Article

Models and issues in data stream systems

Authors:

Rajeev Motwani,

Jennifer WidomAuthors Info & Claims

PODS '02: Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

Pages 1 - 16

https://doi.org/10.1145/543613.543615

Published: 03 June 2002 Publication History

Abstract

In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work relevant to data stream systems and current projects in the area, the paper explores topics in stream query languages, new requirements and challenges in query processing, and algorithmic issues.

References

[1]

S. Acharya, P. B. Gibbons, and V. Poosala. Congressional samples for approximate answering of group-by queries. In Proc. of the 2000 ACM SIGMOD Intl. Conf. on Management of Data, pages 487-498, May 2000.

Digital Library

[2]

S. Acharya, P. B. Gibbons, V. Poosala, and S. Ramaswamy. Join synopses for approximate query answering. In Proc. of the 1999 ACM SIGMOD Intl. Conf. on Management of Data, pages 275-286, June 1999.

Digital Library

[3]

M. Ajtai, T. Jayram, R. Kumar, and D. Sivakumar. Counting inversions in a data stream. manuscript, 2001.

[4]

N. Alon, P. Gibbons, Y. Matias, and M. Szegedy. Tracking join and self-join sizes in limited storage. In Proc. of the 1999 ACM Symp. on Principles of Database Systems, pages 10-20, 1999.

Digital Library

[5]

N. Alon, Y. Matias, and M. Szegedy. The space complexity of approximating the frequency moments. In Proc. of the 1996 Annual ACM Symp. on Theory of Computing, pages 20-29, 1996.

Digital Library

[6]

M. Altinel and M. J. Franklin. Efficient filtering of XML documents for selective dissemination of information. In Proc. of the 2001 Intl. Conf. on Very Large Data Bases, pages 53-64, Sept. 2000.

Digital Library

[7]

A. Arasu, B. Babcock, S. Babu, J. McAlister, and J. Widom. Characterizing memory requirements for queries over continuous data streams. In Proc. of the 2002 ACM Symp. on Principles of Database Systems, June 2002. Available at http://dbpubs.stanford.edu/pub/2001-49.

Digital Library

[8]

R. Avnur and J. Hellerstein. Eddies: Continuously adaptive query processing. In Proc. of the 2000 ACM SIGMOD Intl. Conf. on Management of Data, pages 261-272, May 2000.

Digital Library

[9]

B. Babcock, M. Datar, and R. Motwani, Sampling from a moving window over streaming data. In Proc. of the 2002 Annual ACM-SIAM Symp. on Discrete Algorithms, pages 633-634, 2002.

Digital Library

[10]

S. Babu and J. Widom. Continuous queries over data streams. SIGMOD Record, 30(3):109-120, Sept. 2001.

Digital Library

[11]

Z. Bar-Yossef, R. Kumar, and D. Sivakumar. Sampling algorithms: Lower bounds and applications. In Proc. of the 2001 Annual ACM Symp. on Theory of Computing, pages 266-275, 2001.

Digital Library

[12]

Z. Bar-Yossef, R. Kumar, and D. Sivakumar. Reductions in streaming algorithms, with an application to counting triangles in graphs. In Proc. of the 2002 Annual ACM-SIAM Symp. on Discrete Algorithms, pages 623-632, 2002.

Digital Library

[13]

S. Bellamkonda, T. Borzkaya, B. Ghosh, A. Gupta, J. Haydu, S. Subramanian, and A. Witkowski. Analytic functions in oracle 8i. Available at http://www-db.stanford.edu/dbseminar/Archive/SpringY2000/speakers/agupta/paper.pdf.

[14]

J. A. Blakeley, N. Coburn, and P. A. Larson. Updating derived relations: Detecting irrelevant and autonomously computable updates. ACM Trans. on Database Systems, 14(3):369-400, 1989.

Digital Library

[15]

D. Carney, U. Cetinternel, M. Cherniack, C. Convey, S. Lee, G. Seidman, M. Stonebraker, N. Tatbul, and S. Zdonik. Monitoring streams --- a new class of dbms applications. Technical Report CS-02-01, Department of Computer Science, Brown University, Feb. 2002.

[16]

K. Chakrabarti, M. N. Garofalakis, R. Rastogi, and K. Shim. Approximate query processing using wavelets. In Proc. of the 2000 Intl. Conf. on Very Large Data Bases, pages 111-122, Sept. 2000.

Digital Library

[17]

M. Charikar, S. Chaudhuri, R. Motwani, and V. Narasayya. Towards estimation error guarantees for distinct values. In Proc. of the 2000 ACM Symp. on Principles of Database Systems, pages 268-279, 2000.

Digital Library

[18]

S. Chaudhuri, G. Das, and V. Narasayya. A robust, optimization-based approach for approximate answering of aggregate queries. In Proc. of the 2001 ACM SIGMOD Intl. Conf. on Management of Data, pages 295-306, May 2001.

Digital Library

[19]

S. Chaudhuri and R. Motwani. On sampling and relational operators. Bulletin of the Technical Committee on Data Engineering, 22:35-40, 1999.

[20]

S. Chaudhuri, R. Motwani, and V. Narasayya. Random sampling for histogram construction: How much is enough? In Proc. of the 1998 ACM SIGMOD Intl. Conf. on Management of Data, pages 436-447, 1998.

Digital Library

[21]

S. Chaudhuri, R. Motwani, and V. Narasayya. On random sampling over joins. In Proc. of the 1999 ACM SIGMOD Intl. Conf. on Management of Data, pages 263-274, June 1999.

Digital Library

[22]

S. Chaudhuri and V. Narasayya. An efficient cost-driven index selection tool for microsoft sql server. In Proc. of the 1997 Intl. Conf. on Very Large Data Bases, pages 146-155, 1997.

Digital Library

[23]

J. Chen, D. J. DeWitt, F. Tian, and Y. Wang. NiagraCQ: A scalable continuous query system for internet databases. In Proc. of the 2000 ACM SIGMOD Intl. Conf. on Management of Data, pages 379-390, May 2000.

Digital Library

[24]

C. Cortes, K. Fisher, D. Pregibon, and A. Rogers. Hancock: a language for extracting signatures from data streams. In Proc. of the 2000 ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, pages 9-17, Aug. 2000.

Digital Library

[25]

M. Datar, A. Gionis, P. Indyk, and R. Motwani. Maintaining stream statistics over sliding windows. In Proc. of the 2002 Annual ACM-SIAM Symp. on Discrete Algorithms, pages 635-644, 2002.

Digital Library

[26]

A. Dobra, J. Gehrke, M. Garofalakis, and R. Rastogi. Processing complex aggregate queries over data streams. In Proc. of the 2002 ACM SIGMOD Intl. Conf. on Management of Data, 2002.

Digital Library

[27]

P. Domingos and G. Hulten. Mining high-speed data streams. In Proc. of the 2000 ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, pages 71-80, Aug. 2000.

Digital Library

[28]

P. Domingos, G. Hulten, and L. Spencer. Mining time-changing data streams. In Proc. of the 2001 ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, pages 97-106, 2001.

Digital Library

[29]

N. Duffield and M. Grossglauser. Trajectory sampling for direct traffic observation. In Proc. of the 2000 ACM SIGCOMM, pages 271-284, Sept. 2000.

Digital Library

[30]

D. B. et al. The New Jersey data reduction report. IEEE Data Engineering Bulletin, 20(4):3-45, 1997.

[31]

C. Faloutsos, M. Ranganathan, and Y. Manolopoulos. Fast subsequence matching in time-series databases. In Proc. of the 1994 ACM SIGMOD Intl. Conf. on Management of Data, pages 419-429, May 1994.

Digital Library

[32]

M. Fang, N. Shivakumar, H. Garcia-Molina, R. Motwani, and J. D. Ullman. Computing iceberg queries efficiently. In Proc. of the 1998 Intl. Conf. on Very Large Data Bases, pages 299-310, 1998.

Digital Library

[33]

J. Feigenbaum, S. Kannan, M. Strauss, and M. Viswanathan. An approximate 11-difference algorithm for massive data streams. In Proc. of the 1999 Annual IEEE Symp. on Foundations of Computer Science, pages 501-511, 1999.

Digital Library

[34]

J. Feigenbaum, S. Kannan, M. Strauss, and M. Viswanathan. Testing and spot checking of data streams. In Proc. of the 2000 Annual ACM-SIAM Symp. on Discrete Algorithms, pages 165-174, 2000.

Digital Library

[35]

P. Flajolet and G. Martin. Probabilistic counting. In Proc. of the 1983 Annual IEEE Symp. on Foundations of Computer Science, 1983.

Digital Library

[36]

H. Garcia-Molina, W. Labio, and J. Yang. Expiring data in a warehouse. In Proc. of the 1998 Intl. Conf. on Very Large Data Bases, pages 500-511, Aug. 1998.

Digital Library

[37]

J. Gehrke, F. Korn, and D. Srivastava. On computing correlated aggregates over continual data streams. In Proc. of the 2001 ACM SIGMOD Intl. Conf. on Management of Data, pages 13-24, May 2001.

Digital Library

[38]

P. Gibbons and S. Tirthapura. Estimating simple functions on the union of data streams. In Proc. of the 2001 ACM Symp. on Parallel Algorithms and Architectures, pages 281-291, 2001.

Digital Library

[39]

A. Gilbert, S. Guha, P. Indyk, Y. Kotidis, S. Muthukrishnan, and M. Strauss. Fast, small-space algorithms for approximate histogram maintenance. In Proc. of the 2002 Annual ACM Symp. on Theory of Computing, 2002.

Digital Library

[40]

A. Gilbert, Y. Kotidis, S. Muthukrishnan, and M. Strauss. Surfing wavelets on streams: One-pass summaries for approximate aggregate queries. In Proc. of the 2001 Intl. Conf. on Very Large Data Bases, pages 79-88, 2001.

Digital Library

[41]

M. Greenwald and S. Khanna. Space-efficient online computation of quantile summaries. In Proc. of the 2001 ACM SIGMOD Intl. Conf. on Management of Data, pages 58-66, 2001.

Digital Library

[42]

S. Guha and N. Koudas. Approximating a data stream for querying and estimation: Algorithms and performance evaluation. In Proc. of the 2002 Intl. Conf. on Data Engineering, 2002.

Digital Library

[43]

S. Guha, N. Koudas, and K. Shim. Data-streams and histograms. In Proc. of the 2001 Annual ACM Symp. on Theory of Computing, pages 471-475, 2001.

Digital Library

[44]

S. Guha, N. Mishra, R. Motwani, and L. O'Callaghan. Clustering data streams. In Proc. of the 2000 Annual IEEE Symp. on Foundations of Computer Science, pages 359-366, Nov. 2000.

Digital Library

[45]

A. Gupta, H. V. Jagadish, and I. S. Mumick. Data integration using self-maintainable views. In Proc. of the 1996 Intl. Conf. on Extending Database Technology, pages 140-144, Mar. 1996.

Digital Library

[46]

P. Haas, J. Naughton, P. Seshadri, and L. Stokes. Sampling-based estimation of the number of distinct values of an attribute. In Proc. of the 1995 Intl. Conf. on Very Large Data Bases, pages 311-322, Sept. 1995.

Digital Library

[47]

J. Hellerstein, M. Franklin, et al. Adaptive query processing: Technology in evolution. IEEE Data Engineering Bulletin, 23(2):7-18, June 2000.

[48]

J. Hellerstein, P. Haas, and H. Wang. Online aggregation. In Proc. of the 1997 ACM SIGMOD Intl. Conf. on Management of Data, pages 171-182, May 1997.

Digital Library

[49]

M. Henzinger, P. Raghavan, and S. Rajagopalan. Computing on data streams. Technical Report TR 1998-011, Compaq Systems Research Center, Palo Alto, California, May 1998.

[50]

P. Indyk. Stable distributions, pseudorandom generators, embeddings and data stream computation. In Proc. of the 2000 Annual IEEE Symp. on Foundations of Computer Science, pages 189-197, 2000.

Digital Library

[51]

Y. E. Ioannidis and V. Poosala. Histogram-based approximation of set-valued query-answers. In Proc. of the 1999 Intl. Conf. on Very Large Data Bases, pages 174-185, Sept. 1999.

Digital Library

[52]

iPolicy Networks home page. http://www.ipolicynetworks.com.

[53]

Z. Ives, D. Florescu, M. Friedman, A. Levy, and D. Weld. An adaptive query execution system for data integration. In Proc. of the 1999 ACM SIGMOD Intl. Conf. on Management of Data, pages 299-310, June 1999.

Digital Library

[54]

H. Jagadish, N. Koudas, S. Muthukrishnan, V. Poosala, K. Sevcik, and T. Suel. Optimal histograms with quality guarantees. In Proc. of the 1998 Intl. Conf. on Very Large Data Bases, pages 275-286, 1998.

Digital Library

[55]

H. Jagadish, I. Mumick, and A. Silberschatz. View maintenance issues for the Chronicle data model. In Proc. of the 1995 ACM Symp. on Principles of Database Systems, pages 113-124, May 1995.

Digital Library

[56]

E. Kushlevitz and N. Nisan. Communication Complexity. Cambridge University Press, 1997.

Digital Library

[57]

L. Liu, C. Pu, and W. Tang. Continual queries for internet scale event-driven information delivery. IEEE Trans. on Knowledge and Data Engineering, 11(4):583-590, Aug. 1999.

Digital Library

[58]

S. Madden and M. J. Franklin. Fjording the stream: An architecture for queries over streaming sensor data. In Proc. of the 2002 Intl. Conf. on Data Engineering, Feb. 2002. (To appear).

Digital Library

[59]

S. Madden, J. Hellerstein, M. Shah, and V. Raman. Continuously adaptive continuous queries over streams. In Proc. of the 2002 ACM SIGMOD Intl. Conf. on Management of Data, June 2002. (To appear).

Digital Library

[60]

G. Manku and R. Motwani. Approximate frequency counts over streaming data. manuscript, 2002.

[61]

G. Manku, S. Rajagopalan, and B. G. Lindsay. Approximate medians and other quantiles in one pass and with limited memory. In Proc. of the 1998 ACM SIGMOD Intl. Conf. on Management of Data, pages 426-435, June 1998.

Digital Library

[62]

G. Manku, S. Rajagopalan, and B. G. Lindsay. Random sampling techniques for space efficient online computation of order statistics of large datasets. In Proc. of the 1999 ACM SIGMOD Intl. Conf. on Management of Data, pages 251-262, June 1999.

Digital Library

[63]

Y. Matias, J. Vitter, and M. Wang. Wavelet-based histograms for selectivity estimation. In Proc. of the 1998 ACM SIGMOD Intl. Conf. on Management of Data, pages 448-459, June 1998.

Digital Library

[64]

Y. Matias, J. Vitter, and M. Wang. Dynamic maintenance of wavelet-based histograms. In Proc. of the 2000 Intl. Conf. on Very Large Data Bases, pages 101-110, Sept. 2000.

Digital Library

[65]

R. Motwani and P. Raghavan. Randomized Algorithms. Cambridge University Press, 1995.

[66]

J. Munro and M. Paterson. Selection and sorting with limited storage. Theoretical Computer Science, 12:315-323, 1980.

[67]

B. Nguyen, S. Abiteboul, G. Cobena, and M. Preda. Monitoring XML data on the web. In Proc. of the 2001 ACM SIGMOD Intl. Conf. on Management of Data, pages 437-448, May 2001.

Digital Library

[68]

V. Poosala and V. Ganti. Fast approximate answers to aggregate queries on a data cube. In Proc. of the 1999 Intl. Conf. on Scientific and Statistical Database Management, pages 24-33, July 1999.

Digital Library

[69]

D. Quass, A. Gupta, I. Mumick, and J. Widom. Making views self-maintainable for data warehousing. In Proc. of the 1996 Intl. Conf. on Parallel and Distributed Information Systems, pages 158-169, Dec. 1996.

Digital Library

[70]

V. Raman, B. Raman, and J. Hellerstein. Online dynamic reordering for interactive data processing. In Proc. of the 1999 Intl. Conf. on Very Large Data Bases, 1999.

Digital Library

[71]

M. Saks and X. Sun. Space lower bounds for distance approximation in the data stream model. In Proc. of the 2002 Annual ACM Symp. on Theory of Computing, 2002.

Digital Library

[72]

U. Schreier, H. Pirahesh, R. Agrawal, and C. Mohan. Alert: An architecture for transforming a passive DBMS into an active DBMS. In Proc. of the 1991 Intl. Conf. on Very Large Data Bases, pages 469-478, Sept. 1991.

Digital Library

[73]

T. K. Sellis. Multiple-query optimization. ACM Trans. on Database Systems, 13(1):23-52, 1988.

Digital Library

[74]

P. Seshadri, M. Livny, and R. Ramakrishnan. Sequence query processing. In Proc. of the 1994 ACM SIGMOD Intl. Conf. on Management of Data, pages 430-441, May 1994.

Digital Library

[75]

P. Seshadri, M. Livny, and R. Ramakrishnan. Seq: A model for sequence databases. In Proc. of the 1995 Intl. Conf. on Data Engineering, pages 232-239, Mar. 1995.

Digital Library

[76]

P. Seshadri, M. Livny, and R. Ramakrishnan. The design and implementation of a sequence database system. In Proc. of the 1996 Intl. Conf. on Very Large Data Bases, pages 99-110, Sept. 1996.

Digital Library

[77]

J. Shanmugasundaram, K. Tufte, D. J. DeWitt, J. F. Naughton, and D. Maier. Architecting a network query engine for producing partial results. In Proc. of the 2000 Intl. Workshop on the Web and Databases, pages 17-22, May 2000.

[78]

R. Snodgrass and I. Ahn. A taxonomy of time in databases. In Proc. of the 1985 ACM SIGMOD Intl. Conf. on Management of Data, pages 236-245, 1985.

Digital Library

[79]

S.-. Standard. On-line analytical processing (sql/olap). Available from http://www.ansi.org/, document#ISO/IEC9075-2/Amd1:2001.

[80]

Stanford Stream Data Management (STREAM) Project. http://www-db.stanford.edu/stream.

[81]

M. Sullivan. Tribeca: A stream database manager for network traffic analysis. In Proc. of the 1996 Intl. Conf. on Very Large Data Bases, page 594, Sept. 1996.

Digital Library

[82]

D. Terry, D. Goldberg, D. Nichols, and B. Oki. Continuous queries over append-only databases. In Proc. of the 1992 ACM SIGMOD Intl. Conf. on Management of Data, pages 321-330, June 1992.

Digital Library

[83]

Traderbot home page. http://www.traderbot.com.

[84]

P. Tucker, D. Maier, T. Sheard, and L. Fegaras. Enhancing relational operators for querying over punctuated data streams. manuscript, 2002. Available at http://www.cse.ogi.edu/dot/niagara/pstream/punctuating.pdf.

[85]

J. Ullman and J. Widom. A First Course in Database Systems. Prentice Hall, Upper Saddle River, New Jersey, 1997.

Digital Library

[86]

T. Urhan and M. Franklin. Xjoin: A reactively-scheduled pipelined join operator. IEEE Data Engineering Bulletin, 23(2):27-33, June 2000.

[87]

S. Viglas and J. Naughton. Rate-based query optimization for streaming information sources. In Proc. of the 2002 ACM SIGMOD Intl. Conf. on Management of Data, June 2002. (To appear).

Digital Library

[88]

J. Vitter. Random sampling with a reservoir. ACM Trans. on Mathematical Software, 11(1):37-57, 1985.

Digital Library

[89]

J. Vitter. External memory algorithms and datastructures. In J. Abello, editor, External Memory Algorithms, pages 1-18. Dimacs, 1999.

Digital Library

[90]

J. Vitter and M. Wang. Approximate computation of multidimensional aggregates of sparse data using wavelets. In Proc. of the 1999 ACM SIGMOD Intl. Conf. on Management of Data, pages 193-204, June 1999.

Digital Library

[91]

J. Vitter, M. Wang, and B. Iyer. Data cube approximation and histograms via wavelets. In Proc. of the 1998 Intl. Conf. on Information and Knowledge Management, Nov. 1998.

Digital Library

[92]

Xml path language (XPath) version 1.0, Nov. 1999. W3C Recommendation available at http://www.w3.org/TR/xpath.

[93]

Yahoo home page. http://www.yahoo.com.

Cited By

Gonzalez ERusso BLaiu MArchibald R(2024)StOKeDMD: Streaming Occupation kernel dynamic mode decompositionApplied Mathematics for Modern Challenges10.3934/ammc.2024022(0-0)Online publication date: 2024
https://doi.org/10.3934/ammc.2024022
Mohandas RSouthern MO’Connell EHayes M(2024)A Survey of Incremental Deep Learning for Defect Detection in ManufacturingBig Data and Cognitive Computing10.3390/bdcc80100078:1(7)Online publication date: 5-Jan-2024
https://doi.org/10.3390/bdcc8010007
Muñoz MRiveros C(2024)Streaming enumeration on nested documentsACM Transactions on Database Systems10.1145/3701557Online publication date: 25-Oct-2024
https://doi.org/10.1145/3701557
Show More Cited By

Index Terms

Models and issues in data stream systems
1. Applied computing
  1. Computers in other domains
    1. Personal computers and PC applications
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing
  2. Information systems applications

Recommendations

Data Stream Management
Data Stream Mining: Challenges and Techniques
ICTAI '10: Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 02

Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Their sheer volume and speed pose a great challenge for the data mining community to mine them. Data streams ...
Real-Time Scheduling for Data Stream Management Systems
ECRTS '05: Proceedings of the 17th Euromicro Conference on Real-Time Systems

Quality-aware management of data streams is gaining moreand more importance with the amount of data produced by streams growing continuously. The resources required for data stream processing depend on different factors and are limited by the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

PODS '02: Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

June 2002

311 pages

ISBN:1581135076

DOI:10.1145/543613

Conference Chair:
Lucian Popa
IBM Almaden Research Center
,
General Chair:
Serge Abiteboul
INRIA
,
Program Chair:
Phokion G. Kolaitis
UC Santa Cruz & IBM Almaden Research Center

Copyright © 2002 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 June 2002

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

SIGMOD/PODS02

Sponsor:

SIGMOD/PODS02: International Conference on Management of Data and Symposium on Principles Database and Systems

June 3 - 5, 2002

Wisconsin, Madison

Acceptance Rates

PODS '02 Paper Acceptance Rate 24 of 109 submissions, 22%;

Overall Acceptance Rate 642 of 2,707 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1,206
Total Citations
View Citations
12,454
Total Downloads

Downloads (Last 12 months)211
Downloads (Last 6 weeks)27

Reflects downloads up to 03 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Gonzalez ERusso BLaiu MArchibald R(2024)StOKeDMD: Streaming Occupation kernel dynamic mode decompositionApplied Mathematics for Modern Challenges10.3934/ammc.2024022(0-0)Online publication date: 2024
https://doi.org/10.3934/ammc.2024022
Mohandas RSouthern MO’Connell EHayes M(2024)A Survey of Incremental Deep Learning for Defect Detection in ManufacturingBig Data and Cognitive Computing10.3390/bdcc80100078:1(7)Online publication date: 5-Jan-2024
https://doi.org/10.3390/bdcc8010007
Muñoz MRiveros C(2024)Streaming enumeration on nested documentsACM Transactions on Database Systems10.1145/3701557Online publication date: 25-Oct-2024
https://doi.org/10.1145/3701557
Jayaram RWoodruff DZhou S(2024)Streaming Algorithms with Few State ChangesProceedings of the ACM on Management of Data10.1145/36511452:2(1-28)Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3651145
Yao ZLi LChen MWei XChen CBaeza-Yates RBonchi F(2024)Approximate Matrix Multiplication over Sliding WindowsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671819(3896-3906)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671819
Bonifati ATommasini RBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)An Overview of Continuous Querying in (Modern) Data SystemsCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654679(605-612)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654679
Cao YFeng YWang HXie XZhou S(2024)Learning to Sketch: A Neural Approach to Item Frequency Estimation in Streaming DataIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.338858946:11(7136-7153)Online publication date: Nov-2024
https://doi.org/10.1109/TPAMI.2024.3388589
Ma COdegbile OMelissourgos DWang HChen S(2024)From CountMin to Super kJoin Sketches for Flow Spread EstimationIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.327966511:3(2353-2370)Online publication date: May-2024
https://doi.org/10.1109/TNSE.2023.3279665
Liu QKing AGe T(2024)Reducing Resource Usage for Continuous Model Updating and Predictive Query Answering in Graph Streams2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00208(2653-2666)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00208
Wu YWu HJia CPeng BZhang ZYang TChen PYang KCui B(2024)Scalable Overspeed Item Detection in Streams2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00094(1157-1170)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00094
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents