Context. The observations carried out with XMM-Newton have produced a very extensive X-ray source catalogue in which the standard pipeline determines the variability of sufficiently bright sources through χ2 and fractional variability tests. Faint sources, however, are not automatically checked for variability, and this means that faint, short timescale transients are overlooked. From dedicated X-ray searches, as well as optical and radio archive searches, we know that some such dim sources can still be identified with high confidence. Aims. Our goal is to find new faint, fast transients in XMM-Newton EPIC-pn observations. To that end we created the EPIC-pn XMM-Newton outburst detector (EXOD) algorithm, which we run on the EPIC-pn full-frame data available in the 3XMM-DR8 catalogue. Methods. In EXOD, we computed the variability of the whole field of view by first binning in time the counts detected in each pixel of the detector. We next computed the difference between the median and maximal number of counts in each time bin and pixel to detect variability. We applied EXOD to 5751 observations in the full frame mode and compared the variability of the detected sources to the standard χ2 and Kolmogorov–Smirnov (KS) variability tests. Results. The algorithm is able to detect periodic and aperiodic variability, with both short and long flares. Of the sources detected by EXOD, 60−95% are also shown to be variable by the standard χ2 and KS tests. EXOD computes the variability over the entire field of view faster than the light curve generation takes for all the individual sources. We detect a total of 2961 X-ray variable sources. After removing the spurious detections, we obtain a net number of 2536 variable sources. Of these we investigate the nature of 35 sources with no previously confirmed classification. Amongst the new sources, we find stellar flares and AGNs, in addition to four extragalactic type I X-ray bursters that double the known neutron-star population in M 31. Conclusions. This algorithm is a powerful tool for the prompt detection of interesting variable sources in XMM-Newton observations. EXOD also detects fast transients that other variability tests would classify as non-variable due to their short duration and low number of counts. This is of increasing importance for the multi-messenger detection of transient sources. Finally, EXOD allows us to identify the nature of compact objects through their variability and to detect rare compact objects. We demonstrate this through the discovery of four extragalactic neutron-star low-mass X-ray binaries, doubling the number of known neutron stars in M 31.

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A recent trend among major organisations is to release their datasets in the cloud over various Database-as-a-Service (DBaaS) providers' premises, creating a use case for multi-cloud querying. As identified in the literature, middlewares with such capabilities should quote the monetary cost and the response time of the queries in order to gain the trust of their users, and also optimise the queries so as to avoid cost overruns and meet the quotations. Considering those requirements, this paper introduces an accurate cost model and an efficient execution plan search strategy for dealing with large-scale multi-cloud queries. The former is an ensemble learning stack leveraging online machine learning models, and the latter is a randomised method inspired by iterative improvement. We evaluated our middleware over simulated providers by using the Join Order Benchmark. Experiments showed that the cost model manages to correct the estimations from the providers. The randomised strategy can produce more efficiently execution plans that yield better performances and a lower monetary cost compared to an exhaustive approach from previous work.