THE GENERAL FRAMEWORK OF BIG DATA

Big data has become one of the most popular terms, especially with the huge technological explosion in all areas of eveiyday life, so what do we mean by big data? What are its most important characteristics and its source?

23.2.1 DEFINITION AND CHARACTERISTICS OF BIG DATA

Big data is defined as data generated through the use of a lot of digital devices, computers, and everything that is connected to the Internet at any given moment. For example, millions of people around the world using mobile phones, send text messages or e-mail, or to view digital content on the network, etc. Through these different usages, a great amount of data can be created.

All these activities leave a digital impact, and then this information together constitutes what is known as big data.

Big data is characterized by its large size, the speed in terms of data- generating and updating, and the multiplicity of body or shape. This means that big data needs a more advanced technology in storage and processing to convert this data from its raw image to value knowledge, which is the essence of benefit.

In 2011, the Mackenzie Global Institute defined big data as any collection of data that is larger than the ability of traditional database tools to capture, store, manage, and analyze that data [3].

The most widely used definition of big data in the industrial field, the definition of Gartner in 2012 one of the leading companies in the field of IT research and consulting, which says that: big data is a high-volume, fast, and diverse information asset, and requires innovative and cost-effective forms of processing Information enables to enhance vision and decision-making

[4].

From the above, big data can be defined as a collection of veiy large, complex data that is difficult to process and managed using methods. Traditional accepted applications are grouped into databases and analyzed, stored, and used in the time of need.

The methods of processing big data, in addition to the huge volume of data produced, stored, and made available under the umbrella, are characterized by other characteristics different from traditional data, or which are stored neatly and coordinated as databases. Experts believe that the most important characteristics of data are [1]:

1. Volume: Experts estimate that by 2020 the Internet will contain approximately 40,000 zettabytes of data ready for analysis and debriefing.
2. Velocity: To process a small set of data stored in databases, companies were analyzing each data set separately and sequentially until they were all completed. But as the volume of data swelled, the need for special systems to ensure the speed of big data analysis was urgently needed. That need led to the creation of special techniques for processing such data.
3. Variety: The number of people who have been displaced by the war has increased.
23.2.2 BIG DATA FORMS, SOURCES, AND IMPORTANCE

Organizations and institutions are currently acquiring additional data from their operational environment at an increasingly rapid pace, such as [4]:

• Web Data: Web behavior data can be captured at the customer level, such as page views, searches, comments, and purchase slot, where it can enhance performance in areas such as best-in-class presentation, form planting, customer segmentation, and targeted ads.
• Text Data: e-mail, news, Facebook posts, and is one of the largest types of application of most data on a large scale.
• Time and Location Data: It makes GPS and mobile phone technologies as well as Wi-Fi infonnation from time and location information a growing source of data. And at the individual level, many organizations come to achieve the power of knowing when and where customers are, and it is equally important to look at time and location data is on a collective level, and many individuals open their time and location data more openly, and many interesting applications begin to appear. So big data from time and location should be treated with extreme caution for their privacy.
• Smart Grid and Sensor Data: Sensor data is collected at present from cars, oil pipes, mill turbines, collected at an extremely high frequency, and as sensor data provide strong infonnation on the perfonnance of engines and machines, it can diagnose problems more easily and faster to develop procedures.
• Social Network Data: Social networking sites or social networking, the most important of which is Facebook, Linkedln, Twitter, and Instagram. It is possible to do link analysis to reveal a network of a particular user. The analysis of the social network can give ideas about ads that may attract selected users. This is done by looking not only at the interests mentioned by customers personally but also knowing what is important within the circle of their friends or colleagues.

According to a report on the coordination of the Economic Commission for Europe, entitled “What the big data means for official statistics” on 10 March 2013, a summary of the data sources was developed, as these data are now generated automatically and continuously in digital format in many different ways, and in general, big data sources can be classified as follows [4,5]:

1. Sources Arising from the Management of a Program: Whether it is a government or non-govemmental programs, such as electronic medical records, hospital visits, insurance records, bank records, and food banks.

2. Commercial or Transaction-Related Sources: Data arising from transactions between two entities, for example, credit card transactions and transactions conducted via the Internet, including by mobile devices.
3. Source of Sensor Networks: Such as satellite imagery, road sensors, climate sensors, and air pollution.
4. Tracking Device Sources: Such as tracking data from mobile phones and GPS.
5. Behavioral Data Sources: Such as the number of internet searches for a product, service, or any other type of information, and the views of a page on the Internet.
6. Sources of Opinion-Related Data: Such as comments and opinions on social media such as Facebook and Twitter.
23.2.3 BIG DATA CHALLENGES

The use of big data in statistics poses many challenges, which is mainly in the following categories:

• Legislative Aspects: The committees are also required to provide the information.
• Aspects of Privacy: Managing public confidence and accepting the reuse of data.
• Financial Aspects: The costs involved in taking data for the benefits achieved.
• Management Aspects: For example, policies and guidance on data management and protection.
• Methodological Aspects: The quality of the data and the appropriateness of statistical means.
• Technological Aspects: i.e., issues relating to information technologies.