A Language for Manipulating Arrays
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Algebraic Optimization of Computations over Scientific Databases
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Enhanced abstract data types in object-relational databases
The VLDB Journal — The International Journal on Very Large Data Bases
Warping indexes with envelope transforms for query by humming
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Adaptive stream resource management using Kalman Filters
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Complex spatio-temporal pattern queries
VLDB '05 Proceedings of the 31st international conference on Very large data bases
SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers
ACM Transactions on Mathematical Software (TOMS) - Special issue on the Advanced CompuTational Software (ACTS) Collection
MauveDB: supporting model-based user views in database systems
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Adaptive cleaning for RFID data streams
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Querying continuous functions in a database system
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
SNQL: a query language for sensor network databases
TELE-INFO'08 Proceedings of the 7th WSEAS International Conference on Telecommunications and Informatics
A goal-oriented programming framework for grid sensor networks with reconfigurable embedded nodes
ACM Transactions on Embedded Computing Systems (TECS)
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Many sensor network applications monitor continuous phenomena by sampling, and fit time-varying models that capture the phenomena's behaviors. We introduce Pulse, a framework for processing continuous queries over these continuous-time data models. Pulse allows users to declaratively specify both their queries and models, and transforms these queries into simultaneous equation systems, which in many cases are significantly cheaper to process than a stream of discrete tuples. Pulse is able to guarantee user-defined error bounds between query results from continuous-time data models and sampled data, including cases of null results. We present a high-level overview of the design and architecture of Pulse and propose several query optimization techniques that are novel to our context, such as the simplification of our equation systems. We also discuss our plans for extending Pulse to support several novel model types, including differential equations and time series, and outline an abstraction to support query processing on these classes of models.