The timestamps of now-relative bitemporal databases are modeled as growing, shrinking, or rectangular regions. The shape of these regions makes it a challenge to design bitemporal operators that a) are consistent with the pointbased interpretation of a temporal database, b) preserve the identity of the argument timestamps, e) ensure locality, and d ) perform eficiently. We identifj, the bitemporal split operator us the basic primitive to implement a wide range of advanced now-relative bitemporal operations. The bitemporal split operator splits each tuple of a bitemporal argument relation, such that equality and standard nontemporal algorithms can be used to implement the bitemporal counterparts with the aforementioned properties. Both a native database algorithm and an SQL implementation are provided. Our performance results show that the bitemporal split operator outperforms related approaches by orders of magnitude and scales well.

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To balance the fluctuations of renewable energies, greater flexibility on the consumption side is required. Moreover, solutions are required to handle the uncertainty related to both production and consumption. In this paper, we propose a probabilistic extension to FlexOffers to capture both the interval in which a given energy resource can be operated and the uncertainty that surrounds it. Probabilistic FlexOffers serve as a support for a method to forecast energy production and consumption of stochastic hybrid systems. We then show how to generate a consumption strategy to match a given consumption assignment within a given flexibility interval. The method is illustrated on a building equipped with solar cells, a heat pump and an ice bank used to feed the air conditioning system.