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1 Internet search engines: Fluctuations in document accessibility Wouter Mettrop CWI, Amsterdam, The Netherlands Paul Nieuwenhuysen Vrije Universiteit.

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Presentation on theme: "1 Internet search engines: Fluctuations in document accessibility Wouter Mettrop CWI, Amsterdam, The Netherlands Paul Nieuwenhuysen Vrije Universiteit."— Presentation transcript:


2 1 Internet search engines: Fluctuations in document accessibility Wouter Mettrop CWI, Amsterdam, The Netherlands Paul Nieuwenhuysen Vrije Universiteit Brussel, and Universitaire Instelling Antwerpen, Belgium Presented at NOM 2000 New York Hilton May 16-18, 2000

3 2 Fluctuations in document accessibility - summary Search engines are often compared on the basis of their size, i.e. the number of documents indexed in their databases. However, searchers should be aware of the fact that documents cannot be retrieved reliably - in the sense that unexpected and annoying fluctuations exist in the result set of documents retrieved by most search engines. Fluctuations are ideally caused by alterations in the Web (documents come and go). However, in some cases they are caused by changes in indexing policy (“indexing fluctuations”), and in some cases the origin is more obscure: documents are expected but not retrieved. We have investigated these obscure fluctuations, by searching repeatedly during a year for several identical test documents. The documents were placed on different sites and remained unchanged. The influences of changes in indexing policy of the engines are excluded. We consider two kinds of obscure fluctuations: 1. “Document fluctuations” appear when test documents disappear from the database with indexed documents (for whatever reason). 2. “Element fluctuations” appear when test documents, that still exist in the database, do not show up in result sets even when they should. This presentation is the result of our tests from October 1998 until December 1999. We have evaluated 13 engines: AltaVista, EuroFerret, Excite, HotBot, InfoSeek, Lycos, MSN, NorthernLight, Snap, WebCrawler and 3 national Dutch engines: Ilse, and Vindex. The outcome of our investigation is in particular important for known-item searches.

4 3 WWW WWW: growing number of WWW servers

5 4 Internet based information sources: how many? how much? In 2000: about 1 billion = 1000 million unique URLs in the total Internet about 10 terabyte (= 10 000 gigabyte) of text data

6 5 Internet information retrieval systems in 2000 Several types of systems exist to retrieve information: »Directories of selected sources categorised by subject, made by humans, mainly for browsing. »Search systems, based on databases with machine made indexes, for word-based searching! »“Meta-search” or “multi-threaded” search systems. We have studied and compared several well-known international (and a few national) word-based Internet search engines.

7 6 Internet information retrieval systems: evaluation criteria Many aspects/criteria can be considered in the evaluation of an Internet search engine, including »coverage of documents present on WWW(studies exist); »number of elements of a document, that are indexed to make them usable for retrieval = “depth of indexing”;… We started to study the depth of indexing and we were soon confronted with the fluctuations in the performance that do exist. We think that these fluctations are another important aspect of performance.

8 7 Internet information retrieval systems: our research group The following persons have been involved in the research: Louise Beijer (Hogeschool van Amsterdam, The Netherlands) Hans de Bruin (Unilever Research Laboratorium, Vlaardingen, The Netherlands) Hans de Man (JdM Documentaire Informatie, Vlaardingen, The Netherlands) Rudy Dokter (PNO Consultants, Hengelo, The Netherlands) Marten Hofstede ( Rijksuniversiteit Leiden, The Netherlands) Wouter Mettrop (CWI, Amsterdam, The Netherlands) Paul Nieuwenhuysen (Vrije Universiteit Brussel, Belgium) Eric Sieverts (Hogeschool van Amsterdam, and RUU, The Netherlands) Hanneke Smulders (Infomare, Terneuzen, The Netherlands) Hans van der Laan (Consultant, Leiderdorp, The Netherlands) Ditmer Weertman (ADLIB, Utrecht, The Netherlands)

9 8 Internet search engines: research on indexing functionality Our method to assess the indexing functionality of search engines: »A “rich” test document with many element types has been created »Identical test documents were placed at 8 sites in 2 countries »A procedure was set up to assess retrieval —in an automatic way —with regular intervals

10 9 Number of our test documents that were retrieved

11 10 Internet search engines : reachability 14 528 queries were sent to 13 search engines. Search engines were 721 times unreachable. The percentage of unreachability varies from nearly 0% to nearly 15%. The studied search engines were reachable for 95% of the queries.

12 11 Internet search engines: elements of test document studied title tag META-tags: keywords, description and author comment tag ALT tag text/URL of a link to a document H3 tag table header text of: an internal link, a reference anchor, a link to a sound file name of a sound file (au/wav/aiff/ra) text of a link to an image name of an image file (gif or jpg; inline or linked to) name of a Java applet (with or without extension class) terms after the first 100 lines in a document (200/…/700) the URL of a document

13 12 Number of the studied document elements that were indexed

14 13 Search engine indexing functionality: conclusions Considerable differences among search engines exist in their depth of indexing! Not “all of the static web” is indexed. »Not each of our test documents/pages. »Not all HTML elements of our test document/page. Some of the studied search engines showed changes in the indexing policy during the experiment  fluctuations…

15 14 Internet search engines: fluctuations - definition A fluctuation appears when the result set of an observation - i.e. » one query or » set of queries misses documents with respect to a frame of reference - i.e. » other observations and » knowledge about Web reality

16 15 Internet search engines: detecting fluctuations Through time: comparing result sets of 1 observation repeatedly performed » Observation = one query or set of queries » Frame of reference = other observations & web-knowledge One moment: consistency of result sets » Observation = one query in set of queries » Frame of reference = other observations

17 16 Internet search engines: types of fluctuations Through time: comparing result sets of 1 observation repeatedly performed » “Document fluctuations” » “Indexing fluctuations” One moment: consistency of result sets » “Element fluctuations”

18 17 A B C

19 18 Document fluctuations: example 1

20 19 Document fluctuations: example 2

21 20 Document fluctuations: experimental results

22 21

23 22 Indexing fluctuations: experimental results

24 23 A1 A2 A3A4A3A4A3A4

25 24 Element fluctuations: example

26 25 Element fluctuations: experimental results

27 26 Percentage of documents missed due to fluctuations

28 27 Internet search engines: fluctuations - quantitative conclusions Many element fluctuations  many document and indexing fluctuations and many document elements indexed Many document fluctuations  not always many element fluctuations Few document elements indexed  few element fluctuations

29 28 Fluctuations: remarks on “correctness” Fluctuations can be seen as “correct”, if they are reflections of alterations in: »(web-) reality — then document, indexing and element fluctuations are incorrect »the indexed database of a search engine — then only element fluctuations are incorrect Users do not care; they miss documents

30 29 Fluctuations: remarks on “size” No relation document / element fluctuations “size” Percentage missed documents determines (with other reducing effects, such as depth of indexing) the effective size of an engine

31 30 Fluctuations: remarks on “importance” Users of information »should be aware of the existence of fluctuations »should observe them systematically Providers of information »should be aware of the existence of fluctuations Quantitative analyses of the web are hindered by fluctuations »scientometrics; citation analysis »fluctuations lower the effective size of an index

32 31 Internet search engines: conclusions of our research Search engines differ in depth of indexing documents. Search engines make mistakes: »They are subject to changes in indexing policy. (“indexing fluctuations”) »They forget documents completely (“document fluctuations”) »They miss documents in their result sets (“element fluctuations”). Considerable differences exist among search engines regarding these fluctuations.

33 32 Internet search engines: recommendations related to fluctuations Fluctuations are “normal”; do not be surprised; do not worry. Do not try to find a simple explanation to fully understand what happens. Known item searchers should repeat the search »when using an engine with many element fluctuations; use other search terms; »when using an engine with many document fluctuations: repeat later.

34 33 Element and indexing fluctuations example

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