Approximate query processing over static sets and sliding windows

Indexing of static and dynamic sets is fundamental to a large set of applications such as information retrieval and caching. Denoting the characteristic vector of the set by B, we consider the problem of encoding sets and multisets to support approximate versions of the operations...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Basat, Ran Ben [verfasserIn] Jo, Seungbum [verfasserIn] Satti, Srinivasa Rao [verfasserIn] Ugare, Shubham [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Streaming Algorithms Sliding window Lower bounds

Übergeordnetes Werk:	Enthalten in: Theoretical computer science - Amsterdam [u.a.] : Elsevier, 1975, 885, Seite 1-14
Übergeordnetes Werk:	volume:885 ; pages:1-14

DOI / URN:	10.1016/j.tcs.2021.06.015

Katalog-ID:	ELV006516017

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245	1	0	\|a Approximate query processing over static sets and sliding windows
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520			\|a Indexing of static and dynamic sets is fundamental to a large set of applications such as information retrieval and caching. Denoting the characteristic vector of the set by B, we consider the problem of encoding sets and multisets to support approximate versions of the operations rank ( i ) (i.e., computing ∑ j ≤ i B [ j ] ) and select ( i ) (i.e., finding min ⁡ { p \| rank ( p ) ≥ i } ) queries. We study multiple types of approximations (allowing an error in the query or the result) and present lower bounds and succinct data structures for several variants of the problem. We also extend our model to sliding windows, in which we process a stream of elements and compute suffix sums. This is a generalization of the window summation problem that allows the user to specify the window size at query time. Here, we provide an algorithm that supports updates and queries in constant time while requiring just ( 1 + o ( 1 ) ) factor more space than the fixed-window summation algorithms.
650		4	\|a Streaming
650		4	\|a Algorithms
650		4	\|a Sliding window
650		4	\|a Lower bounds
700	1		\|a Jo, Seungbum \|e verfasserin \|4 aut
700	1		\|a Satti, Srinivasa Rao \|e verfasserin \|0 (orcid)0000-0003-0636-9880 \|4 aut
700	1		\|a Ugare, Shubham \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Theoretical computer science \|d Amsterdam [u.a.] : Elsevier, 1975 \|g 885, Seite 1-14 \|h Online-Ressource \|w (DE-627)265784174 \|w (DE-600)1466347-8 \|w (DE-576)074891030 \|7 nnns
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912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
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912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
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912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
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912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4393
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936	b	k	\|a 54.10 \|j Theoretische Informatik
951			\|a AR
952			\|d 885 \|h 1-14

Indexfelder

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matchkey_str	basatranbenjoseungbumsattisrinivasaraoug:2021----:prxmtqeyrcsigvrttcesn
hierarchy_sort_str	2021
bklnumber	54.10
publishDate	2021
allfields	10.1016/j.tcs.2021.06.015 doi (DE-627)ELV006516017 (ELSEVIER)S0304-3975(21)00357-1 DE-627 ger DE-627 rda eng 004 DE-600 54.10 bkl Basat, Ran Ben verfasserin aut Approximate query processing over static sets and sliding windows 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Indexing of static and dynamic sets is fundamental to a large set of applications such as information retrieval and caching. Denoting the characteristic vector of the set by B, we consider the problem of encoding sets and multisets to support approximate versions of the operations rank ( i ) (i.e., computing ∑ j ≤ i B [ j ] ) and select ( i ) (i.e., finding min ⁡ { p \| rank ( p ) ≥ i } ) queries. We study multiple types of approximations (allowing an error in the query or the result) and present lower bounds and succinct data structures for several variants of the problem. We also extend our model to sliding windows, in which we process a stream of elements and compute suffix sums. This is a generalization of the window summation problem that allows the user to specify the window size at query time. Here, we provide an algorithm that supports updates and queries in constant time while requiring just ( 1 + o ( 1 ) ) factor more space than the fixed-window summation algorithms. Streaming Algorithms Sliding window Lower bounds Jo, Seungbum verfasserin aut Satti, Srinivasa Rao verfasserin (orcid)0000-0003-0636-9880 aut Ugare, Shubham verfasserin aut Enthalten in Theoretical computer science Amsterdam [u.a.] : Elsevier, 1975 885, Seite 1-14 Online-Ressource (DE-627)265784174 (DE-600)1466347-8 (DE-576)074891030 nnns volume:885 pages:1-14 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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 AR 885 1-14
spelling	10.1016/j.tcs.2021.06.015 doi (DE-627)ELV006516017 (ELSEVIER)S0304-3975(21)00357-1 DE-627 ger DE-627 rda eng 004 DE-600 54.10 bkl Basat, Ran Ben verfasserin aut Approximate query processing over static sets and sliding windows 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Indexing of static and dynamic sets is fundamental to a large set of applications such as information retrieval and caching. Denoting the characteristic vector of the set by B, we consider the problem of encoding sets and multisets to support approximate versions of the operations rank ( i ) (i.e., computing ∑ j ≤ i B [ j ] ) and select ( i ) (i.e., finding min ⁡ { p \| rank ( p ) ≥ i } ) queries. We study multiple types of approximations (allowing an error in the query or the result) and present lower bounds and succinct data structures for several variants of the problem. We also extend our model to sliding windows, in which we process a stream of elements and compute suffix sums. This is a generalization of the window summation problem that allows the user to specify the window size at query time. Here, we provide an algorithm that supports updates and queries in constant time while requiring just ( 1 + o ( 1 ) ) factor more space than the fixed-window summation algorithms. Streaming Algorithms Sliding window Lower bounds Jo, Seungbum verfasserin aut Satti, Srinivasa Rao verfasserin (orcid)0000-0003-0636-9880 aut Ugare, Shubham verfasserin aut Enthalten in Theoretical computer science Amsterdam [u.a.] : Elsevier, 1975 885, Seite 1-14 Online-Ressource (DE-627)265784174 (DE-600)1466347-8 (DE-576)074891030 nnns volume:885 pages:1-14 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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 AR 885 1-14
allfields_unstemmed	10.1016/j.tcs.2021.06.015 doi (DE-627)ELV006516017 (ELSEVIER)S0304-3975(21)00357-1 DE-627 ger DE-627 rda eng 004 DE-600 54.10 bkl Basat, Ran Ben verfasserin aut Approximate query processing over static sets and sliding windows 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Indexing of static and dynamic sets is fundamental to a large set of applications such as information retrieval and caching. Denoting the characteristic vector of the set by B, we consider the problem of encoding sets and multisets to support approximate versions of the operations rank ( i ) (i.e., computing ∑ j ≤ i B [ j ] ) and select ( i ) (i.e., finding min ⁡ { p \| rank ( p ) ≥ i } ) queries. We study multiple types of approximations (allowing an error in the query or the result) and present lower bounds and succinct data structures for several variants of the problem. We also extend our model to sliding windows, in which we process a stream of elements and compute suffix sums. This is a generalization of the window summation problem that allows the user to specify the window size at query time. Here, we provide an algorithm that supports updates and queries in constant time while requiring just ( 1 + o ( 1 ) ) factor more space than the fixed-window summation algorithms. Streaming Algorithms Sliding window Lower bounds Jo, Seungbum verfasserin aut Satti, Srinivasa Rao verfasserin (orcid)0000-0003-0636-9880 aut Ugare, Shubham verfasserin aut Enthalten in Theoretical computer science Amsterdam [u.a.] : Elsevier, 1975 885, Seite 1-14 Online-Ressource (DE-627)265784174 (DE-600)1466347-8 (DE-576)074891030 nnns volume:885 pages:1-14 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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 AR 885 1-14
allfieldsGer	10.1016/j.tcs.2021.06.015 doi (DE-627)ELV006516017 (ELSEVIER)S0304-3975(21)00357-1 DE-627 ger DE-627 rda eng 004 DE-600 54.10 bkl Basat, Ran Ben verfasserin aut Approximate query processing over static sets and sliding windows 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Indexing of static and dynamic sets is fundamental to a large set of applications such as information retrieval and caching. Denoting the characteristic vector of the set by B, we consider the problem of encoding sets and multisets to support approximate versions of the operations rank ( i ) (i.e., computing ∑ j ≤ i B [ j ] ) and select ( i ) (i.e., finding min ⁡ { p \| rank ( p ) ≥ i } ) queries. We study multiple types of approximations (allowing an error in the query or the result) and present lower bounds and succinct data structures for several variants of the problem. We also extend our model to sliding windows, in which we process a stream of elements and compute suffix sums. This is a generalization of the window summation problem that allows the user to specify the window size at query time. Here, we provide an algorithm that supports updates and queries in constant time while requiring just ( 1 + o ( 1 ) ) factor more space than the fixed-window summation algorithms. Streaming Algorithms Sliding window Lower bounds Jo, Seungbum verfasserin aut Satti, Srinivasa Rao verfasserin (orcid)0000-0003-0636-9880 aut Ugare, Shubham verfasserin aut Enthalten in Theoretical computer science Amsterdam [u.a.] : Elsevier, 1975 885, Seite 1-14 Online-Ressource (DE-627)265784174 (DE-600)1466347-8 (DE-576)074891030 nnns volume:885 pages:1-14 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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 AR 885 1-14
allfieldsSound	10.1016/j.tcs.2021.06.015 doi (DE-627)ELV006516017 (ELSEVIER)S0304-3975(21)00357-1 DE-627 ger DE-627 rda eng 004 DE-600 54.10 bkl Basat, Ran Ben verfasserin aut Approximate query processing over static sets and sliding windows 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Indexing of static and dynamic sets is fundamental to a large set of applications such as information retrieval and caching. Denoting the characteristic vector of the set by B, we consider the problem of encoding sets and multisets to support approximate versions of the operations rank ( i ) (i.e., computing ∑ j ≤ i B [ j ] ) and select ( i ) (i.e., finding min ⁡ { p \| rank ( p ) ≥ i } ) queries. We study multiple types of approximations (allowing an error in the query or the result) and present lower bounds and succinct data structures for several variants of the problem. We also extend our model to sliding windows, in which we process a stream of elements and compute suffix sums. This is a generalization of the window summation problem that allows the user to specify the window size at query time. Here, we provide an algorithm that supports updates and queries in constant time while requiring just ( 1 + o ( 1 ) ) factor more space than the fixed-window summation algorithms. Streaming Algorithms Sliding window Lower bounds Jo, Seungbum verfasserin aut Satti, Srinivasa Rao verfasserin (orcid)0000-0003-0636-9880 aut Ugare, Shubham verfasserin aut Enthalten in Theoretical computer science Amsterdam [u.a.] : Elsevier, 1975 885, Seite 1-14 Online-Ressource (DE-627)265784174 (DE-600)1466347-8 (DE-576)074891030 nnns volume:885 pages:1-14 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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 AR 885 1-14
language	English
source	Enthalten in Theoretical computer science 885, Seite 1-14 volume:885 pages:1-14
sourceStr	Enthalten in Theoretical computer science 885, Seite 1-14 volume:885 pages:1-14
format_phy_str_mv	Article
bklname	Theoretische Informatik
institution	findex.gbv.de
topic_facet	Streaming Algorithms Sliding window Lower bounds
dewey-raw	004
isfreeaccess_bool	false
container_title	Theoretical computer science
authorswithroles_txt_mv	Basat, Ran Ben @@aut@@ Jo, Seungbum @@aut@@ Satti, Srinivasa Rao @@aut@@ Ugare, Shubham @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	265784174
dewey-sort	14
id	ELV006516017
language_de	englisch
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author	Basat, Ran Ben
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topic_title	004 DE-600 54.10 bkl Approximate query processing over static sets and sliding windows Streaming Algorithms Sliding window Lower bounds
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title	Approximate query processing over static sets and sliding windows
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title_full	Approximate query processing over static sets and sliding windows
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title_sort	approximate query processing over static sets and sliding windows
title_auth	Approximate query processing over static sets and sliding windows
abstract	Indexing of static and dynamic sets is fundamental to a large set of applications such as information retrieval and caching. Denoting the characteristic vector of the set by B, we consider the problem of encoding sets and multisets to support approximate versions of the operations rank ( i ) (i.e., computing ∑ j ≤ i B [ j ] ) and select ( i ) (i.e., finding min ⁡ { p \| rank ( p ) ≥ i } ) queries. We study multiple types of approximations (allowing an error in the query or the result) and present lower bounds and succinct data structures for several variants of the problem. We also extend our model to sliding windows, in which we process a stream of elements and compute suffix sums. This is a generalization of the window summation problem that allows the user to specify the window size at query time. Here, we provide an algorithm that supports updates and queries in constant time while requiring just ( 1 + o ( 1 ) ) factor more space than the fixed-window summation algorithms.
abstractGer	Indexing of static and dynamic sets is fundamental to a large set of applications such as information retrieval and caching. Denoting the characteristic vector of the set by B, we consider the problem of encoding sets and multisets to support approximate versions of the operations rank ( i ) (i.e., computing ∑ j ≤ i B [ j ] ) and select ( i ) (i.e., finding min ⁡ { p \| rank ( p ) ≥ i } ) queries. We study multiple types of approximations (allowing an error in the query or the result) and present lower bounds and succinct data structures for several variants of the problem. We also extend our model to sliding windows, in which we process a stream of elements and compute suffix sums. This is a generalization of the window summation problem that allows the user to specify the window size at query time. Here, we provide an algorithm that supports updates and queries in constant time while requiring just ( 1 + o ( 1 ) ) factor more space than the fixed-window summation algorithms.
abstract_unstemmed	Indexing of static and dynamic sets is fundamental to a large set of applications such as information retrieval and caching. Denoting the characteristic vector of the set by B, we consider the problem of encoding sets and multisets to support approximate versions of the operations rank ( i ) (i.e., computing ∑ j ≤ i B [ j ] ) and select ( i ) (i.e., finding min ⁡ { p \| rank ( p ) ≥ i } ) queries. We study multiple types of approximations (allowing an error in the query or the result) and present lower bounds and succinct data structures for several variants of the problem. We also extend our model to sliding windows, in which we process a stream of elements and compute suffix sums. This is a generalization of the window summation problem that allows the user to specify the window size at query time. Here, we provide an algorithm that supports updates and queries in constant time while requiring just ( 1 + o ( 1 ) ) factor more space than the fixed-window summation algorithms.
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title_short	Approximate query processing over static sets and sliding windows
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author2	Jo, Seungbum Satti, Srinivasa Rao Ugare, Shubham
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up_date	2024-07-06T21:38:44.493Z
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Approximate query processing over static sets and sliding windows

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