A Self-Adaptive Regression-Based Multivariate Data Compression Scheme with Error Bound in Wireless Sensor Networks

Wireless sensor networks (WSNs) have limited energy and transmission capacity, so data compression techniques have extensive applications. A sensor node with multiple sensing units is called a multimodal or multivariate node. For multivariate stream on a sensor node, some data streams are elected as the base functions according to the correlation coefficient matrix, and the other streams from the same node can be expressed in relation to one of these base functions using linear regression. By designing an incremental algorithm for computing regression coefficients, a multivariate data compression scheme based on self-adaptive regression with infinite norm error bound for WSNs is proposed. According to error bounds and compression incomes, the self-adaption means that the proposed algorithms make decisions automatically to transmit raw data or regression coefficients, and to select the number of data involved in regression. The algorithms in the scheme can simultaneously explore the temporal and multivariate correlations among the sensory data. Theoretically and experimentally, it is concluded that the proposed algorithms can effectively exploit the correlations on the same sensor node and achieve significant reduction in data transmission. Furthermore, the algorithms perform consistently well even when multivariate stream data correlations are less obvious or non-stationary. </jats:p

Sucrose Monoester Micelles Size Determined by Fluorescence Correlation Spectroscopy (FCS)

One of the several uses of sucrose detergents, as well as other micelle forming detergents, is the solubilization of different membrane proteins. Accurate knowledge of the micelle properties, including size and shape, are needed to optimize the surfactant conditions for protein purification and membrane characterization. We synthesized sucrose esters having different numbers of methylene subunits on the substituent to correlate the number of methylene groups with the size of the corresponding micelles. We used Fluorescence Correlation Spectroscopy (FCS) and two photon excitation to determine the translational D of the micelles and calculate their corresponding hydrodynamic radius, Rh. As a fluorescent probe we used LAURDAN (6-dodecanoyl-2-dimethylaminonaphthalene), a dye highly fluorescent when integrated in the micelle and non-fluorescent in aqueous media. We found a linear correlation between the size of the tail and the hydrodynamic radius of the micelle for the series of detergents measured

Extended wavelets for multiple measures

Several studies have demonstrated the effectiveness of the Haar wavelet decomposition as a tool for reducing large amounts of data down to compact wavelet synopses that can be used to obtain fast, accurate approximate answers to user queries. Although originally designed for minimizing the overall mean-squared (i.e., L 2-norm) error in the data approximation, recently proposed methods also enable the use of Haar wavelets in minimizing other error metrics, such as the relative error in data value reconstruction, which is arguably the most important for approximate query answers. Relatively little attention, however, has been paid to the problem of using wavelet synopses as an approximate query answering tool over complex tabular datasets containing multiple measures, such as those typically found in real-life OLAP applications. Existing decomposition approaches will either operate on each measure individually, or treat all measures as a vector of values and process them simultaneously. As we demonstrate in this article, these existing individual or combined storage approaches for the wavelet coefficients of different measures can easily lead to suboptimal storage utilization, resulting in drastically reduced accuracy for approximate query answers. To address this problem, in this work, we introduce the notion of an extended wavelet coefficient as a flexible, efficient storage method for wavelet coefficients over multimeasure data. We also propose novel algorithms for constructing effective (optimal or near-optimal) extended wavelet-coefficient synopses under a given storage constraint, for both sum-squared error and relative-error norms. Experimental results with both real-life and synthetic datasets validate our approach, demonstrating that our techniques consistently obtain significant gains in approximation accuracy compared to existing solutions. © 2007 ACM

Issues in complex event processing: Status and prospects in the Big Data era

Many Big Data technologies were built to enable the processing of human generated data, setting aside the enormous amount of data generated from Machine-to-Machine (M2M) interactions and Internet-of-Things (IoT) platforms. Such interactions create real-time data streams that are much more structured, often in the form of series of event occurrences. In this paper, we provide an overview on the main research issues confronted by existing Complex Event Processing (CEP) techniques, with an emphasis on query optimization aspects. Our study expands on both deterministic and probabilistic event models and spans from centralized to distributed network settings. In that, we cover a wide range of approaches in the CEP domain and review the current status of techniques that tackle efficient query processing. These techniques serve as a starting point for developing Big Data oriented CEP applications. Therefore, we further study the issues that arise upon trying to apply those tec hniques over Big Data enabling technologies, as is the case with cloud platforms. Furthermore, we expand on the synergies among Predictive Analytics and CEP with an emphasis on scalability and elasticity considerations in cloud platforms with potentially dispersed resource pools