Real life,real users,and real needs:a study and analysis
of user queries on the web
Bernard J.Jansen a ,Amanda Spink b,*,Tefko Saracevic c
a
Department of Electrical Engineering and Computer Science,United States Military Academy,West Point,
NY 10996,USA
b
School of Information Sciences and Technology,The Pennsylvania State University,State College,PA 16801,USA c
School of Communication,Information and Library Studies,Rutgers University,4Huntington Street,New Brunswick,
NJ 08903,USA
Abstract
We analyzed transaction logs containing 51,473queries posed by 18,113users of Excite ,a major Internet search service.We provide data on:(i)sessions Dchanges in queries during a session,number of pages viewed,and use of relevance feedback;(ii)queries Dthe number of search terms,and the use of logic and modi?ers;and (iii)terms Dtheir rank/frequency distribution and the most highly used search terms.We then shift the focus of analysis from the query to the user to gain insight to the characteristics of the Web user.With these characteristics as a basis,we then conducted a failure analysis,identifying trends among user mistakes.We conclude with a summary of ?ndings and a discussion of the implications of these ?ndings.#2000Elsevier Science Ltd.All rights reserved.
1.Introduction
A panel session at the 1997ACM Special Interest Group on Research Issues In Information Retrieval conference entitled ``Real Life Information Retrieval:Commercial Search Engines''included representatives from several Internet search services.Doug Cutting represented Excite ,one of the major services.Graciously,he o ered to make available a set of user queries as submitted to his service for research.The analysis we present here on the nature of sessions,queries,and terms resulted from this o er.Interestingly,the ?rst two authors expressed their
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*Corresponding author.
interest independently of each other,then met via email,exchanged messages and data,and conducted collaborative research exclusively through the Internet,before ever meeting in person at a Rutgers conference in February 1998,when the results were ?rst presented.In itself,this is an example of how the Internet changed and is changing the conduct of research.We will argue in the conclusions that real life Internet searching is changing information retrieval (IR)as well.While Internet search engines are based on IR principles,Internet searching is very di erent from IR searching as traditionally practised and researched in online databases,CD-ROMs and online public access catalogs (OPACS).Internet IR is a di erent IR,with a number of implications that could portend changes in other areas of IR as well.
With the phenomenal increase in usage of the Web,there has been a growing interest in the study of a variety of topics and issues related to use of the Web.For instance,on the hardware side,Crovella and Bestavros (1996)studied client-side tra c;and Abdulla,Fox and Abrams,(1997)analyzed server usage.On the software side,there have been many descriptive evaluations of Web search engines (e.g.Lynch,1997).Statistics of Web use appear regularly (e.g.Kehoe,Pitkow &Morton,1997;FIND/SVP,1997),but as soon as they appear,they are out of date.The coverage of various Web search engine services was analyzed in several works.A recent article on this topic by Lawrence and Giles (1998)attracted a lot of attention.The pattern of Web sur?ng by users was analyzed as well (Huberman,Pirolli,Pitkow &Lukose,1998).However,to date there has been no large-scale,quantitative or qualitative study of Web searching.
How do they search the Web ?What do they search for on the Web ?These questions are addressed in a large scale and academic manner in this study.Given the recent yearly exponential increase in the estimated number of Web users,this lack of scholarly research is surprising and disappointing.In contrast,there have been an abundance of user studies of on-line public access categories (OPAC)users.Many of these studies are reviewed in Peters (1993).Similarly,there are numerous studies of users of traditional IR systems.The combined proceedings of the International Conference on Research Issues in Information Retrieval (ACM SIGIR)present many of these studies.
In the area of Web users,however,there were only two narrow studies that we could ?nd.One focused on the THOMAS system (Croft,Cook &Wilder,1995)and contained some general information about users at that site.However,this study focused exclusively on the THOMAS Web site,did not attempt to characterize Web searching in a systematic way,and is devoted primarily to a description of the THOMAS system.The second paper was by Jones,Cunningham and McNab (1998)and focused again on a single Web site,the New Zealand Digital Library,which contains computer science technical reports.Given the technical nature of this site,it is questionable whether these users represent Web users in general.There is a small but growing body of Web user studies compared to the numerous studies of OPAC and IR system use.
In this paper,we report results from a major and ongoing study of users'searching behavior on the Web.We examined a set of transaction logs of users'searches from Excite (https://www.doczj.com/doc/873650871.html,).This study involved real users,using real queries,with real information needs,using a real search engine.The strength of this study is that it involved a real slice of life on the Web.The weakness is that it involved only a slice Dan observable artifact of what the users actually did,without any information about the users themselves or about the results
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B.J.Jansen et al./Information Processing and Management36(2000)207±227209 and https://www.doczj.com/doc/873650871.html,ers are anonymous,but we can identify one or a sequence of queries originating with a speci?c user.We know when they searched and what they searched for,but we do not know anything beyond that.We report on artifactual behavior,but without a context. However,the observation and analysis of such behavior provide for a fascinating and surprising insight into the interaction between users and the search engines on the Web.More importantly,this study provides detailed statistics currently lacking on Web user behavior.It also provides a basis for comparison with similar studies of user searching of more traditional IR and OPAC systems.
The Web has a number of search engines.The approaches to searching,including algorithms,displays,modes of interaction and so on,vary from one search engine to another. Still,all Web search engines are IR tools for searching highly diverse and distributed information resources as found on the Web.But by the nature of the Web resources,they are faced with di erent issues requiring di erent solutions than the search engines found in well organized systems,such as in DIALOG,or in lab experiments,such as in the Text Retrieval Conference(TREC)(Sparck Jones,1995).Moreover,from all that we know,Web users span a vastly broader and thus probably di erent population of users(Spink,Bateman&Jansen, 1999)and information needs,which may greatly a ect the queries,searches,and interactions. Thus,it is of considerable interest to examine the similarities and/or di erences in Web searching compared to traditional IR systems.In either case,it is potentially a very di erent IR.
The signi?cance of this study is the same as all other related studies of IR interaction, queries and searching.By axiom and from lessons learned from experience and numerous studies:
``The success or failure of any interactive system and technology is contingent on the extent to which user issues,the human factors,are addressed right from the beginning to the very end,right from theory,conceptualization,and design process to development,evaluation,and to provision of services''(Saracevic,1997).
2.Related IR studies
In this paper,we concentrate on users'sessions,queries,and terms as key variables in IR interaction on the Web.While there are many papers that discuss many aspects of Web searching,most of those are descriptive,prescriptive,or commentary.Other than the two mentioned previously,we could not?nd any similar studies of Web searching.However,there were several studies that included data on searching of existing,mostly commercial,IR systems,and we culled data from those to provide a basis for comparison between searches as done on the Web and those as done on IR systems outside the Web.A representative sample of such studies is reviewed.
The studies cited below concentrated on di erent aspects and variables related to searching, using di erent methodologies and are di cult to compare.Still,each of them had data on the mean number of search terms in queries constructed by the searchers under study as follows:
.Fenichel(1981):Novice searchers:7.9.Moderately experienced:9.6.Experienced:14.4
.Hsieh-yee (1993):Familiar topics:Novices:8.77.Experienced:7.28.Non-familiar topics:Novices:9.67.Experienced:9.00
.Bates,Wilde and Siegfried (1993):Humanities scholars:14.95.Spink and Saracevic (1997):Experienced searchers:14.8.
The studies indicated that searches by various populations contain a range of some 7±15terms.As will be discussed below,this is a considerably higher range than the mean number of terms found in this study that concentrated on Web searches from the Excite search engine.3.Background on Excite and data
Founded in 1994,Excite Inc.is a major Internet media public company which o ers free Web searching and a variety of other services.The company and its services are described at its Web site (https://www.doczj.com/doc/873650871.html,),thus not repeated here.Only the search capabilities relevant to out results are summarized.
Excite searches are based on the exact terms that a user enters in the query,however,capitalization is disregarded,with the exception of logical commands AND,OR,and AND NOT.Stemming is not available.An online thesaurus and concept linking method called Intelligent Concept Extraction (ICE)is used,to ?nd related terms in addition to terms entered.Search results are provided in ranked relevance order.A number of advanced search features are available.Those that pertain to our results are described here:
.As to search logic,Boolean operators AND,OR,AND NOT,and parentheses can be used,but these operators must appear in ALL CAPS and with a space on each side.When using Boolean operator ICE (concept-based search mechanism)is turned o .
.A set of terms enclosed in quotation marks (no space between quotation marks and terms)returns web sites with the terms as a phrase in the exact order they were entered.
.A +(plus)sign before a term (no space)requires that the term must be in an answer.
.A à(minus)sign before a term (no space)requires that the term must NOT be in an answer.We denote plus and minus signs,and quotation marks,as modi?ers.
.A page of search results contains ten answers at a time ranked as to relevance.For each site provided is the title,URL (Web site address),and a summary of its contents.Results can also be displayed by site and titles only.A user can click on the title to go to the Web site.A user can also click for the next page of ten answers.In addition,there is a clickable option More Like This ,which is a relevance feedback mechanism to ?nd similar sites..When More Like This is clicked,Excite enters and counts this as a query with zero terms.Each transaction record contained three ?elds.With these three ?elds,we were able to locate a user's initial query and recreate the chronological series of actions by each user in a session:1.Time of Day :measured in hours,minutes,and seconds from midnight of 9March https://www.doczj.com/doc/873650871.html,er Identi?cation :an anonymous user code assigned by the Excite server.3.Query Terms :exactly as entered by the given user.
Focusing on our three levels of analysis Dsessions,queries,and terms Dwe de?ned our variables in the following way.
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1.Session:A session is the entire series of queries by a user over a number of minutes or hours.A session could be as short as one query or contain many queries.
2.Query:A query consists of one or more search terms,and possibly includes logical operators and modi?ers.
3.Term:A term is any unbroken string of characters(i.e.a series of characters with no space between any of the characters).The characters in terms included everythingDletters, numbers,and symbols.Terms were words,abbreviations,numbers,symbols,URLs,and any combination thereof.We counted logical operators in capitals as terms.However,in a separate analysis we isolated them as commands,not terms.
The raw data collected are very https://www.doczj.com/doc/873650871.html,ers entered terms,commands and modi?ers in all kinds of ways,including many misspellings and mistakes.In many cases,Excite conventions were not followed.We count these deviations as mistakes and report them in the failure analysis portion of the paper.For the most part,we took the data`as is,'i.e.,we did not `clean'the data in any wayDthese queries represent real searches by real users.The only normalization we undertook in one of the counts(unique termsDnot case sensitive)was to disregard capitalization,because Excite disregards it as well.(i.e.TOPIC,topic and Topic retrieve the same answers).Excite does not o er automatic stemming,thus topic and topics count as two unique terms,and`?'or`?'as stemming commands at the end of terms are mistakes,but when used counted as separate terms.We also analyzed a cleaned set of terms, that is we removed term modi?ers such as the+oràsigns.We took great care in the derivation of counts due to the`messiness'of data.This paper extends?ndings from(Jansen, Spink,Bateman&Saracevic,1998a,b;Jansen,Spink&Saracevic,1998c).
4.Results
First,what is the pattern of user queries?We looked at the number of queries by each speci?c user and how successive queries di ered from other queries by the same user.We classi?ed the51,474queries as to unique,modi?ed,or identical as shown in Table1.
A unique query was the?rst query by a user(this represents the number of users).A modi?ed query is a subsequent query in succession(second,third F F F)by the same user with terms added to,removed from,or both added to and removed from the unique query.Unique and modi?ed queries together represent those queries where the user did something with terms. Identical queries are queries by the same user that are identical to the query preceding it.They
Table1
Unique,modi?ed,and identical queries
Query type Number Percent of all queries
Unique18,09835%
Modi?ed11,24922%
Identical22,12743%
Total100%
can come about in two ways.The ?rst possibility is that the user retyped the query.Studies have shown that users often do this (Peters,1993).The second possibility is that the query was generated by Excite .When a user views the second and further pages (i.e.,a page is a group of 10results)of results from the same query,Excite provides another query,but a query that is identical to the preceding one.Our analysis did not allow disambiguation of these two causes of identical queries.
The unique plus modi?ed queries (where users actively entered or modi?ed terms)amounted to 29,437queries or 57%of all queries.If we assume that all identical queries were generated as a request to view subsequent pages,then 43%of queries come as a result of a desire to view more pages after the ?rst one.Modi?cations and viewing are further elaborated in the next two tables.
4.1.Modi?cations
Some users used only one query in their session,others used a number of successive queries.The average session,including all three query types,was 2.84queries per session.This means that a number of users went on to either modify their query,view subsequent results,or both.The average session length,ignoring identical queries,was 1.6queries per user.Table 2lists the number of queries per user.
This analysis includes only the 29,337unique and modi?ed queries.We ignored the identical queries because as stated above,it was impossible to interpret them meaningfully in this context,in order to concentrate only on those queries where users themselves did something to the queries.A substantial majority of users (67%)did not go beyond their ?rst and only query.
Table 2
Number of queries per user Queries per user Number of users Percent of users 112,068672350119313217458335287 1.661440.807790.448320.189360.2010170.091170.041280.0413150.081420.011520.011710.0125
1
0.01
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B.J.Jansen et al./Information Processing and Management36(2000)207±227213 Query modi?cation was not a typical occurrence.This?nding is contrary to experiences in searching regular IR systems,where modi?cation of queries is much more common.Having said this,however,33%of the users did go beyond their?rst query.Approximately14%of users had entered three or more queries.These percentages of33%and14%are not insigni?cant proportions of system users.It suggests that a substantial percentage of Web users do not?t the stereotypical na?ve Web user.These sub-populations of users should receive further study.They could represent sub-populations of Web users with more experience or higher motivation who perform query modi?cation on the Web.
We also examined how user modi?ed their queries.These results are display in Table3. Here we concentrate on the11,247queries that were modi?ed by either an increase or a decrease in the number of terms from one user's query to that user's next query(i.e.,successive queries by the same user at time T and T+1).Zero change means that the user modi?ed one or more terms in a query,but did not change the number of terms in the successive query. Increase or decrease of one means that one term was added to or subtracted from the preceding query.Percent is based on the number of queries in relation to all modi?ed(11,247) queries.
We can see that users typically do not add or delete much in respect to the number of terms
Table3
Changes in number of terms in successive queries
Increase in terms Number Percent
0390934.76 1214019.03 210689.50 3367 3.26 4155 1.38 5700.62 6220.20 760.05 8100.09 910.01 1040.04 Decrease in terms Number Percent à1183716.33à29378.33à3388 3.45à4181 1.61à5760.68à6460.41à7140.12à880.07à920.02à1060.05
in their successive queries.Modi?cations to queries are done in small increments,if at all.The most common modi?cation is to change a term.This number is reˉected in the queries with zero (0)increase or decrease in terms.About one in every three queries that is modi?ed still had the same number of terms as the preceding one .In the remaining 7338successive queries where terms were either added or subtracted about equal number had terms added as subtracted (52±48%)Dthus users go both ways in increasing and decreasing number of terms in queries.About one in ?ve queries that is modi?ed has one more term than the preceding one,and about one in six has one less term .4.2.Viewing of results
Excite displays query results in groups of ten.Each time that a user accesses another group of 10,which we term another page,an identical query is generated.We analyzed the number of pages each user viewed and the percentage that this represented based on the total number of users.The results are shown in Table 4.
The mean number of pages examined per user was 2.35.Most users,58%of them,did not access any results past the ?rst page.Were they so satis?ed with the results that they did not need to view more?Were a few answers good enough?Is the precision that high?Are the users after precision?Or did they just give up and get tired of viewing results?Using only transaction logs,we cannot provide answers to these questions.But in any case,this result combined with the small number of queries per session has interesting implications for recall and may illustrate a need for high precision in Web IR algorithms.For example,using a classical measurement of precision,any search result beyond the tenth position in the list would be meaningless for 58%of Web users.Another possible interpretation is that people use partially relevant items in the ?rst page to avoid further searching through subsequent pages.Given the hypertext nature of the Web,partially relevant items (Spink,Greisdorf &Bateman,1998)in the top ten maybe used as a jumping o point to ?nd relevant items.For example,a user looking for a faculty member's homepage at a university does not retrieve the faculty's homepage in the top ten but gets the university homepage.Rather than continue searching,the user starts browsing,beginning with the university page.4.3.Queries
From the session level of analysis,we then moved to the query level.The basic statistics related to queries and search terms are given in Table 5.
We analyzed the queries based on length (i.e.number of terms),structure (use of Boolean operators and modi?ers),and failure analysis (deviations from published rules of query construction).We also identi?ed the number of users of Boolean logic and modi?ers.
4.3.1.Length
On the average,a query contained 2.21terms.Table 6shows the ranking of all queries by number of terms.
Percent is the percentage of queries containing that number of terms relative to the total number of queries.Web queries are short.About 62%of all queries contained one or two
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Table4
Number of pages viewed per user
Pages viewed Number of users Percent of all users
110,47458
2336319
315639
48965
55303
63542
72521
81530.85
91090.60
10850.47
11750.41
12470.26
13310.17
14290.16
15250.14
16280.15
17130.07
1840.02
19140.08
2090.05
2130.02
2240.02
2350.03
2470.04
2540.02
2670.04
2720.01
2830.02
2910.01
3240.02
3310.01
4010.01
4310.01
4910.01
5020.01
5510.01
Table5
Numbers of users,queries,and terms
No.of users Total no.
of queries
Non-unique
terms
Mean no.of
terms per query(range)
Unique terms with
case sensitive
Unique terms without
case sensitive
18,11351,473113,793 2.21(0±10)27,45921,862
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terms.Fewer than 4%of the queries had more than 6terms.As mentioned,we could not ?nd any other data on Web searches from a major Web search engine,thus,the only comparisons are with the two smaller studies by Croft et al.(1995)and Jones et al.(1998).The query length observed in our research is similar to results from these two studies.This deviates signi?cantly from traditional IR searching.As shown above,the mean number of search terms used in searching of regular IR systems ranged from about 7to 15.This is about three to seven times higher than the mean number of times found in this study,and our count is on the high side,because we counted operators as well.Admittedly,the circumstances and context between searches done by users of IR systems such as DIALOG,and searches of the Web,done by the general Internet population,may be vastly di erent.Thus this comparison may have little meaning.
4.3.2.Relevance feedback
A note should be made on queries with zero terms (last row of Table 6).As mentioned,when a user enters a command for relevance feedback (More Like This ),the Excite transaction log counts that as a query,but a query with zero terms.Thus,the last row represents the largest possible number of queries that used relevance feedback,or a combination of those and queries where users made some mistake that triggered this result.Assuming they were all relevance feedback,only 5%of queries used that feature Da small use of relevance feedback capability.In comparison,a study involving IR searches conducted by professional searchers as they interact with users found that some 11%of search terms came from relevance feedback (Spink &Saracevic,1997),albeit this study looked at human initiated relevance feedback.Thus,in these two studies,relevance feedback on the Web is used half as much as in traditional IR searches.This in itself warrants further study,particularly given the low use of this potentially highly useful and certainly highly vaunted feature.
4.3.3.Structure
Next,we examined the structure of queries,focusing ?rst on how many of the 51,473queries explicitly utilized Boolean operators or modi?ers (see Table 7).
Table 6
Number of terms in queries (N queries=51,473)Terms in query Number of queries Percent of all queries 101850.3691250.2482240.4474840.94661715215844378973924218216,19131115,874310
2584
5
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The Number column lists the number of queries that contained that particular Boolean operator or modi?er.The next column is the percentage that number represents of all queries.Incorrect means the number of queries containing a speci?c operator or modi?er that was constructed not following Excite rules Dthey could be considered as mistakes.The last column is the percentage of queries containing a given operator or modi?er that were incorrectly constructed.We discuss the failures in a later section.
From Table 7,at least one thing is obvious DBoolean operators were not used much,with AND receiving the greatest use.These numbers were signi?cantly lower than those reported by Jones et al.(1998),and signi?cantly lower than studies of searches from IR systems and OPAC systems [Croft et al.(1995)did not report this information].Modi?ers were used a little more often,with the `+'and ``''(i.e.,phrase searching)being used the most.For example,based on what we reviewed so far in this paper,we have a large set of queries that are extremely short,seldom modi?ed,and very simple in structure.Yet,the vast majority of users never viewed anything beyond the ?rst 10results.Is the recall and precision rate of Excite that good?Is something else at work here?One interpretation may be that users only glance at the ?rst page to see how poorly they performed their search.Rather than taking time to learn the detailed procedures of Excite ,they try anything (trial and error)and then try to judge from the hits what they did wrong.
Table 7
Use of Boolean operators and modi?ers in queries (N queries=51,473)Operator or modi?er Number of queries Percent of all queries Incorrect Percent incorrect AND 40948130932OR
1770.344626AND NOT 1050.203937()
2730.5300+(plus)30106118239à(minus)176********``''
3282
6
179
5
Table 8
Use of logic and modi?ers by users (N users=18,113)Operator or modi?er Number of users using it Percent of all users Incorrect Percent incorrect AND 832541850OR
3901128AND NOT 470919()
120100+(plus)826530330à(minus)508336238``''
1019
6
32
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4.3.4.Number of users
In Table 8,we examine how many of the 18,113users,opposed to the number of queries,used any Boolean logic (?rst four rows)or modi?ers (last three rows)in their queries (regardless of how many queries they had).
We relate these numbers to the number of queries.Incorrect means the number of users committing mistakes by not following Excite rules as stated in instructions for use of these operators and modi?ers.Percent incorrect is the proportion of those users using a given operator or modi?er incorrectly or as a mistake.The user population that incorporated Boolean operators was very small.Only 6%of the 18,113users used any of the Boolean capabilities,and these were used in less than 10%of the 51,473queries.A minuscule percentage of users and queries used OR or AND NOT.Only about 1%of users and 12%of queries used nested logic as expressed by a use of parentheses.The `+'and `à'modi?ers were used by about the same number of people that used Boolean operators.Together `+'and `à'were used by 1334or 7%of users in 4776(9%)queries.The ability to create phrases (terms enclosed by quotation marks)was also seldom used Donly 6%of users and 6%of queries used them.From this,it appears that a small number of users account for the occurrences of the more sophisticated queries,indicating that there is little experimentation by users during their sessions.About 5%of the users account for the 8.5%of queries that contained Boolean operators.We discuss the rami?cations of this ?nding for system design later in the paper.
5.Failure analysis
Next,we turn to a discussion of the surprisingly high number of incorrect uses or mistakes.When they used it,50%of users made a mistake in the use of the Boolean AND;28%made an error in uses of OR,and only 19%used AND NOT incorrectly,but only 47users,a negligible percent,used AND NOT at all.The most common mistake was not capitalizing the Boolean operator,as required by the Excite search engine.For example,a correct query would be:information AND processing.The most common mistake would be:information and processing.
When we look at queries,32%contained an incorrect use of AND,26%of OR,and 37%of AND NOT.`AND'presents a special problem,so we did a further analysis.We had 4094queries that used AND in some form (as `AND,'``And,or `and').Some queries had more than one AND.Altogether,there were 4828appearances of all forms of AND:3067as `AND',41as `And,'and 1720as `and.'If considered as Boolean operators,the last two or 1761instances were mistakes.Most of them were,but not all.In a number of queries `and'was used as conjunction e.g.as in query College and university harassment policy .Unfortunately,we could not distinguish the intended use of `and'as a conjunction from that as a mistake,thus our count of AND mistakes is on the high end.
There was a similarly high percentage of mistakes in the use of plus and minus operators Drespectively 30%and 38%.Most of the time,spaces were used incorrectly.Minus presents an especially vexing problem,because it is also used in phrases such as pre-teen .Thus,our count of mistakes is at the high end.It is easy to see that Web users are not up to Boolean,and even less to follow searching rules.At the very least,system redesign seems to be in order.The most
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B.J.Jansen et al./Information Processing and Management36(2000)207±227219 common mistake was stringing all the terms of the query together,as in a mathematical formula.For example,a correct query would be:+information+processing.The most common mistake would be:+information+processing(with no space between information and the next+).Consistent spacing rules between Boolean operators and term modi?er may solve this problem.In the use of Boolean operator,a space between the operator and the term is required.With the use of term modi?ers,the space must not be there.
There were also a large number of queries that incorporated searching techniques that Excite does not support.These failures can be classi?ed as a carry over from user learning associated with other search engines,including those from other Web,OPAC,and IR systems.For example,there were26occurrences of the proximity operator NEAR.There were79uses of the`:'as a separator for terms.There were numerous occurrences of`.'used as a term separator.The symbol`&'was used in-lieu of the Boolean AND over200times.These symbols are common in many other search engines.
6.Terms
We also analyzed user queries according to the terms they included.A term was any series of characters bounded by white space.There were113,793terms(all terms from all queries). After eliminating duplicate terms,there were21,862unique terms that were non-case sensitive (in other words,all upper cases are here reduced to lower case).In this distribution logical operators AND,OR,NOT were also treated as terms,because they were used not only as operators but also as conjunctions(we already discussed the case of`and.'and presented the ?gures for various forms of the term,thus subtraction can be easily done).We discuss terms from the perspective of their occurrence,their?t with known distributions,and classi?cation into some broader subject headings.
6.1.Occurrences
We constructed a complete rank-frequency table for all113,793terms.Out of the complete rank-frequency-table we took the top terms,i.e.those that appeared100times or more,as presented in Table9.
The74terms that were used100or more times across all queries appeared a total of20,698 times as search terms.They represent only0.34%of all unique terms,yet they account for 18.2%of all113,776search terms in all queries.If we delete the9121occurrences of11 common terms that do not carry any content by themselves(and,of,the,in,for,+,on,to,or, &,a),we are left with63subject terms that have a total frequency of11,577occurrencesDthat is0.29%of unique subject terms account for10.3%of all terms in all queries.The high appearance of`+'represents a probable mistakeDthe inclusion of space between the sign and a term,as required by Excite rules.
Similarly,`&''was used often as a part of an abbreviation,such as in AT&T,but also as a substitute for logical AND,as in Ontario&map.In the latter case,it is a mistake and would appear as a separate term.On the other end of the distribution we have9790terms that appeared only once.These terms amounted to44.78%of all unique terms and8.6%of all
terms in all queries.The tail end of unique terms is very long and warrants in itself a linguistic investigation.In fact,the whole area of query language needs further investigation.There are no comprehensive studies of terms,the distribution of those terms,the modi?cation of those terms,etc.,of Web queries.Such studies have potential to bene?t IR system and Web site development.6.2.Term categories
In order to ascertain some broad subjects of searching,we classi?ed the 63top subject terms into a set of common themes.Admittedly,such a classi?cation is arbitrary and each reader can use his/her own criteria.Still a rough picture emerges.These subjects are displayed in Table 10.About 25%of the highest used terms apparently dealt with some or other sexual topic.However,that represents fewer than 3%of all terms.Of course,if one classi?es additional terms further down the distribution (such as those listed in the ``Gender''category as Sexual )the percent will be higher.We perused the rest of the terms and came to the conclusion than no more than some two dozen of the other terms will unmistakably fall into that category.If we added them all together,the frequency of terms in Sexual will increase but not that much,
Table 9
Listing of terms occurring more than 100times (????=expletive)Term
Frequency Term Frequency Term Frequency and (incl.`AND',&`And')4828&188estate 123Of 1266stories 186magazine 123The 791p ????182computer 122Sex 763college 180news 121Nude 647naked 180texas 119Free 610adult 179games 118In
593state 176war 117Pictures 457big
170john 115For 340basketball 166de
113New 334men
163internet 111+
330employment 157car
110University 291school 156wrestling 110Women 262jobs
155high
109Chat 256american 153company 108On 252real 153ˉorida 108Gay 234world 152business 107Girls 223black 150service 106Xxx 222porn 147video 105To 218photos 142anal 104Or 213york 140erotic 104Music 209A
132stock 102Software 204Young 132art 101Pics 202History 131city 100Ncaa 201Page
131porno
100
Home
196
Celebrities
129
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and particularly not in relation to thousands of terms in other categories that are widely spread across all frequencies.In other words,as to frequency of appearance of terms among the 63highest frequency terms,those in category Sexual have the highest frequency of all categories,but still three out of every four terms of the 63highest frequency terms are not sexual;if extended to the frequency of use of all terms we estimate that 39out of 40of all terms were not sexual.
While the category Sexual is certainly big,in comparison to all other categories in no way does it dominate searching.Interest in other categories is high.Of the 63highest terms,16%are modi?ers (free,new,big F F F ),10%deal with places (state,american F F F ),8%with economics (employment,jobs F F F ),and the rest with social activities,education,sports,computing,and arts.In other words,Web searching does cover a gamut of human interests.It is very diverse.In light of this,the stereotypical view of the Web user searching primarily for sexual information may not be valid.
There are two other groupings not listed in the table that should be noted.First,there were 1398queries for various uniform resource locators (URL).Although no one URL made the top of the list,if lumped together as a category,it was one of,if not the largest query category.The second group was searching for multimedia documents (e.g.images,videos,and audio ?les).There were 708queries for these multimedia ?les,with many of these terms looking for speci?c formats.6.3.Distribution of terms
We constructed a graph of rank±frequency distribution of all terms.This graph is shown in Fig.1.
The resulting distribution seems to be unbalanced at the ends of the graph,the high and low ranking terms.In the center and lower regions,the graph follows the traditional slope of a Zipf distribution representing the distribution of words in long English texts.At the beginning,it falls o very gently,and toward the end it shows discontinuities and an unusually long tail,representing terms with a frequency of one.A trend line is plotted on the ?gure with the corresponding equation.The trend line is approximately that of the Zipf distribution.A proper Zipf distribution would be a straight line with slope of à1.The trend line does not plot well for the higher frequency terms due to the large number of terms occurring only once or twice.We wondered if the number of modi?ers (e.g.`+',`à',``,etc.)and the number of queries with all terms strung together (e.g.+information+processing+journal)could be a ecting the rank±frequency distribution,that is,if the number of modi?ers,stray characters,and run-on terms,were creating such a long tail of single occurrence terms.Therefore,we decided to clean all terms and re-plot the rank±frequency graph.In cleaning terms,we removed all modi?ers and separated all terms that were obviously strung together.Due to the varying nature of the terms,this could not be done automatically.For example,one could not just remove `+'from all terms because,for example,with c++(the programming language),the `+'is part of a valid term.In the cleaning process,all 113,793terms were qualitatively examined.In most cases,a decision would clearly be made on whether or not to clean the term.In cases were there was doubt,the term was not modi?ed.Once clean,we again generated a rank±frequency (log)plot.This rank±frequency plot is shown in Fig.2.
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Fig.1.Rank vs frequency (log)of all
terms.
Fig.2.Rank (log)vs frequency (log)of cleaned terms.
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Overall,the graph exhibits the same characteristics as before,a few terms o the scale,a fairly broad middle,ending with of several plateaus:and a long tail of terms used only one time.The only noticeable change is in the length of the plateaus,some are shorter and some are longer.The trend line again is approximately that of the Zipf distribution,with only a slight increase in slope.Again,the tails of the graph in no way resemble a Zipf distribution.This warrants further study of the ends of the rank±frequency distribution.Also,for researchers,this shows that there is little bene?t in expending the energy to clean terms,as the change in the distribution is slight.A comparison of the original and the cleaned data appears in Table 11.
Fig.3is the original and cleaned rank±frequency (log)plots overlaid along with the trend lines.
6.4.Summary of results
The analysis involved 51,473queries from 18,113users,having all together 113,776terms,of which 21,862were unique terms (disregarding capitalization).We provide the highlights of our ?ndings:
.Most users did not have many queries per search.The mean number of queries per user was 2.8.However,a sizable percentage of users did go on to either modify their original query or view subsequent results.
.Web queries are short.On the average,a query contained 2.21terms.Queries used in searching regular IR systems are some three to seven times larger.About one in three queries had one term only,two in three had one or two terms,and four in ?ve had one,two or three terms.Fewer than 4%of the queries were comprised of more than 6terms.
.Relevance feedback was rarely used.About one in 20queries used the feature More Like This .In comparison with professionally assisted IR searching,relevance feedback is apparently used only half as much on the Web.
.Boolean operators were seldom used.One in 18users used any Boolean capabilities,and of the users employing them,every second user made a mistake,as de?ned by Excite rules.As to the queries,about one in 12queries contained a Boolean operator,and in those AND was used by far the most.About one in 190queries used nested logic.About one in every three queries that used Boolean operators or a parentheses did not enter them as required by Excite .Web searchers are reluctant to use Boolean searches and when using them they have great di culty in getting them right.
Table 11
Comparison of original and cleaned terms Measure
Original Cleaned Percent change Total terms 113,793117,608 3.35Unique terms
21,86218,942à13.36Terms occurring once
97907805à20.28Terms occurring 100times or more
73
91
24.66
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.The `+'and `à'modi?ers that specify the mandatory presence or absence of a term were used more than Boolean operators.About 1in 12users employed them.About one in 11queries incorporated a `+'or `à'modi?er.But a majority of these uses were mistakes (about two out of three).
.The ability to create phrases (terms enclosed by quotation marks)was seldom,but correctly used Dwhile only one in 16queries contained a phrase,mistakes were negligible.
.Most users searched one query only and did not follow with successive queries.The average session,ignoring identical queries,includes 1.6queries.About two in three users submitted a single query,and 6in 7did not go beyond two queries.
.On average,users viewed 2.35pages of results (where one page equals ten hits).Over half the users did not access result beyond the ?rst page.More than three in four users did not go beyond viewing two pages.
.The distribution of the frequency of use of terms in queries was highly skewed.A few terms were used repeatedly and a lot of terms were used only once.On the top of the list,the 63subject terms that had a frequency of appearance of 100or more represented only one third of one percent of all terms,but they accounted for about one of every 10terms used in all queries.Terms that appeared only once amounted to half of the unique terms.
.There is a lot of searching about sex on the Web,but all together it represents only a small proportion of all searches.When the top frequency terms are classi?ed as to subject,the top category is Sexual .As to the frequency of appearance,about one in every four terms in the list of 63highest used terms can be classi?ed as sexual in nature.But while sexual terms are high as a category,they still represent a very small proportion of all terms.A great many other subjects are searched,and the diversity of subjects searched is very
high.
Fig.3.Rank (log)Dfrequency (log)plots of original and cleaned terms.
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7.Conclusions and future research
We investigated a large sample of searches on the Web,represented by logs of queries from Excite ,a major Web search provider.However,we consider this study as a starting point.We have begun the analysis of a new sample of over 1million queries.We will compare the results from this study with those of the larger study to isolate similarities and/or di erences.In this larger study,we will address many of the research questions raised in this paper.While Web search engines follow the basic principles of IR,Web search users seem to di er signi?cantly from users of traditional IR systems,such as those represented by users of DIALOG or assumed (and highly arti?cial)users of TREC.It is still IR,but a very di erent IR.Web users are certainly not comfortable with Boolean operators and other advanced means of searching.They certainly do not frequently browse the results,beyond the ?rst page or so.These facts in themselves emphasize the need to approach design of Web IR systems,search engines,and even Web site design in a signi?cantly di erent way than the design of IR systems as practised to date.They also point to the need for further and in-depth study of Web users.For instance:.The low use of advanced searching techniques would seem to support the continued research into new types of user interfaces,intelligent user interfaces,or the use of software agents to aid users in a much simpli?ed and transparent manner.
.The impact of a large number of unique terms on key term lists,thesauri,association methods,and latent semantic indexing deserves further investigation Dthe present methods are not attuned to the richness in the spread of terms.
.The area of relevance feedback also deserves further investigation.Among others,the question of actual low use of this feature should be addressed in contrast to assumptions about high usefulness of this feature in IR research.If users use it so little,what is the impetus for testing relevance feedback in the present form?Users are voting with their ?ngers,but research is going the other way?
.In itself,the work on investigation and classi?cation of a large number of highly diverse queries presents a theoretical and methodological challenge.The impact of producing a more re?ned classi?cation may be reˉected in making browsing easier for users and precision possibly higher Dboth highly desirable features.Also,research into the language of Web queries would be of bene?t to producers of information and data for Web users.Certainly,the Web is a marvelous new technology.The fact that the authors of this paper met and collaborated via the Web is an indication of the Web's potential impact.People have always been unpredictable in how they will use any new technology.The impact that new technology has on existing systems is also unpredictable.It seems that this is the case with the Web as well.In the end,it all comes down to the users and the uses people make of the Web.Maybe they are searching the Web in ways that designers and IR researchers have not contemplated or assumed,as yet.Acknowledgements
The authors gratefully acknowledge the assistance of Graham Spencer,Doug Cutting,Amy Smith and Catherine Yip of Excite Inc.in providing the data and information for this
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国内外流体力学研究机构 2008-05-08 09:08:34|分类：C FD |标签：|字号大中小订阅 1.北京航空航天大学流体力学研究所 http://www.bu https://www.doczj.com/doc/873650871.html,/dept5/stress.htm 包括国家计算流体力学重点实验室（由李椿萱院士和张函信院士主持）和流体力学开放实验室 2. 美国布朗大学流体机械研究中心 http://www.cfm.b https://www.doczj.com/doc/873650871.html, 了解流体机械的诸多方面 3.美国ssesco公司CFD技术服务中心 https://www.doczj.com/doc/873650871.html,/files/cfd_main.html 美国一个著名的计算流体服务机构，解决C FD计算和工程问题的专家 4.英国Cra nfield大学CFD研究中心 http://www.cra https://www.doczj.com/doc/873650871.html,/sme/cfd/ 主要介绍C FD的在各个领域的应用。 5.欧洲流体湍流及燃烧研究协会（Europe an Research C ommunity On Flow, Turbulence And Combustion ） http://lmfwww.epfl.ch/lmf/ERC OFTAC/ 领导管理欧洲的流体，湍流及燃烧方面的科研教育和工业的联合组织。 6.美国国家航空和宇宙航行局 http://www.nasa.go v/ NASA的各项动态和进展，信息很多。 7. 加拿大计算流体力学学会(The CFD Society of Can ada ) http://www.cfdsc.ca/english/index.html 介绍计算流体力学的进展和应用 8. CFD免费软件下载中心(CFD codes list - free softwa re) http://www.cfdsc.ca/english/index.html CFD免费软件下载(ft p) 9. 美国普林斯顿大学空气动力学实验室(the Princeton Gas Dyn amics Lab ) http://www.p https://www.doczj.com/doc/873650871.html,/~gasd yn/index.ht ml 进行流体力学的前沿研究 10. 澳大利亚Monash 大学湍流研究所(The Turbulence Research Laborato ry at Monash Uni versity ) https://www.doczj.com/doc/873650871.html,.au/~julio/TRL/ 进行湍流的理论和实验研究及应用 11. 美国Syracuse 大学超音速中心(S yracuse University cente r for h ype rsonics)
教师即研究者 一、“教师即研究者”探源 20世纪60年代，英国学校委员会和拉菲尔德基金会为培养青年学生对人文课程的兴趣和爱好，联合发起了“人文课程研究”运动。斯腾豪斯作为这个中心的负责人，在这次运动中首次提出了“教师即研究者”概念，要求把教师的教学和研究结合起来，力图改变教师在课程、教学和学习中的原有定位。后来他的学生埃利奥特和凯米斯推广了他的这种思想，分别提出“教师即行动研究者”和“教师即解放的行动研究者”的命题。20世纪80年代，“教师即研究者”的理念从西方传入我国，开始融入我国教育改革、课程改革、教师专业化运动之中。二、肯定“教师即研究者” 我认为，“教师即研究者”，并不是说所有的教师都是研究者，而是说教师要成为研究者，且必须成为研究者。史密斯将“教师做研究”定义为：“教师对学校和课堂工作进行的一种系统的、有目的的探究，是教师与研究者、教学与研究的统一。”这一界定清楚地将教师做研究与专家学者做研究区分开来。实践中，教育专家的研究对象更多的是普遍意义上的或宏观层面上的教育问题和教育现象，因此，他们的研究不可能有效解决教师面对的一些实际教育教学问题；而教师做的研究则直接与他们的教育教学实践相关，是为了实践、通过实践和在实践中的研究，其研究对象是与学校教学相关的人、事、物和观念等。教师做研究表现为教师反思自己，习以为常的教学行为背后的教学观念并改变它；探讨自己或他人成功的教育经验等。理查德·普林格在《教师是研究人员》一文中说：“当我谈到教师是研究人员的时候，我并没有把他们同那些用控制组和实验组进行复杂试验，并用各种技术手段来验证和测量成果的人相比，而只是想到他们是那种认真地使实践理论化或者是能系统而严格地考虑本人所从事的工作的人。”由此可见，教师做研究实际上包含了两类主体和两种过程：两类主体即“教师”和“研究者”；两种过程即“教学”和“研究”。在教师做研究中的两类主体实际上合并成了同一主体，那就是“教师，即研究者”；在教师做研究中两种过程实际上是相辅相成的，即边教学，边研究；边研究，边教学。 教师成为研究者主要有以下几方面的原因: 1、做一名研究型教师是时代的要求
基于服务蓝图的呷哺呷哺服务过程研究 北京工商大学研一企业管理1班高翁玉10011313448 【摘要】伴随着我国餐饮业的发展，各类型的饭店数量剧增，中式快餐也迅速发展，服务质量和管理水平不如人意的问题凸显了出来。本文主要以呷哺呷哺的吧台式火锅为例，运用目前日渐成熟的服务蓝图法对呷哺呷哺整个服务过程进行详细分析，旨在得出服务失败的关键点，并对其进行针对性改进，达到提高服务质量的目的。 【关键词】服务流程服务质量服务蓝图呷哺呷哺 质量是企业的生命线，质量管理是企业管理的一个重要方面。现代质量管理发展基本上经历了质量的事后检验阶段、统计质量控制阶段以及全面质量管理阶段。但是由于服务不同于有形产品，它具有无形性、顾客参与服务生产过程、服务的生产与消费同步发生等特征，因此服务质量是以顾客感知为导向的，我们通常以顾客满意度来衡量服务质量。在服务质量控制上也相对困难，20世纪80年代在美国学者Lynn Shostack和Jane Kingman-Brunkage等专家的努力下，服务蓝图法（Blueprinting Technique）应运而生。本文主要通过对呷哺呷哺的案例分析，介绍如何应运服务蓝图法提升服务质量。 1.服务蓝图简介 服务蓝图是一种服务系统图，它详细地描绘了服务系统的如下几个方面：顾客消费行为过程、服务实施过程（服务传递过程）、顾客接触点、顾客与服务员的角色、服务中的有形展示等。服务蓝图与其他流程图最为显著的区别是包括了顾客及其看待服务过程的观点。实际上，在设计有效的服务蓝图时，值得借鉴的一点是从顾客对过程的观点出发，逆向工作导入实施系统。每个行为部分中的方框图表示出相应水平上执行服务的人员执行或经历服务的步骤。服务蓝图的作用主要有：用于设计新服务；用于服务管理创新与服务质量提升；用于培训；用于标准管理与知识管理；用于可视化管理等等。 服务蓝图的基本构成要素包括四个方面：四种行为、连接行为的流向线、分割行为的三条分界线和设置在顾客行为上方的有形展示，如图1所示。
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服务蓝图 一、服务蓝图的概念 就像盖一所房子必须有建筑图纸一样，服务蓝图是详细描画服务系统的图片或地图，服务过程中涉及到的不同人员可以理解并客观使用它，而无论他的角色或个人观点如何。服务蓝图直观上同时从几个方面展示服务：描绘服务实施的过程、接待顾客的地点、顾客雇员的角色以及服务中的可见要素。它提供了一种把服务合理分块的方法，再逐一描述过程的步骤或任务、执行任务的方法和顾客能够感受到的有形展示。 制定蓝图在应用领域和技术上都有广泛的应用，包括后勤工业工程、决策理论和计算机系统分析等。 二、蓝图的构成：服务蓝图的主要构成 蓝图包括顾客行为、前台员工行为、后台员工行为和支持过程。绘制服务蓝图的常规并非一成不变，因此所有的特殊符号、蓝图中分界线的数量，以及蓝图中每一组成部分的名称都可以因其内容和复杂程度而有所不同。当你深刻理解蓝图的目的，并把它当成一个有用工具而不是什么设计服务的条条框框，所有问题就迎刃而解了。
顾客行为部分包括顾客在购买、消费和评价服务过程中的步骤、选择、行动和互动。例如，在法律服务中，顾客行为包括：决定找律师、给律师打电话、面谈、再打电话、收到文件和帐单。 与顾客行为平行的部分是服务人员行为。那些顾客能看到的服务人员表现出的行为和步骤是前台员工行为。例如，在法律服务中委托人（客户）可以看到的律师（服务人员）行为，是最初会面、中间会面和最终出具法律文件。 那些发生在幕后，支持前台行为的雇员行为称作后台员工行为。在上例中律师在幕后所做的任何准备，包括会面准备和最终文件交接准备都属于蓝图中的这一部分，还包括顾客和律师或其他一线员工的电话联系。 蓝图中的支持过程部分包括内部服务和支持服务人员履行的服务步骤和互动行为。在上例中，任何支持性的服务，诸如由受雇人员所进行的法律调查、准备文件的行为和秘书为会面做的准备工作都包括在蓝图中的支持过程的部分。 服务蓝图与其他流程图最为显著的区别是包括了顾客及其看待服务过程的观点。实际上，在设计有效的服务蓝图时，值得借鉴的一点是
应用场景分析---假设 在这段视频中，我将展示如何使用应用场景分析- 用于数据挖掘的“假设”表分析工具。这个工具只是用于Excel 的众多数据挖掘外接程序之一，并且我们为每个外接程序都制作了视频。 该应用场景分析工具使用逻辑回归算法，可用于对两种类型的应用场景进行建模，并且报告对输入数据中的单行或整个表的影响。 “假设”分析有助于您了解“如果我这样更改，将会有什么结果？”此工具将基于它从您的数据中分析出的成果来帮助您做出决策，例如，裁减营销人员将会对销售额产生的影响。在本教程中，我们将使用呼叫中心数据来了解如何减少“各问题平均所用时间”(Average Time Per Issue)。我所使用的Excel 数据来自https://www.doczj.com/doc/873650871.html,。如果您使用自己的电子表格，只要记住为了找到有意义的模式，必须从有价值的少量数据开始，但数据至少要有50 行。 向导 我们对Level2Operators和“各问题平均所用时间”(Average time per issue) 很感兴趣，为了更好地进行演示，我要隐藏一些列，这样更容易看清结果。 1.开始时，选择“表分析工具示例”(Table Analysis Tools Sample) 选项卡，然后单击表 内的任何地方以激活表分析工具。 2.在“表工具”(Table Tools) 菜单下，选择“分析”(Analyze) 选项卡，从而打开“表 分析工具”(Table Analysis Tools) 功能区。 3.单击“应用场景分析”(Scenario Analysis)，然后单击“假设”(What-If) 以启动该向 导。 4.选择“各问题平均所用时间”(Average time per issue) 作为要更改的列。 5.选择“百分比”(Percentage)，然后键入80。这样做的意思是：平均而言，我们愿 意在每个问题上稍微多花一点时间。 6.如果“更改”(Change) 列包含连续数值，您也可以在值中指定所需的增减量。例如， 我可以选择“每个问题的平均服务时间”(Service Average time per issue) 并将更改指定为一个确切值。 7.在“影响目标”(What happens to) 框中，选择将会受“各问题平均所用时间”(Average time per issue) 变化影响的列。我要选择Level2Operators。如果我降低我的“各问题平均所用时间”预期值，将会需要多少2 级运营商呢？ 8.如果我现在单击“运行”(Run)，将对所有列执行分析。我不这样做，而是打开“选 择分析时要使用的列…”(Choose columns to be used for analysis…)，然后取消选中FactCallCenterID和TotalOperators。通过简化我的分析，可以改进性能和准确性。 但是要小心，不要取消选中将用于“目标”(Target) 或“更改”(Change) 的列。 9.我将对“整个表”(Entire table) 作出预测，并且单击“运行”(Run)。 10.我的结果将作为新列添加到原始数据表的右侧。这些列显示了由于更改“各问题平 均所用时间”(Average time per issue) 而对Level2Operators产生的影响。第一列显示了如果我们进行这样的更改，是需要增加还是减少 2 级运营商的数量。最后一列为各行显示了调查结果的置信度。 现在，我们来针对单行数据进行“假设”分析。 1.对于单行数据，该工具将在对话框的“结果”(Results) 窗格中报告结果。如果找到 了成功的解决方案，该工具将显示结果。例如，“假设”工具可能会告诉您：如果您
教师教育研究素养 一、“教师即研究者”观念的提出 当人类社会出现以教育和教学为职业的人——教师后，人们对教师的角色认识就没有停止过，并且形成了各种各样的观念，但是把教师作为研究者的观念却经历的了一个艰辛的过程。 20世纪50年代以来，随着教育改革的不断深入，人们开始反思专门教育研究者的研究与教师的消极无为不协调的现象。 20世纪80年代以来，伴随着社会发展对教育教学要求的提高，对教师专业化的探讨达到了空前高度，“教师即研究者”也成为教育界乃至全社会普遍认同的理念和努力追求的目标。 总之，教育行动研究打出的口号就是“教师即研究者”，也就是说教师应该承担研究任务，应该成为研究者。 二、一线教师进行教育研究的优势和素养 1.教师工作于真实的教育教学情境之中，最了解教学的困难、问题与需求，能及时清晰地知觉到问题的存在。 2.教师与学生的共同交往构成了教师的教育教学生活，因此教师能准确地从学生的学习中了解到自己教学的成效，了解到师生互动需要改进的方面，尤其是能从教育教学现场中、从学生的文件中获得第一手资料，这为研究提供了良好的条件。 3.实践性是教育教学研究的重要品性，教师是教育教学实践的主体，针对具体的、真实的问题所采取的变革尝试，能够在实践中得到检验，进而产生自己的知识，建构适合情境的教学理论。 当然，一线教师要真正成为研究者，还应当具备以下素养： 1．有对于教育教学改进的热情，有教育研究的意识。 2．养成终身学习的习惯。 3．自我反思和批判能力。 4．掌握教育教学研究的基本方法。 5．独立的教育研究精神。 三、教师教育研究的意义 1．教师的教育研究有利于解决教育教学实际问题，提高教育教学质量。 教育教学是培养人的工作，其间充满着各种各样的问题。教育本身的复杂性和创造性，使得教师必须基于他对教学实践的判断和深思做出决定，对自己的行为进行审慎的、理智的安排。通过研究不仅能增进教师对有效教学的认识，扩展教师对新思想新方法的运用，引发他们对教育教学信念的追求，而且更能增进教师对学生学习需求的关注和了解，更有效地促进和指导学生的学习和成长。 2．教师的教育研究可以使课程、教学与教师真正的融为一体 现代课程与教学观认为，课程不是一项事先规范教师执行的规定或计划，也不是一套教材或教材包含的纲要和内容，而是一种特定形式的教学实际说明；教学也不是转化课程内容以达成学生学习的过程，而是师生共同建构知识的过程。课程中的教育观念只有通过教师的
第一部分：研究机构和本课题研究团队简介 一、上海易居房地产研究院简介 上海易居房地产研究院成立于2005年9月，是由上海市社会科学界联合会主管，并经上海市社会团体管理局登记注册，具有法人实体地位的专业房地产研究机构。院内汇聚了著名高校及科研院所、知名企业等数十位学者和上百位业界精英，形成了不同知识结构相辅相成、优势互补的团队结构。研究院致力于不断加强房地产业领域的重大理论和应用问题研究，持续推动房地产产学研一体化的发展，建立完善房地产研究的良性运作机制，切实促进房地产业持续健康发展。依托企业的丰富实战经验和相关高校齐备的学科体系，在研究、培训、咨询等方面拥有雄厚的专业实力。经常性的为各级政府相关部门、行业协会、大型房企、金融机构等提借项目咨询和课题研究服务，并于2009年7月荣获“上海市优秀民办社会科学研究机构”称号。目前已成为国内公认的一流的房地产研究机构。 二、专家简介 （1）领军人物：张永岳教授上海易居房地产研究院院长、华东师范大学商学院院长 主持或主要参与过《关于加快建立健全上海市民住房保障体系的研究》、《上海市政策性商品住房制度及其运行机制研究》、《房地产二三级市场联动》、《住宅服务市场体系研究》等课题50余个。撰写或主编出版过：《商品经济学入门》、《房地产经济学》、《国际房地产概述》等著作教材10余
部。发表过《关于社会主义市场经济问题思考》、《上海住宅问题研究》等论文100余篇。曾获得过上海市科学技术进步奖、上海市哲学社会科学优秀成果奖、国家建设部、上海房地局科技进步奖、全国房地产优秀课题、优秀论文奖等。 近年承担省级以上社科研究项目例举 近年相关课题项目例举
U N IV ERSI T Y ED UCA T IO N SCI EN CE大学教育科学2010年第2期(总第120期)“课程即研究假设”、“教师即行动研究者” ———斯滕豪斯课程观之要义＊ 王立忠,刘要悟 (湖南师范大学 教育科学学院,湖南 长沙 410081) [摘 要]斯滕豪斯的课程观视“课程即研究假设”、“课堂即实验室”、“教师即研究者”,倡导教师的课程行动研究,凸现了教师“专业主义”的理论取向,提出了课程设计的“过程模式”及相关的知识观、教师观、教学观和评价观,对我国正在推进的基础教育课程改革及教师教育观念的更新有重要的启示。 [关键词]斯滕豪斯;课程观;研究假设;行动研究;过程模式 [中图分类号]G423 [文献标识码]A [文章编号]1672-0717(2010)02-0097-04 我国的基础教育课程改革正在向纵深推进。此次课程改革以学生是学习的主体为基本理念,强调要为学生创设主动参与、乐于探究、交流合作等多元发展的机会,倡导教师在教学过程中引导学生质疑、调查、探究、体验并在教学实践中学习,与学生积极互动,共同发展。凡此种种,都基于教学第一线的教师对课程的理解以及教育教学理念的转变。故此,重温英国课程专家斯滕豪斯的“课程即研究假设”、“教师即行动研究者”的理念具有重要的现实意义。 一、课程是有待教学实践验证的“研究 假设” “课程”是什么?有课程研究者认为课程是指学习者的经验或体验;也有课程研究者主张课程是一系列的教学目标;以学生家长为代表的社会大众认为课程便是学校教学科目;教育行政人员则认为课程是一种教学计划,等等。对课程内涵理解的多样性反映了课程的复杂性。 1975年,斯滕豪斯出版了他的课程论名著《课程研究与开发导论》(An Introduction to Cur-riculum Research and Development),在对由博比特、查特斯、泰勒等创立的课程的目标模式进行分析批判的基础上,提出了课程的过程模式,并将课程定义为“研究假设”(Curriculum as H ypothe-sis)。他从教师专业发展的视角出发,认为课程应该是一套教学内容和教学方法的建议和说明,以说明在何种逻辑前提之下此套课程具有教育价值,在何种条件之下此套课程可以在课堂上实际进行。为了验证此套课程是否具有教育价值以及是否在课堂情境中具有教学的可行性,可以将课程视为有待教师在处于课堂情境的教学过程中加以考验的一套“研究假设”[1]。 “课程即研究假设”的基本假定是:外在的课程设计人员可以从课堂情境之外提供课程法则并要求教师遵循其课程指令,但却无法完全保证学生能够有效地进行学习。由于学生各不相同且课堂情境也有差异,因此,每一位教师都必须根据个别的课堂情境中的实际经验,去接受、修正或拒绝任何普遍性的规则或原理[2]。这样的话,教师就必须在教学实践中从事课程探究以获得对不同学生和不同课堂情境的教育理解。 从课程开发的角度而言,所有的课程皆是有关知识、教学与学习本质的“研究假设”。知识本身不是可以事前明确界定或规定的课程目标,并且概念本身并不是单一绝对的知识结构;因此,教学的目的不是要学生寻求统一的标准答案,而是引导学生在探究过程中进行没有标准答案的讨论与学习,引导学生进入多元社会文化的对话中,经由讨论或 ＊[收稿日期]2009-12-10 [作者简介]王立忠(1963-),男,甘肃镇原人,湖南师范大学博士研究生,主要从事课程与教学论研究。
5G十大细分应用场景研究报告 一、前言 5G是第五代移动通信技术的简称，作为4G通信技术的延伸，将在全社会数字化转型进程中担负着不可替代的重要使命。5G时代，“人”与“人”、“人”与“物”和“物”与“物”之间原有的互联互通界线将被打破，所有的“人”和“物”都将存在于一个有机的数字生态系统里，数据或者信息将通过最优化的方式进行传递。从全球视角来看，目前5G无论是在技术、标准、产业生态还是网络部署等方面都取得了阶段性的成果，5G 落地的最后一环——应用场景正逐渐成为业界关注的焦点。 （一）5G性能指标和关键技术 相较于4G，在传输速率方面，5G峰值速率为10-20Gbps，提升了10-20倍，用户体验速率将达到0.1Gbps-1Gbps，提升了10-100倍；流量密度方面，5G目标值为10Tbs/km2，提升了100倍；网络能效方面，5G提升了100倍；可连接数密度方面，5G每平方公里可联网设备的数量高达100万个，提升了10倍；频谱效率方面，5G相对于4G提升了3-5倍；端到端时延方面，5G将达到1ms级，提升了10倍；移动性方面，5G支持时速高达500km/h 的通信环境，提升了1.43倍。具体指标对比如表1-1所示。
表1-1 5G与4G关键性能指标对比 为了达到性能指标的要求，5G将综合运用大规模多天线技术（Massive MIMO）、新型多址、新型信息编码、毫米波通信、超密集组网、D2D等关键技术。除此之外，5G还将引入全新的构架解决方案——允许在通用物理信息基础设施上创建一组逻辑上独立的网络，称之为“网络切片”。网络切片可以根据垂直行业的业务需求量身定制，使5G能够真正成为全社会共用的新一代信息基础设施。 （二）本报告研究的5G十大应用场景 ITU 定义了5G 三大应用场景：增强型移动宽带（eMBB）、海量机器类通信（mMTC）及低时延高可靠通信（uRLLC），如图1所示。eMBB 场景主要提升以“人”为中心的娱乐、社交等个人消费业务的通信体验，适用于高速率、大带宽的移动宽带业务。mMTC 和uRLLC 则主要面向物物连接的应用场景，其中eMTC 主要满足海量物联的通信需求，面向以传感和数据采集为目标的应用场景；uRLLC 则基于其低时延和高可靠的特点，主要面向垂直行业的特殊应用需求。本报告立足ITU定义的三大应用场景，并结合当前5G 应用的实际情况和未来发展趋势，主要研究VR/AR、超高清
教师即研究者 苏霍姆林斯基曾说：“如果你想让教师的劳动能给教师带来乐趣，使天天上课不至于变成一种单调乏味的义务，那你就应该引导每一位教师走上从事研究的这条幸福的道路上来”。 在创新教育改革中，要求教师应成为研究者，“教师即研究者”是新课程对教师的要求。 一：对“教师即研究者”理念的理解 （一）“教师即研究者”的内涵 史密斯（Cochran Smith）将教师做研究定义为：“教师对学校和课堂工作进行的一种系统的、有目的的探究，是教师与研究者、教学与研究的统一。”教师所做的研究直接与他们的教育教学实践相关，是为了实践、通过实践和在实践中的研究。研究对象是与学校和教育教学实践相关的人、事、物和观念等，研究结果能解决教师所面对的一些实际的教育教学问题。教师做研究表现为教师反思自己习以为常的教育教学行为背后的教育教学观念并改变教育教学行为，探讨自己或他人的成功的教育经验等。教育研究就是有目的、有计划地采用一定的方法，按照一定的步骤去认识教育现象、探索教育规律、改进教育实践的活动。其研究的目的是发现并解决教育情境中的问题，改进教育实践。 （二）教师进行教育研究的优势 教师进行研究有很多优势，这些优势主要有：第一，教师工作于真实的教育教学情境之中，最了解教学的困难、问题与需求，能及时清晰地知觉到问题的存在。从课堂教学实践中能得到第一手材料，总结出自己的理论经验，再应用于实践，这样能取得较好的教学效果。第二，教师与学生的共同交往构成了教师的教育教学生活，因此教师能准确地从学生的学习中了解到自己教学的成效，了解到师生互动需要改进的方面，尤其是能从教育教学现场中、从沉重的文件（如考卷、作业、作文、周记）中获得第一手资料，这为研究提供了良好的条件。第三，实践性是教育教学研究的重要品性，教师是教育教学实践的主体，针对具体的、真实的问题据采取的变革尝试，能够在实践中得到检验，进而产生自己的知识，建构适合情境的教学理论。这些优势是专家学者所不具备的，教师应当增强信念，
先对“使用场景”（或者叫用户场景）做一个简单的分析。 designed by zander 使用场景的三个关键因素：对象（用户）、动作（需求）、情景（场景）。
正如上文列出的三个对应因素『同学甲』、『回家』、『同学聚会结束』，正常的连词成句是同学甲聚餐结束后要回家，这句话很快会形成一个情景，同学甲走路回家、同学甲给老公打电话来接、同学甲打的回家等，决定她回家方式的原因可能有很多，聚餐地离家的远近、选择回家的方式、是否着急回家等，为什么最后选择滴滴打车呢。 其实我们看到的需求可能就是『用户要回家』，但是一个成功的用户场景能够支持一个需求健康的发展是需要考虑很多关键因素。诸如现在如雨后春笋般的O2O各种疯长，有的坚强的活下来了，有的则销声匿迹了。 使用场景的宏观和微观分析 宏观的使用场景，当然这样讲可能不太专业，但是比较容易理解一点，那就是用户在什么样的需求下使用这个产品。比如设计一款打蛋器，我们需要考虑主要用户群是谁，打蛋器要解决或者突破的问题是什么，在什么场合使用，那么所建立的用户场景可能就是厨房里的主妇做午餐的情景。 微观的使用场景，则是在上述宏观场景大前提下，具体的使用细节，比如用完打蛋器后放置的情景，或者用户使用时的握持力度和角度等。 其实互联网产品也是一样的，产品是为那类人解决什么问题可视为宏观场景，用户具体的搜索和浏览等场景为微观场景。宏观使用场景解决的是服务上的用户体验，微观的使用场景则体现的是一个产品细节上的交互体验。好的用户体验是能够在产品的宏观使用场景与微观使用场景都做到体贴而不叨扰，提醒而不打扰。 当我们在分析产品时，我们在分析什么？看到很多产品体验报告，一上来就谈信息架构，画原型，吐槽产品的交互，逻辑等。试问，体验报告是写给谁看的，我的理解是写给别人的，让别人从不知道这个产品到了解，然后知道这个产品的优点以及存在的缺点等，所以，在分析产品之前，先对产品的宏观使用场景与微观使用场景做个简单的人物角色建模与使用场景建模，让别人先对这个产品是什么，怎么用有个初步的了解。 情景的两个关键点：时间与空间。 为什么说情景有时间与空间这两个维度呢？ 10年前，我们也有打车的需求，为什么那时候没人开发个APP出来满足用户叫车的需求呢？好吧，这个问题有点弱，也许大家会说那个时候还没有智能手机嘞，APP个鬼啊。 今天，在大沙漠上走失，你突然想到用滴滴打车叫个车吧，会有车来吗？ 由以上两个例子可以看出，合适的情景需要合适的时间与空间允许。比如现在很多APP对顶部搜索
研究所名称 所在省（或直辖市） 北方交通大学信息科学研究所 北京科技大学矿业研究所 北京林业大学林业研究所 北京师范大学北京市辐射中心 北京市计量科学研究所 北京市农林科学院 北京邮电大学 北京自动化系统工程研究设计院 大庆石油天然气地质研究所 大庆石油学院石油天然气钻采工程研究所 地质矿产部海洋地质研究所 地质矿产部沈阳地质矿产研究所 地质矿产部石油地质中心实验室 地质矿产部水文地质工程地质研究所 地质矿产部西安地质矿产研究所 地质矿产部岩溶地质研究所 地质矿产部郑州矿产综合利用研究所 电力工业部电力科学研究院 电力工业部武汉高压研究所 电子工业部蚌埠接插件继电器研究所 电子工业部长沙半导体设备研究所 电子工业部电视电声研究所 电子工业部东北微电子研究所 电子工业部杭州计算机外部设备研究所 电子工业部华北计算技术研究所 电子工业部华东电子工程研究所 电子工业部华东微电子技术研究所 电子工业部南京电子技术工程研究所 电子工业部平凉半导体专用设备研究所 电子工业部上海微电机研究所 电子工业部四川压电与声光技术研究所 电子工业部天津电子材料研究所 电子工业部西安导航技术研究所 电子工业部中国电波传播研究所
研究所名称 所在省（或直辖市） 煤炭科学研究总院太原分院 内蒙古自治区中蒙医研究所 南京大学配位化学研究所 南京大学天体物理研究室 南开大学分子生物学研究所 南开大学现代光学研究所 农业部环境保护科研监测所 青海省新能源研究所 清华大学微电子学研究院 山东省海洋水产研究所 山东省科学院 山东省农业科学院蔬菜研究所 山西省农业科学院果树研究所 陕西省农业科学院 陕西省西安高压电器研究所 上海交通大学大规模集成电路研究所 上海交通大学光纤技术研究所 上海交通大学水下工程研究所 上海市计算技术研究所 上海市农业科学院 上海医科大学肝癌研究所 上海医科大学手外科研究所 首都钢铁总公司冶金研究所 水利部长春机械研究所 水利部牧区水利科学研究所 水利部南京水文水资源研究所 水利部西北水利科学研究所 四川大学激光物理与化学研究所 四川大学生物技术研究中心 四川大学植物研究所 四川省农业科学院 天津大学信息与控制研究所 天津市农业科学院 铁道部上海铁路局科学技术研究所
服务蓝图 服务蓝图的主要构成 蓝图包括顾客行为、前台员工行为、后台员工行为和支持过程。绘制服务蓝图的常规并非一成不变，因此所有的特殊符号、蓝图中分界线的数量，以及蓝图中每一组成部分的名称都可以因其内容和复杂程度而有所不同。当你深刻理解蓝图的目的，并把它当成一个有用工具而不是什么设计服务的条条框框，所有问题就迎刃而解了。 顾客行为部分包括顾客在购买、消费和评价服务过程中的步骤、选择、行动和互动。这一部分紧紧围绕着顾客在采购、消费和评价服务过程中所采用的技术和评价准展开。 与顾客行为平行的部分是服务人员行为。那些顾客能看到的服务人员表现出的行为和步骤是前台员工行为。这部分则紧围绕前台员工与顾客的相互关系展开。 那些发生在幕后，支持前台行为的雇员行为称作后台员工行为。它围绕支持前台员工的活动展开。 蓝图中的支持过程部分包括内部服务和支持服务人员履行的服务步骤和互动行为。这一部分覆盖了在传递服务过程中所发生的支持接触员工的各种内部服务、步骤和各种相互作用。 服务蓝图与其他流程图最为显著的区别是包括了顾客及其看待服务过程的观点。实际上，在设计有效的服务蓝图时，值得借鉴的一点是从顾客对过程的观点出发，逆向工作导入实施系统。每个行为部分中的方框图表示出相应水平上执行服务的人员执行或经历服务的步骤。 4个主要的行为部分由3条分界线分开。 第1条是互动分界线，表示顾客与组织间直接的互动。一旦有一条垂直线穿过互动分界线，即表明顾客与组织间直接发生接触或一个服务接触产生。 下一条分界线是极关键的可视分界线，这条线把顾客能看到的服务行为与看不到的分开。看蓝图时，从分析多少服务在可视线以上发生、多少在以下发生入手，可以很轻松地得出顾客是否被提供了很多可视服务。这条线还把服务人员在前台与后台所做的工作分开。比如，在医疗诊断时，医生既进行诊断和回答病人问题的可视或前台工作，也进行事先阅读病历、事后记录病情的不可视或后台工作。 第3条线是内部互动线，用以区分服务人员的工作和其他支持服务的工作和工作人员。垂直线穿过内部互动线代表发生内部服务接触。 蓝图的最上面是服务的有形展示。最典型的方法是在每一个接触点上方都列出服务的有形展示。
第28卷 第6期2005年6月 计 算 机 学 报 CH INESE JOURNA L OF COM PU TERS Vo l.28N o.6 June 2005 以用户为中心的场景设计方法研究 王丹力1) 华庆一2) 戴国忠 1) 1) (中国科学院软件研究所 北京 100080) 2) (西北大学计算机系 西安 710069) 收稿日期:2004 06 09;修改稿收到日期:2005 04 25.本课题得到国家 九七三 重点基础研究发展规划项目基金(2002CB312103)、国家自然科学基金(60373056,60033020)资助.王丹力,女,1966年生,博士,副研究员,主要研究方向为人机交互、场景设计和可用性等.E mail:dlw ang@https://www.doczj.com/doc/873650871.html,.华庆一,男,1956年生,教授,博士生导师,主要研究方向为人机交互的概念建模.戴国忠,男,1944年生,研究员,博士生导师,主要研究方向为人机交互、多通道用户界面、虚拟现实等. 摘 要 图形用户界面(G U I)的可用性在于给用户的任务提供了有用的图形表示和操作,以使用户无须分心于那些不必要的交互和领域特征.然而,当前的GU I 设计通常反映了系统的状态和操作,迫使人们学习和适应预定义的系统任务.以用户为中心的GU I 设计旨在提高系统可用性,但是众多的以用户为中心的设计研究只给出一些设计准则和经验性的方法,缺乏对实际软件设计的可操作指导.基于场景的设计是GU I 设计的有效方法,然而,场景本身并没给出好的实现以用户为中心设计的方法.该文在研究以用户为中心的设计和基于场景的设计的基础上,提出一种以用户为中心的场景设计方法,力图给出一种提高G U I 可用性的更有效的方法.该方法的核心是:用场景描述方式来体现用户为中心的设计思想,并将其贯穿到系统开发的各个阶段,真正实现从用户的角度设计系统.最后以A T M 机的界面设计为例说明使用该方法进行设计的有效性. 关键词 以用户为中心的设计;基于场景的设计;以用户为中心的场景设计;图形用户界面中图法分类号T P 391 Research on User Centered Scenario Based Design WAN G Dan Li 1) H U A Qing Yi 2) DAI Guo Zho ng 1) 1)(I nstitute of Sof tw ar e,Chinese A cad emy of Sc ienc es,B eij ing 100080) 2) (Dep ar tment of Comp uter ,N or thw e st Univ e rsity ,X i an 710069) Abstract A usable Gr aphical User Interface (GU I)provides its users w ith presentatio n and ma nipulatio n of useful inter activ e graphics for their tasks at hand w itho ut becom ing bo gg ed dow n in accidental interaction and dom ain features.Current GU I desig n,how ever ,o ften im plicates the system s state and features,enforcing the users on learning and adapting pr edefined sy stem tasks.U ser Centred Design (UCD)for GU I aim s at providing hig hly usability fo r the system.But mo st ex isted approaches on U CD provide only some empir ical cr iteria w ithout the operational guideline for the process of GUI desig n.In contr act,Scenario Based Desig n (SBD)is an effective method for the design pro cess o f GU I,w hich em plo ys scenarios as a central representation thro ug ho ut the entire sy stem lifecycle.H ow ever,scenar io s by themselves do not m ake a g ood starting po int for U https://www.doczj.com/doc/873650871.html,er Centered Scenario Based Desig n,the purpo se o f w hich is to in crease the effectiv ity for the usable GUI softw are.The co re of this approach is that scenarios are used to describe the details of the desig n process,and all these descriptio ns ar e based on UCD,fro m the users point of view and serves users.And a simple ex ample about AT M is given to val idate the effectiveness of the U CSBD. Keywords UCD;SBD;user centered scenario based desig n;gr aphical user interface
1、中为咨询（zwzyzx）（中国） 深圳中为智研咨询有限公司（简称“中为咨询”）是中国领先的产业与市场研究服务供应商。公司围绕客户的需求持续努力，与客户真诚合作，在调查报告、研究报告、市场调查分析报告、商业计划书、可行性研究、IPO咨询等领域构筑了全面专业优势。中为咨询致力于为企业、投资者和政府等提供有竞争力的调查研究解决方案和服务，持续提升客户体验，为客户创造价值。目前，中为咨询的研究成果和解决方案已经应用于3万多家企业，涉及机械设备、工控电子、信息通讯、食品餐饮、旅游酒店、批发零售、建筑装饰、家电家居、文化传媒、汽车与交通、化工化学、医疗医药、能源环保、公共事务等领域，并向海外市场拓展。中为咨询是以自己的知识和智慧，帮助企业、事业成功、推动社会文明的建设发展，中为咨询的生命在市场，中为咨询的成功就在于帮助别人出主意获得成功；帮助别人成功的业绩越多，深圳中为智研咨询有限公司自身的社会信誉不断提高，在市场上占有的份额也变大。目前集团包括深圳中为慧数信息咨询有限公司与深圳万海格华管理咨询有限公司等多个子公司与办事处。 中为咨询注重竞争，更注重合作，注重竞争中的合作。为了向客户提供更多的有价值的咨询产品（好的计划、方案），中为咨询人不断改进咨询的工作方式，不仅在观察、分析问题后，以提交建议或咨询报告，更重要的是长期合作。中为咨询向客户提高咨询服务有特定的工作流程。中为咨询是通过“知识产品”提高企业经营效益。中为咨询服务工作重要一环不是取得报告，而是实施改进方案，把实施方案作为中为咨询服务的重要步骤。中为咨询始终把引进优秀的研究投资人才作为公司的核心目标之一，中为咨询员工拥有多种专业学历背景：统计学、金融学、产业经济学、市场营销学、国际贸易学、经济学、社会学、数学等数十个专业。中为咨询现有350名员工中本科以上学历占90%，65%具有双学位、硕士及博士学位。企业大多数员工曾在国内多家知名产业研究所与证券研究机构有过丰富的从业经验。高素质的专业人才是中为咨询的核心财富，也是中为咨询提供优质服务及践行客户价值的保证。 2、Ipsos益普索（法国） 益普索（Ipsos）是全球领先的市场研究集团，于1975年成立于法国巴黎，1999年在巴黎上市，是全球唯一由研究专业人士拥有并管理的市场研究集团。益普索是全球第三大研究集团，2014年集团全球营业额22.184亿美元，在全球87个国家设有办公室。益普索在六大研究领域为客户提供专业的洞察和服务：广告与品牌研究，客户满意度与忠诚度研究，营销研究，媒介研究，公共事务研究，以及调研管理服务。益普索于2000年进入中国，已成为中国较大的市场研究公司，在上海、北京、广州、成都、武汉等5个城市设有办公室。企业拥有丰富的专业研究产品线和行业专长，研究领域覆盖广告和品牌研究、营销研究、媒介研究、公众事务与社会研究、满意度与忠诚度研究、数据采集与处理，汽车研究以及金融与服务研究。服务范围覆盖了快消、金融、汽车、IT/电信、医药保健等众多行业。益普索洞察市场潜力，预测市场趋势，助力您品牌的健康发展，并协助您建立长期良好的客户关系。企业将研究数据和大数据进行智慧的结合，企业预测市场、测试广告、研究数字时代的多媒