Julie Letchner

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A major problem in detecting events in streams of data is that the data can be imprecise (<i>e.g.</i> RFID data). However, current state-ofthe-art event detection systems such as Cayuga [14], SASE [46] or SnoopIB[1], assume the data is <i>precise</i>. Noise in the data can be captured using techniques such as hidden Markov models. Inference on these models(More)
Knowledge of the physical locations of mobile devices such as laptops or PDA’s is becoming increasingly important with the rise of location-based services such as specialized web search, navigation, and social network applications; furthermore, location information is a key foundation for high-level activity inferencing. In this paper we propose a novel(More)
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(More)
Cascadia is a system that provides RFID-based pervasive computing applications with an infrastructure for specifying, extracting and managing meaningful high-level events from raw RFID data. Cascadia provides three important services. First, it allows application developers and even users to specify events using either a declarative query language or an(More)
A large amount of the world's data is both sequential and imprecise. Such data is commonly modeled as Markovian streams; examples include words/sentences inferred from raw audio signals, or discrete location sequences inferred from RFID or GPS data. The rich semantics and large volumes of these streams make them difficult to query efficiently. In this(More)
Building applications on top of sensor data streams is challenging because sensor data is noisy. A model-based view can reduce noise by transforming raw sensor streams into streams of probabilistic state estimates, which smooth out errors and gaps. The authors propose a novel model-based view, the Markovian stream, to represent correlated probabilistic(More)
Lahar is a warehousing system for Markovian streams—a common class of uncertain data streams produced via inference on probabilistic models. Example Markovian streams include text inferred from speech, location streams inferred from GPS or RFID readings, and human activity streams inferred from sensor data. Lahar supports OLAP-style queries on Markovian(More)
A large amount of the world’s data is both sequential and low-level. Many applications need to query higher-level information (e.g., words and sentences) that is inferred from these low-level sequences (e.g., raw audio signals) using a model (e.g., a hidden Markov model). This inference process is typically statistical, resulting in high-level sequences(More)
The Cascadia system provides RFID-based pervasive computing applications with an infrastructure for specifying, extracting and managing meaningful high-level events from raw RFID data. Cascadia allows application developers and even users to specify events of interest using either a declarative query language or a graphical interface with an intuitive(More)