Introduction

A basic understanding of networking is important for anyone managing a server. Not only is it essential for getting your services online and running smoothly, it also gives you the insight to diagnose problems.

This document will provide a basic overview of some common networking concepts. Here we will discuss basic terminologies.

This guide is operating system agnostic, but should be very helpful when implementing features and services that utilize networking on your server.

Networking Glossary

Before we begin discussing networking with any depth, we must define some common terms that you will see throughout this guide, and in other guides and documentation regarding networking.

These terms will be expanded upon in the appropriate sections that follow:

• Connection: In networking, a connection refers to pieces of related information that are transfered through a network. This generally infers that a connection is built before the data transfer (by following the procedures laid out in a protocol) and then is deconstructed at the at the end of the data transfer.

• Packet: A packet is, generally speaking, the most basic unit that is transfered over a network. When communicating over a network, packets are the envelopes that carry your data (in pieces) from one end point to the other.

Packets have a header portion that contains information about the packet including the source and destination, timestamps, network hops, etc. The main portion of a packet contains the actual data being transfered. It is sometimes called the body or the payload.

• Network Interface: A network interface can refer to any kind of software interface to networking hardware. For instance, if you have two network cards in your computer, you can control and configure each network interface associated with them individually.

A network interface may be associated with a physical device, or it may be a representation of a virtual interface. The “loopback” device, which is a virtual interface to the local machine, is an example of this.

• LAN: LAN stands for “local area network”. It refers to a network or a portion of a network that is not publicly accessible to the greater internet. A home or office network is an example of a LAN.

• WAN: WAN stands for “wide area network”. It means a network that is much more extensive than a LAN. While WAN is the relevant term to use to describe large, dispersed networks in general, it is usually meant to mean the internet, as a whole.

If an interface is said to be connected to the WAN, it is generally assumed that it is reachable through the internet.

• Protocol: A protocol is a set of rules and standards that basically define a language that devices can use to communicate. There are a great number of protocols in use extensively in networking, and they are often implemented in different layers.

Some low level protocols are TCP, UDP, IP, and ICMP. Some familiar examples of application layer protocols, built on these lower protocols, are HTTP (for accessing web content), SSH, TLS/SSL, and FTP.

• Port: A port is an address on a single machine that can be tied to a specific piece of software. It is not a physical interface or location, but it allows your server to be able to communicate using more than one application.

• Firewall: A firewall is a program that decides whether traffic coming into a server or going out should be allowed. A firewall usually works by creating rules for which type of traffic is acceptable on which ports. Generally, firewalls block ports that are not used by a specific application on a server.

• NAT: NAT stands for network address translation. It is a way to translate requests that are incoming into a routing server to the relevant devices or servers that it knows about in the LAN. This is usually implemented in physical LANs as a way to route requests through one IP address to the necessary backend servers.

• VPN: VPN stands for virtual private network. It is a means of connecting separate LANs through the internet, while maintaining privacy. This is used as a means of connecting remote systems as if they were on a local network, often for security reasons

Electronic Commerce

E-Commerce or Electronic Commerce means buying and selling of goods, products, or services over the internet. E-commerce is also known as electronic commerce or internet commerce. These services provided online over the internet network. Transaction of money, funds, and data are also considered as E-commerce. These business transactions can be done in four ways: Business to Business (B2B), Business to Customer (B2C), Customer to Customer (C2C), Customer to Business (C2B). The standard definition of E-commerce is a commercila transaction which is happened over the internet. Online stores like Amazon, Flipkart, Shopify, Myntra, Ebay, Quikr, Olx are examples of E-commerce websites. By 2020, global retail e-commerce can reach up to $27 Trillion. Let us learn in detail about what is the advantages and disadvantages of E-commerce and its types.

E-Commerce or Electronic Commerce

E-commerce is a popular term for electronic commerce or even internet commerce. The name is self-explanatory, it is the meeting of buyers and sellers on the internet. This involves the transaction of goods and services, the transfer of funds and the exchange of data.

Types of E-Commerce Models

Electronic commerce can be classified into four main categories. The basis for this simple classification is the parties that are involved in the transactions. So the four basic electronic commerce models are as follows,

1. Business to Business

This is Business to Business transactions. Here the companies are doing business with each other. The final consumer is not involved. So the online transactions only involve the manufacturers,whole salers and retailers etc.

2. Business to Consumer

Business to Consumer. Here the company will sell their goods and/or services directly to the consumer. The consumer can browse their websites and look at products, pictures, read reviews. Then they place their order and the company ships the goods directly to them. Popular examples are Amazon, Flipkart, Jabong etc.

3. Consumer to Consumer

Consumer to consumer, where the consumers are in direct contact with each other. No company is involved. It helps people sell their personal goods and assets directly to an interested party. Usually, goods traded are cars, bikes, electronics etc. OLX, Quikr etc follow this model.

4. Consumer to Business

This is the reverse of B2C, it is a consumer to business. So the consumer provides a good or some service to the company. Say for example an IT freelancer who demos and sells his software to a company. This would be a C2B transaction.

Advantages of E-Commerce

• E-commerce provides the sellers with a global reach. They remove the barrier of place (geography). Now sellers and buyers can meet in the virtual world, without the hindrance of location.

• Electronic commerce will substantially lower the transaction cost. It eliminates many fixed costs of maintaining brick and mortar shops. This allows the companies to enjoy a much higher margin of profit.

• It provides quick delivery of goods with very little effort on part of the customer. Customer complaints are also addressed quickly. It also saves time, energy and effort for both the consumers and the company.

• One other great advantage is the convenience it offers. A customer can shop 24×7. The website is functional at all times, it does not have working hours like a shop.

• Electronic commerce also allows the customer and the business to be in touch directly, without any intermediaries. This allows for quick communication and transactions. It also gives a valuable personal touch.

Disadvantages of E-Commerce

• The start-up costs of the e-commerce portal are very high. The setup of the hardware and the software, the training cost of employees, the constant maintenance and upkeep are all quite expensive.

• Although it may seem like a sure thing, the e-commerce industry has a high risk of failure. Many companies riding the dot-com wave of the 2000s have failed miserably. The high risk of failure remains even today.

• At times, e-commerce can feel impersonal. So it lacks the warmth of an interpersonal relationship which is important for many brands and products. This lack of a personal touch can be a disadvantage for many types of services and products like interior designing or the jewelry business.

• Security is another area of concern. Only recently, we have witnessed many security breaches where the information of the customers was stolen. Credit card theft, identity theft etc. remain big concerns with the customers.

• Then there are also fulfillment problems. Even after the order is placed there can be problems with shipping, delivery, mix-ups etc. This leaves the customers unhappy and dissatisfied.

Programming language

Computer programming languages allow us to give instructions to a computer in a language the computer understands. Just as many human-based languages exist, there are an array of computer programming languages that programmers can use to communicate with a computer. The portion of the language that a computer can understand is called a “binary.” Translating programming language into binary is known as “compiling.” Each language, from C Language to Python, has its own distinct features, though many times there are commonalities between programming languages.

These languages allow computers to quickly and efficiently process large and complex swaths of information. For example, if a person is given a list of randomized numbers ranging from one to ten thousand and is asked to place them in ascending order, chances are that it will take a sizable amount of time and include some errors.

Language Types

 

Machine and assembly languages

A machine language consists of the numeric codes for the operations that a particular computer can execute directly. The codes are strings of 0s and 1s, or binary digits (“bits”), which are frequently converted both from and to hexadecimal (base 16) for human viewing and modification. Machine language instructions typically use some bits to represent operations, such as addition, and some to represent operands, or perhaps the location of the next instruction. Machine language is difficult to read and write, since it does not resemble conventional mathematical notation or human language, and its codes vary from computer to computer.

Assembly language is designed to be easily translated into machine language. Although blocks of data may be referred to by name instead of by their machine addresses, assembly language does not provide more sophisticated means of organizing complex information. Like machine language, assembly language requires detailed knowledge of internal computer architecture. It is useful when such details are important, as in programming a computer to interact with peripheral devices (printers, scanners, storage devices, and so forth).

Algorithmic languages

Algorithmic languages are designed to express mathematical or symbolic computations. They can express algebraic operations in notation similar to mathematics and allow the use of subprograms that package commonly used operations for reuse. They were the first high-level languages.

FORTRAN

The first important algorithmic language was FORTRAN(formula translation), designed in 1957 by an IBM team led by John Backus. It was intended for scientific computations with real numbers and collections of them organized as one- or multidimensional arrays. Its control structures included conditional IF statements, repetitive loops (so-called DO loops), and a GOTO statement that allowed nonsequential execution of program code. FORTRAN made it convenient to have subprograms for common mathematical operations, and built libraries of them.

FORTRAN was also designed to translate into efficient machine language. It was immediately successful and continues to evolve.

 ALGOL

ALGOL (algorithmic language) was designed by a committee of American and European computer scientists during 1958–60 for publishing algorithms, as well as for doing computations. Like LISP (described in the next section), ALGOL had recursive subprograms—procedures that could invoke themselves to solve a problem by reducing it to a smaller problem of the same kind. ALGOL introduced block structure, in which a program is composed of blocks that might contain both data and instructions and have the same structure as an entire program. Block structure became a powerful tool for building large programs out of small components.

ALGOL contributed a notation for describing the structure of a programming language, Backus–Naur Form, which in some variation became the standard tool for stating the syntax(grammar) of programming languages. ALGOL was widely used in Europe, and for many years it remained the language in which computer algorithms were published. Many important languages, such as Pascal and Ada (both described later), are its descendants.

LISP

LISP(list processing) was developed about 1960 by John McCarthy at the Massachusetts Institute of technology (MIT) and was founded on the mathematical theory of recursive functions (in which a function appears in its own definition). A LISP program is a function applied to data, rather than being a sequence of procedural steps as in FORTRAN and ALGOL. LISP uses a very simple notation in which operations and their operands are given in a parenthesized list. For example, (+ a (* b c)) stands for a + b*c. Although this appears awkward, the notation works well for computers. LISP also uses the list structure to represent data, and, because programs and data use the same structure, it is easy for a LISP program to operate on other programs as data.

LISP became a common language for artificial Intelligence (AI) programming, partly owing to the confluence of LISP and AI work at MIT and partly because AI programs capable of “learning” could be written in LISP as self-modifying programs. LISP has evolved through numerous dialects, such as Scheme and Common LISP.

C

The C programming language was developed in 1972 by Dennis Richtie and Brian Kernighan at the AT&T Corporation for programming computer operating systems. Its capacity to structure data and programs through the composition of smaller units is comparable to that of ALGOL. It uses a compact notation and provides the programmer with the ability to operate with the addresses of data as well as with their values. This ability is important in systems programming, and C shares with assembly language the power to exploit all the features of a computer’s internal architecture. C, along with its descendant C++, remains one of the most common languages.

 

Business-oriented languages

 

COBOL

COBOL (common business oriented language) has been heavily used by businesses since its inception in 1959. A committee of computer manufacturers and users and U.S. government organizations established CODASYL (Committee on Data Systems and Languages) to develop and oversee the language standard in order to ensure its portability across diverse systems.

COBOL uses an English-like notation—novel when introduced. Business computations organize and manipulate large quantities of data, and COBOL introduced the record data structures for such tasks. A record clusters heterogenous data—such as a name, an ID number, an age, and an address—into a single unit. This contrasts with scientific languages, in which homogenous arrays of numbers are common. Records are an important example of “chunking” data into a single object, and they appear in nearly all modern languages.

 

 

Computer programming language

SQL

SQL (structured query language) is a language for specifying the organization of databases (collections of records). Databases organized with SQL are called relational, because SQL provides the ability to query a database for information that falls in a given relation. For example, a query might be “find all records with both last name  Smith and city New York.” Commercial database programs commonly use an SQL-like language for their queries.

Education-oriented languages

BASIC

BASIC (beginner’s all-purpose symbolic instruction code) was designed at Dartmouth College in the mid-1960s by John Kemeny and Thomas Kurtz. It was intended to be easy to learn by novices, particularly non-computer science majors, and to run well on a time-sharing computer with many users. It had simple data structures and notation and it was interpreted: a BASIC program was translated line-by-line and executed as it was translated, which made it easy to locate programming errors.

Its small size and simplicity also made BASIC a popular language for early personal computers. Its recent forms have adopted many of the data and control structures of other contemporary languages, which makes it more powerful but less convenient for beginners.

PASCAL

About 1970 Niklaus Wirth of Switzerland designed Pascal to teach structured programming, which emphasized the orderly use of conditional and loop control structures without GOTO statements. Although Pascal resembled ALGOL in notation, it provided the ability to define data types with which to organize complex information, a feature beyond the capabilities of ALGOL as well as FORTRAN and COBOL. User-defined data types allowed the programmer to introduce names for complex data, which the language translator could then check for correct usage before running a program.

During the late 1970s and ’80s, Pascal was one of the most widely used languages for programming instruction. It was available on nearly all computers, and, because of its familiarity, clarity, and security, it was used for production software as well as for education. 

HyperTalk

HyperTalk was designed as “programming for the rest of us” by Bill Atkinson for Apple’s Macintosh. Using a simple English-like syntax, Hypertalk enabled anyone to combine text, graphics, and audio quickly into “linked stacks” that could be navigated by clicking with a mouse on standard buttons supplied by the program. Hypertalk was particularly popular among educators in the 1980s and early ’90s for classroom multimedia presentations. Although Hypertalk had many features of object-oriented languages (described in the next section), Apple did not develop it for other computer platforms and let it languish; as Apple’s market share declined in the 1990s, a new cross-platform way of displaying multimedia left Hypertalk all but obsolete (see the section World Wide Web Display Languages ).

Object Oriented languages

Object-oriented languages help to manage complexity in large programs. Objects package data and the operations on them so that only the operations are publicly accessible and internal details of the data structures are hidden. This information hiding made large-scale programming easier by allowing a programmer to think about each part of the program in isolation. In addition, objects may be derived from more general ones, “inheriting” their capabilities. Such an object hierarchy made it possible to define specialized objects without repeating all that is in the more general ones.

Object-oriented programming began with the Simula language (1967), which added information hiding to ALGOL. Another influential object-oriented language was Smalltalk (1980), in which a program was a set of objects that interacted by sending messages to one another. 

C++

The C++ language, developed by Bjarne Stroustrup at AT&T in the mid-1980s, extended C by adding objects to it while preserving the efficiency of C programs. It has been one of the most important languages for both education and industrial programming. Large parts of many operating systems were written in C++. C++, along with Java, has become popular for developing commercial software packages that incorporate multiple interrelated applications. C++ is considered one of the fastest languages and is very close to low-level languages, thus allowing complete control over memory allocation and management. This very feature and its many other capabilities also make it one of the most difficult languages to learn and handle on a large scale.

C#

C# (pronounced C sharp like the musical note) was developed by Anders Hejlsberg at Microsoft in 2000. C# has a syntax similar to that of C and C++ and is often used for developing games and applications for the Microsoft Windows Operating System.

Java

In the early 1990s, Java was designed by Sun Microsystem,Inc., as a programming language for the World Wide Web (WWW). Although it resembled C++ in appearance, it was fully object-oriented. In particular, Java dispensed with lower-level features, including the ability to manipulate data addresses, a capability that is neither desirable nor useful in programs for distributed systems. In order to be portable, Java programs are translated by a Java Virtual Machine specific to each computer platform, which then executes the Java program. In addition to adding interactive capabilities to the internet through Web “applets,” Java has been widely used for programming small and portable devices, such as mobile telephones.

Visual Basic

Visual Basic was developed by Microsoft to extend the capabilities of BASIC by adding objects and “event-driven” programming: buttons, menus, and other elements of graphical user interfaces (GUIs). Visual Basic can also be used within other Microsoft software to program small routines. Visual Basic was succeeded in 2002 by Visual Basic .NET, a vastly different language based on C#, a language with similarities to C++.

Python

The open-source language Python was developed by Dutch programmer Guido van Rossum in 1991. It was designed as an easy-to-use language, with features such as using indentation instead of brackets to group statements. Python is also a very compact language, designed so that complex jobs can be executed with only a few statements. In the 2010s, Python became one of the most popular programming languages, along with Java and JavaScript.

 

Data processing

Data processing occurs when data is collected and translated into usable information. Usually performed by a data scientist or team of data scientists, it is important for data processing to be done correctly as not to negatively affect the end product, or data output.

Data processing starts with data in its raw form and converts it into a more readable format (graphs, documents, etc.), giving it the form and context necessary to be interpreted by computers and utilized by employees throughout an organization.

Six stages of data processing

1. Data collection

Collecting data is the first step in data processing. Data is pulled from available sources, including data lakes and data warehoses. It is important that the data sources available are trustworthy and well-built so the data collected (and later used as information) is of the highest possible quality.

2. Data preparation

Once the data is collected, it then enters the data prepration stage. Data preparation, often referred to as “pre-processing” is the stage at which raw data is cleaned up and organized for the following stage of data processing. During preparation, raw data is diligently checked for any errors. The purpose of this step is to eliminate bad data (redundant, incomplete, or incorrect data) and begin to create high-quality data for the best business intelligence.

3. Data input

The clean data is then entered into its destination (perhaps a CRM like Salesforce or a data warehouse like redshift), and translated into a language that it can understand. Data input is the first stage in which raw data begins to take the form of usable information.

4. Processing

During this stage, the data inputted to the computer in the previous stage is actually processed for interpretation. Processing is done using machine learning algorithms, though the process itself may vary slightly depending on the source of data being processed (data lakes, social networks, connected devices etc.) and its intended use (examining advertising patterns, medical diagnosis from connected devices, determining customer needs, etc.).

5. Data output/interpretation

The output/interpretation stage is the stage at which data is finally usable to non-data scientists. It is translated, readable, and often in the form of graphs, videos, images, plain text, etc.). Members of the company or institution can now begin to self-serve the data  for their own data analytics projects.

6. Data storage

The final stage of data processing is storage . After all of the data is processed, it is then stored for future use. While some information may be put to use immediately, much of it will serve a purpose later on. Plus, properly stored data is a necessity for compliance with data protection legislation like GDPR. When data is properly stored, it can be quickly and easily accessed by members of the organization when needed.

Data processing can be defined by the following steps

• Data capture, or data collection,
• Data storage,
• Data conversion (changing to a usable or uniform format),
• Data cleaning and error removal,
• Data validation (checking the conversion and cleaning),
• Data separation and sorting (drawing patterns, relationships, and creating subsets),
• Data summarization and aggregation (combining subsets in different groupings for more information),
• Data presentation and reporting.

There are different types of data processing techniques, depending on what the data is needed for. Types of data processing at a bench level may include:

• Statistical,
• Algebraical,
• Mapping and plotting,
• Forest and tree method,
• Machine learning,
• Linear models,
• Non-linear models,
• Relational processing, and
• Non-relational processing.

These are methodology and techniques which can be applied within the key types of data processing.

What we’re going to discuss in this article is the five main hierarchical types of data processing. Or, in other words, the overarching types of systems in data analytics.

Data Processing by Application Type

The first two key types of data processing I’m going to talk about are scientific data processing and commercial data processing.

1. Scientific Data Processing

When used in scientific study or research and development work, data sets can require quite different methods than commercial data processing.

Scientific data is a special type of data processing that is used in academic and research fields.

It’s vitally important for scientific data that there are no significant errors that contribute to wrongful conclusions. Because of this, the cleaning and validating steps can take a considerably larger amount of time than for commercial data processing.

Scientific data processing needs to draw conclusions, so the steps of sorting and summarization often need to be performed very carefully, using a wide variety of processing tools to ensure no selection biases or wrong relationships are produced.

Scientific data processing often needs a topic expert additional to a data expert to work with quantities.

2. Commercial Data Processing

Commercial data processing has multiple uses, and may not necessarily require complex sorting. It was first used widely in the field of marketing, for customer relationship management applications, and in banking, billing, and payroll functions.

Most of the data caught in these applications is standardized and somewhat error proofed. That is capture fields eliminate errors, so in some cases, raw data can be processed directly, or with minimum and largely automated error checking.

Commercial data processing usually applies standard relational databases and uses batch processing. However, some, in particular, technology applications may use non-relational databases.

There are still many applications within commercial data processing that lean towards a scientific approach, such as predictive market research. These may be considered a hybrid of the two methods.

Data Processing Types by Processing Method

Within the main areas of scientific and commercial processing, different methods are used for applying the processing steps to data. The three main types of data processing we’re going to discuss are automatic/manual, batch, and real-time data processing.

3. Automatic versus Manual Data Processing

It may not seem possible, but even today people still use manual data processing. Bookkeeping data processing functions can be performed from a ledger, customer surveys may be manually collected and processed, and even spreadsheet-based data processing is now considered somewhat manual. In some of the more difficult parts of data processing, a manual component may be needed for intuitive reasoning.

The first technology that led to the development of automated systems in data processing was punch cards used in census counting. Punch cards were also used in the early days of payroll data processing.

The Rise of Computers for Data Processing

Computers started being used by corporations in the 1970s when electronic data processing began to develop. Some of the first applications for automated data processing in the way of specialized databases were developed for customer relationship management (CRM) to drive better sales.

Electronic data management became widespread with the introduction of the personal computer in the 1980s. Spreadsheets provided simple electronic assistance for even everyday data management functions such as personal budgeting and expense allocations.

Database Management

Database management provided more automation of data processing functions, which is why I refer to spreadsheets as a now rather manual tool in data management. The user is required to manipulate all the data in a spreadsheet, almost like a manual system, only the calculations are automated. Whereas in a database, users can extract data relationships and reports relatively easily, providing the setup and entries are correctly managed.

Autonomous databases now look to be a data processing method of the future, especially in the commercial data processing. Oracle and Peloton are poised to offer users more automation with what is termed a “self-driving” database.

This development in the field of automatic data processing, combined with machine learning tools for optimizing and improving service, aims to make accessing and managing data easier for end-users, without the need for highly specialized data professionals in-house.

4. Batch Processing

To save computational time, before the widespread use of distributed systems architecture, or even after it, stand-alone computer systems apply batch processing techniques. This is particularly useful in financial applications or where data requires additional layers of security, such as medical records.

Batch processing completes a range of data processes as a batch by simplifying single commands to provide actions to multiple data sets. 

This is a little like the comparison of a computer spreadsheet to a calculator in some ways. A calculation can be applied with one function, that is one step, to a whole column or series of columns, giving multiple results from one action. The same concept is achieved in batch processing for data. A series of actions or results can be achieved by applying a function to a whole series of data. In this way, computer processing time is far less.

Batch processing can complete a queue of tasks without human intervention, and data systems may program priorities to certain functions or set times when batch processing can be completed.

Banks typically use this process to execute transactions after the close of business, where computers are no longer involved in data capture and can be dedicated to processing functions.

5. Real-Time Data Processing

For commercial uses, many large data processing applications require real-time processing. That is they need to get results from data exactly as it happens. One application of this that most of us can identify with is tracking stock market and currency trends. The data needs to be updated immediately since investors buy in real-time and prices update by the minute. Data on airline schedules and ticketing and GPS tracking applications in transport services have similar needs for real-time updates.

Stream Processing

The most common technology used in real-time processing is stream processing. The data analytics are drawn directly from the stream, that is, at the source. Where data is used to draw conclusions without uploading and transforming, the process is much quicker.

Data Virtualization

Data virtualization techniques are another important development in real-time data processing, where the data remains in its source form, the only information is pulled for the needs of data processing. The beauty of data virtualization is where transformation is not necessary, it is not done, so the error margin is reduced.

Data virtualization and stream processing mean that data analytics can be drawn in real-time much quicker, benefiting many technical and financial applications, reducing processing times and errors.

Other than these popular Data processing Techniques there are three more processing techniques which are mentioned below-

6. Online Processing

This data processing technique is derived from Automatic data processing. This technique is now known as immediate or irregular access handling. Under this technique, the activity by the framework is prepared at the time of operation/processing. It can be viewed easily with the continuous preparation of data sets. This processing method highlights the fast contribution of the exchange of data and connects directly with the databases.

7. Multi Processing

This is the most commonly used data processing technique. However, it is used all over the globe where we have computer-based setups for Data capture and processing.

As the name suggests – Multiprocessing is not bound to one single CPU but has a collection of several CPUs. As the various set of processing devices are included in this method, therefore the outcome efficiency is very useful.

The tasks are broken into frames and then sent to the multiprocessors for processing. The result obtained is expected to be in less time and the output is increased. The additional benefit is every processing unit is independent, thus failure of any will not impact the working of other processing units.

8. Time Sharing

This kind of Data processing is entirely based on time. In this, one unit of processing data is used by several users. Each user is allocated with the set timings on which they need to work on the same CPU/processing Unit.

Intervals are divided into segments, and thus to users, so there is no collapse of timings which makes it a multi-access system. This processing technique is also widely used and mostly entertained in startups.

Quick Tips to Analyze Best Processing Techniques

1. Understanding your requirement is a major point before choosing the best processing techniques for your Project.

2. Filter your data in a much more precise manner so you can apply processing techniques.

3. Recheck your filtered data again in a way that it still represents the first requirement and you don’t miss out on any important fields in it.

4. Think about the OUTPUT which you would like to have so you can follow your idea.

5. Now you have the filter data and the output you wish to have, check the best and most reliable processing technique.

6. Once you choose your technique as per your requirement, it will be easy to follow up for the end result.

7. The chosen technique must be checked simultaneously so there are no loopholes in order to avoid mistakes.

8. Always apply ETL functions to recheck your datasets.

9. With this, don’t forget to apply a timeline to your requirement, as without a specific timeline, it is useless to apply energy.

10. Test your OUTPUT again with the initial requirement for better delivery.

Threat

In computer security, a threat is a potential negative action or event facilitated by a vulnerability that results in an unwanted impact to a computer system or application.

Physical Threats

A physical threat is a potential cause of an incident that may result in loss or physical damage to the computer systems.

The following list classifies the physical threats into three (3) main categories;

• Internal: The threats include fire, unstable power supply, humidity in the rooms housing the hardware, etc.

• External: These threats include Lightning, floods, earthquakes, etc.

• Human: These threats include theft, vandalism of the infrastructure and/or hardware, disruption, accidental or intentional errors.

To protect computer systems from the above mentioned physical threats, an organization must have physical security control measures.

The following list shows some of the possible measures that can be taken:

• Internal: Fire threats could be prevented by the use of automatic fire detectors and extinguishers that do not use water to put out a fire. The unstable power supply can be prevented by the use of voltage controllers. An air conditioner can be used to control the humidity in the computer room.

• External: Lightning protection systems can be used to protect computer systems against such attacks. Lightning protection systems are not 100% perfect, but to a certain extent, they reduce the chances of Lightning causing damage. Housing computer systems in high lands are one of the possible ways of protecting systems against floods.

• Humans: Threats such as theft can be prevented by use of locked doors and restricted access to computer rooms.

Non-physical threats

A non-physical threat is a potential cause of an incident that may result in;

• Loss or corruption of system data

• Disrupt business operations that rely on computer systems

• Loss of sensitive information

• Illegal monitoring of activities on computer systems

• Cyber Security Breaches

• Others

The non-physical threats are also known as logical threats. The following list is the common types of non-physical threats;

• Virus

• Trojans

• Worms

• Spyware

• Key loggers

• Adware

• Denial of Service Attacks

• Distributed Denial of Service Attacks

• Unauthorized access to computer systems resources such as data

• Phishing

• Other Computer Security Risks

Types of Computer Security Threats

There are several types of computer security threats such as Trojans, Virus, Adware, Malware, Rootkit, hackers and much more. Check some of the most harmful types of computer Security Threats.

COMPUTER VIRUS

A Computer Virus is a malicious program, which replicates itself and infects the files and programs of your PC and can make them non-functional.

COMPUTER WORMS

A self-replicating computer program that spreads malicious codes, computer worms make use of the network to send copies of the original codes to other PCS. It can also go to the extent of sending transferring documents utilizing the email of the user.

SCAREWARE

Scareware is a malware that tricks victims to buy software by displaying fake virus alerts. A scareware infected PC may get pop-ups of fake malware threats and to get rid of those, users are prompted to purchase a fake anti-malware software.

KEYLOGGER

Also known as a keystroke logger, Keyloggers can track the real-time activity of a user on his computer. Keylogger runs in the background and records all keystrokes made by a user and passes the information to the hacker with the motive to steal password and banking details.

ROOTKIT

A rootkit is considered extremely dangerous as they appear to be legitimate files and deceives the computer user. Rootkit masks viruses and worms and makes them appear as necessary files. These are very difficult to remove and only an antivirus with the anti-rootkit feature can remove a rootkit.

Tips for Best Computer Security

For best computer security, you must follow certain guidelines, which are also called computer best practices. 1. Use the best antivirus software, which not only provides protection to your PC but also internet protection and guards against cyber threats. 2. Do not download untrusted email attachments and these may carry harmful malware. 3. Never download software from unreliable sites as they may come with a virus that may infect your system as soon as you install the software.

To protect computer systems from the above-mentioned threats, an organization must have logical security measures in place. The following list shows some of the possible measures that can be taken to protect cyber security threats

 

To protect against viruses, Trojans, worms, etc. an organization can use anti-virus software. In additional to the anti-virus software, an organization can also have control measures on the usage of external storage devices and visiting the website that is most likely to download unauthorized programs onto the user’s computer.

Unauthorized access to computer system resources can be prevented by the use of authentication methods. The authentication methods can be, in the form of user ids and strong passwords, smart cards or biometric, etc.

Intrusion-detection/prevention systems can be used to protect against denial of service attacks.There are other measures too that can be put in place to avoid denial of service attacks.

Summary

• A threat is any activity that can lead to data loss/corruption through to disruption of normal business operations.

• There are physical and non-physical threats

• Physical threats cause damage to computer systems hardware and infrastructure. Examples include theft, vandalism through to natural disasters.

• Non-physical threats target the software and data on the computer systems.

 

Multimedia computer

A multimedia computer is a computer that is optimized for high multimedia performance. Early home computers lacked the power and storage necessary for true multimedia.

The Basic Elements of Multimedia

• Text

• Graphic

• Animation

• Video

• Audio

TEXT 

Text are characters that are used to create words, sentences, and paragraphs.

Graphics 

A digital representation of non-text information, such as a drawing, chart, or photograph.

Animation 

Animation is Flipping through a series of still images. It is a series of graphics that create an illusion of motion.

Video

Video means the photographic images that are played back at speeds of 15 to 30 frames a second and the provide the appearance of full motion.

Audio 

Audio means music, speech, or any other sound.

Categorization

Two types of Multimedia presentation

• Linear Presentation

• Non-linear Interactive

 

Linear active content progresses often without any navigational control for the viewer such as a cinema presentation.

Non-linear uses interactivity to control progress as with a video game or self-paced computer based training. Hypermedia is an example of non-linear content.

Usage

Multimedia finds its application in various areas including, but not limited to:

• Advertisements

• Art

• Education

• Entertainment

• Engineering

• Medicine

• Mathematics

• Business

• Scientific research

Advantages of using Multimedia

• It is very user-friendly. It doesn’t take much energy out of the user, in the sense that you can sit and watch the presentation, you can read the text and hear the audio. 

• It is multi sensorial. It uses a lot of the user’s senses while making use of multimedia, for example hearing, seeing and talking.It is integrated and interactive. All the different mediums are integrated through the digitisation process. Interactivity is heightened by the possibility of easy feedback.

• It is flexible. Being digital, this media can easily be changed to fit different situations and audiences.

• It can be used for a wide variety of audiences, ranging from one person to a whole group.

Disadvantages of using Multimedia

• Information overload. Because it is so easy to use, it can contain too much information at once.

• It takes time to compile. Even though it is flexible, it takes time to put the original draft together.

• It can be expensive. As mentioned in one of my previous posts, multimedia makes use of a wide range of resources, which can cost you a large amount of money.

• Too much makes it unpractical. Large files like video and audio has an effect of the time it takes for your presentation to load. Adding too much can mean that you have to use a larger computer to store the files.

 

 

 

Development

Computer science, the study of computers and computing, including their theoretical and algorithmic foundations, hardware and software, and their uses for processing information. The discipline of computer science includes the study of algorithms and data structures, computer and network design, modeling data and information processes, and artificial intelligence. Computer science draws some of its foundations from mathematics and engineering and therefore incorporates techniques from areas such as queueing theory, probability and statistics, and electronic circuit design. Computer science also makes heavy use of hypothesis testing and experimentation during the conceptualization, design, measurement, and refinement of new algorithms, information structures, and computer architectures.

Computer science is considered as part of a family of five separate yet interrelated disciplines: computer engineering, computer science, information systems, information technology, and software engineering. This family has come to be known collectively as the discipline of computing. These five disciplines are interrelated in the sense that computing is their object of study, but they are separate since each has its own research perspective and curricular focus. (Since 1991 the Association for Computing Machinery [ACM], the IEEE Computer Society [IEEE-CS], and the Association for Information Systems [AIS] have collaborated to develop and update the taxonomy of these five interrelated disciplines and the guidelines that educational institutions worldwide use for their undergraduate, graduate, and research programs.)

The major subfields of computer science include the traditional study of computer architecture, programming languages, and software development. However, they also include computational science (the use of algorithmic techniques for modeling scientific data), graphics and visualization, human-computer interaction, databases and information systems, networks, and the social and professional issues that are unique to the practice of computer science. As may be evident, some of these subfields overlap in their activities with other modern fields, such as bioinformatics and computational chemistry. These overlaps are the consequence of a tendency among computer scientists to recognize and act upon their field’s many interdisciplinary connections.

Development Of Computer Science

Computer science emerged as an independent discipline in the early 1960s, although the electronic digital computer that is the object of its study was invented some two decades earlier. The roots of computer science lie primarily in the related fields of mathematics, electrical engineering, physics, and management information systems.

Mathmeatics is the source of two key concepts in the development of the computer—the idea that all information can be represented as sequences of zeros and ones and the abstract notion of a “stored program.” In the binary number system, numbers are represented by a sequence of the binary digits 0 and 1 in the same way that numbers in the familiar decimal system are represented using the digits 0 through 9. The relative ease with which two states (e.g., high and low voltage) can be realized in electrical and electronic devices led naturally to the binary digit, or bit, becoming the basic unit of data storage and transmission in a computer system.

Electrical Engineering provides the basics of circuit design—namely, the idea that electrical impulses input to a circuit can be combined using Boolean Algebra to produce arbitrary outputs. (The Boolean algebra developed in the 19th century supplied a formalism for designing a circuit with binary input values of zeros and ones [false or true, respectively, in the terminology of logic] to yield any desired combination of zeros and ones as output.) The invention of the transistor and the miniaturization of circuits, along with the invention of electronic, magnetic, and optical media for the storage and transmission of information, resulted from advances in electrical engineering and physics.

Managment Information Systems, originally called data processing  systems, provided early ideas from which various computer science concepts such as sorting, searching, databases, information retrievel, and graphical user interfaces evolved. Large corporations housed computers that stored information that was central to the activities of running a business—payroll, accounting, inventory management, production control, shipping, and receiving.