Web Intelligence

Twitter has grown significantly in the past several years and provides a new vector for data collection, offering individual users and companies valuable insights. This presents a technical challenge to collect and analyze all the data in an efficient manner. Traditional relational databases have not been able to provide acceptable response times that this new problem presents, and focus has started shifting to newer technologies such as NoSQL databases. In this paper, we try to answer a question as follows: “If I want to store and access millions of tweets for data analysis, which database systems should I choose?” We selected four popular SQL and NoSQL database systems and tested on different twitter dataset varying from one million to fifty million tweets. Each workload test involves running a core set of data operation commands. The experiment results are promising and provide guideline for choosing the most efficient database systems based on different user requirements.

Over the past several years, as the development of Internet, social media websites such as Twitter and Weibo have received much attention due to their enormous users. A lot of research has been done on sentiment analysis and opinion mining in these websites. However the number of research on using the data in the social media websites to predict the stock market price movement is limited. Behavioral economics and behavioral finance believe that public mood is correlated with economic indicators and financial decisions are significantly driven by emotions. This paper first presents a Chinese emotion mining approach and discusses whether the public emotions or opinions in the Chinese social media websites could be used to predict the stock market price in China. The experimental results demonstrate that the emotions automatically extracted from the large scale Weibo posts represent the real public opinions about some special topics of the stock market in China. Some public mood states extracted such as the “Happiness” and “Disgust” states are highly correlated with the change of stock price according to the Granger causality analysis. Finally, a nonlinear autoregressive model with exogenous sentiment inputs is proposed to predict the stock price movement.

Detecting similar question is a fundamental and essential research problem for constructing similar question dataset for the research of question-answering, short text similarity calculating, and sentence paragraphing. This paper explores the previous assumption about similar question detection and analyzes its existing problem. Afterwards, we propose an automated approach to detecting similar questions based on the calculation of question topical diversity using different ways of topical feature generation methods. The experiment dataset are Yahoo! 4,482,757 questions with answers. The results present that our approach achieves a precision of 74% and a recall of 74% as the best performance compared with baseline methods, demonstrating its effectiveness in similar question group detection.

Technology plays an important role in helping organizations control quality and costs, and take advantage of opportunities in a highly competitive and increasingly complex business environment. Cloud computing offers greater access to computing power, storage, software, and remote data centres through the web. This research aims to confirm the factors to be considered for cloud computing adoption in Australian regional municipal governments. The research involved data from interviews with IT managers from selected regional municipal governments, and survey data from 480 IT staff across 47 regional municipal governments. The major factors to be considered for the adoption of cloud computing in regional municipal governments were identified as Internet connectivity, Internet speed, availability, reliability, data storage location, security, data sovereignty, cost, integration, data backup, provider dependability, employees’ knowledge, and transportability. The findings of this research may help managers increase their awareness about factors to be considered when regional municipal governments planning to adopt cloud computing.

The use of intelligent technologies for providing useful recommendations to patients suffering chronic diseases may play a positive role in improving the general life quality of patients and help reduce the workload and cost involved in their daily healthcare. The objective of this study is to develop an intelligent recommender system based on predictive analysis for advising patients in the telehealth environment concerning whether they need to take the body test one day in advance by analyzing medical measurements of a patient for the past k days. The proposed algorithms supporting the recommender system have been validated using a time series telehealth data recorded from heart disease patients which were collected from May to January 2012, from our industry collaborator Tunstall. The experimental results show that the proposed system yields satisfactory recommendation accuracy and offer a promising way for saving the workload for patients to conduct body tests every day. This study highlights the possible usefulness of the computerized analysis of time series telehealth data in providing appropriate recommendations to patients suffering chronic diseases such as heart diseases patients.