Hot Research Topics in Modern Data Management

Serverless Computing for Data Management

• Tian Zhang, Dong Xie, Feifei Li, Ryan Stutsman: Narrowing the Gap Between Serverless and its State with Storage Functions (pdf)
• Vikram Sreekanti, Chenggang Wu, Xiayue Charles Lin, Johann Schleier-Smith, Jose M. Faleiro, Joseph E. Gonzalez, Joseph M. Hellerstein, Alexey Tumanov: Cloudburst: Stateful Functions-as-a-Service (pdf)

Computer Algebra Systems with Deep Learning

• Guillaume Lample, François Charton: Deep Learning for Symbolic Mathematics. (pdf)

Exponential Smoothing its Application in Time Series Forecasting

• Gardner Jr., E. S. (2006). Exponential smoothing: The state of the art-Part II. International Journal of Forecasting, 22(4), 637–666. (pdf)
• Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5–10. (pdf) 
• Hyndman, R. J., & Kostenko, A. V. (2007). Minimum sample size requirements for seasonal forecasting models. Foresight, (6), 12–15. (pdf) 

Cardinality  Estimation

• Leis, V., Radke, B., Gubichev, A., Kemper, A., & Neumann, T. (2017). Cardinality Estimation Done Right: Index-Based Join Sampling. In CIDR. (pdf)
• Kipf, A., Kipf, T., Radke, B., Leis, V., Boncz, P., & Kemper, A. (2019). Learned Cardinalities: Estimating Correlated Joins with Deep Learning. In CIDR. (pdf)

Creating Datasets for Weak-Supervision

• Ratner, Alexander and Bach, Stephen H. and Ehrenberg, Henry and Fries, Jason and Wu, Sen and Ré, Christopher (2017). Snorkel: Rapid Training Data Creation with Weak Supervision in VLDB. (pdf)
• Bach, Stephen H. and Rodriguez, Daniel and Liu, Yintao and Luo, Chong and Shao, Haidong and Xia, Cassandra and Sen, Souvik and Ratner, Alex and Hancock, Braden and Alborzi, Houman and et al. (2019). Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale. In SIGMOD, Industrial Paper (Google). (pdf)

Join Cardinality Estimation

• V. Leis, A. Gubichev, A. Mirchev, P. A. Boncz, A. Kemper, T. Neumann: How Good Are Query Optimizers, Really?, PVLDB ‘15. (pdf) 
• Walter Cai, M. Balazinska, D. Suciu: Pessimistic Cardinality Estimation: Tighter Bounds for Intermediate Join Cardinalities, SIGMOD ‘19. (pdf)

 Fault-Tolerant Persistent Memory Programming

• Lu Zhang, Steven Swanson. “Pangolin: A Fault-Tolerant Persistent Memory Programming Library”. USENIX ATC ’19: Proceedings of the 2019 USENIX Conference on Usenix Annual Technical Conference, July 2019, Pages 897–911. (pdf)

• Arthur Martens, Rouven Scholz, Phil Lindow, Niklas Lehnfeld, Rüdiger Kapitza. “Dependable Non-Volatile Memory”. SYSTOR ’18: Proceedings of the 11th ACM International Systems and Storage Conference, June 2018. (pdf)

Entity Matching

• Ebraheem, Muhammad, et al. “Distributed representations of tuples for entity resolution.” Proceedings of the VLDB Endowment 11.11 (2018): 1454-1467. (pdf)
• Mudgal, Sidharth, et al. “Deep learning for entity matching: A design space exploration.” Proceedings of the 2018 International Conference on Management of Data. 2018. (pdf)

Chit-chat models

• Zhang, Saizheng, et al. “Personalizing Dialogue Agents: I have a dog, do you have pets too?.” arXiv preprint arXiv:1801.07243 (2018). (pdf)

GPU-based Pipelined Query Processing

• Paul, Johns, Jiong He, and Bingsheng He. “GPL: A GPU-based pipelined query processing engine.” Proceedings of the 2016 International Conference on Management of Data. 2016. (pdf)

Compression Algorithm Synthesis

• Steven Claggett, Sahar Azimi, and Martin Burtscher. “SPDP: An automatically synthesized lossless compression algorithm for floating-point data.” 2018 Data Compression Conference. (pdf)
• Martin Burtscher, Hari Mukka, Annie Yang, Fabod Hesaaraki: “Real-time synthesis of compression algorithms for scientific data.” SC ’16: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (pdf)

Lossy Compression of Climate Data

• Julian Kunkel, Anastasiia Novikova, Eugen Betke, and Armin Schaare. “Toward Decoupling the Selection of Compression Algorithms from Quality Constraints.” Proceedings of the IEEE PDSW-DISCS ’16 1st Joint International Workshop on Parallel Data Storage & Data Intensive Scalable Computing Systems at SC’16. (pdf)

Decomposing Physical Query Operators

• Jens Dittrich, Joris Nix: The Case for Deep Query Optimization: CIDR 2020 (pdf)
  
• Stratos Idreos, Kostas Zoumpatianos, Manos Athanassoulis, Niv Dayan, Brian Hentschel, Michael S. Kester, Demi Guo, Lukas M. Maas, Wilson Qin, Abdul Wasay, Yiyou Sun: The periodic table of data structures. IEEE Data Eng. Bull., 41(3), 2018. (pdf)

Compressed Hash-Tables for Efficient Query Processing

• Tim Gubner, Viktor Leis, and Peter Boncz. “Efficient Query Processing with Optimistically Compressed Hash Tables & Strings in the USSR” Proceedings of the 36th IEEE International Conference on Data Engineering, ICDE 2020 [accepted paper] (pdf)
• Ronald Barber, Guy M. Lohman, Ippokratis Pandis, Vijayshankar Raman, Richard Sidle, Gopi K. Attaluri, Naresh Chainani, Sam Lightstone, and David Sharpe. “Memory-Efficient Hash Joins” PVLDB 8(4): 353-364 (2014) (pdf)

Kick-off-Meeting

• Kick-off Folien
• wissenschaftliches Arbeiten
• Powerpoint-Vorlage

Ein Proseminar soll die Fähigkeit vermitteln,

• sich anhand von wissenschaftlicher Fachliteratur über ein Problem selbständig zu informieren,
• eine Zusammenfassung des Standes der Wissenschaft in einem mündlichen Vortrag von 20 Minuten zu präsentieren,
• seine Auffassung in einer Diskussion zu vertreten und
• sich mit wissenschaftlichen Texten kritisch auseinanderzusetzen