Due to the demand for homogeneous, intelligent, and automated access to data measured anywhere and from any device, Machine-to-Machine (M2M) platforms are evolving as globally-intended multi-layer solutions that provide such access, abstracting from all technology-specific tasks. In order to preserve the stability of their potentially huge data-handling systems and the usefulness of their Big Data, M2M platforms must maintain some data selection and filtering logic. A challenge that appears in modern M2M platforms is related to the decoupling of the front end (devices, area networks) from the backend (applications, databases). Because of this decoupling, domain-specific tricks cannot be applied any more for filtering at the front end. This paper presents a solution using domain-specific filtering thresholds in a domain-agnostic platform, as well as filtering flows and algorithms tailored to modern M2M platforms. Their combination assembles the first filtering solution that supports the unified handling of heterogeneous filters. In an evaluation from the utility-monitoring domain, instances of our approach showed high efficiency of configuration and were the only ones to achieve, for example, forwarding less than 25% of the captured data maintaining a coverage ratio bigger than 50% for all considered applications.