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.
The aim of this work was to develop a technique to speed up complex joins in an incremental visual query system. When designing a visual, highly interactive interface for ad-hoc (read-only) queries, fast response times are of paramount importance. While a column-oriented DBMS reduces the inherent latency found in relational DBMS, there is still the question of how to index the data, especially so as to support complex joins. Equi-joins that involve a many-to-many relationship are an example of complex joins that arise frequently and whose efficient processing is essential for fast query processing. We present OVI-3, a NoSQL visual query system based on incremental querying that uses a simple directory-based indexing scheme for faster processing of such complex joins. The system has been piloted using real data from a student database at Aalto University. The results demonstrated that for certain complex joins the presented indexing scheme outperforms SQL queries from a data server, especially for queries involving anti-joins (negation), where OVI-3 provided an orders of magnitude speed improvement.
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