Evaluating Probabilistic Queries over Uncertain Matching

2012 ◽  
Author(s):  
Reynold Cheng ◽  
Jian Gong ◽  
David W. Cheung ◽  
Jiefeng Cheng
Keyword(s):  
Probabilistic Queries

scholarly journals A statistics-based sensor selection scheme for continuous probabilistic queries in sensor networks

2006 ◽  
Vol 35 (1) ◽  
pp. 33-58 ◽  
Author(s):  
Song Han ◽  
Edward Chan ◽  
Reynold Cheng ◽  
Kam-Yiu Lam
Keyword(s):  
Sensor Networks ◽  
Sensor Selection ◽  
Selection Scheme ◽  
Probabilistic Queries

Sensors, Uncertainty Models, and Probabilistic Queries

2005 ◽  
pp. 613-618 ◽  
Author(s):  
Reynold Cheng ◽  
Sunil Prabhakar
Keyword(s):  
Time Instant ◽  
Network Bandwidth ◽  
User Query ◽  
Data Value ◽  
Query Answer ◽  
Fire Alarm System ◽  
Probabilistic Queries ◽  
The Status ◽  
Recorded Data ◽  
Uncertainty Models

Sensors are often used to monitor the status of an environment continuously. The sensor readings are reported to the application for making decisions and answering user queries. For example, a fire alarm system in a building employs temperature sensors to detect any abrupt change in temperature. An aircraft is equipped with sensors to track wind speed, and radars are used to report the aircraft’s location to a military application. These applications usually include a database or server to which the sensor readings are sent. Limited network bandwidth and battery power imply that it is often not practical for the server to record the exact status of an entity it monitors at every time instant. In particular, if the value of an entity (e.g., temperature, location) monitored is constantly evolving, the recorded data value may differ from the actual value. Querying the database can then produce incorrect results. Consider a simple example where a user asks the database: “Which room has a temperature between 10oF and 20oF?” If we represent temperature values of rooms A and B stored in the database by A0 and B0 respectively, we can see from Figure 1(a) that the answer to the user query is “Room B”. In reality, the temperature values of both rooms may have changed to newer values, A1 and B1, as shown in Figure 1(b), where the true query answer should be “Room A”. Unfortunately, because of transmission delay, these newest pieces of information are not propagated in time to the system to supply fresh data to the query, and consequently the query is unable to yield a correct answer.

Evaluation of probabilistic queries over imprecise data in constantly-evolving environments

2007 ◽  
Vol 32 (1) ◽  
pp. 104-130 ◽  
Author(s):  
Reynold Cheng ◽  
Dmitri V. Kalashnikov ◽  
Sunil Prabhakar
Keyword(s):  
Imprecise Data ◽  
Probabilistic Queries

Quality Assurances of Probabilistic Queries

2007 ◽  
pp. 189-210
Author(s):  
Reynold Cheng ◽  
Edward Chan ◽  
Kam-Yiu Lam
Keyword(s):  
Probabilistic Queries

scholarly journals When do Numbers Really Matter?

2002 ◽  
Vol 17 ◽  
pp. 265-287 ◽  
Author(s):  
H. Chan ◽  
A. Darwiche
Keyword(s):  
Experimental Results ◽  
Network Robustness ◽  
Belief Network ◽  
Network Parameters ◽  
Probabilistic Queries ◽  
Theoretical Question ◽  
The Impact

Common wisdom has it that small distinctions in the probabilities (parameters) quantifying a belief network do not matter much for the results of probabilistic queries. Yet, one can develop realistic scenarios under which small variations in network parameters can lead to significant changes in computed queries. A pending theoretical question is then to analytically characterize parameter changes that do or do not matter. In this paper, we study the sensitivity of probabilistic queries to changes in network parameters and prove some tight bounds on the impact that such parameters can have on queries. Our analytic results pinpoint some interesting situations under which parameter changes do or do not matter. These results are important for knowledge engineers as they help them identify influential network parameters. They also help explain some of the previous experimental results and observations with regards to network robustness against parameter changes.

Learning Probabilistic Graphical Models

2012 ◽  
pp. 223-236
Author(s):  
Juan I. Alonso-Barba ◽  
Jens D. Nielsen ◽  
Luis de la Ossa ◽  
Jose M. Puerta
Keyword(s):  
Graphical Models ◽  
Statistical Models ◽  
Relevant Literature ◽  
Joint Probability ◽  
Probabilistic Graphical Models ◽  
Compact Representation ◽  
Joint Probability Distribution ◽  
Inference Algorithms ◽  
Probabilistic Queries ◽  
Local Parameters

Probabilistic Graphical Models (PGM) are a class of statistical models that use a graph structure over a set of variables to encode independence relations between those variables. By augmenting the graph by local parameters, a PGM allows for a compact representation of a joint probability distribution over the variables of the graph, which allows for efficient inference algorithms. PGMs are often used for modeling physical and biological systems, and such models are then in turn used to both answer probabilistic queries concerning the variables and to represent certain causal and/or statistical relations in the domain. In this chapter, the authors give an overview of common techniques used for automatic construction of such models from a dataset of observations (usually referred to as learning), and they also review some important applications. The chapter guides the reader to the relevant literature for further study.

scholarly journals Hypothesized diprotomeric enzyme complex supported by stochastic modelling of palytoxin-induced Na/K pump channels

2018 ◽  
Vol 5 (3) ◽  
pp. 172155 ◽  
Author(s):  
Gabriel D. Vilallonga ◽  
Antônio-Carlos G. de Almeida ◽  
Kelison T. Ribeiro ◽  
Sergio V. A. Campos ◽  
Antônio M. Rodrigues
Keyword(s):  
Protein Interactions ◽  
Single Channel ◽  
Stochastic Modelling ◽  
Reaction Rates ◽  
Cell Physiology ◽  
Protein Protein Interactions ◽  
Conformational Substates ◽  
Pumping System ◽  
Electrophysiological Recordings ◽  
Probabilistic Queries

The sodium–potassium pump (Na + /K + pump) is crucial for cell physiology. Despite great advances in the understanding of this ionic pumping system, its mechanism is not completely understood. We propose the use of a statistical model checker to investigate palytoxin (PTX)-induced Na + /K + pump channels. We modelled a system of reactions representing transitions between the conformational substates of the channel with parameters, concentrations of the substates and reaction rates extracted from simulations reported in the literature, based on electrophysiological recordings in a whole-cell configuration. The model was implemented using the UPPAAL-SMC platform. Comparing simulations and probabilistic queries from stochastic system semantics with experimental data, it was possible to propose additional reactions to reproduce the single-channel dynamic. The probabilistic analyses and simulations suggest that the PTX-induced Na + /K + pump channel functions as a diprotomeric complex in which protein–protein interactions increase the affinity of the Na + /K + pump for PTX.

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