Randomized Algorithms for Tracking Distributed Count, Frequencies, and Ranks
We show that randomization can lead to significant improvements for a few fundamental problems in distributed tracking. Our basis is the count-tracking problem, where there are k players, each holding a counter n i that gets incremented over time, and the goal is to track an ε -approximation of thei...
Gespeichert in:
| Veröffentlicht in: | Algorithmica Jg. 81; H. 6; S. 2222 - 2243 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
Springer US
01.06.2019
Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 0178-4617, 1432-0541 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | We show that randomization can lead to significant improvements for a few fundamental problems in distributed tracking. Our basis is the
count-tracking
problem, where there are
k
players, each holding a counter
n
i
that gets incremented over time, and the goal is to track an
ε
-approximation of their sum
n
=
∑
i
n
i
continuously at all times, using minimum communication. While the deterministic communication complexity of the problem is
Θ
(
k
/
ε
·
log
N
)
, where
N
is the final value of
n
when the tracking finishes, we show that with randomization, the communication cost can be reduced to
Θ
(
k
/
ε
·
log
N
)
. Our algorithm is simple and uses only
O
(1) space at each player, while the lower bound holds even assuming each player has infinite computing power. Then, we extend our techniques to two related distributed tracking problems:
frequency-tracking
and
rank-tracking
, and obtain similar improvements over previous deterministic algorithms. Both problems are of central importance in large data monitoring and analysis, and have been extensively studied in the literature. |
|---|---|
| AbstractList | We show that randomization can lead to significant improvements for a few fundamental problems in distributed tracking. Our basis is the count-tracking problem, where there are k players, each holding a counter ni that gets incremented over time, and the goal is to track an ε-approximation of their sum n=∑ini continuously at all times, using minimum communication. While the deterministic communication complexity of the problem is Θ(k/ε·logN), where N is the final value of n when the tracking finishes, we show that with randomization, the communication cost can be reduced to Θ(k/ε·logN). Our algorithm is simple and uses only O(1) space at each player, while the lower bound holds even assuming each player has infinite computing power. Then, we extend our techniques to two related distributed tracking problems: frequency-tracking and rank-tracking, and obtain similar improvements over previous deterministic algorithms. Both problems are of central importance in large data monitoring and analysis, and have been extensively studied in the literature. We show that randomization can lead to significant improvements for a few fundamental problems in distributed tracking. Our basis is the count-tracking problem, where there are k players, each holding a counter n i that gets incremented over time, and the goal is to track an ε -approximation of their sum n = ∑ i n i continuously at all times, using minimum communication. While the deterministic communication complexity of the problem is Θ ( k / ε · log N ) , where N is the final value of n when the tracking finishes, we show that with randomization, the communication cost can be reduced to Θ ( k / ε · log N ) . Our algorithm is simple and uses only O (1) space at each player, while the lower bound holds even assuming each player has infinite computing power. Then, we extend our techniques to two related distributed tracking problems: frequency-tracking and rank-tracking , and obtain similar improvements over previous deterministic algorithms. Both problems are of central importance in large data monitoring and analysis, and have been extensively studied in the literature. |
| Author | Yi, Ke Huang, Zengfeng Zhang, Qin |
| Author_xml | – sequence: 1 givenname: Zengfeng orcidid: 0000-0003-2671-7483 surname: Huang fullname: Huang, Zengfeng email: huangzf@fudan.edu.cn organization: School of Data Science, Fudan University – sequence: 2 givenname: Ke surname: Yi fullname: Yi, Ke organization: The Hong Kong University of Science and Technology – sequence: 3 givenname: Qin surname: Zhang fullname: Zhang, Qin organization: Indiana University Bloomington |
| BookMark | eNp9kE1LAzEURYNUsK3-AVcDbhtNMh9JlqVaFQqC1HXITJKafiQ1ySzqrze2guBC3uJt7rnvcUZg4LzTAFxjdIsRoncRoaouIcIMIlSXGB7OwBBXJYGorvAADBGmDFYNphdgFOMaIUwob4Zg8Sqd8jv7qVUx3a58sOl9FwvjQ7EMsttYtyrubUzBtn3KmZnvXZoU86A_eu06q-OkyA1FrtnES3Bu5Dbqq589Bm_zh-XsCS5eHp9n0wXsSswTJC3Vsq0YbwlHpTaV4RXVVLaaKEYUb1SjCWKdNg2rmakJVzVVqjVMK4w6Xo7Bzal3H3x-Iyax9n1w-aQgBFfHaXKKnVJd8DEGbURnk0zWuxSk3QqMxLc7cXInsjtxdCcOGSV_0H2wOxkO_0PlCYo57FY6_H71D_UFJuCEhQ |
| CitedBy_id | crossref_primary_10_1007_s00453_020_00755_x |
| Cites_doi | 10.1145/2481528.2481530 10.1007/s00446-008-0055-3 10.1145/2213556.2213562 10.1016/B978-155860869-6/50038-X 10.1145/378580.378687 10.1145/2213977.2214063 10.1137/1116025 10.1145/1921659.1921667 10.14778/1454159.1454225 10.1145/872757.872764 10.1109/INFCOM.2011.5935005 10.1007/978-3-642-24100-0_27 10.1145/375663.375670 10.1007/978-3-642-02927-1_10 10.1016/0304-3975(80)90061-4 10.1007/s00454-006-1269-4 10.1016/0167-6423(82)90012-0 10.1145/2160158.2160163 10.1145/1989323.1989401 10.1145/1166074.1166084 10.1145/1559795.1559820 10.1109/SFCS.1977.24 10.1145/1066157.1066161 10.1145/1142473.1142507 |
| ContentType | Journal Article |
| Copyright | Springer Science+Business Media, LLC, part of Springer Nature 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018. |
| Copyright_xml | – notice: Springer Science+Business Media, LLC, part of Springer Nature 2018 – notice: Springer Science+Business Media, LLC, part of Springer Nature 2018. |
| DBID | AAYXX CITATION JQ2 |
| DOI | 10.1007/s00453-018-00531-y |
| DatabaseName | CrossRef ProQuest Computer Science Collection |
| DatabaseTitle | CrossRef ProQuest Computer Science Collection |
| DatabaseTitleList | ProQuest Computer Science Collection |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1432-0541 |
| EndPage | 2243 |
| ExternalDocumentID | 10_1007_s00453_018_00531_y |
| GrantInformation_xml | – fundername: Research Grants Council, University Grants Committee (HK) grantid: GRF-16200415 – fundername: National Natural Science Foundation of China grantid: 61802069 funderid: http://dx.doi.org/10.13039/501100001809 – fundername: Research Grants Council, University Grants Committee (HK) grantid: GRF-621413; GRF-16211614 – fundername: Shanghai Science and Technology Development Foundation grantid: 18YF1401200 funderid: http://dx.doi.org/10.13039/100012543 |
| GroupedDBID | -4Z -59 -5G -BR -EM -~C -~X .86 .DC .VR 06D 0R~ 0VY 199 1N0 203 23M 2J2 2JN 2JY 2KG 2KM 2LR 2~H 30V 4.4 406 408 409 40D 40E 5GY 5VS 67Z 6NX 78A 8TC 8UJ 95- 95. 95~ 96X AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABSXP ABTEG ABTHY ABTKH ABTMW ABWNU ABXPI ACAOD ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEFQL AEGAL AEGNC AEJHL AEJRE AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. BA0 BDATZ BGNMA BSONS CS3 CSCUP DDRTE DL5 DNIVK DPUIP EBLON EBS EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV LAS LLZTM M4Y MA- N9A NB0 NPVJJ NQJWS NU0 O93 O9G O9I O9J OAM P19 P9O PF- PT4 PT5 QOK QOS R89 R9I RHV RNS ROL RPX RSV S16 S27 S3B SAP SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TN5 TSG TSK TSV TUC U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z7X Z83 Z88 Z8R Z8W Z92 ZMTXR ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC AEZWR AFDZB AFHIU AFOHR AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION JQ2 |
| ID | FETCH-LOGICAL-c319t-2b7eab489b2903ef4f947e7abe2d82d96d6e208cef6858f529d57ddbf8ed10c93 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 4 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000465544600004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0178-4617 |
| IngestDate | Thu Oct 02 16:29:32 EDT 2025 Sat Nov 29 02:20:28 EST 2025 Tue Nov 18 22:35:45 EST 2025 Fri Feb 21 02:43:10 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Keywords | Continuous distributed tracking Distributed streaming Randomized algorithms |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c319t-2b7eab489b2903ef4f947e7abe2d82d96d6e208cef6858f529d57ddbf8ed10c93 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-2671-7483 |
| PQID | 2214141416 |
| PQPubID | 2043795 |
| PageCount | 22 |
| ParticipantIDs | proquest_journals_2214141416 crossref_citationtrail_10_1007_s00453_018_00531_y crossref_primary_10_1007_s00453_018_00531_y springer_journals_10_1007_s00453_018_00531_y |
| PublicationCentury | 2000 |
| PublicationDate | 2019-06-01 |
| PublicationDateYYYYMMDD | 2019-06-01 |
| PublicationDate_xml | – month: 06 year: 2019 text: 2019-06-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Algorithmica |
| PublicationTitleAbbrev | Algorithmica |
| PublicationYear | 2019 |
| Publisher | Springer US Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer Nature B.V |
| References | Cormode, G., Hadjieleftheriou, M.: Finding frequent items in data streams. In: Proceedings of the International Conference on Very Large Data Bases (2008) Manjhi, A., Shkapenyuk, V., Dhamdhere, K., Olston, C.: Finding (recently) frequent items in distributed data streams. In: Proceedings of the IEEE International Conference on Data Engineering (2005) Arackaparambil, C., Brody, J., Chakrabarti, A.: Functional monitoring without monotonicity. In: Proceedings of the International Colloquium on Automata, Languages, and Programming (2009) Yi, K., Zhang, Q.: Optimal tracking of distributed heavy hitters and quantiles. In: Proceedings of the ACM Symposium on Principles of Database Systems (2009) Patt-ShamirBShafrirAApproximate distributed top-k queriesDistrib. Comput.200821112210.1007/s00446-008-0055-3 CormodeGMuthukrishnanSYiKZhangQContinuous sampling from distributed streamsJ. ACM201259210291568110.1145/2160158.2160163(Preliminary version in PODS’10) MetwallyAAgrawalDAbbadiAAn integrated efficient solution for computing frequent and top-k elements in data streamsACM Trans. Database Syst.20063131095113310.1145/1166074.1166084 SuriSTothCZhouYRange counting over multidimensional data streamsDiscrete Comput. Geom.200636633655226755010.1007/s00454-006-1269-4 Agarwal, P.K., Cormode, G., Huang, Z., Phillips, J.M., Wei, Z., Yi, K.: Mergeable summaries. In: Proceedings of the ACM Symposium on Principles of Database Systems (2012) Tirthapura, S., Woodruff, D.P.: Optimal random sampling from distributed streams revisited. In: Proceedings of the International Symposium on Distributed Computing (2011) Cormode, G., Garofalakis, M., Muthukrishnan, S., Rastogi, R.: Holistic aggregates in a networked world: distributed tracking of approximate quantiles. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2005) Woodruff, D.P.: Efficient and Private Distance Approximation in the Communication and Streaming Models. PhD thesis, Massachusetts Institute of Technology (2007) FellerWAn Introduction to Probability Theory and Its Applications1968New YorkWiley0155.23101 Manku, G., Motwani, R.: Approximate frequency counts over data streams. In: Proceedings of the International Conference on Very Large Data Bases (2002) Bar-Yossef, Z.: The complexity of massive data set computations. PhD thesis, University of California at Berkeley (2002) MisraJGriesDFinding repeated elementsSci. Comput. Program.1982214315269546310.1016/0167-6423(82)90012-0 MunroJIPatersonMSSelection and sorting with limited storageTheor. Comput. Sci.19801231532358931210.1016/0304-3975(80)90061-4 CormodeGMuthukrishnanSYiKAlgorithms for distributed functional monitoringACM Trans. Algorithms201172Article 21278643710.1145/1921659.1921667(Preliminary version in SODA’08) ChanH-LLamTWLeeL-KTingH-FContinuous monitoring of distributed data streams over a time-based sliding windowAlgorithmica2011623–41088111128711391236.68011 Yao, A.C.: Probabilistic computations: towards a unified measure of complexity. In: Proceedings of the IEEE Symposium on Foundations of Computer Science (1977) CormodeGThe continuous distributed monitoring modelACM SIGMOD Rec.201342151410.1145/2481528.2481530 Babcock, B., Olston, C.: Distributed top-k monitoring. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2003) Huang, Z., Wang, L., Yi, K., Liu, Y.: Sampling based algorithms for quantile computation in sensor networks. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2011) Woodruff, D.P., Zhang, Q.: Tight bounds for distributed functional monitoring. In: Proceedings of the ACM Symposium on Theory of Computing (2012) Huang, Z., Yi, K., Liu, Y., Chen, G.: Optimal sampling algorithms for frequency estimation in distributed data. In: IEEE INFOCOM (2011) Greenwald, M., Khanna, S.: Space-efficient online computation of quantile summaries. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2001) Keralapura, R., Cormode, G., Ramamirtham, J.: Communication-efficient distributed monitoring of thresholded counts. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2006) Gibbons, P.B., Tirthapura, S.: Estimating simple functions on the union of data streams. In: Proceedings of the ACM Symposium on Parallelism in Algorithms and Architectures (2001) VapnikVNChervonenkisAYOn the uniform convergence of relative frequencies of events to their probabilitiesTheory Probab. Appl.19711626428010.1137/1116025 B Patt-Shamir (531_CR22) 2008; 21 G Cormode (531_CR10) 2012; 59 A Metwally (531_CR19) 2006; 31 531_CR15 531_CR14 531_CR13 531_CR12 531_CR29 531_CR28 531_CR27 G Cormode (531_CR9) 2011; 7 JI Munro (531_CR21) 1980; 12 VN Vapnik (531_CR25) 1971; 16 S Suri (531_CR23) 2006; 36 G Cormode (531_CR6) 2013; 42 531_CR1 531_CR2 531_CR3 531_CR4 531_CR7 531_CR26 531_CR8 531_CR24 J Misra (531_CR20) 1982; 2 531_CR18 531_CR17 531_CR16 W Feller (531_CR11) 1968 H-L Chan (531_CR5) 2011; 62 |
| References_xml | – reference: ChanH-LLamTWLeeL-KTingH-FContinuous monitoring of distributed data streams over a time-based sliding windowAlgorithmica2011623–41088111128711391236.68011 – reference: Cormode, G., Garofalakis, M., Muthukrishnan, S., Rastogi, R.: Holistic aggregates in a networked world: distributed tracking of approximate quantiles. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2005) – reference: Patt-ShamirBShafrirAApproximate distributed top-k queriesDistrib. Comput.200821112210.1007/s00446-008-0055-3 – reference: Greenwald, M., Khanna, S.: Space-efficient online computation of quantile summaries. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2001) – reference: SuriSTothCZhouYRange counting over multidimensional data streamsDiscrete Comput. Geom.200636633655226755010.1007/s00454-006-1269-4 – reference: Babcock, B., Olston, C.: Distributed top-k monitoring. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2003) – reference: MisraJGriesDFinding repeated elementsSci. Comput. Program.1982214315269546310.1016/0167-6423(82)90012-0 – reference: Woodruff, D.P.: Efficient and Private Distance Approximation in the Communication and Streaming Models. PhD thesis, Massachusetts Institute of Technology (2007) – reference: Manku, G., Motwani, R.: Approximate frequency counts over data streams. In: Proceedings of the International Conference on Very Large Data Bases (2002) – reference: Manjhi, A., Shkapenyuk, V., Dhamdhere, K., Olston, C.: Finding (recently) frequent items in distributed data streams. In: Proceedings of the IEEE International Conference on Data Engineering (2005) – reference: MunroJIPatersonMSSelection and sorting with limited storageTheor. Comput. Sci.19801231532358931210.1016/0304-3975(80)90061-4 – reference: Keralapura, R., Cormode, G., Ramamirtham, J.: Communication-efficient distributed monitoring of thresholded counts. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2006) – reference: Woodruff, D.P., Zhang, Q.: Tight bounds for distributed functional monitoring. In: Proceedings of the ACM Symposium on Theory of Computing (2012) – reference: Yao, A.C.: Probabilistic computations: towards a unified measure of complexity. In: Proceedings of the IEEE Symposium on Foundations of Computer Science (1977) – reference: MetwallyAAgrawalDAbbadiAAn integrated efficient solution for computing frequent and top-k elements in data streamsACM Trans. Database Syst.20063131095113310.1145/1166074.1166084 – reference: Agarwal, P.K., Cormode, G., Huang, Z., Phillips, J.M., Wei, Z., Yi, K.: Mergeable summaries. In: Proceedings of the ACM Symposium on Principles of Database Systems (2012) – reference: CormodeGThe continuous distributed monitoring modelACM SIGMOD Rec.201342151410.1145/2481528.2481530 – reference: Gibbons, P.B., Tirthapura, S.: Estimating simple functions on the union of data streams. In: Proceedings of the ACM Symposium on Parallelism in Algorithms and Architectures (2001) – reference: Tirthapura, S., Woodruff, D.P.: Optimal random sampling from distributed streams revisited. In: Proceedings of the International Symposium on Distributed Computing (2011) – reference: Cormode, G., Hadjieleftheriou, M.: Finding frequent items in data streams. In: Proceedings of the International Conference on Very Large Data Bases (2008) – reference: CormodeGMuthukrishnanSYiKAlgorithms for distributed functional monitoringACM Trans. Algorithms201172Article 21278643710.1145/1921659.1921667(Preliminary version in SODA’08) – reference: VapnikVNChervonenkisAYOn the uniform convergence of relative frequencies of events to their probabilitiesTheory Probab. Appl.19711626428010.1137/1116025 – reference: Yi, K., Zhang, Q.: Optimal tracking of distributed heavy hitters and quantiles. In: Proceedings of the ACM Symposium on Principles of Database Systems (2009) – reference: Arackaparambil, C., Brody, J., Chakrabarti, A.: Functional monitoring without monotonicity. In: Proceedings of the International Colloquium on Automata, Languages, and Programming (2009) – reference: Bar-Yossef, Z.: The complexity of massive data set computations. PhD thesis, University of California at Berkeley (2002) – reference: Huang, Z., Wang, L., Yi, K., Liu, Y.: Sampling based algorithms for quantile computation in sensor networks. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2011) – reference: Huang, Z., Yi, K., Liu, Y., Chen, G.: Optimal sampling algorithms for frequency estimation in distributed data. In: IEEE INFOCOM (2011) – reference: CormodeGMuthukrishnanSYiKZhangQContinuous sampling from distributed streamsJ. ACM201259210291568110.1145/2160158.2160163(Preliminary version in PODS’10) – reference: FellerWAn Introduction to Probability Theory and Its Applications1968New YorkWiley0155.23101 – volume: 42 start-page: 5 issue: 1 year: 2013 ident: 531_CR6 publication-title: ACM SIGMOD Rec. doi: 10.1145/2481528.2481530 – volume: 21 start-page: 1 issue: 1 year: 2008 ident: 531_CR22 publication-title: Distrib. Comput. doi: 10.1007/s00446-008-0055-3 – ident: 531_CR1 doi: 10.1145/2213556.2213562 – ident: 531_CR18 doi: 10.1016/B978-155860869-6/50038-X – ident: 531_CR12 doi: 10.1145/378580.378687 – ident: 531_CR27 doi: 10.1145/2213977.2214063 – volume: 16 start-page: 264 year: 1971 ident: 531_CR25 publication-title: Theory Probab. Appl. doi: 10.1137/1116025 – ident: 531_CR26 – volume: 7 start-page: Article 21 issue: 2 year: 2011 ident: 531_CR9 publication-title: ACM Trans. Algorithms doi: 10.1145/1921659.1921667 – ident: 531_CR8 doi: 10.14778/1454159.1454225 – ident: 531_CR3 doi: 10.1145/872757.872764 – volume: 62 start-page: 1088 issue: 3–4 year: 2011 ident: 531_CR5 publication-title: Algorithmica – ident: 531_CR15 doi: 10.1109/INFCOM.2011.5935005 – ident: 531_CR24 doi: 10.1007/978-3-642-24100-0_27 – volume-title: An Introduction to Probability Theory and Its Applications year: 1968 ident: 531_CR11 – ident: 531_CR13 doi: 10.1145/375663.375670 – ident: 531_CR2 doi: 10.1007/978-3-642-02927-1_10 – ident: 531_CR17 – volume: 12 start-page: 315 year: 1980 ident: 531_CR21 publication-title: Theor. Comput. Sci. doi: 10.1016/0304-3975(80)90061-4 – volume: 36 start-page: 633 year: 2006 ident: 531_CR23 publication-title: Discrete Comput. Geom. doi: 10.1007/s00454-006-1269-4 – ident: 531_CR4 – volume: 2 start-page: 143 year: 1982 ident: 531_CR20 publication-title: Sci. Comput. Program. doi: 10.1016/0167-6423(82)90012-0 – volume: 59 start-page: 10 issue: 2 year: 2012 ident: 531_CR10 publication-title: J. ACM doi: 10.1145/2160158.2160163 – ident: 531_CR14 doi: 10.1145/1989323.1989401 – volume: 31 start-page: 1095 issue: 3 year: 2006 ident: 531_CR19 publication-title: ACM Trans. Database Syst. doi: 10.1145/1166074.1166084 – ident: 531_CR29 doi: 10.1145/1559795.1559820 – ident: 531_CR28 doi: 10.1109/SFCS.1977.24 – ident: 531_CR7 doi: 10.1145/1066157.1066161 – ident: 531_CR16 doi: 10.1145/1142473.1142507 |
| SSID | ssj0012796 |
| Score | 2.2297869 |
| Snippet | We show that randomization can lead to significant improvements for a few fundamental problems in distributed tracking. Our basis is the
count-tracking... We show that randomization can lead to significant improvements for a few fundamental problems in distributed tracking. Our basis is the count-tracking... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 2222 |
| SubjectTerms | Algorithm Analysis and Problem Complexity Algorithms Communication Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Finishes Lower bounds Mathematics of Computing Randomization Theory of Computation Tracking problem |
| Title | Randomized Algorithms for Tracking Distributed Count, Frequencies, and Ranks |
| URI | https://link.springer.com/article/10.1007/s00453-018-00531-y https://www.proquest.com/docview/2214141416 |
| Volume | 81 |
| WOSCitedRecordID | wos000465544600004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1432-0541 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0012796 issn: 0178-4617 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1bS8MwFA46ffDFecXplDz45gJtlrXJ41CHDzJkXthbaW463DppqzB_vUl6GYoKSh-bHMJJTs-XnssHwGnMsGBcm2sqDwUigklEFfORbbbmxwHvdYl0ZBPhcEjHY3ZTFoVlVbZ7FZJ0X-q62M2iD5v7Q5E7OWixCtaMu6PWHEe3D3XsAIeOlcvyziNiHHRZKvO9jM_uaIkxv4RFnbcZNP-3zi2wWaJL2C-OwzZYUckOaFbMDbA05F1wPYoTOZ9N3pWE_enjPJ3kT7MMGgALjfMS9vc5vLAtdS0blhljK9fzDhykReK1uV13oJEAjZjnbA_cDy7vzq9QyauAhDG4HGEeqpgTyjhmXldpohkJVRhzhSXFkgUyUNijQmnbnF73MJO9UEquqZK-J1h3HzSSeaIOAAw8wrUVYIAEYT6lKjYQQxNfMd7FftACfqXeSJRNxy33xTSq2yU7dUVGXZFTV7RogbN6zkvRcuPX0e1q16LS_LIIY5-4xyygU-3S8vXP0g7_NvwIbBgAxYrUsTZo5OmrOgbr4i2fZOmJO5YfxOfcMw |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS8MwEA86BX1xfuJ0ah58c4E1y9rkcahj4hwyp-ytNB_V4dZJW4X515uk7YaigtLHXo5wyfV-ae7uB8BpwLBgPNTHVO4JRASTiCrmINNszQlc3mwQackmvF6PDofsNi8KS4ps9-JK0n6p58VuBn2Y3B-K7M5Bs2WwQnTEMol8_buH-d0B9iwrl-GdR0QH6LxU5nsdn8PRAmN-uRa10aZd_t88N8FGji5hK9sOW2BJRdugXDA3wNyRd0C3H0RyOhm9Kwlb48dpPEqfJgnUABbq4CXM73N4YVrqGjYsLWMq19MabMdZ4rU-Xdeg1gC1mudkF9y3LwfnHZTzKiChHS5FmHsq4IQyjlm9oUISMuIpL-AKS4olc6WrcJ0KFZrm9GETM9n0pOQhVdKpC9bYA6VoGql9AN064aFRoIEEYQ6lKtAQIySOYryBHbcCnMK8vsibjhvui7E_b5dszeVrc_nWXP6sAs7mY16ylhu_SleLVfNz90t8jB1iHz2BWrFKi9c_azv4m_gJWOsMbrp-96p3fQjWNZhiWRpZFZTS-FUdgVXxlo6S-Nhu0Q8f998X |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bS8MwFA46RXxxXnE6NQ--ueCaZW3yOJxDcQzxhm-luelwdtJWYf56k7SdF1QQ6WPTQzg54Xxpzvk-APYjhgXj2hxTeSAQEUwiqpiHLNmaF_m83SLSiU0EgwG9vWXnH7r4XbV7eSWZ9zRYlqY4O3yS-nDa-GaRiK0DoshFEZrMgjliRYPsef3yZnqPgAOn0GU16BExybpom_nexufU9I43v1yRuszTq_5_zstgqUCdsJOHyQqYUfEqqJaKDrDY4GugfxHFcvw4fFUSdkZ342SY3T-m0ABbaJKasL_VYddS7VqVLDPGdrRnDdhL8oJsc-puQGMBGjMP6Tq47h1fHZ2gQm8BCbMRM4R5oCJOKOOYNVtKE81IoIKIKywplsyXvsJNKpS2pPW6jZlsB1JyTZX0moK1NkAlHsdqE0C_Sbi2BgzAIMyjVEUGemjiKcZb2PNrwCtdHYqCjNxqYozCKY2yc1do3BU6d4WTGjiYfvOUU3H8OrpermBYbMs0xNgj7jETaJQr9v76Z2tbfxu-BxbOu72wfzo42waLBmOxvLqsDipZ8qx2wLx4yYZpsuui9Q1sxuf7 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Randomized+Algorithms+for+Tracking+Distributed+Count%2C+Frequencies%2C+and+Ranks&rft.jtitle=Algorithmica&rft.au=Huang%2C+Zengfeng&rft.au=Yi%2C+Ke&rft.au=Zhang%2C+Qin&rft.date=2019-06-01&rft.pub=Springer+US&rft.issn=0178-4617&rft.eissn=1432-0541&rft.volume=81&rft.issue=6&rft.spage=2222&rft.epage=2243&rft_id=info:doi/10.1007%2Fs00453-018-00531-y&rft.externalDocID=10_1007_s00453_018_00531_y |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0178-4617&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0178-4617&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0178-4617&client=summon |