R P Sundarraj

Healthcare organizations (HCO) are under pressure to adopt emerging solutions with a view to improving the quality and efficiency of their operations, patient care and clinical decisions. In the literature, while recommendations have been put forth to follow an organizational approach to the adoption and implementation of analytics, there is a paucity of research into what specifically constitutes healthcare-analytics (HA), as well the antecedents that can explain and predict its adoption-decisions. In this paper, we fill this gap, by first proposing a typology for HA and an adoption-decision model that integrates the TOE framework with DeLone and McLean IS Success Model. We use a case study for the initial validation of the typology and the model. Our study reveals that the HCO studied has the same types of the data proposed in our typology, although the usage of the data and current/future adoption of analytics depends on many factors beyond simply technology.

Smartphone applications are emerging as popular media for promoting one's products through in-app advertisements. Today, there are a number of organizations, known as supply-side-platforms (SSP), who aggregate and auction these ad-spaces from different suppliers/publishers. Advertisers (or their intermediaries) place bid for these spaces based on different relevance criteria (e.g., the location and device of the app-user, the app's IAB category etc.), the impression value, clickthrough value, and the conversion value. After the received ads are filtered based on relevance, the SSP is often still faced with a number of options for ad-placement, each having different revenues owing to differences in clickthrough rates etc. Moreover, the SSP has to decide on the ad-placement in real-time. In this paper, we consider the SSP's ad-placement problem in the aforementioned situation. We propose an optimization model to maximize the SSP's revenues. Based on computational experience with this model, we develop a rule-based online algorithm that appears to be viable as a real-time solution.

With the proliferation of new innovative technologies, electronic gadgets like smartphones loaded with sophisticated user friendly apps are getting popularity among large proportion of phone users. As a result, advertisers are using apps as a medium of communication to showcase their products through in-app advertising. These advertisements are managed by ad aggregators through DSPs & by ad-space aggregators through SSPs. We need to allow most profitable ads to be pushed into appropriate apps so that overall SSP's revenue is maximized.Inapp advertising helps both the SSPs as well as DSPs to earn revenue. In this paper, we aim to maximize the revenue of SSP by developing machine learning based rule inferring technique from the output of mathematical model. Our initial computational results show the efficacy of online algorithm over previous research.

Decision-making under risk or uncertainty is quite common in various environments. According to decision-maker's preferences, there are three types of attitudes toward risk: Risk averse, risk prone or risk neutral. The aim of this work is to perform a cross-cultural analyzes of the risk preferences of subjects who belong to two different cultures. A questionnaire is applied with Brazilians and Indians to capture their preferences in order to observe the attitudes towards risk and check if they act in different way. Using the concept of Certainty Equivalent, it was found they present the same attitude when facing risk situations, being risk averse. At the end of this step, the information serves as an input to consider when negotiating with a counterpart from different cultures.

Cloud computing offers computing and storage services which can be dynamically developed, composed and deployed on virtualized infrastructure. Cloud providers holding excess spare capacity, incentivize customers to purchase it by selling them in a market (spot market), where the prices are derived dynamically based on supply and demand. The cloud providers allow clients to bid on this excess capacity by allocating resources to bidders while their bids exceed a intermittently changing dynamic spot price. In this paper we have used game theory to model the bidding strategies of bidders in a spot market who are attempting to procure the cloud instances, as a prisoner dilemma game. We then analyze real time data from Amazon EC2 spot market to validate this model. In a single shot prisoner dilemma game mutual defection is the Nash equilibrium. We find that a majority (approx. 85%) of bidders choose to Defect which is in-line with the single shot classical prisoner dilemma game. However considering that most bidders in a spot market are repetitive bidders, we propose a Co-operation strategy which is in-line with the Iterated Prisoner Dilemma Game. © 2012 IEEE.

Cloud computing offers computing and storage services which can be dynamically developed, composed and deployed on virtualized infrastructure. Cloud providers possessing excess spare capacity, incentivize clients to purchase it by selling them in a market (spot market), where the prices are derived dynamically based on supply and demand. The cloud providers allow clients to bid on this spare capacity by granting resources to bidders while their bids exceed a periodically changing dynamic spot price. In this paper, a generalized second price auction is proposed for the spot market by viewing the spot instances as heterogeneous units. The concept of heterogeneity has been introduced into the spot market by considering the inter-price time of spot prices.

Utility computing models such as cloud computing provide a variety of computing services using several pricing options. Flexibility of requirements and availability of pricing options, enable a consumer to make trade-offs between cost and time. These trade-offs are governed by consumers' behavior with respect to how they discount price vis-à-vis time of delivery. In this paper, we study this trade-off for the cloud-computing context, based on theoretical models from the time-discounting (intertemporal) literature. We test this model using an experimental study with 162 participants.

The importance of foresight at the organizational level, as well as the ways to measure it have begun to be studied in the recent literature on strategic management. One overarching conclusion that stems from these studies is that organizational foresight is not developed in isolation, but evolves as an aggregation of capabilities (in the sense of the microfoundational literature) throughout the organization. In this context, some key research questions that emerge are: how does an individual develop foresight and how does it contribute to the organization? Studies concerning individual foresight are, however, quite sparse. In this paper, we take up the aforementioned first question of individual foresight development. An exploratory study involving case interviews has been used to find theoretical support for developing an individual foresight measure.

Personalization of movie recommendations is a widely researched topic. Personalization is usually carried out using local resources that are available at one's disposal. This local resource presents a snapshot of user preference at a particular moment. It doesn't address the long term user preferences. These concerns can be addressed using resources available with the user. This paper proposes a model that taps the user browsing history with emphasis on smartphone browsing history to personalize movie recommendations. The browsing history and movie plot summaries are used to generate a similarity score. The obtained score is incorporated into a latent factor model that computes latent user and item features. This model enables prediction of user ratings under sparsity and cold-start scenarios using user browsing history and eventually fetches movies that are similar to the ones the user liked.

In service oriented architecture, workflow discovery and web service composition (WSC) are two major challenges and many works have been carried out in the recent years. In this work, we address the problem of WSC in a multi-cloud environment. All existing methods assume that a feasible solution exists and solves the WSC problem, however when user constraints are hard, a feasible solution might not exist and models fail to return an optimal solution. To address this problem, we propose an Integer Linear programming model where violations in global constraints are permitted but with an associated penalty. The results show that our method performs significantly better than the standard method when user constraints are hard.