data structuring

Data structuring is the process of organizing information so it can be accessed easily and manipulated quickly. This is an important skill to have in any programming language, and especially one that deals with large amounts of data.

In this guide, I will explain what data structuring is, how to think about it, and provide some real-world examples. By the end of this guide, you should be able to create your own data structures, understand when they are useful, and know how to implement them in your own software projects.

 

Big Data– The Unstructured Data Pool

Big Data– The Unstructured Data Pool Big Data is data that is collected, stored and processed in such a way that it can be analyzed using advanced analytics techniques. This type of data can come from a wide variety of sources such as transactional databases, sensor networks, and unstructured text documents (like news articles). In fact, according to Gartner, by 2020, 90% of all enterprise information will be generated from unstructured sources. However, only 10% of that data will be actually be structured – that is, put into databases or other kinds of files that can be analyzed. This leaves the other 90% of the data “unstructured data” that cannot be used for analysis. There are many definitions of “big data” but the one used here is data that is so large and complex that it cannot be processed using conventional tools. In general, big data refers to datasets that are too large and complex for an organization to manage using conventional tools. As a rule of thumb, if you have more than about 1,000 GB of data, you have big data. The amount of data you have depends on how you are storing it and what you are doing with it. For example, if you are just counting words or characters in your documents, you probably have less than 100 MB of data. However, if you are analyzing these same documents using advanced analytics techniques such as topic modeling or sentiment analysis, you could be generating hundreds of gigabytes (or even terabytes) of data.

 

Data Structures and Their Properties

A data structure is basically the way the data is arranged or structured. There are many different kinds of data structures, and each has its own properties. For example, a linked list has these properties: It is easy to add or remove items from the list. It is easy to find out where an item is in the list. It is easy to find the head or tail of the list. It is easy to tell if a specific item is in the list. These are called “operations” on a data structure. On the other hand, a search tree has these properties: It is easy to find an item in the tree. It is easy to tell if an item is in the tree. It is easy to add or remove items from the tree. However, it is difficult to find out where an item is in the tree. This makes a search tree useful for searching very large databases.

In this study, a dataset was built using past data from hundreds of thousands of web requests made by the author of the study against a specific target. The dataset contained data extracted from hundreds of thousands of Internet attacks launched against a specific target over an extended period of time. The dataset was used to train a machine-learning algorithm that could detect with high accuracy whether a new sample of data (an Internet attack) came from the same distribution as the training set.

Algorithms

Data structure algorithms are used to determine how the data is stored in memory. There are three basic data structures: linked lists, arrays and stacks. Each data structure has its own advantages and disadvantages. For example, an array can be very fast at random access but is slow at inserting or deleting elements. On the other hand, a linked list is very slow at random access but is very fast at insertion or deletion. Stacks are somewhere in the middle – they are usually fast enough for general use but not as fast as arrays. These data structures are used in conjunction with various data types like integers, floating-point numbers, characters and strings. There are many different algorithms for choosing the best data structure for a particular application.

Storage Options and Their Features

1.  Data structures have a set of rules that govern how data is organized. Data structures are used to define a framework for data. Data is organized into data structures in different ways depending on the type of data. For example, a person’s name is a string (sequence of characters) and therefore it is stored as a string in a file or database. However, a person’s address is typically given as a sequence of numbers, and therefore it is stored as a number in a file or database. This means that there are different data structures for different types of data. In general, there are three main categories of data structures: Sequential – This type of data structure is used to store data that has a sequential nature such as a sequence of characters, numbers, dates, times, etc. Sequential data structures are also called arrays. Structured – A structured data structure is used to store data that has a defined pattern. An example of structured data would be an address that is given as a sequence of numbers like 123 Elm Street, Apartment 2A, Scranton, PA

2. Unstructured – Unstructured data is any data that does not fit neatly into either of the other two categories. For example, a person’s address may contain information about where he or she lives, but it is not given in a defined pattern like an address. Also, a person’s address may consist of a random collection of characters like abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ123 Elm Street, Apartment 2A, Scranton, PA

3. Sequential Data Structures are arrays and are used to store data that has a sequential nature such as a sequence of characters, numbers, dates, times, etc. The first data structure that we will discuss is the array. An Array An array is a sequential data structure that consists of multiple items called elements. Each element has a unique identifier (index) that refers to that element. There are many different notations used to represent arrays. One of the most common notations is (x). For example: An array with three elements would be written like this: [1] [2] [3] In some notations, brackets are used to enclose an entire array. For example: {1, 2, 3} Sometimes the brackets are used to indicate the range of an array’s indices. For example: {1, 5} indicates that the first element of the array has an index between 1 and 2, which means that the array has two elements. Another common notation for representing an array is (x, y, z), where x represents the number of elements in the array, and each y, z represents the position of one of those elements. For example: (3, 1,

4. would indicate that there are three elements in the array, and the first element is at position 1, the second element is at position 2, and the third element is at position

5. Structured Data Structures are used to store data that has a defined pattern. An example of structured data would be an address that is given as a sequence of numbers like 123 Elm Street, Apartment 2A, Scranton, PA

6. The first data structure that we will discuss that fits this description is the list. A-List A list is a structured data structure that consists of multiple items called elements. Each element has a unique identifier (index) that refers to that element. There are many different notations used to represent lists. One of the most common notations is [1], [2], [3], etc. For example A list with two elements would be written like this: [1] [2] In some notations, brackets are used to enclose a list. For example: {1, 2} Sometimes the brackets are used to indicate the range of a list’s indices. For example: {1, 5} would indicate that the first element of the list has an index between 1 and 2, which means that the list has two elements. Another common notation for representing a list is (x, y), where x represents the number of elements in the list, and each y represents the position of one of those elements. For example: (2,

7. would indicate that there are two elements in the list, and the first element is at position 1, and the second element is at position

Examples of data structures

Examples of data structures are records, arrays, lists, hashes, maps, trees, stacks, and queues. A record is simply a set of information that has a unique identifier. An array is a collection of items where each item has an identity (its “index”) and some associated data. A list is a sequential array. A hash table is an associative array. A map is a collection of identities (keys) with associated data. A tree is a collection of nodes where each node contains a set of data and may have zero or more children nodes. A stack is a collection of elements with an empty top element and a fixed length. A queue is similar to a stack, but the top element can be removed and added to the end of the queue at any time.

Why data structure is important?

The data structure is important because it affects how fast your code runs. A properly structured data file will be much easier to process than an unstructured data file. For example, if you have a data file with hundreds of thousands of records, an unstructured data file will be extremely difficult to process. On the other hand, if your data file is properly structured, you can easily access any particular piece of information with a simple command. This makes it easy to process and also easy to search.

A good way to think about data structure is to imagine that you are building a house. Imagine that you have a large pile of dirt in front of you. In one corner of the dirt, a pile is a group of people who are responsible for putting together the actual house. They are called the “constructors.” In another corner of the dirt, a pile is a group of people who are responsible for putting the house together once it has been built. They are called the “assemblers.” The constructor and assembler roles are analogous to the role of the programmer and the person who actually uses the software respectively.

Now, let’s say you want to build a house. If you give the job of constructing the house to the group of people in the dirt pile, you will almost certainly end up with a house that is not only poorly constructed but also, will not even be livable. This is because these people do not have the training or the skill to put together a solid structure. On the other hand, if you give the job of putting the house together to the group of people in the dirt pile, the house will be assembled properly, but it will still not be livable. This is because these people do not have the training or the skill to make the house “liveable” – which means that they will not have the ability to properly heat and cool the house, put in proper plumbing and electrical connections, and so on.

The analogy between the construction of a computer program and the construction of a house is not perfect, but it is a useful one. A skilled programmer will be able to create a highly functional piece of software even if he has no idea how to properly structure the data. A skilled housebuilder will be able to create a livable house even if he has no clue about programming.

A good way to think about data structure is to imagine that you are building a house. Imagine that you have a large pile of dirt in front of you. In one corner of the dirt, a pile is a group of people who are responsible for putting together the actual house. They are called the “constructors.” In another corner of the dirt, a pile is a group of people who are responsible for putting the house together once it has been built. They are called the “assemblers.” The constructor and assembler roles are analogous to the role of the programmer and the person who actually uses the software respectively.

Now, let’s say you want to build a house. If you give the job of constructing the house to the group of people in the dirt pile, you will almost certainly end up with a house that is not only poorly constructed but also, will not even be livable. This is because these people do not have the training or the skill to put together a solid structure. On the other hand, if you give the job of putting the house together to the group of people in the dirt pile, the house will be assembled properly, but it will still not be livable. This is because these people do not have the training or the skill to make the house “liveable” – which means that they will not have the ability to properly heat and cool the house, put in proper plumbing and electrical connections, and so on.

The analogy between the construction of a computer program and the construction of a house is not perfect, but it is a useful one. A skilled programmer will be able to create a highly functional piece of software even if he has no idea how to properly structure the data. A skilled housebuilder will be able to create a livable house even if he has no clue about programming.

 

Choosing the best data structure

1. Choosing the best data structure is an important part of data mining. A data structure is a way of organizing your data so that it is easy to access. For example, a spreadsheet is a data structure where the data is organized in rows and columns. The data in each row represents a separate piece of information, and the data in each column represents a separate attribute of that piece of information. A database is a more sophisticated data structure that allows the user to query the data in many different ways. A relational database is the most common type of database. It has three major parts: (

2. tables, (

3. fields, and (

4. relations. Each table has columns and rows. Each column represents a field of data, and each row represents a record. The term relation refers to the connections or relationships among the fields in different tables. In a relational database, the tables are connected by relations.

 

Structuring Data

Data structuring is the process of deciding how your data should be organized so that it can be effectively processed. For example, if you are working with tabular data, you need to decide what columns (or fields) you should include in your data structure. What you choose will have a significant impact on how easy it is to analyze the data and draw conclusions from it. You may want to include all available information about each record or you may only want the key pieces of information. Maybe you will only want the latest information for each record. Or maybe you want to include all records regardless of when they were recorded. Whatever you decide, it should be something that makes sense for your application.

 

Relational Databases

Relational Databases (RDBMS) are the most widely used form of database management system. They were introduced by IBM in 1972 and have been enormously successful. An RDBMS is a collection of data organized in tables consisting of rows (records) and columns (fields). The tables are called relations. The data in an RDBMS can be thought of as a multidimensional array where each record is a different “slice” of that array. This means that each record has a unique identifier (called a key) that can be used to find it in the database. Keys are used both to identify records (to locate them) and to retrieve data from those records (to extract them). Keys can be anything that uniquely identifies a record, such as a person’s name, social security number, account number, address, telephone number, or e-mail address.

 

How to choose a data structure?

How to choose a data structure? This is a question that comes up time and time again when people are faced with the task of learning a new programming language or technology. There are many choices available to you, but they can all be boiled down to three basic types: linked lists, arrays, and hash tables. These are the three most common data structures used in computer programming.

A linked list is very similar to an assembly line. Imagine a long line of people standing in front of a single person who has the job of putting a single piece of information on a piece of paper for each person as they pass by. Each of these pieces of paper represents a node or “element” in a linked list. The first node would contain the name of the person who sent the request, the second node would contain the name of the person who created the post, and so on. The last node would contain a null value, which means it represents a “null element” or a “dead end” in the line of communication.

An array is much like a stack of paper plates. Imagine that every person in the line just sent a plate to the next person in line. Then, imagine that every person adds one more plate to the stack until there are no more plates left. In this analogy, the stack of plates is the computer data structure called an array.

A hash table is very similar to a telephone book. Imagine that each person in the line has a contact number written on a piece of paper and sends that piece of paper to the next person along with the phone number. The piece of paper representing the last person’s contact information would have a null value. This means that there are no more numbers to look up in the phone book for that person. In this analogy, the phone book is the computer data structure called a hash table.

 

Characteristics of data structures

A data structure is a way in which your data is organized. For example, a linked list is a data structure that organizes data in a linear fashion. Another data structure is an array, which is a table or grid-like arrangement of data. A hash table is a data structure that organizes data based on how it is represented as a key (or identifier) in the data.

The characteristics of a data structure are important because they determine how the data is processed and stored. For example, if the data is organized in a linear fashion (such as a linked list), it can be traversed easily in any order. However, if the data is arranged in a grid-like fashion (such as an array), it can only be accessed at specific locations. A hash table has certain advantages over other data structures, and those advantages are based on the characteristics of the data structure. A hash table tends to perform better when the data is highly unstructured and has no inherent order. For example, a hash table performs best when the data is a collection of words in a dictionary.

 

Types of data structures

1. Arrays

Arrays are ordered collections of elements, where each element has a specific position or “coordinate” in the array. An array can have any number of dimensions, such as rows and columns in a matrix or an X and Y coordinate in a 2-D plane. Arrays are extremely useful in nearly all aspects of computing and can be found in everything from the periodic table to satellite imagery to the layout of an operating room.

 

2. Linked Lists

Linked lists are data structures that are composed of nodes. Each node contains a value, a link to a next node, and a link to a previous node. The last node contains a null pointer instead of a link to a previous node. A linked list is often used as a collection of data items. A linked list is also known as a singly-linked list or an indirect list. A doubly-linked list has a link to the previous and next node in the list. A list is considered doubly-linked if there is a link to both the previous and next node. A singly-linked list has only one link to the previous node. A doubly-linked list has two links to the previous node. A doubly-linked list is sometimes called a forward list because the nodes are ordered in such a way that the first node is the one that contains the most recent data item, and the last node is the one that contains the least recent data item.

 

3. Stacks

Stacks are data structures that are used to keep related data together. They are often implemented as last-in-first-out (LIFO) or first-in-last-out (FILL) structures. For example, if you have a stack of books, you might use a LIFO structure so that the book you are currently reading is on the top of the stack. If it turns out that you are interested in reading another book that is similar to the one you are reading, you can remove the book from the top of the stack and read the new book. If it turns out that you are not interested in the book you just removed, you can put the book back on the stack and continue reading other books. Stacks are very useful for managing large amounts of data.

 

4. Queues

Queues in data structures are data structures used to store information about items that need to be processed in some sequential order. An example of a queue is a line at the post office or a linked list. In computer science, a queue is a structure with three important operations: en queue, dequeue and peek. Enqueue adds an element to the front of the queue, dequeue removes the first element in the queue, and peek allows you to look at the current front element without removing it.

Queues are used extensively in operating systems, where they are often referred to as first-in-first-out (FIFO) queues.

 

5. Hash Tables

Hash Tables in data structures are used to access (lookup) information very quickly. A hash function is a mathematical function that takes a variable-length input string and maps it to an integer. This integer is called the “hash code” or “hash index.” A hash table contains an entry for each distinct possible value that the hash function could produce. When the hash function is applied to a key (data item), the entry in the hash table containing that key is accessed and its contents are used to determine what the key is. If there are multiple entries with the same key, the entry with the lowest index is accessed. The entry is then looked up in a linked list or another data structure that keeps track of the location of all the entries with a particular key. In this way, a hash table performs a very fast look-up on a key.

 

6. Trees

Trees are the basic data structure used in information retrieval systems. Data structures are simply the building blocks from which information is created. They can be arranged in different ways to create different information. The simplest data structure is a list. A list is like an outline with boxes and bullets. Each item in the list represents a single piece of information – like a sentence or a word in a sentence. Lists are very easy to understand and manipulate. Another common data structure is a table. A table is like a list with columns and rows. A table is used to represent tabular data – information that has been recorded in rows and columns. Tables are used when the data being represented is naturally organized into rows and columns. For example, it is often the case that the employees of a company are organized by department and then alphabetically within each department. In this case, each employee would be represented by a row in a table, and each department would be a column in the table. Of course, there would also be a column that identified each employee by name. Tables are easy to understand and manipulate. But, they can get very large and complex very quickly. This makes them difficult to work with if you are not careful. A third data structure is a tree. A tree is a data structure that resembles a family tree. Each node in the tree represents a single piece of information – like a leaf or a branch on a tree. Each “parent” node represents information that is more detailed than the information represented by one or more of its child nodes. The root node of the tree represents the most general information about the data, while the leaves represent the specific, detailed information. Trees are compact and easy to understand and manipulate. They can grow and change as your dataset grows and changes. This makes them the preferred choice when there is a lot of unstructured data.

 

7. Heaps

Heaps in data structures are a type of self-organizing data structure used for fast retrieval of small portions (heaps) of data from a much larger dataset. A heap is a data structure in which each element has a priority level associated with it. The highest-priority elements are at the top of the heap, and the lowest-priority elements are at the bottom of the heap. Data is retrieved from the heap by repeatedly cutting off the bottom-most elements and returning these to the user. As elements are cut off the heap, they are reinserted at the top of the heap in their proper order. This process continues until all the elements have been removed from the heap and then the entire heap is delivered to the user. Heaps are ideally suited for dealing with large datasets because they can be built very efficiently and because they naturally arrange data in the way that is most useful for human comprehension.

 

8. Graphs

 

Graphs in data structures are mathematical objects used to model real-world objects and relationships. A simple graph has nodes (also known as vertices) and edges. A node represents a particular thing and an edge represents a relationship between two things. For example, in a social network, a person may be represented by a node and the relationships that person has with other people may be represented by edges connecting that person to the other persons. In a web graph, each node might represent a website and the edges would represent the hyperlinks between the websites. Another common use of graphs in data structures is to represent data from a survey in the form of a graph. In this case, the nodes represent the different questions in the survey and the edges represent the correlation between the different questions.

Graphs are extremely useful in data structures because they can compactly represent large amounts of data. For example, in the case of a social network, it is often useful to know what groups of friends a person has in common. This can be represented by looking at the subgraph of the person’s social network that is induced by these common friends. Similarly, in the case of a web graph, it is useful to know which websites are most frequently linked to one another. This can be represented by looking at the subgraph of the web graph that is induced by these commonly linked websites.

Conclusion

In any case, it’s important to remember that data alone is meaningless. You need to know how to interpret that data in order to do something with it. And that’s where most people mess up. They focus too much on the “how to” of data retrieval and not enough on the “why”.

When it comes to data, the first thing you should be thinking about is what you want to do with that data. If you don’t have a compelling reason for getting or keeping that data, then you shouldn’t be worrying about how to retrieve it.

In any case, it’s important to remember that data alone is meaningless. You need to know how to interpret that data in order to do something with it. And that’s where most people mess up. They focus too much on the “how-to” of data retrieval and not enough on the “why”.

When it comes to data, the first thing you should be thinking about is what you want to do with that data. If you don’t have a compelling reason for getting or keeping that data, then you shouldn’t be worrying about how to retrieve it.

By Muthali Ganesh

I am an engineer wih a masters in business administration from Chennai, India. I love discovering and sharing hacks.