  Many modern applications produce massive streams of data series and maintain
them in indexes to be able to explore them through nearest neighbor search.
Existing data series indexes, however, are expensive to operate as they issue
many random I/Os to storage. To address this problem, we recently proposed
Coconut, a new infrastructure that organizes data series based on a new
sortable format. In this way, Coconut is able to leverage state-of-the-art
indexing techniques that rely on sorting for the first time to build, maintain
and query data series indexes using fast sequential I/Os. In this
demonstration, we present Coconut Palm, a new exploration tool that allows to
interactively combine different indexing techniques from within the Coconut
infrastructure and to thereby seamlessly explore data series from across
various scientific domains. We highlight the rich indexing design choices that
Coconut opens up, and we present a new recommender tool that allows users to
intelligently navigate them for both static and streaming data exploration
scenarios.
