Publication

From A to E: Analyzing TPC’s OLTP Benchmarks -- The obsolete, the ubiquitous, the unexplored

Publications associées (28)

High-Level Synthesis of Dynamically Scheduled Circuits

High-Level Synthesis (HLS) tools generate hardware designs from high-level programming languages. These tools almost universally build datapaths that are controlled using a centralized controller which relies on a static, compile-time schedule to determine ...

EPFL2021

Micro-architectural analysis of in-memory OLTP: Revisited

Anastasia Ailamaki, Danica Porobic, Utku Sirin

Micro-architectural behavior of traditional disk-based online transaction processing (OLTP) systems has been investigated extensively over the past couple of decades. Results show that traditional OLTP systems mostly under-utilize the available micro-archi ...

2021

Scalable Multi-Query Execution using Reinforcement Learning

Anastasia Ailamaki, Panagiotis Sioulas

The growing demand for data-intensive decision support and the migration to multi-tenant infrastructures put databases under the stress of high analytical query load. The requirement for high throughput contradicts the traditional design of query-at-a-time ...

Association for Computing Machinery2021

A mechanical approach for the maximum punching resistance of shear-reinforced slab-column connections

Aurelio Muttoni, Miguel Fernández Ruiz, João Tiago Ramos Bernardo de Santa Rita Simões, Diego Hernández Fraile

The use of punching shear reinforcement is currently considered as one of the most convenient practices to enhance the punching shear strength and deformation capacity of slab-column connections in flat slabs. Intensive research has been performed in the l ...

Fédération Internationale du Béton (fib)2021

Representation Learning for Multi-relational Data

Eda Bayram

Recent years have witnessed a rise in real-world data captured with rich structural information that can be better depicted by multi-relational or heterogeneous graphs. However, research on relational representation learning has so far mostly focused on th ...

EPFL2021

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Jean-Pierre Hubaux, Jean-Philippe Léonard Bossuat, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Juan Ramón Troncoso-Pastoriza, Sinem Sav, Apostolos Pyrgelis

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network ...

INTERNET SOC2021

Efficient Protocols for Enforcing Causal Consistency in Geo-Replicated Key-Value Data Stores

Kristina Spirovska

Modern large-scale data platforms manage colossal amount of data, generated by the ever-increasing number of concurrent users. Geo-replicated and sharded key-value data stores play a central role when building such platforms. As the strongest consistency m ...

EPFL2020

Contact networks across ages: computational analysis of epidemiological dynamics

Gianrocco Lazzari

For decades mathematical modeling in epidemiology has helped understanding the dynamics of infectious diseases, as well as describe possible intervention scenarios to prevent and control them. However, such models were relying on several assumptions, such ...

EPFL2020