Integrating IoT Analytics into Marketing Decision Making: A Smart Data-Driven Approach

With the advent of the Internet of Things (IoT), businesses have gained access to vast amounts of data generated by interconnected devices. Leveraging IoT analytics and marketing intelligence, organizations can extract valuable insights from this data to enhance decision-making processes. This paper presents a comprehensive methodology for data-driven decision-making in the context of IoT analytics and marketing intelligence. A real-time example is used to illustrate the application of this methodology, followed by an inference and discussion of the results. The rise of IoT has enabled real-time data collection from a wide array of interconnected devices, offering unprecedented opportunities for businesses to gain actionable insights. This paper focuses on the intersection of IoT analytics and marketing intelligence, exploring how data-driven decision-making can empower organizations to optimize their marketing strategies, customer experiences, and overall business performance

I Understand What You Are Saying: Leveraging Deep Learning Techniques for Aspect Based Sentiment Analysis

Despite widespread use of online reviews in consumer purchase decision making, the potential value of online reviews in facilitating digital collaboration among product/service providers, consumers, and online retailers remains under explored. One of the significant barriers to realizing the above potential lies in the difficulty of understanding online reviews due to their sheer volume and free-text form. To promote digital collaborations, we investigate aspect based sentiment dynamics of online reviews by proposing a semi-supervised, deep learning facilitated analytical pipeline. This method leverages deep learning techniques for text representation and classification. Additionally, building on previous studies that address aspect extraction and sentiment identification in isolation, we address both aspects and sentiments analyses simultaneously. Further, this study presents a novel perspective to understanding the dynamics of aspect based sentiments by analyzing aspect based sentiment in time series. The findings of this study have significant implications with regards to digital collaborations among consumers, product/service providers and other stakeholders of online reviews

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

Detecting Feature Requests of Third-Party Developers through Machine Learning: A Case Study of the SAP Community

The elicitation of requirements is central for the development of successful software products. While traditional requirement elicitation techniques such as user interviews are highly labor-intensive, data-driven elicitation techniques promise enhanced scalability through the exploitation of new data sources like app store reviews or social media posts. For enterprise software vendors, requirements elicitation remains challenging because app store reviews are scarce and vendors have no direct access to users. Against this background, we investigate whether enterprise software vendors can elicit requirements from their sponsored developer communities through data-driven techniques. Following the design science methodology, we collected data from the SAP Community and developed a supervised machine learning classifier, which automatically detects feature requests of third-party developers. Based on a manually labeled data set of 1,500 questions, our classifier reached a high accuracy of 0.819. Our findings reveal that supervised machine learning models are an effective means for the identification of feature requests

Evaluation of sleep stage classification using feature importance of EEG signal for big data healthcare

Sleep analysis is widely and experimentally considered due to its importance to body health care. Since its sufficiency is essential for a healthy life, people often spend almost a third of their lives sleeping. In this case, a similar sleep pattern is not practiced by every individual, regarding pure healthiness or disorders such as insomnia, apnea, bruxism, epilepsy, and narcolepsy. Therefore, this study aims to determine the classification patterns of sleep stages, using big data for health care. This used a high-dimensional FFT extraction algorithm, as well as a feature importance and tuning classifier, to develop accurate classification. The results showed that the proposed method led to more accurate classification than previous techniques. This was because the previous experiments had been conducted with the feature selection model, with accuracy implemented as a performance evaluation. Meanwhile, the EEG Sleep Stages classification model in this present report was composed of the feature selection and importance of the extraction stage. The previous and present experiments also reached the highest values of accuracy, with the Random Forest and SVM models using 2000 and 3000 features (87.19% and 89.19%, respectively. In this article, we proposed an analysis that the feature importance subsequently influenced the model's accuracy. This was because the proposed method was easily fine-tuned and optimized for each subject to improve sensitivity and reduce false negative occurrences