This paper provides a survey of the stateof-the-art and future directions of one of the most important emerging technologies within Business Analytics (BA), namely Prescriptive Analytics (PSA). BA focuses on data-driven decision making and consists of three phases: Descriptive, Predictive, and Prescriptive Analytics. While Descriptive and Predictive Analytics allow us to analyze past and predict future events, respectively, these activities do not provide any direct support for decision making. Here, PSA fills the gap between data and decisions. We have observed an increasing interest for in-DBMS PSA systems in both research and industry. Thus, this paper aims to provide a foundation for PSA as a separate field of study. To do this, we first describe the different phases of BA. We then survey classical analytics systems and identify their main limitations for supporting PSA, based on which we introduce the criteria and methodology used in our analysis. We next survey, categorize, and discuss the state-of-the-art within emerging, so-called PSA + , systems, followed by a presentation of the main challenges and opportunities for next generation PSA systems. Finally, the main findings are discussed and directions for future research are outlined.

Demand Response (DR) schemes are e ective tools to maintain a dynamic balance in energy markets with higher integration of uctuating renewable energy sources. DR schemes can be used to harness residential devices' exibility and to utilize it to achieve social and nancial objectives. However, existing DR schemes su er from low user participation as they fail at taking into account the users' requirements. First, DR schemes are highly demanding for the users, as users need to provide direct information, e.g. via surveys, on their energy consumption preferences. Second, the user utility models based on these surveys are hard-coded and do not adapt over time. ird, the existing scheduling techniques require the users to input their energy requirements on a daily basis. As an alternative, this paper proposes a DR scheme for user-oriented direct load-control of residential appliances operations. Instead of relying on user surveys to evaluate the user utility, we propose an online data-driven approach for estimating user utility functions, purely based on available load consumption data, that adaptively models the users' preference over time. Our scheme is based on a day-ahead scheduling technique that transparently prescribes the users with optimal device operation schedules that take into account both nancial bene ts and user-perceived quality of service. To model day-ahead user energy demand and exibility, we propose a probabilistic approach for generating exibility models under uncertainty. Results on both real-world and simulated datasets show that our DR scheme can provide signi cant nancial bene ts while preserving the user-perceived quality of service.