A service-oriented system for distributed data querying and integration on Grids
Future Generation Computer Systems
Cost-Based Vectorization of Instance-Based Integration Processes
ADBIS '09 Proceedings of the 13th East European Conference on Advances in Databases and Information Systems
A Vision for Next Generation Query Processors and an Associated Research Agenda
Globe '09 Proceedings of the 2nd International Conference on Data Management in Grid and Peer-to-Peer Systems
Future Generation Computer Systems
Cost-based vectorization of instance-based integration processes
Information Systems
Automated web service query service
International Journal of Web and Grid Services
QoS optimization for thermal-aware cyber-physical systems
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
Efficient load balancing in partitioned queries under random perturbations
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
Adaptive parallelization of queries to data providing web service operations
Transactions on Large-Scale Data- and Knowledge-Centered Systems V
Adaptive memory-aware chunk sizing techniques for data-intensive queries over web services
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Self-managing solutions have recently attracted a lot of interest from the database community. The need for self-* properties is more evident in distributed applications comprising heterogeneous and autonomous databases and functionality providers. Such resources are typically exposed as Web Services (WSs), which encapsulate remote DBMSs and functions called from within database queries. In this setting, database queries are over WSs, and the data transfer cost becomes the main bottleneck. To reduce this cost, data is shipped to and from WSs in chunks; however the optimum chunk size is volatile, depending on both the resources' runtime properties and the query. In this paper we propose a robust control theoretical solution to the problem of optimizing the data transfer in queries over WSs, by continuously tuning at runtime the block size and thus tracking the optimum point. Also, we develop online system identification mechanisms that are capable of estimating the optimum block size analytically. Both contributions are evaluated via both empirical experimentation in a real environment and simulations, and have been proved to be more effective and efficient than static solutions.