Improved characters distance sampling for online and offline text searching

Sampled string matching is a very effective technique to reduce the search time for a pattern within a text at the cost of a small amount of additional memory, used for storing a partial index of the text. This approach has recently received some interest and has been applied to improve both online...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Faro, Simone [verfasserIn] Marino, Francesco Pio [verfasserIn] Pavone, Arianna [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Text processing Experimental algorithms String matching

Übergeordnetes Werk:	Enthalten in: Theoretical computer science - Amsterdam [u.a.] : Elsevier, 1975, 946
Übergeordnetes Werk:	volume:946

DOI / URN:	10.1016/j.tcs.2022.12.034

Katalog-ID:	ELV009137246

Internformat


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520			\|a Sampled string matching is a very effective technique to reduce the search time for a pattern within a text at the cost of a small amount of additional memory, used for storing a partial index of the text. This approach has recently received some interest and has been applied to improve both online and offline string matching solutions, improving standard solutions by more than 50%. However, this improvement is currently only achievable in the case of texts on large-sized alphabets, and remains small (or absent) in the case of small-sized alphabets. In this article we propose an extension of the approach to text-sampling, known as Character Distance Sampling, to the case of small alphabets, obtaining an improvement of up to 98% compared to standard solutions in the case of online string matching. We also extend this approach to the case of offline string matching, introducing a sampled version of the suffix array, obtaining performances up to 5 times higher than the search obtained on the standard suffix array. Differently from what has been done by previous solutions, our idea is not based on the reduction of the number of indexed suffixes, but on the construction of the index directly on the sampled text.
650		4	\|a Text processing
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650		4	\|a String matching
700	1		\|a Marino, Francesco Pio \|e verfasserin \|4 aut
700	1		\|a Pavone, Arianna \|e verfasserin \|4 aut
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matchkey_str	farosimonemarinofrancescopiopavoneariann:2022----:mrvdhrcesitneapigoolnadf
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bklnumber	54.10
publishDate	2022
allfields	10.1016/j.tcs.2022.12.034 doi (DE-627)ELV009137246 (ELSEVIER)S0304-3975(22)00764-2 DE-627 ger DE-627 rda eng 004 VZ 54.10 bkl Faro, Simone verfasserin (orcid)0000-0001-5937-5796 aut Improved characters distance sampling for online and offline text searching 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sampled string matching is a very effective technique to reduce the search time for a pattern within a text at the cost of a small amount of additional memory, used for storing a partial index of the text. This approach has recently received some interest and has been applied to improve both online and offline string matching solutions, improving standard solutions by more than 50%. However, this improvement is currently only achievable in the case of texts on large-sized alphabets, and remains small (or absent) in the case of small-sized alphabets. In this article we propose an extension of the approach to text-sampling, known as Character Distance Sampling, to the case of small alphabets, obtaining an improvement of up to 98% compared to standard solutions in the case of online string matching. We also extend this approach to the case of offline string matching, introducing a sampled version of the suffix array, obtaining performances up to 5 times higher than the search obtained on the standard suffix array. Differently from what has been done by previous solutions, our idea is not based on the reduction of the number of indexed suffixes, but on the construction of the index directly on the sampled text. Text processing Experimental algorithms String matching Marino, Francesco Pio verfasserin aut Pavone, Arianna verfasserin aut Enthalten in Theoretical computer science Amsterdam [u.a.] : Elsevier, 1975 946 Online-Ressource (DE-627)265784174 (DE-600)1466347-8 (DE-576)074891030 nnns volume:946 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.10 Theoretische Informatik VZ AR 946
spelling	10.1016/j.tcs.2022.12.034 doi (DE-627)ELV009137246 (ELSEVIER)S0304-3975(22)00764-2 DE-627 ger DE-627 rda eng 004 VZ 54.10 bkl Faro, Simone verfasserin (orcid)0000-0001-5937-5796 aut Improved characters distance sampling for online and offline text searching 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sampled string matching is a very effective technique to reduce the search time for a pattern within a text at the cost of a small amount of additional memory, used for storing a partial index of the text. This approach has recently received some interest and has been applied to improve both online and offline string matching solutions, improving standard solutions by more than 50%. However, this improvement is currently only achievable in the case of texts on large-sized alphabets, and remains small (or absent) in the case of small-sized alphabets. In this article we propose an extension of the approach to text-sampling, known as Character Distance Sampling, to the case of small alphabets, obtaining an improvement of up to 98% compared to standard solutions in the case of online string matching. We also extend this approach to the case of offline string matching, introducing a sampled version of the suffix array, obtaining performances up to 5 times higher than the search obtained on the standard suffix array. Differently from what has been done by previous solutions, our idea is not based on the reduction of the number of indexed suffixes, but on the construction of the index directly on the sampled text. Text processing Experimental algorithms String matching Marino, Francesco Pio verfasserin aut Pavone, Arianna verfasserin aut Enthalten in Theoretical computer science Amsterdam [u.a.] : Elsevier, 1975 946 Online-Ressource (DE-627)265784174 (DE-600)1466347-8 (DE-576)074891030 nnns volume:946 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.10 Theoretische Informatik VZ AR 946
allfields_unstemmed	10.1016/j.tcs.2022.12.034 doi (DE-627)ELV009137246 (ELSEVIER)S0304-3975(22)00764-2 DE-627 ger DE-627 rda eng 004 VZ 54.10 bkl Faro, Simone verfasserin (orcid)0000-0001-5937-5796 aut Improved characters distance sampling for online and offline text searching 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sampled string matching is a very effective technique to reduce the search time for a pattern within a text at the cost of a small amount of additional memory, used for storing a partial index of the text. This approach has recently received some interest and has been applied to improve both online and offline string matching solutions, improving standard solutions by more than 50%. However, this improvement is currently only achievable in the case of texts on large-sized alphabets, and remains small (or absent) in the case of small-sized alphabets. In this article we propose an extension of the approach to text-sampling, known as Character Distance Sampling, to the case of small alphabets, obtaining an improvement of up to 98% compared to standard solutions in the case of online string matching. We also extend this approach to the case of offline string matching, introducing a sampled version of the suffix array, obtaining performances up to 5 times higher than the search obtained on the standard suffix array. Differently from what has been done by previous solutions, our idea is not based on the reduction of the number of indexed suffixes, but on the construction of the index directly on the sampled text. Text processing Experimental algorithms String matching Marino, Francesco Pio verfasserin aut Pavone, Arianna verfasserin aut Enthalten in Theoretical computer science Amsterdam [u.a.] : Elsevier, 1975 946 Online-Ressource (DE-627)265784174 (DE-600)1466347-8 (DE-576)074891030 nnns volume:946 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.10 Theoretische Informatik VZ AR 946
allfieldsGer	10.1016/j.tcs.2022.12.034 doi (DE-627)ELV009137246 (ELSEVIER)S0304-3975(22)00764-2 DE-627 ger DE-627 rda eng 004 VZ 54.10 bkl Faro, Simone verfasserin (orcid)0000-0001-5937-5796 aut Improved characters distance sampling for online and offline text searching 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sampled string matching is a very effective technique to reduce the search time for a pattern within a text at the cost of a small amount of additional memory, used for storing a partial index of the text. This approach has recently received some interest and has been applied to improve both online and offline string matching solutions, improving standard solutions by more than 50%. However, this improvement is currently only achievable in the case of texts on large-sized alphabets, and remains small (or absent) in the case of small-sized alphabets. In this article we propose an extension of the approach to text-sampling, known as Character Distance Sampling, to the case of small alphabets, obtaining an improvement of up to 98% compared to standard solutions in the case of online string matching. We also extend this approach to the case of offline string matching, introducing a sampled version of the suffix array, obtaining performances up to 5 times higher than the search obtained on the standard suffix array. Differently from what has been done by previous solutions, our idea is not based on the reduction of the number of indexed suffixes, but on the construction of the index directly on the sampled text. Text processing Experimental algorithms String matching Marino, Francesco Pio verfasserin aut Pavone, Arianna verfasserin aut Enthalten in Theoretical computer science Amsterdam [u.a.] : Elsevier, 1975 946 Online-Ressource (DE-627)265784174 (DE-600)1466347-8 (DE-576)074891030 nnns volume:946 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.10 Theoretische Informatik VZ AR 946
allfieldsSound	10.1016/j.tcs.2022.12.034 doi (DE-627)ELV009137246 (ELSEVIER)S0304-3975(22)00764-2 DE-627 ger DE-627 rda eng 004 VZ 54.10 bkl Faro, Simone verfasserin (orcid)0000-0001-5937-5796 aut Improved characters distance sampling for online and offline text searching 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sampled string matching is a very effective technique to reduce the search time for a pattern within a text at the cost of a small amount of additional memory, used for storing a partial index of the text. This approach has recently received some interest and has been applied to improve both online and offline string matching solutions, improving standard solutions by more than 50%. However, this improvement is currently only achievable in the case of texts on large-sized alphabets, and remains small (or absent) in the case of small-sized alphabets. In this article we propose an extension of the approach to text-sampling, known as Character Distance Sampling, to the case of small alphabets, obtaining an improvement of up to 98% compared to standard solutions in the case of online string matching. We also extend this approach to the case of offline string matching, introducing a sampled version of the suffix array, obtaining performances up to 5 times higher than the search obtained on the standard suffix array. Differently from what has been done by previous solutions, our idea is not based on the reduction of the number of indexed suffixes, but on the construction of the index directly on the sampled text. Text processing Experimental algorithms String matching Marino, Francesco Pio verfasserin aut Pavone, Arianna verfasserin aut Enthalten in Theoretical computer science Amsterdam [u.a.] : Elsevier, 1975 946 Online-Ressource (DE-627)265784174 (DE-600)1466347-8 (DE-576)074891030 nnns volume:946 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.10 Theoretische Informatik VZ AR 946
language	English
source	Enthalten in Theoretical computer science 946 volume:946
sourceStr	Enthalten in Theoretical computer science 946 volume:946
format_phy_str_mv	Article
bklname	Theoretische Informatik
institution	findex.gbv.de
topic_facet	Text processing Experimental algorithms String matching
dewey-raw	004
isfreeaccess_bool	false
container_title	Theoretical computer science
authorswithroles_txt_mv	Faro, Simone @@aut@@ Marino, Francesco Pio @@aut@@ Pavone, Arianna @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	265784174
dewey-sort	14
id	ELV009137246
language_de	englisch
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author	Faro, Simone
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topic_title	004 VZ 54.10 bkl Improved characters distance sampling for online and offline text searching Text processing Experimental algorithms String matching
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title	Improved characters distance sampling for online and offline text searching
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title_full	Improved characters distance sampling for online and offline text searching
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title_sort	improved characters distance sampling for online and offline text searching
title_auth	Improved characters distance sampling for online and offline text searching
abstract	Sampled string matching is a very effective technique to reduce the search time for a pattern within a text at the cost of a small amount of additional memory, used for storing a partial index of the text. This approach has recently received some interest and has been applied to improve both online and offline string matching solutions, improving standard solutions by more than 50%. However, this improvement is currently only achievable in the case of texts on large-sized alphabets, and remains small (or absent) in the case of small-sized alphabets. In this article we propose an extension of the approach to text-sampling, known as Character Distance Sampling, to the case of small alphabets, obtaining an improvement of up to 98% compared to standard solutions in the case of online string matching. We also extend this approach to the case of offline string matching, introducing a sampled version of the suffix array, obtaining performances up to 5 times higher than the search obtained on the standard suffix array. Differently from what has been done by previous solutions, our idea is not based on the reduction of the number of indexed suffixes, but on the construction of the index directly on the sampled text.
abstractGer	Sampled string matching is a very effective technique to reduce the search time for a pattern within a text at the cost of a small amount of additional memory, used for storing a partial index of the text. This approach has recently received some interest and has been applied to improve both online and offline string matching solutions, improving standard solutions by more than 50%. However, this improvement is currently only achievable in the case of texts on large-sized alphabets, and remains small (or absent) in the case of small-sized alphabets. In this article we propose an extension of the approach to text-sampling, known as Character Distance Sampling, to the case of small alphabets, obtaining an improvement of up to 98% compared to standard solutions in the case of online string matching. We also extend this approach to the case of offline string matching, introducing a sampled version of the suffix array, obtaining performances up to 5 times higher than the search obtained on the standard suffix array. Differently from what has been done by previous solutions, our idea is not based on the reduction of the number of indexed suffixes, but on the construction of the index directly on the sampled text.
abstract_unstemmed	Sampled string matching is a very effective technique to reduce the search time for a pattern within a text at the cost of a small amount of additional memory, used for storing a partial index of the text. This approach has recently received some interest and has been applied to improve both online and offline string matching solutions, improving standard solutions by more than 50%. However, this improvement is currently only achievable in the case of texts on large-sized alphabets, and remains small (or absent) in the case of small-sized alphabets. In this article we propose an extension of the approach to text-sampling, known as Character Distance Sampling, to the case of small alphabets, obtaining an improvement of up to 98% compared to standard solutions in the case of online string matching. We also extend this approach to the case of offline string matching, introducing a sampled version of the suffix array, obtaining performances up to 5 times higher than the search obtained on the standard suffix array. Differently from what has been done by previous solutions, our idea is not based on the reduction of the number of indexed suffixes, but on the construction of the index directly on the sampled text.
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title_short	Improved characters distance sampling for online and offline text searching
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