Stream Packing for Asynchronous Multi-Context Systems using ASP

11/17/2016
by   Stefan Ellmauthaler, et al.
0

When a processing unit relies on data from external streams, we may face the problem that the stream data needs to be rearranged in a way that allows the unit to perform its task(s). On arrival of new data, we must decide whether there is sufficient information available to start processing or whether to wait for more data. Furthermore, we need to ensure that the data meets the input specification of the processing step. In the case of multiple input streams it is also necessary to coordinate which data from which incoming stream should form the input of the next process instantiation. In this work, we propose a declarative approach as an interface between multiple streams and a processing unit. The idea is to specify via answer-set programming how to arrange incoming data in packages that are suitable as input for subsequent processing. Our approach is intended for use in asynchronous multi-context systems (aMCSs), a recently proposed framework for loose coupling of knowledge representation formalisms that allows for online reasoning in a dynamic environment. Contexts in aMCSs process data streams from external sources and other contexts.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/20/2015

Asynchronous Multi-Context Systems

In this work, we present asynchronous multi-context systems (aMCSs), whi...
research
01/08/2013

Answer Set Programming for Stream Reasoning

The advance of Internet and Sensor technology has brought about new chal...
research
07/09/2019

Contextual One-Class Classification in Data Streams

In machine learning, the one-class classification problem occurs when tr...
research
02/18/2020

Managing multiple data streams in R

It is often useful to tap secondary information from a running R script....
research
08/15/2019

Double-Coupling Learning for Multi-Task Data Stream Classification

Data stream classification methods demonstrate promising performance on ...
research
02/18/2023

RecNet: Early Attention Guided Feature Recovery

Uncertainty in sensors results in corrupted input streams and hinders th...
research
06/17/2020

Ranking and benchmarking framework for sampling algorithms on synthetic data streams

In the fields of big data, AI, and streaming processing, we work with la...

Please sign up or login with your details

Forgot password? Click here to reset