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Computer Science > 1.3 Exchanging Data > Flashcards

1.3 Exchanging Data Flashcards

1
Q

1.3.1 (a) Purpose of Compression

A

Reduces the size of files
Reduces download time
Reduce storage requirements
Makes the best use of bandwidth
Lossy or Lossless Compression
When a compressed file arrives it needs to be uncompressed to be read

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2
Q

1.3.1 (a) Bandwidth

A

Lots of data is sent and streamed over the internet everyday
Considerably speed up transmission times if compression is used

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3
Q

1.3.1 (a) Lossy Compression

A

Reduces the size of a file, by removing non essential data
Could store a lower number of colours (colour depth), or a larger area of pixels as a single colour, both reduce the quality of the compressed image
Usually used with images, audio and video, where loss in quality is not noticed -> Considered an acceptable compromise of quality vs file size

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4
Q

1.3.1 (a) Lossless Compression

A

Does not sacrifice any quality during compression
Store the binary number for the colour, followed by the number of pixels that occur in this colour consecutively
It can be restored to its original quality when uncompressed
Method is only effective on images with large areas of continuous colours
Ideal for vector style images like logos
Less ideal for full photos with few repeating colours

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5
Q

1.3.1 (a) File Types and Compression

A

File type determines which compression method is best, some are not suited for lossy compression
Text documents and executable programs must not loose any of the data during compression -> Lossless compression must be used

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6
Q

1.3.1 Compression Summary

A

Reduces the size of a file
Makes files quicker to transfer
Files take up less storage space

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7
Q

1.3.1 (a) Lossy Summary

A

Some data is lost
Slight reduction in quality
Large reduction in file size
Images, audio, video

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8
Q

1.3.1 (a) Lossless Summary

A

None of the original data is lost
Original file can be recreated when uncompressed
Suitable for executable files and text documents

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9
Q

1.3.1 (b) Dictionary Encoding

A

Lossless Compression
Ideal for text based documents
Builds an index (like a table)
Every data item or file entry is recorded, with an indexed reference or unique code
File contains a dictionary index, sequence of occurrences in the original file

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10
Q

1.3.1 (b) Run Length Encoding

A

Lossless compression
Compression of Images
Good for bitmap images (made up of discrete pixels)
We need to recreate every pixel for lossless compression
Store the image in frequency/data pairs
1 number represents the number of continuous pixels
Other number represents the colour of the pixel

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11
Q

1.3.1 (c) Encryption

A

Encoding a message so it can only be read by the sender and the intended recipient

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12
Q

1.3.1 (c) Caesar Cipher

A

Replaces each letter in the alphabet with another letter a fixed distance from the original letter
For decryption, the recipient needs to know how many places the alphabet has been shifted by (the key)
Can still be cracked without the key

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13
Q

1.3.1 (b) Symmetric Encryption

A

A single key is used to encrypt and decrypt the message
Both parties know the key and both parties must keep it a secret
The same key can be used multiple times - But using unique keys makes it more difficult to crack
Danger of cracking: Interception of the key
Duplication of the key production process to acquire a copy of the key
Less secure than asymmetric encryption

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14
Q

1.3.1 (c) Asymmetric Encryption

A

Two different keys are used to encrypt and decrypt the message, the keys are unique from each other
Virtually impossible to derive one key from the other making it more secure
Keys are generated so that anything encrypted with one of the keys can be decrypted with the other - Key pairs
One key is made our public key and distribute, other is private and not
Can be shared on key safes secure severs on the cloud, to be accessed
People send your encrypted message using your public key
You decrypt using your private key

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15
Q

1.3.1 (c) Digital Fingerprints

A

Asymmetric Encryption
You encrypt your message using your private key, anyone can decrypt using your public key
Shows where the message came from

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16
Q

1.3.1 (c) Final Encryption Solution

A

Combined Encryption Key: Your private key, their public key
Use the remaining keys to decrypt
No-one else can read the message, both people know it is secure
Confirmation of authenticity
Confirms message has not been modified

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17
Q

1.3.1 (d) Hashing Function

A

Transforms a string of character into a fixed length value or key that represents the original input string
E.g. SHA-1, MD5

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18
Q

1.3.1 (d) Hashing Features

A

A slight change in the original message produces a totally different hash value
One way process -> Cannot get to original value even with access to the original algorithm

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19
Q

1.3.1 (d) Use of hashing

A

Prevents passwords and pins from being read by a hacker
The hashing function value can be sent and stored in the online system - Cannot be interpreted
To check if the password is correct, the hashing value is compared to the stored value

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20
Q

1.3.1 (d) Other uses of Hashing

A

Used for quick searching, insertion and deletion of data from data structures
Data structure does not need to be searched
Hashing function applied to the input, finds the item immediately without sorting
Provides a constant time complexity

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21
Q

1.3.2 (a) Database

A

Organised collection of data
Organising data into a database allows for easy:
Adding
Modification
Deletion
Searching

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22
Q

1.3.2 (a) Electronic Databases Benefits

A

Easier to retrieve, add, delete, update and modify data
Easier backup and copies of data
Can be accessed by multiple people at the same time from different location

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23
Q

1.3.2 (a) Storing Data on a Database

A

Data is stored in: Table/Entity/File
A table contains: Rows/Records/Tuples
Table contains: Columns/Fields/Attributes

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24
Q

1.3.2 (a) Flat Files

A

Contains only a single table
Saved as a comma separated values file (CSV)
Quick to set up, little expertise to maintain
Suitable for storing small amounts of data
Simple
E.g. Contact Details
Ineffictient

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1.3.2 (a) Why is a flat file inefficient
Contains lots of repetitive data Taking up more unnecessary space Slow to query Become difficult to maintain
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1.3.2 (a) Separating a Flat File
Can be split up into multiple tables Removes repeated data A link is needed: relationship The way the relationship is drawn tells us how the tables are related to each other Becomes a relational database
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1.3.2 (a) Relational Databases
A common filed in both tables There must be one field in each table where each record is uniquely identifiable -> Primary key field Foreign Key -> Primary key of a related table
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1.3.2 (a) Relationships Between Tables
One to one One to many Many to Many - Not considered good database design and need normalising Use entity relationship diagrams (ERDs) Tables are represented by boxes with the name inside
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1.3.2 (a) Using Indexing and Secondary Keys
Databases are useful if we can query to retrieve information Database automatically maintains an index of primary keys to be located quickly and easily The index provides the position of each record according to its primary key - Makes retrieval of records faster - Do not need to be searched sequentially Secondary fields can be indexed: Used for fields that are commonly used as search criteria Create a secondary index
30
1.3.2 (a) Handling Data
Capturing: How do we get the data into the database initially Selecting: How do we query the data and retrieve it Managing: How do we manage, manipulate, add, edit and delete data Exchanging: How do we exchange the data with other people / systems
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1.3.2 (b) Capturing Data
Using paper-based data capture forms Mostly manual - Read and typed into the system by a human To maximise efficiency - Tactics used when designing the data capture forms
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1.3.2 (b) Maximise Data Capture Efficiency
Every Part of the form is clearly labelled Instruction to complete the form in a black pen Instruction to complete the form in capital letters Use tick boxes Squares for entering each letter separately - Reduces errors made by inputting the data due to poor legibility
33
1.3.2 (b) Optical Character Recognition (OCR)
Automatic Data Capture Method Speeds up data input Technology automatically reads text by interpreting the shape of the letter - Works best on typed text Used by road cameras on number plates Used by post office on addresses
34
1.3.2 (b) Optical Mark Recognition (OMR)
Automated Data Capture Method Used for multiple-choice tests and lottery tickets Fast and efficient way of collecting data and inputting it into a database - Reduces the possibility of human error
35
1.3.2 (b) Automated Data Capture Methods
Optical Character Recognition Optical Mark Recognition Magnet Stripes QR Codes Chip and Pin Sensors Barcodes
36
1.3.2 (b) Selecting Data SQL
Structured Query Language Common Query Language for databases Structured Query Language: Set of commands (Fast and Efficient) - Allows retrieval, deletion and manipulation SELECT, FROM, WHERE
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1.3.2 (b) Selecting Data QBE
Query By Example Graphical Query Language - Uses visual tables QBE is converted into executable statements Finer details of SQL syntax is not needed Used in front ends of databases
38
1.3.2 (b) QBE and SQL Comparison
Both: Specify Tables Specify Fields Specify Criteria Specify Output Sorting Use Boolean Expressions
39
1.3.2 (b) Managing Data
Adding, Editing, Deleting Uses a database manipulation language (DML) e.g. SQL Uses commands Or uses facilities built into a database management system (DBMS)
40
1.3.2 (b) Database Management Systems (DBMS)
Provide a layer of abstraction for the user and programmer Hides underlying structure of the data and ensures it remains integral by: Preventing the Creation of Duplicate Primary Keys Enforcing Validation Rules Providing Secure Access Providing Encryption Providing Program / Data Independence Managing Users
41
1.3.2 (b) Exchanging Data
Between Databases, systems or applications e.g. Accounting Programs and Spreadsheet Software Common Data Exchange Formats Manual Data Exchange Methods Automatic Data Exchange Methods
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1.3.2 (b) Data Exchange Formats
XML and JSON Human Readable, open formats for structuring data Standards for storing and transporting data Systems can exchange data and be sure of the format it arrives in
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1.3.2 (b) Comma Separate Values (CSV) File
Each Record is Stored on a Separate Line, Fields are comma separated Structure is fixed and know Routines are written to extract data in this file System allows data to be outputted in this format
44
1.3.2 (b) Manual Data Exchange Methods
Format for data exchange must be agreed Consider how the data can make its way to another system Memory Stick Optical Media Removable Hard Disk Email Paper-Based
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1.3.2 (b) Automatic
Bypass manual exchange - Two databases interface with each other They can read / write directly to and from each other's tables Electronic Data Interchange - Live Connection Method Protocol / Between two systems to facilitate data exchange Used in automatic orders when stocks are low EDI significantly increases the speed of data transmission and the efficiency of processes for users Errors are replicated across multiple systems
46
1.3.2 (c) Normalisation to 3NF
Consider lists of fields with repeating data across records - Takes up unnecessary additional space Worsens as the database grows Many to many relationships are not good practice We can create composite primary key or add new fields to ensure that a primary key is valid Databases are labelled with their normal form (nature of arrangement) Normalising involves splitting tables and arranging data
47
1.3.2 (c) 1NF Conditions
Field Names Are Unique Values in fields are from the same domain - stored values must be the same type (split up into unique types) Values in fields are atomic - Each box contains 1 value No two records can be identical Each table needs a primary key - Could generate a new field or use a composite key
48
1.3.2 (c) 2NF Conditions
Must already be in 1NF Must not have any partial dependencies - Each table must serve its own purpose No M:M relationships
49
1.3.2 (c) Partial Dependancies
Only with Composite Keys Every Record Can be searched for via the primary key One or more fields depend on part of the primary key - Split into two tables both containing the partial dependency Trick: If a field or group of fields can be inferred from a filed that is not the primary key, they are key candidates for splitting into a separate table
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1.3.2 (c) Fixing M:M Relationships
Creating a Linking Table Assign the primary keys from previous two tables as the composite key for the new linking table The linking table becomes the many in a 1:M relationships with each table
51
1.3.2 (c) 3NF Conditions
he data is already in 2NF Transitive dependencies are removed - Non-Key dependencies - The value of a field is determined by the value of another field that is not part of the primary key Split fields apart, which determines others into multiple tables each serving a single purpose Removes repeating data
52
1.3.2 (e) Referential Integrity - Muti-User Dases
Hold vast amounts of data and support simultaneous users - E.g. NHS Users can be given different access rights: Query and Run Reports Add and Modify Delete Records Results in multiple transactions taking place simultaneously Must not corrupt the data base or cause inconsistency - Looses its accuracy DBMS ensure the data is consistent
53
1.3.2 (e) Data Integrity
The maintenance and consistency of data in a data store The data store must reflect the reality that it represents Vital Importance
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1.3.2 (e) Referential Integrity
Ensures data integrity in a relational database Accuracy and Consistency of data within a relationship If references to a deleted record are kept this is inconsistent - Orphaned entries that relate to an employee who no longer exists Maintain referential integrity - Enforce a cascade delete retains the primary key relationship between tables - May cause necessary records to be deleted Also prevents us from adding a record to a table if there is not a matching record in the linked table
55
1.3.2 (f) Transaction Processing
Any information processing that is divided into individual operations (transactions) Each transaction must fully succeed or fail - Cannot be partially complete Relational databases have a base functionality: Create, Read, Update, Delete - CRUD which map to SQL statements Conforms to ACID rules - Describe the properties required by all database transactions
56
1.3.2 (f) Atomicity
A change to a database is: Completely Preformed Not at all Preformed Half Completed Change must not be saved back to the database
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1.3.2 (f) Consistency
A change in a database must retain the overall state of the database E.g. Transactions must be balanced (cannot loose memory)
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1.3.2 (f) Isolation
A transaction must not be interrupted by another transaction Other users or processes cannot access the data concerned Record Locking: Placed in a read-only / invisible state during the transaction to other processes Once completed the lock must be removed
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1.3.2 (f) Durability
Once a change has been made to a database, it must not be lost due to a system failure Makes sure the DBMS writes the effects of transactions immediately back to permanent secondary storage Rather than being forced in a temporary volatile state (holding temporarily)
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1.3.2 (f) Database Redundancy
When the same piece of data exists in multiple places

Computer Science (4 decks)

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