Data Analysis in Trade

2019 ◽  
pp. 62-65
Author(s):  
Bhuvaneswari R ◽  
Malathi M ◽  
Ravi D
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Efficient Method ◽  
Data Warehousing ◽  
Synthetic Data ◽  
Approximate Algorithm ◽  
Data Sets ◽  
Source File ◽  
A New Technique ◽  
Excel File

Data Warehousing is arisen in order to overcome the issues faced by the excel file. When several database is maintained by the excel file a conflict will arise such that which source file belongs to which excel sheet. In order to overcome the problem a data warehousing is used and a new technique used in data warehousing is soft concatenation mapping which depicts a relationship between column during transformation and concatenation .At that time many redundancy and loss of data may occur in order to overcome the issue an efficient method is used that is used to derive an approximate algorithm. This approximation is used both in real time and synthetic data sets.

scholarly journals An Innovative Method to Extract Data in a Real-time Data Warehousing Environment

2021 ◽  
Author(s):  
Flavio de Assis Vilela ◽  
Ricardo Rodrigues Ciferri
Keyword(s):  
Real Time ◽  
Data Warehousing ◽  
Data Extraction ◽  
Synthetic Data ◽  
Knowledge Discovery In Databases ◽  
Data Repository ◽  
Time Data ◽  
Innovative Method ◽  
Real Time Data ◽  
Time Requirements

ETL (Extract, Transform, and Load) is an essential process required to perform data extraction in knowledge discovery in databases and in data warehousing environments. The ETL process aims to gather data that is available from operational sources, process and store them into an integrated data repository. Also, the ETL process can be performed in a real-time data warehousing environment and store data into a data warehouse. This paper presents a new and innovative method named Data Extraction Magnet (DEM) to perform the extraction phase of ETL process in a real-time data warehousing environment based on non-intrusive, tag and parallelism concepts. DEM has been validated on a dairy farming domain using synthetic data. The results showed a great performance gain in comparison to the traditional trigger technique and the attendance of real-time requirements.

scholarly journals A new method for the in vivo identification of degenerated material property ranges of the human eye: feasibility analysis based on synthetic data

2021 ◽  
Author(s):  
Stefan Muench ◽  
Mike Roellig ◽  
Daniel Balzani
Keyword(s):  
Real Time ◽  
Test Data ◽  
Synthetic Data ◽  
New Method ◽  
Data Sets ◽  
New Approach ◽  
Full Field ◽  
Material Parameters ◽  
Virtual Test

AbstractThis paper proposes a new method for in vivo and almost real-time identification of biomechanical properties of the human cornea based on non-contact tonometer data. Further goal is to demonstrate the method’s functionality based on synthetic data serving as reference. For this purpose, a finite element model of the human eye is constructed to synthetically generate full-field displacements from different data sets with keratoconus-like degradations. Then, a new approach based on the equilibrium gap method combined with a mechanical morphing approach is proposed and used to identify the material parameters from virtual test data sets. In a further step, random absolute noise is added to the virtual test data to investigate the sensitivity of the new approach to noise. As a result, the proposed method shows a relevant accuracy in identifying material parameters based on full-field displacements. At the same time, the method turns out to work almost in real time (order of a few minutes on a regular workstation) and is thus much faster than inverse problems solved by typical forward approaches. On the other hand, the method shows a noticeable sensitivity to rather small noise amplitudes rendering the method not accurate enough for the precise identification of individual parameter values. However, analysis show that the accuracy is sufficient for the identification of property ranges which might be related to diseased tissues. Thereby, the proposed approach turns out promising with view to diagnostic purposes.

scholarly journals Frontiers of Real-Time Data Analysis

2011 ◽  
Vol 49 (1) ◽  
pp. 72-100 ◽  
Author(s):  
Dean Croushore
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Data Sets ◽  
Current Analysis ◽  
Time Data ◽  
The Past ◽  
Data Revisions ◽  
The World ◽  
Real Time Data ◽  
The Impact

In the past ten years, researchers have explored the impact of data revisions in many different contexts. Researchers have examined the properties of data revisions, how structural modeling is affected by data revisions, how data revisions affect forecasting, the impact of data revisions on monetary policy analysis, and the use of real-time data in current analysis. This paper summarizes many of the questions for which real-time data analysis has provided answers. In addition, researchers and institutions have developed better real-time data sets around the world. Still, additional research is needed in key areas and research to date has uncovered even more fruitful areas worth exploring. (JEL C52, C53, C80, E01)

Modelling and simulation for metabolomics data analysis

2005 ◽  
Vol 33 (6) ◽  
pp. 1427-1429 ◽  
Author(s):  
P. Mendes ◽  
D. Camacho ◽  
A. de la Fuente
Keyword(s):  
Data Analysis ◽  
Synthetic Data ◽  
Network Models ◽  
Large Data ◽  
Biochemical Network ◽  
Data Sets ◽  
Metabolomics Data ◽  
Analysis Methods ◽  
True Knowledge ◽  
Data Analysis Methods

The advent of large data sets, such as those produced in metabolomics, presents a considerable challenge in terms of their interpretation. Several mathematical and statistical methods have been proposed to analyse these data, and new ones continue to appear. However, these methods often disagree in their analyses, and their results are hard to interpret. A major contributing factor for the difficulties in interpreting these data lies in the data analysis methods themselves, which have not been thoroughly studied under controlled conditions. We have been producing synthetic data sets by simulation of realistic biochemical network models with the purpose of comparing data analysis methods. Because we have full knowledge of the underlying ‘biochemistry’ of these models, we are better able to judge how well the analyses reflect true knowledge about the system. Another advantage is that the level of noise in these data is under our control and this allows for studying how the inferences are degraded by noise. Using such a framework, we have studied the extent to which correlation analysis of metabolomics data sets is capable of recovering features of the biochemical system. We were able to identify four major metabolic regulatory configurations that result in strong metabolite correlations. This example demonstrates the utility of biochemical simulation in the analysis of metabolomics data.

scholarly journals Portable auricular device for real-time swallow and chew detection

2016 ◽  
Vol 2 (1) ◽  
pp. 129-133 ◽  
Author(s):  
Konrad C. Steimer ◽  
Christoph Zimmermann ◽  
Jennifer Zeilfelder ◽  
Christian Pylatiuk ◽  
Wilhelm Stork
Keyword(s):  
Speech Recognition ◽  
Food Intake ◽  
Low Power ◽  
Real Time ◽  
State Of The Art ◽  
Portable Device ◽  
Data Sets ◽  
Binary Classifier ◽  
Pressure Signal ◽  
A New Technique

AbstractMonitoring a person’s nutritional consumption is costly and complex. To solve this problem a new technique is proposed to draw conclusions of a person’s food intake. The air pressure signal, recorded in the external acoustic meatus, is used to detect swallow and chew events. A portable device has been developed to record this pressure signal. Due to the constraint of running on a low-power microcontroller, real-time algorithms, used in pattern and speech recognition, were used to develop methods to automatically detect swallow and chew events. A binary classifier was trained by means of manually annotated data sets. Direct comparisons with state of the art technology and tests with several subjects are provided for evaluation purposes.

scholarly journals Stochastic clustering and pattern matching for real-time geosteering

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. ID13-ID24
Author(s):  
Mingqi Wu ◽  
Yinsen Miao ◽  
Neilkunal Panchal ◽  
Daniel R. Kowal ◽  
Marina Vannucci ◽  
...  
Keyword(s):  
Real Time ◽  
Performance Test ◽  
Synthetic Data ◽  
Optimization Procedure ◽  
Bayesian Optimization ◽  
Earth Model ◽  
Data Sets ◽  
Drilling Process ◽  
Pattern Similarity ◽  
Reliability Performance

We have developed a Bayesian statistical framework for quantitative geosteering in real time. Two types of contemporary geosteering approaches, model based and stratification based, are introduced. The latter is formulated as a Bayesian optimization procedure: The log from a pilot reference well is used as a stratigraphic signature of the geologic structure in a given region; the observed log sequence acquired along the wellbore is projected into the stratigraphic domain given a proposed earth model and directional survey; the pattern similarity between the converted log and the signature is measured by a correlation coefficient; then stochastic searching is performed on the space of all possible earth models to maximize the similarity under constraints of the prior understanding of the drilling process and target formation; finally, an inference is made based on the samples simulated from the posterior distribution using stochastic approximation Monte Carlo in which we extract the most likely earth model and the associated credible intervals as a quantified confidence indicator. We extensively test our method using synthetic and real geosteering data sets. Our method consistently achieves good performance on synthetic data sets with high correlations between the interpreted and the reference logs and provides similar interpretations as the geosteering geologists on four real wells. We also conduct a reliability performance test of the method on a benchmark set of 200 horizontal wells randomly sampled from the Permian Basin. Our Bayesian framework informs geologists with key drilling decisions in real time and helps them navigate the drilling bit into the target formation with confidence.

scholarly journals A stochastic space-time rainfall forecasting system for real time flow forecasting I: Development of MTB conditional rainfall scenario generator

2000 ◽  
Vol 4 (4) ◽  
pp. 603-615 ◽  
Author(s):  
D. Mellor ◽  
J. Sheffield ◽  
P. E. O'Connell ◽  
A. V. Metcalfe
Keyword(s):  
Real Time ◽  
Synthetic Data ◽  
Stochastic Modelling ◽  
Model Space ◽  
Space Time ◽  
Data Sets ◽  
Rainfall Forecasting ◽  
Radar Images ◽  
Time Flow ◽  
Deterministic Modelling

Abstract. The need for the development of a method for generating an ensemble of rainfall scenarios, which are conditioned on the observed rainfall, and its place in the HYREX programme is discussed. A review of stochastic models for rainfall, and rainfall forecasting techniques, is followed by a justification for the choice of the Modified Turning Bands (MTB) model in this context. This is a stochastic model of rainfall which is continuous over space and time, and which reproduces features of real rainfall fields at four distinct scales: raincells, cluster potential regions, rainbands and the overall outline of a storm at the synoptic scale. The model can be used to produce synthetic data sets, in the same format as data from a radar. An inversion procedure for inferring a construction of the MTB model which generates a given sequence of radar images is described. This procedure is used to generate an ensemble of future rainfall scenarios which are consistent with a currently observed storm. The combination of deterministic modelling at the large scales and stochastic modelling at smaller scales, within the MTB model, makes the system particularly suitable for short-term forecasts. As the lead time increases, so too does the variability across the set of generated scenarios. Keywords: MTB model, space-time rainfall field model, rainfall radar, HYREX, real-time flow forecasting

Crossline wavefield reconstruction from multicomponent streamer data: Part 2 — Joint interpolation and 3D up/down separation by generalized matching pursuit

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. WB69-WB85 ◽  
Author(s):  
Ali Özbek ◽  
Massimiliano Vassallo ◽  
Kemal Özdemir ◽  
Dirk-Jan van Manen ◽  
Kurt Eggenberger
Keyword(s):  
Particle Motion ◽  
Matching Pursuit ◽  
Synthetic Data ◽  
Basis Functions ◽  
Seismic Events ◽  
Data Sets ◽  
Optimal Basis ◽  
A New Technique ◽  
Simultaneous Interpolation ◽  
Streamer Data

Computation of the 3D upgoing/downgoing separated wavefield at any desired position within a marine streamer spread is enabled by multicomponent streamers that can measure the crossline and vertical components of water-particle motion in addition to the pressure. We introduce the concept of simultaneous interpolation and deghosting and describe a new technique, generalized matching pursuit (GMP), to achieve this. This method is based on the matching-pursuit technique and iteratively reconstructs the signal as a combination of optimal basis functions. In the GMP method, the basis functions describing the unknown 3D upgoing wavefield are filtered by appropriate forward ghost operators before being matched to the multicomponent measurements. As a data-dependent method, GMP can operate on data samples that are highly aliased in the crossline direction without relying on assumptions about seismic events such as linearity. The technique is naturally suitable for data with only a small number of samples that may be irregularly spaced. We demonstrate the efficacy and robustness of the GMP method on several synthetic data sets of increasing complexity and in the presence of noise.

What the 3Vs Acronym Didn't Put Into Perspective?

2019 ◽  
pp. 28-60
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Quality ◽  
Real Time ◽  
Data Sharing ◽  
Dynamic Processes ◽  
Data Sets ◽  
Multiple Sources ◽  
Evolving Data ◽  
Integrate Data

Data sizes have been growing exponentially within many companies. Facing this size of data—meta tagged piecemeal, produced in real-time, and arrives in continuous streams from multiple sources—analyzing the data to spot patterns and extract useful information is harder still. This includes the ever-changing landscape of data and their associated characteristics, evolving data analysis paradigms, challenges of computational infrastructure, data quality, complexity, and protection in addition to the data sharing and access, and—crucially—our ability to integrate data sets and their analysis toward an improved understanding. In this context, this second chapter will cover the issues and challenges that are hiding behind the 3Vs phenomenon. It gives a platform to complete the first chapter and proceed to different big data issues and challenges and how to tackle them in the dynamic processes.

DTI data analysis: application of fiber tracking to group averaged data sets

2010 ◽  
Vol 41 (01) ◽  
Author(s):  
HP Müller ◽  
A Unrath ◽  
A Riecker ◽  
AC Ludolph ◽  
J Kassubek
Keyword(s):  
Data Analysis ◽  
Fiber Tracking ◽  
Data Sets ◽  
Analysis Application
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