Wu He

Internet of Things in Industries: A Survey

Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of radio-frequency identification (RFID), and wireless, mobile, and sensor devices. A wide range of industrial IoT applications have been developed and deployed in recent years. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art IoT in industries systematically.

Social media competitive analysis and text mining: A case study in the pizza industry

Abstract Social media have been adopted by many businesses. More and more companies are using social media tools such as Facebook and Twitter to provide various services and interact with customers. As a result, a large amount of user-generated content is freely available on social media sites. To increase competitive advantage and effectively assess the competitive environment of businesses, companies need to monitor and analyze not only the customer-generated content on their own social media sites, but also the textual information on their competitors’ social media sites. In an effort to help companies understand how to perform a social media competitive analysis and transform social media data into knowledge for decision makers and e-marketers, this paper describes an in-depth case study which applies text mining to analyze unstructured text content on Facebook and Twitter sites of the three largest pizza chains: Pizza Hut, Dominos Pizza and Papa Johns Pizza. The results reveal the value of social media competitive analysis and the power of text mining as an effective technique to extract business value from the vast amount of available social media data. Recommendations are also provided to help companies develop their social media competitive analysis strategy.

Developing Vehicular Data Cloud Services in the IoT Environment

The advances in cloud computing and internet of things (IoT) have provided a promising opportunity to resolve the challenges caused by the increasing transportation issues. We present a novel multilayered vehicular data cloud platform by using cloud computing and IoT technologies. Two innovative vehicular data cloud services, an intelligent parking cloud service and a vehicular data mining cloud service, for vehicle warranty analysis in the IoT environment are also presented. Two modified data mining models for the vehicular data mining cloud service, a Naïve Bayes model and a Logistic Regression model, are presented in detail. Challenges and directions for future work are also provided.

Integration of Distributed Enterprise Applications: A Survey

Many industrial enterprises acquire disparate systems and applications over the years. The need to integrate these different systems and applications is often prominent for satisfying business requirements and needs. In an effort to help researchers in industrial informatics understand the state-of-the-art of the enterprise application integration, we examined the architectures and technologies for integrating distributed enterprise applications, illustrated their strengths and weaknesses, and identified research trends and opportunities in this increasingly important area.

A state-of-the-art survey of cloud manufacturing

Rapid development in cloud computing has made an impact on the manufacturing industry. Consequently, cloud manufacturing has been proposed and has become a hot topic in the past 3 years. Many technologies such as service-oriented architecture, resource virtualisation, service ontology and modelling, service composition and management, and product data integration have been used to build the architecture of cloud manufacturing platforms. In this article, the authors survey the state of the art in the area of cloud manufacturing, identify recent research directions, and discuss potential research opportunities.

A survey of security risks of mobile social media through blog mining and an extensive literature search

Purpose – As mobile malware and virus are rapidly increasing in frequency and sophistication, mobile social media has recently become a very popular attack vector. The purpose of this paper is to survey the state-of-the-art of security aspect of mobile social media, identify recent trends, and provide recommendations for researchers and practitioners in this fast moving field. Design/methodology/approach – This paper reviews disparate discussions in literature on security aspect of mobile social media though blog mining and an extensive literature search. Based on the detailed review, the author summarizes some key insights to help enterprises understand security risks associated with mobile social media. Findings – Risks related to mobile social media are identified based on the results of the review. Best practices and useful tips are offered to help enterprises mitigate risks of mobile social media. This paper also provides insights and guidance for enterprises to mitigate the security risks of mobile ...

Improving user experience with case-based reasoning systems using text mining and Web 2.0

Many CBR systems have been developed in the past. However, currently many CBR systems are facing a sustainability issue such as outdated cases and stagnant case growth. Some CBR systems have fallen into disuse due to the lack of new cases, case update, user participation and user engagement. To encourage the use of CBR systems and give users better experience, CBR system developers need to come up with new ways to add new features and values to the CBR systems. The author proposes a framework to use text mining and Web 2.0 technologies to improve and enhance CBR systems for providing better user experience. Two case studies were conducted to evaluate the usefulness of text mining techniques and Web 2.0 technologies for enhancing a large scale CBR system. The results suggest that text mining and Web 2.0 are promising ways to bring additional values to CBR and they should be incorporated into the CBR design and development process for the benefit of CBR users.

Gaining competitive intelligence from social media data: Evidence from two largest retail chains in the world

– Social media analytics uses data mining platforms, tools and analytics techniques to collect, monitor and analyze massive amounts of social media data to extract useful patterns, gain insight into market requirements and enhance business intelligence. The purpose of this paper is to propose a framework for social media competitive intelligence to enhance business value and market intelligence. , – The authors conducted a case study to collect and analyze a data set with nearly half million tweets related to two largest retail chains in the world: Walmart and Costco in the past three months during December 1, 2014-February 28, 2015. , – The results of the case study revealed the value of analyzing social media mentions and conducting sentiment analysis and comparison on individual product level. In addition to analyzing the social media data-at-rest, the proposed framework and the case study results also indicate that there is a strong need for creating a social media data application that can conduct real-time social media competitive intelligence for social media data-in-motion. , – So far there is little research to guide businesses for social media competitive intelligence. This paper proposes a novel framework for social media competitive intelligence to illustrate how organizations can leverage social media analytics to enhance business value through a case study.

A bilingual approach for conducting Chinese and English social media sentiment analysis

Propose a bilingual approach for conducting social media sentiment analysis.Test the approach with movie reviews collected from online social network sites.Experiments show that the proposed approach is effective and has high accuracy. Due to the advancement of technology and globalization, it has become much easier for people around the world to express their opinions through social media platforms. Harvesting opinions through sentiment analysis from people with different backgrounds and from different cultures via social media platforms can help modern organizations, including corporations and governments understand customers, make decisions, and develop strategies. However, multiple languages posted on many social media platforms make it difficult to perform a sentiment analysis with acceptable levels of accuracy and consistency. In this paper, we propose a bilingual approach to conducting sentiment analysis on both Chinese and English social media to obtain more objective and consistent opinions. Instead of processing English and Chinese comments separately, our approach treats review comments as a stream of text containing both Chinese and English words. That stream of text is then segmented by our segment model and trimmed by the stop word lists which include both Chinese and English words. The stem words are then processed into feature vectors and then applied with two exchangeable natural language models, SVM and N-Gram. Finally, we perform a case study, applying our proposed approach to analyzing movie reviews obtained from social media. Our experiment shows that our proposed approach has a high level of accuracy and is more effective than the existing learning-based approaches.

Service strategies of small cloud service providers: A case study of a small cloud service provider and its clients in Taiwan

Abstract Small enterprises play an important role in the technology innovation and economic development of most countries all over the world, particularly in Taiwan. Due to a lack of financial resources and expertise, small enterprises tend to find novel ways to utilize IT resources in order to reduce IT adoption costs, to achieve better flexibility, business agility and scalability, and to react faster to market demands. Whereas Taiwan has been promoting cloud computing to help Taiwanese enterprises adopt more effective information technologies, we found that the service strategies of small cloud service providers are individually differentiated in order to survive in the competitive cloud computing market. This paper reports a case study of a small e-learning service provider and its four clients in Taiwan. Some novel insights are revealed through this case study and recommendations are provided accordingly.