Wissenschaftliche Artikel

Gottlob, G., Lanzinger, M., Okulmus, C., & Pichler, R. (2024). Fast parallel hypertree decompositions in logarithmic recursion depth. ACM Transactions on Database Systems, 49(1), 1–43. https://doi.org/10.1145/3638758 ( reposiTUm)
Gottlob, G., Lanzinger, M., Pichler, R., & Razgon, I. (2023). Fractional covers of hypergraphs with bounded multi-intersection. Theoretical Computer Science, 979, Article 114204. https://doi.org/10.1016/j.tcs.2023.114204 ( reposiTUm)
Khamis, M. A., Ngo, H. Q., Pichler, R., Suciu, D., & Wang, Y. R. (2022). Datalog in Wonderland. SIGMOD RECORD, 51(2), 6–17. https://doi.org/10.1145/3552490.3552492 ( reposiTUm)
Lanzinger, M., Sferrazza, S., & Gottlob, G. (2022). MV-Datalog+-: Effective Rule-based Reasoning with Uncertain Observations. Theory and Practice of Logic Programming, 22(5), 678–692. https://doi.org/10.1017/S1471068422000199 ( reposiTUm)
Gottlob, G., Okulmus, C., & Pichler, R. (2022). Fast and parallel decomposition of constraint satisfaction problems. Constraints, 27, 284–326. https://doi.org/10.1007/s10601-022-09332-1 ( reposiTUm)
Fischl, W., Gottlob, G., Longo, D. M., & Pichler, R. (2021). HyperBench: A Benchmark and Tool for Hypergraphs and Empirical Findings. ACM Journal on Experimental Algorithmics, 26, 1–40. https://doi.org/10.1145/3440015 ( reposiTUm)
Bliem, B., Morak, M., Moldovan, M., & Woltran, S. (2020). The Impact of Treewidth on Grounding and Solving of Answer Set Programs. Artificial Intelligence, 67, 35–80. https://doi.org/10.1613/jair.1.11515 ( reposiTUm)
Creignou, N., Kröll, M., Pichler, R., Skritek, S., & Vollmer, H. (2019). A complexity theory for hard enumeration problems. Discrete Applied Mathematics, 268, 191–209. https://doi.org/10.1016/j.dam.2019.02.025 ( reposiTUm)

Beiträge in Tagungsbänden

Grasmann, L., Pichler, R., & Selzer, A. (2023). Integration of Skyline Queries into Spark SQL. In F. Geerts & B. Vandevoort (Eds.), Proceedings 26th International Conference on Extending Database Technology (EDBT 2023) (pp. 337–350). OpenProceedings.org. https://doi.org/10.48786/edbt.2023.27 ( reposiTUm)
Merkl, T. C., Pichler, R., & Skritek, S. (2023). Diversity of Answers to Conjunctive Queries. In F. Geerts & B. Vandevoort (Eds.), 26th International Conference on Database Theory (pp. 10:1-10:19). Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH, Dagstuhl Publishing. https://doi.org/10.4230/LIPICS.ICDT.2023.10 ( reposiTUm)
Corrêa, A., Hecher, M., Helmert, M., Longo, D. M., Pommerening, F., & Woltran, S. (2023). Grounding Planning Tasks Using Tree Decompositions and Iterated Solving. In Proceedings of the Thirty-Third International Conference on Automated Planning and Scheduling (pp. 100–108). Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/icaps.v33i1.27184 ( reposiTUm)
Wang, Y. R., Khamis, M. A., Ngo, H. Q., Pichler, R., & Suciu, D. (2022). Optimizing Recursive Queries with Progam Synthesis. In SIGMOD ’22: Proceedings of the 2022 International Conference on Management of Data (pp. 79–93). Association for Computing Machinery (ACM). https://doi.org/10.1145/3514221.3517827 ( reposiTUm)
Khamis, M. A., Ngo, H. Q., Pichler, R., Suciu, D., & Wang, Y. R. (2022). Convergence of Datalog over (Pre-) Semirings. In PODS ’22: Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (p. 105). Association for Computing Machinery. https://doi.org/10.1145/3517804.3524140 ( reposiTUm)
Gottlob, G., Lanzinger, M., Okulmus, C., & Pichler, R. (2022). Fast Parallel Hypertree Decompositions in Logarithmic Recursion Depth. In Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (pp. 325–336). Association for Computing Machinery. https://doi.org/10.1145/3517804.3524153 ( reposiTUm)
Lanzinger, M., Sferrazza, S., & Gottlob, G. (2022). New Perspectives for Fuzzy Datalog (Extended Abstract). In Proceedings of the 4th International Workshop on the Resurgence of Datalog in Academia and Industry (Datalog-2.0 2022) co-located with the 16th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR} 2022) (pp. 42–47). http://hdl.handle.net/20.500.12708/175762 ( reposiTUm)
Chen, H., Gottlob, G., Lanzinger, M., & Pichler, R. (2021). Semantic Width and the Fixed-Parameter Tractability of Constraint Satisfaction Problems. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. IJCAI 2020, Yokohama, Japan. https://doi.org/10.24963/ijcai.2020/239 ( reposiTUm)
Gottlob, G., Okulmus, C., & Pichler, R. (2021). Fast and Parallel Decomposition of Constraint Satisfaction Problems. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. IJCAI 2020, Yokohama, Japan. https://doi.org/10.24963/ijcai.2020/161 ( reposiTUm)
Lanzinger, M. (2021). Tractability Beyond ß-Acyclicity for Conjunctive Queries with Negation. In L. Libkin (Ed.), Proceedings of the 40th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (pp. 355–369). https://doi.org/10.1145/3452021.3458308 ( reposiTUm)
Haret, A., Lackner, M., Pfandler, A., & Wallner, J. P. (2020). Proportional Belief Merging. In V. Conitzer & F. Sha (Eds.), Proceedings of the AAAI Conference on Artificial Intelligence (pp. 2822–2829). AAAI Press. https://doi.org/10.1609/aaai.v34i03.5671 ( reposiTUm)
Gottlob, G., Lanzinger, M., Longo, D. M., Okulmus, C., & Pichler, R. (2020). The HyperTrac Project: Recent Progress and Future Research Directions on Hypergraph Decompositions. In Integration of Constraint Programming, Artificial Intelligence, and Operations Research (pp. 3–21). Springer. https://doi.org/10.1007/978-3-030-58942-4_1 ( reposiTUm)
Skritek, S. (2019). Towards Reconciling Certain Answers and {SPARQL:} Bag Semantics to the Rescue? In Proceedings of the 13th Alberto Mendelzon International Workshop on Foundations of Data Management, Asunci{’{o}}n, Paraguay, June 3-7, 2019 (pp. 1–5). CEUR-WS.org. http://hdl.handle.net/20.500.12708/57864 ( reposiTUm)
Mengel, S., & Skritek, S. (2019). Characterizing Tractability of Simple Well-Designed Pattern Trees with Projection. In P. Barcelo & M. Calautti (Eds.), 22nd International Conference on Database Theory, {ICDT} 2019 (pp. 20:1-20:18). Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik. https://doi.org/10.4230/LIPIcs.ICDT.2019.20 ( reposiTUm)
Kröll, M., & Carmeli, N. (2019). On the Enumeration Complexity of Unions of Conjunctive Queries. In Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems  - PODS ’19. SIGMOD/PODS 2019 - 38th Symposium on Principles of Database Systems 2019, Amsterdam, Netherlands (the). ACM. https://doi.org/10.1145/3294052.3319700 ( reposiTUm)
Gottlob, G., Pichler, R., & Okulmus, C. (2019). Parallel Computation of Generalized Hypertree Decompositions. In Proceedings of the 13th Alberto Mendelzon International Workshop on Foundations of Data Management, Asunci{’{o}}n, Paraguay, June 3-7, 2019 (pp. 1–5). CEUR-WS.org. http://hdl.handle.net/20.500.12708/57860 ( reposiTUm)
Fischl, W., Longo, D. M., Gottlob, G., & Pichler, R. (2019). HyperBench. In Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems - PODS ’19. SIGMOD/PODS 2019 - 38th Symposium on Principles of Database Systems 2019, Amsterdam, Netherlands (the). ACM. https://doi.org/10.1145/3294052.3319683 ( reposiTUm)
Gottlob, G., Lanzinger, M., & Pichler, R. (2019). Semantic Width Revisited (Extended Abstract). In Proceedings of the 13th Alberto Mendelzon International Workshop on Foundations of Data Management, Asunci{’{o}}n, Paraguay, June 3-7, 2019 (pp. 1–5). CEUR-WS.org. http://hdl.handle.net/20.500.12708/55515 ( reposiTUm)
Kröll, M., Peterfreund, L., Freydenberger, D., & Kimmelfeld, B. (2019). Complexity Bounds for Relational Algebra over Document Spanners. In Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems  - PODS ’19. SIGMOD/PODS 2019 - 38th Symposium on Principles of Database Systems 2019, Amsterdam, Netherlands (the). https://doi.org/10.1145/3294052.3319699 ( reposiTUm)
Fischl, W., Gottlob, G., Longo, D. M., & Pichler, R. (2019). HyperBench: {A} Benchmark and Tool for Hypergraphs and Empirical Findings. In Proceedings of the 13th Alberto Mendelzon International Workshop on Foundations of Data Management, Asunci{’{o}}n, Paraguay, June 3-7, 2019.}, (pp. 1–5). CEUR-WS.org. http://hdl.handle.net/20.500.12708/55517 ( reposiTUm)
Csar, T., Lackner, M., & Pichler, R. (2018). Computing the Schulze Method for Large-Scale Preference Data Sets. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. IJCAI - International Joint Conference on Artificial Intelligence, Stockholm, Sweden. ijcai.org. https://doi.org/10.24963/ijcai.2018/25 ( reposiTUm)
Fischl, W., Gottlob, G., & Pichler, R. (2018). General and Fractional Hypertree Decompositions: Hard and Easy Cases. In Proceedings of the 37th {ACM} {SIGMOD-SIGACT-SIGAI} Symposium on Principles of Database Systems (pp. 17–32). ACM. https://doi.org/10.1145/3196959.3196962 ( reposiTUm)