Problem Frames 7 - Model domains and real worlds.

Slides:



Advertisements
Similar presentations
Data Flow Diagramming Rules Processes –a process must have at least one input –a process must have at least one output –a process name (except for the.
Advertisements

Proceedings of the Conference on Intelligent Text Processing and Computational Linguistics (CICLing-2007) Learning for Semantic Parsing Advisor: Hsin-His.
Chapter 2- Visual Basic Schneider1 Chapter 2 Problem Solving.
Conditional Probability and Independence. Learning Targets 1. I can calculate conditional probability using a 2-way table. 2. I can determine whether.
Methods: Deciding What to Design In-Young Ko iko.AT. icu.ac.kr Information and Communications University (ICU) iko.AT. icu.ac.kr Fall 2005 ICE0575 Lecture.
Problem Frames 8 - Variant frames. Variants Model Operator Description Connection Control.
Model Domains and Real Worlds Book: Problem Frames: Analyzing and structuring software development problems Author: Michael Jackson Presenter: Ryan Waggoner.
PROCESS MODELING Transform Description. A model is a representation of reality. Just as a picture is worth a thousand words, most models are pictorial.
Object (Data and Algorithm) Analysis Cmput Lecture 5 Department of Computing Science University of Alberta ©Duane Szafron 1999 Some code in this.
1 Institute for Software Research, International Methods of Software Development Problem Frames 1 (This lecture is largely based on material graciously.
2-1 Sample Spaces and Events Conducting an experiment, in day-to-day repetitions of the measurement the results can differ slightly because of small.
Problem Frames 10 & 111 Decomposition and Composition Problem Frames: Ch. 10,11.
Advanced Accounting Information Systems
3.1 Chapter 3 Data and Signals Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
 Dr. Syed Noman Hasany.  Review of known methodologies  Analysis of software requirements  Real-time software  Software cost, quality, testing and.
Process Modeling SYSTEMS ANALYSIS AND DESIGN, 6 TH EDITION DENNIS, WIXOM, AND ROTH © 2015 JOHN WILEY & SONS. ALL RIGHTS RESERVED. 1 Roberta M. Roth.
Function: Definition A function is a correspondence from a first set, called the domain, to a second set, called the range, such that each element in the.
Learning Objectives for Section 2.1 Functions
Datasheets II: Sum, sort, filter, and find your data Overview: Sum it up, and more Access 2007 makes it easier than ever to perform math functions on your.
CASE Tools And Their Effect On Software Quality Peter Geddis – pxg07u.
Chapter 1: Introduction to Statistics
Derivative of the logarithmic function
What different types of taxes are deducted from employee’s paychecks? LESSON DO NOW.
Huseyin Ergin and Eugene Syriani. PROBLEM (DIDN’T CHANGE) Development of model transformation is still an error-prone and hard task. One reason is the.
Mathematical Processes GLE  I can identify the operations needed to solve a real-world problem.  I can write an equation to solve a real-world.
Concepts and Terminology Introduction to Database.
1 Random Variables and Discrete probability Distributions Chapter 7.
Problem Analysis and Structure Models and Frames.
5 Minute Check Find the prime factorization for each number
Scientific Inquiry & Skills
University of Toronto Department of Computer Science © Steve Easterbrook. This presentation is available free for non-commercial use with attribution.
Discrete Distributions The values generated for a random variable must be from a finite distinct set of individual values. For example, based on past observations,
FUN with Functions! Lesson Study Group B February 2012.
Identifying Relations and Functions A relation is a set of ordered pairs. The domain of the relation is x-coordinate of the ordered pair. It is also considered.
Analysis Modeling. Function Modeling & Information Flow  Information is transformed as it flows through a computer-based system. The system accepts input.
Procedures for managing workflow components Workflow components: A workflow can usually be described using formal or informal flow diagramming techniques,
Computational Paradigms and Process Frameworks. State-Oriented Models Examples: –Automata (DFAs, NFAs, PDAs) –Turing Machines A finite state machine is.
SAMPLING TECHNIQUES. Definitions Statistical inference: is a conclusion concerning a population of observations (or units) made on the bases of the results.
The Static Analysis Model Class Diagrams Prof. Hany H. Ammar, CSEE, WVU, and Dept. of Computer Science, Faculty of Computers and Information, Cairo University.
Probability (Ch. 6) Probability: “…the chance of occurrence of an event in an experiment.” [Wheeler & Ganji] Chance: “…3. The probability of anything happening;
Logic Signals and Gates. Binary Code Digital logic hides the pitfalls of the analog world by mapping the infinite set of real values for a physical quantity.
Simulation is the process of studying the behavior of a real system by using a model that replicates the system under different scenarios. A simulation.
Mathematical Preliminaries
Intermediate 2 Computing Unit 2 - Software Development.
7.8 Inverse Functions and Relations Horizontal line Test.
The Interpreter Pattern (Behavioral) ©SoftMoore ConsultingSlide 1.
Confidence Interval Estimation For statistical inference in decision making: Chapter 9.
Write a function rule for a graph EXAMPLE 3 Write a rule for the function represented by the graph. Identify the domain and the range of the function.
1 Information System Analysis Topic-3. 2 Entity Relationship Diagram \ Definition An entity-relationship (ER) diagram is a specialized graphic that illustrates.
CME Mathematics II Chapter 4 Functions Objectives: Identify and describe patterns in tables Use differences to decide what type of function can fit a table.
1 CS 430 Database Theory Winter 2005 Lecture 3: A Fifty Minute Introduction to Data Modeling.
Copyright © Cengage Learning. All rights reserved. Fundamentals.
Beyond Scenarios: Generating State Models from Use Cases An approach for the synthesis of State transition graphs from Use Cases Supporting Use Cases Based.
Chapter5 Statistical and probabilistic concepts, Implementation to Insurance Subjects of the Unit 1.Counting 2.Probability concepts 3.Random Variables.
Functions Algebra of Functions. Functions What are functions?
Elements, Compounds, and Mixtures
Chapter 6 The Traditional Approach to Requirements.
Mrs.Volynskaya Functions
Modern Systems Analysis and Design Third Edition
Function Rules, Tables, and Graphs
Lesson 1.1 How do you evaluate algebraic expressions and powers?
Splash Screen.
Confidence intervals for the difference between two means: Independent samples Section 10.1.
Relations.
ECE 352 Digital System Fundamentals
Functions and Tables.
Splash Screen.
The Selection Problem.
ECE 352 Digital System Fundamentals
Presentation transcript:

Problem Frames 7 - Model domains and real worlds

Adding a “model” domain Technique for information display problems A problem frame variant A way of decomposing a problem into two subproblems Makes the problem simpler Describes the machine more accurately

Information Display: problem frame diagram Real world Display Information machine C C Display - Real world RW!C1 IM!E2 C3 Y4

Information Display with model Real world Display machine C C Display - Model RW!C1 IM!E2 C3 Y4 Model X X Modeling machine Model - Real world Y6 MM!E5 DM!E7

Decomposing Invent the model Split requirements into requirements for building the model, and requirements for displaying the results Make two specifications

Composition Two requirements should compose to form the original requirements –Except for extra model phenomena Two specifications should compose to form the original specification –Compositions should be easy, because –One creates model, the other reads it

Why use a model? Lets machine remember phenomena from the past –Model determines the questions that can be answered –Model indicates memory that is needed Lets machine carry out some calculations incrementally

Why use a model? Can model defined terms as if they correspond to separate phenomena Can model processes of a conceptual domain as if they were physical entities Can capture and embody inference rules Can provide surrogates for private phenomena of the modelled domain

Model imperfections Model makes assumptions Continuously varying phenomena are modeled by discrete samples Samples are gathered at different times Time lag: sample is gathered after event happened

Model Imperfections Incompleteness: missing samples or values –Value doesn’t exist –Value is unknown Errors

Example: Experimental voltages Measure voltages at 32 points in a circuit Display voltages as columns side by side on the screen Display average voltage over all the points

More experimental voltages Display the average voltage of each point since the experiment began –Discrete –Finite number of samples –Time lag Model: for each point, a count and a total

More experimental voltages Average over last 5 minutes –Must keep set of samples for 5 minutes Maximum voltage –Keep maximum for each point

Example: Payroll System Inputs: Time cards, New employee form, benefit choice, raise, W4 form Outputs: Pay checks, W1 form, W2 form, check and forms to insurance company

Payroll problem diagram fitted to information display frame Payroll System Payroll forms Output C C d a b c Requirements for payroll a: Pf! {time cards, new employee form, benefit choice, raise, W4} C1 b: PS! {paychecks, W1, W2, checks and forms to insurance} E2 c: Pf! {time cards, new employee form, benefit choice, raise, W4} C3 d: O! {paychecks, W1, W2, checks and forms to insurance} Y4

Payroll input Real world Payroll DB C X Y6 RW!C1 PI!E5 C3 DB - Real world Reports machine Payroll DB Reports X C Y4 MD!Y7 RM!E2 Y6 Reports -DB

Checkwriting machine Payroll DB Paycheck X C Y4 PD!Y7 CM!E2 Y6 Paychecks - DB W2 machine Payroll DB W2 X C Y4 PD!Y7 WM!E2 Y6 W2 - DB

Model Domains are common Compilers (Transformation) –symbol table, abstract syntax tree Robotics (controlled behavior) –map, where we are on the map Commanded behavior, Workpieces –undo, selections

Conclusion Models are useful for information display Models are probably useful for other problem frames Models are an example of a problem variant, and an example of decomposing problems and composing specifications