In online internet advertising, machine learning models are widely used to compute the likelihood of a user engaging with product related advertisements. However, the performance of traditional machine learning models is often impacted due to variations in user and advertiser behavior. For example, search engine traffic for florists usually tends to peak around Valentine's day, Mother's day, etc. To overcome, this challenge, in this manuscript we propose three models which are able to incorporate the effects arising due to variations in product demand. The proposed models are a combination of product demand features, specialized data sampling methodologies and ensemble techniques. We demonstrate the performance of our proposed models on datasets obtained from a real-world setting. Our results show that the proposed models more accurately predict the outcome of users interactions with product related advertisements while simultaneously being robust to fluctuations in user and advertiser behaviors.

COTS products can play various architectural roles in software systems: as interfaces to problem-specific functionality, as components that provide such functionality itself, and as intermediary connectors and components in more complex systems. In doing so, COTS products impose their own, unique constraints on organization and functionality. Over the last ten years, we have gained considerable experience with adopting, adapting, and living with the limitations of COTS products. Our goal was to adapt the COTS product to make it fit the application rather than adapting the application needs to make them fit the COTS product - thus, in essence, adapting the COTS product without access to its source code or documentation (a unique form of maintenance). We report on a large set of experiences involving eight COTS products and a wide range of COTS-Based Software Systems - most of which were done with and for industrial partners or government agencies. This experience report attempts to both give a feeling for how applications can be augmented with such COTS interfaces and also tries to tease out the specific architectural issues that anyone adapting COTS products is certain to face.

We exploit the spreadsheet metaphor to make deductive problem-solving methods available to the vast population of spreadsheet end users. In particular, we show how the function-based problem-solving capabilities of spreadsheets can be extended to include logical deductive methods in a way that is consistent with the existing spreadsheet "look and feel." We also show a spreadsheet-based framework for authoring logic implication rules. This framework was conceived with the objective of reproducing many of the characteristics that make spreadsheet programming accessible to end users. In the proposed framework, rule authors describe the semantics of a binary relation by constructing a functional spreadsheet model that computes the image of that binary relation. This model is subsequently translated into a collection of logic implication rules. We implemented this deductive spreadsheet system on top of Microsoft Excel and adopting the World Wide Web Consortium (W3C) standard ontology language OWL+SWRL formalisms.

Knowledge acquisition research concerned with the development of knowledge acquisition tools is in need of a methodological approach to evaluation. This paper describes experimental methodology to conduct studies and experiments of users modifying knowledge bases with knowledge acquisition tools. The paper also reports on the lessons learned from several experiments that have been performed using this methodology. The hope is that it will help others design user evaluations of knowledge acquisition tools. Ideas are discussed for improving the current methodology and some open issues that remain.