Gabriel Campero Durand

M.Sc. Gabriel Campero Durand

Fakultät Informatik
AG Datenbanken & Software Engineering
Universitätsplatz 2, 39106, Magdeburg, G29-125
Vita
Projekte

Aktuelle Projekte

Publikationen
  • Iya Arefyeva, David Broneske, Gabriel Campero, Marcus Pinnecke, and Gunter Saake. Memory Management Strategies in CPU/GPU Database Systems: A Survey. In BDAS. Springer, September 2018.
     
  • Bala Gurumurthy, David Broneske, Marcus Pinnecke, Gabriel Campero Durand, and Gunter Saake. SIMD Vectorized Hashing for Grouped Aggregation. In Advances in Databases and Information Systems, September 2018. To appear.
     
  • Roman Zoun, Gabriel Campero Durand, Kay Schallert, Apoorva Patrikar, David Broneske, Wolfram Fenske, Robert Heyer, Dirk Benndorf, and Gunter Saake. Protein Identification as a Suitable Application for Fast Data Architecture. In International Workshop on Biological Knowledge Discovery and Data Mining (BIOKDD-DEXA). IEEE, September 2018. To appear.
     
  • Roman Zoun, Kay Schallert, Atin Janki, Rohith Ravindran, Gabriel Campero Durand, Wolfram Fenske, David Broneske, Robert Heyer, Dirk Benndorf, and Gunter Saake. Streaming FDR Calculation for Protein Identication. In Advances in Databases and Information Systems, September 2018. To appear.
     
  • Xiao Chen, Kirity Rapuru, Gabriel Campero Durand, and Eike Schallehn. Performance Comparison of Three Spark-Based Implementations of Parallel Entity Resolution. In International Workshop on Big Data Management in Cloud Systems (BDMICS-DEXA). Springer, To appear, September 2018.
     
  • Iya Arefyeva, Gabriel Campero Durand, Marcus Pinnecke, David Broneske, and Gunter Saake. Low-Latency Transaction Execution on Graphics Processors: Dream or Reality?. Ninth International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures (ADMS), August 2018. To Appear.
     
  • Gabriel Campero Durand, Marcus Pinnecke, Rufat Piriyev, Mahmoud Mohsen, David Broneske, Gunter Saake, Maya Sekeran, Fabian Rodriguez, and Laxmi Balami. GridFormation: Towards Self-Driven Online Data Partitioning using Reinforcement Learning. In First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM), June 2018. (PDF)
     
  • Yusra Shakeel, Jacob Krüger, Ivonne von Nostitz-Wallwitz, Christian Lausberger, Gabriel Campero Durand, Gunter Saake, and Thomas Leich. (Automated) Literature Analysis - Threats and Experiences. In International Workshop on Software Engineering for Science, SE4Science. ACM, May 2018. Accepted.
     
  • Gabriel Campero Durand, Jingyi Ma, Marcus Pinnecke, and Gunter Saake. Piecing together large puzzles, efficiently: Towards scalable loading into graph database systems. In Grundlagen von Datenbanken, May 2018. To appear.
     
  • Gabriel Campero Durand, Anusha Janardhana, Marcus Pinnecke, Yusra Shakeel, Jacob Krüger, Thomas Leich, and Gunter Saake. Exploring Large Scholarly Networks with Hermes. In International Conference on Extending Database Technology, EDBT, pages 650–653. OpenProceedings, March 2018.
     
  • Gabriel Campero Durand, Marcus Pinnecke, David Broneske, and Gunter Saake. Backlogs and interval timestamps: Building blocks for supporting temporal queries in graph databases. In Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference (EDBT/ICDT 2017), Venice, Italy, March 21-24, 2017., volume 1810. CEUR-WS, 2017. (PDF)
     
  • Marcus Pinnecke, David Broneske, Gabriel Campero Durand, and Gunter Saake. Are Databases Fit for Hybrid Workloads on GPUs? A Storage Engine’s Perspective. In Proceedings of the International Workshop on Big Data Management on Emerging Hardware, San Diego, USA, April 22, 2017, pages 1599–1606, 2017. (PDF)
     
  • Gabriel Campero. Best Practices for Developing Graph Database Applications: A Case Study Using Apache Titan. Master thesis, University of Magdeburg, Germany, January 2017.
     

 

Forschung
  • Leveraging AI Techniques in Building HTAP Databases supporting Network Analysis
  • HTAP: Benchmarking HTAP goals, Self-Tuning/Evolutionary Features, Machine Learning Components for Building and Tuning Databases, Reinforcement Learning and MABs in Data Management, Forecasts and Time Series Analysis for Data Management, Exploiting Novel Hardware/Heterogeneous HTAP (with a focus on GPUs), Adaptive Storage Models, Data Partitioning, Transaction Processing, Flexible/Probabilistic Index Structures, Approximate Query Processing, Code Generation, DSLs, Support for Data Science/Machine Learning Workloads, Larger Than Memory Data Management, Optimization for Shared Queries, Exploiting RDMA, Uses for Embeddings in Query Processing
  • Graph Databases and Network Analysis: Graph Query Languages, Query Processing and Optimization, Data Structures, Summarization and Embeddings, Search Engine Processing, Data Loading, Static/Dynamic Network Analysis, Scholarly Network Analysis, Workload Characterization, Fraud Detection
  • Multi-Model Databases: Supporting Non-Relational Models as Materialized Views in a RDBMS, Alternative Approaches for Primary Storage (Key-value Stores, Object Stores), Adaptive Schemas, Machine-Learning-supported management for data across models, Raw Query Processing
  • Cloud Workloads: Container Management, Tracing and Profiling of Large Scale Systems, Analysis of Performance Data, Hardware-Sensitivity for Cloud Applications, Verification of Distributed Systems with Fault Injection
  • Others: Large Scale Machine Learning, Sign-Language Translation, Big Code, Voice Interfaces for Data Systems, Speculative Parallelism, Intelligent Personal Assistants

Letzte Änderung: 07.03.2018 - Ansprechpartner:

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