Presentation of Meandre: Semantic-Driven Data-Intensive Flows in the Clouds at eScience 2008 by Bernie Acs
Data-intensive flow computing allows efficient processing of large volumes of data otherwise unapproachable. This paper introduces a new semantic-driven data-intensive flow infrastructure which: (1) provides a robust and transparent scalable solution from a laptop to large-scale clusters, (2) creates an unified solution for batch and interactive tasks in high-performance computing environments, and (3) encourages reusing and sharing components. Banking on virtualization and cloud computing techniques, the Meandre infrastructure is able to create and dispose Meandre clusters on demand, being transparent to the final user. This paper also presents a prototype of such clustered infrastructure and some results obtained using it.
Presentation of Meandre: Semantic-Driven Data-Intensive Flows in the Clouds at eScience 2008 by Bernie Acs
Data-intensive flow computing allows efficient processing of large volumes of data otherwise unapproachable. This paper introduces a new semantic-driven data-intensive flow infrastructure which: (1) provides a robust and transparent scalable solution from a laptop to large-scale clusters, (2) creates an unified solution for batch and interactive tasks in high-performance computing environments, and (3) encourages reusing and sharing components. Banking on virtualization and cloud computing techniques, the Meandre infrastructure is able to create and dispose Meandre clusters on demand, being transparent to the final user. This paper also presents a prototype of such clustered infrastructure and some results obtained using it.
Services Oriented Infrastructure in a Web2.0 WorldLexumo
Tom Maguire discusses applying SOA Web 2.0 technologies, and open standards to the problems faced by IT in an ever changing world.
This session was recorded at EMC World 2007 in Orlando Florida
API's, Freebase, and the Collaborative Semantic webDan Delany
A presentation about the state of the collaborative semantic web, including:
- What?
- Why?
- Where do we stand?
- A case study on Metaweb's Freebase project
Presentation given during a tour of Australia, in May 2009. The targeted audience are people who are already familiar with the fundamentals of Semantic Web, and this presentation gives an overview of what is happening at W3C
Services Oriented Infrastructure in a Web2.0 WorldLexumo
Tom Maguire discusses applying SOA Web 2.0 technologies, and open standards to the problems faced by IT in an ever changing world.
This session was recorded at EMC World 2007 in Orlando Florida
API's, Freebase, and the Collaborative Semantic webDan Delany
A presentation about the state of the collaborative semantic web, including:
- What?
- Why?
- Where do we stand?
- A case study on Metaweb's Freebase project
Presentation given during a tour of Australia, in May 2009. The targeted audience are people who are already familiar with the fundamentals of Semantic Web, and this presentation gives an overview of what is happening at W3C
This session describes the architecture and implementation of an embeddable, extensible enterprise content management core for Java EE and simpler platforms. The presentation starts by describing the general architectural concepts used as building blocks:
• A schema and document model, reusing XML schemas and making good use of XML namespaces, where document types are built with several facets
• A repository model, using hierarchy and versioning, with the Content Repository API for Java (JSR 170) being one of the possible back ends
• A query model, based on the Java Persistence query language (JSR 220) and reusing the path-based concepts from Java Content Repositories (JCR)
• A fine-grained security model, compatible with WebDAV concepts and designed to provide flexible security policies
• An event model using synchronous and asynchronous events, allowing bridging through Java Message Service (JMS) or other systems to other event-enabled frameworks
• A directory model, representing access to external data sources using the same concepts as for documents but taking advantage of the specificities of the data back ends
Suitable abstraction layers are put in place to provide the required level of flexibility. One of the main architectural tasks is to find commonalities in all the systems used (or whose use is planned in the future) so framework users need to learn and use a minimal number of concepts. The result is a set of concepts that are fundamental to enterprise document management and are usable through direct Java technology-based APIs, Java EE APIs, or SOA. The presentation shows, for each of the main components, which challenges have been met and overcome when building a framework in which all components are designed to be improved and replaced by different implementations without sacrificing backward compatibility with existing ones.
The described implementation, Nuxeo Core, can be embedded in a basic Java technology-based framework based on OSGi (such as Eclipse) or in one based on Java EE, according to the needs of the application using it. This means that the core has to function without relying on Java EE services but also has to take advantage of them when they are available (providing clustering, messaging, caching, remoting, and advanced deployment).
Introduction to NetGuardians' Big Data Software StackJérôme Kehrli
NetGuardians is executing it's Big Data Analytics Platform on three key Big Data components underneath: ElasticSearch, Apache Mesos and Apache Spark. This is a presentation of the behaviour of this software stack.
Asynchronous development in javascript can be a very powerful development paradigm. Ajax applications make use of this paradigm. This presentation will provide an insight about the important things to consider while creating Rich Internet applications
My presentation of our work at the Text Mining Workshop 2008 held in conjunction with Eighth SIAM International Conference on Data Mining (SDM 2008) in Atlanta, GA on April 26, 2008.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
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Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
1. SEASR:
Meandre: !
Semantic-Driven Data-Intensive !
Flows in the Clouds
Xavier Llora, Bernie Acs, Loretta Auvil, Boris Capitanu, Michael Welge, David Goldberg
National Center for Supercomputing Applications!
University of Illinois at Urbana-Champaign
{xllora, acs1, lauvil, capitanu, mwelge, deg}@illinois.edu
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
3. SEASR: Design Goals
• Transparency
– From a single laptop to a HPC cluster
– Not bound to a particular computation fabric
– Allow heterogeneous development
• Intuitive programming paradigm
– Modular Components, Flows, and Reusable
– Foster Collaboration and Sharing
• Open Source
• Service Orientated Architecture (SOA)
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
4. Meandre: Infrastructure
• SEASR/Meandre Infrastructure:
– Dataflow execution paradigm
– Semantic-web driven
– Web Oriented
– Supports publishing services
– Modular components
– Encapsulation and execution mechanism
– Promotes reuse, sharing, and collaboration
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
5. Meandre: Data Driven Execution
• Execution Paradigms
– Conventional programs perform computational tasks by
executing a sequence of instructions.
– Data driven execution revolves around the idea of
applying transformation operations to a flow or stream
of data when it is available.
• Dataflow Approach
– May have zero to many inputs
– May have zero to many outputs
– Performs a logical operation when data is available
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
6. Meandre: Dataflow Example
Value1
Sum
Value2
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
7. Meandre: Dataflow Example
• Dataflow Addition Example
– Logical Operation ‘+’
Value1
– Requires two inputs
Sum
– Produces one output
Value2
• When two inputs are available
– Logical operation can be preformed
– Sum is output
• When output is produced
– Reset internal values
– Wait for two new input values to become available
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
8. Meandre: The Dataflow Component
• Data dictates component execution semantics
Inputs Outputs
Component
P
Descriptor in RDF! The component !
of its behavior
implementation
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
9. Meandre: Component Metadata
• Describes a component
• Separates:
– Components semantics (black box)
– Components implementation
• Provides a unified framework:
– Basic building blocks or units (components)
– Complex tasks (flows)
– Standardized metadata
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
10. Meandre: Semantic Web Concepts
• Relies on the usage of the resource description framework
(RDF) which uses simple notation to express graph relations
written usually as XML to provide a set of conventions and
common means to exchange information
• Provides a common framework to share and reuse data
across application, enterprise, and community boundaries
• Focuses on common formats for integration and combination
of data drawn from diverse sources
• Pays special attention to the language used for recording how
the data relates to real world objects
• Allows navigation to sets of data resources that are
semantically connected.
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
11. Meandre: Metadata Ontologies
• Meandre's metadata relies on three ontologies:
– The RDF ontology serves as a base for defining
Meandre descriptors
– The Dublin Core Elements ontology provides basic
publishing and descriptive capabilities in the description
of Meandre descriptors
– The Meandre ontology describes a set of relationships
that model valid components, as understood by the
Meandre execution engine architecture
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
12. Meandre: Components in RDF
@prefix meandre: <http://www.meandre.org/ontology/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
Existing!
@prefix dc: <http://purl.org/dc/elements/1.1/> .
Standards
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix : <#> .
<http://dita.ncsa.uiuc.edu/meandre/e2k/components/limited-iterations>
meandre:name quot;Limited iterationsquot;^^xsd:string ;
rdf:type meandre:executable_component ;
dc:creator quot;Xavier Lloraquot;^^xsd:string ;
dc:date quot;2007-11-17T00:32:35quot;^^xsd:date ;
dc:description quot;Allows only a limited number of
iterationsquot;^^xsd:string ;
dc:format quot;java/classquot;^^xsd:string ;
dc:rights quot;University of Illinois/NCSA Open Source
Licensequot;^^xsd:string ;
meandre:execution_context
<http://norma.ncsa.uiuc.edu/public-dav/Meandre/demos/E2K/V1/resources/
colt.jar> ,
<http://norma.ncsa.uiuc.edu/public-dav/Meandre/demos/E2K/V1/resources/
gacore.jar> ,
<http://dita.ncsa.uiuc.edu/meandre/e2k/components/limited-
The SEASR project and its Meandre infrastructure!
iterations/implementation/> ,
are sponsored by The Andrew W. Mellon Foundation
<http://norma.ncsa.uiuc.edu/public-dav/Meandre/demos/E2K/V1/
resources/gacore-meandre.jar> ,
<http://norma.ncsa.uiuc.edu/public-dav/Meandre/demos/E2K/V1/
13. Meandre: Components Types
• Components are the basic building block of any
computational task.
• There are two kinds of Meandre components:
– Executable components
• Perform computational tasks that require no human
interactions during runtime
• Processes are initialized during flow startup and are fired when
in accordance to the policies defined for it.
– Control components
• Used to pause dataflow during user interaction cycles
• WebUI may be a HTML Form, Applet, or Other user interface
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
14. Meandre: Component Assemblies
• Defined by connecting outputs from one component to the
inputs of another.
– Cyclical connections are supported
– Components may have
• Zero to many inputs
• Zero to many output
• Properties that control runtime behavior
• Described using RDF
– Enables storage, reuse, and sharing like components
– Allows discovery and dynamic execution
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
15. Meandre: Flow (Complex Tasks)
• A flow is a collection of connected components
Read
Merge
P
P
Show
Get
P
P
Do
P
Dataflow execution
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
16. Meandre: Create, Publish, & Share
• “Components” and “Flows” have RDF descriptors
– Easily shared, fosters sharing, & reuse
– Allow machines to read and interpret
– Independent of the implementations
– Combine different implementation & platforms
– Components: Java, Python, Lisp, Web Services
– Execution: On a Laptop or a High Performance Cluster
• A “Location” is RDF descriptor of one to many
components, one to many flows, and their
implementations
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
17. Meandre: Repository & Locations
• Each location represents a set components/flows
• Users can
– Combine different locations together
– Create components
– Assemble flows
– Share components and flows
• Repositories Help
– Administrate complex environments
– Organize components and flows
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
18. Meandre: Metadata Properties
• Components and Flows share properties such as
component name, creator, creation date, description, tags,
and rights.
• Components specific metadata to describe the
components' behavior, it’s location, type of
implementation, firing policy, runnable, format, resource
location, and execution context
• Flow specific metadata describes the directed graph of
components, components instances, connectors,
connector instance data port source, connector, instance
data port target, connector instance source, connector
instance target, instance name
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
20. Wrapping With Components
• Component provides inputs, outputs, properties
• You code
– Inside!
– Call from!
– A WS front end
– Interactive application
– Request/response cycles
21. Meandre: Programming Paradigm
• The programming paradigm creates complex
tasks by linking together a bunch of specialized
components. Meandre's publishing mechanism
allows components develop by third parties to be
assembled in a new flow.
• There are two ways to develop flows :
– Meandre’s Workbench visual programming tool
– Meandre’s ZigZag scripting language
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
22. Meandre: Workbench Existing Flow
Components
Flows
Locations
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
23. Meandre: ZigZag Script Language
• ZigZag is a simple language for describing data-
intensive flows
– Modeled on Python for simplicity.
– ZigZag is declarative language for expressing the
directed graphs that describe flows.
• Command-line tools allow ZigZag files to compile
and execute.
– A compiler is provided to transform a ZigZag program
(.zz) into Meandre archive unit (.mau).
– Mau(s) can then be executed by a Meandre engine.
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
24. Meandre: ZigZag Script Language
• As an example the Flow Diagram
– The flow below pushes two strings that get concatenated and
printed to the console
–
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
25. Meandre: ZigZag Script Language
• ZigZag code that represents example flow:
#
# Imports the three required components and creates the component aliases
#
import <http://localhost:1714/public/services/demo_repository.rdf>
alias <http://test.org/component/push_string> as PUSH
alias <http://test.org/component/concatenate-strings> as CONCAT
alias <http://test.org/component/print-object> as PRINT
#
# Creates four instances for the flow
#
push_hello, push_world, concat, print = PUSH(), PUSH(), CONCAT(), PRINT()
#
# Sets up the properties of the instances
#
push_hello.message, push_world.message = quot;Hello quot;, quot;world!quot;
#
# Describes the data-intensive flow
#
@phres, @pwres = push_hello(), push_world()
@cres = concat( string_one: phres.string; string_two: pwres.string )
print( object: cres.concatenated_string )
#
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
26. Meandre: ZigZag Script Language
• Automatic Parallelization
– Multiple instances of a component could be run in parallel to boost
throughput.
– Specialized operator available in ZigZag Scripting to cause multiple
instances of a given component to used
• Consider a simple flow example show in the diagram
• The dataflow declaration would look like
#
# Describes the data-intensive flow
#
@pu = push()
@pt = pass( string:pu.string )
print( object:pt.string )
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
27. Meandre: ZigZag Script Language
• Automatic Parallelization
– Adding the operator [+AUTO] to middle component
# Describes the data-intensive flow
#
@pu = push()
@pt = pass( string:pu.string ) [+AUTO]
print( object:pt.string )
– [+AUTO] tells the ZigZag compiler to parallelize the “pass
component instance” by the number of cores available on
system.
– [+AUTO] may also be written [+N] where N is an numeric
value to use for example [+10].
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
28. Meandre: ZigZag Script Language
• Automatic Parallelization
– Adding the operator [+4] would result in a directed graph
# Describes the data-intensive flow
#
@pu = push()
@pt = pass( string:pu.string ) [+4]
print( object:pt.string )
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
29. Meandre: Flows to MAU
• Flows can be executed using their RDF
descriptors
• Flows can be compiled into MAU
• MAU is:
– Self-contained representation
– Ready for execution
– Portable
– The base of flow execution in grid environments
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
30. Meandre: The Architecture
• The design of the Meandre architecture follows
three directives:
– provide a robust and transparent scalable solution from
a laptop to large-scale clusters
– create an unified solution for batch and interactive tasks
– encourage reusing and sharing components
• To ensure such goals, the designed architecture
relies on four stacked layers and builds on top of
service-oriented architectures (SOA)
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
31. Meandre: Basic Single Server
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
32. Meandre MDX: Cloud Computing
• Servers can be
– instantiated on demand
– disposed when done or on demand
• A cluster is formed by at least one server
• The Meandre Distributed Exchange (MDX)
– Orchestrates operational integrity by managing cluster
configuration and membership using a shared database
resource.
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
33. Meandre MDX: The Picture
MDX Backbone
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
34. Meandre MDX: The Architecture
• Virtualization infrastructure
– Provide a uniform access to the underlying execution environment.
It relies on virtualization of machines and the usage of Java for
hardware abstraction.
• IO standardization
– A unified layer provides access to shared data stores, distributed
file-system, specialized metadata stores, and access to other
service-oriented architecture gateways.
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
35. Meandre MDX: The Architecture
• Data-intensive flow infrastructure
– Provide the basic Meandre execution engine for data-intensive
flows, component repositories and discovery mechanisms,
extensible plugins and web user interfaces (webUIs).
• Interaction layer
– Can provide self-contained applications via webUIs, create plugins
for third-party services, interact with the embedding application
that relies on the Meandre engine, or provide services to the cloud.
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
36. SEASR:
Meandre: !
Semantic-Driven Data-Intensive !
Flows in the Clouds
Xavier Llora, Bernie Acs, Loretta Auvil, Boris Capitanu, Michael Welge, David Goldberg
National Center for Supercomputing Applications!
University of Illinois at Urbana-Champaign
{xllora, acs1, lauvil, capitanu, mwelge, deg}@illinois.edu
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation