1. Federated Hierarchical Filter Grids
STTR-funded project with Indiana, Caltech and Deep Web Technologies
A Grid infrastructure for Data Analysis
Integrates with the LHC Tiered Computing Model
Directly supports general Scientific Analysis
In the HEP case, the Gridlet is instantiated as a Rootlet
The FHFG Architecture Composed of Information Service Gridlets managed by general Grid system services with a portlet-based portal user interface

2. Portal Raw Data  Data  Information  Knowledge  Wisdom Decisions
Another Grid
Decisions
Another Grid SS SS SS SS SS FS FS OS MD MD FS
Portal OS OS FS OS
SOAP Messages OS FS FS FS
Another Service FS FS MD MD OS MD OS OS FS
Other
Service FS FS FS FS MD OS OS OS FS FS FS MD MD FS
Filter Service OS
Another Grid FS
MetaData FS FS FS MD
Sensor Service SS SS SS SS SS SS SS SS SS SS
Database
Another Service

3. Filter Grids Three Features:
Information services present data through traditional interfaces
Filters that accept data between these interfaces, transform and re-present
Streaming connections between all services:
High performance
Archiving
Security
Fault tolerance
Narada Brokering
Filter Grids are built from Information resources wrapped as Web Services and Basic Filters that either transform or aggregate Information. Information Services and Filters support identical Service Interfaces.

4. IS BFS Information Resource Request/Select Status MultiResolution Get=
InformationService
Issue Queries
MultiResolution Put
BFS =
Filter Resource
Basic Filter Service
Request/Select
Status
MultiResolution Get
Filters either transform or Aggregate Information

5. HEP Event Analysis using Filter Grids
Analysis tool of choice is Root.
Typical analysis activity is
Loading many files containing event data
Passing each event through a selection filter
Subjecting each selected event to a set of algorithms
Creating summary information in the form of histograms/tables/files
Analysis: starts with small event samples, then applied to much larger samples
Frequently these are remotely located in the Grid
Our HEP implementation is a Filter Grid consisting of Clarens-hosted “Rootlets”.
Each Rootlet is a full instance of the Root application, but limited in scope:
The user’s Root loads a Clarens plug-in
The Clarens interface to the Dataset Location Service allows a list of remote datasets to be generated
The client contacts each remote Grid node, connects to the Clarens server there, and instantiates a Rootlet
The user’s analysis selection code is passed over the network to the Rootlet
The list of event data files is passed to the Rootlet
The Rootlet executes, and terminates.
The output histograms/tables/files are then made available via the Clarens server, and fetched, aggregated and processed as required.

6. Root embedded in a Clarens server
Rootlets
Root embedded in a Clarens server
Tier3
Tier2
Analysis.C, Analysis.h   
Clarens Plugin
XML/RPC
Root nTuples 
GBytes
Root nTuples 
~10 TBytes
Physicist at Tier3 using Root on GBytes of ntuples
Loads Clarens Root plugin. Connects to Clarens at Tier2. Sends analysis code (.C/.h files).
Clarens creates Rootlet, passes it .C/.h files
Rootlet runs analysis code on TBytes of ntuples, creating high statistics output data.
Root at Tier3 receives and plots data

7. Higgs diphoton Analysis using Rootlets