Difference between revisions of "Data Assessment and Harmonisation APIs"
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==Time-Series-Service== | ==Time-Series-Service== | ||
− | * '''Description''': | + | * '''Description''': management of the entire life-cycle of datasets representing time series |
* '''Type''': gCube Web Service | * '''Type''': gCube Web Service | ||
* '''Protocol''': SOAP | * '''Protocol''': SOAP | ||
* '''Framework''': gCore | * '''Framework''': gCore | ||
* '''Key Features''': | * '''Key Features''': | ||
+ | :* ''import and export in multiple formats (e.g. SDMX, csv)''; | ||
+ | :* ''semi-automatic curation''; | ||
+ | :* ''aggregation, grouping, union, denormalisation and filtering''; | ||
+ | :* ''versioning''; | ||
+ | :* ''logging of time series changes for provenance''; | ||
* [[Time_Series_Management|'''Documentation''']] | * [[Time_Series_Management|'''Documentation''']] | ||
+ | |||
==Occurrence Data Management== | ==Occurrence Data Management== | ||
− | * '''Description''': | + | * '''Description''': a set of features for performing assessment and harmonization on occurrence points of species |
* '''Type''': gCube Web Service | * '''Type''': gCube Web Service | ||
* '''Protocol''': SOAP | * '''Protocol''': SOAP | ||
* '''Framework''': gCore | * '''Framework''': gCore | ||
* '''Key Features''': | * '''Key Features''': | ||
− | * ''' | + | ** ''Import from SPD'' |
− | ==Odm-client-library== | + | ** ''Occurrence Points visualization, aggregation and transformation'' |
− | * '''Description''': | + | ** ''Merge, Subtraction and Intersection operations'' |
+ | ** ''Points Clustering'' | ||
+ | ** ''Anomaly Point Detection'' | ||
+ | * [[Occurrence Data Reconciliation|'''Documentation''']] | ||
+ | |||
+ | ==Odm-client-library (Occurrence Data Management (ODM) Client Library== | ||
+ | * '''Description''': Client proxies for [[#ODM API|ODM (Occurrence Data Management)]] endpoints. | ||
* '''Type''': Java | * '''Type''': Java | ||
* '''Protocol''': Local | * '''Protocol''': Local | ||
* '''Framework''': [https://gcube.wiki.gcube-system.org/gcube/index.php/Integration_and_Interoperability_Facilities_Framework:_Client_Libraries CL] | * '''Framework''': [https://gcube.wiki.gcube-system.org/gcube/index.php/Integration_and_Interoperability_Facilities_Framework:_Client_Libraries CL] | ||
* '''Key Features''': | * '''Key Features''': | ||
+ | :* ''transparent endpoint discovery and caching'' | ||
+ | :* ''transparent fault tolerance over endpoint replicas'' | ||
* '''Wiki Doc''': | * '''Wiki Doc''': |
Latest revision as of 10:40, 4 October 2012
Data Assessment and Harmonisation APIs provide access to services that deal with a great heterogeneity of data sources having different characteristics both in terms of data types they offer and of data behavior differentiating across the same type. This page outlines the design rationale for those APIs.
Contents
Overview
Name | Description | Type | Protocol | Framework |
Time-Series-Service | gCube Web Service | SOAP | gCore | |
Occurrence Data Management | gCube Web Service | SOAP | gCore | |
Odm-client-library | Java | Local | CL |
Time-Series-Service
- Description: management of the entire life-cycle of datasets representing time series
- Type: gCube Web Service
- Protocol: SOAP
- Framework: gCore
- Key Features:
- import and export in multiple formats (e.g. SDMX, csv);
- semi-automatic curation;
- aggregation, grouping, union, denormalisation and filtering;
- versioning;
- logging of time series changes for provenance;
Occurrence Data Management
- Description: a set of features for performing assessment and harmonization on occurrence points of species
- Type: gCube Web Service
- Protocol: SOAP
- Framework: gCore
- Key Features:
- Import from SPD
- Occurrence Points visualization, aggregation and transformation
- Merge, Subtraction and Intersection operations
- Points Clustering
- Anomaly Point Detection
- Documentation
Odm-client-library (Occurrence Data Management (ODM) Client Library
- Description: Client proxies for ODM (Occurrence Data Management) endpoints.
- Type: Java
- Protocol: Local
- Framework: CL
- Key Features:
- transparent endpoint discovery and caching
- transparent fault tolerance over endpoint replicas
- Wiki Doc: