Search (36 results, page 1 of 2)

0.03846892 = product of:
  0.07693784 = sum of:
    0.059274152 = weight(_text_:services in 104) [ClassicSimilarity], result of:
      0.059274152 = score(doc=104,freq=4.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.344191 = fieldWeight in 104, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046875 = fieldNorm(doc=104)
    0.017663691 = product of:
      0.035327382 = sum of:
        0.035327382 = weight(_text_:management in 104) [ClassicSimilarity], result of:
          0.035327382 = score(doc=104,freq=2.0), product of:
            0.15810528 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.046906993 = queryNorm
            0.22344214 = fieldWeight in 104, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.046875 = fieldNorm(doc=104)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Exploiting context information in a web search engine helps fine-tuning web services and applications to deliver custom-made information to end users. While context, including user and environment information, cannot be exploited efficiently in the wired Internet interaction type, it is becoming accessible with the mobile web where users have an intimate relationship with their handsets. In this type of interaction, context plays a significant role enhancing information search and therefore, allowing a search engine to detect relevant content in all digital forms and formats. This chapter proposes a context model and an architecture that promote integration of context information for individuals and social communities to add value to their interaction with the mobile web. The architecture relies on efficient knowledge management of multimedia resources for a wide range of applications and web services. The research is illustrated with a corporate case study showing how efficient context integration improves usability of a mobile search engine.

0.03819228 = product of:
  0.07638456 = sum of:
    0.06049643 = weight(_text_:services in 1605) [ClassicSimilarity], result of:
      0.06049643 = score(doc=1605,freq=6.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.3512885 = fieldWeight in 1605, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1605)
    0.015888125 = product of:
      0.03177625 = sum of:
        0.03177625 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
          0.03177625 = score(doc=1605,freq=2.0), product of:
            0.1642603 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046906993 = queryNorm
            0.19345059 = fieldWeight in 1605, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1605)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22

0.01836472 = product of:
  0.07345888 = sum of:
    0.07345888 = sum of:
      0.035327382 = weight(_text_:management in 3246) [ClassicSimilarity], result of:
        0.035327382 = score(doc=3246,freq=2.0), product of:
          0.15810528 = queryWeight, product of:
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.046906993 = queryNorm
          0.22344214 = fieldWeight in 3246, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.046875 = fieldNorm(doc=3246)
      0.038131498 = weight(_text_:22 in 3246) [ClassicSimilarity], result of:
        0.038131498 = score(doc=3246,freq=2.0), product of:
          0.1642603 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046906993 = queryNorm
          0.23214069 = fieldWeight in 3246, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=3246)
  0.25 = coord(1/4)

Date

20. 1.2015 18:30:22

Source

Aslib journal of information management. 68(2016) no.5, S.566-588

0.016171718 = product of:
  0.06468687 = sum of:
    0.06468687 = weight(_text_:services in 3891) [ClassicSimilarity], result of:
      0.06468687 = score(doc=3891,freq=14.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.3756214 = fieldWeight in 3891, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.02734375 = fieldNorm(doc=3891)
  0.25 = coord(1/4)

Content

"Let us start with your positive experiences with Google. I have known Google longer than most. At Opera, we were the first to add their search into the browser interface, enabling it directly from the search box and the address field. At that time, Google was an up-and-coming geeky company. I remember vividly meeting with Google's co-founder Larry Page, his relaxed dress code and his love for the Danger device, which he played with throughout our meeting. Later, I met with the other co-founder of Google, Sergey Brin, and got positive vibes. My first impression of Google was that it was a likeable company. Our cooperation with Google was a good one. Integrating their search into Opera helped us deliver a better service to our users and generated revenue that paid the bills. We helped Google grow, along with others that followed in our footsteps and integrated Google search into their browsers. Then the picture for you and for opera darkened. Yes, then things changed. Google increased their proximity with the Mozilla foundation. They also introduced new services such as Google Docs. These services were great, gained quick popularity, but also exposed the darker side of Google. Not only were these services made to be incompatible with Opera, but also encouraged users to switch their browsers. I brought this up with Sergey Brin, in vain. For millions of Opera users to be able to access these services, we had to hide our browser's identity. The browser sniffing situation only worsened after Google started building their own browser, Chrome. ...
How should Google be regulated? We should limit the amount of information that is being collected. In particular we should look at information that is being collected across sites. It should not be legal to combine data from multiple sites and services. The fact that these sites and services are using the same underlying technology does not change the fact that the user's dealings is with a site at a time and each site should not have the right to share the data with others. I believe this the cornerstone of laws in many countries today, but these laws need to be enforced. Data about us is ours alone and it should not be possible to sell it. We should also limit the ability to target users individually. In the past, ads on sites were ads on sites. You might know what kind of users visited a site and you would place tech ads on tech sites and fashion ads on fashion sites. Now the ads follow you individually. That should be made illegal as it uses data collected from multiple sources and invades our privacy. I also believe there should be regulation as to how location data is used and any information related to our mobile devices. In addition, regulators need to be vigilant as to how companies that have monopoly power use their power. That kind of goes without saying. Companies with monopoly powers should not be able to use those powers when competing in an open market or using their monopoly services to limit competition."

0.015303934 = product of:
  0.061215736 = sum of:
    0.061215736 = sum of:
      0.029439485 = weight(_text_:management in 109) [ClassicSimilarity], result of:
        0.029439485 = score(doc=109,freq=2.0), product of:
          0.15810528 = queryWeight, product of:
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.046906993 = queryNorm
          0.18620178 = fieldWeight in 109, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.0390625 = fieldNorm(doc=109)
      0.03177625 = weight(_text_:22 in 109) [ClassicSimilarity], result of:
        0.03177625 = score(doc=109,freq=2.0), product of:
          0.1642603 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046906993 = queryNorm
          0.19345059 = fieldWeight in 109, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=109)
  0.25 = coord(1/4)

Abstract

This chapter shows that the wider use of Web search engines, reconsidering the theoretical and methodological frameworks to grasp new information practices. Beginning with an overview of the recent challenges implied by the dynamic nature of the Web, this chapter then traces the information behavior related concepts in order to present the different approaches from the user perspective. The authors pay special attention to the concept of "information practice" and other related concepts such as "use", "activity", and "behavior" largely used in the literature but not always strictly defined. The authors provide an overview of user-oriented studies that are meaningful to understand the different contexts of use of electronic information access systems, focusing on five approaches: the system-oriented approaches, the theories of information seeking, the cognitive and psychological approaches, the management science approaches, and the marketing approaches. Future directions of work are then shaped, including social searching and the ethical, cultural, and political dimensions of Web search engines. The authors conclude considering the importance of Critical theory to better understand the role of Web Search engines in our modern society.

Date

20. 4.2012 13:22:37

0.015303934 = product of:
  0.061215736 = sum of:
    0.061215736 = sum of:
      0.029439485 = weight(_text_:management in 1627) [ClassicSimilarity], result of:
        0.029439485 = score(doc=1627,freq=2.0), product of:
          0.15810528 = queryWeight, product of:
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.046906993 = queryNorm
          0.18620178 = fieldWeight in 1627, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1627)
      0.03177625 = weight(_text_:22 in 1627) [ClassicSimilarity], result of:
        0.03177625 = score(doc=1627,freq=2.0), product of:
          0.1642603 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046906993 = queryNorm
          0.19345059 = fieldWeight in 1627, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1627)
  0.25 = coord(1/4)

Date

20. 1.2015 18:30:22

Source

Aslib journal of information management. 67(2015) no.6, S.663-686

0.015303934 = product of:
  0.061215736 = sum of:
    0.061215736 = sum of:
      0.029439485 = weight(_text_:management in 5288) [ClassicSimilarity], result of:
        0.029439485 = score(doc=5288,freq=2.0), product of:
          0.15810528 = queryWeight, product of:
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.046906993 = queryNorm
          0.18620178 = fieldWeight in 5288, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5288)
      0.03177625 = weight(_text_:22 in 5288) [ClassicSimilarity], result of:
        0.03177625 = score(doc=5288,freq=2.0), product of:
          0.1642603 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046906993 = queryNorm
          0.19345059 = fieldWeight in 5288, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5288)
  0.25 = coord(1/4)

Date

20. 1.2015 18:30:22

Source

Aslib journal of information management. 71(2019) no.3, S.310-324

0.015303934 = product of:
  0.061215736 = sum of:
    0.061215736 = sum of:
      0.029439485 = weight(_text_:management in 5493) [ClassicSimilarity], result of:
        0.029439485 = score(doc=5493,freq=2.0), product of:
          0.15810528 = queryWeight, product of:
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.046906993 = queryNorm
          0.18620178 = fieldWeight in 5493, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5493)
      0.03177625 = weight(_text_:22 in 5493) [ClassicSimilarity], result of:
        0.03177625 = score(doc=5493,freq=2.0), product of:
          0.1642603 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046906993 = queryNorm
          0.19345059 = fieldWeight in 5493, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5493)
  0.25 = coord(1/4)

Date

20. 1.2015 18:30:22

Source

Aslib journal of information management. 71(2019) no.3, S.325-343

0.014818538 = product of:
  0.059274152 = sum of:
    0.059274152 = weight(_text_:services in 1284) [ClassicSimilarity], result of:
      0.059274152 = score(doc=1284,freq=4.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.344191 = fieldWeight in 1284, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046875 = fieldNorm(doc=1284)
  0.25 = coord(1/4)

Abstract

This article offers a comparative analysis of the personal profiling capabilities of the two most important free citation-based academic search engines, namely, Microsoft Academic Search (MAS) and Google Scholar Citations (GSC). Author profiles can be useful for evaluation purposes once the advantages and the shortcomings of these services are described and taken into consideration. In total, 771 personal profiles appearing in both the MAS and the GSC databases were analyzed. Results show that the GSC profiles include more documents and citations than those in MAS but with a strong bias toward the information and computing sciences, whereas the MAS profiles are disciplinarily better balanced. MAS shows technical problems such as a higher number of duplicated profiles and a lower updating rate than GSC. It is concluded that both services could be used for evaluation proposes only if they are applied along with other citation indices as a way to supplement that information.

0.01040843 = product of:
  0.04163372 = sum of:
    0.04163372 = product of:
      0.08326744 = sum of:
        0.08326744 = weight(_text_:management in 2406) [ClassicSimilarity], result of:
          0.08326744 = score(doc=2406,freq=16.0), product of:
            0.15810528 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.046906993 = queryNorm
            0.5266582 = fieldWeight in 2406, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2406)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

LCSH

Google (Firm) / Management
Internet industry / Management / United States

RSWK

Google Inc. / Management
Google Inc. / Strategisches Management

Subject

Google Inc. / Management
Google Inc. / Strategisches Management
Google (Firm) / Management
Internet industry / Management / United States

0.008731907 = product of:
  0.03492763 = sum of:
    0.03492763 = weight(_text_:services in 4201) [ClassicSimilarity], result of:
      0.03492763 = score(doc=4201,freq=2.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.2028165 = fieldWeight in 4201, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4201)
  0.25 = coord(1/4)

Abstract

Purpose - The purpose of this paper is to form one of a series which will give an overview of so-called "transformational" areas of digital library technology. The aim will be to assess how much real transformation these applications are bringing about, in terms of creating genuine user benefit and also changing everyday library practice. Design/methodology/approach - An overview of the present state of development of the one-stop shop library search engine, with particular reference to its relationship with the underlying bibliographic databases to which it provides a simplified single interface. Findings - The paper finds that the success of federated searching has proved valuable but limited to date in creating a one-stop shop search engine to rival Google Scholar; but the persistent value of the bibliographic databases sitting underneath a federated search system means that a harvesting search engine could well answer the need for a true one-stop search engine for academic and scholarly information. Research limitations/implications - This paper is based on the hypothesis that Google's success in providing such an apparently high degree of access to electronic journal services is not what it seems, and that it does not render library discovery tools obsolete. It argues that Google has not diminished the pre-eminent role of library bibliographic databases in mediating access to e-journal text, although this hypothesis needs further research to validate or disprove it. Practical implications - The paper affirms the value of bibliographic databases to practitioner librarians and the potential of single interface discovery tools in library practice. Originality/value - The paper uses statistics from US LIS sources to shed light on UK discovery tool issues.

0.008731907 = product of:
  0.03492763 = sum of:
    0.03492763 = weight(_text_:services in 3854) [ClassicSimilarity], result of:
      0.03492763 = score(doc=3854,freq=2.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.2028165 = fieldWeight in 3854, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3854)
  0.25 = coord(1/4)

Abstract

Web search engines, such as Google and Bing, are constantly employing results from knowledge organization and various visualization features to improve their search services. Knowledge graph, a large repository of structured knowledge represented by formal languages such as RDF (Resource Description Framework), is used to support entity search feature of Google and Bing (Demartini, 2016). When a user searchs for an entity, such as a person, an organization, or a place in Google or Bing, it is likely that a knowledge cardwill be presented on the right side bar of the search engine result pages (SERPs). For example, when a user searches the entity Benedict Cumberbatch on Google, the knowledge card will show the basic structured information about this person, including his date of birth, height, spouse, parents, and his movies, etc. The knowledge card, which is used to present the result of entity search, is generated from knowledge graphs. Therefore, the quality of knowledge graphs is essential to the performance of entity search. However, studies on the quality of knowledge graphs from the angle of entity coverage are scant in the literature. This study aims to investigate the coverage of entities of knowledge graphs behind Google and Bing.

0.0063552503 = product of:
  0.025421001 = sum of:
    0.025421001 = product of:
      0.050842002 = sum of:
        0.050842002 = weight(_text_:22 in 344) [ClassicSimilarity], result of:
          0.050842002 = score(doc=344,freq=2.0), product of:
            0.1642603 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046906993 = queryNorm
            0.30952093 = fieldWeight in 344, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=344)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

18. 9.2018 18:22:18

0.0063552503 = product of:
  0.025421001 = sum of:
    0.025421001 = product of:
      0.050842002 = sum of:
        0.050842002 = weight(_text_:22 in 1149) [ClassicSimilarity], result of:
          0.050842002 = score(doc=1149,freq=2.0), product of:
            0.1642603 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046906993 = queryNorm
            0.30952093 = fieldWeight in 1149, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1149)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

17.12.2013 11:02:22

0.0063552503 = product of:
  0.025421001 = sum of:
    0.025421001 = product of:
      0.050842002 = sum of:
        0.050842002 = weight(_text_:22 in 1484) [ClassicSimilarity], result of:
          0.050842002 = score(doc=1484,freq=2.0), product of:
            0.1642603 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046906993 = queryNorm
            0.30952093 = fieldWeight in 1484, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1484)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

13. 9.2014 14:45:22

0.0063552503 = product of:
  0.025421001 = sum of:
    0.025421001 = product of:
      0.050842002 = sum of:
        0.050842002 = weight(_text_:22 in 4996) [ClassicSimilarity], result of:
          0.050842002 = score(doc=4996,freq=2.0), product of:
            0.1642603 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046906993 = queryNorm
            0.30952093 = fieldWeight in 4996, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=4996)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

19. 2.2019 17:22:00

0.004766437 = product of:
  0.019065749 = sum of:
    0.019065749 = product of:
      0.038131498 = sum of:
        0.038131498 = weight(_text_:22 in 93) [ClassicSimilarity], result of:
          0.038131498 = score(doc=93,freq=2.0), product of:
            0.1642603 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046906993 = queryNorm
            0.23214069 = fieldWeight in 93, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=93)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

17. 4.2012 14:25:22

0.004766437 = product of:
  0.019065749 = sum of:
    0.019065749 = product of:
      0.038131498 = sum of:
        0.038131498 = weight(_text_:22 in 105) [ClassicSimilarity], result of:
          0.038131498 = score(doc=105,freq=2.0), product of:
            0.1642603 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046906993 = queryNorm
            0.23214069 = fieldWeight in 105, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=105)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

17. 4.2012 15:22:11

0.004766437 = product of:
  0.019065749 = sum of:
    0.019065749 = product of:
      0.038131498 = sum of:
        0.038131498 = weight(_text_:22 in 108) [ClassicSimilarity], result of:
          0.038131498 = score(doc=108,freq=2.0), product of:
            0.1642603 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046906993 = queryNorm
            0.23214069 = fieldWeight in 108, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=108)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

20. 4.2012 13:19:22

0.004766437 = product of:
  0.019065749 = sum of:
    0.019065749 = product of:
      0.038131498 = sum of:
        0.038131498 = weight(_text_:22 in 1491) [ClassicSimilarity], result of:
          0.038131498 = score(doc=1491,freq=2.0), product of:
            0.1642603 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046906993 = queryNorm
            0.23214069 = fieldWeight in 1491, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=1491)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 9.2014 18:54:11