Designing a DTC Verification System Jennifer Mahoney NOAA/ESRL 21 Feb 2007.

Slides:



Advertisements
Similar presentations
Multi-level SLA Management for Service-Oriented Infrastructures Wolfgang Theilmann, Ramin Yahyapour, Joe Butler, Patrik Spiess consortium / SAP.
Advertisements

Chapter 1 Business Driven Technology
ARCH-05 Application Prophecy UML 101 Peter Varhol Principal Product Manager.
© 2005 Prentice Hall13-1 Stumpf and Teague Object-Oriented Systems Analysis and Design with UML.
May 17, Capabilities Description of a Rapid Prototyping Capability for Earth-Sun System Sciences RPC Project Team Mississippi State University.
1 Software & Grid Middleware for Tier 2 Centers Rob Gardner Indiana University DOE/NSF Review of U.S. ATLAS and CMS Computing Projects Brookhaven National.
8.
Chapter 3 Database Management
R R R CSE870: Advanced Software Engineering (Cheng): Intro to Software Engineering1 Advanced Software Engineering Dr. Cheng Overview of Software Engineering.
Data Sources Data Warehouse Analysis Results Data visualisation Analytical tools OLAP Data Mining Overview of Business Intelligence Data visualisation.
Components and Architecture CS 543 – Data Warehousing.
Chapter 14 The Second Component: The Database.
Introduction to Systems Analysis and Design
Software Process and Product Metrics
DASHBOARDS Dashboard provides the managers with exactly the information they need in the correct format at the correct time. BI systems are the foundation.
Data Warehousing: Defined and Its Applications Pete Johnson April 2002.
Chapter 1 The Systems Development Environment
Chapter 6: The Traditional Approach to Requirements
Module 3: Business Information Systems
The Network Enabled Verification Service (NEVS) in Support of NNEW Capability Evaluation Sean Madine ESRL/GSD/FVS 15 September 2010.
The Evergreen, Background, Methodology and IT Service Management Model
The Design Discipline.
INFO425: Systems Design INFORMATION X Finalizing Scope (functions/level of automation)  Finalizing scope in terms of functions and level of.
1 Autonomic Computing An Introduction Guenter Kickinger.
Chapter 5 Lecture 2. Principles of Information Systems2 Objectives Understand Data definition language (DDL) and data dictionary Learn about popular DBMSs.
9/14/2012ISC329 Isabelle Bichindaritz1 Database System Life Cycle.
Data Warehouse Overview September 28, 2012 presented by Terry Bilskie.
RUP Design RUP Artifacts and Deliverables
CHAPTER 8: MANAGING DATA RESOURCES. File Organization Terms Field: group of characters that represent something Record: group of related fields File:
7.1 Managing Data Resources Chapter 7 Essentials of Management Information Systems, 6e Chapter 7 Managing Data Resources © 2005 by Prentice Hall.
PLoS ONE Application Journal Publishing System (JPS) First application built on Topaz application framework Web 2.0 –Uses a template engine to display.
Information: Policy, Strategy and Systems Module Overview
Lecture 7: Requirements Engineering
©Ian Sommerville 2006Software Engineering, 8th edition. Chapter 11 Slide 1 Design.
Chapter 10 Analysis and Design Discipline. 2 Purpose The purpose is to translate the requirements into a specification that describes how to implement.
6.1 © 2010 by Prentice Hall 6 Chapter Foundations of Business Intelligence: Databases and Information Management.
MANAGING DATA RESOURCES ~ pertemuan 7 ~ Oleh: Ir. Abdul Hayat, MTI.
9 Systems Analysis and Design in a Changing World, Fourth Edition.
NA-MIC National Alliance for Medical Image Computing UCSD: Engineering Core 2 Portal and Grid Infrastructure.
© 2010 Health Information Management: Concepts, Principles, and Practice Chapter 5: Data and Information Management.
Near Real-Time Verification At The Forecast Systems Laboratory: An Operational Perspective Michael P. Kay (CIRES/FSL/NOAA) Jennifer L. Mahoney (FSL/NOAA)
Panel Discussion for the DTC Verification System 23 Feb 2007.
Foundations of Business Intelligence: Databases and Information Management.
Fundamentals of Information Systems, Second Edition 1 Information and Decision Support Systems.
CASE (Computer-Aided Software Engineering) Tools Software that is used to support software process activities. Provides software process support by:- –
Managing Enterprise GIS Geodatabases
The Systems Development Environment Systems Analysis and Design II.
McGraw-Hill/Irwin © 2013 The McGraw-Hill Companies, Inc., All Rights Reserved. Chapter 5 Information Systems Concepts.
1 Software Engineering: A Practitioner’s Approach, 6/e Chapter 9: Design Engineering Software Engineering: A Practitioner’s Approach, 6/e Chapter.
Diagnostic verification and extremes: 1 st Breakout Discussed the need for toolkit to build beyond current capabilities (e.g., NCEP) Identified (and began.
5. 2Object-Oriented Analysis and Design with the Unified Process Objectives  Describe the activities of the requirements discipline  Describe the difference.
Verification of C&V Forecasts Jennifer Mahoney and Barbara Brown 19 April 2001.
Managing Data Resources File Organization and databases for business information systems.
WEB BASED DSS Aaron Atuhe. KEY CONCEPTS When software vendors propose implementing a Web-Based Decision Support System, they are referring to a computerized.
Data Mining and Data Warehousing: Concepts and Techniques What is a Data Warehouse? Data Warehouse vs. other systems, OLTP vs. OLAP Conceptual Modeling.
Advanced Software Engineering Dr. Cheng
16CS202 & Software Engineering
Discovering Computers 2010: Living in a Digital World Chapter 14
Chapter 1: Introduction to Systems Analysis and Design
ITEC 3220A Using and Designing Database Systems
Software Engineering Development of procedures and systematic applications that are used on electronic machines. Software engineering incorporates various.
Systems Analysis and Design in a Changing World, 6th Edition
Graduation Project Kick-off presentation - SET
The Network-Enabled Verification Service (NEVS)
MANAGING DATA RESOURCES
SDM workshop Strawman report History and Progress and Goal.
Data Warehouse Overview September 28, 2012 presented by Terry Bilskie
Chapter 1: Introduction to Systems Analysis and Design
Data Warehouse Overview September 28, 2012 presented by Terry Bilskie
Chapter 1: Introduction to Systems Analysis and Design
Presentation transcript:

Designing a DTC Verification System Jennifer Mahoney NOAA/ESRL 21 Feb 2007

Talk Overview Define verification system Present the key components that will form the DTC verification system framework  Allow flexibility  Serve a variety of users Discuss the complexities of developing the DTC verification system

Acknowledgments ESRL/GSD Verification System Engineering Team  Sean Madine  Nick Matheson  Missy Petty  Dan Schaffer

What is a Verification System?

What is a DTC Verification System?

Verification Analyses as a Function of Software Characteristics

DTC Verification Analysis Activities

Software Characteristics

Non-Functional Requirements Important Operational Considerations  Stability  Reliability  Security Important Development Considerations  Support for collaborative interaction  Adaptability to other environments  Extensibility to other scientific problems  Maintainability  Portability to other organizations of the ‘System’

Verification Mechanics Aggregate/ Combine to Produce Specific Metrics Pre-process Forecasts Pre-process Observations Create Displays Verification System Components- Historical Perspective Good solution for a small set well defined questions System structure remains consistent, but can expand forecast dataset Solution needs to change when want to add observation datasets, new verification mechanics and assessment techniques, integrate non- meteorological information, and inter-compare a variety of forecast systems

Verification System Components: Data Management Structuring System Data Warehouse Finest resolution Forecast/observation pairs + forecast/obs attributes

Verification System Components: Data Management Structuring System Data Warehouse Finest resolution Forecast/observation pairs + forecast/obs attributes Data Marts

Verification System Components: Data Management Structuring System Data Warehouse Finest resolution Forecast/observation pairs + forecast/obs attributes Data Marts Intercompare Forecast Systems Produce Statistical Information Aggregation driven by specific data relationships

Verification Mechanics Pre-process Forecasts Pre-process Observations Displays DTC Verification System Components Store Finest Granularity Verification Elements Apply storage Optimization Strategy Database info Query interrogation to compute score

Verification System Complexities Management of complex interdependent relationships between datasets  Event-driven component initiation Eliminate unnecessary time delays between data execution steps Handle abnormal delays in data access, processing, and user access Account for differences between operational deployments  Multi workflow requests Incorporate relationships with the addition of new datasets, verification methodologies, and forecast intercomparisons Provide only meaningful workflow comparisons (i.e., par down list of all possible choices to meet user analysis questions)

Verification System Complexities Effective access to the results  Provide tiered optimizations Fast turn around of pre-defined set of analyses Flexibility to define varying set of analyses Support wide-range of user needs  Allow for wide variety of graphical and standard displays Traditional statistical displays Displays that allow the integration of verification information

Summary Verification system will mainly support the forecast intercomparison evaluation (e.g., core tests) function of the DTC Scientific conceptualism developed for the toolkit will feed into the DTC verification system DTC requirements for the verification system require complex data management and interrogation strategies Tiered approaches for data access are required to provide maximum system flexibility to end users

Future Efforts Gather verification system requirements from the workshop Develop a Functional Requirements document Begin preliminary development of the system concepts by the end of the year

Contributors to the DTC System Development NCAR – scientific verification concepts and toolkit capabilities NCEP – bridge from the current NCEP verification system functionality to the DTC verification system framework GSD – verification system framework by extending database management and web access strategies DTC users – functional requirements for the verification system

Questions?