An Analytics Environment Architecture for Industrial Cyber-Physical Systems Big Data Solutions
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Industrial Big Data Analytics for Quality Improvement in Complex Systems
Industrial Big Data Analytics for Quality Improvement in Complex Systems Dr. Kaibo Liu Department of Industrial and Systems Engineering University of Wisconsin-Madison 1 Lab for System Informatics and Data Analytics (SIDA) Background • A.P. 2013-now, Department of industrial and Systems Engineering, UW-Madison • Ph.D. 2013, Industrial Engineering (Minor: Machine Learning), Georgia Institute of Technology • M.S. 2011, Statistics, Georgia Institute of Technology • B.S. 2009, Industrial Engineering and Engineering Management, Hong Kong University of Science and Technology, Hong Kong 2 Lab for System Informatics and Data Analytics (SIDA) My Research & Expertise Research Interests Expertise System Informatics and data analytics: • Complex system modeling and Engineering performance assessment Multidisciplinary approach • Data fusion for online process monitoring, diagnosis and prognostics Operation Statistics/ Research/ Machine • Statistical learning, data mining, and Control Learning decision making Multi-disciplinary Research Sensor Measurement System Degradation Spatiotemporal Field and Monitoring Strategy Analysis and Prognostics Modeling and Prediction Overall, my research goal is to make sense of big data for better decision making! 3 Lab for System Informatics and Data Analytics (SIDA) Sensor Measurement and Monitoring Strategy 4 Lab for System Informatics and Data Analytics (SIDA) Objective-oriented sensor system designs in complex systems Objective Approaches • Obtain an optimal sensor allocation design at • A best allocation subsets -
Data Model Standards and Guidelines, Registration Policies And
Data Model Standards and Guidelines, Registration Policies and Procedures Version 3.2 ● 6/02/2017 Data Model Standards and Guidelines, Registration Policies and Procedures Document Version Control Document Version Control VERSION D ATE AUTHOR DESCRIPTION DRAFT 03/28/07 Venkatesh Kadadasu Baseline Draft Document 0.1 05/04/2007 Venkatesh Kadadasu Sections 1.1, 1.2, 1.3, 1.4 revised 0.2 05/07/2007 Venkatesh Kadadasu Sections 1.4, 2.0, 2.2, 2.2.1, 3.1, 3.2, 3.2.1, 3.2.2 revised 0.3 05/24/07 Venkatesh Kadadasu Incorporated feedback from Uli 0.4 5/31/2007 Venkatesh Kadadasu Incorporated Steve’s feedback: Section 1.5 Issues -Change Decide to Decision Section 2.2.5 Coordinate with Kumar and Lisa to determine the class words used by XML community, and identify them in the document. (This was discussed previously.) Data Standardization - We have discussed on several occasions the cross-walk table between tabular naming standards and XML. When did it get dropped? Section 2.3.2 Conceptual data model level of detail: changed (S) No foreign key attributes may be entered in the conceptual data model. To (S) No attributes may be entered in the conceptual data model. 0.5 6/4/2007 Steve Horn Move last paragraph of Section 2.0 to section 2.1.4 Data Standardization Added definitions of key terms 0.6 6/5/2007 Ulrike Nasshan Section 2.2.5 Coordinate with Kumar and Lisa to determine the class words used by XML community, and identify them in the document. -
Big Data for Measuring the Information Society
BIG DATA FOR MEASURING THE INFORMATION SOCIETY COUNTRY REPORT SWEDEN 1 / 44 Acknowledgments: The big data analysis contained in this report was carried out by Ivan Vallejo Vall, Market Analyst at the ICT Data and Statistics Division within the Telecommunication Development Bureau of ITU. Fredrik Eriksson, Statistician in the same division, made substantial contributions to the analysis. The Internet Foundation in Sweden (IIS) provided the data used in this report, as well as insightful information on the methodology of the data collection. IIS’s cooperation is duly acknowledged. 2 / 44 CONTENTS Contents ........................................................................................................................................................ 3 1. Background and Context ........................................................................................................................... 4 1.1. Project Description ............................................................................................................................. 4 1.2. Pilot Country Context ......................................................................................................................... 4 1.3. Stakeholders in the pilot and project timeline .................................................................................. 6 2. Getting Access to the Data: Procedures, Legal Documents and Challenges ............................................ 7 2.1. Legal Documents and Challenges ..................................................................................................... -
Data Analytics EMEA Insurance Data Analytics Study Contents
A little less conversation, a lot more action – tactics to get satisfaction from data analytics EMEA Insurance data analytics study Contents Foreword 01 Introduction from the authors 04 Vision and strategy 05 A disconnect between analytics and business strategies 05 Articulating a clear business case can be tricky 07 Tactical projects are trumping long haul strategic wins 09 The ever evolving world of the CDO 10 Assets and capability 12 Purple People are hard to find 12 Data is not always accessible or trustworthy 14 Agility and traditional insurance are not natural bedfellows 18 Operationalisation and change management 23 Operating models have no clear winner 23 The message is not always loud and clear 24 Hearts and minds do not change overnight 25 Are you ready to become an IDO? 28 Appendix A – The survey 30 Appendix B – Links to publications 31 Appendix C – Key contacts 32 A little less conversation, a lot more action – tactics to get satisfaction from data analytics | EMEA Insurance data analytics study Foreword The world is experiencing the fastest pace of data expansion and technological change in history. Our work with the World Economic Forum in 2015 identified that, within financial services, insurance is the industry which is most ripe for disruption from innovation owing to the significant pressure across the value chain. To build on this work, our report ‘Turbulence ahead – The future of general insurance’, set out various innovations transforming the industry and subsequent scenarios for The time is now the future. It identified that innovation within the insurance industry is no longer led by insurers themselves. -
Constructing a Meta Data Architecture
0-471-35523-2.int.07 6/16/00 12:29 AM Page 181 CHAPTER 7 Constructing a Meta Data Architecture This chapter describes the key elements of a meta data repository architec- ture and explains how to tie data warehouse architecture into the architec- ture of the meta data repository. After reviewing these essential elements, I examine the three basic architectural approaches for building a meta data repository and discuss the advantages and disadvantages of each. Last, I discuss advanced meta data architecture techniques such as closed-loop and bidirectional meta data, which are gaining popularity as our industry evolves. What Makes a Good Architecture A sound meta data architecture incorporates five general characteristics: ■ Integrated ■ Scalable ■ Robust ■ Customizable ■ Open 181 0-471-35523-2.int.07 6/16/00 12:29 AM Page 182 182 Chapter 7 It is important to understand that if a company purchases meta data access and/or integration tools, those tools define a significant portion of the meta data architecture. Companies should, therefore, consider these essential characteristics when evaluating tools and their implementation of the technology. Integrated Anyone who has worked on a decision support project understands that the biggest challenge in building a data warehouse is integrating all of the dis- parate sources of data and transforming the data into meaningful informa- tion. The same is true for a meta data repository. A meta data repository typically needs to be able to integrate a variety of types and sources of meta data and turn the resulting stew into meaningful, accessible business and technical meta data. -
Padma Vibhushan Shri Dhirubhai H. Ambani (28Th December, 1932 - 6Th July, 2002) Reliance Group - Founder and Visionary Profile
Communications Annual Report 2015-16 Padma Vibhushan Shri Dhirubhai H. Ambani (28th December, 1932 - 6th July, 2002) Reliance Group - Founder and Visionary Profile Reliance Communications Limited is the flagship Company of Reliance Group, one of the leading business houses in India. Reliance Communications is India’s foremost and truly integrated telecommunications service provider. The Company has a customer base of over 111 million including over 2.6 million individual overseas retail customers. Reliance Communications corporate clientele includes over 39,000 Indian and multinational corporations including small and medium enterprises and over 290 global, regional and domestic carriers. Reliance Communications has established a pan-India, next generation, integrated (wireless and wireline), convergent (voice, data and video) digital network that is capable of supporting best-of-class services spanning the entire communications value chain, covering over 21,000 cities and towns and over 400,000 villages. Reliance Communications owns and operates the world’s largest next generation IP enabled connectivity infrastructure, comprising over 280,000 kilometers of fibre optic cable systems in India, USA, Europe, Middle East and the Asia Pacific region. Mission: Excellence in Communication Arena To attain global best practices and become a world-class communication service provider – guided by its purpose to move towards greater degree of sophistication and maturity. To work with vigour, dedication and innovation to achieve excellence in service, quality, reliability, safety and customer care as the ultimate goal. To earn the trust and confidence of all stakeholders, exceeding their expectations and make the Company a respected household name. To consistently achieve high growth with the highest levels of productivity. -
Predictive Maintenance and Intelligent Sensors in Smart Factory: Review
sensors Review Predictive Maintenance and Intelligent Sensors in Smart Factory: Review Martin Pech , Jaroslav Vrchota * and Jiˇrí Bednáˇr Department of Management, Faculty of Economics, University of South Bohemia in Ceske Budejovice, Studentska 13, 370 05 Ceske Budejovice, Czech Republic; [email protected] (M.P.); [email protected] (J.B.) * Correspondence: [email protected] Abstract: With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems’ decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper’s main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories. Citation: Pech, M.; Vrchota, J.; Keywords: intelligent sensors; maintenance; smart factory; Industry 4.0 Bednáˇr,J. Predictive Maintenance and Intelligent Sensors in Smart Factory: Review. Sensors 2021, 21, 1470. https://doi.org/10.3390/ 1. Introduction s21041470 Industry sets the direction for the world economy, accounting for more than 70% of Academic Editors: Susana Vieira, the world’s total material production [1], depending on the regional economy’s develop- João Figueiredo and João Miguel da ment. -
Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment
Available online at www.sciencedirect.com ScienceDirect Procedia CIRP 16 ( 2014 ) 3 – 8 Product Services Systems and Value Creation. Proceedings of the 6th CIRP Conference on Industrial Product-Service Systems Service innovation and smart analytics for Industry 4.0 and big data environment Jay Lee*, Hung-An Kao, Shanhu Yang NSF I/UCRC Center for Intelligent Maintenance Systems (IMS), University of Cincinnati, Cincinnati, OH 45221-0072, USA * Corresponding author. Tel.: +1-513-556-3412; fax: +1-513-556-4647. E-mail address: [email protected] Abstract Today, in an Industry 4.0 factory, machines are connected as a collaborative community. Such evolution requires the utilization of advance- prediction tools, so that data can be systematically processed into information to explain uncertainties, and thereby make more “informed” decisions. Cyber-Physical System-based manufacturing and service innovations are two inevitable trends and challenges for manufacturing industries. This paper addresses the trends of manufacturing service transformation in big data environment, as well as the readiness of smart predictive informatics tools to manage big data, thereby achieving transparency and productivity. © 2014 TheElsevier Authors. B.V. Pub Thislished is an by open Elsevier access B.V. article under the CC BY-NC-ND license Selection(http://creativecommons.org/licenses/by-nc-nd/3.0/ and peer-review under responsibility of the Internationa). l Scientific Committee of “The 6th CIRP Conference on Industrial Product- ServiceSelection Systems” and peer-review in the person under of the responsibility Conference ofChair the ProfessorInternational Hoda Scientifi ElMaraghy. c Committee of “The 6th CIRP Conference on Industrial Product-Service Systems” in the person of the Conference Chair Professor Hoda ElMaraghy” Keywords: Manufacturing servitization; predictive maintenance; industrial big data 1. -
An Enterprise Information System Data Architecture Guide
An Enterprise Information System Data Architecture Guide Grace Alexandra Lewis Santiago Comella-Dorda Pat Place Daniel Plakosh Robert C. Seacord October 2001 TECHNICAL REPORT CMU/SEI-2001-TR-018 ESC-TR-2001-018 Pittsburgh, PA 15213-3890 An Enterprise Information System Data Architecture Guide CMU/SEI-2001-TR-018 ESC-TR-2001-018 Grace Alexandra Lewis Santiago Comella-Dorda Pat Place Daniel Plakosh Robert C. Seacord October 2001 COTS-Based Systems Unlimited distribution subject to the copyright. This report was prepared for the SEI Joint Program Office HQ ESC/DIB 5 Eglin Street Hanscom AFB, MA 01731-2116 The ideas and findings in this report should not be construed as an official DoD position. It is published in the interest of scientific and technical information exchange. FOR THE COMMANDER Norton L. Compton, Lt Col, USAF SEI Joint Program Office This work is sponsored by the U.S. Department of Defense. The Software Engineering Institute is a federally funded research and development center sponsored by the U.S. Department of Defense. Copyright 2001 by Carnegie Mellon University. Requests for permission to reproduce this document or to prepare derivative works of this document should be addressed to the SEI Licensing Agent. NO WARRANTY THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT INFRINGEMENT. -
Relilance Navi Mumbai - 400 710, India +91 22 3038 6286 Fax: +91 22 3037 6622
Reliance Communications Limited Dhirubhai Ambani Knowledge City Tel: +91 2230373333 ReLIlANce Navi Mumbai - 400 710, India +91 22 3038 6286 Fax: +91 22 3037 6622 www.rcom.co.in September 18, 2019 The General Manager The Manager Corporate Relationship Department National Stock Exchange of India Ltd. SSE Limited Exchange Plaza, C/1, Block G Phiroze Jeejeebhoy Towers Bandra - Kurla Complex, Bandra (East) Dalal Street, Fort, Mumbai 400 051 Mumbai 400 001 Fax No.: 26598237/38/8347/48/ Fax No.: 22722037/39/41/61/3121/3719 66418124/25/26 SSE Scrip Code: 532712 NSE Symbol: RCOM Dear Sir, Sub: Press Release In continuation with our letter dated September 17, 2019, we enclose herewith the Press Release issued by Global Cloud Xchange (HGCX"), Bermuda, which is self explanatory. Kindly inform your members accordingly. Thanking you. Yours faithfully, For Reliance Communicqtions Limited ~&vu-P+cr Rakesh Gupta Company Secretary Encl.:- as above. Registered Office: Reliance Communications Limited. H Block, 1st Floor, Dhirubhai Ambani Knowledge City, Navi Mumbai - 400 710 CIN No.: L45309MH2004PLC147531 MEDIA RELEASE NEW Global Cloud Xchange Announces Plan to Secure Financial Future Company initiates voluntary, pre-packaged Chapter 11 restructuring with support from more than 75 percent of lenders Proposed plan of reorganization positions the Company to aggressively pursue growth and development with $150 million in bond debt reduction, new working capital financing and strong new ownership NEW YORK, 15 September 2019: Global Cloud Xchange (“GCX” or the “Company”) today announced a pre-packaged Plan of Reorganization (the “Plan”) that will support its long-term growth and development by reducing bond debt by $150 million, providing a permanent capital structure that includes working capital facility and transitioning the business to new ownership. -
ER Studio Data Architect Quick Start Guide
Product Documentation ER Studio Data Architect Quick Start Guide Version 11.0 © 2015 Embarcadero Technologies, Inc. Embarcadero, the Embarcadero Technologies logos, and all other Embarcadero Technologies product or service names are trademarks or registered trademarks of Embarcadero Technologies, Inc. All other trademarks are property of their respective owners. Embarcadero Technologies, Inc. is a leading provider of award-winning tools for application developers and database professionals so they can design systems right, build them faster and run them better, regardless of their platform or programming language. Ninety of the Fortune 100 and an active community of more than three million users worldwide rely on Embarcadero products to increase productivity, reduce costs, simplify change management and compliance and accelerate innovation. The company's flagship tools include: Embarcadero® Change Manager™, CodeGear™ RAD Studio, DBArtisan®, Delphi®, ER/Studio®, JBuilder® and Rapid SQL®. Founded in 1993, Embarcadero is headquartered in San Francisco, with offices located around the world. Embarcadero is online at www.embarcadero.com. March, 2015 Embarcadero Technologies 2 CONTENTS Introducing ER/Studio Data Architect............................................................................ 5 Notice for Developer Edition Users ................................................................................... 5 Product Benefits by Audience ............................................................................................ 5 What's -
Trade Marks Journal No: 1795 , 01/05/2017 Class 28
Trade Marks Journal No: 1795 , 01/05/2017 Class 28 3378457 25/05/2016 [International Registration No. : 1303365] Hunter, Jeff 15478 Sylvan Street Van Nuys CA 91411 United States of America Coker, Kristina 15478 Sylvan Street Van Nuys CA 91411 United States of America Proposed to be Used IR DIVISION Athletic equipment, namely, guards for protecting the hand and wrist. 4801 Trade Marks Journal No: 1795 , 01/05/2017 Class 29 3363961 21/12/2015 [International Registration No. : 1301504] Laixishi Shunxiang Peanut Products Co., Ltd. Zhanjiahui Village, Yuanshang Town, Laixi City, Qingdao City Shandong Province China Proposed to be Used IR DIVISION Processed sesame; processed manufactured bean; processed hazelnuts; processed pine nut; potato crisps; peanut butter; refined nuts; nuts, prepared; processed melon seeds. 4802 Trade Marks Journal No: 1795 , 01/05/2017 Class 29 3387243 16/03/2016 [International Registration No. : 1305603] FUJIAN HAISHAN FOODS CO., LTD. Xiaye Village, Chengxi, Longhai Fujian China Proposed to be Used IR DIVISION Charcuterie; fish (food products made from -); fruits, tinned [canned (Am.)]; meat, tinned [canned (Am.)]; seafoods, tinned [canned (Am.)]; vegetables, tinned [canned (Am.)]; vegetables, preserved; pickled vegetables. 4803 Trade Marks Journal No: 1795 , 01/05/2017 Class 29 3387514 20/03/2016 [International Registration No. : 1305990] Qingdao Chengyang Vegetables and Aquatic Products Wholesale Market Co., Ltd. Chengyang Village, Chengyang District, Qingdao City Shandong Province China Proposed to be Used IR DIVISION Meat; shellfish, not live; shrimps, not live; frozen fruits; vegetables, preserved; milk products; edible oils; jellies for food; vegetable salads. 4804 Trade Marks Journal No: 1795 , 01/05/2017 Class 30 2910059 15/09/2014 [International Registration No.