By Reagan W. Moore (auth.), Jean-Marc Pierson (eds.)
This ebook constitutes the completely refereed post-proceedings of the 1st overseas Workshop on info administration in Grids, DMG 2005, held in Trondheim, Norway in September 2005 at the side of VLDB 2005.
The nine revised complete papers offered including 2 invited papers have been rigorously reviewed and chosen from 24 submissions for inclusion within the publication. The papers deal with all present study actions with regards to information administration in dynamic, heterogeneous and cross-organizational environments, i.e. grids. They exhibit exact services within the administration of very huge, largely allotted databases. Conversely, Grids supply a unique and extremely interesting box of analysis for database scientists either by way of program domain names and basic research.
Read or Download Data Management in Grids: First VLDB Workshop, DMG 2005, Trondheim, Norway, September 2-3, 2005, Revised Selected Papers PDF
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Extra resources for Data Management in Grids: First VLDB Workshop, DMG 2005, Trondheim, Norway, September 2-3, 2005, Revised Selected Papers
July 25–27, 2005) 125–134 16. 1. (October 16, 2003) Adapting to Changing Resource Performance in Grid Query Processing Anastasios Gounaris1 , Jim Smith2 , Norman W. A. uk Abstract. The Grid provides facilities that support the coordinated use of diverse resources, and consequently, provides new opportunities for wide-area query processing. However, Grid resources, as well as being heterogeneous, may also exhibit unpredictable, volatile behaviour. Thus, query processing on the Grid needs to be adaptive, in order to cope with evolving resource characteristics, such as machine load.
Resolve schematic heterogeneity). Approaches like SchemaSQL remedy this situation, but are still limited to the relational data model. XQuery oﬀers suﬃcient expressiveness to turn data to metadata and vice versa, but is limited to the XML realm alone. So we might be tempted to deﬁne Towards Dynamic Information Integration 25 yet another bloated SQL-like language, with the severe drawback that every new metamodel we add will also likely require new language constructs. A natural alternative is viewing both metadata and data described with the PALADIN metamodel as a typed, attributed multigraph.
The 2nd row in Table 1 shows the results when the experiment is repeated, and the adaptation is retrospective (type R1 of response). 53 units). 3% of the execution). This is because it is now more costly to perform log management, as the tuples already sent to remote evaluators need to be discarded and redistributed in a tidy manner. 45). The same general pattern is observed for Q2 as well, using the second method to create imbalance artiﬁcially. In this case, the perturbation is caused in one 2 Without adaptivity, the machines ﬁnish at the same time (the diﬀerence is in the order of fractions of seconds).