Big Data Algorithms Analytics And Applications Pdf

[Rhett Espinoza]

Overallthe book reports on state-of-the-art studies and achievements in algorithms, analytics, and applications of Big Data. It provides readers with the basis for further efforts in this challenging scientific field that will play a leading role in next-generation database, data warehousing, data mining, and cloud computing research. It also explores related applications in diverse sectors, covering technologies for media/data communication, elastic media/data storage, cross-network media/data fusion, and SaaS.

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The business of Fintech is predominantly operated with the help of 3As - Algorithms, Analytics and Applications. Algorithms are powerful mathematical and computational tools that underpin the functionality and success of fintech applications. Algorithms in fintech have significantly improved the efficiency of financial processes. Complex algorithms can automate tasks such as risk assessment, portfolio management, fraud detection, and credit scoring, which were previously time-consuming and resource-intensive. This automation not only reduces human error but also accelerates the decision-making process, enabling financial institutions to deliver services faster and more accurately.

In the digital age, vast amounts of data are generated every second. Algorithms in fintech help analyze this data and extract meaningful insights from it. With machine learning algorithms, financial institutions can gain a deeper understanding of customer behavior, market trends, and risk factors. These data-driven insights empower businesses to make well-informed decisions, personalize services, and tailor offerings to individual customer needs.

Fintech algorithms have transformed the way financial services are delivered to consumers. By analyzing user data, preferences, and behavior, algorithms can personalize financial products and services for each customer. From customized investment portfolios to personalized budgeting and saving plans, fintech algorithms ensure that individuals receive tailored recommendations that align with their unique financial goals.

Risk assessment and management are integral to the functioning of the financial sector. Algorithms in fintech enable sophisticated risk modeling and evaluation. By analyzing historical data and market trends, algorithms can identify potential risks and vulnerabilities, thereby assisting financial institutions in making better risk management decisions. This ability to predict and mitigate risks contributes to the overall stability of the financial system.

The security of financial transactions and customer data is paramount. Fintech algorithms play a vital role in safeguarding against cyber threats and fraudulent activities. Advanced algorithms can detect suspicious patterns and anomalies in real-time, enabling rapid response and preventing potential security breaches. This bolstered security builds trust between financial institutions and their customers, fostering long-lasting relationships.

Fintech algorithms have led to the development of efficient and seamless payment systems. From peer-to-peer payments to mobile wallets, algorithms have made it easier for users to conduct transactions securely and conveniently. These algorithms ensure swift processing, enabling customers to carry out financial activities without disruptions. Algorithms help to create fintech apps that can provide services in a myriad ways.

In conclusion, algorithms are the backbone of fintech, driving innovation and transforming the financial industry. Their ability to automate processes, analyze data, enhance security, and personalize user experiences has revolutionized the way we manage our finances. As technology continues to evolve, algorithms will remain at the forefront of fintech developments, bringing greater convenience, efficiency, and trust to the financial world.

Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification of these applications, identify the gaps, and provide insights for future research. We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines, and support vector regression. This survey also points to the fact that the literature is particularly lacking on the applications of BDA for demand forecasting in the case of closed-loop supply chains (CLSCs) and accordingly highlights avenues for future research.

Nowadays, businesses adopt ever-increasing precision marketing efforts to remain competitive and to maintain or grow their margin of profit. As such, forecasting models have been widely applied in precision marketing to understand and fulfill customer needs and expectations [1]. In doing so, there is a growing attention to analysis of consumption behavior and preferences using forecasts obtained from customer data and transaction records in order to manage products supply chains (SC) accordingly [2, 3].

A variety of statistical analysis techniques have been used for demand forecasting in SCM including time-series analysis and regression analysis [10]. With the advancements in information technologies and improved computational efficiencies, big data analytics (BDA) has emerged as a means of arriving at more precise predictions that better reflect customer needs, facilitate assessment of SC performance, improve the efficiency of SC, reduce reaction time, and support SC risk assessment [11].

With SCM efforts aiming at satisfying customer demand while minimizing the total cost of supply, applying machine-learning/data analytics algorithms could facilitate precise (data-driven) demand forecasts and align supply chain activities with these predictions to improve efficiency and satisfaction. Reflecting on these opportunities, in this paper, first a taxonmy of data sources in SCM is proposed. Then, the importance of demand management in SCs is investigated. A meta-research (literature review) on BDA applications in SC demand forecasting is explored according to categories of the algorithms utilized. This review paves the path to a critical discussion of BDA applications in SCM highlighting a number of key findings and summarizing the existing challenges and gaps in BDA applications for demand forecasting in SCs. On that basis, the paper concludes by presenting a number of avenues for future research.

Data in the context of supply chains can be categorized into customer, shipping, delivery, order, sale, store, and product data [18]. Figure 1 provides the taxonomy of supply chain data. As such, SC data originates from different (and segmented) sources such as sales, inventory, manufacturing, warehousing, and transportation. In this sense, competition, price volatilities, technological development, and varying customer commitments could lead to underestimation or overestimation of demand in established forecasts [19]. Therefore, to increase the precision of demand forecast, supply chain data shall be carefully analyzed to enhance knowledge about market trends, customer behavior, suppliers and technologies. Extracting trends and patterns from such data and using them to improve accuracy of future predictions can help minimize supply chain costs [20, 21].

Big data analytics (BDA) has been increasingly applied in management of SCs [23], for procurement management (e.g., supplier selection [24], sourcing cost improvement [25], sourcing risk management [26], product research and development [27], production planning and control [28], quality management [29], maintenance, and diagnosis [30], warehousing [31], order picking [32], inventory control [33], logistics/transportation (e.g., intelligent transportation systems [34], logistics planning [35], in-transit inventory management [36], demand management (e.g., demand forecasting [37], demand sensing [38], and demand shaping [39]. A key application of BDA in SCM is to provide accurate forecasting, especially demand forecasting, with the aim of reducing the bullwhip effect [14, 40,41,42].

Big data is defined as high-volume, high-velocity, high-variety, high value, and high veracity data requiring innovative forms of information processing that enable enhanced insights, decision making, and process automation [43]. Volume refers to the extensive size of data collected from multiple sources (spatial dimension) and over an extended period of time (temporal dimension) in SCs. For example, in case of freight data, we have ERP/WMS order and item-level data, tracking, and freight invoice data. These data are generated from sensors, bar codes, Enterprise resource planning (ERP), and database technologies. Velocity can be defined as the rate of generation and delivery of specific data; in other words, it refers to the speed of data collection, reliability of data transferring, efficiency of data storage, and excavation speed of discovering useful knowledge as relate to decision-making models and algorithms. Variety refers to generating varied types of data from diverse sources such as the Internet of Things (IoT), mobile devices, online social networks, and so on. For instance, the vast data from SCM are usually variable due to the diverse sources and heterogeneous formats, particularly resulted from using various sensors in manufacturing sites, highways, retailer shops, and facilitated warehouses. Value refers to the nature of the data that must be discovered to support decision-making. It is the most important yet the most elusive, of the 5 Vs. Veracity refers to the quality of data, which must be accurate and trustworthy, with the knowledge that uncertainty and unreliability may exist in many data sources. Veracity deals with conformity and accuracy of data. Data should be integrated from disparate sources and formats, filtered and validated [23, 44, 45]. In summary, big data analytics techniques can deal with a collection of large and complex datasets that are difficult to process and analyze using traditional techniques [46].

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