Focus Group: Data Analytics (Prof. Gemulla)
Our research interests are mainly in machine learning and data processing. In particular:
- Machine learning with structured data (such as relational data)
- Machine learning with semi-structured data (such as multi-relational graphs)
- Combining the above with unstructured knowledge (such as text)
- Efficient and scalable methods and systems for data-intensive processing
People
Former PhD students
Kaustubh Beedkar, Luciano del Corro, Kiril Gashteovski, Stefan Kain, Adrian Kochsiek, Faraz Makari Manshadi, Alexander Renz-Wieland, Daniel Ruffinelli, Christina Teflioudi, Yanjie Wang
Data and Software
- GraSH: Multi-fidelity HPO for graph learning
- DistKGE: A knowledge graph embedding library for multi-GPU and multi-machine training
- AdaPM: A fully adaptive parameter manager
- LibKGE: A knowledge graph embedding library
- Lapse: A parameter server with dynamic parameter allocation
- OPIEC: An open information extraction corpus
- MinIE: Open information extractor (spiritual successor to ClausIE)
- DSGDpp: Various parallel algorithms for matrix factorization (including DSGD++)
- DESQ: Frequent sequence mining with subsequence constraints
- Rounding rank: algorithms for computing rounding-rank decompositions
- CORE: Context-aware open relation extraction with factorization machines
- FINET: Context-aware fine-grained named entity typing
- Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning
- ClausIE: Clause-Based Open Information Extraction
- LEMP: Fast Retrieval of Large Entries in a Matrix Product
- LASH: Large-Scale Sequence Mining with Hierarchies
- MG-FSM: Large-Scale Frequent Sequence Mining
Teaching
If you are interested in writing a seminar, Bachelor or Master thesis with us, please read the following guidelines.
If you are within the intranet of University of Mannheim, you can access lecture videos / materials here. If not, ask me.
Current semester (HWS26)
- CS 560: Large-Scale Data Management (Master Course)
- IE 675b: Machine Learning (Master course)
- SM 445/
CS707: Seminar “Machine Learning on Structured Data” (Bachelor/Master seminar) - Data Analytics Team Project: Your Project, Your Team
Previous semester (FSS26)
I was on sabbatical leave. The course “CS 303: Praktische Informatik II” was taught by Prof. Frederik Armknecht. The course “IE 678 Deep Learning” was taught by Prof. Margret Keuper. We were still offering:
- SM445/
CS 707: Seminar “Machine Learning on Structured Data” (Bachelor/Master seminar) - Data Analytics Team Project: Your Project, Your Team (3 projects accepted)
Previous courses (not taught anymore)
- Hot Topics in Machine Learning (UMA, MSc, 2016–2017); pre-cursor to IE675b Machine Learning
- Database Systems II (UMA, MSc, 2015); pre-cursor to CS560 Large-Scale Data Management
- Algorithmen und Datenstrukturen (PDF, 20 MB) (UMA, BSc, 2014)
- Data Mining and Matrices (PDF, 17 MB) (MPII/UMA, MSc, 2013–2018)
- Scalable Uncertainty Management (PDF, 10 MB) (MPII, MSc, 2012–2013)
- Datenbankprogrammierung (PDF, 3 MB) (TUD, Diploma, DB2 certification course, 2005–2007)
Publications
See also Google Scholar and DBLP.
| 2026 | S. Forbat, R. Gemulla Is Graph Mixup Beneficial? Investigating Interpolation And Empirical Performance of Graph Mixup Methods [pdf, poster (PDF), resources] ICML, 2026 |
| C. Meilicke, R. Gemulla, J. Naegelen, H. Stuckenschmidt A Simple Baseline for Inductive Knowledge Base Completion [pdf, resources] KR (short paper), 2026 | |
| A. Berndt, V. Bekmyradov, R. Gemulla, M. Kessel, T. Bach, S. Baltes Can We Classify Flaky Tests Using Only Test Code? An LLM-Based Empirical Study [pdf] In SANER (RENE track), 2026 | |
| A. Berndt, T. Bach, R. Gemulla, M. Kessel, S. Baltes On the Flakiness of LLM-generated Tests for Industrial and Open-Source Database Management Systems [pdf] In ICSE (SEIP track), 2026 | |
| 2024 | D. Ruffinelli, R. Gemulla Beyond Link Prediction: On Pre-Training Knowledge Graph Embeddings [pdf, resources] In Repl4NLP workshop, 2024 |
| 2023 | A. Kochsiek, R. Gemulla A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs [pdf, resources] In EMNLP Findings, 2023 |
| A. Kochsiek, A. Saxena, I. Nair, R. Gemulla Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction [pdf, resources] In Repl4NLP workshop, 2023 | |
| A. Renz-Wieland, A. Kieslinger, R. Gericke, R. Gemulla, Z. Kaoudi, V. Markl Good Intentions: Adaptive Parameter Management via Intent Signaling [pdf, resources] In CIKM, 2023 | |
| 2022 | A. Kochsiek, F. Niesel, R. Gemulla Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings [pdf, resources] In ECML-PKDD, 2022 |
| A. Saxena, A. Kochsiek, R. Gemulla Sequence-to-Sequence Knowledge Graph Completion and Question Answering [pdf, , video, resources] In ACL, pp. 2814-2828, 2022 | |
| A. Renz-Wieland, R. Gemulla, Z. Kaoudi, V. Markl NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access [pdf, resources] In SIGMOD, pp. 481–495, 2022 | |
| 2021 | A. Kochsiek, R. Gemulla Parallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques [pdf, resources] In PVLDB, 15(3), 2021 |
| A. Renz-Wieland, T. Drobisch, R. Gemulla, Z. Kaoudi, V. Markl Just Move It! Dynamic Parameter Allocation in Action [pdf, demo] In PVLDB (demo), 14(12), 2021. | |
| 2020 | A. Renz-Wieland, R. Gemulla, S. Zeuch, V. Markl Dynamic Parameter Allocation in Parameter Servers [pdf, resources] In PVLDB, 13(12), pp. 1877-1890, 2020 |
| S. Broscheit, K. Gashteovski, Y. Wang, Rainer Gemulla Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction [pdf, resources] In ACL, 2020 | |
| D. Ruffinelli, S. Broscheit, R. Gemulla You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings [pdf, video, resources, OpenReview] In ICLR, 2020 | |
| S. Broscheit, D. Ruffinelli, A. Kochsiek, P. Betz, R. Gemulla LibKGE – A knowledge graph embedding library for reproducible research [pdf, resources] In EMNLP (demo), 2020 | |
| K. Gashteovski, R. Gemulla, B. Kotnis, S. Hertling, C. Meilicke On Aligning OpenIE Extractions with Knowledge Bases: A Case Study [pdf, slides (PDF), resources] In Eval4NLP, 2020 | |
| 2019 | Y. Wang, D. Ruffinelli, R. Gemulla, S. Broscheit, C. Meilicke On Evaluating Embedding Models for Knowledge Base Completion [pdf] In RepL4NLP workshop, 2019 |
| K. Beedkar, R. Gemulla, W. Martens A Unified Framework for Frequent Sequence Mining with Subsequence Constraints [pdf (journal version), pdf (author version), resources] In TODS, 2019 | |
| K. Gashteovski, S. Wanner, S. Hertling, S. Broscheit, R. Gemulla OPIEC: An Open Information Extraction Corpus [pdf, poster (PDF), resources, OpenReview] In AKBC, 2019 | |
| A. Renz-Wieland, M. Bertsch, R. Gemulla Scalable Frequent Sequence Mining With Flexible Subsequence Constraints [pdf, poster (PDF)] In ICDE, 2019 | |
| Preprints (2019) | Y. Wang, S. Broscheit, R. Gemulla A Relational Tucker Decomposition for Multi-Relational Link Prediction [arXiv] 2019 |
| 2018 | C. Meilicke, M. Fink, Y. Wang, D. Ruffinelli, R. Gemulla, and H. Stuckenschmidt Fine-grained Evaluation of Rule- and Embedding-based Systems for Knowledge Graph Completion [pdf, resources] In ISWC, 2018 |
| J. Pfeiffer, S. Broscheit, R. Gemulla, M. Göschl A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval [pdf] In BioNLP workshop, 2018 | |
| S. Broscheit, R. Gemulla, M. Keuper Learning Distributional Token Representations from Visual Features [pdf] In RepL4NLP workshop, 2018 | |
| Y. Wang, R. Gemulla, H. Li On Multi-Relational Link Prediction with Bilinear Models [pdf, resources] In AAAI, 2018 | |
| 2017 | K. Gashteovski, R. Gemulla, L. del Corro MinIE: Minimizing Facts in Open Information Extraction [pdf, poster (PDF), resources] In EMNLP, pp. 2620-2630, 2017 |
| C. Teflioudi, R. Gemulla Exact and Approximate Maximum Inner Product Search with LEMP [pdf (journal version), pdf (author version), resources] In TODS, 42(1) Art. 5, 2017 | |
| 2016 | S. Neumann, R. Gemulla, P. Miettinen What You Will Gain By Rounding: Theory and Algorithms for Rounding Rank [pdf, tech report, resources] In ICDM, pp. 380–389, 2016 |
| K. Beedkar, R. Gemulla DESQ: Frequent Sequence Mining with Subsequence Constraints [pdf, tech report, resources] In ICDM (short paper), pp. 793–798, 2016 | |
| 2015 | L. Del Corro, A. Abujabal, R. Gemulla, G. Weikum FINET: Context-Aware Fine-Grained Named Entity Typing [pdf, slides (PDF), resources] In EMNLP, pp. 868–878, 2015 |
| F. Petroni, L. Del Corro, R. Gemulla CORE: Context-Aware Open Relation Extraction with Factorization Machines [pdf, slides (PDF), resources] In EMNLP, pp. 1763-1773, 2015 | |
| K. Beedkar, K. Berberich, R. Gemulla, I. Miliaraki Closing the Gap: Sequence Mining at Scale [pdf (journal version), pdf (author version), resources] In TODS, 40(2) Art. 8, 2015 | |
| C. Teflioudi, R. Gemulla, O. Mykytiuk LEMP: Fast Retrieval of Large Entries in a Matrix Product [pdf, slides (PDF), resources] In SIGMOD, pp. 107–122, 2015 | |
| K. Beedkar, R. Gemulla LASH: Large-Scale Sequence Mining with Hierarchies [pdf, slides (PDF), resources] In SIGMOD, pp. 491–503, 2015 | |
| R. Gemulla A Self-Portrayal of GI Junior Fellow Rainer Gemulla: Data Analysis at Scale [pdf (journal version), pdf (author version)] it – Information Technology 57(2), pp. 130–132 , 2015 | |
| 2014 | L. Del Corro, R. Gemulla, G. Weikum Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning [pdf, resources] In EMNLP, pp. 374–385, 2014 |
| P. Roy, J. Teubner, R. Gemulla Low-Latency Handshake Join [pdf] In PVLDB, 7(9), pp. 709–720, 2014 | |
| L. Qu, Y. Zhang, R. Wang, L. Jiang, R. Gemulla, G. Weikum Senti-LSSVM: Sentiment-Oriented Multi-Relation Extraction with Latent Structural SVM [pdf] In TACL, 2, pp. 155–168, 2014 | |
| D. Erdös, R. Gemulla, E. Terzi Reconstructing Graphs from Neighborhood Data [pdf (author version), pdf (journal version)] In TKDD, 8(4), 2014 | |
| 2013 | F. Makari, C. Teflioudi, R. Gemulla, P. J. Haas, Y. Sismanis Shared-Memory and Shared-Nothing Stochastic Gradient Descent Algorithms for Matrix Completion [pdf (author version), pdf (journal version), resources] In KAIS (special issue: best papers of ICDM 2012), pp. 1–31, 2013 |
| F. Makari, R. Gemulla A Distributed Approximation Algorithm for Mixed Packing-Covering Linear Programs [pdf] In NIPS 2013 Biglearn workshop (poster), 2013 | |
| F. Makari, B. Awerbuch, R. Gemulla, R. Khandekar, J. Mestre, M. Sozio A Distributed Algorithm for Large-Scale Generalized Matching [pdf, slides (PDF)] The analysis of the number of binary search steps (Lemma 2) contains a bug; see our Biglearn paper (PDF) for a corrected version. In PVLDB, 6(9), pp. 613–624, 2013 | |
| I. Miliaraki, K. Berberich, R. Gemulla, S. Zoupanos Mind the Gap: Large-Scale Frequent Sequence Mining [pdf, slides (PDF), resources] In SIGMOD, pp. 797–808, 2013 | |
| L. Del Corro, R. Gemulla ClausIE: Clause-Based Open Information Extraction [pdf, slides (PDF), resources] In WWW, pp. 355–366, 2013 | |
| R. Gemulla, P. J. Haas, W. Lehner Non-Uniformity Issues and Workarounds in Bounded-Size Sampling [pdf (author version), pdf (journal version), resources] In The VLDB Journal, 22(6), pp. 753–772, 2013 | |
| K. Beedkar, L. Del Corro, R. Gemulla Fully Parallel Inference in Markov Logic Networks [pdf] In BTW, pp. 205–224, 2013 | |
| 2012 | D. Erdös, R. Gemulla, E. Terzi Reconstructing Graphs from Neighborhood Data [pdf, slides (PDF)] In ICDM, pp. 231–240, 2012 |
| C. Teflioudi, F. Makari, R. Gemulla Distributed Matrix Completion [pdf, slides (PDF), resources] In ICDM, pp. 655–664, 2012 | |
| L. Qu, R. Gemulla, G. Weikum A Weakly Supervised Model for Sentence-Level Semantic Orientation Analysis with Multiple Experts [pdf] In EMNLP-CoNLL, pp. 149–159, 2012 | |
| 2011 | R. Gemulla, P. J. Haas, Y. Sismanis, C. Teflioudi, F. Makari Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent [pdf, slides (PDF), resources] In NIPS 2011 Biglearn workshop, 2011 (best paper award) |
| R. Gemulla, E. Nijkamp, P. J. Haas, Y. Sismanis Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent [pdf, slides (PDF), resources] In KDD, pp. 69–77, 2011 | |
| K. Beyer, V. Ercegovac, R. Gemulla, A. Balmin, M. Eltabakh, C.C. Kanne, F. Ozcan, E. Shekita Jaql: A Scripting Language for Large Scale Semistructured Data Analysis [pdf] In PVLDB (industrial track), 4(11), pp. 1272-1283, 2011 | |
| M. Y. Eltabakh, Y. Tian, F. Özcan, R. Gemulla, A. Krettek, J. McPherson CoHadoop: Flexible Data Placement and Its Exploitation in Hadoop [pdf] In PVLDB, 4(9), pp. 575–585, 2011 | |
| R. Gemulla, P. J. Haas, E. Nijkamp, Y. Sismanis Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent [pdf] IBM Research Report RJ10481, March 2011 Revised February, 2013 | |
| B. Schlegel, R. Gemulla, W. Lehner Memory-Efficient Frequent-Itemset Mining [pdf] In EDBT, pp. 461–472, 2011 | |
| 2010 | S. Das, Y. Sismanis, K. S. Beyer, R. Gemulla, P. J. Haas, J. McPherson. Ricardo: Integrating R and Hadoop [pdf] In SIGMOD (industrial track), pp. 987–998, 2010 |
| B. Schlegel, R. Gemulla, W. Lehner. Fast Integer Compression using SIMD Instructions [pdf] In DAMON, pp. 34–40, 2010 | |
| 2009 | K. Beyer, R. Gemulla. P. J. Haas, B. Reinwald, Y. Sismanis. Distinct-Value Synopses for Multiset Operations [pdf, technical perspective (PDF) by Surajit Chaudhuri] In Commun. ACM, 52(10), pp. 87–95, 2009 |
| B. Schlegel, R. Gemulla, W. Lehner. k-Ary Search on Modern Processors [pdf, slides (PPTX)] In DAMON, pp. 52–60, 2009 | |
| 2008 | R. Gemulla. Sampling Algorithms for Evolving Datasets [pdf, summary (PDF), slides (PPTX)] Ph.D. thesis, Technische Universität Dresden, 2009 URL for citations: nbn-resolving.de/urn:nbn:de:bsz:14-ds-1224861856184-11644 |
| R. Gemulla, P. Rösch and W. Lehner. Linked Bernoulli Synopses: Sampling Along Foreign Keys [pdf, slides (PPTX)] In SSDBM, pp. 6–23, 2008 | |
| R. Gemulla and W. Lehner. Sampling Time-Based Sliding Windows in Bounded Space [pdf, slides (PPTX)] As observed by Hu et al., the lower bound of Ω(k log N) stated in Theorem 1 should read Ω(k log(N/k)). In SIGMOD, pp. 379–392, 2008 | |
| P. Rösch, R. Gemulla and W. Lehner. Designing Random Sample Synopses with Outliers [pdf, poster (PDF)] In ICDE (poster), pp. 1400-1402, 2008 | |
| 2007 | R. Gemulla, W. Lehner and P.J. Haas. Maintaining Bounded-Size Sample Synopses of Evolving Datasets [pdf] The resizing algorithm proposed in this article contains a bug; see my Ph.D. thesis (PDF) or our 2013 VLDB Journal (PDF) paper for a corrected version. In The VLDB Journal, Special Issue: Best Papers of VLDB 2006, pp. 173–201, 2007 |
| K. Beyer, P. J. Haas, B. Reinwald, Y. Sismanis and R. Gemulla. On Synopses for Distinct-Value Estimation Under Multiset Operations [pdf, slides (PPT)] In SIGMOD, pp. 199–210, 2007 | |
| R. Gemulla, W. Lehner and P. J. Haas. Maintaining Bernoulli Samples over Evolving Multisets [pdf, slides (PPT)] In PODS, pp. 93–102, 2007 | |
| 2006 | R. Gemulla, W. Lehner and P. J. Haas. A Dip in the Reservoir: Maintaining Sample Synopses of Evolving Datasets [pdf, slides (PPT)] In VLDB, pp. 595–606, 2006 |
| A. Klein, R. Gemulla, P. Rösch and W. Lehner. Derby/ In SIGMOD (demo), pp. 757–759, 2006 | |
| R. Gemulla and W. Lehner. Deferred Maintenance of Disk-Based Random Samples [pdf, slides (PPT)] In EDBT, pp. 423–441, 2006 |