Past talks

Oct 01, 2021

Optimizing machine learning prediction queries and beyond on modern data engines

Konstantinos Karanasos (Microsoft's Gray Systems Lab - Azure Data's applied research group)

Prediction queries are widely used across industries to perform advanced analytics and draw insights from data. They include a data processing part (e.g., for joining, filtering, cleaning, featurizing the datasets) and a machine learning (ML) part invoking one or more trained models to perform predictions. These parts have so far been optimized in isolation, leaving significant opportunities for optimization unexplored.

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Oct 01, 2021

Optimisation of Inference Queries

Ziyu Li (TU Delft)

The wide adoption of machine learning (ML) in diverse application domains is resulting in an explosion of available models described by, and stored in model repositories. In application contexts where inference needs are dynamic and subject to strict execution constraints – such as in video processing – the manual selection of an optimal set of models from a large model repository is a nearly impossible task and practitioners typically settle for models with a good average accuracy and performance.

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Jul 16, 2021

Data-Intensive Systems in the Microsecond Era

Pinar Tozun (ITU Copenhagen)

Late 2000s and early 2010s have seen the rise of data-intensive systems optimized for in-memory execution. Today, it has been increasingly clear that just optimizing for main memory is neither economically viable nor strictly necessary for high performance. Modern SSDs, such as Z-NAND and Optane, can access data at a latency of around 10 microseconds.

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Jul 16, 2021

Charting the Design Space of Query Execution using VOILA

Tim Gubner (CWI)

atabase architecture, while having been studied for four decades now, has delivered only a few designs with well-understood properties. These few are followed by most actual systems. Acquiring more knowledge about the design space is a very time-consuming process that requires manually crafting prototypes with a low chance of generating material insight.

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Jul 02, 2021

DuckDQ: Data Quality Validation for Machine Learning Pipelines

Till Döhmen (RWTH Aachen University and Fraunhofer FIT)

Data quality validation plays an important role in ensuring the correct behaviour of productive machine learning (ML) applications and services. Observing a lack of existing solutions for quality control in medium-sized production ML systems, we developed DuckDQ: A lightweight and efficient Python library for protecting machine learning pipelines from data errors.

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Jun 18, 2021

Teseo and the Analysis of Structural Dynamic Graphs

Dean De Leo (CWI)

Teseo is a new system for the storage and analysis of dynamic structural graphs in main-memory, with the addition of transactional support. It introduces a novel design based on sparse arrays, large arrays interleaved with gaps, and a fat tree, where the graph is ultimately stored. Our design contrasts with early systems for the analysis of dynamic graphs, which often lack transactional support and are anchored to a vertex table as a primary index.

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Jun 18, 2021

MxTasks: How to Make Efficient Synchronization and Prefetching Easy

Jens Teubner (TU Dortmund)

The hardware environment has changed rapidly in recent years: Many cores, multiple sockets, and large amounts of main memory have become a commodity. To benefit from these highly parallel systems, the software has to be adapted. Sophisticated latch-free data structures and algorithms are often meant to address the situation.

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Jun 04, 2021

Evaluating Matching Techniques with Valentine

Christos Koutras (Delft University of Technology)

Data scientists today search large data lakes to discover and integrate datasets. In order to bring together disparate data sources, dataset discovery methods rely on some form of schema matching: the process of establishing correspondences between datasets. Traditionally, schema matching has been used to find matching pairs of columns between a source and a target schema. However, the use of schema matching in dataset discovery methods differs from its original use.

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Jun 04, 2021

Making Distributed Deep Learning Adaptive

Peter Pietzuch (Imperial College London)

When using distributed machine learning (ML) systems to train models on a cluster of worker machines, users must configure a large number of parameters: hyper-parameters (e.g. the batch size and the learning rate) affect model convergence; system parameters (e.g. the number of workers and their communication topology) impact training performance. Some of these parameters, such as the number of workers, may also change in elastic machine learning scenarios. In current systems, adapting such parameters during training is ill-supported.

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May 21, 2021

FASTER: Simplifying Storage for the Modern Edge-Cloud

Badrish Chandramouli (Microsoft Research)

Managing state efficiently in modern applications written for the cloud and edge is hard. In the FASTER project, we have been creating building blocks such as FasterKV and FasterLog to alleviate this problem using techniques such as epoch protection, tiered storage, and asynchronous recoverability.

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May 21, 2021

Distributed Transactions on Serverless Stateful Functions

Martijn De Heus (Delft University of Technology)

The cloud promised to make computing accessible by making compute resources easily available and by simplifying computing challenges. While the former has definitely been achieved, there is room for improvement to simplify (distributed) computing challenges. Deployment, scalability and state management are not straightforward to achieve using most cloud offerings. Especially modern microservice architectures face many challenges that distract developers from providing business value. Serverless computing models (like Function-as-a-Service) can greatly simplify deployment and scalability challenges, however the problem of state management remains unsolved by most cloud offerings.

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May 07, 2021

Materialize: SQL Incremental View Maintenance

Frank McSherry (Materialize, Inc.)

While OLTP engines excel at maintaining invariants under transactional workloads, and OLAP engines excel at ad-hoc analytics, the relational database is not presently an excellent tool for maintaining the results of computations as data change. This space is currently occupied largely by microservices, bespoke tools that suffer from all the problems you might expect of systems that do not provide many of the ACID properties, and which anecdotally consume engineering departments with their maintenance overhead.

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May 07, 2021

Clonos: Consistent Causal Recovery for Highly-Available Streaming Dataflows

Pedro Silvestre (Imperial College London)

Stream processing lies in the backbone of modern businesses, being employed for mission critical applications such as real-time fraud detection, car-trip fare calculations, traffic management, and stock trading. Large-scale applications are executed by scale-out stream processing systems on thousands of long-lived operators, which are subject to failures.

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Apr 23, 2021

TED Learn: Towards Technology-Enabled Database Education

Sourav Bhowmick (NTU Singapore)

There is continuous demand for database-literate professionals in today’s market due to widespread usage of relational database management system (RDBMS) in the commercial world. Such commercial demand has played a pivotal role in the offering of database systems course as part of an undergraduate computer science (CS) degree program in major universities around the world. Furthermore, not all working adults dealing with RDBMS have taken an undergraduate database course. Hence, they often need to undergo on-the-job training or attend relevant courses in higher institutes of learning to acquire database literacy. Database courses in major institutes rely on textbooks, lectures, and off-the-shelf RDBMS to impart relevant knowledge such as how SQL queries are processed.

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Apr 23, 2021

PG-Keys: Keys for Property Graphs

George Fletcher (TU/e)

I report on a community effort between industry and academia to shape the future of property graph constraints. The standardization for a property graph query language is currently underway through the ISO Graph Query Language (GQL) project. Our position is that this project should pay close attention to schemas and constraints, and should focus next on key constraints.

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Apr 09, 2021

Factorization Matters in Large Graphs

Nikolay Yakovets (TU/e)

Evaluation of complex graph pattern queries on large graphs often leads to “explosion” of intermediate results (IR) which, in turn, considerably slows down query processing. In this talk, I will present WireFrame, our recent two-step factorization-based solution which aims to drastically reduce the IR during query processing.

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Apr 09, 2021

Aggregation Support for Modern Graph Analytics

Alin Deutsch (UC San Diego)

In this talk I will describe how GSQL, TigerGraph’s graph query language, supports the specification of aggregation in graph analytics. GSQL makes several unique design decisions with respect to both the expressive power, the semantics, and the evaluation complexity of the specified aggregation.

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Mar 26, 2021

LeanStore: A High-Performance Storage Engine for Modern Hardware

Viktor Leis (Friedrich Schiller University Jena)

LeanStore is a high-performance OLTP storage engine optimized for many-core CPUs and NVMe SSDs. The goal of the project is to achieve performance comparable to in-memory systems when the data set fits into RAM, while being able to fully exploit the bandwidth of fast NVMe SSDs for large data sets.

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Mar 26, 2021

Vectorized query processing over encrypted data with DuckDB and Intel SGX

Sam Ansmink (CWI, UvA, and VU)

Data confidentiality is an increasingly important requirement for customers outsourcing databases to the cloud. The common approach to achieve data confidentiality in this context is by using encryption. However, processing queries over encrypted data securely and efficiently remains an open issue. To this day, many different approaches to designing encrypted database management systems (EDBMS) have been suggested, for example by using homomorphic encryption or trusted execution environments such as Intel SGX.

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Mar 12, 2021

Three techniques for exploiting string compression in data systems

Peter Boncz (CWI & VU)

Actual data in real-life database often is in the form of strings. Strings take significantly more volume than fixed-size data, causing I/O, network traffic, memory traffic and cache traffic. Further, operations on strings tend to be significantly more expensive than operations on e.g. integers, which CPUs do support quite efficiently (let alone GPUs, TPUs - which even do not acknowledge the existense of string data).

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Mar 12, 2021

OtterTune : An Automatic Database Configuration Tuning Service

Andy Pavlo (Carnegie Mellon University)

Database management systems (DBMS) expose dozens of configurable knobs that control their runtime behavior. Setting these knobs correctly for an application’s workload can improve the performance and efficiency of the DBMS. But such tuning requires considerable efforts from experienced administrators, which is not scalable for large DBMS fleets. This problem has led to research on using machine learning (ML) to devise strategies to optimize DBMS knobs for any application automatically.

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Feb 26, 2021

Integrating Columnar Techniques and Factorization into GraphflowDB

Semih Salihoglu (University of Waterloo)

Graph database management systems (GDBMSs) in contemporary jargon refer to systems that adopt the property graph model and often power applications such as fraud detection and recommendations that require very fast joins of records that represent many-to-many relationships, often beyond the performance that existing relational systems generally provide. In this talk, I will give an overview of GraphflowDB, which is an in-memory GDBMS we are developing at University of Waterloo.

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Feb 26, 2021

1000 days of DuckDB - The Pareto Principle still holds

Hannes Mühleisen (CWI)

It has been almost 1000 days since we started work on DuckDB. In this talk, we reflect on the process and revisit the initial goals. We also describe major additions to the system such as automatic query parallelization.

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