TimeSeries offers facilities supporting the management of the entire life-cycle (creation, curation, manipulation and publication) of datasets representing time series, i.e. tabular data representing observations of a given event or phenomenon at different time intervals. Time series are used in many domains ranging from statistics to signal processing and econometrics.
TimeSeries offers a rich set of facilities ranging from those supporting the assessment of data correctness to those supporting the verification of the compliance of data with given code lists, the aggregation and filtering of data.
This document outlines the design rationale, key features, and high-level architecture, as well as the options deployment.
The goal of this service is to offer a single entry for processing, assessing and harmonizing time series.
The service is able to import data using different protocols.
The subsystem provides for:
- comprehensive facilities for time series data management
- The subsystem offers a comprehensive set of data management facilities oriented to support the entire life-cycle of time series data. Facilities include data import, data curation, data manipulation, data visualisation and processing;
- user-friendly interface
- The subsystem offers a graphical user interface where users can visualize the data and perform the basic operation in a very user-friendly environment;
- integrated access to related services
- The subsystem is conceived to integrated with other services including data processing (R) and data publishing (Geospatial Data Visualization);
This represents an endpoint for users who want to process time series in order to extract information.
The subsystem comprises the following components:
- TimeSeries service: the service core;
- TimeSeries client library: a library to connect to the service.
A diagram of the relationships between these components is reported in the following figure:
All the components of the service must be deployed together in a single node. This subsystem can be replicated on multiple hosts and scopes, this does not guarantee a performance improvement because this is associated to the requests which are made towards the database.
The deployment follows the schema of the Architecture
Well suited Use Cases
The Service is particularly suited to support processing on large dataset of time series and to collect statistics on such data.