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Evolution of Database Technology
Introduction and Database Technology By EM Bakker September 11, 2012 Databases and Data Mining 1 DBDM Introduction Databases and Data Mining Projects at LIACS Biological and Medical Databases and Data Mining CMSB (Phenotype Genotype), DIAL CGH DB Cyttron: Visualization of the Cell GRID Computing VLe: Virtual Lab e-Science environments DAS3/DAS4 super computer Research on Fundamentals of Databases and Data Mining Database integration Data Mining algorithms Content Based Retrieval September 11, 2012 Databases and Data Mining 2 DBDM Databases (Chapters 1-7): The Evolution of Database Technology Data Preprocessing Data Warehouse (OLAP) & Data Cubes Data Cubes Computation Grand Challenges and State of the Art September 11, 2012 Databases and Data Mining 3 DBDM Data Mining (Chapters 8-11): Introduction and Overview of Data Mining Data Mining Basic Algorithms Mining data streams Mining Sequence Patterns Graph Mining September 11, 2012 Databases and Data Mining 4 DBDM Further Topics Mining object, spatial, multimedia, text and Web data Mining complex data objects Spatial and spatiotemporal data mining Multimedia data mining Text mining Web mining Applications and trends of data mining Mining business & biological data Visual data mining Data mining and society: Privacy-preserving data mining September 11, 2012 Databases and Data Mining 5 [R] Evolution of Database Technology September 11, 2012 Databases and Data Mining 6 Evolution of Database Technology 1960s: (Electronic) Data collection, database creation, IMS -
Available in PDF Format
Defining Business Rules ~ What Are They Really? the Business Rules Group formerly, known as the GUIDE Business Rules Project Final Report revision 1.3 July, 2000 Prepared by: Project Manager: David Hay Allan Kolber Group R, Inc. Butler Technology Solutions, Inc. Keri Anderson Healy Model Systems Consultants, Inc. Example by: John Hall Model Systems, Ltd. Project team: Charles Bachman David McBride Bachman Information Systems, Inc. Viasoft Joseph Breal Richard McKee IBM The Travelers Brian Carroll Terry Moriarty Intersolv Spectrum Technologies Group, Inc. E.F. Codd Linda Nadeau E.F. Codd & Associates A.D. Experts Michael Eulenberg Bonnie O’Neil Owl Mountain MIACO Corporation James Funk Stephanie Quarles S.C. Johnson & Son, Inc. Galaxy Technology Corporation J. Carlos Goti Jerry Rosenbaum IBM USF&G Gavin Gray Stuart Rosenthal Coldwell Banker Dyna Systems John Hall Ronald Ross Model Systems, Ltd. Ronald G. Ross Associates Terry Halpin Warren Selkow Asymetrix Corporation CGI/IBM David Hay Dan Tasker Group R, Inc. Dan Tasker John Healy Barbara von Halle The Automated Reasoning Corporation Spectrum Technologies Group, Inc. Keri Anderson Healy John Zachman Model Systems Consultants, Inc. Zachman International, Inc. Allan Kolber Dick Zakrzewski Butler Technology Solutions, Inc. Northwestern Mutual Life - ii - GUIDE Business Rules Project Final Report Table of Contents 1. Introduction 1 Project Scope And Objectives 1 Overview Of The Paper 2 The Rationale 2 A Context for Business Rules 4 Definition Of A Business Rule 4 Categories of Business Rule 6 2. Formalizing Business Rules 7 The Business Rules Conceptual Model 8 3. Formulating Business Rules 9 The Origins Of Business Rules — The Model 10 Types of Business Rule 13 Definitions 14 4. -
The Rise of the Sharing Economy Impact on the Transportation Space
The rise of the sharing economy Impact on the transportation space In a world of shared assets, changing economics and customer preferences are increasingly driving transportation players not to go it alone. In only a few short years, the sharing economy has become gain a broader user base, pricing may become more a ubiquitous concept. As recently noted in the Wall Street transparent and assets more fungible between traditional Journal, “there’s an Uber for everything now,” ranging from market verticals. This could allow players to expand beyond Shyp, which uses a network of individual providers to pick their traditional lines of business to offer adjacent services, up, pack and ship items using their own cars, to Zeel, which without having to do it the old fashioned way: investing taps a network of independent licensed therapists to offer huge sums of capital to build capabilities and acquire customers same-day, in-home massages.1 While mobile companies. It seems the sharing economy has the power apps have facilitated this type of collaborative consumption, to bring not only customers but also competitors closer changing consumer preferences may be the biggest together. indicator that the sharing economy is here to stay. Younger Indeed, any industry could potentially benefit from, or people in particular embrace the core idea of eschewing be disrupted by, the rise of collaborative consumption and individual ownership, and its accompanying higher costs, in the proliferation of asset-sharing models. However, due favor of on-demand access to a flexible, lower-cost network to its natural fragmentation and asset intensity, the sharing of shared assets or service providers.2 And, these younger economy is especially relevant to core transportation consumers may have more and more networks to choose companies as well as to heavy users of transportation from as pure-play technology companies continue to enter services. -
Charles Bachman
Charles Bachman Born December 11, 1924, Manhattan, Kan.; Proposer of a network approach to storing data as in the Integrated Data Store (IDS) and developer of the OSI Reference Model. Education: BS, mechanical engineering, Michigan State University, 1948; MS, mechanical engineering, University of Pennsylvania, 1950. Professional Experience: Dow Chemical Corporation, 1950-1960; General Electric Company, 1960-1970; Honeywell Information Systems, 1970-1981; Cullinet, 1981- 1983; founder and chairman, BACHMAN Information Systems, 1983-present. Honors and Awards: ACM Turing Award, 1973; distinguished fellow, British Computer Society, 1978. Bachman began his service to the computer industry in 1958 by chairing the SHARE Data processing Committee that developed the IBM 709 Data Processing Package (9PAC), and which preceded the development of the programming language Cobol. He continued this development work through the American National Standards Institute SPARC Study Group on Data Base Management Systems (ANSI/ SPARC/DBMS) that created the layer architecture and conceptual schema for database systems. This work led to the development of the international “Reference Model for Open Systems Integration,” which included the basic idea of a seven- layer architecture, the basis of the OSI networking standard. Bachman received the 1973 ACM Turing Award for his development of the Integrated Data Store, which lifted database work from the status of a specialty to first-class citizenship in computing. IDS provided an elegant logical framework for organizing large on-line collections of variously interrelated data. The system had pragmatic significance also in taking into account advice on expected usage patterns, to improve physical data layouts. The facilities of IDS were fully integrated into the Cobol language and so became available for full-scale practical use. -
Computer in the Organization*
i^Av COMPUTER IN THE ORGANIZATION* Martin Greenberger** Sloan School of Management and Project MAC Massachusetts Institute of Technology 207-66 ^Prepared for the special September 1966 issue arranged by the Scientific American on the subject of information and computers, **0n leave during 1965-66 as a Guggenheim Fellow at the Center for Research in Management Science, University of California, Berkeley. "Some of the work reported herein was supported (in part) by Project MAC, an M.I.T. research program sponsored by the Ad- vanced Research Projects Agency, Department of Defense, under Office of Naval Research Contract Number Nonr - 4102(01). Reproduction in whole or in part is permitted for any purpose of the United States Government." -1- CONTENTS Synopsis 2 I. Introduction 4 II. The Past 7 Specialization and the Division of Labor 7 Organization 9 Accumulation 12 Mechanization 13 [I. The Present 16 Automation 16 Computer Routines 18 Program Organization 20 Real-Time Systems 22 IV. The Future 26 Current Trends 26 On-Line Synthesis and Control 33 The Automated Information System 34 The Automated Memory 36 V. Postscript 39 Notes 43 -2- Synopsis In its commercial form, the digital computer has been with us for only a decade and a half. Yet its profound significance for the future of civilization is already well established. This is a remarkable achieve- ment for such a technological stripling. It makes us eager to anticipate the benefits that the electronic prodigy will one day deliver to us. But it also disturbs our sense of orderly history, and causes the more cautious and thoughtful among us to recoil from too unreserved an enthusiam for the brazen young technology. -
CMU 15-445/645 Database Systems (Fall 2018 :: Query Optimization
Query Optimization Lecture #13 Database Systems Andy Pavlo 15-445/15-645 Computer Science Fall 2018 AP Carnegie Mellon Univ. 2 ADMINISTRIVIA Mid-term Exam is on Wednesday October 17th → See mid-term exam guide for more info. Project #2 – Checkpoint #2 is due Friday October 19th @ 11:59pm. CMU 15-445/645 (Fall 2018) 4 QUERY OPTIMIZATION Remember that SQL is declarative. → User tells the DBMS what answer they want, not how to get the answer. There can be a big difference in performance based on plan is used: → See last week: 1.3 hours vs. 0.45 seconds CMU 15-445/645 (Fall 2018) 5 IBM SYSTEM R First implementation of a query optimizer. People argued that the DBMS could never choose a query plan better than what a human could write. A lot of the concepts from System R’s optimizer are still used today. CMU 15-445/645 (Fall 2018) 6 QUERY OPTIMIZATION Heuristics / Rules → Rewrite the query to remove stupid / inefficient things. → Does not require a cost model. Cost-based Search → Use a cost model to evaluate multiple equivalent plans and pick the one with the lowest cost. CMU 15-445/645 (Fall 2018) 7 QUERY PLANNING OVERVIEW System Catalog Cost SQL Query Model Abstract Syntax Annotated Annotated Tree AST AST Parser Binder Rewriter Optimizer (Optional) Name→Internal ID Query Plan CMU 15-445/645 (Fall 2018) 8 TODAY'S AGENDA Relational Algebra Equivalences Plan Cost Estimation Plan Enumeration Nested Sub-queries Mid-Term Review CMU 15-445/645 (Fall 2018) 9 RELATIONAL ALGEBRA EQUIVALENCES Two relational algebra expressions are equivalent if they generate the same set of tuples. -
The Evolution of Lisp
1 The Evolution of Lisp Guy L. Steele Jr. Richard P. Gabriel Thinking Machines Corporation Lucid, Inc. 245 First Street 707 Laurel Street Cambridge, Massachusetts 02142 Menlo Park, California 94025 Phone: (617) 234-2860 Phone: (415) 329-8400 FAX: (617) 243-4444 FAX: (415) 329-8480 E-mail: [email protected] E-mail: [email protected] Abstract Lisp is the world’s greatest programming language—or so its proponents think. The structure of Lisp makes it easy to extend the language or even to implement entirely new dialects without starting from scratch. Overall, the evolution of Lisp has been guided more by institutional rivalry, one-upsmanship, and the glee born of technical cleverness that is characteristic of the “hacker culture” than by sober assessments of technical requirements. Nevertheless this process has eventually produced both an industrial- strength programming language, messy but powerful, and a technically pure dialect, small but powerful, that is suitable for use by programming-language theoreticians. We pick up where McCarthy’s paper in the first HOPL conference left off. We trace the development chronologically from the era of the PDP-6, through the heyday of Interlisp and MacLisp, past the ascension and decline of special purpose Lisp machines, to the present era of standardization activities. We then examine the technical evolution of a few representative language features, including both some notable successes and some notable failures, that illuminate design issues that distinguish Lisp from other programming languages. We also discuss the use of Lisp as a laboratory for designing other programming languages. We conclude with some reflections on the forces that have driven the evolution of Lisp. -
The Final 50 Feet Urban Goods Delivery System
Seattle Department of Transportation THE FINAL 50 FEET URBAN GOODS DELIVERY SYSTEM Research Scan and Data Collection Project FINAL REPORT January 19, 2018 University of Washington Supply Chain Transportation and Logistics Center Urban Freight Lab 111 Wilson Ceramic Lab Box 352700 Seattle, WA 98195-2700 CONTENTS /ŶƚƌŽĚƵĐƟŽŶ .............................................................................................................................. 5 Chapter 1 —Industry Sector Research Scan .............................................................6 The Growth of E-Commerce: Moving More Goods, More Quickly ...........................................8 KŶůŝŶĞƐŚŽƉƉĞƌƐ͛ĞdžƉĞĐƚĂƟŽŶƐĨŽƌƐĞƌǀŝĐĞĂƌĞĂůƐŽƌŝƐŝŶŐ ...................................................... 10 Urban Goods Delivery Firms Must Provide Fast and Reliable Delivery Times ........................ 13 dŚƌĞĞWĂƌĐĞůŽŵƉĂŶLJWƌŽĮůĞƐ͗/ŶǀĞƐƟŶŐŝŶ'ƌŽǁƚŚ ........................................................... 13 New Technologies Are Transforming the Urban Goods Delivery System ............................... 16 /ŶĐƌĞĂƐĞĚǀŝƐŝďŝůŝƚLJŽĨĂƐƐĞƚƐ ................................................................................................. 16 ^ŚĂƌĞĚͲƵƐĞŵŽďŝůŝƚLJ͗ƐƚĂƌƚͲƵƉƐŝŶƚŚĞĞdžƉƌĞƐƐĂŶĚƉĂƌĐĞůĚĞůŝǀĞƌLJƐĞĐƚŽƌƐ ........................... 17 ůŽƵĚďĂƐĞĚƐĞƌǀŝĐĞƐĂŶĚŽƉĞŶ͕ƌĞĂůͲƟŵĞĚĂƚĂ ................................................................... 18 ^ŵĂƌƚWĂƌŬŝŶŐ^ŽůƵƟŽŶƐ ....................................................................................................... 19 ŚĂƉƚĞƌϮͲhƌďĂŶ'ŽŽĚƐĞůŝǀĞƌLJWƌŽĮůĞƐĨŽƌWƌŽƚŽƚLJƉĞƵŝůĚŝŶŐƐ -
Electronic Call Market Trading Nicholas Economides and Robert A
VOLUME 21 NUMBER 3 SPRING 1995 Electronic Call Market Trading Nicholas Economides and Robert A. Schwartz Hidden Limit Orders on the NYSE Thomas H.Mclnish and Robert A. Wood Benchmark Orthogonality Properties David E. Tierney and Jgery K Bailey - Equity Style ClassXcations Jon A. Christophmon "'Equity Style Classi6ications": Comment Charles A. Trzn'nka Corporate Governance: There's Danger in New Orthodoxies Bevis Longstreth Industry and Country EfZ'ects in International Stock Returns StmL. Heston and K. Ceert Routuenhont Currency Risk in International Portfolios: How Satisfying is Optimal Hedging? Grant W Gardner and Thieny Wuilloud Fundamental Analysis, Stock Prices, and the Demise of Miniscribe Corporation Philip D. Drake and John W Peavy, LLI Market Timing Can Work in the Real World Glen A. Lmm,Jt., and Gregory D. Wozniak "Market Timing Can Work in the Real World": Comment Joe Brocato and ER. Chandy Testing PSR Filters with the Stochastic Dominance Approach Tung Liang Liao and Peter Shyan-Rong Chou Diverfication Benefits for Investors in Real Estate Susan Hudson- Wonand Bernard L. Elbaum A PUBLICATION OF INSTITUTIONAL INVESTOR, INC. Electronic Call Market Trading Let competition increase @n'ency. Nicholas Economides and Robert A. Schwartz ince Toronto became the first stock exchange to computerize its execution system in 1977, electronic trading has been instituted in Tokyo S(1 982), Paris (1986), Australia (1990), Germany (1991). Israel (1991), Mexico (1993). Switzerland (1995), and elsewhere around the globe. Quite likely, by the year 2000, floor trading will be totally eliminat- ed in Europe, predominantly in favor of electronic con- tinuous markets. Some of the new electronic systems are call mar- kets, however, including the Tel Aviv StockExchange, the Paris Bourse (for thinner issues), and the Bolsa Mexicana's intermediate market. -
Robert Alan Saunders
SMITHSONIAN INSTITUTION LEMELSON CENTER FOR THE STUDY OF INVENTION AND INNOVATION Robert Alan Saunders Transcript of an interview conducted by Christopher Weaver at National Museum of American History Washington, D.C., USA on 29 November 2018 with subsequent additions and corrections For additional information, contact the Archives Center at 202-633-3270 or [email protected] All uses of this manuscript are covered by an agreement between the Smithsonian Institution and Robert Alan Saunders dated November 29, 2018. For additional information about rights and reproductions, please contact: Archives Center National Museum of American History Smithsonian Institution MRC 601 P.O. Box 37012 Washington, D.C. 20013-7012 Phone: 202-633-3270 TDD: 202-357-1729 Email: [email protected] Web: http://americanhistory.si.edu/archives/rights-and-reproductions Preferred citation: Robert Alan Saunders, “Interview with Robert Alan Saunders,” conducted by Christopher Weaver, November 29, 2018, Video Game Pioneers Oral History Collection, Archives Center, National Museum of American History, Smithsonian Institution, Washington, DC. Acknowledgement: The Smithsonian’s Lemelson Center for the Study of Invention and Innovation gratefully acknowledges financial support from the Entertainment Software Association and Coastal Bridge Advisors for this oral history project. For additional information, contact the Archives Center at 202-633-3270 or [email protected] Abstract Robert Saunders begins discussing his early family life, education, and early exposure to electrical engineering. He next recounts his time at MIT, recalling members of the Tech Model Railroad Club and his work with the TX-0 and PDP-1 computers. Saunders discusses the contributions of Spacewar! team members to the project and his development of the original PDP-1 game controllers. -
SOA Education Day for CMU Students and Faculty
IBM SOA Roadshow SOA Education Day For CMU Students and Faculty Frank Stein Program Director IBM Federal SOA Institute © 2007 IBM Corporation IBM SOA Architect Summit Agenda 9:00 – 9:15am Welcome – Dr. Randy Bryant, Dean, SCS 9:15 – 9:45am Introduction to SOA – Frank Stein 9:45 – 10:15am Leveraging Information in SOA – Mark Sherman 10:15 – 10:45am SOA Governance and SOA Lifecycle – Naveen Sachdeva 10:45 – 11:15am Reuse, Community, Ecosystems & Catalogs – Jay Palat 11:15 – 12:00pm IBM’s SOA Experience, Trends, Outlook – Sanjay Bose 12:00 – 1:15pm Lunch Break & Faculty Reception 1:15 – 2:00pm Business Process Modeling Overview – Tom McManus & Emilio Zegarra 2:00 – 3:15pm Innov8 3D BPM “Serious Game” – Dave Daniel 2 SOA Education Day at CMU IBM SOA Roadshow Overview Presentation: SOA: A Foundation for Improved Agility and Economic Value Frank Stein Program Director IBM Federal SOA Institute © 2007 IBM Corporation IBM SOA Architect Summit Cloud Computing Announcement – Oct 10, 2007 • IBM and Google will provide hw, sw, and services to assist the academic community to explore Internet-scale computing. •CMU, MIT, Stanford, U of Washington, UC-Berkeley, U of MD 4 SOA Education Day at CMU IBM SOA Architect Summit Agenda Why SOA SOA Technical Concepts What’s Next for SOA Summary 5 SOA Education Day at CMU IBM SOA Architect Summit Businesses are Placing a Premium on Innovation THINK THINK 6 SOA Education Day at CMU IBM SOA Architect Summit Innovation Impacts Business Models Is Your Architecture Ready? “ On a flat earth, the most important -
Carlos Guardia Rivas Ibm Spain
CARLOS GUARDIA RIVAS IBM SPAIN EXECUTIVE IT SPECIALIST - IBM SOFTWARE GROUP WHAT IS THE ROLE OF DATA WITHIN YOUR ACTIVITY AT IBM? IBM is closely related to data and is a big data producer. For example, a few years ago IBM bought The Weather Company. This company generates climate data, compiles it and sells it to, especially, insurance companies and also to other private companies that make use of this kind of data. Even if IBM uses external data repositories, it is mainly a data producer. As for the tools, at IBM we have a very complete portfolio of analytics tools, ranging from importing data from diverse sources, data integration in diverse formats, data cleaning, replication to other systems if needed… Our most famous analytics tool is Watson, which is basically a set of tools that allows you to do any kind of projection on the data: from visualization to prediction. In conclusion, we are a company that offers solutions in the main data-related areas. This is the field in which IBM is heavily investing. DOES IBM USE OPEN DATA FROM PUBLIC SOURCES? We mainly use our own IBM-produced data and tools. We have a big variety of databases: structured, non-structured, analysis-oriented… CONSIDERING THAT MOST OF THE DATA IS GENERATED BY IBM, WHAT KIND OF STANDARDS DO YOU APPLY? Our own customers demand us to adjust to specific standards when they use our tools. Therefore, we comply with all the typical standards in data management. If we didn’t do so, we wouldn’t sell anything! We are always keeping an eye on the market’s demand for specific standards.