162 citations · 369 across the 16 of their papers we have counts for
18 papers
FastPair: GPU-Optimized String Decoding
Joseph Isaacs, Francesco Gargiulo, Peter Boncz +5
Modern data systems compress data at rest and decompress it only when needed to preserve interconnect bandwidth. This design is often inefficient on GPU-based compute platforms bec…
Stop Indexing at Full Precision: Revisiting Clustering for Vector Embeddings
Leonardo Kuffo, Peter Boncz
In this study, we revisit three widely used techniques in vector search and utilize them to optimize vector embedding indexing through clustering: dimensionality reduction, quantiz…
DuckDB-SGX2: The Good, The Bad and The Ugly within Confidential Analytical Query Processing
Ilaria Battiston, Lotte Felius, Sam Ansmink +2
We provide an evaluation of an analytical workload in a confidential computing environment, combining DuckDB with two technologies: modular columnar encryption in Parquet files (da…
OpenIVM: a SQL-to-SQL Compiler for Incremental Computations
Ilaria Battiston, Kriti Kathuria, Peter Boncz
This demonstration presents a new Open Source SQL-to-SQL compiler for Incremental View Maintenance (IVM). While previous systems, such as DBToaster, implemented computational funct…
Improving Data Minimization through Decentralized Data Architectures
Ilaria Battiston, Peter Boncz
In this research project, we investigate an alternative to the standard cloud-centralized data architecture. Specifically, we aim to leave part of the application data under the co…
The LDBC Social Network Benchmark Interactive workload v2: A transactional graph query benchmark with deep delete operations
David Püroja, Jack Waudby, Peter Boncz +1
The LDBC Social Network Benchmark's Interactive workload captures an OLTP scenario operating on a correlated social network graph. It consists of complex graph queries executed con…
The Linked Data Benchmark Council (LDBC): Driving competition and collaboration in the graph data management space
Gábor Szárnyas, Brad Bebee, Altan Birler +30
Graph data management is instrumental for several use cases such as recommendation, root cause analysis, financial fraud detection, and enterprise knowledge representation. Efficie…
In-Memory Indexed Caching for Distributed Data Processing
Alexandru Uta, Bogdan Ghit, Ankur Dave +2
Powerful abstractions such as dataframes are only as efficient as their underlying runtime system. The de-facto distributed data processing framework, Apache Spark, is poorly suite…
An Epidemiological Model for contact tracing with the Dutch CoronaMelder App
Peter Boncz
We present an epidemiological model for the effectiveness of CoronaMelder, the Dutch digital contact tracing app developed on top of the Google/Apple Exposure Notification framewor…
The Future is Big Graphs! A Community View on Graph Processing Systems
Sherif Sakr, Angela Bonifati, Hannes Voigt +38
Graphs are by nature unifying abstractions that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users a…
The LDBC Graphalytics Benchmark
Alexandru Iosup, Ahmed Musaafir, Alexandru Uta +15
In this document, we describe LDBC Graphalytics, an industrial-grade benchmark for graph analysis platforms. The main goal of Graphalytics is to enable the fair and objective compa…
The LDBC Social Network Benchmark
Renzo Angles, János Benjamin Antal, Alex Averbuch +21
The Linked Data Benchmark Council's Social Network Benchmark (LDBC SNB) is an effort intended to test various functionalities of systems used for graph-like data management. For th…
Extracting Novel Facts from Tables for Knowledge Graph Completion (Extended version)
Benno Kruit, Peter Boncz, Jacopo Urbani
We propose a new end-to-end method for extending a Knowledge Graph (KG) from tables. Existing techniques tend to interpret tables by focusing on information that is already in the…
Estimating Cardinalities with Deep Sketches
Andreas Kipf, Dimitri Vorona, Jonas Müller +6
We introduce Deep Sketches, which are compact models of databases that allow us to estimate the result sizes of SQL queries. Deep Sketches are powered by a new deep learning approa…
Learned Cardinalities: Estimating Correlated Joins with Deep Learning
Andreas Kipf, Thomas Kipf, Bernhard Radke +3
We describe a new deep learning approach to cardinality estimation. MSCN is a multi-set convolutional network, tailored to representing relational query plans, that employs set sem…
Adaptive Geospatial Joins for Modern Hardware
Andreas Kipf, Harald Lang, Varun Pandey +4
Geospatial joins are a core building block of connected mobility applications. An especially challenging problem are joins between streaming points and static polygons. Since point…
G-CORE: A Core for Future Graph Query Languages
Renzo Angles, Marcelo Arenas, Pablo Barceló +9
We report on a community effort between industry and academia to shape the future of graph query languages. We argue that existing graph database management systems should consider…
From Cooperative Scans to Predictive Buffer Management
Michał Świtakowski, Peter Boncz, Marcin Żukowski
In analytical applications, database systems often need to sustain workloads with multiple concurrent scans hitting the same table. The Cooperative Scans (CScans) framework, which…