Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
Optimization of dynamic query evaluation plans
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Iterators, schedulers, and distributed-memory parallelism
Software—Practice & Experience
Incremental data structures and algorithms for dynamic query interfaces
ACM SIGMOD Record
Multidatabase Query Optimization
Distributed and Parallel Databases
Efficient mid-query re-optimization of sub-optimal query execution plans
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Cost-based query scrambling for initial delays
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An adaptive query execution system for data integration
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Combining fuzzy information from multiple systems
Journal of Computer and System Sciences
Decomposition—a strategy for query processing
ACM Transactions on Database Systems (TODS)
Exception handling: issues and a proposed notation
Communications of the ACM
The state of the art in distributed query processing
ACM Computing Surveys (CSUR)
A History of Data-Flow Languages
IEEE Annals of the History of Computing
Efficient Querying of Distributed Resources in Mediator Systems
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
Self-monitoring query execution for adaptive query processing
Data & Knowledge Engineering
A taxonomy of scientific workflow systems for grid computing
ACM SIGMOD Record
Compiled Query Execution Engine using JVM
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Query optimization over web services
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
A pay-as-you-go framework for query execution feedback
Proceedings of the VLDB Endowment
Maestro: a self-organizing peer-to-peer dataflow framework using reinforcement learning
Proceedings of the 18th ACM international symposium on High performance distributed computing
Query optimizers: time to rethink the contract?
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
An object-oriented version model for context-aware data management
WISE'07 Proceedings of the 8th international conference on Web information systems engineering
Coordinating services for accessing and processing data in dynamic environments
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems - Volume Part I
Proceedings of the 5th International Workshop on Web APIs and Service Mashups
A bottom-up, knowledge-aware approach to integrating and querying web data services
ACM Transactions on the Web (TWEB)
Exploratory search framework for Web data sources
The VLDB Journal — The International Journal on Very Large Data Bases
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The efficient execution of data-intensive computations over services is a challenging task: data are retrieved from remote sources and therefore are not available in the query engine until after the execution of these calls, but the system must be inherently efficient thereafter, by guaranteeing that data is immediately cached and processed efficiently, according to the best query plan. In this chapter, we present a flexible execution model for search computing queries, named Panta Rhei. The proposed execution engine paradigm adopts the producer/consumer model and supports both data-driven and event-driven synchronization, and their interplay. Query plans are modeled as directed graphs, whose nodes are processing units and whose edges are either control or data flows. While control flows synchronize service calls and unit execution, data flows transfer data between units that process data flows to produce query results. We present the specification of Panta Rhei by formally defining the units for data production, consumption, manipulation, and caching, as well as the control and data flows. Finally, we discuss how a query plan is expressed in terms of a query execution plan.