The Role of Mathematics in Big Data

Posted: August 26th, 2021

The Role of Mathematics in Big Data

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The Role of Mathematics in Big Data

            By definition, big data refers to extremely large data sets that can be analyzed computationally to reveal patterns, trends, and associations. It has become one of the most promising frontiers for innovative research and development of business, and computer science(Chen, Chiang & Storey, 2012). For organizations, enterprises, and individual lives, big data is slowly becoming a strategic asset. Also, it has been adopted to reinforce the security standards of several nations around the world. Until now, the United States has been the leader in the exploitation of big data for the past two years. From a utility point of view, big data has brought about imperative economic opportunities that can be exploited by organizations. The most prominent organizations in bid data are the International Data Corporation which hasprojected that the worldwide big data and analytics market may reach a monetary value of $220 billion by 2022.  Moreover, the company predicts that big data-related marketing services in the pacific region may grow from the current $5 billion to $8 billion by the year 2021 (Chen, Chiang & Storey, 2012). Therefore, the already witnessed positive results and predictions show that big data holds immense potential in terms of realizing rapid economic advancement in the years ahead.

Role of Mathematics in Bid Data

Big data and its associated analytics borrow a lot from mathematics. However, no sufficient pieces of literature show the important role that mathematics plays in strengthening big data (Chen, Chiang & Storey, 2012). Scrutiny reveals that big data employs a deep sense of mathematical modeling. The mathematical structures and frameworks are used in the bid data model to facilitatethe execution of rigorous mathematical analyses performed on data sets(Lee, et al., 2013). Additionally, being that the mathematical operator of big data is usually BIG, mathematical models provide an excellent avenue for examining the cardinality of big data. Recent studies have shown that mathematical volume is the very first attribute for big data. The assertion is directly linked to the large amounts of data analyzed(Lee, et al., 2013). As such, the bigger the volume of data, the more the importance presumed to be for its intended users. Thus, through discrete mathematical concepts to measure theory and real function theory, it becomes easy to determine the cardinality of large data sets.

Besides proving the cardinality of big data, mathematics is also useful in the modeling for searching data sets. As such, a piece of data is regarded as a member of a larger data set (Udell & Townsend, 2019). For example, we can assign a given data to set a unique identifier and an attribute. Upon invoking this identifier and the associated attribute, the data piece can easily be retried from the pool of big data(Lee, et al., 2013). The logic that is being incorporated by technological companies like Google in helping people retrieve specific pieces of information from the interwebs.

In addition, in the identification of patterns within data sets, mathematical concepts are key. Once data patterns are identified, it becomes even easier for programmers to create accurate big data algorithms(Chen, Chiang & Storey, 2012). Classification is a branch of mathematics that deals with assigning categories to a collection of data for purposes of facilitating accurate predictions and data patterns for seamless analysis (Lee, et al., 2013). In this way, it is possible to formulate accurate classification algorithms that in turn make a user adequately aware of the various classes of particular instances in the dataset. Therefore, a predictive mathematical model can then be generated to help in the future identification of particular instances of a problem.

At the core of big data, there is usually the writing and reading of millions of lines of code. The requirement is made possible by the existence of numerous programming languages such as java and python (Lee, et al., 2013). At the core of these lines of codes is usually mathematical data that is executed in line with the specified mathematical equations and logic-based thinking that directs the computers(Lee, et al., 2013). Therefore, without this firm mathematical basis, big data would not be as sophisticated and effective as it has come to be known in the present day.

Today, the global population is expanding at a rapid rate as well, the demand in the number and complexities of human needs with subsequent impact on the nature of jobs. That is, in the current big data era, the traditional menial and rudimentary jobs are slowly fading away. Instead, database jobs are sprouting in nearly every sector (Chen, Chiang & Storey, 2012). In response to this development, companies are clamoring to recruit highly competent mathematicians (Udell & Townsend, 2019). Hence, it is important for job seekers to have basic mathematical skills at the very least for a rewarding job in the fast-expanding big data companies. In addition, subtraction, series, sequences, linear regression, and statistics are the principal areas within mathematics that are key in seeking to maneuver around big data(Chen, Chiang & Storey, 2012). More than anyone else, it is easier for mathematicians to utilize big data analytics toolsfor generating useful information such as targeted marketing, trend analysis and brand recognition for businesses(Lee, et al., 2013). Therefore, as the years go by, the number of these mathematician employees of big data companies is projected to keep increasing.

The usefulness of mathematics is also exhibited in the practical application of the conceptof the associative array. The concept is often applied in the creation of the Dynamic Distributed Dimensional Data Model for conducting graphical analysis and machine learning (Udell & Townsend, 2019). As such, aspiring data specialists should havean extensive understanding of associative arrays and associated properties. It is a well-known mathematical fact is that there exists a close relationship between associative arrays and data linearity. Hence, thorough knowledge of these spheres of mathematics is crucial in the successful creation and implementation of big data projects.

Conclusion

Mathematics is an important part of big data. Were it for the existence of the above discussed mathematical concepts and models, big data would not be as sophisticated and useful as it is presently. In this case, further exploration needs to be directed towards mathematical modeling to facilitate the extensive development of big data and analytics. One-way through which this heavy involvement of mathematics in big data has been the usage of the crucial fuzzy subsets theory. Hence, the above discussion clearly shows that mathematical theory affirms that the BIG operation can be used as an abstraction of technologies, tools, and systems to support data management and processing. Therefore, in the further research and development of big data, its analytics, and business intelligence should endeavor to explore mathematical concepts.

References

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165-1188.

Lee, Y. M., An, L., Liu, F., Horesh, R., Chae, Y. T., & Zhang, R. (2013). Applying science and mathematics to big data for smarter buildings. Annals of the NEW YORK Academy of Sciences, 1295(1), 18-25.

Udell, M., & Townsend, A. (2019). Why are big data matrices approximately low rank?. SIAM Journal on Mathematics of Data Science, 1(1), 144-160.