DOCSLIB.ORG

From Theory to Rractical Deployment the Science Behind Predrol the Rremise Behind Predictive Policing Predictive Rolicing Eviden

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
From Theory to Rractical Deployment the Science Behind Predrol the Rremise Behind Predictive Policing Predictive Rolicing Eviden Q PREDPOL' THE PREDICTIVE POLICING COMPANY." From Theory to Rractical Deployment The Science Behind PredRol Policing crime patterns is hard. While crime may afflict the same neighborhoods year after year, the day-to-day fluctuations in where and when crimes occur are large. Extensive research has shown that day-to-day crime patterns are the result of: (1) crime generators that are fixed features of The Rremise Behind the environment; (2) repeat and near-repeat victimization that leads previous victims and their neighbors to be at greater risk of follow-on crimes; (3) the routine activity patterns of Predictive Policing offenders that keep risk local; and (4) substantial random noise. Each of these processes is well known empirically, but when put together their impact on how crime hotspots emerge, spread and disappear is incredibly complex. It is very hard to predict where crime If one can accurately predict will occur in the next 10-12 hours given where it occurred yesterday. where and when crimes will Knowledge, skills and experience can reliably direct officers to the top two or three occur; then law enforcement riskiest locations in their operational environment. It is much harder for them to Identify personn'el can disrupt those and choose between locations ~here the risk may be lower and highly variable from day to day. If an officer chooses to go to a location typically fourth in line for crime risk, but a crimes before they happen. crime occurred at a location fifth in line on that day, then an opportunity to disrupt crime is missed. Using high-powered mathematics and real-time crime data, PredPol evaluates yesterday's crimes in the context of all crimes occurring over a long time horizon and wide spatial field to calculate accurate probabilities of where and when crime will occur today. Officers using this information can make it harder for offenders to commit crimes in those locations leading to a net reduction in crime. Predictive Rolicing Evidence from the Field If predictive policing does not perform better than existing practice, or makes the job of the officer on the street harder, then it is better to stick with existing practice. Controlled experimental trials of PredPol were conducted with the Kent Police, UK, and Los Angeles Police Department, USA, with the goal of assessing what (if any) advantages stem from the use of predictive policing. In each experiment, PredPol was tested head-to­ head against crime analysts using existing practice. PredPol predicts crime In 500' x 500' (150m x 150m) boxes with the number of boxes deployed in a given shift calibrated to the policing resources available. The experimental deployments involved placement of 20 prediction boxes per shift in designated policing divisions. Analysts were tasked with deploying the same number of prediction boxes per shift using all of the tools at their disposal. In Kent West Division, the analyst focused on intelligence-led policing practices, while Los Angeles Foothill Division focused on crime hot spot mapping. In the absence of directed police patrol effects, PredPol predicts between 1.6-2.5 more crime than existing practice. Increased opportunities to impact crime are therefore of a similar magnitude using PredPol. 831.311.45501 [email protected] 1www.predpol.com PREDICT CRIME IN REAL TIME® Q PREDPOL' THE PREDICTIVE POLICING COMPANY.~ Accurate prediction leads not only to unique opportunities to disrupt crime, but also to serve the public at the scale at which crime occurs. In live deployments In both Kent and Los Angeles, officers were directed to use available time between calls or appointments to police what they see. Kent police officers capitalized on these opportunities: 06105113 Whilst potro!fing PredPo/ Zone 8 Harmer Street, Gravesend, Neighborhood officers attended a call to a suspicious mole seen rooking into vehicfes. This male was seen to then break into a vehicle and due to the close proximity the officers they were quickly on scene and detained the mole after a short foot chose. The male, a prolific offender, hod 2 Sot Navs in his pocket stolen from 2 vehicles in Royal Pier rood. If officers hod not already been in the zone they would not have been in o position to catch the offender as he committed the crime. 04106113 PredPof duty in Gravesend with 4 officers in 2 managed ro visit all PredPo/ boxes at feast once during the shrft with different teams to show a high and varied presence, consisting offoot and mobile patrol. While checking vehicles they noticed 2 SAT NAV systems and money in the way of change on display, ~ve traced the registered owners of the vehicfes and gave them advice over the phone, which was much appreciated. 02105113 From a local resident who has lived in the west side of Moidstone for many years: "great to see police around here again you hove really made a big difference in cleaning up this port of town". Disrupting crime during each and every shift can lead to significant crime declines over time. In Kent, the first six months of PredPol deployment produced a steady decline In violent crime of -6%. In los Angeles, the first six months of deployment saw a -12% decline In property crimes overall with a -25% decline in burglary alon.e. The Limits of Predictive Policing PredPo/ is about predicting where and when crime will occur; not who will commit a crime. PredPol is not criminal profiling. It does not use any information about individuals or populations and their characteristics. The patterns inherent In the crimes themselves provide ample information to predict where and when crimes will occur In the future. Predictive policing disrupts the short-term, situational causes of crime. lt does not solve criminality, or the propensity for individuals to commit crime. Predictive policing Is therefore not a replacement for policy and community engagement strategies needed to steer people clear of criminal careers in the first place. Key Contacts PredPol- The Predictive Policing Company Kent Police Dr. P. jeffrey Brantingham Detective Chief Superintendent jon Sutton je/[email protected] [email protected] Los Angeles Police Department Mr. Mark Johnson, Head of Analysis Captain Sean Malinowski [email protected] sean.ma/[email protected] 831.331.4550 [email protected] I www.predpotrom PREDICT CRIME IN REAL TlMf® @ 2013 PredPol, Inc. All dghls reserved. No part of this publication de sui bed herein may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted In any form or by means elettronlc, mechanical, photocopying, recording. or otherwise without the permission of PredPol,ln(, Page I of2 From: Donnie Fowler <[email protected]> To: [email protected], [email protected], [email protected] Date: Friday, September 06, 2013 10: 12AM Subject: Completing the Predictive Policing Deployment Chief Suhr, Susan, & Rod - Happy September to you. I write to let you see the SFPD predictive policing system as it will run when you are ready to deploy. It's one thing when I see deployments in another city and something else entirely when I see predictions where I live here in S. F. You can find it with the simple log-in name and password below: * once you log in, click on red box at the top right and select the whole city or any district Per our most recent conversation, we were waiting for the Oracle work to finalize before finishing our integration with you on general property crimes and on the first-in-the-r;Jation rollout of gun violence predictions·(along with Seattle and Atlanta). If my memory is correct, you thought September would be the likely time. In the last few months, we have had tremendous success with deployments from Alhambra, CA, to Norcross, GA, to Kent, England. You will see some of our work in the attached white papers on results and on gun violence. There are also a few news stories below my signature. All the best, Donnie DONNIE FOWLER Director of Business Development ..4th Street, ..I San Francisco, CA 94114 C:41···~ PREDPOL.COM The Predictive Policing Company In the News ... Fox News· Atlanta, August 2013 ("On the first day, they apprehended a suspect in the middle of committing a burglary.") http://www.myfoxatlanta.com/story/23177698/computer-tries-to-predict The Economist, July 2013 ("easier to foresee wrongdoing and spot likely wrongdoers") http://www.economist.com/news/briefinq/21582042-it-gettinq-easier-foresee-wrongdoinq-and-spot-likely- http://sfmail04.sfgov.org/mail/gsuhr.nsf/(%24Inbox)/8C34B97B743B991 D6276BA51 EE321... 9/23/13 Page 2 of2 wrongdoers-dent-even-think-about-it NBC News- Los Angeles, January 2013 ("a cliff-like drop when predictive policing began") http://www.nbclosangeles.com/news/locai/LAPD-Chief-Charlie-Beck-Predictive-Policinq-Forecasts-Crime-185970452.html Current TV with Santa Cruz Crime Analyst & Fmr. Michigan Gov. Jennifer Granholm, January 2013 ("Science fiction has become science fact.") https://current.box.com/s/c6bxouhsdugp3ifngz4s San Francisco Chronicle, August 2012 ("a new web-bases system to monitor real· time crime data and predict when the next violence will occur") http://www.sfgate.com/bayarea/article/SF-mayor-announces-antiviolence-strategy-3770653.php CBS Evening News - National, March 2012 ("It's not how many people you catch, it's how many crimes you prevent") http://www.cbsnews.com/videolwatch/?id~7 404996n Attachments: White Paper Predicting Gun Violence (2013 White Paper Science and Testing of Predictive July).pdf Policing (2013).pdf http://sfmail04.sfgov.org/mail/gsuhr.nsf/(%241nbox)/8C34B97B743B991D6276BA5l EE32l... 9/23/13 Page I of3 From: Suzy Loftus <[email protected]> To: Donnie Fowler <[email protected]>, "[email protected]" <[email protected]> Date: Thursday, May 09, 2013 01:42PM Subject: RE: Predictive Policing & Gun Violence Thanks, Donnie.
Load more

Recommended publications
  • Googling the Grey: Open Data, Web Services, and Semantics
    Archaeologies: Journal of the World Archaeological Congress (Ó 2010) DOI 10.1007/s11759-010-9146-4 Googling the Grey: Open Data, Web Services, and Semantics Eric C. Kansa, School of Information, UC Berkeley, Berkeley, CA, USA E-mail: [email protected] RESEARCH Sarah Whitcher Kansa, Alexandria Archive Institute, San Francisco, CA, USA Margie M. Burton and Cindy Stankowski, San Diego Archaeological Center, Escondido, CA, USA ABSTRACT ________________________________________________________________ Primary data, though an essential resource for supporting authoritative archaeological narratives, rarely enters the public record. Lack of primary data publication is also a major obstacle to cultural heritage preservation and the goals of cultural resource management (CRM). Moreover, access to primary data is key to contesting claims about the past and to the formulation of credible alternative interpretations. In response to these concerns, experimental systems have implemented a variety of strategies to support online publication of primary data. Online data dissemination can be a powerful tool to meet the needs of CRM professionals, establish better communication and collaborative ties with colleagues in academic settings, and encourage public engagement with the documented record of the past. This paper introduces the ArchaeoML standard and its implementation in the Open Context system. As will be discussed, the integration and August 2010 online dissemination of primary data offer great opportunities for making archaeological knowledge creation more participatory and transparent. However, different strategies in this area involve important trade-offs, and all face complex conceptual, ethical, legal, and professional challenges. ________________________________________________________________ Volume 6 Number 2 Since Open Context is operated in the United States, the European Union’s database protection laws are less applicable.
    [Show full text]
  • Foundations of Temporal Text Networks
    Vega et al. Applied Network Science (2018) 3:25 Applied Network Science https://doi.org/10.1007/s41109-018-0082-3 RESEARCH Open Access Foundations of Temporal Text Networks Davide Vega* and Matteo Magnani *Correspondence: [email protected] Abstract InfoLab, Department of Information Three fundamental elements to understand human information networks are the Technology, Uppsala University, Uppsala, Sweden individuals (actors) in the network, the information they exchange, that is often observable online as text content (emails, social media posts, etc.), and the time when these exchanges happen. An extremely large amount of research has addressed some of these aspects either in isolation or as combinations of two of them. There are also more and more works studying systems where all three elements are present, but typically using ad hoc models and algorithms that cannot be easily transfered to other contexts. To address this heterogeneity, in this article we present a simple, expressive and extensible model for temporal text networks, that we claim can be used as a common ground across different types of networks and analysis tasks, and we show how simple procedures to produce views of the model allow the direct application of analysis methods already developed in other domains, from traditional data mining to multilayer network mining. Keywords: Network, Text, Time, Model, Temporal text network, Human information network Introduction A large amount of human-generated information is available online in the form of text exchanged between individuals at specific times. Examples include social network sites, online forums and emails. The public accessibility of several of these sources allows us to observe our society at various scales, from focused conversations among small groups of individuals to broad political discussions involving heterogeneous audiences from large geographical areas (Zhou et al.
    [Show full text]
  • Database-Driven Web Mashups
    Database-Driven Web Mashups Andrei Vancea Michael Grossniklaus Moira C. Norrie Institute for Information Systems ETH Zurich CH-8092 Zurich, Switzerland {vancea,grossniklaus,norrie}@inf.ethz.ch Abstract news feeds being combined into a single feed. An enterprise mashup uses the general mashup techniques within a com- In most web mashup applications, the content is gener- pany’s own internal applications. It usually combines data ated using either web feeds or an application programming from both internal and external sources. Finally, a business interface (API) based on web services. Both approaches mashup is a combination of all of the above that makes the have limitations. Data models provided by web feeds are result available for a business application. not powerful enough to permit complex data structures to In this paper, we argue that current approaches to the de- be transmitted. APIs based on web services are usually velopment of web mashup applications lack a powerful data different for each web application, and thus different im- model for data interchange. As mentioned above, in most plementations of the APIs are required for each web ser- cases, the content is generated using feeds or an applica- vice that a web mashup application uses. We propose a tion programming interface (API) based on web services. database-driven approach to web mashups that supports In the former case, the data models provided by web feeds integration at the database level and enables mashup de- (RSS or Atom) lack flexibility and do not permit complex velopers to work with a uniform abstract model and have data structures to be transmitted.
    [Show full text]
  • Sturbridge, Brimfield, Holland and Wales
    SPENCER FAMILY DENTAL Gentle Caring State of the Art Dentistry For The Whole Family Cosmetic Dentistry • Restorative Dentistry • Preventative Dentistry CROWNS • CAPS • BRIDGES • COMPLETE and PARTIAL DENTURES New We Strive NON SURGICAL GUM TREATMENT • ROOT CANAL THERAPY Patients SURGICAL SERVICES For Painless Welcome BREATH CLINIC-WE TREAT CHRONIC BAD BREATH Dentistry HERBAL DENTAL PRODUCTS • All Instruments Fully Sterilized • Most Insurances Accepted Dr. Nasser S. Hanna Conveniently Located On Route 9 • (Corner of Greenville St. & Main St.) 284 Main St., Spencer 508-885-5511 Mailed free to requesting homes in Sturbridge, Brimfield, Holland and Wales Vol. VII, No. 50 PROUD MEDIA SPONSOR OF RELAY FOR LIFE OF THE GREATER SOUTHBRIDGE AREA! COMPLIMENTARY HOME DELIVERY ONLINE: WWW.STURBRIDGEVILLAGER.NET Friday, December 13, 2013 THIS WEEK’S QUOTE Board fields “The only thing that OML complaint overcomes hard luck is CREAMER ACCUSED OF DEFAMATORY COMMENTS hard work.” BY CHELSEA DAVIS Photo courtesy John Shevlin Harry Golden VILLAGER STAFF WRITER This design represents the intersection of Route 20 and Route 131 STURBRIDGE — The Board of becoming a rotary after the proposed improvement plans. Selectmen addressed an Open Meeting Law complaint made against them at their INSIDE Monday, Dec. 2 meeting. “[The written response is] acknowledg- Residents get look ing it was a somewhat awkward discus- ALMANAC ............2 sion,” said Town Administrator Shaun Suhoski. “It addresses the five items that POLICE LOGS........5 were on page 2 of the complaint.” at Route 20 project OPINION ............10 The complaint made by Monique Marinelli, wife of Sturbridge Fire CALENDAR .........12 Lieutenant John Marinelli, mostly BY CHELSEA DAVIS walking distances to these VILLAGER STAFF WRITER OBITUARIES .......12 addressed the alleged fault of former Board facilities, [and] protects and of Selectmen Chairman Thomas Creamer STURBRIDGE — A pres- takes advantage of the SPORTS ............14 in a Nov.
    [Show full text]
  • Open Meeting Law, Public Records Law and the Use of Technology
    Open Meeting Law, Public Records Law and the Use of Technology Town of Wilmington Mark R. Reich, Esq. November 30, 2020 2 Disclaimer This information is provided as a service by KP Law, P.C. This information is general in nature and does not, and is not intended to, constitute legal advice as to particular issues. Neither the provision nor receipt of this information creates an attorney-client relationship between the presenter and the recipient. You are advised not to take, or to refrain from taking, any action based on this information without consulting legal counsel about the specific issue(s). 3 Municipal Use of Social Media – Legal Issues ■ Open Meeting Law, G.L. c.30A, §§18-25 ■ Communications among a quorum of board members on social media can constitute an open meeting law violation ■ Click here for a discussion of the OML and social media: http://www.k-plaw.com/wp-content/uploads/2017/01/Open- Meeting-Law-and-Social-Media-Potential-Pitfalls.pdf ■ Public Records Law, G.L. c.66, §10 ■ Records retention ■ Policy to retain copies of social media pages and posts ■ Current guidance recommends taking a periodic “snapshot” of the social media sites in order to meet records retention obligations ■ Do not post information that is not public record 4 Open Meeting Law – Overview Massachusetts Open Meeting Law - G.L. c. 30A, §§ 18-25 • Purpose of OML is to eliminate the secrecy surrounding deliberations and decisions on which public policy is based • Applies to “a deliberation by a public body with respect to any matter within the body’s
    [Show full text]
  • Massachusetts Municipal Association Annual Meeting January, 2020 Disclaimer This Information Is Provided As a Service by KP Law, P.C
    Massachusetts Municipal Association Annual Meeting January, 2020 Disclaimer This information is provided as a service by KP Law, P.C. This information is general in nature and does not, and is not intended to, constitute legal advice. Neither the provision nor receipt of this information creates an attorney-client relationship between the presenter and the recipient. You are advised not to take, or to refrain from taking, any action based on this information without consulting legal counsel about the specific issue(s). All materials © Copyright 2020 KP Law, P.C. All rights reserved. Overview– Sunshine Laws . Open meeting , public records and conflict of interest laws exist in virtually every state . Purpose of such laws is to eliminate much of the secrecy surrounding deliberations and decisions on which public policy is based . Under the Open Meeting Law (“OML”), public bodies can only conduct business through public meetings, held in accordance with the OML, unless an exemption allowing an executive session exists All materials © Copyright 2020 KP Law, P.C. All rights reserved. OML –Legal Requirements • Meeting Notices/Agendas • Meeting must be properly posted 1) timing 2) location 3) detail • Minutes • Accurate, timely prepared and approved • Verbatim transcripts not required, but significantly detailed • Guidance from AG on process for approving • Attorney General’s Office promulgated revisions to the Open Meeting Law regulations in 2017 All materials © Copyright 2020 KP Law, P.C. All rights reserved. OML –Meetings Meeting includes
    [Show full text]
  • Multilingual Catalogue Strategies
    CEN CWA 15992 WORKSHOP July 2009 AGREEMENT ICS 35.240.60 English version Harmonization of data interchange in tourism This CEN Workshop Agreement has been drafted and approved by a Workshop of representatives of interested parties, the constitution of which is indicated in the foreword of this Workshop Agreement. The formal process followed by the Workshop in the development of this Workshop Agreement has been endorsed by the National Members of CEN but neither the National Members of CEN nor the CEN Management Centre can be held accountable for the technical content of this CEN Workshop Agreement or possible conflicts with standards or legislation. This CEN Workshop Agreement can in no way be held as being an official standard developed by CEN and its Members. This CEN Workshop Agreement is publicly available as a reference document from the CEN Members National Standard Bodies. CEN members are the national standards bodies of Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland and United Kingdom. EUROPEAN COMMITTEE FOR STANDARDIZATION COMITÉ EUROPÉEN DE NORMALISATION EUROPÄISCHES KOMITEE FÜR NORMUNG Management Centre: Avenue Marnix 17, B-1000 Brussels © 2009 CEN All rights of exploitation in any form and by any means reserved worldwide for CEN national Members. Ref. No.:CWA 15992:2009 E CWA 15992:2009 (E) Contents Contents
    [Show full text]
  • 11 Bio.Tools Studentships 145 11.1 Requirements
    bio.tools Documentation Release latest Jul 21, 2021 Contents 1 What is bio.tools? 3 1.1 Objectives................................................3 1.2 Scope...................................................3 1.3 Technical components..........................................4 1.4 bio.tools Tool Identifiers.........................................4 1.5 Docs overview..............................................5 1.6 Getting involved : a quickstart guide...................................5 2 Contributors Guide 7 2.1 bio.tools community site.........................................7 2.2 Feature requests & issues........................................7 2.3 Mailing list................................................7 2.4 Hangouts.................................................8 2.5 Task management............................................8 2.6 Editing tool descriptions.........................................8 3 Quickstart guide 9 3.1 Create an account............................................9 3.2 Add content...............................................9 3.3 Update a resource............................................ 11 3.4 Remove a resource............................................ 12 3.5 Search for a tool............................................. 12 3.6 References................................................ 12 4 Curators Guide 13 4.1 General guidelines............................................ 14 4.2 Attribute guidelines........................................... 17 4.3 Tool type guidelines..........................................
    [Show full text]
  • Ualbertabusiness
    SPRING 2017 THE FUTURE ISSUE LIVE. WORK. LEARN. ualbertabusiness BRIGHT SPOT AHEAD The upside of Canada’s electricity crisis PAGE 20 PAGE 13 PAGE 6 WHEELS OF FORTUNE NEW WORLD ORDER ENGINEERING THRILLS Will ride-shares and AI spell an Trade and turmoil in One man’s quest to build the end to the personal car? the age of Trump ultimate amusement park ride Make a world of difference. Donate and help our students experience life-changing opportunities during their studies. Donations to the Student Experience fund at the Alberta School of Business have an enormous impact. Your support provides students with opportunities that change the way they see the world, and inspires them to pursue their passions. To learn more visit ualberta.ca/business/give or call 1-877-992-7587. SPRING 2017 3 FRONT CONTENTS SPRING 2017 ISSUE A magazine for alumni and friends of the Alberta School of Business FRONT FEATURES DEAN Joseph Doucet 04 Message from the Dean 16 Leading Light SUPERVISING EDITOR 05 From the Web by Michael Hingston Heather Newton Canada’s electricity industry is at a EDITOR Omar Mouallem crossroads and the future is anyone’s guess ART DIRECTOR Make a world of difference. LIVE Sarah Jackson ASSOCIATE EDITOR 20 MaaS Effect Angela Johnston 06 Engineering an by Tim Querengesser ASSISTANT EDITOR Donate and help our students experience life-changing The private vehicle is looking costly, Mike Kendrick Adrenaline Rush wasteful, dirty and plain old passé as more How Guy Nelson got in the business CLASS NOTES EDITOR opportunities during their studies. people take advantage of the “mobility-as- Jacqueline Kokic of thrill rides a-service” ecosystem.
    [Show full text]
  • Oversight of the Federal Communications Commission: the National Broadband Plan
    OVERSIGHT OF THE FEDERAL COMMUNICATIONS COMMISSION: THE NATIONAL BROADBAND PLAN HEARING BEFORE THE SUBCOMMITTEE ON COMMUNICATIONS, TECHNOLOGY, AND THE INTERNET OF THE COMMITTEE ON ENERGY AND COMMERCE HOUSE OF REPRESENTATIVES ONE HUNDRED ELEVENTH CONGRESS SECOND SESSION MARCH 25, 2010 Serial No. 111–109 ( Printed for the use of the Committee on Energy and Commerce energycommerce.house.gov U.S. GOVERNMENT PRINTING OFFICE 76–021 WASHINGTON : 2012 For sale by the Superintendent of Documents, U.S. Government Printing Office Internet: bookstore.gpo.gov Phone: toll free (866) 512–1800; DC area (202) 512–1800 Fax: (202) 512–2104 Mail: Stop IDCC, Washington, DC 20402–0001 VerDate Mar 15 2010 04:07 Dec 12, 2012 Jkt 076021 PO 00000 Frm 00001 Fmt 5011 Sfmt 5011 E:\HR\OC\A021.XXX A021 smartinez on DSK6TPTVN1PROD with COMMITTEE ON ENERGY AND COMMERCE HENRY A. WAXMAN, California Chairman JOHN D. DINGELL, Michigan JOE BARTON, Texas Chairman Emeritus Ranking Member EDWARD J. MARKEY, Massachusetts RALPH M. HALL, Texas RICK BOUCHER, Virginia FRED UPTON, Michigan FRANK PALLONE, JR., New Jersey CLIFF STEARNS, Florida BART GORDON, Tennessee NATHAN DEAL, Georgia BOBBY L. RUSH, Illinois ED WHITFIELD, Kentucky ANNA G. ESHOO, California JOHN SHIMKUS, Illinois BART STUPAK, Michigan JOHN B. SHADEGG, Arizona ELIOT L. ENGEL, New York ROY BLUNT, Missouri GENE GREEN, Texas STEVE BUYER, Indiana DIANA DEGETTE, Colorado GEORGE RADANOVICH, California Vice Chairman JOSEPH R. PITTS, Pennsylvania LOIS CAPPS, California MARY BONO MACK, California MICHAEL F. DOYLE, Pennsylvania GREG WALDEN, Oregon JANE HARMAN, California LEE TERRY, Nebraska TOM ALLEN, Maine MIKE ROGERS, Michigan JANICE D. SCHAKOWSKY, Illinois SUE WILKINS MYRICK, North Carolina HILDA L.
    [Show full text]
  • Edit Master Title Style DATA CENTER
    DATAClick CENTER to edit Master title style DATA CENTER Make sense of your data David Brock, Founder and Director Data Center Massachusetts Institute of Technology SOLUTION XML PROBLEM <CallsPerDay> 2575 </CallsPerDay> PROBLEM “CallsPerDay” ? That’s not even a word ANOTHER PROBLEM <CompanyData> <CompanyName> Fidelity Employer Services Company </CompanyName> <Location> Merrimack </location> <CallData> <RecordDate> Tue Aug 11, 2004 </RecordDate> <CallsPerDay> 2575 </CallsPerDay> </CallData> </CompanyData> MORE PROBLEMS <CompanyData> <CompanyName> <CorporateRecords> Fidelity Employer <Company> Services Company Fidelity Employer </CompanyName> Services Company <Location> </Company> Merrimack <Records Data= “Tue Aug </location> ? 11, 2004” > <CallData> <Calls Units=“PerDay”> <RecordDate> 2575 Tue Aug 11, 2004 </Calls> </RecordDate> </CorporateRecords> <CallsPerDay> 2575 </CallsPerDay> </CallData> </CompanyData> OBSERVATION <CompanyData> “CallsPerDay” <CompanyName> Fidelity Services </CompanyName> <Location> vocabulary Merrimack </location> <CallData> grammar <RecordDate> Tue Aug 11,2005 </RecordDate> <CallsPerDay> 2575 </CallsPerDay> </CallData> </CompanyData> STANDARDS? 4ML ARMLARML BiblioML CIDX eBIS-XML HTTP-DRP MatML ODRL PrintTalk SHOE UML XML F AML ARMLARML BCXML xCIL ECML HumanML MathML OeBPS ProductionML SIF UBL XML Key AML ASMLASML BEEP CLT eCo HyTime MBAM OFX PSL SMML UCLP XMLife AML ASMLASML BGML CNRP EcoKnow IML MISML OIL PSI SMBXML UDDI XML MP AML ASTMASTM BHTML ComicsML edaXML ICML MCF OIM QML SMDL UDEF XML News AML ATMLATML
    [Show full text]
  • Data Analytics Subgroup Report
    Data Analytics Subgroup report Paolo Alcini - EMA (subgroup lead) Gianmario Candore – EMA (subgroup lead) Marek Lehmann - EMA Luis Pinheiro - EMA Antti Hyvärinen - Fimea Hans Ovelgonne – CBG-MED Mateja Sajovic - JAZMP Panagiotis Telonis - EMA Kevin Horan – HPRA (until May 2018) Massimiliano Falcinelli – EMA (from September 2018) See websites for contact details Heads of Medicines Agencies www.hma.eu European Medicines Agency www.ema.europa.eu An agency of the European Union Table of content Executive summary ..................................................................................................... 7 Data Standardisation ................................................................................................ 7 Information technology ............................................................................................ 8 Data manipulation ................................................................................................... 9 Artificial intelligence ................................................................................................. 9 Conclusions ........................................................................................................... 10 Acknowledgments .................................................................................................. 11 Data Analytics – Standardisation................................................................................. 12 1. Standardisation....................................................................................................
    [Show full text]