8 Search Results for "Hübschle-Schneider, Lorenz"

Document
DOI: 10.4230/LIPIcs.SEA.2026.24
ZOR Filters: Fast and Smaller Than Fuse Filters

Authors: Antoine Limasset

Published in: LIPIcs, Volume 371, 24th International Symposium on Experimental Algorithms (SEA 2026)

Abstract
Probabilistic membership filters support fast approximate membership queries with controlled false-positive probability ε and are widely used across storage, analytics, networking, and bioinformatics [Chang et al., 2008; Niv Dayan et al., 2018; Broder and Mitzenmacher, 2004; Harris and Medvedev, 2020; Marchet and Limasset, 2023; Chikhi et al., 2025; Hernandez-Courbevoie et al., 2025]. In the static setting, low-overhead methods such as XOR, Fuse, and BuRR have been proposed [Graf and Lemire, 2020; Graf and Lemire, 2022; Dillinger et al., 2022; Ulrich and Renard, 2023]. Among these, Fuse filters are known for near-optimal query throughput. For XOR/Fuse-style peeling constructions, however, build success is only high probability, which complicates deterministic builds. We introduce ZOR filters, a deterministic continuation of XOR/Fuse-style constructions that guarantees termination while preserving the same XOR-based query mechanism. ZOR replaces restart-on-failure with deterministic peeling that abandons a small fraction of keys, and restores false-positive-only semantics by storing the remainder in a compact auxiliary structure. In our experiments, the abandoned fraction drops below 1% for moderate arity (e.g., N ≥ 5), so the auxiliary handles a negligible fraction of keys. As a result, ZOR filters can be substantially more memory-efficient than Fuse filters, with overhead below 1%, while not yet matching the near-optimal overhead of BuRR (below 0.1%). In query performance, ZOR-pure is close to Fuse and faster than BuRR on positive queries, while the complete interleaved variant trades additional negative-query latency for deterministic continuation. Relative to optimised Fuse/BuRR implementations [Graf and Lemire, 2022; Dillinger et al., 2022], the current ZOR prototype remains slower in construction because deterministic peeling requires explicit incidence handling; reducing this construction gap is an important direction for future work.
Cite as

Antoine Limasset. ZOR Filters: Fast and Smaller Than Fuse Filters. In 24th International Symposium on Experimental Algorithms (SEA 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 371, pp. 24:1-24:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{limasset:LIPIcs.SEA.2026.24,
  author =	{Limasset, Antoine},
  title =	{{ZOR Filters: Fast and Smaller Than Fuse Filters}},
  booktitle =	{24th International Symposium on Experimental Algorithms (SEA 2026)},
  pages =	{24:1--24:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-422-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{371},
  editor =	{Aum\"{u}ller, Martin and Finocchi, Irene},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2026.24},
  URN =		{urn:nbn:de:0030-drops-260281},
  doi =		{10.4230/LIPIcs.SEA.2026.24},
  annote =	{Keywords: Data structure, Approximate Set Membership, Static filter}
}
Document
DOI: 10.4230/LIPIcs.SEA.2026.19
Breaking 2-Cores for Invertible Bloom Lookup Tables by Structure Prediction

Authors: Vojtěch Gaďurek and Pavel Veselý

Published in: LIPIcs, Volume 371, 24th International Symposium on Experimental Algorithms (SEA 2026)

Abstract
Invertible Bloom Lookup Tables (IBLTs) provide a highly space-efficient way to reconstruct small sets resulting from a large number of insertions and deletions of elements, such as in streaming or distributed computation of the symmetric difference of similar sets. The set recovery process succeeds if the IBLT size is at least 1.22 times the size of the encoded set; otherwise, a 2-core occurs with high probability in the corresponding random hypergraph. However, the sets in practice often exhibit structure that allows for performance beyond worst-case bounds. Here, we demonstrate that structured sets - such as the k-mers in the symmetric difference of two closely related genomes - can be recovered with an IBLT of significantly smaller size. We achieve this by employing structure-aware predictors to break the 2-core whenever the recovery process gets stuck. Importantly, this approach modifies only the decoding procedure, leaving the IBLT data structure unchanged. We prove that even a weak matching-based predictor enables the recovery of 27% more elements than the nominal IBLT size. Equipped with simple predictors for k-mers of genomic datasets, we demonstrate that recovering a symmetric difference with high probability can be done with an IBLT of size only 66% of the encoded set size for k = 31, improving the space efficiency by almost a factor of two. Moreover, we design an improved method for k-mers with large k that combines subsampling with nearly perfect prediction via fingerprinting and achieves a scaling property, requiring only O(M log M) bits for recovering M k-mers, instead of Θ(k⋅M) bits of the standard IBLT. Overall, our results highlight the possibility of significant space-efficiency improvements for IBLTs on datasets with predictable structure.
Cite as

Vojtěch Gaďurek and Pavel Veselý. Breaking 2-Cores for Invertible Bloom Lookup Tables by Structure Prediction. In 24th International Symposium on Experimental Algorithms (SEA 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 371, pp. 19:1-19:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{gadurek_et_al:LIPIcs.SEA.2026.19,
  author =	{Ga\v{d}urek, Vojt\v{e}ch and Vesel\'{y}, Pavel},
  title =	{{Breaking 2-Cores for Invertible Bloom Lookup Tables by Structure Prediction}},
  booktitle =	{24th International Symposium on Experimental Algorithms (SEA 2026)},
  pages =	{19:1--19:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-422-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{371},
  editor =	{Aum\"{u}ller, Martin and Finocchi, Irene},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2026.19},
  URN =		{urn:nbn:de:0030-drops-260237},
  doi =		{10.4230/LIPIcs.SEA.2026.19},
  annote =	{Keywords: Invertible Bloom Lookup Table, symmetric difference, k-mer sets}
}
Document
DOI: 10.4230/LIPIcs.ESA.2025.9
MorphisHash: Improving Space Efficiency of ShockHash for Minimal Perfect Hashing

Authors: Stefan Hermann

Published in: LIPIcs, Volume 351, 33rd Annual European Symposium on Algorithms (ESA 2025)

Abstract
A minimal perfect hash function (MPHF) maps a set of n keys to unique positions {1, …, n}. Representing an MPHF requires at least log₂(e)≈ 1.443 bits per key. ShockHash is a technique to construct an MPHF and requires just slightly more space. It gives each key two random candidate positions. If each key can be mapped to one of its two candidate positions such that there is exactly one key mapped to each position, then an MPHF is found. If not, ShockHash repeats the process with a new set of random candidate positions. ShockHash has to store how many repetitions were required and for each key to which of the two candidate positions it is mapped. However, when a given set of candidate positions can be used as MPHF then there is not only one but multiple ways of mapping the keys to one of their candidate positions such that the mapping results in an MPHF. This redundancy makes up for the majority of the remaining space overhead in ShockHash. In this paper, we present MorphisHash which almost completely eliminates this redundancy. Our theoretical result is that MorphisHash saves Θ(ln(n)) bits in expectation compared to ShockHash. This corresponds to a factor of 20 less space overhead in practice. Just like ShockHash, MorphisHash can be used as a building block within RecSplit to obtain MorphisHash-RS. When compared for same space consumption, MorphisHash-RS can be constructed up to 21 times faster than ShockHash-RS. The technique to accomplish this might be of a more general interest to compress data structures.
Cite as

Stefan Hermann. MorphisHash: Improving Space Efficiency of ShockHash for Minimal Perfect Hashing. In 33rd Annual European Symposium on Algorithms (ESA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 351, pp. 9:1-9:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{hermann:LIPIcs.ESA.2025.9,
  author =	{Hermann, Stefan},
  title =	{{MorphisHash: Improving Space Efficiency of ShockHash for Minimal Perfect Hashing}},
  booktitle =	{33rd Annual European Symposium on Algorithms (ESA 2025)},
  pages =	{9:1--9:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-395-9},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{351},
  editor =	{Benoit, Anne and Kaplan, Haim and Wild, Sebastian and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2025.9},
  URN =		{urn:nbn:de:0030-drops-244779},
  doi =		{10.4230/LIPIcs.ESA.2025.9},
  annote =	{Keywords: compressed data structure, perfect hashing, random graph, pseudoforest, component}
}
Document
DOI: 10.4230/LIPIcs.ESA.2025.99
Engineering Minimal k-Perfect Hash Functions

Authors: Stefan Hermann, Sebastian Kirmayer, Hans-Peter Lehmann, Peter Sanders, and Stefan Walzer

Published in: LIPIcs, Volume 351, 33rd Annual European Symposium on Algorithms (ESA 2025)

Abstract
Given a set S of n keys, a k-perfect hash function (kPHF) is a data structure that maps the keys to the first m integers, where each output integer can be hit by at most k input keys. When m = ⌈n/k⌉, the resulting function is called a minimal k-perfect hash function (MkPHF). Applications of kPHFs can be found in external memory data structures or to create efficient 1-perfect hash functions, which in turn have a wide range of applications from databases to bioinformatics. Several papers from the 1980s look at external memory data structures with small internal memory indexes. However, actual k-perfect hash functions are surprisingly rare, and the area has not seen a lot of research recently. At the same time, recent research in 1-perfect hashing shows that there is a lack of efficient kPHFs. In this paper, we revive the area of k-perfect hashing, presenting four new constructions. Our implementations simultaneously dominate older approaches in space consumption, construction time, and query time. We see this paper as a possible starting point of an active line of research, similar to the area of 1-perfect hashing.
Cite as

Stefan Hermann, Sebastian Kirmayer, Hans-Peter Lehmann, Peter Sanders, and Stefan Walzer. Engineering Minimal k-Perfect Hash Functions. In 33rd Annual European Symposium on Algorithms (ESA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 351, pp. 99:1-99:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{hermann_et_al:LIPIcs.ESA.2025.99,
  author =	{Hermann, Stefan and Kirmayer, Sebastian and Lehmann, Hans-Peter and Sanders, Peter and Walzer, Stefan},
  title =	{{Engineering Minimal k-Perfect Hash Functions}},
  booktitle =	{33rd Annual European Symposium on Algorithms (ESA 2025)},
  pages =	{99:1--99:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-395-9},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{351},
  editor =	{Benoit, Anne and Kaplan, Haim and Wild, Sebastian and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2025.99},
  URN =		{urn:nbn:de:0030-drops-245685},
  doi =		{10.4230/LIPIcs.ESA.2025.99},
  annote =	{Keywords: Compressed Data Structures, Perfect Hashing}
}
Document
DOI: 10.4230/LIPIcs.SEA.2025.25
Blocked Bloom Filters with Choices

Authors: Johanna Elena Schmitz, Jens Zentgraf, and Sven Rahmann

Published in: LIPIcs, Volume 338, 23rd International Symposium on Experimental Algorithms (SEA 2025)

Abstract
Probabilistic filters are approximate set membership data structures that represent a set of keys in small space, and answer set membership queries without false negative answers, but with a certain allowed false positive probability. Such filters are widely used in database systems, networks, storage systems and in biological sequence analysis because of their fast query times and low space requirements. Starting with Bloom filters in the 1970s, many filter data structures have been developed, each with its own advantages and disadvantages, e.g., Blocked Bloom filters, Cuckoo filters, XOR filters, Ribbon filters, and more. We introduce Blocked Bloom filters with choices that work similarly to Blocked Bloom filters, except that for each key there are two (or more) alternative choices of blocks where the key’s information may be stored. When inserting a key, we select the block using a cost function which takes into account the current load and the additional number of bits to be set in the candidate blocks. The result is a filter that partially inherits the advantages of a Blocked Bloom filter, such as the ability to insert keys rapidly online or the ability to slightly overload the filter with only a small penalty to the false positive rate. At the same time, it avoids the major disadvantage of a Blocked Bloom filter, namely the larger space consumption. Our new data structure uses less space at the same false positive rate, or has a lower false positive rate at the same space consumption as a Blocked Bloom filter. We discuss the methodology, cost functions for block selection, engineered implementation, a detailed performance evaluation and use cases in bioinformatics of Blocked Bloom filters with choices, showing that they can be of practical value. The implementation of the evaluated filters and the workflows used are provided via Gitlab at https://gitlab.com/rahmannlab/blowchoc-filters.
Cite as

Johanna Elena Schmitz, Jens Zentgraf, and Sven Rahmann. Blocked Bloom Filters with Choices. In 23rd International Symposium on Experimental Algorithms (SEA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 338, pp. 25:1-25:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{schmitz_et_al:LIPIcs.SEA.2025.25,
  author =	{Schmitz, Johanna Elena and Zentgraf, Jens and Rahmann, Sven},
  title =	{{Blocked Bloom Filters with Choices}},
  booktitle =	{23rd International Symposium on Experimental Algorithms (SEA 2025)},
  pages =	{25:1--25:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-375-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{338},
  editor =	{Mutzel, Petra and Prezza, Nicola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2025.25},
  URN =		{urn:nbn:de:0030-drops-232631},
  doi =		{10.4230/LIPIcs.SEA.2025.25},
  annote =	{Keywords: Probabilistic filter, Bloom filter, power of two choices}
}
Document
DOI: 10.4230/LIPIcs.SEA.2022.4
Fast Succinct Retrieval and Approximate Membership Using Ribbon

Authors: Peter C. Dillinger, Lorenz Hübschle-Schneider, Peter Sanders, and Stefan Walzer

Published in: LIPIcs, Volume 233, 20th International Symposium on Experimental Algorithms (SEA 2022)

Abstract
A retrieval data structure for a static function f: S → {0,1}^r supports queries that return f(x) for any x ∈ S. Retrieval data structures can be used to implement a static approximate membership query data structure (AMQ), i.e., a Bloom filter alternative, with false positive rate 2^{-r}. The information-theoretic lower bound for both tasks is r|S| bits. While succinct theoretical constructions using (1+o(1))r|S| bits were known, these could not achieve very small overheads in practice because they have an unfavorable space-time tradeoff hidden in the asymptotic costs or because small overheads would only be reached for physically impossible input sizes. With bumped ribbon retrieval (BuRR), we present the first practical succinct retrieval data structure. In an extensive experimental evaluation BuRR achieves space overheads well below 1% while being faster than most previously used retrieval data structures (typically with space overheads at least an order of magnitude larger) and faster than classical Bloom filters (with space overhead ≥ 44%). This efficiency, including favorable constants, stems from a combination of simplicity, word parallelism, and high locality. We additionally describe homogeneous ribbon filter AMQs, which are even simpler and faster at the price of slightly larger space overhead.
Cite as

Peter C. Dillinger, Lorenz Hübschle-Schneider, Peter Sanders, and Stefan Walzer. Fast Succinct Retrieval and Approximate Membership Using Ribbon. In 20th International Symposium on Experimental Algorithms (SEA 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 233, pp. 4:1-4:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{dillinger_et_al:LIPIcs.SEA.2022.4,
  author =	{Dillinger, Peter C. and H\"{u}bschle-Schneider, Lorenz and Sanders, Peter and Walzer, Stefan},
  title =	{{Fast Succinct Retrieval and Approximate Membership Using Ribbon}},
  booktitle =	{20th International Symposium on Experimental Algorithms (SEA 2022)},
  pages =	{4:1--4:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-251-8},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{233},
  editor =	{Schulz, Christian and U\c{c}ar, Bora},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2022.4},
  URN =		{urn:nbn:de:0030-drops-165385},
  doi =		{10.4230/LIPIcs.SEA.2022.4},
  annote =	{Keywords: AMQ, Bloom filter, dictionary, linear algebra, randomized algorithm, retrieval data structure, static function data structure, succinct data structure, perfect hashing}
}
Document
DOI: 10.4230/LIPIcs.ESA.2019.59
Parallel Weighted Random Sampling

Authors: Lorenz Hübschle-Schneider and Peter Sanders

Published in: LIPIcs, Volume 144, 27th Annual European Symposium on Algorithms (ESA 2019)

Abstract
Data structures for efficient sampling from a set of weighted items are an important building block of many applications. However, few parallel solutions are known. We close many of these gaps both for shared-memory and distributed-memory machines. We give efficient, fast, and practicable algorithms for sampling single items, k items with/without replacement, permutations, subsets, and reservoirs. We also give improved sequential algorithms for alias table construction and for sampling with replacement. Experiments on shared-memory parallel machines with up to 158 threads show near linear speedups both for construction and queries.
Cite as

Lorenz Hübschle-Schneider and Peter Sanders. Parallel Weighted Random Sampling. In 27th Annual European Symposium on Algorithms (ESA 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 144, pp. 59:1-59:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{hubschleschneider_et_al:LIPIcs.ESA.2019.59,
  author =	{H\"{u}bschle-Schneider, Lorenz and Sanders, Peter},
  title =	{{Parallel Weighted Random Sampling}},
  booktitle =	{27th Annual European Symposium on Algorithms (ESA 2019)},
  pages =	{59:1--59:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-124-5},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{144},
  editor =	{Bender, Michael A. and Svensson, Ola and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2019.59},
  URN =		{urn:nbn:de:0030-drops-111800},
  doi =		{10.4230/LIPIcs.ESA.2019.59},
  annote =	{Keywords: categorical distribution, multinoulli distribution, parallel algorithm, alias method, PRAM, communication efficient algorithm, subset sampling, reservoir sampling}
}
Document
DOI: 10.4230/OASIcs.ATMOS.2014.138
Speed-Consumption Tradeoff for Electric Vehicle Route Planning

Authors: Moritz Baum, Julian Dibbelt, Lorenz Hübschle-Schneider, Thomas Pajor, and Dorothea Wagner

Published in: OASIcs, Volume 42, 14th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (2014)

Abstract
We study the problem of computing routes for electric vehicles (EVs) in road networks. Since their battery capacity is limited, and consumed energy per distance increases with velocity, driving the fastest route is often not desirable and may even be infeasible. On the other hand, the energy-optimal route may be too conservative in that it contains unnecessary detours or simply takes too long. In this work, we propose to use multicriteria optimization to obtain Pareto sets of routes that trade energy consumption for speed. In particular, we exploit the fact that the same road segment can be driven at different speeds within reasonable intervals. As a result, we are able to provide routes with low energy consumption that still follow major roads, such as freeways. Unfortunately, the size of the resulting Pareto sets can be too large to be practical. We therefore also propose several nontrivial techniques that can be applied on-line at query time in order to speed up computation and filter insignificant solutions from the Pareto sets. Our extensive experimental study, which uses a real-world energy consumption model, reveals that we are able to compute diverse sets of alternative routes on continental networks that closely resemble the exact Pareto set in just under a second---several orders of magnitude faster than the exhaustive algorithm.
Cite as

Moritz Baum, Julian Dibbelt, Lorenz Hübschle-Schneider, Thomas Pajor, and Dorothea Wagner. Speed-Consumption Tradeoff for Electric Vehicle Route Planning. In 14th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems. Open Access Series in Informatics (OASIcs), Volume 42, pp. 138-151, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@InProceedings{baum_et_al:OASIcs.ATMOS.2014.138,
  author =	{Baum, Moritz and Dibbelt, Julian and H\"{u}bschle-Schneider, Lorenz and Pajor, Thomas and Wagner, Dorothea},
  title =	{{Speed-Consumption Tradeoff for Electric Vehicle Route Planning}},
  booktitle =	{14th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems},
  pages =	{138--151},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-75-0},
  ISSN =	{2190-6807},
  year =	{2014},
  volume =	{42},
  editor =	{Funke, Stefan and Mihal\'{a}k, Mat\'{u}s},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2014.138},
  URN =		{urn:nbn:de:0030-drops-47583},
  doi =		{10.4230/OASIcs.ATMOS.2014.138},
  annote =	{Keywords: electric vehicles, shortest paths, route planning, bicriteria optimization, algorithm engineering}
}
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