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The BigSur System™

Introduction

Our BigSur System is the inheritor of the intellectual capital of interdisciplinary global-change research dating from the 1992 Sequoia 2000 Project, and subsequent 1995 BigSur Project at the University of California, Berkeley, and includes the insights of Turing Award winner Jim Gray, and world renown Professor Michael Stonebraker, among other great minds. Add to the mix the transition from University prototype to real-world product at the hands of a veteran of the computer industry of (at the time) 20 years experience, our own Richard Troy, working in close concert with a major U.S. research center (LaRC), and you have a very capable product.

Overview | Perspective | Implementation | Primary Features | Major Components



The Overview

The BigSur System implements a science computing environment able to handle the needs of a single researcher, a research site, or an entire research community distributed across the Internet. Science, as an activity, is immersed in the challenge of data-management; BigSur is a general-purpose science system which brings modern database and high performance computing technology to bear on the most difficult challenges in scientific computing. BigSur learns about scientific data-types, the processes that operate against them, the visualization tools and access methods used to view and manipulate them. It learns the structural organization into which scientific data belong, and it manages that organization for its users. BigSur is taught the processing flow of data into and out of scientific processes and can then automate that processing. BigSur records the relationships as new scientific objects are created and retains the lineage and associations between them and their processes. The entire history of objects are therefore known, and this information can be used to create new objects when older data are superseded.

For a visual interpretation of one facet of the underlying concepts of The BigSur System™ the following diagram, apply named "Tracking Lineage", may be helpful. The BigSur System™ provides the framework and "glue" represented by the rum colored arrows.

(click for larger image)
Tracking Lineage Diagram

Print a full size *.tiff image as well.

BigSur knows about more than just scientific objects. It also knows about your compute resources, where you want processing performed, and can cite your documents and web links as references to your data objects. If you teach it, it knows about people and teams with whom you collaborate, what software tools they use, what scientific processes they have and what data-types those processes can operate on. It can publish your work to their systems if you wish, cause their processes to be executed, and can use and reference their data-products as inputs to your own scientific processes - all at the control of the investigators in charge and with the permissions they may or may not grant, of course. Best of all, it can "encapsulate" existing environs whole, as is, without changing their fundamentals. And where few resources are available, it can act in a more limited mode and provide many of these features without need for full implementation on the part of your collaborators.

 


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Perspective

Among the challenges of performing Science in the modern era, perhaps the most ignored is management of the big picture. Most researchers already have favorite or discipline specific data-types and visualization tools, but are woefully lacking in management of the meta-data -- the data about the data. Most often this meta-data is held in the brains of researchers and graduate students, and sometimes in notebooks, and is nearly never available online. It needs to be recorded and available in a common repository. If sufficient meta-data is managed in a system, it provides an opportunity for automation and standardization of methods of access and processing.

We believe that a Science System needs to be flexible and learn the paradigms, data-types and functions (processes) of researchers, rather than the other way around. And we believe that the system must be as 'light weight' and trim as is possible - the less there is to learn and mess with the better, so long as it is functional. The functionality domain is literally from data source (eg: sensors) to end-users desk-top - the furthest "end to end" possible. Of course, many, maybe most users won't want or need to reach all the way to each end, but we feel it is imperative to handle such breadth - it is far easier to ignore features you don't need than try to add them later.


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Implementation

The BigSur System is made up of a collection of core components. Central to this collection is the Science-Tools Database (STDB), which is further extensible for work in specific disciplines. Our Geo, and Med extensions to STDB provide Geographic and Medical Record capababilities, respectively. For the execution of Scientific Processes, the Distributed Processing System (DPS) provides the framework - BigSur's DPS is the world's first true grid computing platform. It, in turn, consists of a number of components, including both a Demand Engine (DPS-DE) which initiates processing when requested to do so, and an Eager Engine (DPS-EE) which initiates processing when parental data products become available.

The system depends upon a Relational Database Management System (RDBMS). To expedite access to the database in an appropriate way, and to enforce security and ensure the system behaves as expected, several management applications are provided as well as a large, sophisticated Application Programming Interface (API), which at the time of this writing, contains not less than 1646 functions (methods).

Generally, customers only need to write code for two basic purposes to meet their specific needs: 1) "Scientific Processes" (or functions) are and will always remain the responsiblilty of the researcher - you need to know what your science is! - and; 2) Customers often wish to have very specific interfaces for their users and/or customers. In both cases, we are expert at helping you make such implementations as you desire, and we offer a suite of services to help you reach your goals. In addition, we offer templates to get your started off on the right foot.


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Primary Features

Many features can be used independently of the others, while some are dependent and build upon the abilities offered by others.

  • Scientific "Notebook"
    • manages scientific objects (data), processes (code and executables), and the relationships between these.
    • learns about researcher's scientific data types and processes, and the relationships and structures into which they belong
    • fully distributed architecture - object meta-data, object data, scientific processes and functions, and authorized users may be anywhere
    • robust knowledge about datatypes - tracks access methods, visualization and processing tools
    • complete scientific object lineage, including full processing history and relationships between data-sets and objects
    • eliminates "shadows" of people in which one person appears to be many by tracking the relationships between actual people and the account names used on participating computers.
    • Our Geo extension conforms to the U.S. Federally mandated Federal Geographic Data Committee (FGDC) Geospatial meta-data standard
  • Workflow System
    • Provides for archetypal process definitions
    • workflow steps can be computing steps, human-interaction steps, or logical (some say virtual) steps that represent some action.
    • Workflows can be themselves named and encapsulated, thus simplifying representations of larger workflows and easing the burdens on making requests therefrom.
  • Scientific Processing
    • Our Distributed Processing System provides the core capability to schedule and automate scientific (or non-scientific) processing
    • Processing may occur on any system, anywhere in a network
    • The Demand-Engine provides processing capability whenever it is desired
    • The Eager-Engine provides processing automatically, as soon as "parent" data objects become available
    • Load-balancing and specialized compute resource control is easily managed
    • "Reaching through firewalls" to ensure security for all compute and storage resources is easily accomplished
  • Distributed Objects - Our meta-data provides an exceptionally robust distribution capability
    • Objects may reside on any system, and in any form
    • Objects may have data or not, as desired
    • Object data may be in multiple parts or may be made up of set(s) of other objects
    • Processing ("lineage") meta-data is fully distributed too - both local and remote inputs to processing are recorded in an identical fashion
    • Copies of objects are also easily managed, as the canonical home location is known
  • Resource Discovery - Robust meta-data provides for easy searching of objects
    • Meta-data describes associations and relationships so discovering the data and processing algorithms of other researchers is easy, as is discovering what data were produced by what Processes (algorithms), etc
    • Special access handle generation creates a unique handle (name) for internal identification of objects which also provide for the ultimate in data access performance due to the ability to use exact-match fetches.
    • Special associative mechanism permits easy and natural groupings of objects to fit researchers needs and greatly aiding searches
    • R-Tree indexing provides for the worlds fastest geo-spatial searches
    • Natural and powerful typing system permits researchers to create their own data-types and object-hierarchies.
  • Publishing System - Provides for publishing of results as they are generated
    • Researchers may publish to any collaborative site by any means desired - email, FTP, etc., including other database systems.
    • Publishing decisions may be based upon object type, process which ran, contact names, etc
  • Multiple-Dimension Objects - Multi-Dimension Arrays
    • Our specialized MDA code allows as many descriptions of a single object as multiple dimension arrays may be defined thus improving access to the object by making natural descriptions available for selections of data
    • Includes "snip" or "slice" functions for extracting subsets so that only minimal amounts of data need be moved through a network

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Major Components

 
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