Longitudinal study of a building-scale RFID ecosystem
Proceedings of the 7th international conference on Mobile systems, applications, and services
Indexing correlated probabilistic databases
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Lahar demonstration: warehousing Markovian streams
Proceedings of the VLDB Endowment
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Lineage processing over correlated probabilistic databases
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Development of foundation models for Internet of Things
Frontiers of Computer Science in China
A generic framework for handling uncertain data with local correlations
Proceedings of the VLDB Endowment
Database-support for continuous prediction queries over streaming data
Proceedings of the VLDB Endowment
Lineage for Markovian stream event queries
Proceedings of the 10th ACM International Workshop on Data Engineering for Wireless and Mobile Access
Probabilistic management of OCR data using an RDBMS
Proceedings of the VLDB Endowment
An RFID and particle filter-based indoor spatial query evaluation system
Proceedings of the 16th International Conference on Extending Database Technology
Approximation trade-offs in a Markovian stream warehouse: An empirical study
Information Systems
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Model-based views have recently been proposed as an effective method for querying noisy sensor data. Commonly used models from the AI literature (e.g., the hidden Markov model) expose to applications a stream of probabilistic and correlated state estimates computed from the sensor data. Many applications want to detect sophisticated patterns of states from these Markovian streams. Such queries are called event queries. In this paper, we present a new Markovian stream storage manager, Caldera. We develop and evaluate Caldera as a component of Lahar, a Markovian stream event query processing system developed in previous work. At the heart of Caldera is a set of access methods for Markovian streams that can improve event query performance by orders of magnitude compared to existing techniques, which must scan the entire stream. Our access methods use new adaptations of traditional B+ tree indexes, and a new index, called the Markov-chain index. They efficiently extract only the relevant timesteps from a stream, while retaining the stream's Markovian properties. We have implemented our prototype system on BDB and demonstrate its effectiveness on both synthetic data and real data from a building-wide RFID deployment.