# Presentation: 542892

## Intelligent Archive Concepts for the Future

- H. K. Ramapriyan*, Gail McConaughy*, Chris Lynnes*, Robert Harberts**, Larry Roelofs**, Steve Kempler*, Ken McDonald*
- * NASA Goddard Space Flight Center
- ** Global Science and Technology, Inc.
- Future Intelligent Earth Observing Systems/
- International Society of Photogrammetry and Remote Sensing
- November 11, 2002
- Ramapriyan@gsfc.nasa.gov

**Notes:**
This work is supported by NASA’s Intelligent Systems Project, specifically the Intelligent Data Understanding area.  The IS Project is part of the larger Program called CICT (Computing, Information and Communication Technology) and is led by NASA Ames research Center.  This is one of several activities under IDU.  There are several basic research activities at universities and NASA centers supported by this program, addressing NASA-relevant problems at a low technology readiness level, with the expectation that some of these will evolve into prototypes and eventual implementation in real missions of the future.

## Outline

- Goal and Objectives
- Problem Statement
- What is an Intelligent Archive?
- Context -- A Knowledge-Building System
- Autonomy
- Conclusion

## Goal

- To create a next generation conceptual archive architecture supported by advanced technology that is able to:
- Increase data utilization by hosting and applying IDU technologies such as:
- Information and knowledge extraction
- Automated data object identification and classification
- Intelligent user interfacing, and system management
- Distributed computing and data storage
- Automate the transformation of data to information and knowledge allowing the user to focus on research/applications rather than data and data system manipulation
- Exploit new and emerging technologies as they become available
- Incorporate lessons learned from existing archives
- Accommodate new data intensive missions without redesign or restructuring

## Technical Objectives

- Formulate concepts and architectures that support data archiving for NASA science research and applications in the 10 to 20 year time frame
- Focus on architectural strategies that will support intelligent processes and functions
- Identify and characterize science and applications scenarios that drive intelligent archive requirements
- Assess technologies and research that will need the development of an intelligent archive
- Identify and characterize potential research projects that will be needed to develop and create an intelligent archive

## The Problem

- Data acquisition and accumulation rates tend to outpace the ability to access and analyze them
- The variety of data implies a heterogeneous and distributed set of data providers that serve a diverse, distributed community of users
- Unassisted human-based manipulation of vast quantities of archived data for discovery is difficult and potentially costly
- To apply remote sensing-based technologies in operational agencies’ decision support systems, it is necessary to demonstrate the feasibility of near-real-time utilization of vast quantities of data and the derived information and knowledge
- The types of data access and usage in future years are difficult to anticipate and will vary depending on the particular research or application environment, its supporting data sources, and its heritage system infrastructure

## Earth Science Data Archive Volume Growth and Moore's Law

**Notes:**
Data storage capacity has improved exponentially over time at a rate similar to the Moore’s Law (doubling capacity every 18 months) improvement of processing speed. However, over the last decade, science data volumes (see Figure 4-1) have risen commensurately with computing power and storage capacity for a given cost as demand keeps pace with capacity. The reason is a complex interplay between science requirements and what can be afforded. Thus, we cannot assume that further advances in storage or computing technology will solve the technological problem posed by future growth in data volumes. Furthermore, growth in gigabytes/$ in storage capacity does not tell the whole story. While this figure has been increasing rapidly in both disk and tape technology areas, the ability to access large quantities of data (i.e., seek time and throughput) has not been keeping pace with storage capacity. 

## NASA Earth Science Enterprise “Data Center” Locations

*NASA Earth Science Enterprise “Data Center” Locations*

- ESE supports 68 data centers (some of which at the same location), widely
- distributed geographically.  Additional data centers, including NOAA’s NCDC
- and Unidata, are networked through membership in the ESIP Federation.

- Chart from Martha Maiden, NASA HQ

**Notes:**
This illustrates the distributed nature of data providers supported by NASA’s Earth Science enterprise.  There are about 70 of them that come in various sizes and serve various purposes.  The nature of the business is such that we expect the distributed nature to continue into the future.

## Precision Agriculture Scenario

- Local System
- Intelligence

- Distributed System
- Intelligence

- Archive System
- Intelligence

- Sensor System
- Intelligence

- Data Providers:

- Data Sources:
- (measurements)

- Networked Distributed
- Infrastructure:

- Virtual Farm:

- Space, Airborne,  In Situ, Smart Dust

- Intelligent Interactive
- User Interface:

**Information, Data &**
**Services Filtered &**
**Scaled to Concerns **
**of a specific Farm**

**Farmer**

**Machinery**
**(command/**
**control)**

**Local **
**Sensors**

**GPS**

**Wired &  Wireless Access**

**Fixed & Mobile UI**

**Notes:**
The Precision Agriculture scenario serves as a partial requirements driver and roadmap for intelligent data understanding applications.  Distilling specific farm-relevant information from an avalanche of data involves a chain of data reduction and information/knowledge extraction services (left side of diagram to top).
The interconnected intelligent systems represent a chain of semantic filters and retrievers that extract and filter relevant information on behalf of people.   The UI presents unambiguous relevant information through intelligent interactive interfaces tailored to the concerns of the the consumer.
System-to-system communication and operation (wired and wireless) interface people, local sensors inputs, and machines in autonomous modes using external operating data and information.  Information could also flow out of the virtual farm acting as a reverse provider, or a cooperative node in a wider network of aggregated services for example. 

## Advanced Weather Prediction Scenario

*Advanced Weather Prediction Scenario*

- Local System
- Intelligence

- Distributed System
- Intelligence

- Archive System
- Intelligence

- Sensor System
- Intelligence

- Data Stores:
- processing

- Data Sources:
- (observations)

- Networked Distributed
- Infrastructure:

**New**
**Data**

**Historical**
**Data**

**Predictions**

- Access, Modeling &
- Assimilation System

**Sensor Web: Terrestrial, Space-based**

**Guidance**
**Direction**
**Sensor Tasking**

**Actual Observations**

**Knowledge Process:**
**Comparison**
**Analysis &**
**Model Refinement**

**Weather Model**** **

**Notes:**
The scenario summarized in this slide reflects the visionary NASA Weather Prediction Technology Investment Study (M. Steiner, B. Atlas, M. Kalb) describing two-way interactive sensor webs and model/assimilation systems.  As time resolutions increase the linkage between model prediction and actual observations gets tighter.  A precision weather model with 15-day prediction target is the goal.  The intelligent systems that are involved in orchestrating this high speed, data and computationally complex process are essential to achieving this goal.

## Distributed Environment - sensors, providers, users

## What Is An Intelligent Archive (IA)?

- An IA includes all items stored to support “end-to-end” research and applications scenarios
- Stored items include:
- Data, information and knowledge (*see next chart*)
- Software and processing needed to manage holdings and improve self-knowledge (e.g., data-mining to create robust content-based metadata)
- Interfaces to algorithms and physical resources to support acquisition of data and their transformation into information and knowledge
- Architecture expected to be highly distributed so that it can easily adapt to include new elements as data and service providers
- Will have evolved functions beyond that of a traditional archive
- Will be based on and exploit technologies in the 10 to 20 year time range
- Will be highly adaptable so as to meet the evolving needs of science research and applications in terms of data, information and knowledge

## Data, Information and Knowledge

- _Data_: an assemblage of measurements and observations, particularly from sensors or instruments, with little or no interpretation applied
- Examples:* Scientific instrument measurements, market past performance*
- _Information_: a summarization, abstraction or transformation of data into a more readily interpretable form
- Examples:* results after performing transformations by data mining, segmentation, classification, etc., such as a Landsat scene spatially indexed based on content, assigned a “class” value, fused with other data types, and subset for an application, for example a GIS.*
- _Knowledge_: a summarization, abstraction or transformation of information that allows our understanding of the physical world
- Examples:* predictions from model forward runs, published papers, output of heuristics, or other techniques applied to information to answer a “what if” question such as “What will the accident rate be if an ice storm hits the Washington D.C. Beltway between Chevy Chase and the Potomac crossing at 7 a.m.?”*

## Context - A Knowledge-Building System

*Context - A Knowledge-Building System*

**ENTERPRISE**
- (earth and space sciences)

- Supported by
**DISTRIBUTED INTELLIGENT **
**SYSTEMS**

-  Processing Systems

- Intelligent Sensors

- Infrastructures of
- Physical and
- Virtual computing
- resources,
-  services,
- communications

- Applications

- Integrated through
**DISTRIBUTED**
**INFRASTRUCTURES**

- Knowledge

- Information

- Data

- Observation

- Transformation
- Loop

- Intelligent Functions & Services:
- Data Understanding
- Data Management
- Data Persistence and Preservation Management

**Notes:**
This chart shows the end-to-end context for an intelligent archive. Note that the end-to-end system is highly distributed. In fact, all but the smallest intelligent archives will be built upon a geographically widely dispersed and generally independently managed set of physical resources and information repositories. Thus, our conceptual architecture will be implemented over a “grid” of computing resources: IA can be viewed as a level of software applications and logical organization applied to the grid.  The presence of feedback loops connecting intelligent algorithms, methods, and technologies within the archive is an important feature. When implemented throughout an enterprise system, these automatic feedback loops create adaptive systems that respond to the need for greater data utilization and improved digital scientific services.

The functions of the Intelligent Archive can be deployed over discrete computing facilities or optionally over a network of facilities as an open, distributed resource. Small to large intelligent archives in the near future can be built and operated either collaboratively or privately because of evolving technologies, standards, and methods. Rapidly changing computing technology will permit the formation of new enterprise system capabilities built upon distributed interconnected infrastructures - a system of systems. With the inclusion of more self-directed automated processes for autonomous discovery, self-aware management, and self-healing, new advantages can be realized for both archive users and archive operators. 

## A Model of IA Focused on Objects and Functions

_**Data Management**_

**Intelligent**
**Permanent**
**Archive**

_**Added Value**_
_**Content**_

- _Cooperating Systems_
- _Interface_

_**Resource Infrastructure**_
_**Interface**_

- Register
- Lookup
- Broker

- Data
- Production
- System

**Intelligent**
**Interim**
**Archive**

_**Operations**_
**(Science;**** ****S/W)**

- Low, Mid, High (TRL)

**•****	****Self-Monitoring**
**•****	****Self-Adjusting**
**•****	****Self-Recovery**
**•****	****Autonomous Performance Tuning**
**•****	****Cooperative Interface Mgmt**

**•****	****Brokering**
**•****	****Store/Retrieve**
**•****	****Querying**
**•****	****Cataloging**
**•****	****Distributing**
**•****	****Registering**
**•****	****Change Formats**

**•****	****Mining**
**•****	****Characterization**
**•****	****Sub-setting**
**•****	****Fusion**

**•****Provisioning**
**•****	****Adapting**
**•****	****Sharing**

- Science
- Model
- System

**Notes:**
This chart shows a view of the IA from the point of view of the functions it should have and its interfaces to other entities making up a knowledge-building system.  The functions needed are shown on the bottom half of the chart.  These are essentially the same whether the IA is “interim” or “permanent”.  
The rounded rectangular boxes represent core system functions with corresponding sub-functions listed in dashed boxes. Intelligent systems and data understanding technology underlie many of the core archive functions. Future operations’ functions will be characterized by intelligent capabilities that increase automated self-regulation, adjusting, recovery, and performance tuning. Intelligent data management consists of such functions as semantic queries, retrieval strategies, registering and cataloging assets for human and machine level access, and learning access patterns for improving storage/retrieval strategies.
We also show our opinion of the technology readiness levels from the point of view of achieving our ideal of intelligent archives in the various sub-function areas. These are shown in colors - red (pink) for low, yellow (orange) for mid  and green for High.  Some of these categorizations may be arguable at a more detailed level since progress is being made in specific sub-areas at various rates.  
 While we have classified Data Management as Mid-TRL because there is significant automation in several of these functions (especially compared the way it was 10 years ago, there is considerable amount of work left to do in areas such as: Semantic Webs to help discover data, Automation of content-based metadata generation, Automated Data quality assurance, Content-based queries, Coincident searches, etc.
There is low-TRL research on-going in the “Added Value Content” area (some supported by the IS Project), but significant amount of work is needed before proving the techniques and scaling them to work in large data volume, distributed environment envisioned in this study.

Provisioning = providing infrastructure resources for particular uses

## Autonomy in an IA

-  Holdings’ Management Autonomy
- Provides data to a science knowledge base in the context of research activities
- Can exploit and use collected data in the context of a science enterprise
- Is aware of its data and knowledge holdings and is constantly searching new and existing data for unidentified objects, features or processes
- Facilitates derivation of information and knowledge using algorithms for Intelligent Data Understanding
- Works autonomously to identify and characterize objects and events, thus enriching the collections of data, information and knowledge
- User Services Autonomy
- Recognizes the value of its results, indexes/formats them properly, and delivers them to concerned individuals
- Interacts with users in human language and visual imagery that can be easily understood by both people and machines
- System Management Autonomy
- Works with other autonomous information system functions to support research
- Manages its resources, activities and functions from sensor to user
- Is aware of and manages the optimization of its own configuration
- Observes its own operation and improves its own performance from sensors to models
- Has awareness of the “state” of its cooperating external partners

**Notes:**
HMA - Storing and managing full representations of data, information, and knowledge
HMA - Building intelligence about transformations on data, information, knowledge, and accompanying services involved in a scientific enterprise
HMA - Performing self-analysis to enrich metadata that adds value to the archive’s holdings
HMA - Performing change detection to develop trending information
SMA - Interacting as a cooperative node in a “web” of other systems to perform knowledge building (where knowledge building involves the transformations from data to information to knowledge) instead of just data pipelining
SMA - Being aware of other nodes in the knowledge building system (participating in open systems interfaces and protocols for virtualization, and collaborative interoperability)

## Conclusion

- End-to-End Knowledge-Building Systems (KBSs) are needed for maximizing utilization of NASA data from missions of future in applications to benefit society
- Intelligent Archives are an essential part of such KBSs
- We have formulated a few ideas and concepts to provide recommendations that we hope will lead to
- research by the computer science community in the near-term
- prototyping to demonstrate feasibility in the mid-term, and
- operational implementation in the period from 2012 to 2025.