Cost-Optimal Outsourcing of Applications into the Clouds. - Immanuel Trummer; Frank Leymann; Ralph Mietzner; Walter Binder (CloudCom, 2010)

Dynamically Selecting Composition Algorithms for Economical Composition as a Service. - Immanuel Trummer; Boi Faltings (ICSOC, 2011)

Optimizing the Tradeoff between Discovery, Composition, and Execution Cost in Service Composition. - Immanuel Trummer; Boi Faltings (ICWS, 2011)

Towards Self-Organizing Service-Oriented Architectures. - Walter Binder; Daniele Bonetta; Cesare Pautasso; Achille Peternier; Diego Milano; Heiko Schuldt; Nenad Stojnic; Boi Faltings; Immanuel Trummer (SERVICES, 2011)

Utility-driven data acquisition in participatory sensing. - Mehdi Riahi; Thanasis G. Papaioannou; Immanuel Trummer; Karl Aberer (EDBT, 2013)

Approximation schemes for many-objective query optimization. - Immanuel Trummer; Christoph Koch 001 (SIGMOD Conference, 2014)

Multi-Objective Quality-Driven Service Selection - A Fully Polynomial Time Approximation Scheme. - Immanuel Trummer; Boi Faltings; Walter Binder (IEEE Trans. Software Eng., 2014)

Mining Subjective Properties on the Web. - Immanuel Trummer; Alon Y. Halevy; Hongrae Lee; Sunita Sarawagi; Rahul Gupta (SIGMOD Conference, 2015)

An Incremental Anytime Algorithm for Multi-Objective Query Optimization. - Immanuel Trummer; Christoph Koch 001 (SIGMOD Conference, 2015)

Probably Approximately Optimal Query Optimization. - Immanuel Trummer; Christoph Koch 001 (CoRR, 2015)

A Fast Randomized Algorithm for Multi-Objective Query Optimization. - Immanuel Trummer; Christoph Koch 001 (SIGMOD Conference, 2016)

Multiple Query Optimization on the D-Wave 2X Adiabatic Quantum Computer. - Immanuel Trummer; Christoph Koch 001 (Proc. VLDB Endow., 2016)

Parallelizing Query Optimization on Shared-Nothing Architectures. - Immanuel Trummer; Christoph Koch 001 (Proc. VLDB Endow., 2016)

Multi-Objective Parametric Query Optimization. - Immanuel Trummer; Christoph Koch 001 (SIGMOD Rec., 2016)

From Massive Parallelization to Quantum Computing: Seven Novel Approaches to Query Optimization. - Immanuel Trummer (2016)

Solving the Join Ordering Problem via Mixed Integer Linear Programming. - Immanuel Trummer; Christoph Koch 001 (SIGMOD Conference, 2017)

Data Vocalization: Optimizing Voice Output of Relational Data. - Immanuel Trummer; Jiancheng Zhu; Mark Bryan (Proc. VLDB Endow., 2017)

The FactChecker: Verifying Text Summaries of Relational Data Sets. - Saehan Jo; Immanuel Trummer; Weicheng Yu; Daniel Liu; Niyati Mehta (CoRR, 2018)

Vocalizing Large Time Series Efficiently. - Immanuel Trummer; Mark Bryan; Ramya Narasimha (Proc. VLDB Endow., 2018)

SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning. - Immanuel Trummer; Samuel Moseley; Deepak Maram; Saehan Jo; Joseph Antonakakis (Proc. VLDB Endow., 2018)

Data Vocalization with CiceroDB. - Immanuel Trummer (CIDR, 2019)

Verifying Text Summaries of Relational Data Sets. - Saehan Jo; Immanuel Trummer; Weicheng Yu; Xuezhi Wang 002; Cong Yu 001; Daniel Liu; Niyati Mehta (SIGMOD Conference, 2019)

Exact Cardinality Query Optimization with Bounded Execution Cost. - Immanuel Trummer (SIGMOD Conference, 2019)

A Holistic Approach for Query Evaluation andResult Vocalization in Voice-Based OLAP. - Immanuel Trummer; Yicheng Wang; Saketh Mahankali (SIGMOD Conference, 2019)

SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning. - Immanuel Trummer; Junxiong Wang; Deepak Maram; Samuel Moseley; Saehan Jo; Joseph Antonakakis (SIGMOD Conference, 2019)

AggChecker: A Fact-Checking System for Text Summaries of Relational Data Sets. - Saehan Jo; Immanuel Trummer; Weicheng Yu; Xuezhi Wang 002; Cong Yu 001; Daniel Liu; Niyati Mehta (Proc. VLDB Endow., 2019)

Mining an "Anti-Knowledge Base" from Wikipedia Updates with Applications to Fact Checking and Beyond. - Georgios Karagiannis; Immanuel Trummer; Saehan Jo; Shubham Khandelwal; Xuezhi Wang 002; Cong Yu 001 (Proc. VLDB Endow., 2019)

BitGourmet: Deterministic Approximation via Optimized Bit Selection. - Saehan Jo; Immanuel Trummer (CIDR, 2020)

Demonstration of BitGourmet: Data Analysis via Deterministic Approximation. - Saehan Jo; Immanuel Trummer (SIGMOD Conference, 2020)

Demonstration of ScroogeDB: Getting More Bang For the Buck with Deterministic Approximation in the Cloud. - Saehan Jo; Jialing Pei; Immanuel Trummer (Proc. VLDB Endow., 2020)

Scrutinizer: A Mixed-Initiative Approach to Large-Scale, Data-Driven Claim Verification. - Georgios Karagiannis; Mohammed Saeed 002; Paolo Papotti; Immanuel Trummer (Proc. VLDB Endow., 2020)

Scrutinizer: Fact Checking Statistical Claims. - Georgios Karagiannis; Mohammed Saeed 002; Paolo Papotti; Immanuel Trummer (Proc. VLDB Endow., 2020)

Demonstrating the Voice-Based Exploration of Large Data Sets with CiceroDB-Zero. - Immanuel Trummer (Proc. VLDB Endow., 2020)

Optimally Summarizing Data by Small Fact Sets for Concise Answers to Voice Queries. - Immanuel Trummer; Connor Anderson 002 (ICDE, 2021)

Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning. - Junxiong Wang; Immanuel Trummer; Debabrota Basu (SIGMOD Conference, 2021)

Demonstrating Robust Voice Querying with MUVE: Optimally Visualizing Results of Phonetically Similar Queries. - Ziyun Wei; Immanuel Trummer; Connor Anderson 002 (SIGMOD Conference, 2021)

Can Deep Neural Networks Predict Data Correlations from Column Names? - Immanuel Trummer (CoRR, 2021)

WebChecker: Towards an Infrastructure for Efficient Misinformation Detection at Web Scale. - Immanuel Trummer (IEEE Data Eng. Bull., 2021)

The Case for NLP-Enhanced Database Tuning: Towards Tuning Tools that "Read the Manual". - Immanuel Trummer (Proc. VLDB Endow., 2021)

UDO: Universal Database Optimization using Reinforcement Learning. - Junxiong Wang; Immanuel Trummer; Debabrota Basu (Proc. VLDB Endow., 2021)

Robust Voice Querying with MUVE: Optimally Visualizing Results of Phonetically Similar Queries. - Ziyun Wei; Immanuel Trummer; Connor Anderson 002 (Proc. VLDB Endow., 2021)

Database Tuning using Natural Language Processing. - Immanuel Trummer (SIGMOD Rec., 2021)

SkinnerDB: Regret-bounded Query Evaluation via Reinforcement Learning. - Immanuel Trummer; Junxiong Wang; Ziyun Wei; Deepak Maram; Samuel Moseley; Saehan Jo; Joseph Antonakakis; Ankush Rayabhari (ACM Trans. Database Syst., 2021)

Procrastinated Tree Search: Black-Box Optimization with Delayed, Noisy, and Multi-Fidelity Feedback. - Junxiong Wang; Debabrota Basu; Immanuel Trummer (AAAI, 2022)

Towards NLP-Enhanced Data Profiling Tools. - Immanuel Trummer (CIDR, 2022)

DB-BERT: A Database Tuning Tool that "Reads the Manual". - Immanuel Trummer (SIGMOD Conference, 2022)

Demonstrating DB-BERT: A Database Tuning Tool that "Reads" the Manual. - Immanuel Trummer (SIGMOD Conference, 2022)

CodexDB: Generating Code for Processing SQL Queries using GPT-3 Codex. - Immanuel Trummer (CoRR, 2022)

CodexDB: Synthesizing Code for Query Processing from Natural Language Instructions using GPT-3 Codex. - Immanuel Trummer (Proc. VLDB Endow., 2022)

BABOONS: Black-Box Optimization of Data Summaries in Natural Language. - Immanuel Trummer (Proc. VLDB Endow., 2022)

From BERT to GPT-3 Codex: Harnessing the Potential of Very Large Language Models for Data Management. - Immanuel Trummer (Proc. VLDB Endow., 2022)

SkinnerMT: Parallelizing for Efficiency and Robustness in Adaptive Query Processing on Multicore Platforms. - Ziyun Wei; Immanuel Trummer (Proc. VLDB Endow., 2022)

Demonstration of ThalamusDB: Answering Complex SQL Queries with Natural Language Predicates on Multi-Modal Data. - Saehan Jo; Immanuel Trummer (SIGMOD Conference Companion, 2023)

Demonstrating NaturalMiner: Searching Large Data Sets for Abstract Patterns Described in Natural Language. - Immanuel Trummer (SIGMOD Conference Companion, 2023)

Quantum Optimisation of General Join Trees. - Manuel Schönberger; Immanuel Trummer; Wolfgang Mauerer (VLDB Workshops, 2023)

JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning. - Kaiwen Wang; Junxiong Wang; Yueying Li; Nathan Kallus; Immanuel Trummer; Wen Sun 002 (CoRR, 2023)

Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes. - Simran Arora; Brandon Yang; Sabri Eyuboglu; Avanika Narayan; Andrew Hojel; Immanuel Trummer; Christopher Ré (Proc. VLDB Endow., 2023)

Demonstrating GPT-DB: Generating Query-Specific and Customizable Code for SQL Processing with GPT-4. - Immanuel Trummer (Proc. VLDB Endow., 2023)

Can Large Language Models Predict Data Correlations from Column Names? - Immanuel Trummer (Proc. VLDB Endow., 2023)

Demonstrating ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Joins via Reinforcement Learning. - Junxiong Wang; Mitchell Gray; Immanuel Trummer; Ahmet Kara 002; Dan Olteanu (Proc. VLDB Endow., 2023)

ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning. - Junxiong Wang; Immanuel Trummer; Ahmet Kara 002; Dan Olteanu (Proc. VLDB Endow., 2023)

Quantum-Inspired Digital Annealing for Join Ordering. - Manuel Schönberger; Immanuel Trummer; Wolfgang Mauerer (Proc. VLDB Endow., 2023)

Large Language Models: Principles and Practice. - Immanuel Trummer (ICDE, 2024)

Towards Out-of-Core Simulators for Quantum Computing. - Immanuel Trummer (Q-DATA@SIGMOD, 2024)

Leveraging Quantum Computing for Database Index Selection. - Immanuel Trummer; Davide Venturelli (Q-DATA@SIGMOD, 2024)

JoinGym: An Efficient Join Order Selection Environment. - Junxiong Wang; Kaiwen Wang; Yueying Li; Nathan Kallus; Immanuel Trummer; Wen Sun 002 (RLJ, 2024)

Demonstrating λ-Tune: Exploiting Large Language Models for Workload-Adaptive Database System Tuning. - Victor Giannakouris; Immanuel Trummer (SIGMOD Conference Companion, 2024)

First Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications (Q-Data). - Ibrahim Sabek; Immanuel Trummer; Stefan Prestel (SIGMOD Conference Companion, 2024)

SMART: Automatically Scaling Down Language Models with Accuracy Guarantees for Reduced Processing Fees. - Saehan Jo; Immanuel Trummer (CoRR, 2024)

λ-Tune: Harnessing Large Language Models for Automated Database System Tuning. - Victor Giannankouris; Immanuel Trummer (CoRR, 2024)

ThalamusDB: Approximate Query Processing on Multi-Modal Data. - Saehan Jo; Immanuel Trummer (Proc. ACM Manag. Data, 2024)

ROME: Robust Query Optimization via Parallel Multi-Plan Execution. - Ziyun Wei; Immanuel Trummer (Proc. ACM Manag. Data, 2024)

Generating Succinct Descriptions of Database Schemata for Cost-Efficient Prompting of Large Language Models. - Immanuel Trummer (Proc. VLDB Endow., 2024)

DB-BERT: making database tuning tools "read" the manual. - Immanuel Trummer (VLDB J., 2024)

DBG-PT: A Large Language Model Assisted Query Performance Regression Debugger. - Victor Giannakouris; Immanuel Trummer (Proc. VLDB Endow., 2024)

Cost-Based Query Optimization for Quantum Computation. - Immanuel Trummer (Q-DATA@SIGMOD, 2025)

Leveraging Quantum Computing for Optimal Data Allocation in Distributed Systems. - Immanuel Trummer (Q-DATA@SIGMOD, 2025)

SwellDB: Dynamic Query-Driven Table Generation with Large Language Models. - Victor Giannakouris; Immanuel Trummer (SIGMOD Conference Companion, 2025)

Demonstrating CEDAR: A System for Cost-Efficient Data-Driven Claim Verification. - Tharushi Jayasekara; Immanuel Trummer (SIGMOD Conference Companion, 2025)

Demonstrating SQLBarber: Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads. - Jiale Lao; Immanuel Trummer (SIGMOD Conference Companion, 2025)

Second Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications (Q-Data). - Ibrahim Sabek; Immanuel Trummer (SIGMOD Conference Companion, 2025)

Customizing Operator Implementations for SQL Processing via Large Language Models. - Immanuel Trummer (IEEE Data Eng. Bull., 2025)

λ-Tune: Harnessing Large Language Models for Automated Database System Tuning. - Victor Giannakouris; Immanuel Trummer (Proc. ACM Manag. Data, 2025)

If All Else Fails, Read the Instructions! A Perspective on 'GPTuner: An LLM-Based Database Tuning System'. - Immanuel Trummer (SIGMOD Rec., 2025)

Generating highly customizable python code for data processing with large language models. - Immanuel Trummer (VLDB J., 2025)

SpareLLM: Automatically Selecting Task-Specific Minimum-Cost Large Language Models under Equivalence Constraint. - Saehan Jo; Immanuel Trummer (Proc. ACM Manag. Data, 2025)

SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads. - Jiale Lao; Immanuel Trummer (CoRR, 2025)