Data Analytics and Business Intelligence Framework for Stock Market Trading

AlArmouty, Batool; Fraihat, Salam

doi:10.1109/ictcs.2019.8923059

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…Both papers [23], and [34] investigate the utilization of business intelligence and data analytics in the stock market. The first paper explores the application of machine learning for pattern discovery and insight generation using online session data, while the second paper focuses on developing a data analytics and business intelligence framework for stock market trading, utilizing an OLAP-applied dashboard for data visualization and analysis [38].…”

Section: B Discussionmentioning

confidence: 99%

“…In conclusion, organizations should carefully consider the costs and benefits of using BI in decisionmaking and develop strategies to ensure a balanced approach. of adopting business intelligence (BI) and machine learning (ML) algorithms [34]. The implementation of BI and ML has proven to enhance data-driven decision-making and increase organizational efficiency, revenue, and competitive advantage.…”

Section: B Discussionmentioning

confidence: 99%

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A Survey on Business Intelligence Approach Based on Deep Learning

2023

IJCCCE

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The article provides an overview of two recent developments in technology: Business Intelligence (BI) and Deep Learning (DL). In order to support decision-making processes, BI entails gathering, integrating, and analyzing data from various sources, while DL uses artificial neural networks to learn and generate predictions from complicated datasets. This paper introduces the concepts and principles and highlights recent developments and applications in different domains of research: education, organizations, stock market, forecasting, decision-making in real-time, and security. However, the fundamental problem with the business intelligence approach is that there is no learning involved. Other limitations and challenges include the capacity that affects the data analysis process, the variety of data in results, and the need for a complete presentation of results in the form of dashboards, scorecards, reports, and portals. The approach choice hinges on the problem's context and requirements and the nature and characteristics of the data. Although BI and DL are widespread, alternative methods may suit well too, such as machine learning, data mining, and statistical analysis. Justifying the selection based on precise needs and goals is crucial. Recurrent neural networks (RNN), convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent units (GRU), and Business intelligence tools are used in the research problem to address these limitations and explore the potential advantages and difficulties of integrating BI and DL to achieve an advantage in a given sector. Index Terms— Business Intelligence Tools; decision making; deep learning algorithms.

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Section: B Discussionmentioning

confidence: 99%

Section: B Discussionmentioning

confidence: 99%

A Survey on Business Intelligence Approach Based on Deep Learning

2023

IJCCCE

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“…This section elaborates the method and detailed implementation behind the proposed approach for predicting customer best offer in the telecom industry. The cross-industry standard process data mining (CRISP-DM) model is widely used in the business domain [25]- [27]. This model is followed throughout our study, as it has a structured approach in using data mining to solve and deal with business problems.…”

Section: Methods and Implementationmentioning

confidence: 99%

An efficient enhanced k-means clustering algorithm for best offer prediction in telecom

Fraihat

Awad

et al. 2022

IJECE

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<span>Telecom companies usually offer several rate plans or bundles to satisfy the customers’ different needs. Finding and recommending the best offer that perfectly matches the customer’s needs is crucial in maintaining customer loyalty and the company’s revenue in the long run. This paper presents an effective method of detecting a group of customers who have the potential to upgrade their telecom package. The used data is an actual dataset extracted from call detail records (CDRs) of a telecom operator. The method utilizes an enhanced k-means clustering model based on customer profiling. The results show that the proposed k-means-based clustering algorithm more effectively identifies potential customers willing to upgrade to a higher tier package compared to the traditional k-means algorithm. Our results showed that our proposed clustering model accuracy was over 90%, while the traditional k-means accuracy was under 70%.</span>

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“…According to their research, the use of big data can improve the operational efficiency in addition to other benefits [13]. Business Intelligence solutions have been implemented in several other domains such as real estate [14], mobile money systems [15], and stock market [16].…”

Section: Martin Et Al Discussed Inmentioning

confidence: 99%

Telecom Big Data: Social Media Sentiment Analysis

Fraihat¹

2020

IJATCSE

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Despite Big Data being one of the trending buzzwords, it is possibly one of the least comprehended terms in business. Big Data revolutionized the way business is organized and managed. Nevertheless, massive data is not as clear as it may appear, especially that originated from social media. Big Data is not only concerned with the amount of data that has changed, but also with the velocity and the structural complexity of the data. Thus, the demand has emerged to look for other techniques and procedures that are structured to deal with this vast amount of data as well as drawing insights out of them. One of the most critical and challenging tasks in social media related data is processing the natural language and defining the implicit and explicit meaning behind unstructured text data. This task refers to understanding and analyzing the text sentiment. Sentiment Analysis (SA) is the process of recognizing the intention or the emotional condition of a client articulation. This paper presents a Business Intelligence (BI) framework using social media data to analyze and define the sentiment meaning of data that comes from digital channel feeds. The implemented Business Intelligence framework consists of four main phases: first, ingesting data from multiple sources: structured and unstructured data. Second, transforming, aggregating, and processing the data through an in-memory processing layer. Third, building the Natural Language Processing (NLP) model to define the meaning behind the customer message or complaint. Finally, the visualization phase, which presents the final output and insights in a single dashboard.

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Data Analytics and Business Intelligence Framework for Stock Market Trading

Cited by 8 publications

References 5 publications

A Survey on Business Intelligence Approach Based on Deep Learning

A Survey on Business Intelligence Approach Based on Deep Learning

An efficient enhanced k-means clustering algorithm for best offer prediction in telecom

Telecom Big Data: Social Media Sentiment Analysis

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