Data Mining Model Performance of Sales Predictive Algorithms Based on Rapidminer Workflows

Massaro, Alessandro; Maritati, Vincenzo; Galiano, Angelo

doi:10.5121/ijcsit.2018.10303

Cited by 26 publications

(20 citation statements)

References 17 publications

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“…• Requirements related to the management of the site and its maintenance. All the user feedbacks and sales results are used for the optimization of the web page by means of a data mining algorithms able to enhance clusters of products most sold and to predict demand marketing (time series forecasting prediction [23]- [24]).…”

Section: Web Marketingmentioning

confidence: 99%

A Case Study of Innovation of an Information Communication System and Upgrade of the Knowledge Base in Industry by ESB, Artificial Intelligence, and Big Data System Integration

Massaro¹,

Calicchio²,

Maritati³

et al. 2018

IJAIA

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In this paper, a case study is analyzed. This case study is about an upgrade of an industry communication system developed by following Frascati research guidelines. The knowledge Base (KB) of the industry is gained by means of different tools that are able to provide data and information having different formats and structures into an unique bus system connected to a Big Data. The initial part of the research is focused on the implementation of strategic tools, which can able to upgrade the KB. The second part of the proposed study is related to the implementation of innovative algorithms based on a KNIME (Konstanz Information Miner) Gradient Boosted Trees workflow processing data of the communication system which travel into an Enterprise Service Bus (ESB) infrastructure. The goal of the paper is to prove that all the new KB collected into a Cassandra big data system could be processed through the ESB by predictive algorithms solving possible conflicts between hardware and software. The conflicts are due to the integration of different database technologies and data structures. In order to check the outputs of the Gradient Boosted Trees algorithm an experimental dataset suitable for machine learning testing has been tested. The test has been performed on a prototype network system modeling a part of the whole communication system. The paper shows how to validate industrial research by following a complete design and development of a whole communication system network improving business intelligence (BI).

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Section: Web Marketingmentioning

confidence: 99%

A Case Study of Innovation of an Information Communication System and Upgrade of the Knowledge Base in Industry by ESB, Artificial Intelligence, and Big Data System Integration

Massaro¹,

Calicchio²,

Maritati³

et al. 2018

IJAIA

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“…In this direction, ANN could predict patient physiological status [17], could be applied for predictive medicine [18], and for the prediction of heart problems [19], suggesting ANN as a good approach to predict patient health status in DSS. In particular, ANN applied in other applications exhibited high performances [20], specifically MLP-ANN provided a good match between experimental and predicted results [21], and good flexibility concerning predictive maintenance and big data analytics [22].…”

Section: Background: Tools and Specifications Useful For Rmp-dss Designmentioning

confidence: 99%

A Study of a Health Resources Management Platform Integrating Neural Networks and DSS Telemedicine for Homecare Assistance

Massaro¹,

Maritati²,

Savino³

et al. 2018

Information

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Abstract:The proposed paper is related to a case of study of an e-health telemedicine system oriented on homecare assistance and suitable for de-hospitalization processes. The proposed platform is able to transfer efficiently the patient analyses from home to a control room of a clinic, thus potentially reducing costs and providing high-quality assistance services. The goal is to propose an innovative resources management platform (RMP) integrating an innovative homecare decision support system (DSS) based on a multilayer perceptron (MLP) artificial neural network (ANN). The study is oriented in predictive diagnostics by proposing an RMP integrating a KNIME (Konstanz Information Miner) MLP-ANN workflow experimented on blood pressure systolic values. The workflow elaborates real data transmitted via the cloud by medical smart sensors and provides a prediction of the patient status. The innovative RMP-DSS is then structured to enable three main control levels. The first one is a real-time alerting condition triggered when real-time values exceed a threshold. The second one concerns preventative action based on the analysis of historical patient data, and the third one involves alerting due to patient status prediction. The proposed study combines the management of processes with DSS outputs, thus optimizing the homecare assistance activities.

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“…A similar conclusion was also drawn in [42], noting the ease of KNIME to gain insight into data. While [43] favored Rapid…”

Section: Data Mining For Sme: a Modelmentioning

confidence: 99%

“…Miner given that workflows can be created to represent several methodologies. It has been observed that the development of an Artificial Neural Network (ANN) was the best predictor for future sales, essentially using historical data and examples to form a conscious within the system which can then be used to predict future events [43]. In another research, it was noted that an ANN to have three layers, Figure 6, with the hidden layer being the analysis of the data to find relationships between the variables [44].…”

Section: Data Mining For Sme: a Modelmentioning

confidence: 99%

Data Mining for Small and Medium Enterprises: A Conceptual Model for Adaptation

Saeed¹

2020

IIM

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The following paper explored data mining issues in Small and Medium Enterprises' (SMEs), firstly exploring the relationship between data mining and economic development. With SME's contributing most employment prospects and output within any emerging economy such as the Kingdom of Saudi Arabia. Adopting technology will improve SME's potential for effective decision making and efficient operations. Hence, it is important that SMEs have access to data mining techniques and implement the most suited into their business to improve their business intelligence (BI). The paper is aimed to critically review the existing literature on data mining in the field of SME to provide a theoretical underpinning for any future work. It has been found data mining to be complicated and fragmented with a multitude of options available for businesses from quite basic systems implemented within Excel or Access to more sophisticated cloud-based systems. For any business, data mining is trade-off between the need for data analysis, and intelligence against the cost and resource-use of the system put in place. Multiple challenges have been identified to data mining, most notably the resource-intensive nature of systems (both in terms of labor and capital) and the security issues of data collection, analysis and storage; with General Data Protection Regulation (GDPR) a key focus for Kingdom of Saudi Arabia businesses. With these challenges the paper suggests that any SME starts small with an internal data mining exercise to digitalize and analyze their customer data, scaling up over time as the business grows and acquires the resources needed to properly manage any system.

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Data Mining Model Performance of Sales Predictive Algorithms Based on Rapidminer Workflows

Abstract: By applying RapidMiner workflows has been processed a dataset originated from different data files

Cited by 26 publications

References 17 publications

A Case Study of Innovation of an Information Communication System and Upgrade of the Knowledge Base in Industry by ESB, Artificial Intelligence, and Big Data System Integration

A Case Study of Innovation of an Information Communication System and Upgrade of the Knowledge Base in Industry by ESB, Artificial Intelligence, and Big Data System Integration

A Study of a Health Resources Management Platform Integrating Neural Networks and DSS Telemedicine for Homecare Assistance

Data Mining for Small and Medium Enterprises: A Conceptual Model for Adaptation

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