OSI Model

What is OSI model?

OSI is stands for Open System Interconnection. And it is ISO standard for worldwide communications. It defines a networking framework to implement protocols in layers. This OSI model have seven layers.

They are,

•        Application layer
•        Presentation layer
•        Session layer
•        Transport layer
•        Network layer
•        Data link layer
•        Physical layer

Controls of those layers are passed through adjacent layers. First the request is taken from the Application layer and it is passed to physical layer.

Functions of these layers

Ø  Application layer- This layer is used to interact with users. Everything at this layer is application specific. Services that are provided by this layers are File transfers, e-mail, and other network software services.

Ø  Presentation layer- This layer is used to translate the information form the application layer and network format, and vice versa (encryption).

Ø  Session layer- This layer is used to establish, manage and terminates connections between applications.

Ø  Transport layer- The transport layer is responsible for delivery of a message from one process to another. And it ensures complete data transfer.

Ø  Network layer- The network layer is responsible for the delivery of packets from the original source to the final destination.

Ø  Data link layer- It’s responsible for transmitting frames from one node to next.

Ø  Physical layer- It is responsible for transmitting individual bits from one node to the next.

In the beginning OSI model is used to describe about the network connections. After that TCP/IP model was created.

Because,

ü  The foundation of the internet was built using the TCP/IP suite and through the spread of the WWW (World Wide Web) and internet.

ü  Department of Defense (DOD) did a research project and they tried to bring International standards which could not be met by the OSI model.

What is TCP/IP model?

TCP/IP is stands for Transmission Control Protocol/Internet Protocol. It is a suite of communication protocols used to connect hosts on the Internet. TCP and IP are the two main protocols which are used by the TCP/IP model.

TCP/IP provides end to end connectivity specifying how data should be formatted, addressed, transmitted, routed and received at the destination. TCP/IP model has four layers.

They are,

    Ø  Application layer

    Ø  Transport layer

    Ø  Internet layer

    Ø  Network access layer

Functions of TCP/IP model layers

  Ø  Application layer- This layer is combination of the Application, Presentation and Session               layer. This layer has ability to use both TCP and UDP protocols.

  Ø  Transport layer- It acts as the delivery service used by the application layer. This layer will            choose the protocol based on the reliability requirements of the data.

  Ø  Internet layer- Responsibilities of this layers are routing and delivery of data. It allows                   communications across networks of the same and different types.

  Ø  Network access layer- This is a combination of Data link and Physical layers of the OSI               model.

Difference between The OSI model and TCP/IP model

In wired or Wireless network, most of the data communication happens by way of packets of information travelling through one or more network. Those networks uses protocols to transmit data.

ë  ISO model is implemented first. So it is a reference model and TCP/IP is implemented using OSI model.

ë  OSI model is a generic, protocol independent standard. And the TCP/IP model is considered to be standards around which the internet has developed.

ë  OSI model has 7 layers and TCP/IP model has 4 layers.

ë  Application layer, Presentation layer and Session layer in OSI model are combined together as an Application layer in TCP/IP model.

ë  And Data link layer and physical layer in OSI model are combined together as a Network access layer in TCP/IP model.

ë  TCP/IP model is not a rigidly designed into strict layers. But the OSI model has rigid layers.

ë  Session layer in OSI model permits two parties to hold ongoing communications. But it is not in TCP/IP model. But its characteristics are provided in Transport layer in TCP/IP model.

ë  Presentation layer in OSI model handles data format information in the network communication. But it is not found in TCP/IP model. Instead of that Application layer in TCP/IP model handles this function.

ë  Applications of OSI model and TCP/IP model

OSI	TCP/IP
FTAM	FTP
VT	SMTP
MHS	TELNET
DS	DNS
CMIP	SNMP

ë  The notion of an application process is common to both OSI and TCP/IP model. But their approaches to constructing application entities are different.

ë  In OSI model transport layer is responsible for reliable data transmission. And it breaks the data in to packets and transmits it. But in TCP/IP model it uses two standard protocols. They are TCP and UDP. Here TCP is used for reliable transmission and UDP is used to un-reliable transmission.

ë  Network layer in OSI model provides both connection less and connection oriented services and the Internet layer in the TCP/IP model is exclusively connectionless.

ë  OSI’s CLNP is functionally identical to the Internet’s IP. Both CLNP and IP are best effort delivery network delivery. Major difference is CLNB accommodates variable length addresses, and IP supports fixed addresses.

ë  In OSI model end systems and intermediate systems use routing protocols to transmit information. In TCP/IP model hosts use a protocol.

ë  Each layers in OSI model handles errors. Transport layer of the OSI model checks source to destination reliability. In TCP/IP model reliability control is done in the transport layer.

ë  Hosts on OSI implementation do not handle network operation but TCP/IP hosts participate in network protocols.

Big Data Mining

Database is collection of information organized in such a way that a computer program can quickly select desired pieces of data. In traditional system databases are organized by fields, records and files. Everything in this databases are data. Data is distinct pieces of information.

Every day 2.5 quintillion of data are created and more than 90 percentage of data are produced within past two years. In past days our data generation has never been so powerful. But now it is increased very much. In 2012, debate between Barak Obama and Mitt Romney triggered about 10 million tweets within 2 hours. And the well-known web site Flickr which is used to post our images faced a problem. It receives 1.8 million photographs every day which has the size of 2MB. Approximately they need 3.6TB storage capacity per day. Those situations demonstrate the rise of Big Data application where data collection has grown admirably. 

The term Big data is used for collection of data sets, which are so large and complex. Those data are very difficult to process using on-hand DBMS tools or traditional applications which are used to process data. We can’t simply handle those data. There are so many risks in capturing, storing, searching, sharing and visualizing the data.

Usually big data might be petabytes or exabytes of data consisting of trillions of records of people. Those data are not belongs to a particular source. There are many sources for these data. Such as customer contact center, web, sales, mobile data, social media and so on. Those data are not structured well. Those are mostly loosely structured and it is often incomplete and inaccessible.

Now a days scientists encounter limitations because of large data sets in many areas. Such as genomics, connectomics, physics simulations, meteorology, and biological research. These limitations affects internet search, business informatics and finance.

Digital data is now everywhere, in every sector, in every economy, in every organizations and user of digital technology. Big data is important for leaders across every sector and consumers of products and services stand to benefit from its application. 

Big data starts with large volume, heterogeneous, autonomous sources with distributed and decentralized control and seeks to explore complex and evolving relationships among data. These characteristics make it an extreme challenge for discovering useful knowledge from the Big Data. Autonomous data sources with distributed and decentralized controls are a main characteristic of Big Data applications. Being autonomous, each data source is able to generate and collect information without involving any centralized control. 

Needs of the stored data have become larger amount in global economy. When we consider a company, every day they increase their number of customers and suppliers. Therefore their operational activities and data transactions also increase in large scale. All over the world there are many devices connected to the network every second such as mobile phones, smart energy meters, automobiles, and industrial machines these devices may sense the data, create the data, and communicate data in the Internet.

Furthermore there are many Social media sites, smartphones, and other consumer devices including PCs and laptops all are contribute data to the entire world.

 More than this multimedia content has played a major role to increase the amount of big data in fastest ascending manner. Each second of high-definition videos, images and audio files those are takes very large amount of data. All together there are considerable amount of big data available.

In typical data mining systems, the mining procedures require computational intensive computing units for data analysis and comparisons. A computing platform is, therefore, needed to have efficient access to, at least, two types of resources. They are data and computing processors.

For small scale data mining tasks, a single desktop computer, which contains hard disk and CPU processors, is sufficient to fulfill the data mining goals. Indeed, many data mining algorithm are designed for this type of problem settings. For Big Data mining, because data scale is far beyond the capacity that a single personal computer can handle, a typical Big Data processing framework will rely on cluster computers with a high-performance computing platform, with a data mining task being deployed by running some parallel programming tools, such as MapReduce or Enterprise Control Language, on a large number of computing nodes.