Stock time series analysis

With 1.5 billion new messages added every day and a yearly data growth rate estimated at 30%-50%, both historical and real time financial data processing becomes increasingly challenging. Market forecasts and risk assessments with thousands of portfolio members, resulting in […]

Visual Analytics in SmartDataLake

The visual analytics layer of SmartDataLake comprises two main components, namely the Visual Analytics Engine and the Visual Explorer. The Visual Analytics Engine interfaces directly with the data virtualization and mining components of the SmartDataLake toolkit, preparing their results for […]

Link Prediction with PathLearn

PathLearn is an open-source component for performing link prediction in Heterogeneous Information Networks (HINs). PathLearn predicts links by modeling the effect of every path that exists between pairs of nodes. Instead of assigning a discrete type to each path, PathLearn […]

Multi-Attribute Similarity Search

SimSearch is a tool that simplifies data exploration by enabling top-k similarity search over large collections of entities involving multiple heterogeneous attributes from different sources. It supports different modes for data access and query over diverse types of attributes, including […]

Approximate Query Processing

The Query Approximation Layer (QAL), developed by the Eindhoven University of Technology, allows users to get approximate results with error guarantees for SQL aggregation queries. QAL introduces a novel adaptive approximate processing engine that constructs the synopses which maximize the […]

Project Facts
SmartDataLake is a Research and Innovation action funded by the Horizon 2020 Framework Programme of the European Union.

Project Full Title: Sustainable Data Lakes for Extreme-Scale Analytics

Topic: ICT-12-2018-2020 - Big Data technologies and extreme-scale analytics

Grant Agreement No: 825041

Duration: 36 months (1/2019 – 12/2021)

Coordinated by : IMSI / Athena RC