Prof. Dr. Rainer Gemulla

Chair of Practical Computer Science I: Data Analytics

Universität Mannheim
B6, 26, Room B 016
D-68159 Mannheim

Tel.: +49 621 181 2480
My PGP public key (id 0x81405E0B30302532)

I am heading the Chair of Practical Computer Science I: Data Analytics at the University of Mannheim. The chair is part of the Data and Web Science Group.

Go to: Research Interests, CV, PhD Students, Teaching, Awards, Professional Activities, Data and Software, Publications

If you consider applying to our lab, please read:

  • Applications should include CV, transcripts, and a short (!) cover letter or will be ignored.
  • We generally do not offer short interships (3 months or less). Applications for such internships will be ignored.
  • If you are a BSc or MSc student here, have very good transcripts, and are interested in a student job with us, contact me directly.


Research Interests

  • Machine learning with semi-structured/structured datadata
  • Combining unstructured and structured knowledge
  • Representation learning for multi-relational graphs
  • Efficient and scalable methods and systems for data-intensive processing

Curriculum Vitae

Since 2014   W3-Professor for Practical Computer Science I, Universität Mannheim, Germany
2010 – 2014   Senior researcher / group leader, Max-Planck-Institut für Informatik, Saarbrücken, Germany
2008 – 2010   Postdoctoral researcher, IBM Almaden Research Center, San Jose, CA, USA
2004 – 2008   PhD in Computer Science, Technische Universität Dresden, Germany

PhD Students

Former PhD students

Kaustubh Beedkar, Luciano del Corro, Stefan Kain, Faraz Makari Manshadi, Christina Teflioudi, Yanjie Wang


If you are interested in writing a seminar, Bachelor or Master thesis with us, please read the following guidelines.

Current semester (HWS 2023)

Previous semester (FSS 2023)


      • Distinguished PC Member Award at EDBT, 2023
      • Outstanding Reviewer Award at NeurIPS, 2021
      • Named Distinguished PC Member for SIGMOD, 2017
      • Junior-Fellow of the Gesellschaft für Informatik (GI), 2013
      • AWS in Education Research Grant Award, 2013
      • Busy Beaver teaching award (winter term 2012/2013)
        for Non-Traditional Data Management (NoSQL and more)
      • IBM's 2011 Pat Goldberg Memorial best paper award in CS, EE and Math
        (for “Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent” with P. J. Haas, E. Nijkamp, and Y. Sismanis; KDD 2011)
      • Best paper of NIPS 2011 Biglearn workshop
        (for “Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent” with P. J. Haas, Y. Sismanis, C. Teflioudi, and F. Makari)
      • Google Focused Research Award 2011: Robust and Scalable Fact Discovery from Web Sources
        (with G. Weikum and M. Theobald)
      • Research Highlight in Communications of the ACM
        (for “Distinct-Value Synopses for Multiset Operations” with K. Beyer, P.J. Haas, B. Reinwald, Y. Sismanis)
      • The VLDB Journal, Special Issue: Best Papers of VLDB 2006
        (for “A Dip in the Reservoir: Maintaining Sample Synopses of Evolving Datasets” with W. Lehner and P.J. Haas)

      Professional Activities

      Associate editor / area chair

      • VLDB: 2021, 2019, 2015
      • TKDE: 2015–2018
      • DASP: 2018
      • EDBT: 2017
      • JISA: 2016
      • CIKM: 2014

      PC member / reviewer (since 2011)

      • 2022: EDBT, IJCAI, ICLR, SDM, SIGMOD
      • 2021: DEEM, EDBT, ICDM, IJCAI, KDML, NeurIPS, SIGMOD (demo), Repl4NLP
      • 2020: AKBC, EDBT, IJCAI, LWDA, Repl4NLP, SUM, TPDS
      • 2018: VLDB, EDBT, DEEM
      • 2016: SIGMOD, ECML-PKDD
      • 2015: BTW, DAMI, GvDB, IS, JODS, JWS, PODS
      • 2014: BDDC, BigData, Buda, DMC, JMLR, SIGMOD, VLDB
      • 2013: AKBC, BigData, BTW, CIKM, DMC, ICALP, TKDE, TML, VLDB
      • 2012: KDD, JMLR, TODS
      • 2011: BTW, IS, VLDB, VLDBJ


      Data and Software

      • 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


      See also Google Scholar and DBLP.

      2023   A. Kochsiek, R. Gemulla
      A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs [pdfresources]
      In EMNLP Findings, 2023
       A. Kochsiek, A. Saxena, I. Nair, R. Gemulla
      Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction [pdfresources]
      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 [pdfresources]
      In CIKM, 2023
      2022   A. Kochsiek, F. Niesel, R. Gemulla
      Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings [pdfresources]
      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 [pdfsource code]
      In SIGMOD, pp. 481–495, 2022
      2021   A. Kochsiek, R. Gemulla
      Parallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques [pdfresources]
      In PVLDB, 15(3), 2021
       A. Renz-Wieland, T. Drobisch, R. Gemulla, Z. Kaoudi, V. Markl
      Just Move It! Dynamic Parameter Allocation in Action [pdfdemo]
      In PVLDB (demo), 14(12), 2021.
      2020   A. Renz-Wieland, R. Gemulla, S. Zeuch, V. Markl
      Dynamic Parameter Allocation in Parameter Servers [pdfsource code]
      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 [pdfresources]
      In ACL, 2020
       D. Ruffinelli, S. Broscheit, R. Gemulla
      You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings [pdfvideoresourcesOpenReview]
      In ICLR, 2020
       S. Broscheit, D. Ruffinelli, A. Kochsiek, P. Betz, R. Gemulla
      LibKGE – A knowledge graph embedding library for reproducible research [pdfsource]
      In EMNLP (demo), 2020
       K. Gashteovski, R. Gemulla, B. Kotnis, S. Hertling, C. Meilicke
      On Aligning OpenIE Extractions with Knowledge Bases: A Case Study [pdfslides, 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 [pdfposterresourcesOpenReview]
      In AKBC, 2019
       A. Renz-Wieland, M. Bertsch, R. Gemulla
      Scalable Frequent Sequence Mining With Flexible Subsequence Constraints [pdfposter]
      In ICDE, 2019
      Y. Wang, S. Broscheit, R. Gemulla
      A Relational Tucker Decomposition for Multi-Relational Link Prediction [arXiv]
      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 [pdfresources]
      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 [pdfresources]
      In AAAI, 2018
      2017   K. Gashteovski, R. Gemulla, L. del Corro
      MinIE: Minimizing Facts in Open Information Extraction [pdfposterresources]
      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 [pdftech reportresources]
      In ICDM, pp. 380–389, 2016
       K. Beedkar, R. Gemulla
      DESQ: Frequent Sequence Mining with Subsequence Constraints [pdftech reportresources]
      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 [pdfslidesresources]
      In EMNLP, pp. 868–878, 2015
       F. Petroni, L. Del Corro, R. Gemulla
      CORE: Context-Aware Open Relation Extraction with Factorization Machines [pdfslidesresources]
      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 [pdfslidesresources]
      In SIGMOD, pp. 107–122, 2015
       K. Beedkar, R. Gemulla
      LASH: Large-Scale Sequence Mining with Hierarchies [pdfslidessource code]
      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 [pdfresources]
      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)source code]
      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 [pdfslides]
      The analysis of the number of binary search steps (Lemma 2) contains a bug; see our Biglearn paper 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 [pdfslidesresources]
      In SIGMOD, pp. 797–808, 2013
       L. Del Corro, R. Gemulla
      ClausIE: Clause-Based Open Information Extraction [pdfslidesresources]
      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)source code]
      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 [pdfslides]
      In ICDM, pp. 231–240, 2012
       C. Teflioudi, F. Makari, R. Gemulla
      Distributed Matrix Completion [pdfslidessource code]
      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 [pdfslidessource code]
      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 [pdfslidessource code]
      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 [pdftechnical perspective by Surajit Chaudhuri]
      In Commun. ACM, 52(10), pp. 87–95, 2009
       B. Schlegel, R. Gemulla, W. Lehner.
      k-Ary Search on Modern Processors [pdfslides]
      In DAMON, pp. 52–60, 2009
      2008   R. Gemulla.
      Sampling Algorithms for Evolving Datasets [pdfsummaryslides]
      Ph.D. thesis, Technische Universität Dresden, 2009
      URL for citations:
       R. Gemulla, P. Rösch and W. Lehner.
      Linked Bernoulli Synopses: Sampling Along Foreign Keys [pdfslides]
      In SSDBM, pp. 6–23, 2008
       R. Gemulla and W. Lehner.
      Sampling Time-Based Sliding Windows in Bounded Space [pdfslides]
      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 [pdfposter]
      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 or our 2013 VLDB Journal 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 [pdfslides]
      In SIGMOD, pp. 199–210, 2007
       R. Gemulla, W. Lehner and P. J. Haas.
      Maintaining Bernoulli Samples over Evolving Multisets [pdfslides]
      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 [pdfslides]
      In VLDB, pp. 595–606, 2006
       A. Klein, R. Gemulla, P. Rösch and W. Lehner.
      Derby/S: A DBMS for Sample-Based Query Answering [pdfposter1poster2]
      In SIGMOD (demo), pp. 757–759, 2006
       R. Gemulla and W. Lehner.
      Deferred Maintenance of Disk-Based Random Samples [pdfslides]
      In EDBT, pp. 423–441, 2006


      Prof. Dr. Rainer Gemulla

      Prof. Dr. Rainer Gemulla

      Chair of Practical Computer Science I: Data Analytics
      University of Mannheim
      School of Business Informatics and Mathematics
      B 6, 26 – Room B 0.16
      68159 Mannheim