What is big data? (+ Big Data applications)

Table of Contents




Preface: big data?(BD )

Big data can be one of the terms exists no superset of the sayings and writings related to information technology, he said.

If you have a query related to the use of BD in Google search, you’ll see that the use of Big Data in medicineeconomicsbanking and accounting, and audit, only a small part of the questions and concerns of interest allocated to this area.

All we hear  BD  or macro data, so we can guess its meaning seems plain language could be said that Big Data, is a large amount of data; The volume is increasing every day and each of us, on any scale we are active, have seen and experienced its effects.

But for a more accurate definition, let’s go to the Gartner Institute and read Gartner’s definition from Big Data.

Gartner Definition

Big Data means information assets [a collection or organization] that:

  • High volumes are
  • With high-speed production and / or  a wide variety of

And they need innovativecost-effective processing methods that can be used to automate processes, make decisions, and improve intuition.

source

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.[2] BD  challenges include capturing datadata storagedata analysis, search, sharingtransfervisualizationquerying, updating, information privacy and data source. Big data was originally associated with three key concepts: volumevariety, and velocity. When we handle BD , we may not sample but simply observe and track what happens. Therefore, BD  often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value.

Current usage of the term BD tends to refer to the use of predictive analyticsuser behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.”[3] Analysis of data sets can find new correlations to “spot business trends, prevent diseases, combat crime and so on.”[4] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet searchesfintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorologygenomics,[5] connectomics, complex physics simulations, biology and environmental research.[6]

3V Big Data Model

In most high-definition, three so-called volume (Volume) and speed (Velocity) and variety  (Variety) see. To the extent that the term 3V is sometimes used to define BD .

For example, PWC, one of the world’s leading management consulting firms, defines BD  with the same 3V.

PWC Big data Definition

Most articles in the Big Data Application  Handbook are based on the same 3V definition .

In his book Customer Relationship Management, Francis Butel follows the same 3V model when it comes to analytical CRM.

Company Ernst & Young ‘s Guide to  Understanding BD  the same definition mentioned and of course to speak a new stirred, a V-quarters is added to it is (V fourth, the first letter Veracity and importance refers to the accuracy and reliability of the data).

As a rule of thumb, you should be convinced that the 3V (or 4V) pattern is a common and well-known model for defining big data, and we can now move on to a more precise definition of each of these components.

What are the applications of BD?

When we talk about BD, we are talking about more than one situation; A situation in which large volumes of data are generated at high speed and a wide variety.

But how to use such a situation requires other knowledge. Scientists Data (Data Scientists), specialists in AI (Artificial Intelligence), and active data mining (Data-mining), including those that can use BD to discover and develop in various fields.

So even though the term uses of large-scale data(BD  Applications) in Persian and English is common, and we used it, we always remember the purpose,  application analysis of large data sets; Otherwise, the volume and variety of data are not useful in itself, and if the proper analysis and processing of bulk data is not done, this data will not be different from other organizational waste resources in terms of resource management.

Typically, addressing the details of these applications is a specialized discussion and goes beyond the area defined for the digital literacy course. But perhaps the following points can be a clue to your further searches and more complete studies:

What is the use of BD? Check out a few simple examples

Applications of BD  in everyday life include routing services such as Waze and the Navigation section of Google Maps. Significant amounts of data related to moving vehicles (actually: mobile phones) are processed continuously and instantly, and appropriate routes are determined based on the destination and user preferences are suggested to them.

In terms of customer relationship management, analytical CRM is one of the areas in which the use of BD  is well known, and reviewing the analytical CRM course can give you a little insight into this.

If you are familiar with market segmentation and customer behavior analysis, you can no doubt imagine how useful BDAnalysis can be and help decision-makers in this area.

If you are familiar with the topic of personalization, you can guess how high-volume data analysis can help you plan for personalized services.

Of course, the personalization of services is possible even without big data analysis. But when you have many options and limited resources, it is natural that personalization based on BD  analysis can estimate the most effective options (compared to the cost of each option) for you.

Recommendation systems are also among the applications of BD  in digital businesses, and the use of Big Data in this field has yielded tangible results.

One of the areas that have always been considered in the Big Data debate is medicine. There are several reasons for this:

  • Extensive and varied numerical information that can be obtained from patients (compared to qualitative domains)
  • The human desire to cooperate in the field of health (assuming that it can have beneficial achievements for them)
  • The many applications and tools that are used in the field of health today and the large amount of data that they produce (just think of your mobile accelerometer sensor that records your movements for a large part of the day)

But in examining the use of BD  in medicine, it is important to distinguish between different areas. For example, the field of forecasting is one of the areas in which there is relatively more hope and has made interesting progress (you may know the Google Flu project, which aimed to predict the statistics and the trend of influenza in different places, based on user search).

The diagnostics and diagnostics industry is the second area that has grown significantly, and the ability to process large volumes of patient images is expected to create valuable opportunities for diagnosis in the future.

The field of treatment and related decisions is the most difficult branch, and yet, we have to wait a long time to see and experience its tangible achievements.

If you are interested in studying more about the application of BD  in medicine and health in general, the following two articles can be a good starting point:

 Big Data Revolution in Healthcare (PDF)

Big Date Revolution in Healthcare

 Big Data Analytics in Healthcare (PDF)

Big data analytics in healthcare

The first file was created by McKenzie and is a bit more general. The second file is more useful in terms of the resources it introduces. Most of the references presented are useful, informative,and simple. Of course, it is natural that we have introduced both files with the beginner audience in mind, and if you want to study professionally in this field, you should go to specialized journals.

SEO is another area that has used BD  analysis extensively, and we have all experienced the results.

In the history of SEO, we have noted that overcoming the era of relying solely on keyword analysis and reaching behavioral analysis algorithms has made traditional black hat SEO methods simply no longer effective. An important part of this achievement should be attributed to the ability to analyze user behavior on a large scale.

These days, if sites can use black techniques to get a place in the top rankings of search results, search engines (especially Google) quickly after sending visitors to these pages and examining their behavior, prefer the inappropriateness and low quality of these pages. And correct their results.

Naturally, the use of BD  in various fields goes far beyond the limited cases mentioned here, and in each case, even specialized books have been written and published.

But in general, you have to keep in mind that BD  is still very young and there is a very long way to go before we can see and experience its applications commercially and widely throughout our lives.

About KSRA

The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.

KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.

Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.

The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.

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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.

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Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.

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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.