DOCSLIB.ORG

Project Football) Aaron Luo, Shravan Byra Michael Rosenston, Ashwin John

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Project Football) Aaron Luo, Shravan Byra Michael Rosenston, Ashwin John Project Basketball (Formerly Project Football) Aaron Luo, Shravan Byra Michael Rosenston, Ashwin John Abstract: Our project seeks to create a convenient and easy to interpret Basketball statistics as a specialized search engine. A user would enter the names of two NBA players as the query. They would then be shown a page that contains pictures of each player, a table of their statistics, relevant headlines, and a radar overlay chart comparing their key statistics. This radar chart would allow fans and analysts to objectively choose the better player simply by checking which player’s overlay covered the most area on the chart. Our tool eliminates the uncertainty of visually comparing multiple numbers by turning all the key statistics into an easy to read chart. Introduction: Basketball fans, reporters, coaches, players, and executives often like to compare the statistics of two players to determine who is superior. Current statistics tables simply give the user a table with the statistics of two players side by side. This data alone makes it challenging to objectively choose the better player in most situations. Eyeballing and weighting the comparisons of multiple statistical categories is difficult and often open to the influence of fan bias. It is hard to keep track of who is better and in what area and by how much. Our tool seeks to remedy this issue, by providing basketball enthusiasts with an easy to read visual comparison. A radar chart lets the user add points around the circle. The interior circles represent increasing measurements with the outermost circle being the maximum. Data is the filled in to extend toward the points. It is essentially a multi axis graph. Each point on the outside is a different basketball statistic. The two different colored areas represent two players. The player with the larger overall player would often be judged to be the better player. A player whose area extends more extremely toward one category would be judged as being better in that statistic. When a user of our tool enters two player names we deliver them one of these charts comparing the two players they requested. Additionally, we return two images of each player. These images are located with an image web crawler that we designed for this purpose. Lastly, we use an API or crawler to pull the statics of the two players and display it under our chart. This way the user receives the old statistics tables they are used to as well as our radar chart. The user then can use our chart to make a quick judgment and if they so choose explore the number more in depth on their own. If applicable, recent headlines of players are also pulled and displayed. Related Work: Basketball-reference.com contains a plethora of basketball statistics for teams and players both past and present. It also provides numbers on the performance of coaches as well as outcomes and box scores of every NBA game ever played. It however does not feature a visual tool to easily compare players. At espn.com/nba, users can look up players, view their statistics, see a photograph of them, and read the latest news on that player. What they can’t do is compare two players with an easily readable graph. NBA.com offers similar features as ESPN, but still no easy way to visually compare two players. Our tool sets itself apart, by allowing users to visually compare two players with a radar chart. With a high number of statistics attributed to each player, it is very difficult to keep track of which player has the best numbers. Most related sites display a numeric comparison of players, but without our radar graph to help interpret the data, it is very difficult to see at first glance who the superior player is. Our tool solves this problem while others fail. Problem Definition: We look to solve the problem of comparing players easily and speedily. We solve this by taking a query of two players from the user and display to them the relevant information in a timely fashion. The input to our system is a query from the user; it consists of two NBA players. Our problem is to then retrieve their statistics using an API, information extractor, or crawler, crawl the web for images, make a radar chart comparing them retrieve relative headlines, and display all of this data in an aesthetically pleasing manner. Returning our output in a visually pleasing and timely fashion is essential. If we take too long, users will become unhappy and not use our product. If the data is display in a disorganized manner, it will be hard to read and interpret. This would reduce the utility of our tool. Methods: The back end was written mainly in python; it consisted of parsing, API calls, crawlers (Scrappy) and text extraction/interpretation. The API we used was limited in its ability to retrieve data: it could only retrieve statistics if the players is currently on a known roster. So, upon receiving input names, the first thing we did run crawlers our crawlers (common seed sites were Google, ESPN.com, NBA.com, sports.yahoo.com, etc). The crawler would find the player’s current team if he is an active player, and retrieve statistics (using information extraction and pattern matching) if the player is an inactive (retired) player. This method also allows for typos in the name as well as up to date data (since the API pulls the most current data to the minute). For inactive players, those statistics are immediately used to create the radar chart, and the graph, statistics, and relative headlines (if any) are sent directly to the front end. For active players, a series of API calls will be used to determine and parse statistics before being handled the same way. Crawler4j is an open source Java Crawler that was used to crawl websites to select photographs to use in our search. The original crawler was modified to find “patterns” on the page which satisfy (.png, .jpg .tff etc.). The Crawler controller was also modified to take input of a player name to start crawling the specific player site to pull images. Since the starting crawl page was Wikipedia.org, the crawler was able to specify what size images it wanted to download. Other pages such as Google images wouldn’t have been able to provide such a precise image size. For the front end we used AngularJS, so that we could use a more MVC design for the website. There aren't really any page reloads because of angular. Index.html is initially loaded and then angular watches the url for changes, when a url changes, it will then load the new view depending on the url extension. So the new view that will be loaded is copied to the <div ng- view></div> tag in index.html, and the appropriate model and controller are loaded with it. The search button is linked to a function in the angular "controller" this function is called every time the button is clicked. The function then calls another function that sends an http post request to the back end flask server. The flask server then sees the post request from that specified url. For instance, if the front end sends a request to "/mySearch", then we add a url rule to the flask server to accept post requests from "/mySearch" and we can then use the information from the post in our search functions. Evaluation/Sample Results: After overcoming some developmental challenges, our tool finally achieved functionality. Sample results on players usually result positively. However, the text extractor robot is not perfect. Some historical players would have missing stats or crazy outputs. I have hardcoded an acceptable range of numbers for each statistic to fall under. If their statistic doesn’t comply, the stat is tossed out. This error could come from two places: 1) certain statistics were not tracked before a certain time, therefore the player’s time/era/age plays a factor; 2) format of statistics was too difficult for the extractor to understand. Tests on misspelled names work just as well (with help from Google). The graph is easily interpretable and helps basketball enthusiasts decide the better player. The statistical tables can be seen clearly and are well organized. The pictures of the players are perfectly sized as to not dominate the page, but still give the user a feel for the players whose performance they are comparing. If required, users also have the option to inspect statistics numerically. This allows for more detailed analysis and versatility. If only one player is selected, position averages are hardcoded in and the player is “compared” to the average player of his position. Conclusions and Future Work: Our tool takes a query from the user in the form of two basketball players. It then returns to them photos of the players, tables of their statistics, relative headlines, and most importantly a radar chart comparing their performance. Our tool is unique and superior to other basketball references when it comes to comparing players. We allow users to easily view a radar chart of two players. This method is quick and objective. This tool if employed by a highly trafficked sports website, could be of great use to basketball fans. Further expansion of our tool could be to allow the comparison of three or more players. The challenge of this is that it makes the graph harder to read with each overlay. Additionally, it requires the sending and receiving of more data.
Load more

Recommended publications
  • Master Thesis
    Structural Complexity of Manufacturing Systems Layout by Valeria Betzabe Espinoza Vega A Thesis Submitted to the Faculty of Graduate Studies through Industrial and Manufacturing Systems Engineering in Partial Fulfillment of the Requirements for the Degree of Master of Applied Science at the University of Windsor Windsor, Ontario, Canada © 2012 Valeria Espinoza Library and Archives Bibliothèque et Canada Archives Canada Published Heritage Direction du Branch Patrimoine de l'édition 395 Wellington Street 395, rue Wellington Ottawa ON K1A 0N4 Ottawa ON K1A 0N4 Canada Canada Your file Votre référence ISBN: 978-0-494-76377-3 Our file Notre référence ISBN: 978-0-494-76377-3 NOTICE: AVIS: The author has granted a non- L'auteur a accordé une licence non exclusive exclusive license allowing Library and permettant à la Bibliothèque et Archives Archives Canada to reproduce, Canada de reproduire, publier, archiver, publish, archive, preserve, conserve, sauvegarder, conserver, transmettre au public communicate to the public by par télécommunication ou par l'Internet, prêter, telecommunication or on the Internet, distribuer et vendre des thèses partout dans le loan, distrbute and sell theses monde, à des fins commerciales ou autres, sur worldwide, for commercial or non- support microforme, papier, électronique et/ou commercial purposes, in microform, autres formats. paper, electronic and/or any other formats. The author retains copyright L'auteur conserve la propriété du droit d'auteur ownership and moral rights in this et des droits moraux qui protege cette thèse. Ni thesis. Neither the thesis nor la thèse ni des extraits substantiels de celle-ci substantial extracts from it may be ne doivent être imprimés ou autrement printed or otherwise reproduced reproduits sans son autorisation.
    [Show full text]
  • Multivariate Process Control with Radar Charts
    SEMATECH 1996 Statistical Methods Symposium, San Antonio Multivariate Process Control with Radar Charts Dave Trindade, Ph.D. Senior Fellow, Applied Statistics AMD, Sunnyvale, CA Introduction ! Multivariate statistical process control ! Monitoring with separate X control charts ! Hotelling T2 statistic ! Radar or web plots ! Microsoft EXCEL capabilities – Matrix manipulation – Graphical Outline of Presentation ! Example of process with two quality characteristics per sample ! Univariate and multivariate considerations ! Calculations and graphical analysis in EXCEL ! Implications Vocabulary ! Multivariate ! Matrix representation ! Hotelling T2 statistic ! Radar or web plots ! Subgroups Vs. individual observations Multivariate SPC ! Consider a process in which two quality characteristics (p = 2) are measured on each of four samples (n = 4) within a subgroup. ! Data on twenty subgroups are available (k = 20) ! Data is from Thomas P. Ryan, Statistical Methods for Quality Improvement Sample Data for Each Subgroup Subgroup Number First Variable X Second Variable X 1 7284794923302810 2 5687334214318 9 3 5573226013226 16 4 44 80 54 74 9 28 15 25 5 9726485836101415 6 8389916230353618 7 4766535812181416 8 8850846931113019 9 5747414614108 10 10 26 39 52 48 7 11 35 30 11 46 27 63 34 10 8 19 9 12 49 62 78 87 11 20 27 31 13 71 63 82 55 22 16 31 15 14 71 58 69 70 21 19 17 20 15 67 69 70 94 18 19 18 35 16 55 63 72 49 15 16 20 12 17 49 51 55 76 13 14 16 26 18 72 80 61 59 22 28 18 17 19 61 74 62 57 19 20 16 14 20 35 38 41 46 10 11 13 16 Multivariate observations are (72,23), (84,30), …, (46, 16).
    [Show full text]
  • Statistical Analysis Plan Revisions Version 1.0 – 16 October 2017
    Risankizumab (ABBV-066) M16-002 (BI 1311.5) – Statistical Analysis Plan Revisions Version 1.0 – 16 October 2017 3.0 Introduction This document is an explanation of differences between the analysis performed after final database lock and those detailed in the SAP. The changes include: ● Section 6.4: Updates to the visit window for mTSS. Rationale: Updated window more accurately encompasses observations appropriate to label as baseline. ● Section 6.5: mTSS and PsAMRIS removed from 6.5 Missing Data Handling. Rationale: No subject level imputation will be performed for continuous imaging data. ● Section 7.1: Removed statistical testing between placebo arm and the mixed two arms at baseline. Rationale: No statistical testing for baseline characteristics. ● Section 9.0: Planned number of visits when injection planned has been capped at 5. Rationale: No subject should have more than 5 planned visits. ● Section 10.1: Table 12 has been updated. Rationale: Some efficacy endpoints have been added and some analyses have changed. ● Section 10.6 List of further efficacy endpoints has been updated. Rationale: Some efficacy endpoints have been added and some analyses have changed ● Section 10.9.10: The addition of a PASI LOCF sensitivity analysis. Rationale: Sensitivity analysis added due to the presence of PASI missing data. ● Section 10.9.12: Updates to what defines a dactylic digit. Rationale: Added a missing component of the definition of dactylitis. ● Section 10.9.20: Updates to the scoring and missing data rules for mTSS. Rationale: Two separate methods of analysis will be performed for mTSS. 2 Risankizumab (ABBV-066) M16-002 (BI 1311.5) – Statistical Analysis Plan Revisions Version 1.0 – 16 October 2017 ● Section 10.9.21: Updated PsAMRIS analysis to be descriptive statistics only.
    [Show full text]
  • Jeong HJ, Kim M, an EA, Et Al. a Strategy to Develop Tailored Patient Safety Culture Improvement Programs with Latent Class Analysis Method
    Biometrics & Biostatistics International Journal Research Article Open Access A strategy to develop tailored patient safety culture improvement programs with latent class analysis method Abstract Volume 2 Issue 2 - 2015 As patient safety can be built only on the bedrock of safety culture, many hospitals Heon-Jae Jeong,1 Minji Kim,2 EunAe An,3 So have designed and implemented their own safety culture improvement programs. 4 4 Although carefully tailored clinical area specific programs would be most effective, Yeon Kim, ByungJoo Song 1Department of Health Policy and Management, Johns Hopkins lack of resources precludes this. This study applied a latent class analysis method Bloomberg School of Public Health, Johns Hopkins University, and classified 72 clinical areas in a large tertiary hospital into four classes according Baltimore, USA to their score patterns in the six domains of the Korean version of Safety Attitudes 2Annenberg School for Communication, University of Questionnaire (SAQ-K). Bayesian information criterion (BIC) and bootstrapped Pennsylvania, USA likelihood ratio test (BLRT) were used in deciding the number of classes. We named 3Leadership Development Division, The Catholic Education the best class estimate in each of the SAQ-K domains the ‘current maxima,’ the goals Foundation, Korea of which other classes aim to improve. Because the job satisfaction domain cannot be 4Performance Improvement Team, Seoul St. Mary’s Hospital, The directly improved, we excluded it from the goal setting process. One class dominated Catholic University of Korea, Korea the other three in most domains. The difference between the best class and others, the ‘room for change,’ can be used in deciding which clinical areas should be focused first Correspondence: Heon-Jae Jeong, Department of Health and how much resources should be invested in developing safety culture improvement Policy and Management, Johns Hopkins Bloomberg School of programs.
    [Show full text]
  • Creating Charts and Graphs Presenting Information Visually Copyright
    Calc Guide Chapter 3 Creating Charts and Graphs Presenting information visually Copyright This document is Copyright © 2005–2013 by its contributors as listed below. You may distribute it and/or modify it under the terms of either the GNU General Public License (http://www.gnu.org/licenses/gpl.html), version 3 or later, or the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), version 3.0 or later. All trademarks within this guide belong to their legitimate owners. Contributors Barbara Duprey Christian Chenal Pierre-Yves Samyn Jean Hollis Weber Laurent Balland-Poirier Shelagh Manton John A Smith Philippe Clément Peter Schofield Feedback Please direct any comments or suggestions about this document to: [email protected] Acknowledgments This chapter is based on Chapter 3 of the OpenOffice.org 3.3 Calc Guide. The contributors to that chapter are: Richard Barnes John Kane Peter Kupfer Shelagh Manton Alexandre Martins Anthony Petrillo Sowbhagya Sundaresan Jean Hollis Weber Linda Worthington Ingrid Halama Publication date and software version Published 8 December 2013. Based on LibreOffice 4.1. Note for Mac users Some keystrokes and menu items are different on a Mac from those used in Windows and Linux. The table below gives some common substitutions for the instructions in this chapter. For a more detailed list, see the application Help. Windows or Linux Mac equivalent Effect Tools > Options LibreOffice > Preferences Access setup options menu selection Right-click Control+click or right-click
    [Show full text]
  • USING RADAR CHART to DISPLAY CLINICAL DATA Shi-Tao Yeh, Glaxosmithkline, King of Prussia, PA
    NESUG 18 Posters USING RADAR CHART TO DISPLAY CLINICAL DATA Shi-Tao Yeh, GlaxoSmithKline, King of Prussia, PA ABSTRACT A radar or star chart graphically shows the size of the target numeric variable among categories. On a radar chart, the chart statistics are displayed along spokes from the center of the chart. The GRADAR procedure in SAS/GRAPH 9.1 creates radar chart. The GRADAR procedure provides five star types with interactive features. The interactive features are: # By an ActiveX control – (pop-up data tips, drill-down links, and interactive menus) # By a Metaview applet – (data tips, drill-down links, or some interactivity such as zooming, panning, and slide shows). # By Static Images – (data tips, and drill-down links, or animation). The hypothetical clinical data used throughout this paper for illustration purpose are: # On-therapy Adverse Events # On-therapy Serious Adverse Events # QTC Change from Baseline # Clinical Laboratory Data with Worst Case in High Direction # ABPM Data ? The SAS products utilized in this paper are SAS BASE and SAS/GRAPH 9.1 on a PC Windows platform. INTRODUCTION The procedure GRADAR is a new graphical procedure provided in SAS/GRAPH 9.1 for creating radar charts. A radar chart is a type of graphical presentation using spokes to illustrate the size of the target numeric variable among categories. On a radar chart, the chart statistics are displayed along spokes from the center of the chart. The radar charts are called in different names, such as star charts, polar chart, spider plots, or cobweb plots, because the radar chart has different types, such as star types of corona, polygon, radial, spoke, and wedge provided from the Procedure GRADAR.
    [Show full text]
  • Placeprofile: Employing Visual and Cluster Analysis to Profile Regions
    PlaceProfile: Employing Visual and Cluster Analysis to Profile Regions based on Points of Interest Rafael Mariano Christofano,´ Wilson Estecio´ Marc´ılio Junior´ a and Danilo Medeiros Eler b Sao˜ Paulo State University (UNESP), Presidente Prudente/Sao˜ Paulo, Brazil Keywords: Area Profiling, Smart Cities, Smart Mobility, POIs, Clustering, Visualization, Google Maps. Abstract: Understanding how commercial and social activities and points of interest are located in a city is essential to plan efficient cities in smart mobility. Over the years, the growth of data sources from distinct online social networks has enabled new perspectives to applications that provide mechanisms to aid in comprehension of how people displaces between different regions within a city. To support enterprises and governments better understand and compare distinct regions of a city, this work proposes a web application called PlaceProfile to perform visual profiling of city areas based on iconographic visualization and to label areas based on cluster- ing algorithms. The visualization results are overlayered on Google Maps to enrich the map layout and aid analyst in understanding region profiling at a glance. Besides, PlaceProfile coordinates a radar chart with areas selected by the user to enable detailed inspection of the frequency of categories of points of interest (POIs). This linked views approach also supports clustering algorithms’ explainability by providing inspections of the attributes used to compute similarities. We employed the proposed approach in a case study in the Sao˜ Paulo city, Brazil. 1 INTRODUCTION gions (Silva, 2014). In the past, to understand mobil- ity and activities in a city, researchers collected sur- Since the work of (Ravenstein, 1885), researchers vey data on small samples and low frequencies.
    [Show full text]
  • Statistical Inference with Paired Observations and Independent Observations in Two Samples
    Statistical inference with paired observations and independent observations in two samples Benjamin Fletcher Derrick A thesis submitted in partial fulfilment of the requirements of the University of the West of England, Bristol, for the degree of Doctor of Philosophy Applied Statistics Group, Engineering Design and Mathematics, Faculty of Environment and Technology, University of the West of England, Bristol February, 2020 Abstract A frequently asked question in quantitative research is how to compare two samples that include some combination of paired observations and unpaired observations. This scenario is referred to as ‘partially overlapping samples’. Most frequently the desired comparison is that of central location. Depend- ing on the context, the research question could be a comparison of means, distributions, proportions or variances. Approaches that discard either the paired observations or the independent observations are customary. Existing approaches evoke much criticism. Approaches that make use of all of the available data are becoming more prominent. Traditional and modern ap- proaches for the analyses for each of these research questions are reviewed. Novel solutions for each of the research questions are developed and explored using simulation. Results show that proposed tests which report a direct measurable difference between two groups provide the best solutions. These solutions advance traditional methods in this area that have remained largely unchanged for over 80 years. An R package is detailed to assist users to per- form these new tests in the presence of partially overlapping samples. Acknowledgments I pay tribute to my colleagues in the Mathematics Cluster and the Applied Statistics Group at the University of the West of England, Bristol.
    [Show full text]
  • Comparative Visual Analytics in a Cohort of Breast Cancer Patients
    Vergleichende visuelle Analyse in einer Kohorte von Brustkrebspatienten DIPLOMARBEIT zur Erlangung des akademischen Grades Diplom-Ingenieur im Rahmen des Studiums Medizinische Informatik eingereicht von Nikolaus Karall, BSc Matrikelnummer 00625631 an der Fakultät für Informatik der Technischen Universität Wien Betreuung: Ao.Univ.Prof. Dipl.-Ing. Dr.techn. Eduard Gröller Mitwirkung: Univ.Ass Renata Raidou, MSc PhD Wien, 3. Mai 2018 Nikolaus Karall Eduard Gröller Technische Universität Wien A-1040 Wien Karlsplatz 13 Tel. +43-1-58801-0 www.tuwien.ac.at Comparative Visual Analytics in a Cohort of Breast Cancer Patients DIPLOMA THESIS submitted in partial fulfillment of the requirements for the degree of Diplom-Ingenieur in Medical Informatics by Nikolaus Karall, BSc Registration Number 00625631 to the Faculty of Informatics at the TU Wien Advisor: Ao.Univ.Prof. Dipl.-Ing. Dr.techn. Eduard Gröller Assistance: Univ.Ass Renata Raidou, MSc PhD Vienna, 3rd May, 2018 Nikolaus Karall Eduard Gröller Technische Universität Wien A-1040 Wien Karlsplatz 13 Tel. +43-1-58801-0 www.tuwien.ac.at Erklärung zur Verfassung der Arbeit Nikolaus Karall, BSc Steingasse 15/8, 1030 Wien Hiermit erkläre ich, dass ich diese Arbeit selbständig verfasst habe, dass ich die verwen- deten Quellen und Hilfsmittel vollständig angegeben habe und dass ich die Stellen der Arbeit – einschließlich Tabellen, Karten und Abbildungen –, die anderen Werken oder dem Internet im Wortlaut oder dem Sinn nach entnommen sind, auf jeden Fall unter Angabe der Quelle als Entlehnung kenntlich gemacht habe. Wien, 3. Mai 2018 Nikolaus Karall v Danksagung Zu allererst möchte ich mich beim Team der Research Division of Computer Graphics der Technischen Universität Wien für das Ermöglichen dieser Diplomarbeit bedanken, besonders bei Meister Eduard Gröller und Renata Raidou, die stets sehr hilfreich und inspirierend war.
    [Show full text]
  • Learn to Create a Radar Chart in R with Data from Our World in Data (2018)
    Learn to Create a Radar Chart in R With Data From Our World in Data (2018) © 2021 SAGE Publications, Ltd. All Rights Reserved. This PDF has been generated from SAGE Research Methods Datasets. SAGE SAGE Research Methods: Data 2021 SAGE Publications, Ltd. All Rights Reserved. Visualization Learn to Create a Radar Chart in R With Data From Our World in Data (2018) How-to Guide for R Introduction In this guide, you will learn how to create a radar chart using the R statistical software. Readers are provided links to the example dataset and encouraged to replicate this example. An additional practice example is suggested at the end of this guide. The example assumes you have downloaded the relevant data files to a folder on your computer and that you are using the R statistical software. The relevant code should, however, work in other environments too. Contents 1. Radar Chart 2. An Example in R: Energy Consumption by Type in the Baltics (2018) 2.1 The R Procedure 2.1.1 Preparing the Data 2.1.2 Single Radar Chart 2.1.3 Multiple Radar Charts 2.1.4 Further Edits 2.2 Exploring the Output 3. Your Turn Page 2 of 17 Learn to Create a Radar Chart in R With Data From Our World in Data (2018) SAGE SAGE Research Methods: Data 2021 SAGE Publications, Ltd. All Rights Reserved. Visualization 1. Radar Chart The radar chart maps three or more different quantitative variables across a series of categories at equal intervals along a circle. The quantity in each category is marked by a marker plotted a certain distance away from the central axis, which is then connected sequentially to the other category value markers to create a unique shape, which can be either filled or unfilled.
    [Show full text]
  • Music-Circles: Can Music Be Represented with Numbers?
    Music-Circles: Can Music Be Represented With Numbers? Seokgi Kim, Jihye Park, Kihong Seong, Namwoo Cho, Junho Min, Hwajung Hong*† Figure 1: Overview of the three pages of Music-Circles. Left: First Page (Circular Barplot, Table, Radar Chart); Middle: Second Page (Survey); Right: Third Page (Cluster Visualization - Bubble Chart, Radar Chart, Table) ABSTRACT 1 INTRODUCTION The world today is experiencing an abundance of music like no other The music industry went through significant adjustments whenever time, and attempts to group music into clusters have become increas- the prominent medium used to listen to music changed. Three ingly prevalent. Common standards for grouping music were songs, major changes in medium for music are notable; Vinyls to CDs, artists, and genres, with artists or songs exploring similar genres CDs to digital downloads, digital downloads to online streaming. of music seen as related. These clustering attempts serve critical The second change from CDs to digital downloads introduced a purposes for various stakeholders involved in the music industry. monumental shift of music from tangible forms to digital forms, For end users of music services, they may want to group their music and the third transition to online streaming led to a demand for so that they can easily navigate inside their music library; for music enhanced analytics of the digitized audio content. This was because streaming platforms like Spotify, companies may want to establish a online streaming platforms offered the complete music library to solid dataset of related songs in order to successfully provide person- listen to by a subscription basis, whereas in the era of downloads, alized music recommendations and coherent playlists to their users.
    [Show full text]
  • Ranking Visualizations of Correlation Using Weber's
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 20, NO. 12DEEM , C BER 2014 1943 Ranking Visualizations of Correlation Using Weber’s Law Lane Harrison, Fumeng Yang, Steven Franconeri, Remco Chang Abstract— Despite years of research yielding systems and guidelines to aid visualization design, practitioners still face the challenge of identifying the best visualization for a given dataset and task. One promising approach to circumvent this problem is to leverage perceptual laws to quantitatively evaluate the effectiveness of a visualization design. Following previously established methodologies, we conduct a large scale (n=1687) crowdsourced experiment to investigate whether the perception of correlation in nine commonly used visualizations can be modeled using Weber’s law. The results of this experiment contribute to our understanding of information visualization by establishing that: 1) for all tested visualizations, the precision of correlation judgment could be modeled by Weber’s law, 2) correlation judgment precision showed striking variation between negatively and positively correlated data, and 3) Weber models provide a concise means to quantify, compare, and rank the perceptual precision afforded by a visualization. Index Terms—Perception, Visualization, Evaluation 1INTRODUCTION The theory and design of information visualization has come a long dS way since Bertin’s seminal work on the Semiology of Graphics [1]. dp= k (1) Years of visualization research has led to systems [17, 18] and guide- S lines [5, 24] that aid the designer in choosing visual representations where dp is the differential change in perception, dS is the differ- based on general data characteristics such as dimensionality and data- ential increase in the data correlation (change in stimulus), and S is type.
    [Show full text]