DeepAI AI Chat
Log In Sign Up

Context agnostic trajectory prediction based on λ-architecture

by   Evangelos Psomakelis, et al.

Predicting the next position of movable objects has been a problem for at least the last three decades, referred to as trajectory prediction. In our days, the vast amounts of data being continuously produced add the big data dimension to the trajectory prediction problem, which we are trying to tackle by creating a λ-Architecture based analytics platform. This platform performs both batch and stream analytics tasks and then combines them to perform analytical tasks that cannot be performed by analyzing any of these layers by itself. The biggest benefit of this platform is its context agnostic trait, which allows us to use it for any use case, as long as a time-stamped geolocation stream is provided. The experimental results presented prove that each part of the λ-Architecture performs well at certain targets, making a combination of these parts a necessity in order to improve the overall accuracy and performance of the platform.


page 10

page 16

page 17

page 18


A Novel Micro-service Based Platform for Composition, Deployment and Execution of BDA Applications

Big Data are growing at an exponential rate and it becomes necessary the...

AlertMix: A Big Data platform for multi-source streaming data

The demand for stream processing is increasing at an unprecedented rate....

GALOIS: A Hybrid and Platform-Agnostic Stream Processing Architecture

With the increasing prevalence of IoT environments, the demand for proce...

Moving Objects Analytics: Survey on Future Location & Trajectory Prediction Methods

The tremendous growth of positioning technologies and GPS enabled device...

Cloud Based Big Data DNS Analytics at Turknet

Domain Name System (DNS) is a hierarchical distributed naming system for...