ASPIS Web Application
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Intro
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ASPIS webapp aims to provide an easy, user friendly, access to the data contained in the DATABASE.
ASPIS webapp will be suited with tools able to easy access and visualize data. Technically, ASPIS
web-app will access to database using a set of dedicated API (GET) who will act as middleware to the
Database queries. A key feature will be the function to visualize several data-set in the same page. The
feature, strongly related to the casual-chain concept is designed to take into account the standard
work-flow of the Space Weather community while they are inspecting correlation and liked phenomena
inside different products. A small and protected amount of server-side and/or client side models should
be run over visualized data for simple and light computation, notwithstanding the intention of the web-app
is to facilitate the data exploration and cross-visualization. Users will be encouraged to implement heavy
computations and advanced data handling using ASPISpy package.

Functionalities
^^^^^^^^^^^^^^^

The web-app will be accessible using a common web browser. Basic functionalities are expected to be
public and will be open to the community:

• Search in the archive based on several keys
• Time based queries
• Spatial queries
• Data visualization
• Data comparison (Multiple plots)
• Access to the Causal-chains
• Causal-chain visualization with multiple plots.
• Quick citation extraction tools based on visualized data.

Advanced functionalities will be protected and will be accessible only to user identification:

• Save search results
• Save data visualization settings
• Run internal, server-side, ASPIS models over an identified data-set
• (optional) Light computation of model and data comparison

The Access Control List (ACL) will be provided by the ASPIS machines.

Time-based multi-plot visualization
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Time is a key link between the most of data-sets provided. So, graphical representation of data shall be
able to facilitate a time-based comparison between the series visualized by a common timeline. In this
way, every relationship between time-based plots can be easily found by the user.

Spatial visualization
.....................

Data-set are generated by instruments spatially referenced by acquisition site (ex. the instrument is
physically mounted on a Spacecraft or on Earth ), by target position (ex. The instrument target is the
Sun) and by line of sight (ex. the instrument acquire 20 square deg in a direction of the sky). This info
can be used to facilitate the user to visualize:

• Instruments positions in a specified time span
• Targets positions in a specified time span
• Targets coverage in instruments line of sights.
• Visualization of specific phenomena in different reference systems

Event-chain visualization
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Causal-chains will get a special section in the webapp. Causal-chains will take advantage from the
other basic visualization plots and can be associated here as a pre-defined organization of data subsets
plots. Web-app dedicated section will be able to list and search all causal-chains. Web-app will be able
to advise the user if, in a nominal visualization plot, the data subset is part of a know causal-chain and
propose a suitable visualization of such a causal-chain.

Data Discovery
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Aspis Data Discovery section is organized in four thematic sub-panels:

• Phenomena
• Targets
• Scientific Topics
• Smart Discovery

The last selection method is strongly related to the causal chain concept. 
The concept is designed to consider several possible workflows of the Space Weather community while searching for correlations or linked phenomena inside different products.
This criterion is called "smart discovery" and takes inspiration from the casual chain concept.
This discovery panel tries to represent a semantic generalization of the "casual chains" contained in the DB, presenting to the user a selection of data products that are linked for their 'conceptual proximity', i.e. they either are often associated by causality or correlation in Space Weather studies or are alternative data sources.
A few smart discovery associations are offered to the user (e.g., interplanetary disturbances studies, flare AR studies, geoeffective disturbances studies), possibly updating the existing associations and adding others, following the researchers' experience and interests.
Of course, jumping from one smart discovery representation to another is possible since some data products are present in more than one representation and can act as pivot points.

On the right size of the Data Discovery panel the list of the user selected products will be shown.
