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Explore FREDDY: Fast Word Embeddings in Database Systems
Word embeddings encode a lot of semantic as well as syntactic features and therefore are useful in many tasks especially in Natural Language Processing and Information Retrieval. FREDDY (Fast woRd EmbedDings Database sYstems), an extended PostgreSQL database system, allowing the user to analyze structured knowledge in ...
BTW2019 - Datenbanksysteme für Business, Technologie und WebBTW2019 - Datenbanksysteme für Business, Technologie und Web
Fighting the Duplicates in Hashing: Conflict Detection-aware Vectorization of Linear Probing
Hash tables are a core data structure in database systems, because they are fundamental for many database operators like hash-based join and aggregation. In recent years, the efficient vectorized implementation using SIMD (Single Instruction Multiple Data) instructions has attracted a lot of attention. Generally, all ...
Fast Approximated Nearest Neighbor Joins For Relational Database Systems
K nearest neighbor search (kNN-Search) is a universal data processing technique and a fundamental operation for word embeddings trained by word2vec or related approaches. The benefits of operations on dense vectors like word embeddings for analytical functionalities of RDBMSs motivate an integration of kNN-Joins. However, ...
NeMeSys – Energy Adaptive Graph Pattern Matching on NUMA-based Multiprocessor Systems
NeMeSys is a NUMA-aware graph pattern processing engine, which leverages intelligent resource management for energy adaptive processing. With modern server systems incorporating an increasing amount of main memory, we can store graphs and compute analytical graph algorithms like graph pattern matching completely in-memory. ...