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Burst (Haas et al., 1989), Garlic (Roth et al., 1999)).
In (Josifovski and Risch, 2002), the authors propose
an approach using the AMOSII mediator database
system, in which a given query is transformed into an
executable object algebraic execution plan. The opti-
mization process is based on built-in algebraic opera-
tors and a built-in cost model for local data.
Moreover, in (Josifovski and Risch, 2002), Dy-
namic Programming, Simulated Annealing, or Ran-
dom can be used as search strategies and so, this ap-
proach is the only system that offers the choice be-
tween different search strategies, as we do. How-
ever, the strategies proposed in (Josifovski and Risch,
2002) are hard-coded, which offers less flexibility
than in our approach.
We finally mention that, in (Stonebraker 2008),
the author stresses that database systems are now
more and more specialized (e.g. OLPT vs. OLAP
systems), and thus, that there is no hope in design-
ing efficient common optimization techniques for all
these systems. We therefore conclude that our ap-
proach of providing a generic framework for the inte-
gration of different optimization techniques can con-
tribute in the design of efficient and flexible query op-
timizers in DHISs.
5 CONCLUSIONS
In this paper, we have proposed a generic framework
for query optimization in the context of DHISs. Our
framework is composed of a set of basic functions,
whose implementation takes into account all aspects
of query optimization such as transformation rule,
cost estimation, construction and annotation execu-
tion plans, and search strategy. The experimental re-
sults reported in this paper show the high flexibility of
our framework used to create or upgrade easily opti-
mizers with high performance. Moreover, our exper-
iments also show that using our generic framework
allows for significant gains of runtime.
Regarding future work, we plan to investigate the
following issues: (i) implementing a cache system in
order to optimize the cost computation of a given exe-
cution plan, (ii) consider cost models in the context of
cloud computing, and (iii) incorporate multi-criteria
optimization techniques in our framework.
REFERENCES
Colorni, A. et al (1991). Distributed optimization by ant
colonies. In Conf´erence europ´eenne sur la vie artifi-
cielle, pages 134–142.
Dang-Ngoc, T.-T. and Travers, N. (2007). Tree graph views
for a distributed pervasive environment. In Interna-
tional Conference on Network-Based Information Sys-
tems (NBIS).
Daniels, D., Selinger, P. G., Haas, L. M., Lindsay, B. G.,
Mohan, C., Walker, A., and Wilms, P. F. (1982). An
introduction to distributed query compilation in r*. In
DDB, pages 291–309.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Op-
timization and Machine Learning. Addison-Wesley.
Haas, L. M., Freytag, J. C., Lohman, G. M., and Pirahesh,
H. (1989). Extensible query processing in starburst.
In ACM SIGMOD, pages 377–388.
Josifovski, V. and Risch, T. (2002). Query decomposition
for a distributed object-oriented mediator system. Dis-
tributed and Parallel Databases, 11:307–336.
Kirkpatrick, S., Gelatt, Jr., C. D., and Vecchi, M. P. (1987).
Readings in computer vision: issues, problems, prin-
ciples, and paradigms. chapter Optimization by simu-
lated annealing, pages 606–615. Morgan Kaufmann.
Liu, L. and Pu, C. (1997). An adaptive object-oriented
approach to integration and access of heteroge-
neous information sources. Distributed and Parallel
Databases, 5:167–205.
Liu, T. (2011). Proposition d’un cadre g´en´erique
d’optimisation de requˆetes dans les environnements
h´et´erog`enes r´epartis. PhD thesis, Universit´e de
Cergy-Pontoise, France. (French).
Naacke, H., Gardarin, G., and Tomasic, A. (1998). Lever-
aging mediator cost models with heterogeneous data
sources. In ICDE, pages 351–360.
Nahar, S., Sahni, S., and Shragowitz, E. (1986). Simulated
annealing and combinatorial optimization. In 23rd
Design Automation Conference.
Roth, M. T., Ozcan, F., and Haas, L. M. (1999). Cost models
do matter: Providing cost information for diverse data
sources in a federated system. In VLDB, pages 599–
610.
Selinger, P. G., Astrahan, M. M., Chamberlin, D. D., Lorie,
R. A., and Price., T. G. (1979). Access path selection
in a relational database management system. In ACM
SIGMOD, pages 23–34.
Stonebraker, M. (2008). Technical perspective - One size
fits all: an idea whose time has come and gone.
CACM, 51(12):76.
Swami, A. (1989). Optimization of large join queries: Com-
bining heuristics and combinatorial techniques. In
ACM SIGMOD, pages 367–376.
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