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Sharing of probabilistically correlated data in peer-to-peer networks

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Rack-Scale Memory Pooling for Datacenters

Stanko Novakovic

The rise of web-scale services has led to a staggering growth in user data on the Internet. To transform such a vast raw data into valuable information for the user and provide quality assurances, it is important to minimize access latency and enable in-me ...

EPFL2017

Dictionary Compression in Point Cloud Data Management

Anastasia Ailamaki, Mirjana Pavlovic

Nowadays, massive amounts of point cloud data can be collected thanks to advances in data acquisition and processing technologies like dense image matching and airborne LiDAR (Light Detection and Ranging) scanning. With the increase in volume and precision ...

2017

Multicloud Resource Allocation

Hao Zhuang

Nowadays our daily life is not only powered by water, electricity, gas and telephony but by "cloud" as well. Big cloud vendors such as Amazon, Microsoft and Google have built large-scale centralized data centers to achieve economies of scale, on-demand res ...

EPFL2017

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

Parallelizing Query Optimization on Shared-Nothing Architectures

Christoph Koch, Immanuel Trummer

Data processing systems offer an ever increasing degree of parallelism on the levels of cores, CPUs, and processing nodes. Query optimization must exploit high degrees of parallelism in order not to gradually become the bottleneck of query evaluation. We s ...

Assoc Computing Machinery2016

Efficient Incremental Data Analysis

Milos Nikolic

Many data-intensive applications require real-time analytics over streaming data. In a growing number of domains -- sensor network monitoring, social web applications, clickstream analysis, high-frequency algorithmic trading, and fraud detections to name a ...

EPFL2016

Fast Queries Over Heterogeneous Data Through Engine Customization

Anastasia Ailamaki, Manolis Karpathiotakis, Ioannis Alagiannis

Industry and academia are continuously becoming more data-driven and data-intensive, relying on the analysis of a wide variety of heterogeneous datasets to gain insights. The different data models and formats pose a significant challenge on performing anal ...

Assoc Computing Machinery2016

Visual Patterns Discovery in Large Databases of Paintings

Frédéric Kaplan, Isabella Di Lenardo, Benoît Laurent Auguste Seguin

The digitization of large databases of works of arts photographs opens new avenue for research in art history. For instance, collecting and analyzing painting representations beyond the relatively small number of commonly accessible works was previously ex ...

2016

NoDB: Efficient Query Execution on Raw Data Files

Anastasia Ailamaki, Miguel Sérgio De Oliveira Branco, Ioannis Alagiannis, Renata Borovica-Gajic

As data collections become larger and larger, users are faced with increasing bottlenecks in their data analysis. More data means more time to prepare and to load the data into the database before executing the desired queries. Many applications already av ...

Assoc Computing Machinery2015

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