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Volumn , Issue , 2007, Pages 1292-1296

A lightweight online framework for query progress indicators

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA STORAGE EQUIPMENT; FEEDBACK; PROBLEM SOLVING; QUERY PROCESSING; RAPID PROTOTYPING;

EID: 34548779669     PISSN: 10844627     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDE.2007.368996     Document Type: Conference Paper
Times cited : (19)

References (17)
  • 1
    • 34548705507 scopus 로고    scopus 로고
    • Monitoring Facility
    • 2
    • DB2 Monitoring Facility. DB2 Online Documentation; www.software.ibm.com, 2000.
    • (2000)
    • DB1
  • 3
    • 0041857839 scopus 로고    scopus 로고
    • On Estimating The Prediction Function and the Number of Unseen Species in Sampling With Replacement
    • S. Boneh, A. Boneh, and R. Caron. On Estimating The Prediction Function and the Number of Unseen Species in Sampling With Replacement. Journal Of the American Statistical Association, 93(441):372, 1998.
    • (1998) Journal Of the American Statistical Association , vol.93 , Issue.441 , pp. 372
    • Boneh, S.1    Boneh, A.2    Caron, R.3
  • 5
    • 29844453835 scopus 로고    scopus 로고
    • When Can We Trust Progress Estimators for SQL Queries
    • S. Chaudhuri, R. Kaushik, and R. Ramamurthy. When Can We Trust Progress Estimators for SQL Queries. SIGMOD, 2005.
    • (2005) SIGMOD
    • Chaudhuri, S.1    Kaushik, R.2    Ramamurthy, R.3
  • 10
    • 0032265203 scopus 로고    scopus 로고
    • Estimating the Number of Classes in a Finite Population
    • P. Haas and L. Stokes. Estimating the Number of Classes in a Finite Population. Journal of the American Statistical Association, Vol 93. No 444, pages 1475-1487, 1998.
    • (1998) Journal of the American Statistical Association , vol.93 , Issue.444 , pp. 1475-1487
    • Haas, P.1    Stokes, L.2
  • 11
    • 0032093823 scopus 로고    scopus 로고
    • Efficient Mid Query Reoptimization of Sub-Optimal Query Execution Plans
    • N. Kabra and D. DeWitt. Efficient Mid Query Reoptimization of Sub-Optimal Query Execution Plans. SIGMOD, 1998.
    • (1998) SIGMOD
    • Kabra, N.1    DeWitt, D.2
  • 12
    • 34548760300 scopus 로고    scopus 로고
    • Increasing the Accuracy and Coverage of SQL Progress Indicators
    • G. Luo, J. Naughton, C. Ellman, and M. Watzke. Increasing the Accuracy and Coverage of SQL Progress Indicators. ICDE, 2004.
    • (2004) ICDE
    • Luo, G.1    Naughton, J.2    Ellman, C.3    Watzke, M.4
  • 13
  • 14
    • 50249142401 scopus 로고    scopus 로고
    • Multi-query SQL Progress Indicators
    • G. Luo, J. Naughton, and P. Yu. Multi-query SQL Progress Indicators. EDBT, 2006.
    • (2006) EDBT
    • Luo, G.1    Naughton, J.2    Yu, P.3
  • 16
    • 34548737215 scopus 로고    scopus 로고
    • Online Framework for Query Progress Indicators
    • C. Mishra and N. Koudas. A Lightweight Online Framework for Query Progress Indicators. http://www.cs.toronto.edu/~cmishra/ProgressBars.pdf.
    • A Lightweight
    • Mishra, C.1    Koudas, N.2
  • 17
    • 85016592179 scopus 로고
    • The Importance of Percent Done Indicators For Computer Human Interfaces
    • B. A. Myers. The Importance of Percent Done Indicators For Computer Human Interfaces. Proceedings of SIGCHI, 1985.
    • (1985) Proceedings of SIGCHI
    • Myers, B.A.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.