IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
High-performance complex event processing over streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
ULDBs: databases with uncertainty and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Entirely declarative sensor network systems
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Sketching probabilistic data streams
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Optimizing mpf queries: decision support and probabilistic inference
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
NexusScout: an advanced location-based application on a distributed, open mediation platform
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
An adaptive RFID middleware for supporting metaphysical data independence
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient pattern matching over event streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Event queries on correlated probabilistic streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Replay-based approaches to revision processing in stream query engines
SSPS '08 Proceedings of the 2nd international workshop on Scalable stream processing system
ICACC '09 Proceedings of the 2009 International Conference on Advanced Computer Control
Online Filtering, Smoothing and Probabilistic Modeling of Streaming data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Probabilistic Inference over RFID Streams in Mobile Environments
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
ZStream: a cost-based query processor for adaptively detecting composite events
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Microsoft CEP server and online behavioral targeting
Proceedings of the VLDB Endowment
Leveraging spatio-temporal redundancy for RFID data cleansing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Active complex event processing: applications in real-time health care
Proceedings of the VLDB Endowment
Distributed inference and query processing for RFID tracking and monitoring
Proceedings of the VLDB Endowment
Tuffy: scaling up statistical inference in Markov logic networks using an RDBMS
Proceedings of the VLDB Endowment
E-Cube: multi-dimensional event sequence analysis using hierarchical pattern query sharing
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Active complex event processing over event streams
Proceedings of the VLDB Endowment
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Recent years have witnessed the emergence of real-time object monitoring applications driven by the explosion of small inexpensive sensors. In many real-world applications, not all sensed events carry the identification of the object whose action they report on, so called "non-ID-ed" events. Reasons range from heterogeneous sensing devices to human's choosing to conceal their identifications. Such non-ID-ed events prevent us from performing object-based analytics, such as tracking, alerting and pattern matching. We propose a probabilistic inference framework, called FISS, to tackle this problem by inferring the missing object identification associated with an event. Specifically, as a foundation we design a time-varying graphic model to capture correspondences between sensed events and objects. Upon this formal model, we elaborate how to adapt the Forward-backward (FB) inference algorithm to continuously infer probabilistic identifications for non-ID-ed events. However, we demonstrate that FB is neither scalable nor efficient over event streams. To overcome this deficiency, we propose a suite of strategies for optimizing its performance, including the selective smoothing technique that significantly reduces the number of random variables that need to be smoothed, and the finish-flag mechanism that enables early termination of backward computations. Our experimental results, using large-volume streams of a real-world healthcare application, demonstrate the accuracy, efficiency, and scalability of FISS. Especially FISS achieves on average 15x higher throughput than our basic FB inference.