Storing and processing temporal data in a main memory column store

Storing and Processing Temporal Data in Main Memory Column Stores

Managing and accessing temporal data is of increasing importance for many applications in industry. Yet, even though there was a significant amount of research in academia during the 1990’s, temporal features were only recently included into the SQL:2011 standard. Therefore, only a few temporal operators and with rather poor performance are currently implemented by commercial database systems. ...

Graph Processing in Main-Memory Column Stores

Scaling Up Concurrent Main-Memory Column-Store Scans: Towards Adaptive NUMA-aware Data and Task Placement

Main-memory column-stores are called to efficiently use modern non-uniform memory access (NUMA) architectures to service concurrent clients on big data. The efficient usage of NUMA architectures depends on the data placement and scheduling strategy of the column-store. Most column-stores choose a static strategy that involves partitioning all data across the NUMA architecture, and employing a s...

Highspeed Graph Processing Exploiting Main-Memory Column Stores

A popular belief in the graph database community is that relational database management systems are generally ill-suited for efficient graph processing. This might apply for analytic graph queries performing iterative computations on the graph, but does not necessarily hold true for short-running, OLTP-style graph queries. In this paper we argue that, instead of extending a graph database manag...

BitMat: A Main Memory RDF Triple Store

BitMat is a main memory based bit-matrix structure for representing a large set of RDF triples, designed primarily to allow processing of conjunctive triple pattern (join) queries. The key aspects are as follows: i) its RDF triple-set representation is compact compared to conventional disk-based and existing main-memory RDF stores, ii) basic join query processing employs logical bitwise AND/OR ...