1 |
Agrawal, S., Chaudhuri, S., and Narasayya, V. R. (2000). Automated selection of materialized views and indexes in SQL databases. In Procs VLDB Conf, pages 496–505.
|
|
2 |
Baralis, E., Paraboschi, S., and Teniente, E. (1997). Materialized views selection in a multidimensional database. Procs VLDB Conference, pages 156–165.
|
|
3 |
Bellatreche, L., Boukhalfa, K., and Mohania, M. (2013). Pruning Search Space of Physical Database Design. Procs DEXA Conference, 63(8):479–488.
|
|
4 |
Bruno, N. (2012). Automated Physical Database Design and Tuning. CRC Press.
|
|
5 |
Chaudhuri, S., Datar, M., and Narasayya, V. (2004). Index selection for databases: A hardness study and a principled heuristic solution. IEEE TKDE, 16(11):1313–1323.
|
|
6 |
Chaudhuri, S. and Weikum, G. (2006). Foundations of automated database tuning. Procs SIGMOD Conference, pages 964–965.
|
|
7 |
Chen, S., Nascimento, M. A., Ooi, B. C., and Tan, K.-L. (2010). Continuous online index tuning in moving object databases. ACM TODS, 35(3):1–51.
|
|
8 |
Chirkova, R. and Yang, J. (2012). Materialized views. Fnd. Trends in DBs, 4(4):295–405.
|
|
9 |
de Oliveira, R. P., Baiao, F., Almeida, A. C., Schwabe, D., and Lifschitz, S. (2019). Outer-˜ tuning: an integration of rules, ontology and RDBMS. In Procs. Brazilian Symposium on Information Systems SBSI, pages 1–8. ACM.
|
|
10 |
Elfayoumy, S. and Patel, J. (1999). Database performance monitoring and tuning using intelligent agent assistants. In Procs Intl Conf Artificial Intelligence, pages 331–335.
|
|
11 |
Hartigan, J. A. and Wong, M. A. (1979). Algorithm AS 136: A K-Means clustering algorithm. Applied Statistics, 28(1):100–108.
|
|
12 |
Hayes-Roth, B. (1985). A blackboard architecture for control. AI, 26(3):251–321.
|
|
13 |
Kimura, H., Huo, G., Rasin, A., Madden, S., and Zdonik, S. B. (2010). CORADD: Correlation aware DB designer mat. views and indexes. PVLDB, 3(1-2):1103–1113.
|
|
14 |
Kwon, O., Im, G. P., and Lee, K. C. (2011). An agent-based web service approach for supply chain collaboration. Scientia Iranica, 18(6):1545–1552.
|
|
15 |
Lawler, A. E. L. and Wood, D. E. (1966). Branch-And-Bound Methods : A Survey Published. Operations Research, 14(4):699–719.
|
|
16 |
Mrozek, D., Malysiak-Mrozek, B., Mikolajczyk, J., and Kozielski, S. (2014). Database Under Pressure – Testing Performance of Database Systems Using Universal MultiAgent Platform. Man-Machine Interactions 3, pages 631–641.
|
|
17 |
Oliveira, R. P. d. (2019). Automatic Combination and Selection of Tuning Actions (in portuguese). Phd thesis, Pontif´ıcia Universidade Catolica do Rio de Janeiro.
|
|
18 |
Schnaitter, K., Abiteboul, S., Milo, T., and Polyzotis, N. (2006). COLT: Continuous On-line Tuning. Procs SIGMOD Conference, pages 1–23.
|
|
19 |
Shasha, D. and Bonnet, P. (2002). Database Tuning: Principles, Experiments, and Troubleshooting Techniques. Elsevier Science.
|
|
20 |
Stonebraker, M. (1989). The Case for Partial Indexes. SIGMOD Conf, 18(4):4–11.
|
|
21 |
Talebian, S. H. and Kareem, S. A. (2010). A lexicographic ordering genetic algorithm for solving multi-objective view selection problem. Procs ICCRD Conf, pages 110–115.
|
|
22 |
The PostgreSQL Global Development Group (2019). Explain PostgreSQL.
|
|
23 |
Tran, Q. T., Jimenez, I., Wang, R., Polyzotis, N., and Ailamaki, A. (2015). Rita: An index-tuning advisor for replicated databases. Procs SSDBM Conf, pages 22:1–22:12.
|
|
24 |
Vijay Kumar, T. and Ghoshal, A. (2009). A reduced lattice greedy algorithm for selecting materialized views. Information Systems, Technology and Management, 31:6–18.
|
|
25 |
Vijay Kumar, T. and Kumar, S. (2012). Materialized view selection using genetic algorithm. Contemporary Computing, 306:225–237.
|
|
26 |
Vijay Kumar, T. and Kumar, S. (2013). Materialized view selection using iterative improvement. Advances in Computing and Information Technology, 178:205–213.
|
|
27 |
Vijay Kumar, T. V., Haider, M., and Kumar, S. (2010). Proposing Candidate Views for Materialization. Information Systems, Technology and Management, 54:89–98.
|
|