International Journal of Information Technology & Decision Making

We consider a problem of marketing decisions for the choice of a product with maximum customer appeal. A widely used technique for this purpose is TURF, or Total Unduplicated Reach and Frequency, which evaluates a union of the events defined by the sample proportion of many products, or flavors of one product. However, when using TURF, it is often impossible to distinguish between subsets of different flavor combinations with practically the same level of coverage. An appropriate tool can be borrowed from cooperative game theory, namely, the Shapley Value, that permits the ordering of flavors by their strength in achieving maximum consumers' reach and provides more stable results than TURF. We describe marketing strategy reasons for using these techniques in the identification of the preferred combinations in media or product mix.

This paper presents the way that the global efficiencies approach has been applied to improve the dynamic reasoning of an intelligent Graphical User Interface (GUI) that is based on the Data Envelopment Analysis (DEA). The GUI provides intelligent help to users during their interaction with an e-mailing system, which is called I-Mailer. For this purpose, I-Mailer constantly reasons about every user action in terms of the user's goals and plans. In case the user is involved in a problematic situation, it transforms the user's problematic action and generates alternative actions to be suggested to the user. The actions suggested should be better than the problematic one in the context of the user's intentions as these are hypothesized by the system. Therefore, DEA is used in order to categorize the candidate alternative actions to be suggested to the user into dominating and dominated ones. However, this process usually results in the production of many alternative actions that could be suggested to the user instead of the one issued. To solve this problem, we have applied the global efficiencies approach to discriminate further among the DEA-efficient alternative actions and select the best alternative action to be suggested to a user. Finally, the system has been evaluated as to the effectiveness of this approach.

The Internet today offers primarily a best-effort service. Research and technology development efforts are currently underway to allow the provisioning of better than best-effort quality of service assurances. In this article, we develop a spot pricing framework for expected bandwidth guaranteed Internet service contracts on a single domain. A nonlinear pricing model that maximizes the total surplus with cost constraints forms the basis for the framework. The spot price process is obtained for different demand profiles of the customer-base and demand arrival characteristics. Simulation modeling and analysis is employed to implement the pricing framework and analyze the price process under different market conditions and changes due to network failures. The framework when implemented at access or exchange points of different service provider domains will provide assured bandwidth for inter-domain traffic.

This paper focuses on the traceability issue as key element in systems design and management of system. The work is presented through the system engineering framework with a main emphasis on requirements process. Effectively in decision making, the need for traceability model is often required in order to find out the link for a considered decision and often the semantics of such link. With the advent for information technology and integrating technical needs and business goal, the traceability find its essence and many models can now be easily implemented automatically. The paper is based on many experiences carried out ranging from audit to requirement evolution issues for impact analysis.

Recently, there has been enormous growth in the amount of commercial and scientific data, such as protein sequences, retail transactions, and web-logs. Such datasets consist of sequence data that have an inherent sequential nature. However, few existing clustering algorithms consider sequentiality. In this paper, we study how to cluster these sequence datasets. We propose a new similarity measure to compute the similarity between two sequences. In the proposed measure, subsets of a sequence are considered, and the more identical subsets there are, the more similar the two sequences. In addition, we propose a hierarchical clustering algorithm and an efficient method for measuring similarity. Using a splice dataset and synthetic datasets, we show that the quality of clusters generated by our proposed approach is better than that of clusters produced by traditional clustering algorithms.

The next-generation Web will increase the need for a highly organized and ever evolving method to store references to Web objects. These requirements could be realized by the development of a new bookmark structure. This paper endeavors to identify the key requirements of such a bookmark, specifically in relation to Web documents, and sets out a suggested design through which these needs may be accomplished. A prototype developed offers such features as the sharing of bookmarks between users and groups of users. Bookmarks for Web documents in this prototype allow more specific information to be stored such as: URL, the document type, the document title, keywords, a summary, user annotations, date added, date last visited and date last modified. Individuals may access the service from anywhere on the Internet, as long as they have a Java-enabled Web browser.

Gathering of novel information from the WWW constitutes a real challenge for artificial intelligence (AI) methods. Large search engines do not offer a satisfactory solution, their indexing cycle is long and they may offer a huge amount of documents. An AI-based assistant agent is studied here, which sorts the availabe links by their estimated value for the user. By using this link-list the best links could be highlighted in the browser, making the user's choices easier during surfing. The method makes use of (i) "experts", i.e. pre-trained text classifiers, forming the long-term memory of the system, (ii) relative values of experts and value estimation of documents based on recent choices of the user. Value estimation adapts fast and forms the short-term memory of the system. All experiments show that surfing based filtering can efficiently highlight 10%–20% of the documents in about five steps, or less.

Being increasingly popular, the Internet greatly changes our lives. We can conveniently receive and send information via the Internet. With the information explosion on the Web, it is becoming crucial to develop means to automatically extract important sentences from the Web articles. In this paper, we propose a method which uses both statistical and structural information for sentence extraction. In addition, following the analysis of human's extractions, several heuristic rules are added to filter out non-important sentences and to prevent similar sentences from being extracted. Our experimental results proved the effectiveness of these means. In particular, once the heuristic rules being added, a significant improvement has been observed.

In this paper, we introduce some operational laws of linguistic variables and define some linguistic preference relations such as incomplete linguistic preference relation and improved linguistic preference relation, etc. We then utilize the extended arithmetic averaging (EAA) operator and the extended weighted arithmetic averaging (EWAA) operator to develop an approach to group decision making based on incomplete linguistic preference relations. Finally, we give an illustrative example to verify the developed approach.