Efficiently Maintaining Distributed Model-Based Views on Real-Time Data Streams

Arion, Alexandru; Jeung, Hoyoung; Aberer, Karl

doi:10.1109/glocom.2011.6133764

Cited by 6 publications

(5 citation statements)

References 25 publications

(25 reference statements)

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“…The producer node keeps approximating an incoming sensor data signal, and then subsequently updates the model parameters of the compression method to the consumer node, only when the maximum error guarantee is violated. This scenario is commonly used in wireless sensor network applications [1,13,31].…”

Section: Transmission Costmentioning

confidence: 99%

“…However, compression techniques built on nonlinear models generally require more coefficients to approximate a given sensor data signal, rendering the size of compressed data relatively larger. Both polynomial and Chebyshev Approximations (CHEB) [1,5,16] are widely used techniques from this compression category. In particular, CHEB is often preferred for data processing, as it can quickly compute a near-optimal approximation for a given signal.…”

Section: Nonlinear-model Compressionmentioning

confidence: 99%

“…• Nonlinear models, such as Polynomial or Chebyshev approximations (CHEB) [1,5,16], capture raw sensordata signals using complex functions.…”

Section: Introductionmentioning

confidence: 99%

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An Evaluation of Model-Based Approaches to Sensor Data Compression

Nguyen

Jeung

Aberer

2013

IEEE Trans. Knowl. Data Eng.

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Abstract-As the volumes of sensor data being accumulated are likely to soar, data compression has become essential in a wide range of sensor-data applications. This has led to a plethora of data compression techniques for sensor data, in particular modelbased approaches have been spotlighted due to their significant compression performance. These methods, however, have never been compared and analyzed under the same setting, rendering a 'right' choice of compression technique for a particular application very difficult. Addressing this problem, this paper presents a benchmark that offers a comprehensive empirical study on the performance comparison of the model-based compression techniques. Specifically, we re-implemented several state-ofthe-art methods in a comparable manner, and measured various performance factors with our benchmark, including compression ratio, computation time, model maintenance cost, approximation quality, and robustness to noisy data. We then provide in-depth analysis of the benchmark results, obtained by using 11 different real datasets consisting of 346 heterogeneous sensor data signals. We believe that the findings from the benchmark will be able to serve as a practical guideline for applications that need to compress sensor data.

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Section: Transmission Costmentioning

confidence: 99%

Section: Nonlinear-model Compressionmentioning

confidence: 99%

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An Evaluation of Model-Based Approaches to Sensor Data Compression

Nguyen

Jeung

Aberer

2013

IEEE Trans. Knowl. Data Eng.

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“…For example, regression coefficients are stored for the regression model. The attribute model params has variable length (e.g., VARCHAR or VARBINARY data types in SQL) and it stores the concatenation of the parameters or their compressed representation, by means of standard lossless compression techniques (refer Section 5.7) or by a bitmap coding of approximate values, as proposed in [3]. Each tuple in the ModelTable corresponds to a model with a particular id and function.…”

Section: Overview Of Sensor Data Compression Systemmentioning

confidence: 99%

“…Many approaches assume that the user provides an accuracy bound, and based on this bound the sensor data is approximated, resulting in compressed representations of the data [24]. A large number of other techniques exploit the fact that sensor data is often correlated; thus, this correlation can be used for approximating one data stream with another [24,67,49,3].…”

Section: Data Compressionmentioning

confidence: 99%

A Survey of Model-based Sensor Data Acquisition and Management

Sathe

Papaioannou

Jeung³

et al. 2012

Managing and Mining Sensor Data

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In recent years, due to the proliferation of sensor networks, there has been a genuine need of researching techniques for sensor data acquisition and management. To this end, a large number of techniques have emerged that advocate model-based sensor data acquisition and management. These techniques use mathematical models for performing various, day-to-day tasks involved in managing sensor data. In this chapter, we survey the state-of-the-art techniques for model-based sensor data acquisition and management. We start by discussing the techniques for acquiring sensor data. We, then, discuss the application of models in sensor data cleaning; followed by a discussion on model-based meth-

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