31,750 research outputs found
Alarm-Based Prescriptive Process Monitoring
Predictive process monitoring is concerned with the analysis of events
produced during the execution of a process in order to predict the future state
of ongoing cases thereof. Existing techniques in this field are able to
predict, at each step of a case, the likelihood that the case will end up in an
undesired outcome. These techniques, however, do not take into account what
process workers may do with the generated predictions in order to decrease the
likelihood of undesired outcomes. This paper proposes a framework for
prescriptive process monitoring, which extends predictive process monitoring
approaches with the concepts of alarms, interventions, compensations, and
mitigation effects. The framework incorporates a parameterized cost model to
assess the cost-benefit tradeoffs of applying prescriptive process monitoring
in a given setting. The paper also outlines an approach to optimize the
generation of alarms given a dataset and a set of cost model parameters. The
proposed approach is empirically evaluated using a range of real-life event
logs
A Utility-Theoretic Approach to Privacy in Online Services
Online offerings such as web search, news portals, and e-commerce applications face the challenge of providing high-quality service to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by introducing methods to personalize services based on special knowledge about users and their context. For example, a user's demographics, location, and past search and browsing may be useful in enhancing the results offered in response to web search queries. However, reasonable concerns about privacy by both users, providers, and government agencies acting on behalf of citizens, may limit access by services to such information. We introduce and explore an economics of privacy in personalization, where people can opt to share personal information, in a standing or on-demand manner, in return for expected enhancements in the quality of an online service. We focus on the example of web search and formulate realistic objective functions for search efficacy and privacy. We demonstrate how we can find a provably near-optimal optimization of the utility-privacy tradeoff in an efficient manner. We evaluate our methodology on data drawn from a log of the search activity of volunteer participants. We separately assess usersā preferences about privacy and utility via a large-scale survey, aimed at eliciting preferences about peoplesā willingness to trade the sharing of personal data in returns for gains in search efficiency. We show that a significant level of personalization can be achieved using a relatively small amount of information about users
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