Scaling up concurrent main-memory column-store scans

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...

Fast Column Scans: Paged Indices for In-Memory Column Stores

Commodity hardware is available in configurations with huge amounts of main memory and it is viable to keep large databases of enterprises in the RAM of one or a few machines. Additionally, a reunification of transactional and analytical systems has been proposed to enable operational reporting on the most recent data. In-memory column stores appeared in academia and industry as a solution to h...

DEMO: Adjustably Encrypted In-Memory Column-Store

Recent databases are implemented as in-memory columnstores. Adjustable encryption offers a solution to encrypted database processing in the cloud. We show that the two technologies play well together by providing an analysis and prototype results that demonstrate the impact of mechanisms at the database side (dictionaries and their compression) and cryptographic mechanisms at the adjustable enc...

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 ...

Graph Processing in Main-Memory Column Stores