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During the last decade, big data management has attracted increasing interest from both the industrial and academic communities. In parallel, Cyber Security has become mandatory due to various and more intensive threats. In June 2022, a group of researchers has met to reflect on their community's impacts on current research challenges. In particular, they have considered four dimensions: (1) dedicated systems being data processing and analytic platforms or time series management systems; (2) graphs analytics and distributed computation; (3) privacy; and (4) new hardware.

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APIs allow companies to export, via the Internet, their skills and know-how, or even to open up new markets and new media for sale. But to fully exploit the advantages of these services, customers, mainly developers, must be equipped with tools giving the possibility of being able to assemble different services together. Fortunately, the notion of service composition is quite advanced, and different tools exist to compose services. However, as APIs with similar functionality are expected to be provided by competing providers, the key challenge is to find the most relevant compositions. This issue has been addressed in the context of QoS-based composite service selection. The downside, in practice, customers choose services based on the number of call limits. In this paper, we propose an approach to select the most relevant compositions based on the notion of call limit. Specifically, we show how the call limits of the individual services can be aggregated to obtain the call limits of a given composition. Then, we introduce the notion of minimal budget skyline, which comprises the most interesting compositions that fit within the customer's budget. In addition, we develop two algorithms, based on effective pruning strategies, to efficiently compute the minimal budget skyline. Finally, we present a thorough experimental evaluation of our approach.