passport/pp2.htm Academics
8.https://www.wendangku.net/doc/4f7062838.html,/~cswebpg/2events.html Rutgers - New Brunswick Career Services --
!!Template!!
9.https://www.wendangku.net/doc/4f7062838.html,/~cswebpg/Access.html Access to Career Services
10.https://www.wendangku.net/doc/4f7062838.html,/~cswebpg/Action_Plan_Emp.html Career Services
11.https://www.wendangku.net/doc/4f7062838.html,/~cswebpg/Grants.html FUNDING OPPORTUNITIES AT RUTGERS
CAREER SERVICES
12.https://www.wendangku.net/doc/4f7062838.html,/~cswebpg/Offices&Hours.html 1996/97 Directory
13.https://www.wendangku.net/doc/4f7062838.html,/~cswebpg/Start.html Welcome to Rutgers University Career Services Homepage
14.https://www.wendangku.net/doc/4f7062838.html,/~cswebpg/assessment.html Career Services
15.https://www.wendangku.net/doc/4f7062838.html,/~cswebpg/camden.html Rutgers-Camden
16.https://www.wendangku.net/doc/4f7062838.html,/~cswebpg/collegeave.html College Ave. Staff
A.5Query:Off Campus Housing
T hese are the results of your search for ‘‘"off campus housing"*.’’
Group-1:_______________________________
Group-2:_______________________________
Group-3:_______________________________
Group-4:_______________________________
Group-5:_______________________________
1.https://www.wendangku.net/doc/4f7062838.html,/~pals/housing.html
2.https://www.wendangku.net/doc/4f7062838.html,/rent
3.https://www.wendangku.net/doc/4f7062838.html,//guide/ready.html Ready Reference Guide
4.https://www.wendangku.net/doc/4f7062838.html,/About_Rutgers/safety/volume.1.shtml Safety Matters
5.https://www.wendangku.net/doc/4f7062838.html,/Directories/CIS_Rolodex/Card.card_id_195821.shtml Off Campus Housing Service (Off-Campus Housing Office, OCHS, OCHO, Renting Apartments, Renting Houses)
6.https://www.wendangku.net/doc/4f7062838.html,/Services/Housing/ Off-Campus Housing Services
7.https://www.wendangku.net/doc/4f7062838.html,/Services/Housing/housing-form.html Off-Campus Housing Search
8.https://www.wendangku.net/doc/4f7062838.html,/cgi-bin/ColHenry/news.by.sub/ru.misc.col-henry/1198
9.https://www.wendangku.net/doc/4f7062838.html,/campus/ochs/ Rutgers University Off-Campus Housing Homepage
10.https://www.wendangku.net/doc/4f7062838.html,/campus/ochs/clinic.html Off-Campus Housing Free Legal Clinic, Spring 1997
B Clusterings done by subjects
This appendix includes the raw data that was the basis for the results presented in this paper.Each table corresponds to a query,and each row corresponds to a different subject and gives the clusters formed by that subject.Even though singleton clusters were not included in the analyses of the paper,they are included in this appendix.
With Documents
1
1
3
8
5,6,8,9
10
5,6,7,9,10
5
4
51,2,5,6,7
1,5,6
4
61,5,6
9,7,5,6
3,4
4,10
1,3,5,6,7,9
1,2,3,6,7,9
5
3,4,10
Subject With Documents
1
14,15
5,6
2 25,2
1,3,4,10
15
11
9,13 4
8
1,3,7,11
1,3,7,11,13,14
10,11
2,3,4
10,11,12,13,14,15
3
4,10,11,12
7,9,13
4,9,10,13,15
12
92,5,7,8,13
4,6,11,14
1,10,15
4,10,11
With Documents
1
6,8,9,10,11,13,14
4
12
16
3,6,8,9,10,12,13,14,15
16
7 3
3,5,6,8,9,10,11,12,14
3
10
9,13
51,3,5,6,9,10,11,12,13,14,15,16
3,5
6,9,10,11,12,13,14,15,16
61,2,3,4,5,6,7,9,10,13,14
4
7
14,15,16 8
2,16
8
2,7
11,15,16
3,5,9,10,13,14
With Documents
1
4,6
3
13
8,9,13,14,15,16
1,2,4,6,10,11 33,8,13,14,16
1,2,3,4,7,9,11
6
1,4,7,11
4579101116
4678000 62,4,7,9,10,11
13
7
6 83,8,14,15,16
2,5,9,10,7
91,2
4,7
9,10,11
8,14,15,16
With Documents
17,9,10,11
2,4
1,6,9,10,11
8
27,8
2,4,11
3
3
4
5
1,7,8,9 5
4,5,6
3
2,4 71,4,6,9
7,9
8
1,8
4
2,3,4,5,6,7
8,9
References
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SAP开发webservice接口教程
SAP开发webservice接口教程 在client=100中进行开发： 1.创建RFC函数 SE80，在函数组下，右击->创建，创建函数模块，填写函数模块名称及描述。 2.函数属性标签页，选择“远程启用的模块”，其余默认不变。 3.函数导入标签页，需要添加调用时传入的参数（表），“传递值”需勾选。 表类型：ZSHR_EMPLOYEER_T （需要自己创建） 行类型：ZSHR_EMPLOYEER （需要自己创建）
4.函数导出标签页，需要添加调用返回的参数（表），“传递值”需勾选。 表类型：ZSHR_EMPLOYEER_OUT_T （需要自己创建） 行类型：ZSHR_EMPLOYEER_OUT （需要自己创建） 5.函数源代码标签页，需要写代码实现把传入的数据保存在透明表中。 至此，函数创建完成。 6.创建Web Services 右击包名创建企业服务，进入如下页面，选择“Service Provider”，因为我们是服务提供者，点击“继续”。
7.选择“Existing ABAP Object (Inside Out)”，点击“继续”。 8.给服务起名，并填写描述，点击“继续”
9.选择“Function Module”，点击“继续”。 10.填写我们第一步创建的函数，并勾选“Map Name”，点击“继续”。 11.SOAP Appl默认不变，Profie下拉框选择第四个选择，即不进行权限认证。点击“继续”。 12.填写对于的包和请求，点击“继续”。 下一步，直接点击“完成”。服务创建成功。
13.配置SOA 使用T-CODE：soamanager，进入web页面的SOA管理（client=100）。 14.点击“简化Web服务配置”，进入如下设置页面，点击“执行”，从列表中找到自己创建的 服务，勾选第一个checkbox，User Name/Password(basic)，点击列表左上角的“保存”，之后页面右上角的“返回”按钮，返回首页。 这一步设置，代表我们只设置用户名/密码的调用认证方式。
【WebService】接口的测试方法
【WebService】接口的测试方法 有以下多种方式： 一、通过WSCaller.jar工具进行测试： 前提：知道wsdl的url。 wsCaller可执行程序的发布方式为一个wsCaller.jar包，不包含Java运行环境。你可以把wsCaller.jar复制到任何安装了Java运行环境（要求安装JRE/JDK 1.3.1或更高版本）的计算机中，用以下命令运行wsCaller： java -jar wsCaller.jar 使用wsCaller软件的方法非常简单，下面是wsCaller的主界面： 首先在WSDL Location输入框中输入你想调用或想测试的Web Service的WSDL位置，如“https://www.wendangku.net/doc/4f7062838.html,/axis/services/StockQuoteService?wsdl”，然后点“Find”按钮。wsCaller就会检查你输入的URL地址，并获取Web Service的WSDL信息。如果信息获取成功，wsCaller会在Service和Operation下拉列表框中列出该位置提供的Web Service服务和服务中的所有可调用的方法。你可以在列表框中选择你要调用或测试的方法名称，选定后，wsCaller窗口中间的参数列表框就会列出该方法的所有参数，包括每个参数的名
称、类型和参数值的输入框（只对[IN]或[IN, OUT]型的参数提供输入框）。你可以输入每个参数的取值。如下图： 这时，如果你想调用该方法并查看其结果的话，只要点下面的“Invoke”按钮就可以了。如果你想测试该方法的执行时间，则可以在“Invoke Times”框中指定重复调用的次数，然后再按“Invoke”按钮。wsCaller会自动调用你指定的方法，如果调用成功，wsCaller会显示结果对话框，其中包括调用该方法所花的总时间，每次调用的平均时间和该方法的返回值（包括返回值和所有输出型的参数）。如下图：
ESB部署WebService接口(统一用户和待办)
1 统一待办(WebService方式) 1.1 概述 门户系统做为用户访问各集成应用系统的统一入口，用户访问企业内部信息资源时只需要登录到门户系统，就可使用门户系统集成的各个应用，而待办做为各系统中用户需要处理的工作，门户系统需要提供收集建投内部应用系统中产生的待办信息，并且进行统一展现的功能，即统一待办功能。 统一待办应用业务涉及到的系统其中包括本期门户系统建设过程中所需集成的OA、WCM、EAM系统。 为保证门户系统接入各应用系统待办信息的规范性，现就各应用系统接入实现做统一要求，以确保门户系统统一待办功能实现的规范性、重用性及安全性。不满足本技术方案提供的接入规则的相关应用系统，应参考本文档完成对应用系统改造后方可进行门户系统统一待办接入工作。 统一待办实现共分为以下部分： 系统待办信息获取 系统待办信息展示 系统待办信息处理 1.2 待办信息获取 设计思路：应用系统通过门户系统提供的webservice接口向门户系统统一待办系统库写入代表信息，如下图
数据获取设计示意图 步骤如下： 1.应用系统需获得最新的待办信息。 2.应用系统通过门户接口，将获得的最新待办信息发送到门户系统。 3.统一待办系统将应用系统提供的待办信息展示给用户。 4.应用系统通过调用集成接口后获得信息，可以判断发送信息操作是否正常。 1.3 待办信息展示 设计思路：应用系统将最新的待办信息发送到统一待办系统中，并最终展示到门户首页上的待办栏目上，如下图 用户 待办栏目页面 待办集中展示设计示意图 场景如下：
在所有的待办类标题前加上”请办理”，待阅类标题前加上”请审阅”。此外，如果信息是未办或者未阅，用红色表示 1.4 待办信息处理 设计思路：用户点击门户系统上“待办栏目”里的一条待办时，弹出一个新页面，首先同应用系统实现SSO，然后跳转到应用系统的待办页面，完成待办处理后，由应用系统调用门户接口通知门户系统，并关闭弹出的待办处理页面，门户系统负责即时刷新门户待办页。如下图： 待办信息集中处理设计示意图
WebService接口代码样例说明
WS接口代码样例 Java代码调用样例 参见WSTest_for_Java.rar附件，该附件为Eclipse工程代码。接口调用参见https://www.wendangku.net/doc/4f7062838.html,info.smsmonitor.Test C代码调用样例 参见WSTest_for_c.tar附件，该附件为标准C工程代码。 附录 Webservice消息发送接口报文样例：
常用的webservice接口
商业和贸易： 1、股票行情数据WEB 服务（支持香港、深圳、上海基金、债券和股票；支持多股票同时查询） Endpoint:https://www.wendangku.net/doc/4f7062838.html,/WebServices/StockInfoWS.asmx Disco:https://www.wendangku.net/doc/4f7062838.html,/WebServices/StockInfoWS.asmx?disco WSDL:https://www.wendangku.net/doc/4f7062838.html,/WebServices/StockInfoWS.asmx?wsdl 支持香港股票、深圳、上海封闭式基金、债券和股票；支持多股票同时查询。数据即时更新。此中国股票行情数据WEB 服务仅作为用户获取信息之目的，并不构成投资建议。支持使用| 符号分割的多股票查询。 2、中国开放式基金数据WEB 服务 Endpoint:https://www.wendangku.net/doc/4f7062838.html,/WebServices/ChinaOpenFundWS.asmx Disco:https://www.wendangku.net/doc/4f7062838.html,/WebServices/ChinaOpenFundWS.asmx?disco WSDL:https://www.wendangku.net/doc/4f7062838.html,/WebServices/ChinaOpenFundWS.asmx?wsdl 中国开放式基金数据WEB 服务，数据每天15：30以后及时更新。输出数据包括：证券代码、证券简称、单位净值、累计单位净值、前单位净值、净值涨跌额、净值增长率(%)、净值日期。只有商业用户可获得此中国开放式基金数据Web Services的全部功能，若有需要测试、开发和使用请QQ：8698053 或联系我们 3、中国股票行情分时走势预览缩略图WEB 服务 Endpoint: https://www.wendangku.net/doc/4f7062838.html,/webservices/ChinaStockSmallImageWS.asmx Disco: https://www.wendangku.net/doc/4f7062838.html,/webservices/ChinaStockSmallImageWS.asmx?disco WSDL: https://www.wendangku.net/doc/4f7062838.html,/webservices/ChinaStockSmallImageWS.asmx?wsdl 中国股票行情分时走势预览缩略图WEB 服务（支持深圳和上海股市的全部基金、债券和股票），数据即时更新。返回数据：2种大小可选择的股票GIF分时走势预览缩略图字节数组和直接输出该预览缩略图。 4、外汇-人民币即时报价WEB 服务 Endpoint: https://www.wendangku.net/doc/4f7062838.html,/WebServices/ForexRmbRateWebService.asmx Disco:https://www.wendangku.net/doc/4f7062838.html,/WebServices/ForexRmbRateWebService.asmx?disco
webservice接口开发
Microsoft .NET体系结构中非常强调Web Service,构建Web Service接口对.NET Framework开发工具有很大的吸引力，因此很多讲建立Web Service机制的文章都是使用.NET Framework开发工具的。 在这篇文章中我们将谈论下面几个方面的问题 1、客户端怎样和Web Service通信的 2、使用已存在的Web Service创建代理对象 3、创建客户端。这包括： Web 浏览器客户端 Windows应用程序客户端 WAP客户端 最好的学习方法是建立一个基于真实世界的实例。我们将使用一个已存在的Web Service，这个Web Service从纳斯达克获得股票价格，客户端有一个简单的接口，该接口的外观和感觉集中了建立接口的多数语句。 客户端描述 三种客户端都接受客户输入的同一股票代码，如果请求成功，将显示公司名和股票价格，如果代码不可用，将显示一个错误信息。客户端都设置有"Get Quote" 和"Reset"按钮以初始化用户的请求。 开发中的注意事项 我使用visual https://www.wendangku.net/doc/4f7062838.html,作为我的集成开发环境，beta版没有结合.NET Mobile Web，因此，我们需要使用文本编辑器创建wap客户端，下一个版本的visual https://www.wendangku.net/doc/4f7062838.html, 将整合入.NET Mobile Web 。 客户端怎样与Web Service通讯 我们先复习一下Web Service的功能，在我得上一篇文章中曾展示一幅图（如图一），a点的用户将通过Internet执行远程调用调用b点web 服务器上的东西，这次通讯由SOAP和HTTP完成。
webservice接口文档
软件项目文档 无线条码库存管理系统 数据库设计报告 版本：<1.0>
版本历史
目录 1文档介绍 (4) 1.1 文档目的 (4) 1.2 文档范围 (4) 1.3 读者对象 (4) 1.4 参考文献 (4) 1.5 术语与缩写解释 (4) 2数据库环境说明 (4) 3数据库的命名规则 (4) 4逻辑设计............................................................................................................................ 错误！未定义书签。5物理设计.. (4) 5.0 表汇总......................................................................................................................... 错误！未定义书签。 5.1 表A ............................................................................................................................. 错误！未定义书签。 5.n 表N ............................................................................................................................. 错误！未定义书签。6存储过程、函数、触发器设计........................................................................................ 错误！未定义书签。7安全性设计........................................................................................................................ 错误！未定义书签。 7.1 防止用户直接操作数据库的方法............................................................................. 错误！未定义书签。 7.2 用户帐号密码的加密方法......................................................................................... 错误！未定义书签。 7.3 角色与权限................................................................................................................. 错误！未定义书签。8优化.................................................................................................................................... 错误！未定义书签。9数据库管理与维护说明.................................................................................................... 错误！未定义书签。
WebService接口实例说明文档
WebService接口说明文档 文档说明 本文档主要讲述如何用CSharp创建一个简单的WebService接口，并使用Java调用这个WebService接口。 准备工作 系统环境：安装JDK1.6或更新版本 开发工具：Microsoft Visual Studio2012、MyEclipse10.5、axis2-1.6.2 C Sharp服务端 1.首先，创建一个Web Service项目。依次点击：文件—新建—项目，在弹出的新建项目窗口中选择 Web下的https://www.wendangku.net/doc/4f7062838.html, 空 Web应用程序。如下图： 2.接下来我们需要创建我们的WebService接口实现文件。鼠标右击我们的项目，依次点击：添加—新 建项，在弹出窗口中选择Web服务。可修改新建项的文件名，注意文件名后缀后.asmx。如下图：
新建完成后我们的项目结构如下： 3.打开我们新建的MyService.asmx下的MyService.asmx.cs文件，可以看到其中已经有默认的 HelloWorld方法。
我们可以直接运行查看下运行的效果，效果如下图： 点击HelloWorld，再点击调用可以看到页面返回：
4.接下来我们完善我们的WebService接口功能。主要对WebService接口进行参数类型的测试，文本型、 布尔型、数值型、类(Class)等。 新增Add()等运算方法： 新增strcat()连接字符串方法： 新增GetBool()返回布尔值方法： 新增GetTest()返回测试类，并新增Test类 运行我们的项目，可以看到我们的结果如下图：
点击add方法测试： 输入add的参数i和j点击调用按钮，可以看到返回计算结果： 5.到此为止我们C Sharp创建的WebService程序完成。接下来看Java如何调用我们的WebService接口。
webservice数据传输系统设计说明书
X X X学院毕业 毕业设计 . 题目： _______ Web Service数据传输 系别：_____________ ______________ 专业：______________ ___________班级：_______________________ __姓名：___________________ ________指导老师：______________________ _____________
数据传输项目需求分析 1 系统概述 (2) 系统简介 (2) 系统功能简介 (2) 系统用户角色 (2) 2 系统假设 (3) 3 串口通信技术 (3) 串口通信的定义 (3) 串口通信与项目之间的联系 (3) 4 服务器与服务器之间的数据传输 (3) Web Service技术和数据库复制技术之间的优缺点 (3) Web Service技术和数据库复制技术之间的对比 (4) Web Service 技术和数据库复制技术的选择 (4) 5 分析和总结 (4) 6 文档历史 (5) 1 系统概述 系统简介 该系统主要是为了实现太阳神有限公司的数据交换传递。 系统功能简介 异构平台间的互通功能；数据备份功能。 系统用户角色 系统管理员：添加、删除普通管理员 普通管理员：可以查看数据库的数据；整理数据集合
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webservice接口实现过程
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WebService接口配置说明
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一、调用地址 http://61.236.127.167/sms/smsservice.asmx?wsdl 二、接口定义 1．SendSMS 发送短信 a.定义 public SendResult SendSMS(string Username, string Password, string Content, string Numbers) b. c.返回值 public class SendResult { ///
/// 发送总数 /// public int Total; ///
用Java调用C# 的WebService接口
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webservice的发布： 1、新建项目https://www.wendangku.net/doc/4f7062838.html, web应用程序工程，名字自拟 2、在新建的项目中添加一个类，名字自拟，用来操作数据库，进行目标数据库的增删改查
注：连接本地数据库的字符串为private String ConServerStr = @"Data Source=xzh-pc\sqlexpress;database=qisou;uid=qisou;pwd=qisou" 连接本地数据库的类可以为 using System; using System.Collections.Generic; using System.Data.SqlClient; using System.Linq; using System.Web; namespace StockManageWebservice { ///
{ sqlCon = new SqlConnection(); sqlCon.ConnectionString = ConServerStr; sqlCon.Open(); } } //关闭销毁数据库，相当于close() publicvoid Dispose() { if (sqlCon != null) { sqlCon.Close(); sqlCon = null; } } ///
定性检测webservice接口说明V1.0.0(1)
浙江省农贸市场信息化综合管理平台 浙江浙大网新中研软件有限公司 2012-11-28 https://www.wendangku.net/doc/4f7062838.html,/
一、查询接口说明 1、定性检测信息上传接口 参数：HashMap
Entry scnbxhEntry = new Entry(); scnbxhEntry.setKey("scnbxh"); scnbxhEntry.setValue("3301060000000036"); hashMap.entry.add(scnbxhEntry); Entry deptidEntry = new Entry(); deptidEntry.setKey("deptid"); deptidEntry.setValue("3301060000000036"); hashMap.entry.add(deptidEntry); Entry jcdwidEntry = new Entry(); jcdwidEntry.setKey("jcdwid"); jcdwidEntry.setValue("33010600000000361"); hashMap.entry.add(jcdwidEntry); Entry sccjidEntry = new Entry(); sccjidEntry.setKey("sccjid"); sccjidEntry.setValue("00000000000001010832"); hashMap.entry.add(sccjidEntry); Entry usernameEntry = new Entry(); usernameEntry.setKey("username"); usernameEntry.setValue("hzgdnmsc"); hashMap.entry.add(usernameEntry); Entry jcyhpwdEntry = new Entry(); jcyhpwdEntry.setKey("jcyhpwd"); jcyhpwdEntry.setValue("1"); hashMap.entry.add(jcyhpwdEntry); Entry spidEntry = new Entry(); spidEntry.setKey("spid"); spidEntry.setValue("04009005"); hashMap.entry.add(spidEntry); Entry jcxmEntry = new Entry(); jcxmEntry.setKey("jcxm"); jcxmEntry.setValue("022012"); hashMap.entry.add(jcxmEntry); Entry jcrqEntry = new Entry(); jcrqEntry.setKey("jcrq"); jcrqEntry.setValue("2012-11-29"); hashMap.entry.add(jcrqEntry);
泛微OA工作流WebService接口使用说明
工作流WebService接口使用说明 一、检查部署是否成功:输入下面的地址 http://192.168.4.183(换成实际的地址):8060(实际的端口号)/services/,界面中有如下服务即可: 采用Web Serverice技术进行协同系统和业务系统进行数据交互，由协同系统方开发实现Web Serverice服务，业务系统方需在本地实现Web Serverice本地代理来进行调用。 协同系统方需提供的服务接口如下： 1、流程创建接口 2、流程流转控制接口 提供流程提交，退回和转发控制接口 3、需业务系统处理的任务列表 根据用户id和流程id获取该人员需要业务系统处理的任务列表，如果需要获取表单的数据在通过reqestid调用获取表单数据接口 4、获取表单数据接口 根据协同系统中流程主键requestid获取表单中所有数据 5、已归档任务列表 根据创建人id和流程id获取已经归档的任务列表 2.2.2接口说明 接口名称参数说明返回值功能描述 创建新流程：doCreateWorkflowReques t WorkflowRequestInfo wri： 请求信息对象 userId: 用户id String：新流程的 requestid 如果小于0表示 失败 -1：创建流程失败 -2：用户没有流程 根据请求信息对 象创建一条新的 流程，请求信息 对象中需包含创 建人、创建流程 id和表单的数
接口对象说明： 1）WorkflowRequestInfo 工作流请求信息/** * 请求ID */ private String requestId; /** * 请求标题 */ private String requestName; /** * 请求重要级别 */ private String requestLevel; /** * 短信提醒 */ private String messageType; /**
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者................................................. ................................................... ..............................................41.2图例................................................. ................................................... ..............................................41.3目的................................................. ................................................... ..............................................4二、业务现状................................................. .............................. ................................................... ...............5三、概要设计................................................. ................................................... (5) 3.1接口通讯方式................................................. ...................................................
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