Fall Andrew U. Frank, October 23, 2011

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Fall 2011. Andrew U. Frank, October 23, 2011"

Transcription

1 Fall 2011 Andrew U. Frank, October 23, 2011 TU Wien, Department of Geoinformation and Cartography, Gusshausstrasse 27-29/E127.1 A-1040 Vienna, Austria Part I Introduction 1 The data producer's perspective Current approaches to geo data quality are pushed by the producers of geo data primarily the National Mapping Agencies NMA to communicate the standards they maintain [nsds, morrison] and to coordinate necessary specication with other agencies, which are potentially users of their data [fcdic]. Some NMA make their data available again substantial fees and use data quality arguments to try to convince potential users that the data are of high quality and therefore the high prices are justied [some german/austrian publication?]. Data producers, similar to producers of other goods, claim that their product is of high quality. Unlike material goods, which are produced for a particular use, data can be used for many purposes - this is what GIS, previously called multipurpose cadastre [], is all about [wisconsin paper]. For a physical good, e.g. a pair of scissors for cutting hair, it is relatively clear what requirements a user has and what 'high quality' means; but note that a pair of scissors for cutting paper is not of high quality if one intents to cut hair (or reverse!). I would interpret high quality as fullls the users requirements for the intended purpose. But what does 'high quality' mean for a good with not yet dened and thus not yet known use? High quality for what? - Here starts the problem of geo data quality! The producer knows the production method, the instruments used etc. and understands their eect on the quality of the data collected. A surveyor can describe the statistical deviations from true values for the coordinates of the 1

2 points determined, assuming a normal distribution and indicating mean and standard deviation of the results. But precision of location is not the only aspect of geo data quality: Geo data quality has multiple aspects. Besides the precision of locations, matters obviously: when the data was collected, which themes are included, and how they are coded etc. In the mid 1980s tentative lists of what data quality aspects need to be included were published [chrisman, frank 1986]. They included, dierentiated between precision and resolution, for the three components of geo data: geometry, thematic data and time [sinton]. As a cope-out aspect, lineage was added, where data producers should describe where the data originated and how it was produced and treated. These views more or less reworked are the state of the art in today's data quality standards [refs]. <<insert a matrix: precision resolution // geometry, time, theme>> Only later was discovered that these aspects were not orthogonal to each other [frank?]. For example, spatial and temporal precision are hard to separate completely - an uncertain location and a sharp time time cannot be dierentiated from a certain location and a uncertain time stamp (g). This is only an application of Sinton's generic description of geographic data with three aspects (location, time, theme), of which one is xed, one varies as the independent variable, and the last is the dependent variable. Practical progress in reporting data quality for data sets is slow, despite the publication of standards. Hunter started a systematic investigations [ref], which revealed not only many missing indications, but often found uninformative values. What should a user do with a description of geometric precision as varying? Not much more is learned from precision between 2m and 10 km. I take this as indicating, that the practitioners among the data producers know that the users hardly ever consult the metadata, and that the data quality values in the metadata hardly ever help the user decide whether to use the dataset or not. This is conrmed by studies of user behavior [ann boin], which reveals other information users use to make the decision whether to use a dataset. The separation of the producers point of view from the perspective of the user introduced by Timpf [] help the research out of the impasse and stagnation. It posed a number of new questions for research: how to describe the user's requirements, how to connect the producers descriptions of data quality with the user requirements; These questions are the major driving force and provide the guideline for the presentation in this course. The practical goal of geo data quality research should be to achieve an operational connection between the data quality description from the producer's perspective which we know how to do and the decision by the user, whether a geo data set is useful for him and he should acquire and use it. 2

3 2 The users perspective If we consider the users perspective on data quality, we have to ask why a user would acquire a dataset and how data quality will aect this decision. It is obvious, that data which is not useful for the user will not be acquired - but what does not useful in this context? To answer the question, why a potential user would acquire some data, we have to look into the users situation. 2.1 Data serve only in decision situations When does a user need data? The only use of data is to improve decisions this is the only use of data! therefore, a user will consider the acquisition of data only when he needs them to make a decision, i.e. specic situation, not some generic need to know. The modern, highly distributed methods of decision making in corporations and public administration produce many situations, where potential decision-makers ask for data, which is, however, always related to some possible decision situation. The decision not to act is a decision as well; decision-makers typically ask for information to help them rst decide if an action by them is necessary and often no further action is observable - meaning that the decision was not to act. 2.2 Model of decision making A model of a decision is required for a formal analysis: a decision is a choice between dierent alternative actions, represented as a 1, a 2,... a n. A person makes a decision between the alternatives such that the outcome of the action he selects promises to be the best, the most advantageous outcome for him. In his mind, the outcome for each of the actions a i is the transformation of the current state s 0 to a new state s i ; the states s i are evaluated by a valuation function v, which produces for each state s i the corresponding value v i. The action which corresponds to the highest value v i is the most advantageous and is therefore selected. <<gure>> Note, that we do not assume that the user knows exactly what state follows from an action and what his valuation of this state will be, after execution. The concept of bounded rationality introduced by Herb Simon [] posits only that the decision maker has some idea of what the outcome will be and how he imagines the value of this outcome. From experience, we all know that we are sometimes very limited in what we know and select actions because we erroneously imagine an outcome which never realizes and we are disappointed when we realize our error in expecting a specic outcome or the error in valuation of an outcome we have imagined much nicer than what is actually achieved. 3

4 Figure 1: Model for decision making; without information, the maximum of v i (i = 1, 2,or 3). Information is acquired if v c, which is the expected value achieved with information, is larger than v i. 2.3 Role of information in decision making Assume a decision maker with the alternatives a 1, a 2,... a n as before, but the additional choice to acquire some data d, which contains information of relevance for the decision (Fig. 2.3). When should the data d be acquired? Lets label the alternatives, when executed after acquiring the data d with primes: a 1, a 2,... a n, to which outcomes s i with valuations v ibelong (Fig. 2). Given the additional information the user has, neither the outcomes nor the valuations are necessary the same as the ones he would expect without the acquired information. A rational decision maker will again select the action among a 1, a 2,... a n and a 1, a 2,... a n which give the best value. The apparent value of the information is the contribution to improve the decision, i.e. the dierence in the maximum of the values v i and the maximum of the values v i.the acquisition of the data was worthwhile if the maximum of the values v i, say v m, is larger than the maximum of the values v i, say v m ; a rational user should be ready to pay the dierence between v m and v m. With the assumption of bounded rationality, one must actually include an additional compound decision a c which is the action of acquiring the data and then select the best decision; the initially, before acquiring the data, expected value of this v c enters in the assessment of the willingness to pay for acquiring data as v c v m. The real value of the data is only revealed after the fact, when the actions are carried out and the real outcome of the decisions is revealed. The eect of acquiring data is often (only) a reduction of risk in a decision, which must be counted as a positive contribution. 4

5 Figure 2: Model for decision making; after acquisition of information, the improvement of the decision through the information can be evaluated. 3 Model of data quality from a user perspective 3.1 When is data correct (from a user perspective) Correctness of data is the pinnacle of data quality. When is data correct? Much has been discussed by data producers and standards state what deviation from values from re-measurements of higher quality is acceptable - often quite arbitrary rules, dictated by practicability, available resources of an agency etc. If we take the perspective of the user, the answer is relatively easy: data is correct if it leads to decisions for actions, which can be carried out and have the expected results. Some simplistic examples: a railway timetable entry is correct, if is leads us to catch the desired train: if we arrive at the station before the indicated time we are able to catch the respective train; navigation instructions are correct, if they can be followed (i.e. not leading to actions prohibited by the driving rules established by law) and lead to the desired goal, i.e. we reach the destination. This denition of correctness of data from a user perspective does not require that the data gives a true description of reality, as is sometimes requested, but only that the eect of deviations from a true description does not inuence the decision substantially - meaning another decision would be better, if the data were better. This leads to an understanding of the value of data and indirectly to the quality of the data always related to a specic decision situation. It hints to a reduced need for quality in the data: lack of correctness in the data is only aecting a decision, if another decision would be better than the one selected based on the erroneous data; given that for a decision we seldom have many options, then only data which is better than helping us to avoid selecting 5

6 the wrong alternative, is necessary. This means that approximate data and heuristic methods for decision making are sucient to select among the few alternatives one has in reality. It is meaningless to ask for data quality from a user perspective without considering a specic decision situation. 3.2 Quality of a decision Assume a decision situation, where the optimal decision is ã m and the decision with the available information is a m, the value of the information is the improvement of the decision and the degraded available information is thus just ã m a m less valuable than the perfect one. Consider the decision making d as a function d from some input data values d i to a decision (a i, v i ). Using ideas from adjustment computations to this decision function, one posit, that the optimal decision ã m results from correct values d for each input data element. In consequence, the contribution for the deviation of each data element from the correct value can be computed - assuming that the deviations are not large, linearization of the function d is permitted. (a i, v i ) = d(d i ) The data quality of a data element is then derived from the contribution it makes to the correctness of the decision. We can compare the decision with information d i compared to the decision we would make with no particular information d 0 (the absence of additional information is just the a particular case of erroneous information). Comparing the corresponding values indicates what contribution this data makes to the decision and says what a rational decision maker would be willing pay for it. [my paper] 4 Summary Data quality is not unlike the quality of other products: producers claim 'high quality', meaning that the data are produced with high quality inputs and carefully arranged operations under permanent control and nally checked against exacting standards. What sounds very similar to material production is somewhat complicated that the denition of dimensions on which to measure data - quantity as well as quality - is considerably more complex than for material goods. Measuring the quantity of data you receive from a source is far more complicated and no widely accepted consensus on how to do it exists - it is denitely not as easy as weighting a bag of potatoes. Measuring the quality is equally dicult and not comparable to non-trivial, but standardized measure of the starch content of said bag of potatoes (some industries pay potatoes for their starch content, which I consider here a quality attribute of potatoes). We have also seen dierence between material goods and data, e.g. non-rival, 6

7 multipurpose, experience good; aect how quality for data is somewhat dierent from quality descriptions for material goods. Considering decision making as a function from data to outcomes shows how the eect of data and data quality on a decision can be analyzed; given that deviation from correct values are small, linearization of the function is possible. The quality of the decision can be calculated by applying Gauss' law of error propagation from the quality of the input data. This decision quality deriving formula is in principle the desired method to translate the data quality descriptions of the producer to the data quality of the user. The restriction in principle indicates that the assumption of normal distribution of the deviations, i.e. that the deviations from perfect quality can be described statistically with standard deviations, is not justied for all data quality aspects. The completeness - technically described by omission and commission rates, for example, needs other statistical methods. To gain some insight, we start an ontological approach next. 7

Oscillations of the Sending Window in Compound TCP

Oscillations of the Sending Window in Compound TCP Oscillations of the Sending Window in Compound TCP Alberto Blanc 1, Denis Collange 1, and Konstantin Avrachenkov 2 1 Orange Labs, 905 rue Albert Einstein, 06921 Sophia Antipolis, France 2 I.N.R.I.A. 2004

More information

The Universe of Discourse Design with Visible Context

The Universe of Discourse Design with Visible Context The Universe of Discourse Design with Visible Context Rational GUI Andrew U. Frank TU Wien, Department of Geoinformation Gusshausstrasse 27-29/E127.1 A-1040 Vienna, Austria frank@geoinfo.tuwien.ac.at for

More information

Bilateral Exposures and Systemic Solvency Risk

Bilateral Exposures and Systemic Solvency Risk Bilateral Exposures and Systemic Solvency Risk C., GOURIEROUX (1), J.C., HEAM (2), and A., MONFORT (3) (1) CREST, and University of Toronto (2) CREST, and Autorité de Contrôle Prudentiel et de Résolution

More information

Software development process

Software development process OpenStax-CNX module: m14619 1 Software development process Trung Hung VO This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 2.0 Abstract A software development

More information

Clustering and scheduling maintenance tasks over time

Clustering and scheduling maintenance tasks over time Clustering and scheduling maintenance tasks over time Per Kreuger 2008-04-29 SICS Technical Report T2008:09 Abstract We report results on a maintenance scheduling problem. The problem consists of allocating

More information

Stock Investing Using HUGIN Software

Stock Investing Using HUGIN Software Stock Investing Using HUGIN Software An Easy Way to Use Quantitative Investment Techniques Abstract Quantitative investment methods have gained foothold in the financial world in the last ten years. This

More information

Is a Single-Bladed Knife Enough to Dissect Human Cognition? Commentary on Griffiths et al.

Is a Single-Bladed Knife Enough to Dissect Human Cognition? Commentary on Griffiths et al. Cognitive Science 32 (2008) 155 161 Copyright C 2008 Cognitive Science Society, Inc. All rights reserved. ISSN: 0364-0213 print / 1551-6709 online DOI: 10.1080/03640210701802113 Is a Single-Bladed Knife

More information

Many algorithms, particularly divide and conquer algorithms, have time complexities which are naturally

Many algorithms, particularly divide and conquer algorithms, have time complexities which are naturally Recurrence Relations Many algorithms, particularly divide and conquer algorithms, have time complexities which are naturally modeled by recurrence relations. A recurrence relation is an equation which

More information

Information and Responsiveness in Spare Parts Supply Chains

Information and Responsiveness in Spare Parts Supply Chains Information and Responsiveness in Spare Parts Supply Chains Table of Contents 1.0 Motivation... 3 2.0 What is Supply Chain?... 3 2.1 Spare Parts Supply Chain... 4 2.2 Spare Part Supply Chain Characteristics...

More information

programming languages, programming language standards and compiler validation

programming languages, programming language standards and compiler validation Software Quality Issues when choosing a Programming Language C.J.Burgess Department of Computer Science, University of Bristol, Bristol, BS8 1TR, England Abstract For high quality software, an important

More information

1 Example of Time Series Analysis by SSA 1

1 Example of Time Series Analysis by SSA 1 1 Example of Time Series Analysis by SSA 1 Let us illustrate the 'Caterpillar'-SSA technique [1] by the example of time series analysis. Consider the time series FORT (monthly volumes of fortied wine sales

More information

DATA QUALITY AND SCALE IN CONTEXT OF EUROPEAN SPATIAL DATA HARMONISATION

DATA QUALITY AND SCALE IN CONTEXT OF EUROPEAN SPATIAL DATA HARMONISATION DATA QUALITY AND SCALE IN CONTEXT OF EUROPEAN SPATIAL DATA HARMONISATION Katalin Tóth, Vanda Nunes de Lima European Commission Joint Research Centre, Ispra, Italy ABSTRACT The proposal for the INSPIRE

More information

GEOGRAPHIC INFORMATION SYSTEMS CERTIFICATION

GEOGRAPHIC INFORMATION SYSTEMS CERTIFICATION GEOGRAPHIC INFORMATION SYSTEMS CERTIFICATION GIS Syllabus - Version 1.2 January 2007 Copyright AICA-CEPIS 2009 1 Version 1 January 2007 GIS Certification Programme 1. Target The GIS certification is aimed

More information

Spatial data quality assessment in GIS

Spatial data quality assessment in GIS Recent Advances in Geodesy and Geomatics Engineering Spatial data quality assessment in GIS DANIELA CRISTIANA DOCAN Surveying and Cadastre Department Technical University of Civil Engineering Bucharest

More information

TOWARDS AN AUTOMATED HEALING OF 3D URBAN MODELS

TOWARDS AN AUTOMATED HEALING OF 3D URBAN MODELS TOWARDS AN AUTOMATED HEALING OF 3D URBAN MODELS J. Bogdahn a, V. Coors b a University of Strathclyde, Dept. of Electronic and Electrical Engineering, 16 Richmond Street, Glasgow G1 1XQ UK - jurgen.bogdahn@strath.ac.uk

More information

Ulrich A. Muller UAM.1994-01-31. June 28, 1995

Ulrich A. Muller UAM.1994-01-31. June 28, 1995 Hedging Currency Risks { Dynamic Hedging Strategies Based on O & A Trading Models Ulrich A. Muller UAM.1994-01-31 June 28, 1995 A consulting document by the O&A Research Group This document is the property

More information

Adjusting Nominal Values to Real Values

Adjusting Nominal Values to Real Values OpenStax-CNX module: m48709 1 Adjusting Nominal Values to Real Values OpenStax This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 4.0 By the end of this section,

More information

EVALUATION BY PARTS OF TRUSTED. Ravi S. Sandhu. George Mason University, Fairfax, VA 22030-4444

EVALUATION BY PARTS OF TRUSTED. Ravi S. Sandhu. George Mason University, Fairfax, VA 22030-4444 Proc. 4th RADC Workshop on Multilevel Database Security, Rhode Island, April 1991, pages 122-126. EVALUATION BY PARTS OF TRUSTED DATABASE MANAGEMENT SYSTEMS Ravi S. Sandhu Center for Secure Information

More information

Exercises Engenharia de Software (cod. 5386 & 6633 )

Exercises Engenharia de Software (cod. 5386 & 6633 ) Exercises Engenharia de Software (cod. 5386 & 6633 ) Departamento de Informática Universidade da Beira Interior Ano lectivo 2010/2011 These exercises are taken from Software Engineering, 9th edition, Pearson

More information

Draft Martin Doerr ICS-FORTH, Heraklion, Crete Oct 4, 2001

Draft Martin Doerr ICS-FORTH, Heraklion, Crete Oct 4, 2001 A comparison of the OpenGIS TM Abstract Specification with the CIDOC CRM 3.2 Draft Martin Doerr ICS-FORTH, Heraklion, Crete Oct 4, 2001 1 Introduction This Mapping has the purpose to identify, if the OpenGIS

More information

IMPLEMENTATION OF A MANAGEMENT AND QUALITY CONTROL SYSTEM UNDER ISO STANDARDS 9001:2000, 19113, 19114,19138 AND 19115 IN CARTOGRAPHIC PRODUCTION

IMPLEMENTATION OF A MANAGEMENT AND QUALITY CONTROL SYSTEM UNDER ISO STANDARDS 9001:2000, 19113, 19114,19138 AND 19115 IN CARTOGRAPHIC PRODUCTION IMPLEMENTATION OF A MANAGEMENT AND QUALITY CONTROL SYSTEM UNDER ISO STANDARDS 9001:2000, 19113, 19114,19138 AND 19115 IN CARTOGRAPHIC PRODUCTION SUMMARY INTRODUCTION JOSELYN A. ROBLEDO CEBALLOS joselyn.robledo@saf.cl

More information

The Time Value of Money

The Time Value of Money The Time Value of Money This handout is an overview of the basic tools and concepts needed for this corporate nance course. Proofs and explanations are given in order to facilitate your understanding and

More information

Introduction to Logistic Regression

Introduction to Logistic Regression OpenStax-CNX module: m42090 1 Introduction to Logistic Regression Dan Calderon This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 Abstract Gives introduction

More information

Productivity of Service Providers: Microeconometric measurement in the case of hair salons

Productivity of Service Providers: Microeconometric measurement in the case of hair salons RIETI Discussion Paper Series 10-E-051 Productivity of Service Providers: Microeconometric measurement in the case of hair salons KONISHI Yoko RIETI NISHIYAMA Yoshihiko Kyoto Institute of Economic Research,

More information

INDIVIDUAL COURSE DETAILS

INDIVIDUAL COURSE DETAILS INDIVIDUAL COURSE DETAILS A. Name of Institution NATIONAL INSTITUTE OF TECHNICAL TEACHERS TRAINING AND RESEARCH TARAMANI CHENNAI 600 113 [An Autonomous Institute under Ministry of Human Resource Development,

More information

PA Common Core Standards Standards for Mathematical Practice Grade Level Emphasis*

PA Common Core Standards Standards for Mathematical Practice Grade Level Emphasis* Habits of Mind of a Productive Thinker Make sense of problems and persevere in solving them. Attend to precision. PA Common Core Standards The Pennsylvania Common Core Standards cannot be viewed and addressed

More information

Intelligent Agents. Based on An Introduction to MultiAgent Systems and slides by Michael Wooldridge

Intelligent Agents. Based on An Introduction to MultiAgent Systems and slides by Michael Wooldridge Intelligent Agents Based on An Introduction to MultiAgent Systems and slides by Michael Wooldridge Denition of an Agent An agent is a computer system capable of autonomous action in some environment, in

More information

Geo-information in The Hague & National SDI hub PDOK

Geo-information in The Hague & National SDI hub PDOK Geo-information in The Hague & National SDI hub PDOK dr.ir. Friso Penninga senior advisor at Municipality of The Hague & tactical advisor at Geonovum Contents 1. Introduction 2. GI in The Hague a. Overview

More information

IMPLICIT COLLUSION IN DEALER MARKETS WITH DIFFERENT COSTS OF MARKET MAKING ANDREAS KRAUSE Abstract. This paper introduces dierent costs into the Dutta-Madhavan model of implicit collusion between market

More information

Microeconomics. Lecture Outline. Claudia Vogel. Winter Term 2009/2010. Part III Market Structure and Competitive Strategy

Microeconomics. Lecture Outline. Claudia Vogel. Winter Term 2009/2010. Part III Market Structure and Competitive Strategy Microeconomics Claudia Vogel EUV Winter Term 2009/2010 Claudia Vogel (EUV) Microeconomics Winter Term 2009/2010 1 / 25 Lecture Outline Part III Market Structure and Competitive Strategy 12 Monopolistic

More information

www.thecustomerexperience.es

www.thecustomerexperience.es www.thecustomerexperience.es 2 three The role of IT systems in managing customer experience Hugo Brunetta The term CRM program is used indistinctly and erroneously to refer to the strategy and the software

More information

Chapter 7. Continuity

Chapter 7. Continuity Chapter 7 Continuity There are many processes and eects that depends on certain set of variables in such a way that a small change in these variables acts as small change in the process. Changes of this

More information

STRUTS: Statistical Rules of Thumb. Seattle, WA

STRUTS: Statistical Rules of Thumb. Seattle, WA STRUTS: Statistical Rules of Thumb Gerald van Belle Departments of Environmental Health and Biostatistics University ofwashington Seattle, WA 98195-4691 Steven P. Millard Probability, Statistics and Information

More information

Approaches to Qualitative Evaluation of the Software Quality Attributes: Overview

Approaches to Qualitative Evaluation of the Software Quality Attributes: Overview 4th International Conference on Software Methodologies, Tools and Techniques Approaches to Qualitative Evaluation of the Software Quality Attributes: Overview Presented by: Denis Kozlov Department of Computer

More information

Two-step competition process leads to quasi power-law income distributions Application to scientic publication and citation distributions

Two-step competition process leads to quasi power-law income distributions Application to scientic publication and citation distributions Physica A 298 (21) 53 536 www.elsevier.com/locate/physa Two-step competition process leads to quasi power-law income distributions Application to scientic publication and citation distributions Anthony

More information

ArcGIS Data Models Practical Templates for Implementing GIS Projects

ArcGIS Data Models Practical Templates for Implementing GIS Projects ArcGIS Data Models Practical Templates for Implementing GIS Projects GIS Database Design According to C.J. Date (1995), database design deals with the logical representation of data in a database. The

More information

1 Uncertainty and Preferences

1 Uncertainty and Preferences In this chapter, we present the theory of consumer preferences on risky outcomes. The theory is then applied to study the demand for insurance. Consider the following story. John wants to mail a package

More information

Framework Data Content Standard Transportation: Transit. Course Information. Prerequisites

Framework Data Content Standard Transportation: Transit. Course Information. Prerequisites Framework Data Content Standard Transportation: Transit Course Information The National Spatial Data Infrastructure (NSDI) Framework is a collaborative initiative to develop a set of commonly used geographic

More information

Intermediate Microeconomics (22014)

Intermediate Microeconomics (22014) Intermediate Microeconomics (22014) I. Consumer Instructor: Marc Teignier-Baqué First Semester, 2011 Outline Part I. Consumer 1. umer 1.1 Budget Constraints 1.2 Preferences 1.3 Utility Function 1.4 1.5

More information

BUSINESS RULES AND GAP ANALYSIS

BUSINESS RULES AND GAP ANALYSIS Leading the Evolution WHITE PAPER BUSINESS RULES AND GAP ANALYSIS Discovery and management of business rules avoids business disruptions WHITE PAPER BUSINESS RULES AND GAP ANALYSIS Business Situation More

More information

Chapter 12 Modal Decomposition of State-Space Models 12.1 Introduction The solutions obtained in previous chapters, whether in time domain or transfor

Chapter 12 Modal Decomposition of State-Space Models 12.1 Introduction The solutions obtained in previous chapters, whether in time domain or transfor Lectures on Dynamic Systems and Control Mohammed Dahleh Munther A. Dahleh George Verghese Department of Electrical Engineering and Computer Science Massachuasetts Institute of Technology 1 1 c Chapter

More information

The program also provides supplemental modules on topics in geometry and probability and statistics.

The program also provides supplemental modules on topics in geometry and probability and statistics. Algebra 1 Course Overview Students develop algebraic fluency by learning the skills needed to solve equations and perform important manipulations with numbers, variables, equations, and inequalities. Students

More information

Statistics for Business Decision Making

Statistics for Business Decision Making Statistics for Business Decision Making Faculty of Economics University of Siena 1 / 62 You should be able to: ˆ Summarize and uncover any patterns in a set of multivariate data using the (FM) ˆ Apply

More information

Geography 4203 / 5203. GIS Modeling. Class 12: Spatial Data Quality and Uncertainty

Geography 4203 / 5203. GIS Modeling. Class 12: Spatial Data Quality and Uncertainty Geography 4203 / 5203 GIS Modeling Class 12: Spatial Data Quality and Uncertainty Some Updates - Progress Reports Progress reports: 11 & 14 April (instead of 14 & 16 April) Class on 16 April: Jeremy Class

More information

Revised Version of Chapter 23. We learned long ago how to solve linear congruences. ax c (mod m)

Revised Version of Chapter 23. We learned long ago how to solve linear congruences. ax c (mod m) Chapter 23 Squares Modulo p Revised Version of Chapter 23 We learned long ago how to solve linear congruences ax c (mod m) (see Chapter 8). It s now time to take the plunge and move on to quadratic equations.

More information

Simultaneous or Sequential? Search Strategies in the U.S. Auto. Insurance Industry. Elisabeth Honka 1. Pradeep Chintagunta 2

Simultaneous or Sequential? Search Strategies in the U.S. Auto. Insurance Industry. Elisabeth Honka 1. Pradeep Chintagunta 2 Simultaneous or Sequential? Search Strategies in the U.S. Auto Insurance Industry Elisabeth Honka 1 University of Texas at Dallas Pradeep Chintagunta 2 University of Chicago Booth School of Business October

More information

Operations management: Special topic: supply chain management

Operations management: Special topic: supply chain management OpenStax-CNX module: m35461 1 Operations management: Special topic: supply chain management Global Text Project This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution

More information

University of Tübingen Working Papers in Economics and Finance No. 75

University of Tübingen Working Papers in Economics and Finance No. 75 University of Tübingen Working Papers in Economics and Finance No. 75 Forward Trading and Collusion of Firms in Volatile Markets by Markus Aichele Faculty of Economics and Social Sciences www.wiwi.uni-tuebingen.de

More information

A Componentware Methodology based on Process Patterns Klaus Bergner, Andreas Rausch Marc Sihling, Alexander Vilbig Institut fur Informatik Technische Universitat Munchen D-80290 Munchen http://www4.informatik.tu-muenchen.de

More information

On computer algebra-aided stability analysis of dierence schemes generated by means of Gr obner bases

On computer algebra-aided stability analysis of dierence schemes generated by means of Gr obner bases On computer algebra-aided stability analysis of dierence schemes generated by means of Gr obner bases Vladimir Gerdt 1 Yuri Blinkov 2 1 Laboratory of Information Technologies Joint Institute for Nuclear

More information

Open Source Project Categorization Based on Growth Rate Analysis and Portfolio Planning Methods

Open Source Project Categorization Based on Growth Rate Analysis and Portfolio Planning Methods Open Source Project Categorization Based on Growth Rate Analysis and Portfolio Planning Methods Stefan Koch and Volker Stix Vienna University of Economics and Business Administration Institute for Information

More information

Ina Minei Reuven Cohen. The Technion. Haifa 32000, Israel. e-mail: faminei,rcoheng@cs.technion.ac.il. Abstract

Ina Minei Reuven Cohen. The Technion. Haifa 32000, Israel. e-mail: faminei,rcoheng@cs.technion.ac.il. Abstract High Speed Internet Access Through Unidirectional Geostationary Satellite Channels Ina Minei Reuven Cohen Computer Science Department The Technion Haifa 32000, Israel e-mail: faminei,rcoheng@cs.technion.ac.il

More information

How to Write a Successful PhD Dissertation Proposal

How to Write a Successful PhD Dissertation Proposal How to Write a Successful PhD Dissertation Proposal Before considering the "how", we should probably spend a few minutes on the "why." The obvious things certainly apply; i.e.: 1. to develop a roadmap

More information

Prot Maximization and Cost Minimization

Prot Maximization and Cost Minimization Simon Fraser University Prof. Karaivanov Department of Economics Econ 0 COST MINIMIZATION Prot Maximization and Cost Minimization Remember that the rm's problem is maximizing prots by choosing the optimal

More information

CHARACTERISTICS IN FLIGHT DATA ESTIMATION WITH LOGISTIC REGRESSION AND SUPPORT VECTOR MACHINES

CHARACTERISTICS IN FLIGHT DATA ESTIMATION WITH LOGISTIC REGRESSION AND SUPPORT VECTOR MACHINES CHARACTERISTICS IN FLIGHT DATA ESTIMATION WITH LOGISTIC REGRESSION AND SUPPORT VECTOR MACHINES Claus Gwiggner, Ecole Polytechnique, LIX, Palaiseau, France Gert Lanckriet, University of Berkeley, EECS,

More information

1 Monopoly Why Monopolies Arise? Monopoly is a rm that is the sole seller of a product without close substitutes. The fundamental cause of monopoly is barriers to entry: A monopoly remains the only seller

More information

Mississippi Private Schools 2015

Mississippi Private Schools 2015 Mississippi Private Schools 2015 Shapefile Tags education, schools, private, K-12 Summary To add to state data clearinghouse the Mississippi private schools point features. Description Point locations

More information

The CORAS Model-based Method for Security Risk Analysis

The CORAS Model-based Method for Security Risk Analysis The CORAS Model-based Method for Security Risk Analysis Folker den Braber, Gyrd Brændeland, Heidi E. I. Dahl, Iselin Engan, Ida Hogganvik, Mass S. Lund, Bjørnar Solhaug, Ketil Stølen, Fredrik Vraalsen

More information

Risk Analysis Approaches to Rank Outliers in Trade Data

Risk Analysis Approaches to Rank Outliers in Trade Data Risk Analysis Approaches to Rank Outliers in Trade Data Vytis Kopustinskas and Spyros Arsenis Abstract The paper discusses ranking methods for outliers in trade data based on statistical information with

More information

Microeconomics II. ELTE Faculty of Social Sciences, Department of Economics. week 9 MARKET THEORY AND MARKETING, PART 3

Microeconomics II. ELTE Faculty of Social Sciences, Department of Economics. week 9 MARKET THEORY AND MARKETING, PART 3 MICROECONOMICS II. ELTE Faculty of Social Sciences, Department of Economics Microeconomics II. MARKET THEORY AND MARKETING, PART 3 Author: Supervised by February 2011 Prepared by:, using Jack Hirshleifer,

More information

A Spatial Data Infrastructure for a Spatially Enabled Government and Society

A Spatial Data Infrastructure for a Spatially Enabled Government and Society Chapter 1 A Spatial Data Infrastructure for a Spatially Enabled Government and Society Abbas Rajabifard Centre for Spatial Data Infrastructures and Land Administration, Department of Geomatics, University

More information

Daniel F. DeMenthon and Larry S. Davis. Center for Automation Research. University of Maryland

Daniel F. DeMenthon and Larry S. Davis. Center for Automation Research. University of Maryland Model-Based Object Pose in 25 Lines of Code Daniel F. DeMenthon and Larry S. Davis Computer Vision Laboratory Center for Automation Research University of Maryland College Park, MD 20742 Abstract In this

More information

Abstract. Introduction

Abstract. Introduction Data Replication and Data Sharing Integrating Heterogeneous Spatial Databases Mark Stoakes and Katherine Irwin Professional Services, Safe Software Inc. Abstract Spatial data warehouses are becoming more

More information

Data Quality; is this the key to driving value out of your investment in SAP? Data Quality; is this the key to

Data Quality; is this the key to driving value out of your investment in SAP? Data Quality; is this the key to Driving Whitby Whitby value Partners Partners from Business Driving Intelligence value from Business Business Intelligence Intelligence Whitby Partners 78 York Street London W1H 1DP UK Tel: +44 (0) 207

More information

FINANCIAL ECONOMICS OPTION PRICING

FINANCIAL ECONOMICS OPTION PRICING OPTION PRICING Options are contingency contracts that specify payoffs if stock prices reach specified levels. A call option is the right to buy a stock at a specified price, X, called the strike price.

More information

Michael Cline. University of British Columbia. Vancouver, British Columbia. cline@cs.ubc.ca. bimanual user interface.

Michael Cline. University of British Columbia. Vancouver, British Columbia. cline@cs.ubc.ca. bimanual user interface. Higher Degree-of-Freedom Bimanual User Interfaces for 3-D Computer Graphics Michael Cline Dept. of Computer Science University of British Columbia Vancouver, British Columbia Canada V6T 1Z4 cline@cs.ubc.ca

More information

Monica Pratesi, University of Pisa

Monica Pratesi, University of Pisa DEVELOPING ROBUST AND STATISTICALLY BASED METHODS FOR SPATIAL DISAGGREGATION AND FOR INTEGRATION OF VARIOUS KINDS OF GEOGRAPHICAL INFORMATION AND GEO- REFERENCED SURVEY DATA Monica Pratesi, University

More information

CFSD 21 ST CENTURY SKILL RUBRIC CRITICAL & CREATIVE THINKING

CFSD 21 ST CENTURY SKILL RUBRIC CRITICAL & CREATIVE THINKING Critical and creative thinking (higher order thinking) refer to a set of cognitive skills or strategies that increases the probability of a desired outcome. In an information- rich society, the quality

More information

Managing large sound databases using Mpeg7

Managing large sound databases using Mpeg7 Max Jacob 1 1 Institut de Recherche et Coordination Acoustique/Musique (IRCAM), place Igor Stravinsky 1, 75003, Paris, France Correspondence should be addressed to Max Jacob (max.jacob@ircam.fr) ABSTRACT

More information

SECTION 2.5: FINDING ZEROS OF POLYNOMIAL FUNCTIONS

SECTION 2.5: FINDING ZEROS OF POLYNOMIAL FUNCTIONS SECTION 2.5: FINDING ZEROS OF POLYNOMIAL FUNCTIONS Assume f ( x) is a nonconstant polynomial with real coefficients written in standard form. PART A: TECHNIQUES WE HAVE ALREADY SEEN Refer to: Notes 1.31

More information

introduction by Stacey Barr

introduction by Stacey Barr There's no such thing as the performance measure stork! after you've conceived your measures, there's actually some unavoidable labour needed to bring them into the world by Stacey Barr introduction In

More information

Possibilistic programming in production planning of assemble-to-order environments

Possibilistic programming in production planning of assemble-to-order environments Fuzzy Sets and Systems 119 (2001) 59 70 www.elsevier.com/locate/fss Possibilistic programming in production planning of assemble-to-order environments Hsi-Mei Hsu, Wen-Pai Wang Department of Industrial

More information

Do Reported End Dates of Treatments Matter for Evaluation Results?

Do Reported End Dates of Treatments Matter for Evaluation Results? Methodische Aspekte zu Arbeitsmarktdaten No. 1/2007 Do Reported End Dates of Treatments Matter for Evaluation Results? An Investigation Based on the German Integrated Employment Biographies Sample Marie

More information

The Case for a New CRM Solution

The Case for a New CRM Solution The Case for a New CRM Solution Customer Relationship Management software has gone well beyond being a good to have capability. Senior management is now generally quite clear that this genre of software

More information

Finite cloud method: a true meshless technique based on a xed reproducing kernel approximation

Finite cloud method: a true meshless technique based on a xed reproducing kernel approximation INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING Int. J. Numer. Meth. Engng 2001; 50:2373 2410 Finite cloud method: a true meshless technique based on a xed reproducing kernel approximation N.

More information

DATA QUALITY IN GIS TERMINOLGY GIS11

DATA QUALITY IN GIS TERMINOLGY GIS11 DATA QUALITY IN GIS When using a GIS to analyse spatial data, there is sometimes a tendency to assume that all data, both locational and attribute, are completely accurate. This of course is never the

More information

Tracking Moving Objects In Video Sequences Yiwei Wang, Robert E. Van Dyck, and John F. Doherty Department of Electrical Engineering The Pennsylvania State University University Park, PA16802 Abstract{Object

More information

Coefficient of Potential and Capacitance

Coefficient of Potential and Capacitance Coefficient of Potential and Capacitance Lecture 12: Electromagnetic Theory Professor D. K. Ghosh, Physics Department, I.I.T., Bombay We know that inside a conductor there is no electric field and that

More information

hep-lat/9510048 26 Oct 1995

hep-lat/9510048 26 Oct 1995 Light meson decay constants beyond the quenched approximation G. M. de Divitiis, R. Frezzotti, M. Guagnelli, M. Masetti and R. Petronzio hep-lat/9510048 26 Oct 1995 Dipartimento di Fisica, Universita di

More information

PS engine. Execution

PS engine. Execution A Model-Based Approach to the Verication of Program Supervision Systems Mar Marcos 1 y, Sabine Moisan z and Angel P. del Pobil y y Universitat Jaume I, Dept. of Computer Science Campus de Penyeta Roja,

More information

Personal geographic Information Management

Personal geographic Information Management Personal geographic Information Management Amin Abdalla and Andrew U. Frank Vienna University of Technology Institute for Geoinformation and Cartography Abstract Traditionally personal information management

More information

Data quality in Accounting Information Systems

Data quality in Accounting Information Systems Data quality in Accounting Information Systems Comparing Several Data Mining Techniques Erjon Zoto Department of Statistics and Applied Informatics Faculty of Economy, University of Tirana Tirana, Albania

More information

APPLICATION OF FREE TACHEOMETRIC STATIONS IN MONITORING OF MONUMENTAL OBJECTS

APPLICATION OF FREE TACHEOMETRIC STATIONS IN MONITORING OF MONUMENTAL OBJECTS APPLICATION OF FREE TACHEOMETRIC STATIONS IN MONITORING OF MONUMENTAL OBJECTS Ryszard Malarski, Kamil Nagórski Warsaw University of Technology, Faculty of Geodesy and Cartography Department of Engineering

More information

1170 M. M. SANCHEZ AND X. CHEN

1170 M. M. SANCHEZ AND X. CHEN STATISTICS IN MEDICINE Statist. Med. 2006; 25:1169 1181 Published online 5 January 2006 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/sim.2244 Choosing the analysis population in non-inferiority

More information

The Complex Dynamics of a Simple Stock Market Model. Moshe Levy, Nathan Persky, and Sorin Solomon

The Complex Dynamics of a Simple Stock Market Model. Moshe Levy, Nathan Persky, and Sorin Solomon The Complex Dynamics of a Simple Stock Market Model Moshe Levy, Nathan Persky, and Sorin Solomon Racah Institute of Physics, Hebrew University, Jerusalem, Israel July 1995 Abstract We formulate a microscopic

More information

Monopolistic Competition, Oligopoly, and maybe some Game Theory

Monopolistic Competition, Oligopoly, and maybe some Game Theory Monopolistic Competition, Oligopoly, and maybe some Game Theory Now that we have considered the extremes in market structure in the form of perfect competition and monopoly, we turn to market structures

More information

Simulations, Games and Experiential Learning Techniques:, Volume1, 1974

Simulations, Games and Experiential Learning Techniques:, Volume1, 1974 THE BUSINESS GAME: A NEW APPROACH TO MANAGERIAL ACCOUNTING Kenneth R. Goosen, Louisiana Tech University The use of business games in marketing, management, and finance has grown tremendously, however,

More information

International Data Centre for Hydrology of Lakes and Reservoirs (HYDROLARE)

International Data Centre for Hydrology of Lakes and Reservoirs (HYDROLARE) Sengupta, M. and Dalwani, R. (Editors). 2008 Proceedings of Taal 2007: The 12th World Lake Conference: 2258-2262 International Data Centre for Hydrology of Lakes and Reservoirs (HYDROLARE) T.P. Gronskaya,

More information

Problem of the Month: Cutting a Cube

Problem of the Month: Cutting a Cube Problem of the Month: The Problems of the Month (POM) are used in a variety of ways to promote problem solving and to foster the first standard of mathematical practice from the Common Core State Standards:

More information

The Business Case for Information Management An Oracle Thought Leadership White Paper December 2008

The Business Case for Information Management An Oracle Thought Leadership White Paper December 2008 The Business Case for Information Management An Oracle Thought Leadership White Paper December 2008 NOTE: The following is intended to outline our general product direction. It is intended for information

More information

Problem of the Month: Perfect Pair

Problem of the Month: Perfect Pair Problem of the Month: The Problems of the Month (POM) are used in a variety of ways to promote problem solving and to foster the first standard of mathematical practice from the Common Core State Standards:

More information

Copulas in Financial Risk Management Dr Jorn Rank Department for Continuing Education University of Oxford A thesis submitted for the diploma in Mathematical Finance 4 August 2000 Diploma in Mathematical

More information

Development. Boston MA 02114 Murray Hill, NJ 07974 [CMU/SEI-95-SR-024]. A rational design process should address. software reuse.

Development. Boston MA 02114 Murray Hill, NJ 07974 [CMU/SEI-95-SR-024]. A rational design process should address. software reuse. Session 5: Key Techniques and Process Aspects for Product Line Development Nancy S. Staudenmayer Dewayne E. Perry Sloan School Software Production Research Massachusetts Institute of Technology Bell Laboratories

More information

Probability Models.S1 Introduction to Probability

Probability Models.S1 Introduction to Probability Probability Models.S1 Introduction to Probability Operations Research Models and Methods Paul A. Jensen and Jonathan F. Bard The stochastic chapters of this book involve random variability. Decisions are

More information

Section 1.1. Introduction to R n

Section 1.1. Introduction to R n The Calculus of Functions of Several Variables Section. Introduction to R n Calculus is the study of functional relationships and how related quantities change with each other. In your first exposure to

More information

The Causal Eect of Unemployment Duration on Wages: Evidence from Unemployment Insurance Extensions

The Causal Eect of Unemployment Duration on Wages: Evidence from Unemployment Insurance Extensions The Causal Eect of Unemployment Duration on Wages: Evidence from Unemployment Insurance Extensions Johannes F. Schmieder Till von Wachter Stefan Bender Boston University UCLA, NBER, Institute for Employment

More information

The rigorous development and verication of a boiler system specication is presented.

The rigorous development and verication of a boiler system specication is presented. Stepwise Development and Verication of a Boiler System Specication Peter Bishop and Glenn Bruns and Stuart Anderson Abstract The rigorous development and verication of a boiler system specication is presented.

More information

For example, estimate the population of the United States as 3 times 10⁸ and the

For example, estimate the population of the United States as 3 times 10⁸ and the CCSS: Mathematics The Number System CCSS: Grade 8 8.NS.A. Know that there are numbers that are not rational, and approximate them by rational numbers. 8.NS.A.1. Understand informally that every number

More information

Jena Research Papers in Business and Economics

Jena Research Papers in Business and Economics Jena Research Papers in Business and Economics Solving symmetric mixed-model multilevel just-in-time scheduling problems Malte Fliedner, Nils Boysen, Armin Scholl 18/2008 Jenaer Schriften zur Wirtschaftswissenschaft

More information

Preliminary Version { Comments Welcome

Preliminary Version { Comments Welcome Option Pricing under the Mixture of Distributions Hypothesis Marco Neumann Diskussionspapier Nr. 208 First Version: December 14, 1997 Current Version: June 27, 1998 Preliminary Version { Comments Welcome

More information