Quantitatively evaluating the effects of price promotions using data mining

Price promotions (also called discount promotions), i.e. short-term temporary price reductions for selected items (Hermann 1989), are frequently used in sales promotions. The main objective of price promotions is to boost sales and increase profits. Quantitative evaluation of the effects of price promotions (QEEPP) is essential and important for sales managers to analyse historical price promotions and informative for devising more effective promotional strategies in the future. However, most previous studies only provide insights into the effects of discount promotions from some specific prospectives, and no approaches have been proposed for comprehensive evaluation of the effects of discount promotions. For example, Hinkle [1965] discovered that price promotions in the off-season are more favourable, and the effects of price promotions are stronger for new products. Peckham [1973] found that price promotions have no impact on long-term trend. Blattberg et al. [1978] identified that different segments respond to price promotions in different ways. Rockney [1991] discovered three basic types of effects: effects on discounted items, effects on substitutes and effects on complementary items.

Multimedia content adaptation service discovery mechanism

Electronic information is becoming increasingly rich in content and varied in format and style while at the same time client devices are getting increasingly varied in their capabilities. This mismatch between rich contents and the end devices capability presents a challenge in providing seamless and ubiquitous access to electronic documents to interested users. Service-oriented content adaptation has emerged as a potential solution to the content-device mismatch problem. Since an adaptation task can potentially be performed by multiple content adaptation services (CAS), an approach for CAS discovery is a fundamental component of service-oriented content adaptation environment. In this paper, we propose a service discovery approach that considers the client device capability and the service’s attributes to discover appropriate CAS while optimizing performance and functionality. The efficiency of the proposed CAS discovery protocol is studied experimentally. The results show that the proposed discovery approach is effective in terms of discovering appropriate content adaptation services.

An adaptive approach to information discovery in multi-dimensional wireless sensor networks

Multidimensional WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multidimensional data). Such networks present unique challenges to data dissemination, data storage and in-network query processing (information discovery). In this paper, we investigate efficient strategies for information discovery in large-scale multidimensional WSNs and propose the Adaptive MultiDimensional Multi-Resolution Architecture (A-MDMRA) that efficiently combines “push” and “pull” strategies for information discovery and adapts to variations in the frequencies of events and queries in the network to construct optimal routing structures. We present simulation results showing the optimal routing structure depends on the frequency of events and query occurrence in the network. It also balances push and pull operations in large scale networks enabling significant QoS improvements and energy savings.

An intelligent content discovery technique for health portal content management

Background: Continuous content management of health information portals is a feature vital for its sustainability and widespread acceptance. Knowledge and experience of a domain expert is essential for content management in the health domain. The rate of generation of online health resources is exponential and thereby manual examination for relevance to a specific topic and audience is a formidable challenge for domain experts. Intelligent content discovery for effective content management is a less researched topic. An existing expert-endorsed content repository can provide the necessary leverage to automatically identify relevant resources and evaluate qualitative metrics.Objective: This paper reports on the design research towards an intelligent technique for automated content discovery and ranking for health information portals. The proposed technique aims to improve efficiency of the current mostly manual process of portal content management by utilising an existing expert-endorsed content repository as a supporting base and a benchmark to evaluate the suitability of new contentMethods: A model for content management was established based on a field study of potential users. The proposed technique is integral to this content management model and executes in several phases (ie, query construction, content search, text analytics and fuzzy multi-criteria ranking). The construction of multi-dimensional search queries with input from Wordnet, the use of multi-word and single-word terms as representative semantics for text analytics and the use of fuzzy multi-criteria ranking for subjective evaluation of quality metrics are original contributions reported in this paper.Results: The feasibility of the proposed technique was examined with experiments conducted on an actual health information portal, the BCKOnline portal. Both intermediary and final results generated by the technique are presented in the paper and these help to establish benefits of the technique and its contribution towards effective content management.Conclusions: The prevalence of large numbers of online health resources is a key obstacle for domain experts involved in content management of health information portals and websites. The proposed technique has proven successful at search and identification of resources and the measurement of their relevance. It can be used to support the domain expert in content management and thereby ensure the health portal is up-to-date and current.

Price dynamics, foreign exchange and anticipated shocks in a tourism economy

Using a dynamic model of an open monetary economy, this paper examines the effects of tourism-related anticipated shocks on goods prices and foreign exchange reserves. Foreign tourists consume mainly non-traded goods in holiday destinations, converting them into exportable goods. This gives rise to a tourism terms-of-trade effect that affects the accumulation of foreign exchange. Announcements of anticipated events bring tourist visits forward, resulting in an initial underadjustment or an over-adjustment in the prices of the non-traded goods when the tourism termsof-trade effect is positive or negative. This leads to an increase or a decrease in foreign reserves in the long run.