Challenges for Event Queries over Markovian Streams

Letchner, Julie; Ré, Christopher; Balazinska, Magda; Philipose, Matthai

doi:10.1109/mic.2008.118

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…Similarly, particle filtering approaches enable event queries [11], [19], [23] of the form of a notification upon a condition, e.g., "Alert the user when entity X enters room A." At any instant, the location of entity X is stored as a probability distribution using particle filtering.…”

Section: Application Performancementioning

confidence: 99%

Blurring snapshots: Temporal inference of missing and uncertain data

Rajamani

Julien

2010

2010 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Abstract-Many pervasive computing applications continuously monitor state changes in the environment by acquiring, interpreting and responding to information from sensors embedded in the environment. However, it is extremely difficult and expensive to obtain a continuous, complete, and consistent picture of a continuously evolving operating environment. One standard technique to mitigate this problem is to employ mathematical models that compute missing data from sampled observations thereby approximating a continuous and complete stream of information. However, existing models have traditionally not incorporated a notion of temporal validity, or the quantification of imprecision associated with inferring data values from past or future observations. In this paper, we support continuous monitoring of dynamic pervasive computing phenomena through the use of a series of snapshot queries. We define a decay function and a set of inference approaches to filling in missing and uncertain data in this continuous query. We evaluate the usefulness of this abstraction in its application to complex spatiotemporal pattern queries in pervasive computing networks.

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Section: Application Performancementioning

confidence: 99%

Blurring snapshots: Temporal inference of missing and uncertain data

Rajamani

Julien

2010

2010 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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“…As in previous work on event queries over RFID data [28,29,39,50], we build the probabilistic view using a Hidden Markov Model (HMM) [36] and apply (Bayesian filtering and smoothing [14]) using a particle filter based inference technique [11]. The output of this process is a stream in which each timestep contains a distribution over possible locations for a tag: e.g., Alice was in her office with probability 0.2, in her neighbor's office with probability 0.1, and in the corridor with probability 0.7.…”

Section: Inferring Information From Rfid Datamentioning

confidence: 93%

Longitudinal study of a building-scale RFID ecosystem

Welbourne

Koscher

Soroush

et al. 2009

Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services

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Radio Frequency IDentification (RFID) deployments are becoming increasingly popular in both industrial and consumer-oriented settings. To effectively exploit and operate such deployments, important challenges must be addressed, from managing RFID data streams to handling limitations in reader accuracy and coverage. Furthermore, deployments that support pervasive computing raise additional issues related to user acceptance and system utility. To better understand these challenges, we conducted a four-week study of a building-scale EPC Class-1 Generation-2 RFID deployment, the "RFID Ecosystem", with 47 readers (160 antennas) installed throughout an 8,000 square meter building. During the study, 67 participants having over 300 tags accessed the collected RFID data through applications including an object finder and a friend tracker and several tools for managing personal data. We found that our RFID deployment produces a very manageable amount of data overall, but with orders of magnitude difference among various participants and objects. We also find that the tag detection rates tend to be low with high variance across the type of tag, participant and object. Users need expert guidance to effectively mount their tags and are encouraged by compelling applications to wear tags more frequently. Finally, probabilistic modeling and inference techniques promise to enable more complex applications by smoothing over gaps and errors in the data, but must be applied with care as they add significant computational and storage overhead.

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“…It accepts declarative event specifications and detects the specified events over incoming RFID data, producing one event stream per specification. Cascadia copes with uncertainty by transforming intermittent RFID streams into smoothed, probabilistic Markovian Streams (MStreams) [19] that capture both the uncertainty of a tag's location at each time step (e.g., a distribution over which rooms the tag might be in) and the correlations between a tag's possible locations (e.g., distributions over entire paths through a building). MStreams abstract away the complexities of sporadic and imprecise data to expose a more uniform model of location over which event specifications are expressed, thus considerably simplifying the requirements of an event specification language.…”

Section: Integrating Lahar Into Cascadiamentioning

confidence: 99%

“…The query signal for an event specification, or event signal, consists of timestep-probability pairs <t,p> which indicate that the event occurred at timestep t with probability p (see Figure 2). Each probability p is derived from an MStream using well-established query answering techniques for probabilistic databases [19,28].…”

Section: Integrating Lahar Into Cascadiamentioning

confidence: 99%

Specification and Verification of Complex Location Events with Panoramic

Welbourne

Balazinska

Borriello

et al. 2010

Lecture Notes in Computer Science

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Abstract. We present the design and evaluation of Panoramic, a tool that enables end-users to specify and verify an important family of complex location events. Our approach aims to reduce or eliminate critical barriers to deployment of emerging location-aware business activity monitoring applications in domains like hospitals and office buildings. Panoramic does not require users to write code, understand complex models, perform elaborate demonstrations, generate test location traces, or blindly trust deterministic events. Instead, it allows end-users to specify and edit complex events with a visual language that embodies natural concepts of space and time. It also takes a novel approach to verification, in which events are extracted from historical sensor data traces and then presented with intelligible, hierarchical visualizations that represent uncertainty with probabilities. We build on our existing software for specifying and detecting events while enhancing it in non-trivial ways to facilitate event specification and verification. Our design is guided by a formative study with 12 non-programmers. We also use location traces from a building-scale radio frequency identification (RFID) deployment in a qualitative evaluation of Panoramic with 10 non-programmers. The results show that end-users can both understand and verify the behavior of complex location event specifications using Panoramic.

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Challenges for Event Queries over Markovian Streams

Cited by 6 publications

References 7 publications

Blurring snapshots: Temporal inference of missing and uncertain data

Blurring snapshots: Temporal inference of missing and uncertain data

Longitudinal study of a building-scale RFID ecosystem

Specification and Verification of Complex Location Events with Panoramic

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