Breadth-First Search

Breadth-first search is a search algorithm to find something in an tree. This search algorithm utilizes a queue to keep track of all the nodes that the algorithm will explore. If you're not sure what a queue is then you should take a look at it here.

What basically happens:
The search algorithm starts at the root node(A), and looks at each node of its children before going on.

What specifically happens:
Let's say that the goal is to find E.
The search tree starts at the root node(A) added into the queue then popped, and adds it's children into the queue. If the node it is at is not a goal, it enqueues its children into the queue. If it is the goal then the algorithm returns and stops. Here are some of the steps:

1. We start with A dequeued from the queue. Since A is not the goal and it has children, A's children are enqueued. Queue = [B, C]

2. B is dequeued from the queue and its children are enqueued. Queue = [C, D, E]

3. C is dequeued from the queue and its children are enqueued. Queue = [D, E, F, G]

4. D is dequeued from the queue and its children are enqueued. Queue = [E, F, G, H, I]

5. E is dequeued from the queue and it is our goal, so we stop.

Think about if we are searching for M. What would our path be and what would happen?

Why don't we need an explored list compared to Depth-first algorithm?
Because we search everything level by level, there is no need to keep track of explored nodes, whereas Depth-first has to go back up the tree.

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Depth-First Search

Depth-first search is a search algorithm to find something in an tree. This search algorithm utilizes a stack to keep track of all the nodes that the algorithm will explore. If you're not sure what a stack is then you can read about it here.

What basically happens:
The search starts at the root node(A), and continues to travel down until it reaches a leaf node. Once it hits a leaf node, it goes a step until it can travel downwards again. This continues until it has either found what it was looking for, or until it has explored all of the nodes and found nothing. The path happens this way: (A, B, D, H, I, E, J, K, C, F, L, G)


What specifically happens:
Let's say our goal is to find G.
The search starts with the root node(A) and pushed into the stack, and continues to travel down until it reaches a leaf node. A leaf node is a node that does not have any children. At every step it checks to see if the node has already been explored. If it has been explored, it is simply ignored and continues looking in the stack. If the node has not been explored, it adds any children(that have not been explored) into the stack and adds itself to the explored list. If it hasn't found the goal, then it continues to search until it has found the goal or that the tree is all explored. Here are some of the steps:

1. A goes down B, D, and stops at H.  Explored list = [A, B, D, H]  Stack = [C, E, I]

2. H is a leaf so it doesn't add any children to the stack, I is popped off and added to the explored list.
    Explored list = [A, B, D, H, I]  Stack = [C, E]

3. I also doesn't have any children to add to stack, E gets popped off. It's added to explored and its children added to the stack.
    Explored list = [A, B, D, H, I, E]  Stack = [C, K, J]

4. J is popped off the stack. Because it is a leaf no children are added to the stack. Same thing happens with K.
    Explored list = [A, B, D, H, I, E, J, K]   Stack = [C]

5. C is popped off the stack and any of its unexplored children are added to the stack.
    Explored list = [A, B, D, H, I, E, J, K, C]  Stack = [G, F]

6. F is popped off the stack and, any unexplored children added.
    Explored list = [A, B, D, H, I, E, J, K, C, F]   Stack = [G, L]

7. L is popped off the stack and there are no children to add to the stack.
    Explored list = [A, B, D, H, I, E, J, K, C, F, L]   Stack = [G]

8. G is popped off the stack and because our goal was to find G, we stop our search.

Imagine that there is a search for M. What would happen? The algorithm should return something that signifies nothing was found.

Why do we need an Explored list?
The reason that we would need an explored list is to prevent already traversed nodes from being rechecked, and prevent its children from being added back into the stack. Without the explored list the algorithm would end up in a loop forever.
Just like looking for something in the grocery store, you wouldn't want to go back to an aisle you already looked in, because you remember that you've looked there already.

I hope this clears up any muddy ideas you have about this search algorithm. Feel free to let me know if I've made any errors, grave or not.

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Database Concepts - A Short Post about the Relational Database Model

via Wikipedia

Relational Database Model:
The relational database model, different from the entity relation model, shows specific data and information about a database table. A relational database model contains two parts: An instance, and a Schema.

Schema:
The schema is specific name of the relation, or the name of each column. If you think back to the Entity-Relationship Diagrams, there are names that have relationships to each other, these names would be the part of the schema. In the above picture, the schema would be the part that says "login", "first", and "last."

Instance:
The instance are the rows of data that pairs with the schema. In the above picture, the instance would be the rows of names.

Relations:
As you can see from the image above, the specific row of information for "Mark" is linked to another table that contains the specific key's phone number. This, is what the Entity Relationship Diagram has drawn out; the connection between one login key to a foreign key.

Usage:
With the Relational Database, we can use query language to query for data. Although our queries can be efficient, the DBMS is mostly responsible for making queries efficient.

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Setting Up MySQL

Hi Everyone!

I know I haven't been posting much these past few months, I was busy with many other things and I decided to take a breather with blogging. However, things have cleared out of my way now, and I will start again with posts.

Installing:

For Linux users, type the following into your command line:

shell>sudo apt-get install mysql-server
shell>sudo apt-get install mysql-client

For Mac and Windows users, you have to download the installer here. Make sure you check 32 or 64 bit! Follow the steps that the install gives, and you should be fine!

Using MySQL:
To start up MySQL:  type the following commands in the terminal:
shell>mysql -u root -p
It would ask you for your password, since you need to be the root user. The installation should ask for you to set a password for MySQL, which is optional. If you do not wish to set a password, you can just click enter when it prompts you to type in a password.

If you did not set a password for MySQL, you can disregard the "-p" part. You can also just type it and click enter when it asks for a password. If you get in, you should see something like this:
Welcome to the MySQL monitor.  Commands end with ; or \g.
Your MySQL connection id is 1
Server version: 5.6.27 MySQL Community Server (GPL)
Copyright (c) 2000, 2015, Oracle and/or its affiliates. All rights reserved.
Oracle is a registered trademark of Oracle Corporation and/or its
affiliates. Other names may be trademarks of their respective
owners.
Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.
mysql>

For Windows users, here's the way to start it up. I haven't tried this but I hope it helps.

To create a database: type the following in the MySQL command line:

mysql>create database database_name;

Notice here that you need a semicolon to show that you're done talking. We'll see later how that semicolon can be changed to something else.

To go into a database: type the following in the MySQL command line:

mysql>use database_name;

type in "mysql --help" in you're computer terminal to see many, many other commands.

Changing MySQL Password:

In order to change your MySQL password you will need to log onto MySQL first. After that you type the following:

mysql>grant all privileges on *.* to root@localhost identified by "new_password";

Here, the new_password should be in the quotes.

Changing the Terminator Symbol:

The semicolon at the end of each command is usually called the terminator, which tells MySQL that you're done with a statement. But what if you wanted to paste a big chunk of commands? Or paste in a function? You can change the terminator symbol before you paste code, and change it back to semicolon afterwards. Here's how to change it:

mysql>DELIMITER YOUR_TERMINATOR

example: mysql>DELIMITER done

In this example, assuming you don't have the word "done" in your code, will end your set of commands when it find the word "done." To change it back to semicolon you would do the same except with a ";"

That's it! Pretty Simple huh? If you have any problems look it up, many people may have the same problem. You can also feel free to post a comment here, and please do so if my post here helped you! I really appreciate knowing that I have helped! 'Til next time.

*It also seems that the spacing of this post is really odd, I'm trying to fix it but I haven't figured out the cause so far.

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Database concepts - ER Diagrams (Continued)

So, in the last post I went over a little about how things are represented in an ER diagram, as well as the reasons to why using an ER diagram is helpful to design a good database. In this post, we'll elaborate on specific rules and guidelines that should be followed ad considered when creating an ER Diagram along with more information on relationships. Let's get started!

The basic setup of the ER diagram is formed in the first post, but there are Integrity constraints that need to be established.
Entity Integrity:
The entity integrity constraints states that a primary key of an entity cannot be NULL. NULL means that the value is unknown. The reason behind this is that specific rows of an entity is identified by the primary key(previous post), and if there is no primary key, then there is no way to identify it.

Referential Integrity:
The referential integrity constraint states a specific constraint between two tables. The primary table's records must exist in order for another table to reference that record.

For example, John sells a used car to Adam. The license of this car is 00A154.
In the Car table, there would first exist an record of this car:
    (License, Year, Model, Manufacturer) --> (00A154,  2014, Civic, Honda)

In the Sells table, there would then exist an entry:
    (Salesman, Client, Car) --> (John, Adam, 00A154)

1. If John were to change this record in table Car to (00B154, 2014, Civic, Honda), the Sells table would not be able to locate a record of 00A154.

2. If John were to delete the record in table Car, the Sells table would also not be able to locate a record of 00A154.

3. If John accidentally inserted the Sells record incorrectly say (John, Adam, 00B154), then when the Sells table try to locate Car 00B154 and it's information, the records would not be able to find it.

4. However, John can insert the Sells record as (John, Adam, NULL).

Foreign Integrity:
Foreign integrity constraints help solve the first two problems we have with the Referential Integrity Constraints.

Cascade Update:
Any time the primary table's record is changed, anything that references it must also be changed. That way there would not have mismatching records when we try to locate the reference.

Cascade Delete:
Any time a primary table's record is deleted, anything that references it must also be deleted. That way there would not have unfound records.


Relationship Constraints: There are key constraints, One-to-One, One-to-Many, and Many-to-Many. Then, there are participation constraints.

One-to-One: One to one means that an entity can at most be related one entity. For example, a man can be married to one woman, and vice versa. Sometimes there would also be specific ranges on the line specifying the relationship as [1:1]. This would be denoted by an arrow:

Man --> marries <-- Woman

One-to-Many:  Let's say in a case that a man can marry many women, but a woman can only marry one man. Sometimes there would also be specific ranges on the line specifying the relationship as [1:N]. The relationship would be a One-to-Many, expressed as the following:

Man -- marries <-- Woman

Many-to-Many: Many-to-Many are represented like the diagram above, just a thin line.

Mechanic -- Repairs -- Car

Participation Constraints: If we want to create a database where everyone must be married, then we would have a participation constraint. The arrows are still present to represent the One-to-One relation, and the line is thickened to show that records MUST be married couples.

Man --> marries <-- Woman

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Database Concepts - The Entity-Relationship Diagram

Via texample.net

In this post I will try to start database ideas with modeling. Here, I will explain what an ER diagram is and how it is used as a database model. In future posts I will explain more into detail of database ideas once I have established a basic bridge. Lets get started!


In order to continue understanding clearly upcoming concepts we need to first establish some more basic basic basic terms:

Primary Keys:
Primary Keys are the significant attributes of an entity. The primary keys are used to identify the specific entity, meaning that they are unique e.g., student IDs. You'll see more of an explanation of what an entity is below.

Foreign Keys:
Foreign Keys are specific attributes that are referencing an original attribute. Continuing with the student ID example, there may be a chart of students that are age 20, identified by their student IDs. That chart's student IDs refer to the original list of Students and their student IDs.
+-------------------+     +------------------------+
| Student |   ID#   |       |      aged 20 IDs       |
|   John    |  1001  |       |          1001              |
|   Sally   |  1002  |       +-----------------------+
+-------------------+

*see here that we have a roster of students, and then a list of students that are 20, we're assuming only John is 20. This is omitted but the roster of students should also include the details of their age.

Summarized:
Primary Keys are unique identifiers, Foreign keys refer to another entity's attributes. So, looking back into the previous post, we have a change.

1. Professors(Pid CHAR(50), Pname CHAR(50), Dept CHAR(50), PRIMARY KEY (Pid))
2. Students(Sid CHAR(50), Sname CHAR(50), Age INTEGER, MajorCHAR(50), PRIMARY KEY (Sid))
3. Departments(Did CHAR(50), Dname CHAR(50), PRIMARY KEY (Did))
4. Courses(Cno INTEGER, Cname CHAR(50), PRIMARY KEY (Cno))
5. EnrolledIn(Sid CHAR(50) REFERENCES Students(Sid), Cno INTEGER, PRIMARY KEY (Sid))

*here we have added things to specify PRIMARY KEY. Also, EnrolledIn has an addition to show that Sid is referring to the table Student(Sid).  With that said, we also need to establish some integrity constraints to avoid big disastrous problems. I'll talk about these once we get the whole picture.

The Entity-Relationship Diagram:
Also called ER diagrams. ER diagrams are used to plan out how different elements of the database are going to be connected and "mapped." They show the relationships between entities their attributes, as well as entity-entity relationships.

Entity:
The entities in the diagram above are the ones in blue(Employee, Salesman, Mechanic, etc.).
Entities are the "things" that are major in a database. In the diagram, we see that the "Important" objects that are the bigger part of this database are going to be the ones in blue. All of the entities are represented as a rectangle.

Attributes:
Attributes are the little details related to each entity.

And there you have it! If you read this then you have just learned the very basics of database concepts! I'm looking forward to writing more about this topic! See you soon.

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Data Structures - Stacks and Queues

In this post we'll go over some things about stacks and queues.
I have in an earlier post wrote an implementation of a queue while explaining lists. You can take a look here.

Queues:
Queues are FIFO, meaning first in first out. The first element put into the queue is dequeued in the before the others. There is a head and a tail that keeps track of the first and last element enqueued.

Priority queues:
Priority queues are used in storing processes and their priorities. When a system has to switch the processor between many many processes going on, it can switch based on priorities. Some processes have lower priority and some, higher.

Circular Queues:
Circular queues are the same as queues, except that it wraps around the end to the beginning again. With circular queues, it would be easy to use an array, since there is only a limited amount of space in a circle.

Stacks:
Stacks are FILO, meaning first in last out. Think of the stack like, a stack of plates. If you stack one plate over another, say five plates, you would take each one off one by one from the fifth one you stacked to the first one at the very bottom. Stacks are used for memory storage in systems, can be allocated for a process.

These two data structures, as you can see my examples of them, are used in important systems and programs.

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Database Concepts - What is a Database? Some beginning notes

What is a database?
A database is a glop of data, a collection of data that models real world things; companies, inventory, websites, etc. The collection of data describes a series of relationships between some entities. (e.g., Annie is taking course number A110 or Josie is subscribed to a specific magazine).

What is a DBMS?
DBMS stands for "Database Management System." As the name says, it's a software that is designed to manage and store the database.

How is a file system different?
A file system is split between main memory and secondary storage, since not everything can be stored in one place. A file system also has multiple users, but it also has access control.

How is this important in CS?
The need for database knowledge is growing; websites have big sets of products, search libraries, the Human Genome project, etc. The concepts of databases also connect to Operating Systems, Programming, theory, or Artificial Intelligence.

What is a data model?
A data model is something that describes the relationships between the entities.
The relational model of relational data consists of tables:
1. The Relation: the rows and columns part of the tables are the relations.
2. The Schema: The schema are the entities and it's attributes.

The different levels of abstraction:
1. View Level/External Level:
       This layer describes how users see this data and database
2. Conceptual Level:
        This level defines the logical part of the database, the connections.
3. Physical Level:
        This level are the schemas that describe those physical files.

Examples of Levels:

Physical Level:

        All the files of each table, not in some order. So just think of a big floating world of tables, I suppose.

Conceptual Level:

1. Professors(Pid CHAR(50), Pname CHAR(50), Dept CHAR(50))
2. Students(Sid CHAR(50), Sname CHAR(50), Age INTEGER, Major CHAR(50))
3. Departments(Did CHAR(50), Dname CHAR(50))
4. Courses(Cno INTEGER, Cname CHAR(50))
5. EnrolledIn(Sid CHAR(50), Cno INTEGER)

So here we have covered some basic knowledge of databases. Please let me know in the comments what I should talk more about, or if I made any mistakes or typos in this post! I hope you enjoy the start of this learning experience with me! I will soon add to this collection of database posts and put the next link here.

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Sorting Algorithms - Insertion Sort

Pic from Geeksquiz

Insertion Sort is utilized to sort a group of elements from smallest to largest. Here's how it works:

Lets say we start with an array A = {15, 16, 12, 7, 9}. This array is not sorted(least to greatest).

Pseudocode:

Insertion-Sort(A)

1.    for j = 2 to A.length do

2.        key = A[j]

3.        i = j - 1

4.        while i > 0 and A[i] > key do

5.            A[i+1] = A[i]

6.            i = i - 1

7.        A[i + 1] = key

Walk-through:

Starting with the unsorted array {15, 16, 12, 7, 9}...

line #1: j is 2. array is unchanged {15, 16, 12, 7, 9}

line #2: key is 16

line #3: i is 1

line#4: loops while i is not going out of bounds and A[i] is bigger than the key(current j that we are comparing)

line#7: we skipped lines 5 and 6 because A[i](15) was not bigger than key(16). We basically copy the key back into i + 1 in which this case it is also j.

line#1: j = 3 array is {15, 16, 12, 7, 9}

line#2: key is 12

line#3: i is 2

line#4: loops while i is not going out of bounds and A[i] is bigger than the key(current j that we are   comparing)

line#5: 16 > 12, so A[i + 1] = A[i] OR {15, 16, 16, 7, 9}

line#6: i is 1

line#7: A[i + 1] = key OR {15, 12, 16, 7, 9}

line#5: 15 > 12, so A[i + 1] = A[i] OR  {15, 15, 16, 7, 9}

line#6: i is 0

line#7: A[i+1]  = key OR {12, 15, 16, 7, 9}

See here that first three elements are now already sorted, if we keep going with the pseudocode we will eventually come to the sorted {7, 9, 12, 15, 16}. Try doing the rest!

Analysis: 

Worst Case: The worse array that can go through this is some array that is sorted in reverse order e.g, {19, 15, 13, 7, 3, 1} 

In this case, the algorithm will have to go through and compare each number to more and more numbers.

For example, the worst case array presented here will first compare 15 once, then compare 13 twice, then 7 three times, 3 four times, and 1 five times:

                                                     1 + 2 + 3 + 4 + 5........

In terms of the length of the array n, it will compare a total of:
                                                            n(n + 1)/ 2  OR  (n^2 + n)/2 times

As you accumulate more knowledge or already have, you will see that O(n^2) is not very nice a time compared to some others.

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