Grasp heuristic for time series compression with piecewise aggregate approximation

The Piecewise Aggregate Approximation (PAA) is widely used in time series data mining because it allows to discretize, to reduce the length of time series and it is used as a subroutine by algorithms for patterns discovery, indexing, and classification of time series. However, it requires setting one parameter: the number of segments to consider during the discretization. The optimal parameter value is highly data dependent in particular on large time series. This paper presents a heuristic for time series compression with PAA which minimizes the loss of information. The heuristic is built upon the well known metaheuristic GRASP and strengthened with an inclusion of specific global search component. An extensive experimental evaluation on several time series datasets demonstrated its efficiency and effectiveness in terms of compression ratio, compression interpretability and classification.

A GRASP for Next Generation Sapphire Image Acquisition Scheduling

This paper investigates an image acquisition scheduling problem for a Canadian surveillance-of-space satellite named Sapphire that takes images of deep space Earth-orbiting objects. For a set of resident space objects (RSOs) that needs to be imaged within the time horizon of one day, the Sapphire image acquisition scheduling (SIAS) problem is to find a schedule that maximizes the “Figure of Merit” of all the scheduled RSO images. To address the problem, we propose an effective GRASP heuristic that alternates between a randomized greedy constructive procedure and a local search procedure. Experimental comparisons with the currently used greedy algorithm are presented to demonstrate the merit of the proposed algorithm in handling the SIAS problem.

A New Elastic Scheduling Task Model in the Node of a Control and Measurement System

The paper presents a new elastic scheduling task model which has been used in the uniprocessor node of a control measuring system. This model allows the selection of a new set of periods for the occurrence of tasks executed in the node of a system in the case when it is necessary to perform additional aperiodic tasks or there is a need to change the time parameters of existing tasks. Selection of periods is performed by heuristic algorithms. This paper presents the results of the experimental use of an elastic scheduling model with a GRASP heuristic algorithm.