To address the classical selectivity estimation problem in database systems, a radically different query processing technique called PlanBouquet was proposed in 2014. In this approach, the estimation process is completely abandoned and replaced with a calibrated selectivity discovery mechanism. The beneficial outcome is that provable guarantees are obtained on worst-case execution performance, thereby facilitating robust query processing. An improved version of PlanBouquet, called SpillBound (SB), which significantly accelerates the selectivity discovery process, and provides platform-independent performance guarantees, was presented two years ago. Notwithstanding its benefits, a limitation of SpillBound is that its guarantees are predicated on expending enormous preprocessing efforts during query compilation, making it suitable only for canned queries that are invoked repeatedly. In this paper, we address this limitation by leveraging the fact that plan cost functions typically exhibit concave down behavior with regard to predicate selectivities. Specifically, we design FrugalSpillBound, which provably achieves extremely attractive tradeoffs between the performance guarantees and the compilation overheads. For instance, relaxing the performance guarantee by a factor of two typically results in at least two orders of magnitude reduction in the overheads. Further, when empirically evaluated on benchmark OLAP queries, the decrease in overheads is even greater, often more than three orders of magnitude. Therefore, FrugalSpillBound substantively extends robust query processing towards supporting ad-hoc queries.

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Network inference is the process of inferring the structure of the unknown underlying network, based on the observations of the propagations of different contagions through the network. All the existing works consider the setting in which the information of the different propagations is available to the computation at the beginning. We introduce the problem of online network inference when the propagation information is revealed dynamically in batches. We present a new greedy heuristic that is amenable for online extension and derive two online inference algorithms. We present extensive experimental results show the computational gains that the online algorithms provide without losing much on the accuracy of the inferences.