Table 14 Benefits and limitations of the Other State of Art Studiesa focusing on the application domain
From: Software architectures for big data: a systematic literature review
Citation # | Benefit | Limitation |
|---|---|---|
19 | A model- free, quantitative, and general-purpose evaluation methodology to extract resilience indexes from, e.g., system logs and process data | Furthering the investigation into the combination of several FOM functions or resilience indexes in systems with several observed variables and more complex hierarchical structures |
20 | Review the main enabling technologies included under the concept of Industry 4.0, identifying the local se- curity threats against those areas and their most representative attack vectors | – |
23 | An overview of the different security solutions proposed for Cyber Physical Systems (CPS) big data storage, access, and analytics. We also discuss big data meeting green challenges in the contexts of CPS. | – |
24 | A comprehensive survey on what is Big Data, comparing methods, its research problems, and trends. Then a survey of Deep Learning, its methods, comparison of frameworks, and algorithms is presented. And at last, application of Deep Learning in Big Data, its challenges, open research problems and future trends are presented | – |
25 | An audio-visua lemotion recognition system using a deep network to extract features and another deep network to fuse the features. | Using other deep architectures to improve the performance of the system |
26 | The sensitive information topics-based sentiment analysis method for big data is proposed. This method integrates topic semantic information into text representation through a neural network model. | The extension of the sentiment dictionary and the emoticons will be considered to improve accuracy of the sentiment dictionary tagging method, thereby losing less texts. |
27 | examine the sentiments toward a brand, via brand authenticity, to identify the reasons for positive or negative sentiments on social media | could use retrospective data to access sort of data (number of likes, retweets) to see if there are sentiment differences between popular tweets and others. |