Incremental sequence-based frequent query pattern mining from XML queries
Abstract Existing algorithms of mining frequent XML query patterns (XQPs) employ a candidate generate-and-test strategy. They involve expensive candidate enumeration and costly tree-containment checking. Further, most of existing methods compute the frequencies of candidate query patterns from scrat...
Ausführliche Beschreibung
Autor*in: |
Li, Guoliang [verfasserIn] |
---|
Format: |
Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2009 |
---|
Schlagwörter: |
---|
Anmerkung: |
© Springer Science+Business Media, LLC 2009 |
---|
Übergeordnetes Werk: |
Enthalten in: Data mining and knowledge discovery - Springer US, 1997, 18(2009), 3 vom: 12. Feb., Seite 472-516 |
---|---|
Übergeordnetes Werk: |
volume:18 ; year:2009 ; number:3 ; day:12 ; month:02 ; pages:472-516 |
Links: |
---|
DOI / URN: |
10.1007/s10618-009-0126-5 |
---|
Katalog-ID: |
OLC2027058311 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | OLC2027058311 | ||
003 | DE-627 | ||
005 | 20230503034703.0 | ||
007 | tu | ||
008 | 200819s2009 xx ||||| 00| ||eng c | ||
024 | 7 | |a 10.1007/s10618-009-0126-5 |2 doi | |
035 | |a (DE-627)OLC2027058311 | ||
035 | |a (DE-He213)s10618-009-0126-5-p | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 400 |a 070 |a 004 |q VZ |
084 | |a 24,1 |2 ssgn | ||
084 | |a LING |q DE-30 |2 fid | ||
100 | 1 | |a Li, Guoliang |e verfasserin |4 aut | |
245 | 1 | 0 | |a Incremental sequence-based frequent query pattern mining from XML queries |
264 | 1 | |c 2009 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
338 | |a Band |b nc |2 rdacarrier | ||
500 | |a © Springer Science+Business Media, LLC 2009 | ||
520 | |a Abstract Existing algorithms of mining frequent XML query patterns (XQPs) employ a candidate generate-and-test strategy. They involve expensive candidate enumeration and costly tree-containment checking. Further, most of existing methods compute the frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XQPs transferred from user query logs. However, it is not straightforward to maintain such discovered frequent patterns in real XML databases as there may be frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. Therefore, a drawback of existing methods is that they are rather inefficient for the evolution of transaction databases. To address above-mentioned problems, this paper proposes an efficient algorithm ESPRIT to mine frequent XQPs without costly tree-containment checking. ESPRIT transforms XML queries into sequences using a one-to-one mapping technique and mines the frequent sequences to generate frequent XQPs. We propose two efficient incremental algorithms, ESPRIT-i and ESPRIT-i+, to incrementally mine frequent XQPs. We devise several novel optimization techniques of query rewriting, cache lookup, and cache replacement to improve the answerability and the hit rate of caching. We have implemented our algorithms and conducted a set of experimental studies on various datasets. The experimental results demonstrate that our algorithms achieve high efficiency and scalability and outperform state-of-the-art methods significantly. | ||
650 | 4 | |a XML query patterns | |
650 | 4 | |a Frequent query patterns | |
650 | 4 | |a XML frequent pattern mining | |
650 | 4 | |a Incremental mining | |
650 | 4 | |a Sequential pattern mining | |
700 | 1 | |a Feng, Jianhua |4 aut | |
700 | 1 | |a Wang, Jianyong |4 aut | |
700 | 1 | |a Zhou, Lizhu |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Data mining and knowledge discovery |d Springer US, 1997 |g 18(2009), 3 vom: 12. Feb., Seite 472-516 |w (DE-627)230491774 |w (DE-600)1386325-3 |w (DE-576)067290434 |x 1384-5810 |7 nnns |
773 | 1 | 8 | |g volume:18 |g year:2009 |g number:3 |g day:12 |g month:02 |g pages:472-516 |
856 | 4 | 1 | |u https://doi.org/10.1007/s10618-009-0126-5 |z lizenzpflichtig |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_OLC | ||
912 | |a FID-LING | ||
912 | |a SSG-OLC-BUB | ||
912 | |a SSG-OLC-MAT | ||
912 | |a SSG-OPC-BBI | ||
912 | |a SSG-OPC-ANG | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_100 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4305 | ||
951 | |a AR | ||
952 | |d 18 |j 2009 |e 3 |b 12 |c 02 |h 472-516 |
author_variant |
g l gl j f jf j w jw l z lz |
---|---|
matchkey_str |
article:13845810:2009----::nrmnasqecbsdrqeturptenii |
hierarchy_sort_str |
2009 |
publishDate |
2009 |
allfields |
10.1007/s10618-009-0126-5 doi (DE-627)OLC2027058311 (DE-He213)s10618-009-0126-5-p DE-627 ger DE-627 rakwb eng 400 070 004 VZ 24,1 ssgn LING DE-30 fid Li, Guoliang verfasserin aut Incremental sequence-based frequent query pattern mining from XML queries 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2009 Abstract Existing algorithms of mining frequent XML query patterns (XQPs) employ a candidate generate-and-test strategy. They involve expensive candidate enumeration and costly tree-containment checking. Further, most of existing methods compute the frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XQPs transferred from user query logs. However, it is not straightforward to maintain such discovered frequent patterns in real XML databases as there may be frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. Therefore, a drawback of existing methods is that they are rather inefficient for the evolution of transaction databases. To address above-mentioned problems, this paper proposes an efficient algorithm ESPRIT to mine frequent XQPs without costly tree-containment checking. ESPRIT transforms XML queries into sequences using a one-to-one mapping technique and mines the frequent sequences to generate frequent XQPs. We propose two efficient incremental algorithms, ESPRIT-i and ESPRIT-i+, to incrementally mine frequent XQPs. We devise several novel optimization techniques of query rewriting, cache lookup, and cache replacement to improve the answerability and the hit rate of caching. We have implemented our algorithms and conducted a set of experimental studies on various datasets. The experimental results demonstrate that our algorithms achieve high efficiency and scalability and outperform state-of-the-art methods significantly. XML query patterns Frequent query patterns XML frequent pattern mining Incremental mining Sequential pattern mining Feng, Jianhua aut Wang, Jianyong aut Zhou, Lizhu aut Enthalten in Data mining and knowledge discovery Springer US, 1997 18(2009), 3 vom: 12. Feb., Seite 472-516 (DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 1384-5810 nnns volume:18 year:2009 number:3 day:12 month:02 pages:472-516 https://doi.org/10.1007/s10618-009-0126-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_70 GBV_ILN_100 GBV_ILN_4012 GBV_ILN_4305 AR 18 2009 3 12 02 472-516 |
spelling |
10.1007/s10618-009-0126-5 doi (DE-627)OLC2027058311 (DE-He213)s10618-009-0126-5-p DE-627 ger DE-627 rakwb eng 400 070 004 VZ 24,1 ssgn LING DE-30 fid Li, Guoliang verfasserin aut Incremental sequence-based frequent query pattern mining from XML queries 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2009 Abstract Existing algorithms of mining frequent XML query patterns (XQPs) employ a candidate generate-and-test strategy. They involve expensive candidate enumeration and costly tree-containment checking. Further, most of existing methods compute the frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XQPs transferred from user query logs. However, it is not straightforward to maintain such discovered frequent patterns in real XML databases as there may be frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. Therefore, a drawback of existing methods is that they are rather inefficient for the evolution of transaction databases. To address above-mentioned problems, this paper proposes an efficient algorithm ESPRIT to mine frequent XQPs without costly tree-containment checking. ESPRIT transforms XML queries into sequences using a one-to-one mapping technique and mines the frequent sequences to generate frequent XQPs. We propose two efficient incremental algorithms, ESPRIT-i and ESPRIT-i+, to incrementally mine frequent XQPs. We devise several novel optimization techniques of query rewriting, cache lookup, and cache replacement to improve the answerability and the hit rate of caching. We have implemented our algorithms and conducted a set of experimental studies on various datasets. The experimental results demonstrate that our algorithms achieve high efficiency and scalability and outperform state-of-the-art methods significantly. XML query patterns Frequent query patterns XML frequent pattern mining Incremental mining Sequential pattern mining Feng, Jianhua aut Wang, Jianyong aut Zhou, Lizhu aut Enthalten in Data mining and knowledge discovery Springer US, 1997 18(2009), 3 vom: 12. Feb., Seite 472-516 (DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 1384-5810 nnns volume:18 year:2009 number:3 day:12 month:02 pages:472-516 https://doi.org/10.1007/s10618-009-0126-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_70 GBV_ILN_100 GBV_ILN_4012 GBV_ILN_4305 AR 18 2009 3 12 02 472-516 |
allfields_unstemmed |
10.1007/s10618-009-0126-5 doi (DE-627)OLC2027058311 (DE-He213)s10618-009-0126-5-p DE-627 ger DE-627 rakwb eng 400 070 004 VZ 24,1 ssgn LING DE-30 fid Li, Guoliang verfasserin aut Incremental sequence-based frequent query pattern mining from XML queries 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2009 Abstract Existing algorithms of mining frequent XML query patterns (XQPs) employ a candidate generate-and-test strategy. They involve expensive candidate enumeration and costly tree-containment checking. Further, most of existing methods compute the frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XQPs transferred from user query logs. However, it is not straightforward to maintain such discovered frequent patterns in real XML databases as there may be frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. Therefore, a drawback of existing methods is that they are rather inefficient for the evolution of transaction databases. To address above-mentioned problems, this paper proposes an efficient algorithm ESPRIT to mine frequent XQPs without costly tree-containment checking. ESPRIT transforms XML queries into sequences using a one-to-one mapping technique and mines the frequent sequences to generate frequent XQPs. We propose two efficient incremental algorithms, ESPRIT-i and ESPRIT-i+, to incrementally mine frequent XQPs. We devise several novel optimization techniques of query rewriting, cache lookup, and cache replacement to improve the answerability and the hit rate of caching. We have implemented our algorithms and conducted a set of experimental studies on various datasets. The experimental results demonstrate that our algorithms achieve high efficiency and scalability and outperform state-of-the-art methods significantly. XML query patterns Frequent query patterns XML frequent pattern mining Incremental mining Sequential pattern mining Feng, Jianhua aut Wang, Jianyong aut Zhou, Lizhu aut Enthalten in Data mining and knowledge discovery Springer US, 1997 18(2009), 3 vom: 12. Feb., Seite 472-516 (DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 1384-5810 nnns volume:18 year:2009 number:3 day:12 month:02 pages:472-516 https://doi.org/10.1007/s10618-009-0126-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_70 GBV_ILN_100 GBV_ILN_4012 GBV_ILN_4305 AR 18 2009 3 12 02 472-516 |
allfieldsGer |
10.1007/s10618-009-0126-5 doi (DE-627)OLC2027058311 (DE-He213)s10618-009-0126-5-p DE-627 ger DE-627 rakwb eng 400 070 004 VZ 24,1 ssgn LING DE-30 fid Li, Guoliang verfasserin aut Incremental sequence-based frequent query pattern mining from XML queries 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2009 Abstract Existing algorithms of mining frequent XML query patterns (XQPs) employ a candidate generate-and-test strategy. They involve expensive candidate enumeration and costly tree-containment checking. Further, most of existing methods compute the frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XQPs transferred from user query logs. However, it is not straightforward to maintain such discovered frequent patterns in real XML databases as there may be frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. Therefore, a drawback of existing methods is that they are rather inefficient for the evolution of transaction databases. To address above-mentioned problems, this paper proposes an efficient algorithm ESPRIT to mine frequent XQPs without costly tree-containment checking. ESPRIT transforms XML queries into sequences using a one-to-one mapping technique and mines the frequent sequences to generate frequent XQPs. We propose two efficient incremental algorithms, ESPRIT-i and ESPRIT-i+, to incrementally mine frequent XQPs. We devise several novel optimization techniques of query rewriting, cache lookup, and cache replacement to improve the answerability and the hit rate of caching. We have implemented our algorithms and conducted a set of experimental studies on various datasets. The experimental results demonstrate that our algorithms achieve high efficiency and scalability and outperform state-of-the-art methods significantly. XML query patterns Frequent query patterns XML frequent pattern mining Incremental mining Sequential pattern mining Feng, Jianhua aut Wang, Jianyong aut Zhou, Lizhu aut Enthalten in Data mining and knowledge discovery Springer US, 1997 18(2009), 3 vom: 12. Feb., Seite 472-516 (DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 1384-5810 nnns volume:18 year:2009 number:3 day:12 month:02 pages:472-516 https://doi.org/10.1007/s10618-009-0126-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_70 GBV_ILN_100 GBV_ILN_4012 GBV_ILN_4305 AR 18 2009 3 12 02 472-516 |
allfieldsSound |
10.1007/s10618-009-0126-5 doi (DE-627)OLC2027058311 (DE-He213)s10618-009-0126-5-p DE-627 ger DE-627 rakwb eng 400 070 004 VZ 24,1 ssgn LING DE-30 fid Li, Guoliang verfasserin aut Incremental sequence-based frequent query pattern mining from XML queries 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2009 Abstract Existing algorithms of mining frequent XML query patterns (XQPs) employ a candidate generate-and-test strategy. They involve expensive candidate enumeration and costly tree-containment checking. Further, most of existing methods compute the frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XQPs transferred from user query logs. However, it is not straightforward to maintain such discovered frequent patterns in real XML databases as there may be frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. Therefore, a drawback of existing methods is that they are rather inefficient for the evolution of transaction databases. To address above-mentioned problems, this paper proposes an efficient algorithm ESPRIT to mine frequent XQPs without costly tree-containment checking. ESPRIT transforms XML queries into sequences using a one-to-one mapping technique and mines the frequent sequences to generate frequent XQPs. We propose two efficient incremental algorithms, ESPRIT-i and ESPRIT-i+, to incrementally mine frequent XQPs. We devise several novel optimization techniques of query rewriting, cache lookup, and cache replacement to improve the answerability and the hit rate of caching. We have implemented our algorithms and conducted a set of experimental studies on various datasets. The experimental results demonstrate that our algorithms achieve high efficiency and scalability and outperform state-of-the-art methods significantly. XML query patterns Frequent query patterns XML frequent pattern mining Incremental mining Sequential pattern mining Feng, Jianhua aut Wang, Jianyong aut Zhou, Lizhu aut Enthalten in Data mining and knowledge discovery Springer US, 1997 18(2009), 3 vom: 12. Feb., Seite 472-516 (DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 1384-5810 nnns volume:18 year:2009 number:3 day:12 month:02 pages:472-516 https://doi.org/10.1007/s10618-009-0126-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_70 GBV_ILN_100 GBV_ILN_4012 GBV_ILN_4305 AR 18 2009 3 12 02 472-516 |
language |
English |
source |
Enthalten in Data mining and knowledge discovery 18(2009), 3 vom: 12. Feb., Seite 472-516 volume:18 year:2009 number:3 day:12 month:02 pages:472-516 |
sourceStr |
Enthalten in Data mining and knowledge discovery 18(2009), 3 vom: 12. Feb., Seite 472-516 volume:18 year:2009 number:3 day:12 month:02 pages:472-516 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
XML query patterns Frequent query patterns XML frequent pattern mining Incremental mining Sequential pattern mining |
dewey-raw |
400 |
isfreeaccess_bool |
false |
container_title |
Data mining and knowledge discovery |
authorswithroles_txt_mv |
Li, Guoliang @@aut@@ Feng, Jianhua @@aut@@ Wang, Jianyong @@aut@@ Zhou, Lizhu @@aut@@ |
publishDateDaySort_date |
2009-02-12T00:00:00Z |
hierarchy_top_id |
230491774 |
dewey-sort |
3400 |
id |
OLC2027058311 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2027058311</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503034703.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2009 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10618-009-0126-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2027058311</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10618-009-0126-5-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">400</subfield><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">24,1</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">LING</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Guoliang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Incremental sequence-based frequent query pattern mining from XML queries</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2009</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC 2009</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Existing algorithms of mining frequent XML query patterns (XQPs) employ a candidate generate-and-test strategy. They involve expensive candidate enumeration and costly tree-containment checking. Further, most of existing methods compute the frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XQPs transferred from user query logs. However, it is not straightforward to maintain such discovered frequent patterns in real XML databases as there may be frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. Therefore, a drawback of existing methods is that they are rather inefficient for the evolution of transaction databases. To address above-mentioned problems, this paper proposes an efficient algorithm ESPRIT to mine frequent XQPs without costly tree-containment checking. ESPRIT transforms XML queries into sequences using a one-to-one mapping technique and mines the frequent sequences to generate frequent XQPs. We propose two efficient incremental algorithms, ESPRIT-i and ESPRIT-i+, to incrementally mine frequent XQPs. We devise several novel optimization techniques of query rewriting, cache lookup, and cache replacement to improve the answerability and the hit rate of caching. We have implemented our algorithms and conducted a set of experimental studies on various datasets. The experimental results demonstrate that our algorithms achieve high efficiency and scalability and outperform state-of-the-art methods significantly.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">XML query patterns</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Frequent query patterns</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">XML frequent pattern mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Incremental mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sequential pattern mining</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Feng, Jianhua</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Jianyong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Lizhu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Data mining and knowledge discovery</subfield><subfield code="d">Springer US, 1997</subfield><subfield code="g">18(2009), 3 vom: 12. Feb., Seite 472-516</subfield><subfield code="w">(DE-627)230491774</subfield><subfield code="w">(DE-600)1386325-3</subfield><subfield code="w">(DE-576)067290434</subfield><subfield code="x">1384-5810</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:18</subfield><subfield code="g">year:2009</subfield><subfield code="g">number:3</subfield><subfield code="g">day:12</subfield><subfield code="g">month:02</subfield><subfield code="g">pages:472-516</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10618-009-0126-5</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-LING</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-BBI</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-ANG</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">18</subfield><subfield code="j">2009</subfield><subfield code="e">3</subfield><subfield code="b">12</subfield><subfield code="c">02</subfield><subfield code="h">472-516</subfield></datafield></record></collection>
|
author |
Li, Guoliang |
spellingShingle |
Li, Guoliang ddc 400 ssgn 24,1 fid LING misc XML query patterns misc Frequent query patterns misc XML frequent pattern mining misc Incremental mining misc Sequential pattern mining Incremental sequence-based frequent query pattern mining from XML queries |
authorStr |
Li, Guoliang |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)230491774 |
format |
Article |
dewey-ones |
400 - Language 070 - News media, journalism & publishing 004 - Data processing & computer science |
delete_txt_mv |
keep |
author_role |
aut aut aut aut |
collection |
OLC |
remote_str |
false |
illustrated |
Not Illustrated |
issn |
1384-5810 |
topic_title |
400 070 004 VZ 24,1 ssgn LING DE-30 fid Incremental sequence-based frequent query pattern mining from XML queries XML query patterns Frequent query patterns XML frequent pattern mining Incremental mining Sequential pattern mining |
topic |
ddc 400 ssgn 24,1 fid LING misc XML query patterns misc Frequent query patterns misc XML frequent pattern mining misc Incremental mining misc Sequential pattern mining |
topic_unstemmed |
ddc 400 ssgn 24,1 fid LING misc XML query patterns misc Frequent query patterns misc XML frequent pattern mining misc Incremental mining misc Sequential pattern mining |
topic_browse |
ddc 400 ssgn 24,1 fid LING misc XML query patterns misc Frequent query patterns misc XML frequent pattern mining misc Incremental mining misc Sequential pattern mining |
format_facet |
Aufsätze Gedruckte Aufsätze |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
nc |
hierarchy_parent_title |
Data mining and knowledge discovery |
hierarchy_parent_id |
230491774 |
dewey-tens |
400 - Language 070 - News media, journalism & publishing 000 - Computer science, knowledge & systems |
hierarchy_top_title |
Data mining and knowledge discovery |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 |
title |
Incremental sequence-based frequent query pattern mining from XML queries |
ctrlnum |
(DE-627)OLC2027058311 (DE-He213)s10618-009-0126-5-p |
title_full |
Incremental sequence-based frequent query pattern mining from XML queries |
author_sort |
Li, Guoliang |
journal |
Data mining and knowledge discovery |
journalStr |
Data mining and knowledge discovery |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
400 - Language 000 - Computer science, information & general works |
recordtype |
marc |
publishDateSort |
2009 |
contenttype_str_mv |
txt |
container_start_page |
472 |
author_browse |
Li, Guoliang Feng, Jianhua Wang, Jianyong Zhou, Lizhu |
container_volume |
18 |
class |
400 070 004 VZ 24,1 ssgn LING DE-30 fid |
format_se |
Aufsätze |
author-letter |
Li, Guoliang |
doi_str_mv |
10.1007/s10618-009-0126-5 |
dewey-full |
400 070 004 |
title_sort |
incremental sequence-based frequent query pattern mining from xml queries |
title_auth |
Incremental sequence-based frequent query pattern mining from XML queries |
abstract |
Abstract Existing algorithms of mining frequent XML query patterns (XQPs) employ a candidate generate-and-test strategy. They involve expensive candidate enumeration and costly tree-containment checking. Further, most of existing methods compute the frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XQPs transferred from user query logs. However, it is not straightforward to maintain such discovered frequent patterns in real XML databases as there may be frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. Therefore, a drawback of existing methods is that they are rather inefficient for the evolution of transaction databases. To address above-mentioned problems, this paper proposes an efficient algorithm ESPRIT to mine frequent XQPs without costly tree-containment checking. ESPRIT transforms XML queries into sequences using a one-to-one mapping technique and mines the frequent sequences to generate frequent XQPs. We propose two efficient incremental algorithms, ESPRIT-i and ESPRIT-i+, to incrementally mine frequent XQPs. We devise several novel optimization techniques of query rewriting, cache lookup, and cache replacement to improve the answerability and the hit rate of caching. We have implemented our algorithms and conducted a set of experimental studies on various datasets. The experimental results demonstrate that our algorithms achieve high efficiency and scalability and outperform state-of-the-art methods significantly. © Springer Science+Business Media, LLC 2009 |
abstractGer |
Abstract Existing algorithms of mining frequent XML query patterns (XQPs) employ a candidate generate-and-test strategy. They involve expensive candidate enumeration and costly tree-containment checking. Further, most of existing methods compute the frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XQPs transferred from user query logs. However, it is not straightforward to maintain such discovered frequent patterns in real XML databases as there may be frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. Therefore, a drawback of existing methods is that they are rather inefficient for the evolution of transaction databases. To address above-mentioned problems, this paper proposes an efficient algorithm ESPRIT to mine frequent XQPs without costly tree-containment checking. ESPRIT transforms XML queries into sequences using a one-to-one mapping technique and mines the frequent sequences to generate frequent XQPs. We propose two efficient incremental algorithms, ESPRIT-i and ESPRIT-i+, to incrementally mine frequent XQPs. We devise several novel optimization techniques of query rewriting, cache lookup, and cache replacement to improve the answerability and the hit rate of caching. We have implemented our algorithms and conducted a set of experimental studies on various datasets. The experimental results demonstrate that our algorithms achieve high efficiency and scalability and outperform state-of-the-art methods significantly. © Springer Science+Business Media, LLC 2009 |
abstract_unstemmed |
Abstract Existing algorithms of mining frequent XML query patterns (XQPs) employ a candidate generate-and-test strategy. They involve expensive candidate enumeration and costly tree-containment checking. Further, most of existing methods compute the frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XQPs transferred from user query logs. However, it is not straightforward to maintain such discovered frequent patterns in real XML databases as there may be frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. Therefore, a drawback of existing methods is that they are rather inefficient for the evolution of transaction databases. To address above-mentioned problems, this paper proposes an efficient algorithm ESPRIT to mine frequent XQPs without costly tree-containment checking. ESPRIT transforms XML queries into sequences using a one-to-one mapping technique and mines the frequent sequences to generate frequent XQPs. We propose two efficient incremental algorithms, ESPRIT-i and ESPRIT-i+, to incrementally mine frequent XQPs. We devise several novel optimization techniques of query rewriting, cache lookup, and cache replacement to improve the answerability and the hit rate of caching. We have implemented our algorithms and conducted a set of experimental studies on various datasets. The experimental results demonstrate that our algorithms achieve high efficiency and scalability and outperform state-of-the-art methods significantly. © Springer Science+Business Media, LLC 2009 |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_70 GBV_ILN_100 GBV_ILN_4012 GBV_ILN_4305 |
container_issue |
3 |
title_short |
Incremental sequence-based frequent query pattern mining from XML queries |
url |
https://doi.org/10.1007/s10618-009-0126-5 |
remote_bool |
false |
author2 |
Feng, Jianhua Wang, Jianyong Zhou, Lizhu |
author2Str |
Feng, Jianhua Wang, Jianyong Zhou, Lizhu |
ppnlink |
230491774 |
mediatype_str_mv |
n |
isOA_txt |
false |
hochschulschrift_bool |
false |
doi_str |
10.1007/s10618-009-0126-5 |
up_date |
2024-07-03T13:36:48.549Z |
_version_ |
1803565199315173376 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2027058311</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503034703.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2009 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10618-009-0126-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2027058311</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10618-009-0126-5-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">400</subfield><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">24,1</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">LING</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Guoliang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Incremental sequence-based frequent query pattern mining from XML queries</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2009</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC 2009</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Existing algorithms of mining frequent XML query patterns (XQPs) employ a candidate generate-and-test strategy. They involve expensive candidate enumeration and costly tree-containment checking. Further, most of existing methods compute the frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XQPs transferred from user query logs. However, it is not straightforward to maintain such discovered frequent patterns in real XML databases as there may be frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. Therefore, a drawback of existing methods is that they are rather inefficient for the evolution of transaction databases. To address above-mentioned problems, this paper proposes an efficient algorithm ESPRIT to mine frequent XQPs without costly tree-containment checking. ESPRIT transforms XML queries into sequences using a one-to-one mapping technique and mines the frequent sequences to generate frequent XQPs. We propose two efficient incremental algorithms, ESPRIT-i and ESPRIT-i+, to incrementally mine frequent XQPs. We devise several novel optimization techniques of query rewriting, cache lookup, and cache replacement to improve the answerability and the hit rate of caching. We have implemented our algorithms and conducted a set of experimental studies on various datasets. The experimental results demonstrate that our algorithms achieve high efficiency and scalability and outperform state-of-the-art methods significantly.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">XML query patterns</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Frequent query patterns</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">XML frequent pattern mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Incremental mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sequential pattern mining</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Feng, Jianhua</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Jianyong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Lizhu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Data mining and knowledge discovery</subfield><subfield code="d">Springer US, 1997</subfield><subfield code="g">18(2009), 3 vom: 12. Feb., Seite 472-516</subfield><subfield code="w">(DE-627)230491774</subfield><subfield code="w">(DE-600)1386325-3</subfield><subfield code="w">(DE-576)067290434</subfield><subfield code="x">1384-5810</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:18</subfield><subfield code="g">year:2009</subfield><subfield code="g">number:3</subfield><subfield code="g">day:12</subfield><subfield code="g">month:02</subfield><subfield code="g">pages:472-516</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10618-009-0126-5</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-LING</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-BBI</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-ANG</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">18</subfield><subfield code="j">2009</subfield><subfield code="e">3</subfield><subfield code="b">12</subfield><subfield code="c">02</subfield><subfield code="h">472-516</subfield></datafield></record></collection>
|
score |
7.397664 |