CPI（居民消费价格指数）是宏观经济的一个重要衡量指标。近年来，我国公布的CPI与公众感受之间存在着较大差异，受到一定的质疑。这其中，既有公众主观感受上的差异，也有CPI调查中数据质量上存在的问题。长期以来，在CPI的编制过程中，调查目录和权数最为受到关注和研究，但原始价格数据采集作为CPI调查中最基础的一道环节，也是在确定了调查目录和权数后，最易影响数据质量的关键环节，却缺乏相关的理论研究与实践探索，在实际操作中暴露出诸多问题。本文以CPI采价方式为中心，探讨了采价工作的重要意义以及当前采价工作的现状和面临的挑战。综合国内外在采价中质量调整的理论研究与在浙江的采价试点实践，讨论了特殊商品采价...CPI (Consumer Price Index) is an important macroeconomic indicator. In recent years, there is a big difference between the CPI data we published and the public feelings, subject to certain doubts. Among these, there are differences between subjective feelings of people, and also doubts about the quality of the issuance of the survey data that CPI presented. For a long time, in the process of compi...学位：公共管理硕士院系专业：公共事务学院_公共管理硕士学号：1392012115029

Caffaro, Jérôme

Chan, Belinda

Gvianishvili, Giorgi

Kaplun, Samuele

Le Meur, Jean-Yves

Marian, Ludmila

Rajman, Martin

Šimko, Tibor

华敏

Xiamen University Institutional Repository

Policy Research on the Improvement of CPI Price Collection

The size of digital libraries is increasing, making navigation and access to information more challenging. Improving the system by observing the users’ activities can help at providing better services to users of very large digital libraries. In this paper we explain how the Invenio open-source software, used by the CERN Document Server (CDS) allows fine grained logging of user behavior. In the first phase, the sequence of actions performed by users of CDS is captured, while in the second phase statistical data is calculated offline. This paper explains these two steps and the results. Although the analyzed system focuses on the high energy physics literature, the process could be applicable to other scientific communities, with and international, large user base

CERN Document Server

Capturing and Analyzing User Behavior in Large Digital Libraries

The size of digital libraries is increasing, making navigation and access to information more challenging. Improving the system by observing the users’ activities can help at providing better services to users of very large digital libraries. In this paper we explain how the Invenio open-source software, used by the CERN Document Server (CDS) allows fine grained logging of user behavior. In the first phase, the sequence of actions performed by users of CDS is captured, while in the second phase statistical data is calculated offline. This paper explains these two steps and the results. Although the analyzed system focuses on the high energy physics literature, the process could be applicable to other scientific communities, with and international, large user base.LI

Infoscience - École polytechnique fédérale de Lausanne

CERN-IT-2010-00210/09/2010Capturing and Analyzing User Behaviorin Large Digital LibrariesGiorgi Gvianishvili, Jean-Yves Le Meur, Tibor Sˇimko, Je´roˆme Caffaro,Ludmila Marian, Samuele Kaplun, Belinda Chan, and Martin RajmanEuropean Organization for Nuclear ResearchCERN, IT Division,CH-1211, Geneva 23, Switzerland{giorgi.gvianishvili,jean-yves.le.meur,tibor.simko,jerome.caffaro,ludmila.marian,samuele.kaplun,belinda.chan}@cern.chhttp://www.cern.chSwiss Federal Institute of TechnologyEPFL, Artificial Intelligence Laboratory,CH-1015, Lausanne, Switzerlandmartin.rajman@epfl.chhttp://www.epfl.chAbstract. The size of digital libraries is increasing, making navigationand access to information more challenging. Improving the system byobserving the users’ activities can help at providing better services tousers of very large digital libraries. In this paper we explain how the In-venio open-source software, used by the CERN Document Server (CDS)allows fine grained logging of user behavior. In the first phase, the se-quence of actions performed by users of CDS is captured, while in thesecond phase statistical data is calculated oﬄine. This paper explainsthese two steps and the results. Although the analyzed system focuseson the high energy physics literature, the process could be applicable toother scientific communities, with and international, large user base.Keywords: Invenio, CDS, Very large digital library, Log analysis, Userbehavior study analysis1 IntroductionDigital libraries are playing a strategic role in the showcasing of research doneby an institution or university, since 1988. Large scientific communities rely onthe digital libraries as a primary resource for storing and acquiring information.One of the challenges is to make navigation in large amounts of data as intuitiveas possible. Our goal is to concentrate on the users’ specific needs in orderto improve and optimize access to information. In addition to the successfulsurvey done in 2008 on the information resources in High-Energy Physics [8] thebehavior of CERN Document Server (CDS) users has been studied.2 Capturing and Analyzing User Behavior in Large Digital LibrariesCDS is an instance of the Invenio software, which is developed and main-tained at CERN. The number of records in CDS exceeds 1 million and continuesto grow, while the number of unique users is more than 40 000, making CDSone of the largest digital libraries in the physics domain [14]. We have loggedusers’ interaction with the search engine and analyzed them using automateddata processing techniques. This automated approach helped to reveal importantpatterns, which are difficult or sometimes even impossible to spot by a human,due to the large amount of data involved.In order to understand which options users can select and which of them wereused or ignored, we first describe the CDS production environment, underlingits core functionalities and possibilities. We then describe the logging phase andthe type of information collected. Finally, we explain the automated extractionof additional information and the results obtainedreturned.2 System DescriptionInvenio [1] [2] [3] [4] is a digital library system which is freely available underGNU General Public License. Invenio consists of a set of modules for maintainingintermediate to large digital library services. It has been actively used at CERNsince 2002. Besides CERN, it is also used in diverse scientific institutions anduniversities worldwide like EPFL [5], DESY [6] and others. The system canhandle not only articles and books, but also theses, photos, videos, etc.Records maintained by Invenio are organized in collections that can be de-fined on top of any query. Users are offered either simple or advanced searchinterfaces. They can query specific fields, such as title, author, etc., sort theresults or apply a ranking criteria (like word similarity).Users can restrict their search to a set of specific collections or sub-collections,and the results returned can be merged into a single list.Users can also customize the output format of the results: by default a sum-mary of the results is displayed (brief HTML) but other formats such as detailedHTML, HTML MARC and others are also provided. Invenio has been translatedinto 26 languages and supports Unicode for information retrieval.Users can also register an account in order to access restricted collections orto use Web 2.0-like services (baskets, alerts, etc.).In CDS, there are approximately 8 000 registered users, representing a largeportion of the high energy physics community. However, the majority of usersare not registered (∼ 40 000 users).Most of the content maintained in CDS are articles and preprints, comingmainly from the high energy physics domain.3 User Logging3.1 First PhaseInvenio software allows user activities to be logged in real-time into MySQLtables. Standard logs collected from the web-server do not provide sufficientCapturing and Analyzing User Behavior in Large Digital Libraries 3information for observing users’ interactions with the system. In addition toweb-server logs we can store information about the query recall, the status ofthe user and the rank of downloaded documents. The main challenges arising inthis phase are:– Defining the data and the events to be captured– Preserving the relationship between stored data– Making logging transparent to end usersTwo tables contain information about user queries, with the following data:user id, date, host name, IP address, HTTP referrer and query recall. Recallis stored as the list of records unique identifiers. Other tables are dedicatedto log the downloads and the accesses to detailed page view information. Foreach record, download time, client host, user id, file format, HTTP referrer anddisplay position are logged. The download table is used not only to store localfile downloads, but also downloads of documents which are hosted on remoteservers, and which cannot be extracted from the web-server logs. To preservethe relationship between stored data query id, user id and IP address are used.These identifiers can uniquely identify the history of a user’s action in the system.3.2 Second PhaseAfter the logs have been collected, a post-processing phase that is more compu-tationally intensive is executed oﬄine. Four types of counts are extracted fromthe logs:– Number of detailed page views: for each record we count the occurrences ofrecord abstract being viewed– Number of downloads: for each record we count the occurrences of the asso-ciated full-text being downloaded– Number of displays: for each record we count the occurrences of the recordbeing listed on the results pages– Number of seens: for each record we count the occurrences of record beingseen. We mark all records seen from the first up to the one on which an ac-tion has been performed (download/view). For example, if a user downloadsrecord #6 we mark all records from #1 up to #6 as seen, since those recordswould have most probably been seen by the user. This count provides us withan approximate result of records seen, since there is no guarantee that theuser has really seen those records.These numbers, can then be used not only for information but also for rankingand for analysis of the relationship among records. They might also suggest thereorganization of the digital library for optimizing its performance. Concerningranking, combining these counts with other attributes like freshness, citationfrequency and Hirsch index is being studied within the scope of the collaborativeD-Rank [13] project at Swiss Federal Institute of Technology (EPFL) and CentralEuropean Organization for Nuclear Research (CERN). The core idea of the4 Capturing and Analyzing User Behavior in Large Digital Librariesproject is to take into consideration a user’s previous interaction with the system:if some subsets of documents have been downloaded or viewed, it is assumed thattheir importance will be preserved; on the other hand, documents which weredisplayed or potentially seen, but not downloaded or viewed will be consideredas less important.4 AnalysisAfter analyzing more than 130 000 queries maintained on CERN DocumentServer (CDS), it can be observed that 73.1% of users are using the Englishinterface which is set by default. Usage of other languages is relatively equallydistributed (Figure 1).Fig. 1. Number of users using non-English search engine interface. The English inter-face has been used 48 507 times (73.1%).The default ordering of the records is ’latest first’, but it can be changedto display results according to word similarity criteria. As we observed, suchtype of adjustments are extremely rare: in the case of CDS only in 1% of thequeries ranking method has been changed from latest first to word similarity.This confirms a habit that has already been observed in the past [9]. It confirmsthat professionals from a specific field look more for the recent publications [10].Yet another explanation can be that options in the interface are not intuitiveenough to users. Sorting has been used in less than 3% of the queries Table 1.The default setting where the descending order of results has been used is in97% of cases.CDS supports a wide variety of output formats, but users change the defaultsetting (condensed display) in only 1.5% of all queries.Advanced search was used in approximately 8% of all queries, using thedefault matching type most of the time (85.4%) (matching all the words). Re-maining matching types and operators used are compared in Table 2 and Table3. As we can observe from the Table 2, besides the default option users aremost often using the ’Regular Expression’ matching type. Such contrast of usingCapturing and Analyzing User Behavior in Large Digital Libraries 5Table 1. Percentage of using various sorting criteria. Default ordering criteria (LatestFirst) was used in 97.2% of cases.Latest First Chronological Order Key Title Year Report Number Author Title Other97.2% 1.0% 0.9% 0.3% 0.1% 0.1% 0.1% 0.3%the default or the most advanced technique can be caused by using pre-definedqueries. Operators used operators (Table 3) suggest that users prefer achievinghigher precision by restricting the criteria (Google behavior). We can observein Table 4 that the same fields are used to query CDS in bothe the simple andadvanced search interfaces.In Table 5 we can observe the 64 most often issued query terms. Although itis possible to enter Boolean expressions and years in the dedicated fields, usersprefer typing them using free text query.Table 2. Percentage of using various matching criteria, in advanced search.Matching Type PercentageAll of the words: 85.4Any of the words: 1.3Exact phrase: 1.5Partial phrase: 0.3Regular expression: 11.5Table 3. Percentage of used operators in the advanced search for defining relationshipamong matching fields.Operator PercentageAND 96.8OR 2.3NOT 0.9Rank of downloads and detailed page views is shown in the Table 6. Topranked records are downloaded/viewed on average 9 times more than ones onthe 9th position. In Table 7 we can see the 10 most often displayed records withcorresponding counts. The search engine returned no results in less than 1.5% ofall queries. The distribution of user access through the day (Figure 2) confirmsthat CDS is the institutional repository.6 Capturing and Analyzing User Behavior in Large Digital LibrariesTable 4. Percentage of using different fields in the simple and advanced search inter-faces.Field Simple Search Advanced Searchany field 49.1 71.8author 15.2 12.8title 19.5 6.5keyword 2.8 3.5report number 3.0 2.6year 9.8 0.6other 0.6 2.2Table 5. List of most often used terms in user queries for 5 weeks, with correspondingfrequencies. (Typically several terms are combined to form query.)Term Frequency % Term Frequency % Term Frequency %lhc 2289 3.5% magnet 237 0.4% energy 184 0.3%cern 1468 2.2% neutrino 235 0.4% collision 184 0.3%atlas 1324 2.0% lhcb 234 0.4% school 178 0.3%physics 1023 1.5% programme 233 0.4% control 176 0.3%higgs 469 0.7% collaboration 220 0.3% model 171 0.3%particle 412 0.6% trigger 218 0.3% technical 171 0.3%detector 404 0.6% performance 213 0.3% first 169 0.2%alice 346 0.5% quantum 213 0.3% training 168 0.2%data 337 0.5% hadron 199 0.3% electron 163 0.2%beam 319 0.5% bulletin 198 0.3% tunnel 161 0.2%lecture 299 0.5% computing 194 0.3% field 160 0.2%design 287 0.4% system 192 0.3% experiment 158 0.2%accelerator 284 0.4% student 189 0.3% academic 157 0.2%muon 258 0.4% collider 189 0.3% logo 154 0.2%theory 239 0.4% introduction 187 0.3% collisions 153 0.2%calorimeter 237 0.4% reconstruction 185 0.3% john 147 0.2%5 ConclusionThanks to its rich mechanisms Invenio is giving a lot of possibilities for observinghow users are interacting with the system. The number of users and recordsmaintained by CDS makes it one of the largest open access digital repository inscience. Capturing and analyzing user logs can provide us with hints on how toimprove the usability of the system. Log analysis results can be combined withother types of user behavior studies, for better understanding the user needs.Log analysis procedure in CDS is done in two phases. In the first step logs arecollected online. The second phase is run oﬄine, for extracting detailed statistics.Collected data can be applied to the new ranking algorithm, building rec-ommendation systems or identifying user communities with common interests.Other possible applications are: construction of query expansion mechanisms,user interface optimization, identifying most requested queries for their opti-Capturing and Analyzing User Behavior in Large Digital Libraries 7Table 6. Rank on which records have been downloaded or detail page viewed, withcorresponding counts. Mostly ’latest first’ ordering has been used.Rank of Results List Download Frequency Rank of Results List Page View Frequency1 1428 1 18852 566 2 9733 353 3 7684 287 4 6185 203 5 4946 180 6 3597 143 7 3818 128 8 2619 117 9 297≥10 4175 ≥10 6676Table 7. Top 10 most often displayed records, with corresponding seen, download andabstract view counts.Displays Seens Views Downloads1 237 17 22 1982 247 23 10 1303 358 54 24 1004 182 9 10 975 139 4 5 806 154 9 36 767 238 25 26 758 234 17 15 639 106 6 0 6310 76 4 2 58mization, defining the most suitable time for running computationally intensivetasks and many others.8 Capturing and Analyzing User Behavior in Large Digital LibrariesFig. 2. Average user access through the day. By dashed lines there are denoted week-ends, while with the solid line workdays.References1. Invenio software website, http://invenio-software.org2. CERN Document Server, http://cds.cern.ch3. Pepe A., Le Meur J.-Y., Simko. T.: Dissemination of scientific results in High EnergyPhysics: the CERN Document Server vision, (2006)4. Pepe A., Baron T., Gracco M., Le Meur J.-Y., Robinson N., Simko T., Vesely M.:CERN Document Server Software: the integrated digital library, (2005)5. Swiss Federal Institute of Technology, Knoweledge and Information Services http://infoscience.epfl.ch/6. DESY, A Research Center of the Helmholtz Association http://desy.de/7. SLAC, Stanford Linear Accelerator Center Library/SPIRES, Stanford Universityhttp://www.slac.stanford.edu/spires/8. Gentil-Beccot A., Mele S., Holtkamp A., O’Connell H. B., Brooks T.C.: InformationResources in High-Energy Physics: Surveying the Present Landscape and Chartingthe Future Course, (2008)9. Jones T., Cunningham S.J., Mcnab R., Boddie S. : A Transaction Log Analysisof a Digital Library , Department of Computer Science, University of Waikato,International Journal on Digital Libraries, pp. 152–169 (1999)10. Tenopir C.: Use and users of electronic library resources: An overview and analysisof recent research studies. Washington, DC: Council on Library and InformationResources, (2003)11. Covey D. T.: Usage and usability assessment: Library practices and concerns.Washington, DC: Council on Library and Information Resources, (2002)12. Papatheodorou C., Kapidaki S., Sfakakis M., Vassiliou A. : Mining User Commu-nities in Digital Libraries, (2003)13. Vesely M., Rajman M., Le Meur J.-Y., Using Bibliographic Knowledge for Rankingin Scientific Publication Databases, Published in IOS Press, (2008)14. Ranking Web of Worl Repositories http://repositories.webometrics.info/top800_rep_inst.asp

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Policy Research on the Improvement of CPI Price Collection

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