  Inverted indexes continue to be a mainstay of text search engines, allowing
efficient querying of large document collections. While there are a number of
possible organizations, document-ordered indexes are the most common, since
they are amenable to various query types, support index updates, and allow for
efficient dynamic pruning operations. One disadvantage with document-ordered
indexes is that high-scoring documents can be distributed across the document
identifier space, meaning that index traversal algorithms that terminate early
might put search effectiveness at risk. The alternative is impact-ordered
indexes, which primarily support top-k disjunctions, but also allow for anytime
query processing, where the search can be terminated at any time, with search
quality improving as processing latency increases. Anytime query processing can
be used to effectively reduce high-percentile tail latency which is essential
for operational scenarios in which a service level agreement (SLA) imposes
response time requirements. In this work, we show how document-ordered indexes
can be organized such that they can be queried in an anytime fashion, enabling
strict latency control with effective early termination. Our experiments show
that processing document-ordered topical segments selected by a simple score
estimator outperforms existing anytime algorithms, and allows query runtimes to
be accurately limited in order to comply with SLA requirements.
