Ongoing time point now is used to state that a tuple is valid from the start point onward. For database systems ongoing time points have far-reaching implications since they change continuously as time passes by. State-of-the-art approaches deal with ongoing time points by instantiating them to the reference time. The instantiation yields query results that are only valid at the chosen time and get invalidated as time passes by. We propose a solution that keeps ongoing time points uninstantiated during query processing. We do so by evaluating predicates and functions at all possible reference times. This renders query results independent of a specific reference time and yields results that remain valid as time passes by. As query results, we propose ongoing relations that include a reference time attribute. The value of the reference time attribute is restricted by predicates and functions on ongoing attributes. We describe and evaluate an efficient implementation of ongoing data types and operations in PostgreSQL.

Outlier mining is an important task for finding anomalous objects. In practice, however, there is not always a clear distinction between outliers and regular objects as objects have different roles w.r.t. different attribute sets. An object may deviate in one subspace, i.e. a subset of attributes. And the same object might appear perfectly regular in other subspaces. One can think of subspaces as multiple views on one database. Traditional methods consider only one view (the full attribute space). Thus, they miss complex outliers that are hidden in multiple subspaces. In this work, we propose Outrank, a novel outlier ranking concept. Outrank exploits subspace analysis to determine the degree of outlierness. It considers different subsets of the attributes as individual outlier properties. It compares clustered regions in arbitrary subspaces and derives an outlierness score for each object. Its principled integration of multiple views into an outlierness measure uncovers outliers that are not detectable in the full attribute space. Our experimental evaluation demonstrates that Outrank successfully determines a high quality outlier ranking, and outperforms state-of-the-art outlierness measures.