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Dan Olteanu

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4 papers
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4

CSL Conference 2023 Conference Paper

Evaluation Trade-Offs for Acyclic Conjunctive Queries

  • Ahmet Kara 0002
  • Milos Nikolic 0001
  • Dan Olteanu
  • Haozhe Zhang 0001

We consider the evaluation of acyclic conjunctive queries, where the evaluation time is decomposed into preprocessing time and enumeration delay. In a seminal paper at CSL'07, Bagan, Durand, and Grandjean showed that acyclic queries can be evaluated with linear preprocessing time and linear enumeration delay. If the query is free-connex, the enumeration delay becomes constant. Further prior work showed that constant enumeration delay can be achieved for arbitrary acyclic conjunctive queries at the expense of a preprocessing time that is characterised by the fractional hypertree width. We introduce an approach that exposes a trade-off between preprocessing time and enumeration delay for acyclic conjunctive queries. The aforementioned prior works represent extremes in this trade-off space. Yet our approach also allows for the enumeration delay and the preprocessing time between these extremes, in particular the delay may lie between constant and linear time. Our approach decomposes the given query into subqueries and achieves for each subquery a trade-off that depends on a parameter controlling the times for preprocessing and enumeration. The complexity of the query is given by the Pareto optimal points of a bi-objective optimisation program whose inputs are possible query decompositions and parameter values.

Highlights Conference 2018 Conference Abstract

Learning Models over Relational Databases

  • Dan Olteanu

ABSTRACT. In this talk I will overview recent results on learning classification and regression models over training datasets defined by feature extraction queries over relational databases. I will show that the complexity of this task can be connected with known notions of widths that measure the complexity of relational queries, such as the fractional hypertree width. This complexity can be much lower than that of the state of the art approach that first materialises the training dataset. Recent joint work with collaborators from industry shows that this approach can speed up real analytical workloads by several orders of magnitude over state-of-the-art systems. I will also highlight on-going work on linear algebra over databases and point out exciting directions for future work. This work is based on long-standing collaboration with Maximilian Schleich and Jakub Zavodny and more recent collaboration with Mahmoud Abo-Khamis, Hung Ngo, and XuanLong Nguyen.

AAAI Conference 2014 Conference Paper

Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF Systems

  • Boris Motik
  • Yavor Nenov
  • Robert Piro
  • Ian Horrocks
  • Dan Olteanu

We present a novel approach to parallel materialisation (i. e. , fixpoint computation) of datalog programs in centralised, main-memory, multi-core RDF systems. Our approach comprises an algorithm that evenly distributes the workload to cores, and an RDF indexing data structure that supports efficient, ‘mostly’ lock-free parallel updates. Our empirical evaluation shows that our approach parallelises computation very well: with 16 physical cores, materialisation can be up to 13. 9 times faster than with just one core.

TCS Journal 2008 Journal Article

World-set decompositions: Expressiveness and efficient algorithms

  • Dan Olteanu
  • Christoph Koch
  • Lyublena Antova

Uncertain information is commonplace in real-world data management scenarios. The ability to represent large sets of possible instances (worlds) while supporting efficient storage and processing is an important challenge in this context. The recent formalism of world-set decompositions (WSDs) provides a space-efficient representation for uncertain data that also supports scalable processing. WSDs are complete for finite world-sets in that they can represent any finite set of possible worlds. For possibly infinite world-sets, we show that a natural generalization of WSDs precisely captures the expressive power of c-tables. We then show that several important problems are efficiently solvable on WSDs while they are NP-hard on c-tables. Finally, we give a polynomial-time algorithm for factorizing WSDs, i. e. an efficient algorithm for minimizing such representations.

v2026.09.13