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Pixels: An Efficient Column Store for Cloud Data Lakes

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As data continues to be generated at exponentially growing rates in heterogeneous formats, fast analytics to extract meaningful information is becoming increasingly important. Systems widely use in-memory caching as one of their primary techniques to speed ...

2017

Elastic Program Transformations

Jonas Benedict Wagner

Performance and reliability are important yet conflicting properties of systems software. Software today often crashes, has security vulnerabilities and data loss, while many techniques that could address such issues remain unused due to performance concer ...

EPFL2017

A discussion on the paper "Role of porosity on the stiffness and stability of (001) surface of the nanogranular C-S-H gel"

Paul Bowen, Aslam Kunhi Mohamed

This paper is a discussion of a recent publication by Sekkal and coworkers, which investigated the elasticity, cohesion, and stability of the C-S-H (001) surface as a function of porosity using molecular dynamics simulations. In our discussion, we highligh ...

Elsevier2017

The typical enterprise data architecture consists of several actively updated data sources (e.g., NoSQL systems, data warehouses), and a central data lake such as HDFS, in which all the data is periodically loaded through ETL processes. To simplify query p ...

2017

Toward timely, predictable and cost-effective data analytics

Renata Borovica-Gajic

Modern industrial, government, and academic organizations are collecting massive amounts of data at an unprecedented scale and pace. The ability to perform timely, predictable and cost-effective analytical processing of such large data sets in order to ext ...

EPFL2016

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Enterprise databases use storage tiering to lower capital and operational expenses. In such a setting, data waterfalls from an SSD-based high-performance tier when it is "hot" (frequently accessed) to a disk-based capacity tier and finally to a tape-based ...

Assoc Computing Machinery2016

Runtime Prediction for Scale-Out Data Analytics

Adrian Daniel Popescu

Many analytics applications generate mixed workloads, i.e., workloads comprised of analytical tasks with different processing characteristics including data pre-processing, SQL, and iterative machine learning algorithms. Examples of such mixed workloads ca ...

EPFL2015

Adaptive Query Processing on Raw Data Files

Ioannis Alagiannis

Nowadays, business and scientific applications accumulate data at an increasing pace. This growth of information has already started to outgrow the capabilities of database management systems (DBMS). In a typical DBMS usage scenario, the user should define ...

EPFL2015

Location choice with longitudinal WiFi data

Michel Bierlaire, Matthieu Marie Cochon de Lapparent, Antonin Danalet, Loïc Tinguely

While moving from diary survey to location-aware technologies, recent data collection techniques provide new insights about location choices. Only few dynamic models of location choice exist in the literature, and none of them to our knowledge correct for ...

Transport and Mobility Laboratory, ENAC, EPFL2015

Correcting for endogeneity using the EMIS method: a case study with revealed preference data

Michel Bierlaire, Anna Fernández Antolín

Endogeneity is an important issue that often arises in discrete choice models leading to biased estimates of the parameters. We propose the extended multiple indicator solution (EMIS) methodology to correct for it and exemplify it with a case study using r ...

2015