Stephen Shaoyi Liao

Exploring alternatives during requirements analysis

Goal-oriented requirements analysis techniques provide ways to refine organizational and technical objectives, to more effectively explore alternatives during requirements definition. After selecting a set of alternatives to achieve these objectives, you can elaborate on them during subsequent phases to make them more precise and complete. The authors argue that goal-oriented analysis complements and strengthens traditional requirements analysis techniques by offering a means for capturing and evaluating alternative ways of meeting business goals. They detail the five main steps that comprise goal-oriented analysis. These steps include goal analysis, softgoal analysis, softgoal correlation analysis, goal correlation analysis, and evaluation of alternatives. To illustrate the main elements of the proposed analysis technique, they explore a typical scenario that involves defining requirements for a meeting scheduling system.

Combining empirical experimentation and modeling techniques: A design research approach for personalized mobile advertising applications

We propose a design research approach combining behaviour and engineering techniques to better support user modeling in personalized mobile advertising applications. User modeling is a practical means of enabling personalization; one important method to construct user models is that of Bayesian networks. To identify the Bayesian network structure variables and the prior probabilities, empirical experimentation is adopted and context, content, and user preferences are the salient variables. User data collected from the survey are used to set the prior probabilities for the Bayesian network. Experimental evaluation of the system shows it is effective in improving user attitude toward mobile advertising.

Mining comparative opinions from customer reviews for Competitive Intelligence

Competitive Intelligence is one of the key factors for enterprise risk management and decision support. However, the functions of Competitive Intelligence are often greatly restricted by the lack of sufficient information sources about the competitors. With the emergence of Web 2.0, the large numbers of customer-generated product reviews often contain information about competitors and have become a new source of mining Competitive Intelligence. In this study, we proposed a novel graphical model to extract and visualize comparative relations between products from customer reviews, with the interdependencies among relations taken into consideration, to help enterprises discover potential risks and further design new products and marketing strategies. Our experiments on a corpus of Amazon customer reviews show that our proposed method can extract comparative relations more accurately than the benchmark methods. Furthermore, this study opens a door to analyzing the rich consumer-generated data for enterprise risk management.

Text mining and probabilistic language modeling for online review spam detection

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

Intelligent agents and financial risk monitoring systems

A society of intelligent agents can work together to monitor financial transactions and yield important information regarding potential financial calamities.

Discovering original motifs with different lengths from time series

Finding previously unknown patterns in a time series has received much attention in recent years. Of the associated algorithms, the k-motif algorithm is one of the most effective and efficient. It is also widely used as a time series preprocessing routine for many other data mining tasks. However, the k-motif algorithm depends on the predefine of the parameter w, which is the length of the pattern. This paper introduces a novel k-motif-based algorithm that can solve the existing problem and, moreover, provide a way to generate the original patterns by summarizing the discovered motifs.

Modeling constraint-based negotiating agents

Most existing decision support systems (DSSs) are hard to fit satisfactorily into emerging working practices or organizational environments. Decision-making is becoming more pluralistic and less hierarchical, determined not so much by position in the corporate hierarchy but much by the argumentative and evidential value. Such decision-making can only be supported by those DSSs with negotiation support facilities. Intelligent negotiation agents can be used to model many decisionmaking negotiation tasks. Such negotiation agents are able to interact and negotiate with users and with each other. In addition, the newly emerging constraint agent technology provides a promising solution for such negotiation agents. In this paper, we present a model by applying constraint negotiation agents for DSSs. An object-oriented constraint language for modeling constraint agents is defined. Within our model, constraint DSS agents are able to reason cooperatively with users or with other agents. Negotiation among agents and users is modeled as a process of interactive constraint satisfaction. Efficient algorithms for the implementation of negotiation-oriented constraint satisfaction are also presented. As manpower costs become more and more expensive and as the negotiation workload increases, the future DSSs will play more important roles in the negotiation process. It is expected that our work will contribute to the future DSS design and development.

Classifying Consumer Comparison Opinions to Uncover Product Strengths and Weaknesses

With the Web 2.0 paradigm, a huge volume of Web content is generated by users at online forums, wikis, blogs, and social networks, among others. These user-contributed contents include numerous user opinions regarding products, services, or political issues. Among these user opinions, certain comparison opinions exist, reflecting customer preferences. Mining comparison opinions is useful as these types of viewpoints can bring more business values than other types of opinion data. Manufacturers can better understand relative product strengths or weaknesses, and accordingly develop better products to meet consumer requirements. Meanwhile, consumers can make purchasing decisions that are more informed by comparing the various features of similar products. In this paper, a novel Support Vector Machine-based method is proposed to automatically identify comparison opinions, extract comparison relations, and display results with the comparison relation maps by mining the volume of consumer opinions posted on the Web. The proposed method is empirically evaluated based on consumer opinions crawled from the Web. The initial experimental results show that the performance of the proposed method is promising and this research opens the door to utilizing these comparison opinions for business intelligence.

A Hybrid Approach for Automatic Incident Detection

This paper presents a hybrid approach to automatic incident detection (AID) in transportation systems. It combines time series analysis (TSA) and machine learning (ML) techniques in light of the fault diagnosis theory. In this approach, the time series component is to forecast the normal traffic for the current time point based on prior (normal) traffic. The ML component aims to detect incidents using features of real-time traffic, predicted normal traffic, as well as differences between the two. We validate our approach using a real-world data set collected in previous research. The results show that the hybrid approach is able to detect incidents more accurately [higher detection rate (DR)] and faster (shorter mean time to detect) under the requirement of a similar false alarm rate (FAR), as compared with state-of-the-art algorithms. This paper lends support to further studies on combining TSA with ML to address problems related to intelligent transportation systems (ITS).

A real-time personalized route recommendation system for self-drive tourists based on vehicle to vehicle communication

Provide personalized route recommendation services for self-drive tourists based on historical and real-time traffic information.Propose a prototype of route recommendation system based on information collected by vehicle to vehicle communication systems (V2VCS).Employ genetic algorithm (GA) to explore an optimal route based on traffic information and personal requirements.The proposed system not only reduces total visiting time but also meets the specific requirements of self-drive tourists. Recently, traffic jams and long queuing problems in tourist hot spots is growing with the increasing number of self-drive tourists. Some recommendation systems have been developed in attempt to relieve these problems. However, all these systems lack information pertaining to real-time traffic as well as the ability of personalization. In this research, we have developed a novel route recommendation system to provide self-drive tourists with real-time personalized route recommendations. This will help to reduce the traffic jams and queuing time in tourist hot spots. It will also help to personalize visiting routes based on the users specific preferences. Ultimately, based on the evaluation results given by experienced self-drive tourists, we have shown that the proposed system not only saves total visiting time, but also meets their specific visiting preferences.