Null Values

nIt is possible for tuples to have a null value, denoted by null, for some of their attributes

nnull signifies an unknown value or that a value does not exist.

nThe result of any arithmetic expression involving null is null.

nAggregate functions simply ignore null values (as in SQL)

nFor duplicate elimination and grouping, null is treated like any other value, and two nulls are assumed to be  the same (as in SQL)

nComparisons with null values return the special truth value: unknown

lIf false was used instead of unknown, then    not (A < 5)
               would not be equivalent to               A >= 5

nThree-valued logic using the truth value unknown:

lOR: (unknown or true)         = true,
       (unknown or false)        = unknown
       (unknown or unknown) = unknown

lAND:   (true and unknown)         = unknown,  
           (false and unknown)        = false,
           (unknown and unknown) = unknown

lNOT:  (not unknown) = unknown

lIn SQL “P is unknown” evaluates to true if predicate P evaluates to unknown

nResult of select  predicate is treated as false if it evaluates to unknown

Rename Operation

nAllows us to name, and therefore to refer to, the results of relational-algebra expressions.

nAllows us to refer to a relation by more than one name.

nExample:

   r x (E)

  returns the expression E under the name X

nIf a relational-algebra expression E has arity n, then

                                         

  returns the result of expression E under the name X, and with the

  attributes renamed to A1 , A2 , …., An .

Banking Example

branch (branch_name, branch_city, assets)

customer (customer_name, customer_street, customer_city)

account (account_number, branch_name, balance)

loan (loan_number, branch_name, amount)

depositor (customer_name, account_number)

borrower (customer_name, loan_number)

Query Languages

nLanguage in which user requests information from the database.

nCategories of languages

lProcedural

lNon-procedural, or declarative

n“Pure” languages:

lRelational algebra

lTuple relational calculus

lDomain relational calculus

nPure languages form underlying basis of query languages that people use.

Relational Algebra

nProcedural language

nSix basic operators

lselect: s

lproject: Õ

lunion: È

lset difference: –

lCartesian product: x

lrename: r

nThe operators take one or  two relations as inputs and produce a new relation as a result.

Keys ( Database )

* Let K Í R

* K is a superkey of R if values for K are sufficient to identify a unique tuple of each possible relation r(R)

# by “possible r ” we mean a relation r that could exist in the enterprise we are modeling.

# Example:  {customer_name, customer_street} and
                 {customer_name}
are both superkeys of Customer, if no two customers can possibly have the same name

4In real life, an attribute such as customer_id would be used instead of customer_name to uniquely identify customers, but we omit it to keep our examples small, and instead assume customer names are unique.

Keys (Cont.)

nK is a candidate key if K is minimal
Example:  {customer_name} is a candidate key for Customer, since it is a superkey and no subset of it is a superkey.

nPrimary key: a candidate key chosen as the principal means of identifying tuples within a relation

lShould choose an attribute whose value never, or very rarely, changes.

lE.g. email address is unique, but may change

Relational Model ( Database system concepts )

Example of a Relation

Attribute Types

* Each attribute of a relation has a name

* The set of allowed values for each attribute is called the domain of the attribute

* Attribute values are (normally) required to be atomic; that is, indivisible

lE.g. the value of an attribute can be an account number,
but cannot be a set of account numbers

* Domain is said to be atomic if all its members are atomic

* The special value null  is a member of every domain

* The null value causes complications in the definition of many operations

** We shall ignore the effect of null values in our main presentation and consider their effect later

Relation Schema

* Formally, given domains D1, D2, …. Dn a relation r is a subset of
        D1 x  D2  x … x Dn
Thus, a relation is a set of n-tuples (a1, a2, …, an) where each ai  Î Di

* Schema of a relation consists of

# attribute definitions

4name

4type/domain

# integrity constraints

Relation Instance

* The current values (relation instance) of a relation are specified by a table

* An element t of r is a tuple, represented by a row in a table

* Order of tuples is irrelevant (tuples may be stored in an arbitrary order)

Instances and Schemas

nSimilar to types and variables in programming languages

nSchema – the logical structure of the database

He.g., the database consists of information about a set of customers and accounts and the relationship between them)

HAnalogous to type information of a variable in a program

HPhysical schema: database design at the physical level

HLogical schema: database design at the logical level

nInstance – the actual content of the database at a particular point in time

HAnalogous to the value of a variable

nPhysical Data Independence – the ability to modify the physical schema without changing the logical schema

HApplications depend on the logical schema

HIn general, the interfaces between the various levels and components should be well defined so that changes in some parts do not seriously influence others.

Data Models

nA collection of tools for describing

Hdata

Hdata relationships

Hdata semantics

Hdata constraints

nEntity-Relationship model

nRelational model

nOther models:

Hobject-oriented model

Hsemi-structured data models

HOlder models: network model and hierarchical model

Database Management System (DBMS)

nCollection of interrelated data

nSet of programs to access the data

nDBMS contains information about a particular 

  enterprise

nDBMS provides an environment that is both 

convenient and efficient to use.

nDatabase Applications:

HBanking: all transactions

HAirlines: reservations, schedules

HUniversities:  registration, grades

HSales: customers, products, purchases

HManufacturing: production, inventory, orders, supply chain

HHuman resources:  employee records, salaries, tax deductions

nDatabases touch all aspects of our lives

Purpose of Database System

* In the early days, database applications were 

   built on top of file systems

* Drawbacks of using file systems to store data:

HData redundancy and inconsistency

4Multiple file formats, duplication of information in different files

HDifficulty in accessing data

4Need to write a new program to carry out each new task

HData isolation — multiple files and formats

HIntegrity problems

4Integrity constraints  (e.g. account balance > 0) become part of program code

4Hard to add new constraints or change existing ones

Memory Interface

Introduction

Simple or complex, every microprocessor-based system has

a memory system. 

Almost all systems contain two main types of memory:

read-only

memory

 (ROM) and

random access memory

(RAM) or read/write memory. 

This chapter explains how to interface both memory types to

the Intel family of microprocessors. 

 MEMORY DEVICES

Before attempting to interface memory to the

microprocessor, it is essential to understand the operation of

memory components. 

In this section, we explain functions of the

four common types of memory: 

read-only memory (ROM) 

Static random access memory (SRAM)

Memory Pin Connections 

address inputs

data outputs or

input/outputs

some type of

selection input

at least one control

input to select a read

or write operation

Address Connections

Memory devices have address inputs to

select a memory location within the device. 

Almost always labeled from A

0

, the least significant address

input, to A

where subscript n can be any value

always labeled as one less than total number

of address pins

A

memory device with 10 address pins has 

its address pins labeled from A

0

 to A

9

.

The number of address pins on a memory device is

determined by the number of memory locations found

within it.

Today, common memory devices have between 1K (1024)

to 1G (1,073,741,824) memory locations. 

with 4G and larger devices on the horizon

Data Centers notes

The Standards You’ll Need in the

Next Generation Data Center

By Paul Rubens

June 6, 2011

Many data centers are reaching the limits of their capacity: space and power

are in short supply, cooling is diffcult to achieve and the management of

servers and their associated cabling is becoming increasingly complex.

The good news is that “next generation” data centers promise increased

automation, greater energy effciency, reduced complexity and lower total cost

of ownership (TCO), using a variety of technologies including blade servers,

virtualization, cloud computing and, particularly, high-speed converged networking.

A crucial step in the evolution of the next generation data center is the

development of standards for the new technologies involved. That’s because

it’s only once these standards have been established that you can invest in next

generation technologies with the knowledge that any hardware you buy will be

compatible with other manufacturers’ products, protecting the value of your

investments and ensuring that you are not locked in with a particular vendor.

Here are three of the most important standards that are emerging for the

data centers of the future:

Data Center Bridging (DCB) and Converged Enhanced Ethernet (CEE)

10Gb/s Ethernet is one of the most signifcant enabling technologies for

next generation data centers, and it was standardized by the IEEE back in

2004. Fast Ethernet networking is important because it makes it possible

for you to use a single networking fabric to transport LAN, storage and

inter-process communication (IPC) traffc. This has a number of benefts,

including simplifying your data center infrastructure, reducing the amount

of hardware you need —including certain types of storage-specifc switch

-

es and host bus adapters (HBAs) — and reducing your administrative and

power and cooling costs.

But carrying the storage traffc that you currently transport on a dedicat

-

ed storage network over a conventional Ethernet network presents some

technical challenges: essentially you are asking an Ethernet network to

do something that it was never designed to do.

This problem prompted the development of a number of enhancements

to Ethernet to make it more suitable for use in data centers as a unifed

networking fabric. The set of network standards that are being defned

for these enhancements are called Data Center Bridging (DCB), and one

implementation of these emerging network standards defned by lead

-

ing networking companies, including Brocade, is known as “converged

enhanced Ethernet,” (CEE).