Variational Bayesian Context-aware Representation for Grocery Recommendation

Grocery recommendation is an important recommendation use case, which aims to predict which items a user might choose to buy in the future, based on their shopping history. However, existing methods only represent each user and item by single deterministic points in a low-dimensional continuous space.


Big data skipping in the cloud

Cloud compute and storage services should be deployed and managed independently; huge datasets need to be shipped from the storage service to the compute service to analyse the data.
Data skipping is a technique which minimizes the amount of data sent across the network for SQL style analytics on structured data.

Discover more


Integration of Mobility Data with Weather Information

Mobility databases usually do not contain weather information, thus hindering the joint analysis of mobility and weather data. Motivated by this evident need of many real-life applications, in this paper, we develop a system for integrating mobility data with external weather sources.


Fog Function: Serverless Fog Computing for Data Intensive IoT Services

To achieve both flexibility and efficiency, we propose a data-centric programming model called Fog Function and also introduce its underlying orchestration mechanism that leverages three types of contexts: data context, system context, and usage context.


Reinforcement Learning based Orchestration for Elastic Services

In order to provide this adaptation efficiently, we propose a Reinforcement Learning (RL) based Orchestration for Elastic Services


BigDataStack: A holistic data-driven stack for big data applications and operations

In this paper we present the architecture of a complete stack (namely BigDataStack), based on a frontrunner infrastructure management system that drives decisions according to data aspects, thus being fully scalable, runtime adaptable and highperformant