TY - JOUR A1 - Kruse, Sebastian A1 - Kaoudi, Zoi A1 - Contreras-Rojas, Bertty A1 - Chawla, Sanjay A1 - Naumann, Felix A1 - Quiane-Ruiz, Jorge-Arnulfo T1 - RHEEMix in the data jungle BT - a cost-based optimizer for cross-platform systems JF - The VLDB Journal N2 - Data analytics are moving beyond the limits of a single platform. In this paper, we present the cost-based optimizer of Rheem, an open-source cross-platform system that copes with these new requirements. The optimizer allocates the subtasks of data analytic tasks to the most suitable platforms. Our main contributions are: (i) a mechanism based on graph transformations to explore alternative execution strategies; (ii) a novel graph-based approach to determine efficient data movement plans among subtasks and platforms; and (iii) an efficient plan enumeration algorithm, based on a novel enumeration algebra. We extensively evaluate our optimizer under diverse real tasks. We show that our optimizer can perform tasks more than one order of magnitude faster when using multiple platforms than when using a single platform. KW - Cross-platform KW - Polystore KW - Query optimization KW - Data processing Y1 - 2020 U6 - https://doi.org/10.1007/s00778-020-00612-x SN - 1066-8888 SN - 0949-877X VL - 29 IS - 6 SP - 1287 EP - 1310 PB - Springer CY - Berlin ER - TY - JOUR A1 - Ladeira, Guenia A1 - Marwan, Norbert A1 - Destro-Filho, Joao-Batista A1 - Ramos, Camila Davi A1 - Lima, Gabriela T1 - Frequency spectrum recurrence analysis JF - Scientific reports N2 - In this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by comparing the signals from participants in two states, eyes open and eyes closed. Firstly, EES were characterized and analysed by means of techniques already known to compare with the results of the innovative technique that we present here. We verified that, standard recurrence quantification analysis by means of EES time series cannot statistically distinguish the two states. However, the new frequency spectrum recurrence quantification exhibit quantitatively whether the participants have their eyes open or closed. In sequence, new quantifiers are created for analysing the recurrence concentration on frequency bands. These analyses show that EES with similar frequency spectrum have different recurrence levels revealing different behaviours of the nervous system. The technique can be used to deepen the study on depression, stress, concentration level and other neurological issues and also can be used in any complex system. KW - Biomedical engineering KW - Brain injuries KW - Computational models KW - Computational neuroscience KW - Data acquisition KW - Data processing KW - Electrical and electronic engineering KW - Neural circuits KW - Visual system Y1 - 2020 U6 - https://doi.org/10.1038/s41598-020-77903-4 SN - 2045-2322 VL - 10 IS - 1 PB - Nature portfolio CY - Berlin ER - TY - JOUR A1 - Schmelzbach, C. A1 - Scherbaum, Frank A1 - Tronicke, Jens A1 - Dietrich, P. T1 - Bayesian frequency-domain blind deconvolution of ground-penetrating radar data JF - Journal of applied geophysics N2 - Enhancing the resolution and accuracy of surface ground-penetrating radar (GPR) reflection data by inverse filtering to recover a zero-phased band-limited reflectivity image requires a deconvolution technique that takes the mixed-phase character of the embedded wavelet into account. In contrast, standard stochastic deconvolution techniques assume that the wavelet is minimum phase and, hence, often meet with limited success when applied to GPR data. We present a new general-purpose blind deconvolution algorithm for mixed-phase wavelet estimation and deconvolution that (1) uses the parametrization of a mixed-phase wavelet as the convolution of the wavelet's minimum-phase equivalent with a dispersive all-pass filter, (2) includes prior information about the wavelet to be estimated in a Bayesian framework, and (3) relies on the assumption of a sparse reflectivity. Solving the normal equations using the data autocorrelation function provides an inverse filter that optimally removes the minimum-phase equivalent of the wavelet from the data, which leaves traces with a balanced amplitude spectrum but distorted phase. To compensate for the remaining phase errors, we invert in the frequency domain for an all-pass filter thereby taking advantage of the fact that the action of the all-pass filter is exclusively contained in its phase spectrum. A key element of our algorithm and a novelty in blind deconvolution is the inclusion of prior information that allows resolving ambiguities in polarity and timing that cannot be resolved using the sparseness measure alone. We employ a global inversion approach for non-linear optimization to find the all-pass filter phase values for each signal frequency. We tested the robustness and reliability of our algorithm on synthetic data with different wavelets, 1-D reflectivity models of different complexity, varying levels of added noise, and different types of prior information. When applied to realistic synthetic 2-D data and 2-D field data, we obtain images with increased temporal resolution compared to the results of standard processing. KW - Deconvolution KW - Inverse filtering KW - Ground penetrating radar KW - GPR KW - Data processing KW - Vertical resolution Y1 - 2011 U6 - https://doi.org/10.1016/j.jappgeo.2011.08.010 SN - 0926-9851 VL - 75 IS - 4 SP - 615 EP - 630 PB - Elsevier CY - Amsterdam ER -