'Multi valued' presentation slideshows

CPSC 668 Distributed Algorithms and Systems

CPSC 668 Distributed Algorithms and Systems. Fall 2006 Prof. Jennifer Welch. Fault-Tolerant Shared Memory Simulations. What if some processors might crash? Can we still provide a shared read/write variable on top of message passing?

808 views • 69 slides

Multi-Valued Logic

821 views • 60 slides

Regular Structures

Regular Structures. Levelized Structures. Standard Lattice Diagrams for continuous, multiple-valued and binary logic. Lattice Structure for Multivalued and Binary Logic. Realizes every binary symmetric function Realizes every non-symmetric function by repeating variables

676 views • 58 slides

Chapter 2 Modeling Data in the Organization

Chapter 2 Modeling Data in the Organization. Jason C. H. Chen, Ph.D. Professor of MIS School of Business Administration Gonzaga University Spokane, WA 99258 chen@gonzaga.edu. EMPLOYEE. derived/ computed. composite. multi-valued. Questions:

923 views • 51 slides

Introduction to Databases

Introduction to Databases. Week One Relational and Object-Oriented Database Concepts Introduction to Database Design and Techniques. Examples of Database Applications. Purchases from the supermarket Purchases using your credit card Booking a holiday at the travel agents

1.08k views • 50 slides

Data Modeling

Data Modeling. Lecture 2 I502. Outline. Understand nature of data Data Flow Data Models Why model?. Data for Decisions and Knowledge. The goal is not just storage of data but support for decision making and learning by key people and groups in the organization

713 views • 45 slides

Entity Relationship Modeling (& Normalization)

Entity Relationship Modeling (& Normalization). Outline. Data Modeling: Big picture E-R Model Attributes types Relationships connectivity, cardinality strength, participation, degree Entities composite entity supertype/subtype Table Normalization normal forms 1NF, 2NF, 3NF.

851 views • 43 slides

Multi-Valued Neurons and Multilayer Neural Network based on Multi-Valued Neurons

Multi-Valued Neurons and Multilayer Neural Network based on Multi-Valued Neurons. MVN and MLMVN. f (-1,1)= -1. f (1,1)= 1. 1. -1. 1. -1. f (1,-1)= -1. f (-1,-1)= - 1. A threshold function is a linearly separable function.

616 views • 43 slides

Modeling Data In The Organization

Modeling Data In The Organization. Chapter 3. Objectives. Definition of terms Importance of data modeling Distinguish unary, binary, and ternary relationships Model different types of attributes, entities, relationships, and cardinalities Draw E-R diagrams for common business situations

629 views • 42 slides

MVSIS Group

Optimization of Multi-Valued Multi-Level Networks. MVSIS Group Minxi Gao,, Jie-Hong Jiang, Yunjian Jiang, Yinghua Li, Alan Mishchenko 1 , Subarna Sinha, Tiziano Villa 2 , and Robert Brayton Dept. of Electrical Engineering and Computer Science University of California, Berkeley.

478 views • 36 slides

Business Computing

Business Computing. Computing:- Computing applies a set of techniques on data/ instructions to define and solve problems pertaining to a specific information-based task. Business Computing:- Computing applied to solve business related problems, is known as business computing.

727 views • 35 slides

Database Design

Database Design. Relational Database A collection of normalized relations (tables). The Three Level ANSI-SPARC Architecture DBTG (Data Base Task Group) Appointed by the Conference on Data Systems and Languages in 1971 Two level approach

433 views • 34 slides

Entity Relationship Model: E-R Modeling

579 views • 30 slides

Mapping an ERD to a Relational Database

Mapping an ERD to a Relational Database. To map an ERD to a relational database, 4 rules are defined to govern how tables are constructed. Rule for entity types Rule for relationships Rule for attributes 4) Rule for participation constraint. Entities

523 views • 28 slides

Quest Notes Migrator for SharePoint

Quest Notes Migrator for SharePoint. Steve Walch, Senior Product Manager. Blog: http://notes2sharepoint.org . November, 2011. Partner Training Webcast Series - Session 3 Advanced Content Migration Jobs. Partner Training Webcast Series. Migration Basics with the NMSP Designer Client

316 views • 23 slides

XML-to-SQL Query Mapping in the Presence of Multi-valued Schema Mappings and Recursive XML Schemas

XML-to-SQL Query Mapping in the Presence of Multi-valued Schema Mappings and Recursive XML Schemas. Speaker: Artem Chebotko Mustafa Atay, Artem Chebotko, Shiyong Lu, and Farshad Fotouhi Department of Computer Science Wayne State University Detroit, MI 48202 USA

332 views • 20 slides

ERD

ERD. Translation to tables. A Table Example. t1 = ( foo,bar,baz,x ) t2 = ( quz,bar,foo,y ) t1[a1] = ( baz ) t2[a2] = ({ y,z }) t1[a?] = ( baz ,{ x,y }) t2[ k ?] = ( quz,bar ). t1. t2. Vector of all key attributes. k? = (k1,k2) a? = (a1,a2). Vector of all non-key attributes.

301 views • 20 slides

Combinational Problems: Unate Covering, Binate Covering, Graph Coloring and Maximum Cliques

Combinational Problems: Unate Covering, Binate Covering, Graph Coloring and Maximum Cliques. Example of application: Decomposition. What will be discussed. In many logic synthesis, formal verification or testing problems we can reduce the problem to some well-known and researched problem

252 views • 19 slides

CS 430 Database Theory

CS 430 Database Theory. Winter 2005 Lecture 15: How to Convert an ER Model to Relations. Issue. We do Conceptual Modeling using ER Models Our databases are relational Look a little like ER models but not exactly How to we convert an ER model to relations for an RDBMS

245 views • 17 slides

Normalization

Normalization. Normalization Introduction. Developed by E.F Codd Normalization of data is a process of analyzing the given relation schema based on their FDs ( Functional Dependencies ) and primary keys to achieve the desirable properties of Minimizing redundancy

377 views • 16 slides