1. Supersite Relational Database System (SRDS) Rudolf Husar, PI Center for Air Pollution Impact and Trend Analysis (CAPITA) Washington University, St. Louis, MO Proposal Presentation to the Supersite Program Nov 30, 2001 a sub- project of St. Louis Midwest Supersite Project, Jay Turner, PI

2. Design, Populate and Maintain a Supersite Relational Database System Facilitate cross-Supersite, regional, comparative data analyses Support analyses by a variety of research groups Include monitoring data from Supersites and auxiliary projects Purpose of the Project:

3. EPA Specs of the Supersite Relational Data System (from RFP) Data Input: –Data input electronically –Modest amount of metadata on sites, instruments, data sources/version, contacts etc. –Simple data structures, formats and convenient submission procedures Data Storage and Maintenance: –Data storage in relational database(s), possibly distributed over multiple servers –A catalog of data holdings and request logs –Supersite data updates quarterly Data Access: –User-friendly web-access by multiple authorized users –Data query by parameter, method, location, date/time, or other metadata –Multiple data output formats (ASCII, spreadsheet, other (dbf, XML)

4. General Approach to SRDS Design Based on consensus, adopt a uniform relational data structure, suitable for regional and cross-Supersite data integration and analysis. We propose a star schema with spatial, temporal, parameter and method dimensions. The ‘original’ data are to be maintained at the respective providers or custodians (Supersites, CIRA, CAPITA..). We propose the creation of flexible ‘adapters’ and web-submission forms for the transfer of data subsets into the uniformly formatted ‘Federated Data Warehouse’. Data users would access the data warehouse manually or through software. We propose data access using modern ‘web services’ protocol, suitable for adding data viewers, processors (filtering, aggregation and fusion) and other value-adding processes.

5. The RDMS Schema: ‘Minimal’ Star Schema‘Minimal’ Star Schema The minimal Sites table includes SiteID, Name and Lat/Lon. The minimal Parameter table consists of ParamterID, Description and Unit The time dimensional table is usually skipped since the time code is self-describing The minimal Fact (Data) table consists of the Obs_Value and the three dimensional codes for Obs_DateTime, Site_ID and Parameter_ID Additional dimension tables may include Method and Data Quality. For integrative, cross-Supersite analysis, data queries by time, location and parameter and method, the database has to have, at the minimum, time, location parameter and method as dimensions The CAPITA data exploration software, Voyager uses this minimal schema. Voyager was in use for the past 12 years successfully encoding and browsing 1000+ datasets worldwide.Voyager The state of California still formats and distributes their AQ data on CDs using Voyager.

6. From Heterogeneous to Homogeneous Schema Individual Supersite SQL databases have varied designs, usually following an more elaborate the ‘snowflake’ pattern (see Database Schema Design for the Federated Data Warehouse ) Database Schema Design for the Federated Data Warehouse Though they have more complicated schemata, these SQL servers can be queried along spatial, temporal, parameter, method dimensions. However, the query to retrieve the same information depends on the particular database schema. A way to homogenize the distributed data is by accessing all the data through a Data Adapter using only a subset of the tables/fields from any particular database (show red in the schemata below) The proposed extracted uniform (abstract) schema is the Minimal Star Schema, (possibly expanded). The final form of the uniformly extracted data schema will be arrived at by Supersite consensus. Subset used Uniform Schema Fact Table Data Adapter Extraction of homogeneous data from heterogeneous sources

7. Live Demo of the Data Warehouse Prototype Uniform Data Query regardless of the native schema: Query by parameter, location, time, method Currently online data are accessible from the CIRA (IMPROVE) and CAPITA SQL servers The hidden DataAdopter - accepts the uniform query - translates the uniform to server-specific query - return DataSet in uniform schema Data Returned in uniform schema A rudimentary viewer displays the data in a table for browsing. http://capita.wustl.edu/DSViewer/DSviewer.aspx

8. Federated Data Warehouse Federated Data Warehouse Architecture Tree-tier architecture consisting of –Provider Tier: Back-end servers containing heterogeneous data, maintained by the federation members –Proxy Tier: Retrieves Provider data and homogenizes it into common, uniform Datasets –User Tier: Accesses the Proxy Server and uses the uniform data for presentation, integration or further processing The Provider servers interact only with the Proxy Server in accordance with the Federation Contract –The contract sets the rules of interaction (accessible data subsets; types of queries submitted by the Proxy) –The Proxy layer allows strong security measures, e.g. through Secure Socket layer The data User interacts only with the generic Proxy Server using flexible Web Services interface –Generic data queries, applicable to all data in the Warehouse (e.g. space, time, parameter data sub-cube) –The data query is addressed to a Web Service provided by the Proxy Server of the Federation –Uniformly formatted, self-describing XML data packages are handed to the user for presentation or further machine processing SQLDataAdapter1 CustomDataAdapter SQLDataAdapter2 SQLServer1 SQLServer2 LegacyServer Presentation Data Access & Use Provider Tier Heterogeneous Data Proxy Tier Data Homogenization, etc. Member Servers Proxy Server User Tier Data Consumption Processing Integration Federated Data Warehouse Fire Wall, Federation Contract Web Service, Uniform Query & Data

9. Federated Warehouse: Data Re-Use and Synergy Data producers maintain their own workspace and resources (data, reports, comments). Part of the resources are shared by creating a common virtual resources. Web-based integration of the resources can be across several dimensions: Spatial scale:Local – global data sharing Data content:Combination of data generated internally and externally The main benefits of sharing are data re-use, data complementing and synergy. To be self-sustaining, the user-benefits of sharing need to outweigh the ‘costs’ of sharing. User Local Global Virtual Shared Resources Data, Knowledge Tools, Methods User Shared part of resources

10. Data Entry to the Supersite Relational Data System: EPA Supersite Data Coordinated Supersite Relational Tables EOSDIS Data Archive NARSTO ORNL DES, Data Ingest Supersite SQL Server 3. DES-SQL Transformer 1. DataAdapter Supersite & other SQL Data Data Query Table Output 2. Direct Web Data Input 1.Batch transfer of large Supersite and other datasets to the SRDS SQL server 2.Web-submission of of relational tables by the data producers/custodians 3.Automatic translation and transfer of NARSTO-archived DES data to SQL

11. Federated Data Warehouse Features As much as possible, data should reside in their respective home environment. ‘Uprooted’ data in decoupled databases tend to decay i.e. can not be easily updated, maintained, enriched. Data Providers would need to ‘open up’ their SQL data servers for limited data subsets and queries, in accordance with a ‘contract’. However, the data structures of the Providers will not need to be changed. Data from the providers will be transferred to the ‘federated data warehouse’ through (1) on-line DataAdapters, (2) Manual web submission and (3) Semi-automated transfer from the NARSTO archive. Retrieval of uniform data from the data warehouse facilitates integration and comparison along the key dimensions (space, time, parameter, method) The open architecture data warehouse (see Web Services) promotes the building of further value chains: Data Viewers, Data Integration Programs, Automatic Report Generators etc..Web Services

12. Data Preparation Procedures: Data gathering, QA/QC and standard formatting is to be done by individual projects The data naming standards, data ingest and archives are by ORNL and NASA Data ingest is to automated, aided by tools and procedures supplied by this project –NARSTO DES-SQL translator –Web submission tools and procedures –Metadata Catalog and I/O facilities Data submissions and access will be password protected as set by the community. Submitted data will be retained in a temporary buffer space and following verification transferred to the shared SQL database. The data access, submissions and other ‘transactions’ will be automatically recorded an summarized in human-readable reports.

13. Data Catalog Data Catalog will be maintained through CAPITA website. Limited metadata (based on user consensus) will be recorded for each dataset User feedback on individual datasets will be through comments/feedback pages An example is the data section of the St. Louis Supersite website.St. Louis Supersite

14. SRDS and Federated Data Warehouse Technologies Server hardware: 2 identical Dell PowerEdge 4400 servers, (SQL server and Web Server); dual processor Xeon, 1 GB memory, 260 GB RAID drives SQL Software: Microsoft SQL 2000 Enterprize Development Server Web Server: Microsoft IIS 2000, including the Data Transformation Services for data ingestion. Programming Environment: Microsoft.Net languages (VB, C#) for creating and programming web objects and ASP.NET to create the distributed web pages. Note: The rapid development of distributed applications was recently made possible by the ubiquity of SOAP/XML as a data transport protocol, and Web Services/.Net as the distributed programming environment. In fact,.NET is still in version Beta2.Web Services

15. Related CAPITA Projects EPA Network Design Project (~$150K/yr –April 2003). Development of novel quantitative methods of network optimization. The network performance evaluation is conducted using the complete PM FRM data set in AIRS which will be available for input into the SRDS. EPA WebVis Project (~$120K/yr - April 2003). Delivery current visibility data to the public through a web-based system. The surface met data are being transferred into the SQL database (Since March 2001) and will be available to SRDS. NSF Collaboration Support Project (~$140K/yr – Dec 2004). Continuing development of interactive web sites for community discussions and for web-based data sharing; (directly applicable to this project) NOAA ASOS Analysis Project (~$50K/yr - May 2002). Evaluate the potential utility of the ASOS visibility sensors (900 sites, one minute resolution) as PM surrogate. Data now available for April-October 2001 – can be incorporated into to the Supersite Relational Data System. St. Louis Supersite Project website (~$50K/yr – Dec 2003). The CAPITA group maintains the St. Louis Supersite website and some auxiliary data. It will also be used for this project

16. Federated Data Warehouse Applications: Distributed ‘Voyager’ XML Web Services Satellite Vector GIS Data XDim Data OLAP Cube SQL Table HTTP Services Text Data Web Page Text Data Time Chart Scatter Chart Text, Table Data View & Process Tier Layered Map Cursor Data Warehouse Tier Data View Manager Connection Manager Data Access Manager Cursor-Query Manager OpenGIS Services Data are rendered by linked Data Views (map, time, text) Distributed data of multiple types (spatial, temporal text ) The Broker handles the views, connections, data access, cursor

17. Live Demo: Distributed ‘Voyager’ http://capita.wustl.edu/view3/DVoyDemo1.aspx http://capita.wustl.edu/view3/DVoyDemo1.aspx Browsing by space, time, parameter dimension (parameters now ‘hard wired’) Data selection from distributed servers Data overlay in time an map views ‘Clickable’ data map and time views s for browsing.

18. Supersite Relational Data System: Schedule First four four months to design of the relational database, associated data transformers, I/O; submitted to the Supersite workgroups for comment In six months, Supersite data preparation and entry begins In Year 2 and Year 3, data sets will be updated by providers as needed; system accessible to data user community Year 1 - 2002Year 2 - 2003Year 2 - 2004 RDMS Design Feed back Impl. & Test SQL Supersite Data Entry Auxiliary Data Entry Other Coordinated Data Entry Supersite, Coordinated and Auxiliary Data Updates

19. Personnel, Management and Facilities Personnel PI, R. B. Husar (10%), Kari Hoijarvi (25%). Software experience at CAPITA, Microsoft, Visala. 20% of project budget ($12k/yr) to consultants: J. Watson, DRI, W. White and J. Turner, WU. Collaborators, (CAPITA associates): B. Schichtel, CIRA, S. Falke, EPA, M. Bezic, Microsoft. Management This project is a sub-project of the St. Louis-Midwest Supersite project, Dr. Jay Turner, PI. Special focus is on supporting large scale, crosscutting, and integrative analysis. This project will leverage the other CAPITA data sharing projects Resources and Facilities CAPITA has the ‘largest known privately held collection of air quality, metrological and emission data’, available in uniform Voyager format and extensively accessed from the CAPITA website The computing and communication facilities include two servers, ten workstations and laptops, connected internally and externally through high-speed networks. Software development tools includes Visual Studio, part of the.NET distributed development environment