View Materialization in a Data Cube

2011 ◽  
Author(s):  
Vikas Agrawal ◽  
P. S. Sundararaghavan ◽  
Mesbah U. Ahmed ◽  
Udayan Nandkeolyar
Keyword(s):  
Data Cube ◽  
View Materialization

View Materialization in a Data Cube

2009 ◽  
pp. 204-223
Author(s):  
Vikas Agrawal ◽  
P. S. Sundararaghavan ◽  
Mesbah U. Ahmed ◽  
Udayan Nandkeolyar
Keyword(s):  
Integer Programming ◽  
Response Time ◽  
Data Warehouse ◽  
Data Cube ◽  
Decision Makers ◽  
Efficient Design ◽  
Linear Integer Programming ◽  
Query Response Time ◽  
View Materialization ◽  
The Right

Data warehouse has become an integral part in developing a DSS in any organization. One of the key architectural issues concerning the efficient design of a data warehouse is to determine the “right” number of views to be materialized in order to minimize the query response time experienced by the decision makers in the organization. We consider a bottleneck objective in designing such a materialization scheme which has the effect of guaranteeing a certain level of performance. We examine linear integer programming formulations, and develop heuristics and report on the performance of these heuristics. We also evaluate heuristics reported in the literature for the view materialization problem with a simpler objective.

View Materialization in a Data Cube

2007 ◽  
Vol 18 (3) ◽  
pp. 1-20 ◽  
Author(s):  
Vikas Agrawal ◽  
P. S. Sundararaghavan ◽  
Mesbah U. Ahmed ◽  
Udayan Nandkeolyar
Keyword(s):  
Data Cube ◽  
View Materialization

Soil Moisture Retrieval Using Data Cube Representation of Radar Scattering

PIERS Online ◽  
2010 ◽  
Vol 6 (6) ◽  
pp. 504-508 ◽  
Author(s):  
Seung-Bum Kim ◽  
Eni Gerald Njoku
Keyword(s):  
Soil Moisture ◽  
Data Cube ◽  
Radar Scattering ◽  
Using Data

Spectroscopy-Based Mapping with Scanning Microwave Impedance Microscopy

2018 ◽  
Author(s):  
Peter De Wolf ◽  
Zhuangqun Huang ◽  
Bede Pittenger
Keyword(s):  
Single Point ◽  
Electrical Characterization ◽  
Principal Component ◽  
High Sensitivity ◽  
Data Cube ◽  
Nanometer Scale ◽  
Learning Approaches ◽  
Data Set ◽  
3D Data ◽  
Higher Dimensional

Abstract Methods are available to measure conductivity, charge, surface potential, carrier density, piezo-electric and other electrical properties with nanometer scale resolution. One of these methods, scanning microwave impedance microscopy (sMIM), has gained interest due to its capability to measure the full impedance (capacitance and resistive part) with high sensitivity and high spatial resolution. This paper introduces a novel data-cube approach that combines sMIM imaging and sMIM point spectroscopy, producing an integrated and complete 3D data set. This approach replaces the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube approach is also applicable to other AFM-based electrical characterization modes.

An Efficient Shot Boundary Detection Using Data-cube Searching Technique

2020 ◽  
Vol 13 (4) ◽  
pp. 798-807
Author(s):  
J. Kavitha ◽  
P. Arockia Jansi Rani ◽  
P. Mohamed Fathimal ◽  
Asha Paul
Keyword(s):  
Texture Features ◽  
Boundary Detection ◽  
Data Cube ◽  
Multimedia Data ◽  
Shot Boundary Detection ◽  
Gradual Transition ◽  
Detection Rates ◽  
Effective Manner ◽  
Shot Boundary ◽  
Shot Transitions

Background:: In the internet era, there is a prime need to access and manage the huge volume of multimedia data in an effective manner. Shot is a sequence of frames captured by a single camera in an uninterrupted space and time. Shot detection is suitable for various applications such that video browsing, video indexing, content based video retrieval and video summarization. Objective:: To detect the shot transitions in the video within a short duration. It compares the visual features of frames like correlation, histogram and texture features only in the candidate region frames instead of comparing the full frames in the video file. Methods: This paper analyses candidate frames by searching the values of frame features which matches with the abrupt detector followed by the correct cut transition frame with in the datacube recursively until it detects the correct transition frame. If they are matched with the gradual detector, then it will give the gradual transition ranges, otherwise the algorithm will compare the frames within the next datacube to detect shot transition. Results:: The total average detection rates of all transitions computed in the proposed Data-cube Search Based Shot Boundary Detection technique are 92.06 for precision, 96.92 for recall and 93.94 for f1 measure and the maximum accurate detection rate. Conclusion:: Proposed method for shot transitions uses correlation value for searching procedure with less computation time than the existing methods which compares every single frame and uses multi features such as color, edge, motion and texture features in wavelet domain.

scholarly journals Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

2021 ◽  
Vol 13 (12) ◽  
pp. 2299
Author(s):  
Andrea Tassi ◽  
Daniela Gigante ◽  
Giuseppe Modica ◽  
Luciano Di Martino ◽  
Marco Vizzari
Keyword(s):  
Land Cover ◽  
National Park ◽  
Time Window ◽  
Central Italy ◽  
Google Earth ◽  
Data Cube ◽  
Landsat 8 ◽  
Change Analysis ◽  
Object Based ◽  
Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

scholarly journals Rotational hyperspectral scanner and related image reconstruction algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Longqiang Luo ◽  
Shuo Li ◽  
Xinli Yao ◽  
Sailing He
Keyword(s):  
Image Reconstruction ◽  
Spectral Resolution ◽  
Reconstruction Algorithm ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Spatial Accuracy ◽  
Scanning Method ◽  
Image Reconstruction Algorithm

AbstractWe design and implement a compact and lightweight hyperspectral scanner. Based on this, a novel rotational hyperspectral scanner was demonstrated. Different from translational scanning, rotational scanning is a moveless and stable scanning method. We also designed a relevant image algorithm to reconstruct the image from an angular recorded hyperspectral data cube. The algorithm works well even with uncertain radial and tangential offset, which is caused by mechanical misalignment. The system shown a spectral resolution of 5 nm after calibration. Finally, spatial accuracy and spectral precision were discussed, based on some additional experiments.

scholarly journals Super-resolution time-resolved imaging using computational sensor fusion

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
C. Callenberg ◽  
A. Lyons ◽  
D. den Brok ◽  
A. Fatima ◽  
A. Turpin ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Single Photon ◽  
Super Resolution ◽  
Numerical Data ◽  
Data Cube ◽  
Fluorescence Lifetime Imaging ◽  
Time Resolved ◽  
Temporal Precision

AbstractImaging across both the full transverse spatial and temporal dimensions of a scene with high precision in all three coordinates is key to applications ranging from LIDAR to fluorescence lifetime imaging. However, compromises that sacrifice, for example, spatial resolution at the expense of temporal resolution are often required, in particular when the full 3-dimensional data cube is required in short acquisition times. We introduce a sensor fusion approach that combines data having low-spatial resolution but high temporal precision gathered with a single-photon-avalanche-diode (SPAD) array with data that has high spatial but no temporal resolution, such as that acquired with a standard CMOS camera. Our method, based on blurring the image on the SPAD array and computational sensor fusion, reconstructs time-resolved images at significantly higher spatial resolution than the SPAD input, upsampling numerical data by a factor $$12 \times 12$$ 12 × 12 , and demonstrating up to $$4 \times 4$$ 4 × 4 upsampling of experimental data. We demonstrate the technique for both LIDAR applications and FLIM of fluorescent cancer cells. This technique paves the way to high spatial resolution SPAD imaging or, equivalently, FLIM imaging with conventional microscopes at frame rates accelerated by more than an order of magnitude.

On the path toward a universal outflow mechanism in light of NGC 4151 and NGC 1068

2020 ◽  
Vol 15 (S359) ◽  
pp. 283-284
Author(s):  
D. May ◽  
J. E. Steiner ◽  
R. B. Menezes
Keyword(s):  
Near Infrared ◽  
Feedback Mechanism ◽  
Data Cube ◽  
Low Velocity ◽  
Ionized Gas ◽  
Ngc 4151 ◽  
Molecular Outflow ◽  
Ngc 1068 ◽  
Field Unit ◽  
First Time

AbstractWe use near-infrared Integral Field Unit (IFU) data to analyze the galaxies NGC 4151 and NGC 1068, which have very different Eddington ratios - ˜50 times lower for NGC 4151. Together with a detailed data cube treatment methodology, we reveal remarkable similarities between both AGN, such as the detection of the walls of an “hourglass” structure for the low-velocity [Fe ii] emission with the high-velocity emission within this hourglass; a molecular outflow - detected for the first time in NGC 4151; and the fragmentation of an expanding molecular bubble into bullets of ionized gas. Such observations suggest that NGC 4151 could represent a less powerful and more compact version of the outflow seen in NGC 1068, suggesting a universal feedback mechanism acting in quite different AGN.

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