Big Data and Its Analysis Methods

Nowadays, in every movement, data is increasing in a high manner. The data is exponentially growing in the market. The data is not only growing but it has been generated, stored, analyzed and acquired also. The reason behind huge data is the use of the Internet. The Internet is not only used on the web, but it is exponentially growing in the web, IoT, and many more electronic devices. It is also used in medical and other industries as well. The volume, type, and size of data are increasing in very different formats. Another side we can say that the price of that datastore is getting less and available full time, but there are lots of problems that will generate like processing for that data, analysis of that data and also integrating that big data into our business for further decision in order to make business faster.

In order to deal with big data, we have to go through emerging new technologies to solve this problem. To achieve the goal of a solution for such problems, we must have to build scalable and elastic solution architecture.

Big data has been mainly generated by big corporate leaders, large scale multinational companies, etc. Sometimes in the sudden rise of big data cause the issue in the unprepared organization. Previously, new technologies appear in publication only. The emerging growth of big data technologies and readymade acceptance of the concept of an organization having small time to develop big data domain.

The reason for this term paper is to deep dive into big data scenarios and problems. I am going to deep dive into its methodology in this paper. I am also going for some limitation and their benefits as well. This paper begins with an overview of big data. After that, I have highlighted some topics like mathematical theories for big data analysis. Other characteristics, like frequency, I will describe it as well. I have also highlighted big data analysis methods in brief.

Big Data Overview

Big data is the application of technologies and some type of techniques in order t process and analyze the very large scale of data, such as any corporation, industry data, etc. Those data are not only large but it is, bit complex as well. So, for us, it might be difficult some time to process using manually database and its management tool. For example, some medical records, photos, and vides, website or software logs, military records, call details and huge scale any e-commerce site having such type of big data.

Here, the term very large means it is a minimum one terabyte. For any organization or industry, big data must be managed properly. It must be copied at another place as well. It must be combined with other data as well. Big data can be used by many people at a time. We should manage such type of scenario with big data management. We can say that if we manage big data on a very effective scale, then we can grow our business in a better approach.

In another word, we can say that big data is a field for systematically use and analyze information from large and complex scale data by using some data processing application software. Big data is only dealing with to analyze large scale data, but its role is to capture data, store that captured data, analyzes that stored data, performs operation such like search, shares, and transfer that data, it needs to visualize as well, we can perform such query on that, we need to update also. It is also necessary to make privacy and data security for data.

Mainly big data is attached with three core concepts: variety, volume, and velocity. We can easily observe big data with some software and its processing withing some time. Nowadays big data is using for user behavior analytics and predictive analytics. Big data analytics is more useful in order to prevent diseases, increase business capitalize, and also used in the crime as well. Governments, advertiser, scientists, medicine predictions, business executives are mainly used big data in order to solve their difficulties in large scale databases. They are facing difficulties in large scale urban information, business information, and Internet searches. Scientists are facing an issue with a large scale of data or complex physics calculations.

Nowadays, our data sets increase at a very high scale. If we talking about the Internet of Things, its devices such as cameras, sensors, RFID, wireless devices, mobiles, computers are rapidly growing in the market. For such type of big data, our traditional software like RDBMS, some desktop software have difficulties to handle it. So, for big data, we need more servers that run parallel to analyze data.

Mathematical Theories for Big Data Analysis

In technical terms, we can say that data are values of the set. I am going to look into big data analytics theory, which is used for optimization and statistics. The theory is also used in probability measurement, topological and metric space. There are two theories under big data analytics named independent and identical distribution and set theory.

Independent and Identical Distribution

Statistics is very useful to analyze the data in graph and tabular form. In big data, statistics is very useful to analyze data, which is called as big data statistics. In a data market, big data is focusing mainly on machine learning and statistics. On other side, statistics and probability are used for the collection of random value to generate identical and independent distribution. In that, all variables are mutually independent.

Here, IID assumptions for generated variables from probability and statistics tend to statistical inferences. So, we can say that IID assumptions are very useful to limit central theorem, which says that the sum of probability distribution with finite variables approaches a normal IID distribution. The normal distribution is nothing but it is an elementary distribution of probability, which might be extended into generalized and normal IID distribution.

Set Theory

The theory for the relational database is relational calculus and relational algebra. Here, relational algebra has been constructed from operations and set of tuples. Mainly, there are five set of operations available, such as join, union, projection, intersection, and selection. Join is used for the cartesian product of two relations. Join is restricted under some criteria. Union operator is used to combining two relations. The projection operation mainly extracts specified attributes from tuples only. In intersection operators, it produces common relations between two tuples. The selection operator gets some tuples from tables and performs such operation on those in order to get certain criteria in the form of the subset.

Big Data Characteristics

Nowadays, communication and computer technology are growing rapidly, which causes the paradigm transition of our living world. We can say that digital technology is highly advanced in every field, at every stage in data. We must have to make sure about its stability, management, and also make sure about the computation of that data. So, big data is a highly emerging technology that is related to data. It’s mainly used to analyze application data. In big data management, cloud computing and files are also used for unstructured data. So, we must have to categorize that data analysis with different characteristics.

In our community, the label ‘big data’ is connected with a computer scientist. Apart from that, computer software is connected with mainly three aspects, such as distributed intelligence, complexity in the algorithm, and problem computability. For us, algorithm complexity and computability of the program become a challenge for software under artificial intelligence.

In computer technology, big data is having 4V characteristics like volume, variety, velocity, and veracity. In volume characteristics, big data is referred to a huge amount of stored data. We can say that it is quantity if stored and generated data. Mainly the size of data is used to determine that value and potential insight and finalize that the data can be considered in big data or not. In a variety, it is referred to as multi-source and multi-type data. It mainly depends on the nature and type of data. Variety characteristics are very helpful for a data scientist who analyzes it for effectively resulting insight. Data fusion has been used in missing place, which is from audio, text, video and images. Velocity characteristic is referred to as the speed of data calculation. It is also depending on the data are processed and generated. Speed is useful to meet challenges and demands for growth and development. Big data is available in real-time as well. Statistics of big data is continues compared to small data and its analysis. There are two types of velocity is available for big data: one is generation frequency and another one is the frequency for publishing, recording, and handling. Finally, veracity refers to the high quality of data. It is also referred to as the total value of captured as well as analyzed data. Data quality is measured with user demand satisfaction and inherent information content. The veracity we can term as data usability and the data value is affecting accurately.

Big Data Analysis Methods

There are many methods or techniques being used in order to process and analyze big data. I will look into some of the methods which are applicable to analyze big data. In order to do the structural analysis, the characteristics of big data have been derived from big data characteristics. Below are some methods for big data analysis.

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Association Rule Learning

The association rule learning method is used for discovering correlations between value in big data. Initially, the association rule learning method is used in a supermarket for point-of-sale (POS) systems. In there it is used for discovering relations between products.

We can use association rule learning in order to increase sales from products in better proximity to one another. If we want to extract information about our customers or visitors from our website or e-commerce site, we can use this method. We can also analyze biological data to create new relationships. Association rule learning also helps us to monitor system logs in order to find miscellaneous or unwanted activities in the server. We can also get analyze data based on purchased products by any customer.

Classification Tree Analysis

The classification tree analysis method is used for identifying categories that a new analysis or observation belongs to. This method requires a correctly identified training set of observations. We can also say historical data for the same. Classification tree analysis also knows as statistical classification.

In order to automate assign some documents based on their categories, we can use the classification tree analysis method of big data analytics. If we want to make groups of categories organisms, we can use this statistical classification method. We can also use this method for developing profiles or portfolios of students who are taking online courses for study.

Genetic Algorithms

The genetic algorithm method is used for the evolution of business. Using this technique, we can optimize our problem which reasons for evolves solution. For biological evolution, we can use the genetic algorithm process.

We can use genetics algorithms to schedule doctors’ hours in emergency rooms at any given time. We can also use it in order to develop puns, jokes, etc. from artificial sources. Using a genetic algorithm, we can create such a type of business process, that can be useful for buyers to remove every step of the buying process.

Machine Learning

Machine learning is mainly known as a subfield of computer science to perform such tasks as computer vision, text analytics, pattern recognition, and speech recognition. It performs these tasks with mathematical optimization and statistics. The machine learning method is mainly used in the field of optical character recognition, which is known as OCR, spam filtering and search engines where they need more analytics and optimization for data.

The machine learning method is divided into two types: supervised learning and unsupervised learning. If we do have input data as a matrix and we want a response in prediction, we can use supervised learning for this type of problem. In which matrix having a number of predictors. A real-time example would be to predict web user probability to click on web ads using any features as predictors. Unsupervised learning is used in such a scenario where groups are similar within each other, but they don’t have a class to learn from. There are many approaches we do have to the task of machine learning which maps predictors to find a group that is having similarity instances in every group. A real-time example of unsupervised learning is the segmentation of customers like for any telecommunication company we must have a user segment based on their phone usage. It is very useful for any marketing department to give an offer based on their use.

Regression Analysis

The regression analysis method is mainly used for manipulating independent variable, such as background music, in order to see how independent variable influences on a dependent variable for example time spent in-store. So, we can say that regression analysis is used to describe how the value of any dependent variable is change based on any independent variable is varied. Mainly regression analysis works with continuous quantitative data we do have like age, speed or height.

If we want to determine the level of any customer satisfaction which affects customer loyalty for any product, we must go with regression analysis. We can also determine the number of calls might be received because of miscellaneous information given to any user. Regression analysis used to find love from online dating sites.

Sentiment Analysis

Sentiment analysis is used to analyze opinionated text, it is having user’s opinions such as organization, events, products and might be individual. Nowadays, many industries are capturing more data of their customers to manage them under sentiment analysis. In the market, finance, marketing, governance, military, political parties, scientist are the major application that belongs for sentimental analysis.

Sentimental analysis is mainly divided into three subgroups named as aspect-based, sentence-level and document-level. The aspect-based method is used to recognize all sentiments in a document and find the aspects of the entity to which sentiment refers. For example, if we are talking for customer product reviews, then it usually having opinions about different aspects of products. Using an aspect-based method, we can get valuable information for different product features that would remain if the sentiment is only classified. The sentence-level technique is used for determining the polarity of any single sentiment. It must first find the difference between objective and subjective sentences. Here, the sentence-level method is very hard from the document-level technique. Document-level techniques can find that the whole document expresses a positive or negative sentiment. This assumption is that if the document contains sentiments about a single entity or not. In document-level techniques, some are divided into two classes like positive or negative.

Social Network Analysis

Social network analysis also knows as network analysis techniques for big data analytics. Social network analysis is mainly used for the telecommunication industry. It is also used by other industries like sociologists, etc. Nowadays, it is being used in many places, for example, to analyze the relationship between people in activities. In social network analysis, nodes are represented as individuals in a network on other side ties, which are used to represent the relationships between individuals.

The social network analysis method is mainly used to see how people who are from different population and they tie with outsiders. We can also find importance from a particular individual within a group using this social network analysis technique. We can also find the minimum number of direct ties that connect two individuals. We can analyze the social structure of a customer base.

A/B Split Testing

A/B split testing method is used to determine what treatments/changes will improve in a given objective variable from the control group which is compared with a variety of other test groups. A real-time example for A/B split testing is a marketing response rate. This technique is also known as bucket testing or split testing.

A real-time example of the split testing technique is to determine images, colors, copy text and layouts that will improve rates from any big e-commerce web site. Here, big data is very useful for us to find and ensure that the groups are sufficient size to detect meaningful differences between treatment and control. We can say that when more than one variable is being manipulated in any treatment, then multivariate generalization of split testing technique, which applies statistical modeling, we can call this as A/B/N testing.

Conclusion

Nowadays, communication and computer technologies are having emerging development. Humans, environments, machines are observed by ground, air, and space-based sensors. In this paper I have added big data overview, mathematical theory, 4V characteristics and then also mentioned techniques/methods for big data analytics. Here, these methods are used for any big data analytics, but spatiotemporal associations analysis is specially used for only geographical data. Just like 4V characteristics of big data analytics given by standard methods in big data are given inexperience, and they are not collectively exhaustive and mutually exclusive in any theory.

References

1. https://en.wikipedia.org/wiki/Big_data
2. ‘Popular Solutions and Techniques for Big Data Analytics’. https://www.peerbits.com/blog/big-data-analytics-techniques-solution.html
3. ‘Big Data Techniques That Create Business Value’. https://www.firmex.com/thedealroom/7-big-data-techniques-that-create-business-value/
4. Hong Shu. ‘Big Data Analytics: Six Techniques’. https://www.researchgate.net/publication/303325341_Big_data_analytics_six_techniques
5. ‘Big Data Statistical Analysis Methods’. https://www.allerin.com/blog/5-big-data-statistical-analysis-methods