Call for Papers: A Special Issue of the International Journal of Information Management on - Application of Soft Computing and Machine Learning in the Big Data Analytics for Smart Cities and Factories
摘要截稿:
全文截稿: 2018-10-31
影响因子: 8.21
期刊难度:
CCF分类: 无
Overview
The ever-increasing pervasiveness of Internet connections and the miniaturization of hardware, together with the success of new distributed computing and storage architectures, such as cloud, fog, mobile, and edge computing, have paved the way for a new generation of data-centric applications, potentially able to the revolutionize information society. Data gathering and sharing are particularly pivotal to our society with the proliferation of the Internet of Things and social networks, and the consequent data processing and information inference are equally important and pose several non-trivial challenges.
In fact, such a vast availability of data requires novel means for the extraction of information and the making of such data/information useful for multiple aims, spanning from the improvement of the city management, the realization of smart cities, the increasing of industrial competitiveness, to the fight against terrorist organizations, just to give some concrete example. On the one hand, the need to store and exchange a large amount of data has led to a radical rethinking of database systems, resulting in NoSQL solutions, and the evolution of communication protocols and computing infrastructures, making cloud computing, and its variants, very popular and widespread.
A similar research and technological advance is mandatory also in the way data are processed, and information is inferred. Novel approaches and research fields are emerging, such as big data analytics, sentiment analysis, or deep learning, where soft computing and machine learning are extensively applied for data mining, in order to handle the scale of the data sets, the geographical distribution of the interacting systems, and the expressiveness of the processing means, etc. A non-marginal aspect also consists in the extraction of the hidden knowledge within these vast data sets and the exploitation of the mentioned approaches to design advanced solutions where applications can learn from data to extract knowledge from past experiences, and to make decisions and predictions based on such obtained knowledge.
Last but not least, big data analytics raises serious management challenges when data are accessed, managed or governed, related to privacy, security, governance and ethical aspects. This is further exacerbated when such solutions are applied to the case of smart cities and factories. However, apart from security and privacy, some particularly important aspects that are typically neglected are the following considerations. On the one hand, there is the data governance, which consists in categorizing, modelling and mapping the data as they are captured and stored, and proper means to optimize such a process are needed in order to maintain a high quality of the mined and analysed data. On the other hand, the cost minimization of the big data analytics consists in properly managing the execution of the Big Data processing in sophisticated data centers by reducing the cost/operational expenditures in terms of the consumed energy (within the context of the green computing), and/or the computation and storage resources, and even by planning the allocation of data intensive activities to data centers or machines under the constraints of good performance, high availability and/or resiliency. These two aspects are emerging challenges since they have an impact on the way organizations adopt the upcoming solutions of Big Data analytics to leverage the data in their business processes, and novel solutions are required in order to address these issues, since the traditional means have been proved to be inadequate.
Soft Computing and Machine Learning are promising technologies able to make changes in the way that people and companies will use the Big data Analytics solutions for knowledge centred activities, such as learning organisations, health care (patients as well as health workers and managers), business intelligence, security in organizations, etc. The cutting-edge benefit of Soft computing and Machine Learning consists in addressing how the organizational theory can exploit these advanced information processing and management concepts within the context of big data analytics, so as to prompt its application to smart cities and factories, and to investigate the impact of these technologies on the internal organization structures and dynamics of enterprises, as well as of societal organizations involved in these application domains. We also solicit contributions highlighting how the use of Machine Learning and Soft Computing may lead to better customer experiences and services, which may help businesses achieve on improved performance, with respect to big data analytics within the context of smart cities and factories.
Topics
The aim of this special issue is to bring together experiences intersecting the two fields of soft computing and machine learning to address the key issues related to data mining in the current large-scale data sharing infrastructures for big data analytics in smart cities and factories, to promote a convergence and cross-fertilization. We also solicit contributions coming from the industrial community to present concrete applications of these novel means in big data analytics and knowledge extraction. The topics of interest for this special issue include
- Machine learning for data mining and knowledge extraction
- Soft computing for big data analytics
- Data mining and knowledge extraction enhanced with computational intelligence
- Data mining and knowledge extraction in the Internet of Things
- Stream processing at a large scale
- Cloud-based services for large data sets processing
- Information forecasting with Machine learning and soft computing
- Knowledge classification with Deep Learning-based solutions
- Processing large data sets with heterogeneous features from multiple different sources
- Soft computing and Machine Learning data fusion in large-scale computing solutions
- Feature selection/classification from social networks
- Privacy concerns and protection within big data analytics solutions
- Protecting data mining methods for big data analytics against attacks and failures
- Data governance within machine learning for smart cities and factories
- Machine Learning for governance of big data analytics
- Soft Computing Models in big data governance
- Big Data Expenditure minimization by means of machine learning and soft computing within smart cities and factories
- Business models where leveraging machine learning and soft computing are used to enhance the quality of big data analytics and to unleash business potential.