A Simple SemanticBased Data Storage Layout for Querying Point Clouds The importance of being able to separate the semantics from the actual (X,Y,Z) coordinates in a point cloud has been actively brought up in recent research. However, there is still no widely used or accepted data layout paradigm on how to efficiently store and manage such semantic point cloud data. In this paper, we present a simple data layout that makes use the semantics and that allows for quick queries. The underlying idea is especially suited for a programming approach (e.g., queries programmed via Python) but we also present an even simpler implementation of the underlying technique on a well known relational database management system (RDBMS), namely, PostgreSQL. The obtained query results suggest that the presented approach can be successfully used to handle point and range queries on large points clouds.

Compressed Data Structures for Astronomical ContentAware Resource Search We introduce an efficient approach that aims at supporting contentbased queries on the Chilean Virtual Observatory. In particular we are interested in retrieving relevant information from virtualobservatory tables. This introduces several challenges that make the informationretrieval process harder. We define an algorithm that uses a compressed data structure to obtain the count of the number of occurrences of a string query within each column of a table. This kind of query has been used in the literature for faceted and semantic search as well as for retrieving information from web tables. This is in order to improve search effectiveness. We show that using only 15%xe2x80x9325% the space of a table our approach contains the table data (and hence the table can be deleted) and is able to answer queries efficiently in a few milliseconds.