Estimating deep web data source size by capture–recapture method

Abstract This paper addresses the problem of estimating the size of a deep web data source that is accessible by queries only. Since most deep web data sources are non-cooperative, a data source size can only be estimated by sending queries and analyzing the returning results. We propose an efficien...
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Gespeichert in:

Autor*in:	Lu, Jianguo [verfasserIn] Li, Dingding

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2009

Schlagwörter:	Deep web Estimators Capture–recapture

Anmerkung:	© Springer Science+Business Media, LLC 2009

Übergeordnetes Werk:	Enthalten in: Information Retrieval Journal - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1999, 13(2009), 1 vom: 13. Aug., Seite 70-95
Übergeordnetes Werk:	volume:13 ; year:2009 ; number:1 ; day:13 ; month:08 ; pages:70-95

Links:	Volltext

DOI / URN:	10.1007/s10791-009-9107-y

Katalog-ID:	SPR013241257

Internformat


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520			\|a Abstract This paper addresses the problem of estimating the size of a deep web data source that is accessible by queries only. Since most deep web data sources are non-cooperative, a data source size can only be estimated by sending queries and analyzing the returning results. We propose an efficient estimator based on the capture–recapture method. First we derive an equation between the overlapping rate and the percentage of the data examined when random samples are retrieved from a uniform distribution. This equation is conceptually simple and leads to the derivation of an estimator for samples obtained by random queries. Since random queries do not produce random documents, it is well known that the traditional methods by random queries underestimate the size, i.e., those estimators have negative bias. Based on the simple estimator for random samples, we adjust the equation so that it can handle the samples returned by random queries. We conduct both simulation studies and experiments on corpora including Gov2, Reuters, Newsgroups, and Wikipedia. The results show that our method has small bias and standard deviation.
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allfields	10.1007/s10791-009-9107-y doi (DE-627)SPR013241257 (SPR)s10791-009-9107-y-e DE-627 ger DE-627 rakwb eng Lu, Jianguo verfasserin aut Estimating deep web data source size by capture–recapture method 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2009 Abstract This paper addresses the problem of estimating the size of a deep web data source that is accessible by queries only. Since most deep web data sources are non-cooperative, a data source size can only be estimated by sending queries and analyzing the returning results. We propose an efficient estimator based on the capture–recapture method. First we derive an equation between the overlapping rate and the percentage of the data examined when random samples are retrieved from a uniform distribution. This equation is conceptually simple and leads to the derivation of an estimator for samples obtained by random queries. Since random queries do not produce random documents, it is well known that the traditional methods by random queries underestimate the size, i.e., those estimators have negative bias. Based on the simple estimator for random samples, we adjust the equation so that it can handle the samples returned by random queries. We conduct both simulation studies and experiments on corpora including Gov2, Reuters, Newsgroups, and Wikipedia. The results show that our method has small bias and standard deviation. Deep web (dpeaa)DE-He213 Estimators (dpeaa)DE-He213 Capture–recapture (dpeaa)DE-He213 Li, Dingding aut Enthalten in Information Retrieval Journal Dordrecht [u.a.] : Springer Science + Business Media B.V., 1999 13(2009), 1 vom: 13. Aug., Seite 70-95 (DE-627)320529789 (DE-600)2015614-5 1573-7659 nnns volume:13 year:2009 number:1 day:13 month:08 pages:70-95 https://dx.doi.org/10.1007/s10791-009-9107-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 13 2009 1 13 08 70-95
spelling	10.1007/s10791-009-9107-y doi (DE-627)SPR013241257 (SPR)s10791-009-9107-y-e DE-627 ger DE-627 rakwb eng Lu, Jianguo verfasserin aut Estimating deep web data source size by capture–recapture method 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2009 Abstract This paper addresses the problem of estimating the size of a deep web data source that is accessible by queries only. Since most deep web data sources are non-cooperative, a data source size can only be estimated by sending queries and analyzing the returning results. We propose an efficient estimator based on the capture–recapture method. First we derive an equation between the overlapping rate and the percentage of the data examined when random samples are retrieved from a uniform distribution. This equation is conceptually simple and leads to the derivation of an estimator for samples obtained by random queries. Since random queries do not produce random documents, it is well known that the traditional methods by random queries underestimate the size, i.e., those estimators have negative bias. Based on the simple estimator for random samples, we adjust the equation so that it can handle the samples returned by random queries. We conduct both simulation studies and experiments on corpora including Gov2, Reuters, Newsgroups, and Wikipedia. The results show that our method has small bias and standard deviation. Deep web (dpeaa)DE-He213 Estimators (dpeaa)DE-He213 Capture–recapture (dpeaa)DE-He213 Li, Dingding aut Enthalten in Information Retrieval Journal Dordrecht [u.a.] : Springer Science + Business Media B.V., 1999 13(2009), 1 vom: 13. Aug., Seite 70-95 (DE-627)320529789 (DE-600)2015614-5 1573-7659 nnns volume:13 year:2009 number:1 day:13 month:08 pages:70-95 https://dx.doi.org/10.1007/s10791-009-9107-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 13 2009 1 13 08 70-95
allfields_unstemmed	10.1007/s10791-009-9107-y doi (DE-627)SPR013241257 (SPR)s10791-009-9107-y-e DE-627 ger DE-627 rakwb eng Lu, Jianguo verfasserin aut Estimating deep web data source size by capture–recapture method 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2009 Abstract This paper addresses the problem of estimating the size of a deep web data source that is accessible by queries only. Since most deep web data sources are non-cooperative, a data source size can only be estimated by sending queries and analyzing the returning results. We propose an efficient estimator based on the capture–recapture method. First we derive an equation between the overlapping rate and the percentage of the data examined when random samples are retrieved from a uniform distribution. This equation is conceptually simple and leads to the derivation of an estimator for samples obtained by random queries. Since random queries do not produce random documents, it is well known that the traditional methods by random queries underestimate the size, i.e., those estimators have negative bias. Based on the simple estimator for random samples, we adjust the equation so that it can handle the samples returned by random queries. We conduct both simulation studies and experiments on corpora including Gov2, Reuters, Newsgroups, and Wikipedia. The results show that our method has small bias and standard deviation. Deep web (dpeaa)DE-He213 Estimators (dpeaa)DE-He213 Capture–recapture (dpeaa)DE-He213 Li, Dingding aut Enthalten in Information Retrieval Journal Dordrecht [u.a.] : Springer Science + Business Media B.V., 1999 13(2009), 1 vom: 13. Aug., Seite 70-95 (DE-627)320529789 (DE-600)2015614-5 1573-7659 nnns volume:13 year:2009 number:1 day:13 month:08 pages:70-95 https://dx.doi.org/10.1007/s10791-009-9107-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 13 2009 1 13 08 70-95
allfieldsGer	10.1007/s10791-009-9107-y doi (DE-627)SPR013241257 (SPR)s10791-009-9107-y-e DE-627 ger DE-627 rakwb eng Lu, Jianguo verfasserin aut Estimating deep web data source size by capture–recapture method 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2009 Abstract This paper addresses the problem of estimating the size of a deep web data source that is accessible by queries only. Since most deep web data sources are non-cooperative, a data source size can only be estimated by sending queries and analyzing the returning results. We propose an efficient estimator based on the capture–recapture method. First we derive an equation between the overlapping rate and the percentage of the data examined when random samples are retrieved from a uniform distribution. This equation is conceptually simple and leads to the derivation of an estimator for samples obtained by random queries. Since random queries do not produce random documents, it is well known that the traditional methods by random queries underestimate the size, i.e., those estimators have negative bias. Based on the simple estimator for random samples, we adjust the equation so that it can handle the samples returned by random queries. We conduct both simulation studies and experiments on corpora including Gov2, Reuters, Newsgroups, and Wikipedia. The results show that our method has small bias and standard deviation. Deep web (dpeaa)DE-He213 Estimators (dpeaa)DE-He213 Capture–recapture (dpeaa)DE-He213 Li, Dingding aut Enthalten in Information Retrieval Journal Dordrecht [u.a.] : Springer Science + Business Media B.V., 1999 13(2009), 1 vom: 13. Aug., Seite 70-95 (DE-627)320529789 (DE-600)2015614-5 1573-7659 nnns volume:13 year:2009 number:1 day:13 month:08 pages:70-95 https://dx.doi.org/10.1007/s10791-009-9107-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 13 2009 1 13 08 70-95
allfieldsSound	10.1007/s10791-009-9107-y doi (DE-627)SPR013241257 (SPR)s10791-009-9107-y-e DE-627 ger DE-627 rakwb eng Lu, Jianguo verfasserin aut Estimating deep web data source size by capture–recapture method 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2009 Abstract This paper addresses the problem of estimating the size of a deep web data source that is accessible by queries only. Since most deep web data sources are non-cooperative, a data source size can only be estimated by sending queries and analyzing the returning results. We propose an efficient estimator based on the capture–recapture method. First we derive an equation between the overlapping rate and the percentage of the data examined when random samples are retrieved from a uniform distribution. This equation is conceptually simple and leads to the derivation of an estimator for samples obtained by random queries. Since random queries do not produce random documents, it is well known that the traditional methods by random queries underestimate the size, i.e., those estimators have negative bias. Based on the simple estimator for random samples, we adjust the equation so that it can handle the samples returned by random queries. We conduct both simulation studies and experiments on corpora including Gov2, Reuters, Newsgroups, and Wikipedia. The results show that our method has small bias and standard deviation. Deep web (dpeaa)DE-He213 Estimators (dpeaa)DE-He213 Capture–recapture (dpeaa)DE-He213 Li, Dingding aut Enthalten in Information Retrieval Journal Dordrecht [u.a.] : Springer Science + Business Media B.V., 1999 13(2009), 1 vom: 13. Aug., Seite 70-95 (DE-627)320529789 (DE-600)2015614-5 1573-7659 nnns volume:13 year:2009 number:1 day:13 month:08 pages:70-95 https://dx.doi.org/10.1007/s10791-009-9107-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 13 2009 1 13 08 70-95
language	English
source	Enthalten in Information Retrieval Journal 13(2009), 1 vom: 13. Aug., Seite 70-95 volume:13 year:2009 number:1 day:13 month:08 pages:70-95
sourceStr	Enthalten in Information Retrieval Journal 13(2009), 1 vom: 13. Aug., Seite 70-95 volume:13 year:2009 number:1 day:13 month:08 pages:70-95
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Deep web Estimators Capture–recapture
isfreeaccess_bool	false
container_title	Information Retrieval Journal
authorswithroles_txt_mv	Lu, Jianguo @@aut@@ Li, Dingding @@aut@@
publishDateDaySort_date	2009-08-13T00:00:00Z
hierarchy_top_id	320529789
id	SPR013241257
language_de	englisch
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author	Lu, Jianguo
spellingShingle	Lu, Jianguo misc Deep web misc Estimators misc Capture–recapture Estimating deep web data source size by capture–recapture method
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topic_title	Estimating deep web data source size by capture–recapture method Deep web (dpeaa)DE-He213 Estimators (dpeaa)DE-He213 Capture–recapture (dpeaa)DE-He213
topic	misc Deep web misc Estimators misc Capture–recapture
topic_unstemmed	misc Deep web misc Estimators misc Capture–recapture
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title	Estimating deep web data source size by capture–recapture method
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title_full	Estimating deep web data source size by capture–recapture method
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title_sort	estimating deep web data source size by capture–recapture method
title_auth	Estimating deep web data source size by capture–recapture method
abstract	Abstract This paper addresses the problem of estimating the size of a deep web data source that is accessible by queries only. Since most deep web data sources are non-cooperative, a data source size can only be estimated by sending queries and analyzing the returning results. We propose an efficient estimator based on the capture–recapture method. First we derive an equation between the overlapping rate and the percentage of the data examined when random samples are retrieved from a uniform distribution. This equation is conceptually simple and leads to the derivation of an estimator for samples obtained by random queries. Since random queries do not produce random documents, it is well known that the traditional methods by random queries underestimate the size, i.e., those estimators have negative bias. Based on the simple estimator for random samples, we adjust the equation so that it can handle the samples returned by random queries. We conduct both simulation studies and experiments on corpora including Gov2, Reuters, Newsgroups, and Wikipedia. The results show that our method has small bias and standard deviation. © Springer Science+Business Media, LLC 2009
abstractGer	Abstract This paper addresses the problem of estimating the size of a deep web data source that is accessible by queries only. Since most deep web data sources are non-cooperative, a data source size can only be estimated by sending queries and analyzing the returning results. We propose an efficient estimator based on the capture–recapture method. First we derive an equation between the overlapping rate and the percentage of the data examined when random samples are retrieved from a uniform distribution. This equation is conceptually simple and leads to the derivation of an estimator for samples obtained by random queries. Since random queries do not produce random documents, it is well known that the traditional methods by random queries underestimate the size, i.e., those estimators have negative bias. Based on the simple estimator for random samples, we adjust the equation so that it can handle the samples returned by random queries. We conduct both simulation studies and experiments on corpora including Gov2, Reuters, Newsgroups, and Wikipedia. The results show that our method has small bias and standard deviation. © Springer Science+Business Media, LLC 2009
abstract_unstemmed	Abstract This paper addresses the problem of estimating the size of a deep web data source that is accessible by queries only. Since most deep web data sources are non-cooperative, a data source size can only be estimated by sending queries and analyzing the returning results. We propose an efficient estimator based on the capture–recapture method. First we derive an equation between the overlapping rate and the percentage of the data examined when random samples are retrieved from a uniform distribution. This equation is conceptually simple and leads to the derivation of an estimator for samples obtained by random queries. Since random queries do not produce random documents, it is well known that the traditional methods by random queries underestimate the size, i.e., those estimators have negative bias. Based on the simple estimator for random samples, we adjust the equation so that it can handle the samples returned by random queries. We conduct both simulation studies and experiments on corpora including Gov2, Reuters, Newsgroups, and Wikipedia. The results show that our method has small bias and standard deviation. © Springer Science+Business Media, LLC 2009
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title_short	Estimating deep web data source size by capture–recapture method
url	https://dx.doi.org/10.1007/s10791-009-9107-y
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up_date	2024-07-03T18:20:50.152Z
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Since most deep web data sources are non-cooperative, a data source size can only be estimated by sending queries and analyzing the returning results. We propose an efficient estimator based on the capture–recapture method. First we derive an equation between the overlapping rate and the percentage of the data examined when random samples are retrieved from a uniform distribution. This equation is conceptually simple and leads to the derivation of an estimator for samples obtained by random queries. Since random queries do not produce random documents, it is well known that the traditional methods by random queries underestimate the size, i.e., those estimators have negative bias. Based on the simple estimator for random samples, we adjust the equation so that it can handle the samples returned by random queries. We conduct both simulation studies and experiments on corpora including Gov2, Reuters, Newsgroups, and Wikipedia. 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