DOCSLIB.ORG

Summaries of the Fourth Annual JPL Airborne Geoscience Workshop October 25-29, 1993 Volume 1

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Summaries of the Fourth Annual JPL Airborne Geoscience Workshop October 25-29, 1993 Volume 1 JPL Publication 93-26, Vol. 1 Summaries of the Fourth Annual JPL Airborne Geoscience Workshop October 25-29, 1993 Volume 1. AVIRIS Workshop Robert O. Green Editor L_ October 25, 1993 National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California This publication was prepared by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. ABSTRACT This publication contains the summaries for the Fourth Annual JPL Airborne GeoscienceWorkshop, held in Washington, D. C. on October 25-29, 1993.The main workshop is divided into three smaller workshops as follows: The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on October 25-26. The summaries for this workshop appear in Volume 1. The Thermal Infrared Multispectral Scanner(TIMS) workshop, on October 27.The summaries for this workshop appear in Volume 2. The Airborne Synthetic Aperture Radar (AIRSAR) workshop, on October 28-29. The summaries for this workshop appear in Volume 3. iii FOREWORD In the text and the figure captions of some of the papers for the AVIRIS and AIRSAR Workshops, reference is made to color slides. A pocket containing a set of all the slides mentioned in those papers appears at the end of this volume. iv CONTENTS Volume 1: AVIRIS Workshop Use of Spectral Analogy to Evaluate Canopy Reflectance Sensitivity to Leaf Optical Property .................................................................................................... 1 Frdd&ic Baret, Vern C. Vanderbilt, Michael D. Steven, and Stephane Jacquemoud A Tool for Manual Endmember Selection and Spectral Unmixing ....................... 3 C. Ann Bateson and Brian Curtiss Lithologic Discrimination and Alteration Mapping From AVIRIS Data, Socorro, New Mexico .................................................................................................... 7 K. K. Beratan, N. DeLillo, A. Jacobson, R. Blom, and C. E. Chapin Automating Spectral Unmixing of AVIRIS Data Using Convex Geometry Concepts ......................................................................................................................... 11 Joseph W. Boardman AVIRIS Calibration Using the Cloud-Shadow Method ........................................... 15 K. L. Carder, P. Reinersman, and R. F. Chen Field Observations Using an AOTF Polarimetric Imaging Spectrometer. ............ 19 Li-Jen Cheng, Mike Hamilton, Colin Mahoney, and George Reyes Instantaneous Field of View and Spatial Sampling of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) ................................................... 23 Thomas G. Chrien and Robert 0. Green Airborne Visible/Infrared Imaging Spectrometer (AVIRIS): Recent Improvements to the Sensor ........................................................................................ 27 Thomas G. Chrien, Robert 0. Green, Charles M. Sarture, Christopher Chovit, Michael L. Eastwood, and Bjorn T. Eng Comparison of Three Methods for Materials Identification and Mapping with Imaging Spectroscopy ......................................................................................... 31 Roger N. Clark, Gregg Swayze, Joe Boardman, and Fred Kruse Comparison of Methods for Calibrating AVIRIS Data to Ground Reflectance ...................................................................................................................... 35 Roger N. Clark, Gregg Swayze, Kathy Heidebrecht, Alexander F. H. Goetz, and Robert 0. Green The U.S. Geological Survey, Digital Spectral Reflectance Library: Version 37 1:0.2 to 3.0 _'n ............................................................................................................... Roger N. Clark, Gregg A. Swayze, Trude V. V. King, Andrea J. Gallagher, and Wendy M. Calvin Application of a Two-Stream Radiative Transfer Model for Leaf Lignin and Cellulose Concentrations From Spectral Reflectance Measurements (Part 1) ............................................................................................................................. 39 James E. Conel, Jeannette van den Bosch, and Cindy I. Grove Application of a Two-Stream Radiative Transfer Model for Leaf Lignin and Cellulose Concentrations From Spectral Reflectance Measurements (Part ................................................................................... 2_ames"E;'Conel , Jeannette van den Bosch, and Cindy I. Grove .................... 45 Discrimination of Poorly Exposed Lithologies in AVIRIS Data ............................. 53 William H. Farrand and Joseph C. Harsanyi Spectral Decomposition of AVIRIS Data ................................................................... 57 Lisa Gaddis, Laurence Soderblom, Hugh Kieffer, Kris Becker, Jim Torson, and Kevin Mullins Remote Sensing of Smoke, Clouds, and Fire Using AVIRIS Data ......................... 61 Bo-Cai Gao, Yoram ]. Kaufman, and Robert O. Green Use of Data from the AVIRIS Onboard Calibrator ................................................... 65 Robert 0. Green Inflight Calibration of AVIRIS in 1992 and 1993 ...................................................... 69 Robert O. Green, James E. Conel, Mark Helmlinger, Jeannette van den Bosch, Chris Chovit, and Tom Chrien Estimation of Aerosol Optical Depth and Additional Atmospheric Parameters for the Calculation of Apparent Reflectance from Radiance Measured by the Airborne Visible/Infrared Imaging Spectrometer ..................... 73 Robert O. Green, James E. Conel, and Dar A. Roberts Use of the Airborne Visible/Infrared Imaging Spectrometer to Calibrate the Optical Sensor on board the Japanese Earth Resources Satellite-1 ...................................................................................................... 77 Robert O. Green, James E. Conel, Jeannette van den Bosch, and Masanobu Shimada A Proposed Update to the Solar Irradiance Spectrum Used in LOWTRAN and MODTRAN ............................................................................................................ 81 Robert O. Green and Bo-Cai Gao vi A Role for AVIRIS in the Landsat and Advanced Land Remote Sensing SystemProgram.............................................................................................................85 Robert O. Green and John J. Simmonds Estimating Dry Grass Residues Using Landscape Integration Analysis .............. 89 Quinn J. Hart, Susan L. Ustin, Lian Duan, and George Scheer Classification of High Dimensional Multispectral Image Data .............................. 93 Joseph P. Hoffbeck and David A. Landgrebe Simulation of Landsat Thematic Mapper Imagery Using AVIRIS Hyperspectral Imagery ................................................................................................. 97 Linda S. Kalman and Gerard R. Peltzer The Effects of AVIRIS Atmospheric Calibration Methodology on Identification and Quantitative Mapping of Surface Mineralogy, Drum Mtns., Utah ......................................................................................................... 101 Fred A. Kruse and John L. Dwyer AVIRIS Spectra Correlated With the Chlorophyll Concentration of a Forest Canopy ............................................................................................................................ 105 John Kupiec, Geoffrey M. Smith, and Paul J. Curran AVIRIS and TIMS Data Processing and Distribution at the Land Processes Distributed Active Archive Center ............................................................................. 109 G. R. Mah and J. Myers Measurements of Canopy Chemistry With 1992 AVIRIS Data at Blackhawk Island and Harvard Forest. ...................................................................... 113 Mary E. Martin and John D. Aber Classification of the LCVF AVIRIS Test Site With a Kohonen Artificial Neural Network ............................................................................................ 117 Erzse'bet Merdnyi, Robert B. Singer, and William H. Farrand Extraction of Auxiliary Data from AVIRIS Distribution Tape for Spectral, Radiometric, and Geometric Quality Assessment. ................................................... 121 Peter Meyer, Robert 0. Green, and Thomas G. Chrien Preprocessing: Geocoding of AVIRIS Data using Navigation, Engineering, DEM, and Radar Tracking System Data .................................................................... 127 Peter Meyer, Steven A. Larson, Earl G. Hansen, and Klaus I. Itten vii The Data Facility of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) .......................................................................................................................... 133 Pia J. Nielsen, Robert O. Green, Alex T. Murray, Bjorn T. Eng, H. Ian Novack, Manuel Solis, and Martin Olah Mapping of the Ronda Peridotite Massif (Spain) From AVIRIS Spectro- Imaging Survey: A First Attempt. ............................................................................... 137 P. C. Pinet, S. Chabrillat, and G. Ceuleneer Spectral Variations in a Collection of AVIRIS Imagery ........................................... 141 John C. Price Monitoring Land Use and Degradation Using Satellite and Airborne Data ................................................................................................................ 145 Terrill W. Ray, Thomas G. Farr, Ronald G. Blom, and Robert E. Crippen The Red Edge in Arid Region Vegetation: 340-1060 nm Spectra ........................... 149 Terrill W. Ray, Bruce C. Murray, A. Chehbouni,
Load more

Recommended publications
  • Phase Relations of Purkinje Cells in the Rabbit Flocculus During Compensatory Eye Movements
    JOURNALOFNEUROPHYSIOLOGY Vol. 74, No. 5, November 1995. Printed in U.S.A. Phase Relations of Purkinje Cells in the Rabbit Flocculus During Compensatory Eye Movements C. I. DE ZEEUW, D. R. WYLIE, J. S. STAHL, AND J. I. SIMPSON Department of Physiology and Neuroscience, New York University Medical Center, New York 1OOM; and Department of Anatomy, Erasmus University Rotterdam, 3000 DR Rotterdam, Postbus 1738, The Netherlands SUMMARY AND CONCLUSIONS 17 cases (14%) showed CS modulation. In the majority (15 of 1. Purkinje cells in the rabbit flocculus that respond best to 17) of these cases, the CS activity increased with contralateral rotation about the vertical axis (VA) project to flocculus-receiving head rotation; these modulations occurred predominantly at the neurons (FRNs) in the medial vestibular nucleus. During sinusoi- higher stimulus velocities. dal rotation, the phase of FRNs leads that of medial vestibular 7. On the basis of the finding that FRNs of the medial vestibular nucleus neurons not receiving floccular inhibition (non-FRNs) . If nucleus lead non-FRNs, we predicted that floccular VA Purkinje the FRN phase lead is produced by signals from the ~~OCCU~US,then cells would in turn lead FRNs. This prediction is confirmed in the the Purkinje cells should functionally lead the FRNs. In the present present study. The data are therefore consistent with the hypothesis study we recorded from VA Purkinje cells in the flocculi of awake, that the phase-leading characteristics of FRN modulation could pigmented rabbits during compensatory eye movements to deter- come about by summation of VA Purkinje cell activity with that mine whether Purkinje cells have the appropriate firing rate phases of cells whose phase would otherwise be identical to that of non- to explain the phase-leading characteristics of the FRNs.
    [Show full text]
  • Dark Data and Its Future Prospects
    International Journal of Engineering Technology Science and Research IJETSR www.ijetsr.com ISSN 2394 – 3386 Volume 5, Issue 1 January 2018 Dark Data and Its Future Prospects Sarang Saxena St. Joseph’s Degree & Pg College ABSTRACT Dark data is data which is acquired through various computer network operations but not used in any manner to derive insights or for decision making. The ability of an organisation to collect data can exceed the throughput at which it can analyse the data. In some cases the organisation may not even be aware that the data is being collected. It’s data that is ever present, unknown and unmanaged. For example, a company may collect data on how users use its products, internal statistics about software development processes, and website visits. However, a large portion of the collected data is never even analysed. In recent times, there are certain businesses which have realised the importance of such data and are working towards finding techniques, methods, processes and developing software so that such data is properly utilized Through this paper I’m trying to find the scope and importance of dark data, its importance in the future, its implications on the smaller firms and organisations in need of relevant and accurate data which is difficult to find but is hoarded by giant firms who do not intend to reveal such information as a part of their practice or have no idea that the data even exists, the amount of data generatedby organisations and the percentage of which is actually utilised, the businesses formed around mining dark data and analysing it, the effects of dark data, the dangers and limitations of dark data.
    [Show full text]
  • Dark Data As the New Challenge for Big Data Science and the Introduction of the Scientific Data Officer
    Delft University of Technology Dark Data as the New Challenge for Big Data Science and the Introduction of the Scientific Data Officer Schembera, Björn; Duran, Juan Manuel DOI 10.1007/s13347-019-00346-x Publication date 2019 Document Version Final published version Published in Philosophy & Technology Citation (APA) Schembera, B., & Duran, J. M. (2019). Dark Data as the New Challenge for Big Data Science and the Introduction of the Scientific Data Officer. Philosophy & Technology, 33(1), 93-115. https://doi.org/10.1007/s13347-019-00346-x Important note To cite this publication, please use the final published version (if applicable). Please check the document version above. Copyright Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons. Takedown policy Please contact us and provide details if you believe this document breaches copyrights. We will remove access to the work immediately and investigate your claim. This work is downloaded from Delft University of Technology. For technical reasons the number of authors shown on this cover page is limited to a maximum of 10. Philosophy & Technology https://doi.org/10.1007/s13347-019-00346-x RESEARCH ARTICLE Dark Data as the New Challenge for Big Data Science and the Introduction of the Scientific Data Officer Bjorn¨ Schembera1 · Juan M. Duran´ 2 Received: 25 June 2018 / Accepted: 1 March 2019 / © The Author(s) 2019 Abstract Many studies in big data focus on the uses of data available to researchers, leaving without treatment data that is on the servers but of which researchers are unaware.
    [Show full text]
  • An Intelligent Navigator for Large-Scale Dark Structured Data
    CognitiveDB: An Intelligent Navigator for Large-scale Dark Structured Data Michael Gubanov, Manju Priya, Maksim Podkorytov Department of Computer Science University of Texas at San Antonio {mikhail.gubanov, maksim.podkorytov}@utsa.edu, [email protected] ABSTRACT Consider a data scientist who is trying to predict hard Volume and Variety of Big data [Stonebraker, 2012] are sig- cider sales in the regions of interest to be able to restock nificant impediments for anyone who wants to quickly un- accordingly for the next season. Since consumption of hard derstand what information is inside a large-scale dataset. cider is correlated with weather, access to the complete weather Such data is often informally referred to as 'dark' due to data per region would improve prediction accuracy. How- the difficulties of understanding its contents. For example, ever, such a dataset is not available internally, hence, the there are millions of structured tables available on the Web, data scientist is considering enriching existing data with de- but finding a specific table or summarizing all Web tables tailed weather data from an external data source (e.g. the to understand what information is available is infeasible or Web). However, the cost to do ETL (Extract-Transform- very difficult because of the scale involved. Load) from a large-scale external data source like the Web Here we present and demonstrate CognitiveDB, an intel- in order to integrate weather data is expected to be high, ligent cognitive data management system that can quickly therefore the data scientist never acquires the missing data summarize the contents of an unexplored large-scale struc- pieces and does not take weather into account.
    [Show full text]
  • An Epidemiology of Big Data
    Syracuse University SURFACE Dissertations - ALL SURFACE May 2014 An Epidemiology of Big Data John Mark Young Syracuse University Follow this and additional works at: https://surface.syr.edu/etd Part of the Social and Behavioral Sciences Commons Recommended Citation Young, John Mark, "An Epidemiology of Big Data" (2014). Dissertations - ALL. 105. https://surface.syr.edu/etd/105 This Dissertation is brought to you for free and open access by the SURFACE at SURFACE. It has been accepted for inclusion in Dissertations - ALL by an authorized administrator of SURFACE. For more information, please contact [email protected]. ABSTRACT Federal legislation designed to transform the U.S. healthcare system and the emergence of mobile technology are among the common drivers that have contributed to a data explosion, with industry analysts and stakeholders proclaiming this decade the big data decade in healthcare (Horowitz, 2012). But a precise definition of big data is hazy (Dumbill, 2013). Instead, the healthcare industry mainly relies on metaphors, buzzwords, and slogans that fail to provide information about big data’s content, value, or purposes for existence (Burns, 2011). Bollier and Firestone (2010) even suggests “big data does not really exist in healthcare” (p. 29). While federal policymakers and other healthcare stakeholders struggle with the adoption of Meaningful Use Standards, International Classification of Diseases-10 (ICD-10), and electronic health record interoperability standards, big data in healthcare remains a widely misunderstood
    [Show full text]
  • Neuronal Couplings Between Retinal Ganglion Cells Inferred by Efficient Inverse Statistical Physics Methods
    Supporting Information Appendix for Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods S. Cocco, S. Leibler, R. Monasson Table of contents: 1. Inference of couplings and of their accuracy within the inverse Ising model (page 2) 2. Limitations of the Ising model: higher-order correlations and couplings (page 7) 3. Algorithm for the Inverse Integrate-and-Fire Problem (page 11) 4. On Cross-correlograms: Analysis of Data, Relationship with Couplings, and Simulations (page 14) 5. Correspondence between Integrate-and-Fire and Ising Inverse Models (page 23) 6. Spatial features of the inferred couplings (page 28) 7. On states and the large-N limit in the inverse Ising model (page 36) 8. Bibliography and footnotes (page 39) Supporting Information Appendix, Section 1: Inference of couplings and of their accuracy within the inverse Ising model I. INFERENCE AND MINIMIZATION OF THE ISING ENTROPY A multi-electrode recording provides the firing times of N recorded cells during a time interval of duration T . In the Ising inverse approach the recording interval is divided into time windows (time-bins) of width ∆t and the data are encoded in T/∆t configurations s = (s1,s2,...,sN ) of the N binary variables si, (i = 1,...,N) called spins (by τ analogy with magnetic systems described by the Ising model). The value of each spin variable is: si = 1, if the cell i is active in the time-bin τ (τ = 1,...,B = T/∆t), si = 0 otherwise. Let pi be the probability that the cell i is active in a given time-bin, and pij be the joint probability that the cells i and j are both active in the same bin.
    [Show full text]
  • IDC Futurescape: Worldwide Data, Integration, and Analytics 2020 Predictions
    IDC FutureScape IDC FutureScape: Worldwide Data, Integration, and Analytics 2020 Predictions Dan Vesset Carl W. Olofson Stewart Bond Maureen Fleming Marci Maddox Lynne Schneider Neil Ward-Dutton Christopher Lee Marshall Jennifer Hamel Shintaro Kusachi Chandana Gopal IDC FUTURESCAPE FIGURE FIGURE 1 IDC FutureScape: Worldwide Data, Integration, and Analytics 2020 Top 10 Predictions Note: Marker number refers only to the order the prediction appears in the document and does not indicate rank or importance, unless otherwise noted in the Executive Summary. Source: IDC, 2019 October 2019, IDC #US44802519 EXECUTIVE SUMMARY When almost 100 CEOs were asked by IDC in a study in August 2019 about the importance of various strategic areas to their organization over the next five years, fully 80% of respondents (second only to focus on digital trust) mentioned data (or more specifically, using data in advanced decision models to affect performance and competitive advantage). And these executives are not simply paying homage to the current trendy topics. According to IDC's Worldwide Big Data and Analytics Tracker and Spending Guide, enterprises worldwide spent $169 billion on BDA software, hardware, and services in 2018. However, most enterprises don't know if they are getting value or how much value they are getting out of data and analytics. Most are not tapping into dark data that is created by sits unused nor are most enterprises even attempting to get a return on their data asset externally. Most have subpar data literacy and incomplete data intelligence. Many are planning under the assumptions of huge potential benefits of artificial intelligence (AI), but lack foundational data, integration, and analytics capabilities that are prerequisites for moving along the AI-based automation evolution path.
    [Show full text]
  • Population-Based Linkage of Big Data in Dental Research
    International Journal of Environmental Research and Public Health Review Population-Based Linkage of Big Data in Dental Research Tim Joda 1,*, Tuomas Waltimo 2, Christiane Pauli-Magnus 3, Nicole Probst-Hensch 4 and Nicola U. Zitzmann 1 1 Department of Reconstructive Dentistry, University Center for Dental Medicine Basel, 4056 Basel, Switzerland; [email protected] 2 Department of Oral Health & Medicine Dentistry, University Center for Dental Medicine Basel, 4056 Basel, Switzerland; [email protected] 3 Department of Clinical Research & Clinical Trial Unit, Faculty of Medicine, University of Basel, 4031 Basel, Switzerland; [email protected] 4 Department of Epidemiology & Public Health, Swiss Tropical & Public Health Institute Basel, University of Basel, 4051 Basel, Switzerland; [email protected] * Correspondence: [email protected]; Tel.: +4161-267-2631 Received: 19 October 2018; Accepted: 23 October 2018; Published: 25 October 2018 Abstract: Population-based linkage of patient-level information opens new strategies for dental research to identify unknown correlations of diseases, prognostic factors, novel treatment concepts and evaluate healthcare systems. As clinical trials have become more complex and inefficient, register-based controlled (clinical) trials (RC(C)T) are a promising approach in dental research. RC(C)Ts provide comprehensive information on hard-to-reach populations, allow observations with minimal loss to follow-up, but require large sample sizes with generating high level of external validity. Collecting data is only valuable if this is done systematically according to harmonized and inter-linkable standards involving a universally accepted general patient consent. Secure data anonymization is crucial, but potential re-identification of individuals poses several challenges.
    [Show full text]
  • Industrial Statistics
    Industrial Statistics Ronald Christensen and Aparna V. Huzurbazar Department of Mathematics and Statistics University of New Mexico Copyright c 2002, 2021 v To Walt and Ed Contents Preface xiii 0.1 Standards xiii 0.2 Six-Sigma xiii 0.3 Lean xiv 0.4 This Book xiv 0.4.1 Computing xv 1 Introduction1 1.1 Four Principles for Quality1 1.1.1 Institute and Maintain Leadership for Quality Improvement2 1.1.2 Create Cooperation3 1.1.3 Train, Retrain, and Educate5 1.1.4 Insist on Action5 1.2 Some Technical Matters5 1.3 The Shewhart Cycle: PDSA6 1.4 Benchmarking7 1.5 Exercises7 2 Basic Tools 9 2.1 Data Collection9 2.2 Pareto and Other Charts 11 2.3 Histograms 13 2.3.1 Stem and Leaf Displays 16 2.3.2 Dot Plots 18 2.4 Box Plots 19 2.5 Cause and Effect Diagrams 19 2.6 Flow Charts 20 3 Probability 23 3.1 Introduction 23 3.2 Something about counting 25 3.3 Working with Random Variables 26 3.3.1 Probability Distributions, Expected Values 27 3.3.2 Independence, Covariance, Correlation 28 3.3.3 Expected values and variances for sample means 30 3.4 Binomial Distribution 32 3.5 Poisson distribution 34 3.6 Normal Distribution 34 vii viii CONTENTS 4 Control Charts 37 4.1 Individuals Chart 38 4.2 Means Charts and Dispersion Charts 40 4.2.1 Process Capability 46 4.2.1.1 Six-Sigma 47 4.3 Attribute Charts 48 4.4 Control Chart Summary 53 4.5 Average Run Lengths 54 4.6 Discussion 55 4.7 Testing Mean Shifts from a Target 56 4.7.1 Exponentially Weighted Moving Average Charts 56 4.7.1.1 Derivations of results 58 4.7.2 CUSUM charts 60 4.8 Computing 61 4.8.1 Minitab 61
    [Show full text]
  • Anomalies and Error Sources
    NICMOS 4 Anomalies and Error Sources In This Chapter... NICMOS Dark Current and Bias / 4-4 Bars / 4-19 Detector Nonlinearity Issues / 4-20 Flatfielding / 4-22 Pixel Defects and Bad Imaging Regions / 4-24 Effects of Overexposure / 4-28 Cosmic Rays of Unusual Size / 4-34 Scattered Earthlight / 4-36 The previous chapter described the basic stages of NICMOS pipeline processing. As with any instrument, however, high quality data reduction does not end with the standard pipeline processing. NICMOS data are subject to a variety of anomalies, artifacts, and instabilities which complicate the task of data reduction and analysis. Most of these can be handled with careful post-facto recalibration and processing, which usually yield excellent, scientific grade data reductions. Careful NICMOS data processing usually requires a certain amount of "hands-on" interaction from the user, who must inspect for data anomalies and treat them accordingly during the reduction procedures. This chapter describes the most common problems affecting NICMOS data at the level of frame-by-frame processing. In some cases, recognizing and treating problems with NICMOS data requires a moderately in-depth understanding of the details of instrumental behavior; problems with dark and bias subtraction are a good example. Where appropriate, this chapter offers a fairly detailed discussion of the relevant workings of the instrument, but the reader should consult the NICMOS Instrument Handbook for further details. Each section of this chapter deals with a different aspect of NICMOS data processing, roughly following the order of the processing steps in the standard STSDAS pipeline. Various potential problems are described and illustrated.
    [Show full text]
  • Visual Analytics on Biomedical Dark Data
    Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 October 2020 Visual Analytics on Biomedical Dark Data Shashwat Aggarwal a, Ramesh Singh b a University of Delhi a National Informatics Center 1. INTRODUCTION In today’s data centralized world, the practice of data visualization has become an indispensable tool in numerous domains such as Research, Marketing, Journalism, Biology etc. Data visualization is the art of efficiently organizing and presenting data in a graphically appealing format. It speeds up the process of decision making and pattern recognition, thereby enabling decision makers to make informed decisions. With the rise in technology, the data has been exploding exponentially, and the world’s scientific knowledge is accessible with ease. There is an enormous amount of data available in the form of scientific articles, government reports, natural language, and images that in total contributes to around 80% of overall data generated as shown in an excerpt from The Digital Universe [1] in Figure 1. However, most of the data lack structure and cannot be easily categorized and imported into regular databases. This type of data is often termed as Dark Data. Data visualization techniques proffer a potential solution to overcome the problem of handling and analyzing overwhelming amounts of such information. It enables the decision maker to look at data differently and more imaginatively. It promotes creative data exploration by allowing quick comprehension of information, the discovery of emerging trends, identification of relationships and patterns etc. Figure 1. Worldwide growth of corporate data categorized by structured and unstructured/dark data over the past decade. 1 © 2020 by the author(s).
    [Show full text]
  • Data Science by Analyticbridge
    Data Science by AnalyticBridge Vincent Granville, Ph.D. Founder, Data Wizard, Managing Partner www.AnalyticBridge.com - www.DataScienceCentral.com Download the most recent version at http://bit.ly/oB0zxn This version was released on 01/03/2013 (123 pages – Gartner’s contribution updated) Previous release was on 06/05/2012 (123 pages) Published by AnalyticBridge. Connect with the Author: . AnalyticBridge – www.analyticbridge.com/profile/VincentGranville . LinkedIn – www.linkedin.com/in/vincentg . Facebook – www.facebook.com/analyticbridge . Twitter – www.twitter.com/analyticbridge . GooglePlus – https://plus.google.com/116460988472927384512 . Quora – www.quora.com/Vincent-Granville 1 Content Introduction Part I - Data Science Recipes 1. New random number generator: simple, strong and fast 2. Lifetime value of an e-mail blast: much longer than you think 3. Two great ideas to create a much better search engine 4. Identifying the number of clusters: finally a solution 5. Online advertising: a solution to optimize ad relevancy 6. Example of architecture for AaaS (Analytics as a Service) 7. Why and how to build a data dictionary for big data sets 8. Hidden decision trees: a modern scoring methodology 9. Scorecards: Logistic, Ridge and Logic Regression 10. Iterative Algorithm for Linear Regression 11. Approximate Solutions to Linear Regression Problems 12. Theorems for Traders 13. Preserving metric and score consistency over time and across clients 14. Advertising: reach and frequency mathematical formulas 15. Real Life Example of Text Mining to Detect Fraudulent Buyers 16. Discount optimization problem in retail analytics 17. Sales forecasts: how to improve accuracy while simplifying models? 18. How could Amazon increase sales by redefining relevancy? 19.
    [Show full text]