Overview
Transport Policy makers need information that will support their decisions regarding the potentiality of the implementation of a particular policy. This information is coming in the form of performance and supporting indicators. These indicators are typically computed from the processing of data residing on dispersed, heterogeneous repositories recording transactional or operational data. Processing may involve collection, validation, harmonisation, aggregation, consolidation, transformation, and/ or application of models.
Currently, there is no systematic way of associating indicators to a particular policy. Furthermore, there is no descriptive information (metadata) on how the indicators were computed, or even worse the variables required to calculate the requested indicators are often unavailable. Additionally, there is no Information Technology (IT) based support for storing, retrieving and analysing computed indicators. Also, the policy maker does not have any facilities to view computed indicators from different policy related perspectives. Finally, policy makers do not have a visualisation environment to view those computed indicators on maps.
ETIS is an integrated solution to the above problems and limitations that policy makers have in their daily work.
The objectives of ETIS-Agent are as follows:
To define and maintain the associations of policies with policy questions, policy questions with performance indicators, and performance indicators with the supporting indicators that are used to compute them.
To allows the policy maker to evaluate the computation methodology that was followed for the production of indicators' definitions and values.
To enable the policy maker to formulate and execute queries on policy-specific performance and supporting indicators
To enable the policy maker to analyse the results of queries on policy-specific performance and supporting indicators from different policy related perspectives by performing On-line Analytical Processing (OLAP).
To support visualisation of the results of queries on policy-specific performance and supporting indicators in tables, graphs and/or on GIS.
In order to meet the objectives, ETIS-Agent was structured as a 'data warehousing' project in the Transport area. The ETIS-Agent system will consist of three components. These are the Data Integration, the Data Storage and the Front-End Environment.
Data Integration refers to those processes through which transactional or operational data (e.g. road tolls) residing in dispersed, heterogeneous data sources is integrated to produce policy-related indicators. These processes refer to collection, validation, harmonisation, aggregation, consolidation, transformation, and/ or application of models on this remote data.
The Data Storage is a repository used to store the variables and the policy related indicators produced from Data Integration. The Data Storage is materialised as a data warehouse. The indicators are stored in the data warehouse as multidimensional structures (data cubes). A multidimensional structure allows the policy maker to view an indicator from a number of policy related perspectives. Data Storage includes a repository containing metadata regarding policy domains, policy questions, performance and supporting indicators and information on the associated computation methods.
The Front-End Environment provides a distinct interface to access, compute and visualise policy related indicators and their metadata. Through this environment, the policy maker will be able to:
- Perform policy to indicators metadata browsing and editing.
- Accesses indicators and apply On Line Analytical Processing operations on them.
- Visualise the retrieved indicators through the following ways:
- Tabular View that will present query results into tabular format.
- Graphical View that will present retrieved data in graphical form (i.e. bar charts)
- GIS View that will enable the display of geo-related query results on maps.
ETIS-Agent, (in coordination with ETIS-BASE and ETIS-LINK projects), has defined a pilot, which was used throughout the system development, from requirements analysis to system validation. The pilot definition included the selection of a suitable transport problem domain area, along with the identification and selection of required data, indicators, data sources, tools etc. The pilot definition was based on a survey and analysis of available studies, models, indicators and data sources.
Furthermore, afte
Funding
Results
In order to meet the objectives, ETIS-Agent was structured as a 'data warehousing' project in the Transport area. The ETIS-Agent system consists of three components. These are the Data Integration, the Data Storage and the Front-End Environment.
Data Integration refers to those processes through which transactional or operational data (e.g. road tolls) residing in dispersed, heterogeneous data sources is integrated to produce policy-related indicators. These processes refer to collection, validation, harmonisation, aggregation, consolidation, transformation, and/ or application of models on this remote data. Moreover, Data Integration includes the loading of the policy-related indicators into the Data Storage (see below) as XML documents.
The Data Storage is a repository used to store the variables and the policy related indicators produced from Data Integration. The Data Storage is materialised as a data warehouse. The indicators are stored in the data warehouse as multidimensional structures (data cubes). A multidimensional structure allows the policy maker to view an indicator from a number of policy related perspectives. Conceptually, the indicator is stored as a set of features that can be viewed along one or more dimensions. For example the performance indicator ‘% of freight traffic’ is a data cube that may be viewed along the following modes or dimensions: road, air, time. The possible viewing dimensions obviously depend on the availability of corresponding underlying data. Data Storage includes a repository containing metadata regarding policy domains, policy questions, performance and supporting indicators and information on the associated computation methods.
The Front-End Environment provides a distinct interface to access, compute and visualise policy related indicators and their metadata. Through this environment, the policy maker will be able to:
- Perform policy to indicators metadata browsing and editing.
- Accesses indicators and apply On Line Analytical Processing operations on them.
- Visualise the retrieved indicators through the following ways:
- Tabular View that will present query results into tabular format.
- Graphical View that will present retrieved data in graphical form (i.e. bar charts)
- GIS View that will enable the display of geo-related query results on maps.
An important note to make here is that the Front-End Environment provides a distinct interface to ac
Policy implications
ETIS-Agent is an integrated solution to the current problems and limitations that policy makers have in their daily work. In ETIS-Agent, each policy is associated with a number of policy questions. Subsequently, each policy question is associated with particular performance indicators. These performance indicators are computed or defined by a number of supporting indicators. All this information is stored inside a consolidated repository, which is kept up to date through automatic procedures, where possible, otherwise through experts. ETIS-Agent allows the policy maker to evaluate the computation methodology that was followed for the production of the indicator definitions and values. The policy maker will be able to formulate and execute queries on policy-specific performance and supporting indicators and view those computed indicators from different policy related perspectives. Finally, the policy maker will be able to visualise the retrieved information in tables, graphs and/or GIS.
The pilot system is fully operational and can be accessed at: http://www.agilis-sa.gr/Etis/
Login as user 'demo' with password 'demo'.
EU Member States can utilise the ETIS-Agent system in order to load their own transport data and support their decision-making processes.