5 citations · 12 across the 7 of their papers we have counts for
8 papers
Approximate Query Processing via Tuple Bubbles
Damjan Gjurovski, Sebastian Michel
We propose a versatile approach to lightweight, approximate query processing by creating compact but tunably precise representations of larger quantities of original tuples, coined…
Compressing (Multidimensional) Learned Bloom Filters
Angjela Davitkova, Damjan Gjurovski, Sebastian Michel
Bloom filters are widely used data structures that compactly represent sets of elements. Querying a Bloom filter reveals if an element is not included in the underlying set or is i…
Optimizing Multiple Multi-Way Stream Joins
Manuel Dossinger, Sebastian Michel
We address the joint optimization of multiple stream joins in a scale-out architecture by tailoring prior work on multi-way stream joins to predicate-driven data partitioning schem…
LMKG: Learned Models for Cardinality Estimation in Knowledge Graphs
Angjela Davitkova, Damjan Gjurovski, Sebastian Michel
Accurate cardinality estimates are a key ingredient to achieve optimal query plans. For RDF engines, specifically under common knowledge graph processing workloads, the lack of sch…
Learning Interesting Categorical Attributes for Refined Data Exploration
Koninika Pal, Sebastian Michel
This work proposes and evaluates a novel approach to determine interesting categorical attributes for lists of entities. Once identified, such categories are of immense value to al…
An LSH Index for Computing Kendall's Tau over Top-k Lists
Koninika Pal, Sebastian Michel
We consider the problem of similarity search within a set of top-k lists under the Kendall's Tau distance function. This distance describes how related two rankings are in terms of…