The document proposes Distributed Hierarchical Hyper-TGraphs (DHHTGraphs) as a model for representing graphs that go beyond plain graphs. DHHTGraphs integrate distribution, hierarchy, and hyperedges seamlessly with established graph concepts. They are defined precisely with a graph formalism and allow specifying domain-specific aspects through graph schemas. The model supports n-ary relationships, abstraction and refinement of elements, and distribution of graphs across networks in a hierarchical structure. It is implemented efficiently and integrated into software through an API.
OPAL: a passe-partout for web forms - WWW 2012 (Demonstration)Giorgio Orsi
Web forms are the interfaces of the deep web. Though modern web browsers provide facilities to assist in form filling, this assistance is limited to prior form fillings or keyword matching. Automatic form understanding enables a broad range of applications, including crawlers, meta-search engines, and usability and accessibility support for enhanced web browsing. In this demonstration, we use a novel form understanding approach, OPAL, to assist in form filling even for complex, previously unknown forms. OPAL associates form labels to fields by analyzing structural properties in the HTML encoding and visual features of the page rendering. OPAL interprets this labeling and classifies the fields according to a given domain ontology. The combination of these two properties, allows OPAL to deal effectively with many forms outside of the grasp of existing form filling techniques. In the UK real estate domain, OPAL achieves >99% accuracy in form understanding.
SQL server dba Online Training is Offering at Glory IT Technologies. We have Certified Working Professionals on this Modules. They trained so many Global Students, We also Provides Corporate Training and Job/Project Support Services to sql server dba . We are Only Institute Delivering Best Online Training Services to this Module.
OPAL: a passe-partout for web forms - WWW 2012 (Demonstration)Giorgio Orsi
Web forms are the interfaces of the deep web. Though modern web browsers provide facilities to assist in form filling, this assistance is limited to prior form fillings or keyword matching. Automatic form understanding enables a broad range of applications, including crawlers, meta-search engines, and usability and accessibility support for enhanced web browsing. In this demonstration, we use a novel form understanding approach, OPAL, to assist in form filling even for complex, previously unknown forms. OPAL associates form labels to fields by analyzing structural properties in the HTML encoding and visual features of the page rendering. OPAL interprets this labeling and classifies the fields according to a given domain ontology. The combination of these two properties, allows OPAL to deal effectively with many forms outside of the grasp of existing form filling techniques. In the UK real estate domain, OPAL achieves >99% accuracy in form understanding.
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Netwerken doen we eigenlijk dagelijks. We doen het op de gekste plaatsen zoals de sportschool, het pompstation, via LinkedIn of Twitter.
Toch willen veel ondernemers zich nog binden aan een netwerk waar ze vooraf voor moeten betalen met geen garantie op succes. "Waarom?"
Door anders naar netwerken te kijken is WeConnect een jong en fris netwerk voor ondernemers geworden.
We geven op aanvraag een training, coaching, brainstormsessie en/of zorgen voor concept realisatie en koppelen deze aan spraakmakende bijeenkomsten en deelnemers.
Wij zorgen dat ondernemers verbonden worden voor samenwerkingen, om leads te genereren en om kennis te delen.
WeConect is hierdoor hét daadkrachtige netwerk in dé branche voor ondernemers geworden.
Netwerken doen we eigenlijk dagelijks. We doen het op de gekste plaatsen zoals de sportschool, het pompstation, via LinkedIn of Twitter.
Toch willen veel ondernemers zich nog binden aan een netwerk waar ze vooraf voor moeten betalen met geen garantie op succes. "Waarom?"
Door anders naar netwerken te kijken is WeConnect een jong en fris netwerk voor ondernemers geworden.
We geven op aanvraag een training, coaching, brainstormsessie en/of zorgen voor concept realisatie en koppelen deze aan spraakmakende bijeenkomsten en deelnemers.
Wij zorgen dat ondernemers verbonden worden voor samenwerkingen, om leads te genereren en om kennis te delen.
WeConect is hierdoor hét daadkrachtige netwerk in dé branche voor ondernemers geworden.
Here is the presentation I did at Trifork GeekNights, January 12+13.
The talk is about my journey into Erlang land, how we need to start thinking about threads and processes in a new way, and some aspects of the work I have done towards implementing Erlang on the JVM platform.
Follow-up discussion either at my blog: http://javalimit.com/ or at http://groups.google.com/group/erjang
2009 - Node XL v.84+ - Social Media Network Visualization Tools For Excel 2007Marc Smith
Overview of the NodeXL project (Network Overview, Discovery and Exploration) that adds social network metrics and visualization features to Excel 2007. Contains updated images from version .84 of the NodeXL project.
Srihitha Technologies provides C Language Training in Ameerpet by real time Experts. For more information about C Language training in Ameerpet call 9394799566 / 9290641808.
The 2.0 Component Model contains a hierarchical structure of XML objects used to enable 2.0 APIs and web services which subsequently allows developers to optimize component reuse and build other 2.0 XML objects.
software development practices like procedural coding are like training wheels, they help when we start development, but are detrimental later. This presentation lists few such practices and their alternatives
This paper presents an extensive experimental study of the state-of-the-art of XML compression tools. The study reports the behavior of nine XML compressors using a large corpus of XML documents which covers the dierent natures and scales of XML documents. In addition to assessing and comparing the performance characteristics of the evaluated XML compression tools, the study tries to assess the effectiveness and practicality of using these tools in the real world. Finally, we provide some guidelines and recommendations which are useful for helping developers and users for making an effective decision for selecting the most suitable XML compression tool for their needs.
1. Distributed Hierarchical
Hyper-TGraphs :
Modeling beyond plain graphs
Daniel Bildhauer, Jürgen Ebert
Institute for Software Technology
University of Koblenz-Landau, Germany
dbildh@uni-koblenz.de
jgralab.uni-koblenz.de
2. jgralab.uni-koblenz.de
Overview
• Motivation ➡ Why?
• DHHTGraphs ➡ What?
• Details ➡ How?
• Implementation ➡ Does it work?
• Conclusion ➡ Current state and
what comes next?
2
6. jgralab.uni-koblenz.de
Issues of graph representation
• Models contain ad modeling profits from:
• N-ary relationships
• Abstraction and refinement
• Distribution
• Graphs are a suitable representation of models
• Above concepts are not supported by plain graphs
• Workarounds & existing solutions not sufficient
6
7. jgralab.uni-koblenz.de
Workarounds & existing solutions
• Workarounds cause extra effort to handle graphs
• Relation-like vertices simulate n-ary relations
• Marking of elements simulates abstraction levels
• Existing extended graph concepts are not sufficient
• Many variants of hyperedges, hierarchy and distribution,
but no integrated solution
• Graph model to restricted or to complex
• Not fully implemented/no convenient API
7
9. jgralab.uni-koblenz.de
Demands to a modern graph
framework
• Seamless integration of distribution, hierarchy and
hyperedges with established graph concepts
• Precise and well-defined graph formalism
• Ability to specify domain-specific aspects &
constraints by graph schemas
• Efficient implementation to handle large graphs
• Seamless integration in modern software by API
9
10. jgralab.uni-koblenz.de
Proposal: DHHTGraphs
• Distribution of graphs over networks
• Hierarchical structuring and refinement
• Refinement of elements by nested graphs
• Refinement of graphs by visibility layers
• Hyperedges with labeled directed ends
• Typing and attribution of vertices and edges
• Ordering of incidences at vertices and edges
• Compatibel to existing concepts as far as possible 10
12. jgralab.uni-koblenz.de
Hypergraphs
v2: Feature
• Typing, attribution and
v1: BusinessProcess
name=“pay order“
name=“payment
method“
ordering of vertices and edges
[1] [1]
• Connection by labeled, i1: realizedProcess
{1}
i3: realizedFeature
directed & ordered incidences
{2}
traversable in both directions e1:
FeatureTraceability Link
• Equality and duality of vertices id=4711
{3}
and edges {4}
i2: usedRule i4: target
• Vertices represent entities,
[1] [1]
edges their relationships
v3: TransformationRule v4: Activity
name=“pay order“
12
13. jgralab.uni-koblenz.de
Metamodeling Hypergraphs
• Modeling language grUML TransformationRule source ModelElement
(graph UML) name: String 0..* name: String
0..1 0..* 0..*
usedRule
• Vertex- and Edge classes define TraceabilityLink
0..* target
types and attributes #vertices per edge
0..* id: int
#edges per vertex
• Incidence classes define Activity
labeled connections
Feature
• Specialization of vertex-, realizedProcess
subsets source
0..* TraceabilityLink 0..*
realizedFeature
subsets source
edge-, and incidence classes 0..* 0..*
ModelElement ModelElement
• Multiplicities and incidence
BusinessProcess Feature
inheritance for vertex- and
edge classes
13
14. jgralab.uni-koblenz.de
Hierarchical graphs
v6: TransformationRule
• Element refinement by tree-like
nesting of graphs in elements i5: usedRule i7: usedRule
• Connections across boundaries
e1: e2:
• Border of nested graphs are of
same kind as nested elements
FeatureTraceability
Link
FeatureTraceability
Link
• Graph refinement by visibility layers i8: target i9: target
for elements
v4: Activity
• Refinements are DHHTGraphs on name=“pay order“
their own v5: Activity
name=“enter credit card details“
14
15. jgralab.uni-koblenz.de
Metamodeling hierarchy
<<nested>>
• Compositions define possible TraceabilityLink
nesting relationships id: int
target
• Tree-like on instance level
ModelElement
constraints
• Stereotyped compositions {kappa=0..4}
define edge nesting
•
0..* ActivityNode
Compositions also define
edge classes (compatibility to partOf
classical graph technology)
0..1
• Allowed visibility indicated by Activity Branch
constraints (kappa)
15
16. jgralab.uni-koblenz.de
Distributed graphs
v2: Feature
• Partitioning and distribution
v1: BusinessProcess
name=“pay order“
name=“payment
method“
across several stations
• Treatment of local and global realizedProcess realizedFeature
graphs in the same way
e1:
• Compatibility to hierarchy FeatureTraceability Link
id=4711
• Distributed graphs are full
usedRule target
DHHTGraphs with support of
distribution and hierarchy
• No domain specific features, v3: TransformationRule v4: Activity
name=“pay order“
thus no metamodeling
16
18. jgralab.uni-koblenz.de
Implementation
• Extension of Java library JGraLab for plain TGraphs
• Typing, attribution... realized by native Java constructs
• Extended symmetric incidence lists as datastructure
• Distribution by Java Remote Method Invocation,
efficient access by element-ids identify machine
• In-memory storage
• 1GB: 106 vertices, 106 edges, 5x106 incidences (creation 7s)
• Breadth first search on that graph in 2,5s on 2,3GHz
18
19. jgralab.uni-koblenz.de
TraceabilityLink
id: int
API ModelElement ModelElement
BusinessProcess Feature
0..* 0..*
process Feature feature
subsets source TraceabilityLink subsets source
0..* 0..*
• Object-oriented access to all
via KM3-like DSL tg
DHHTGraph properties
public interface TraceabilityLink extends Edge {
• Equal treatment of complete public int get_id();
graphs and all subgraphs public void set_id(int _id);
public TraceabilityLink getNextTraceabilityLink();
• Seamless integration in public TraceabilityLink_source getFirst_source();
applications by generated public TraceabilityLink_rule getFirst_rule();
}
schema-specific API
(interface+implementation) public interface FeatureTraceabilityLink extends TraceabilityLink {
public FeatureTraceabilityLink getNextFeatureTraceabilityLink();
public FeatureTraceabilityLink_process getFirst_process();
public FeatureTraceabilityLink_activity getFirst_activity();
}
19
21. jgralab.uni-koblenz.de
Main design decisions
• Vertices and edges are dual
• Vertices represent entities, edges their relations
• Distribution and hierarchy are compatible
• Subgraphs are DHHTGraphs on their own
• Nested vertices and edges have only vertices or
edges on their border, respectively
• Seamless from definition to implementation
21
22. jgralab.uni-koblenz.de
The future: Querying
DHHTGraphs
• Based on existing TGraph query language GReQL
from process:V{Process}
with process (-->{FeatureTraceabilityLink_process}
<--{TraceabiliyLink_rule})+
& {TransformationRule @
thisVertex.name=“MyRule“}
report process -->{FeatureTraceabilityLink} end
22
23. jgralab.uni-koblenz.de
Conclusion
• Seamless realization of
• Hyperedges with labeled ends
• Nesting of graphs in elements
• Abstraction levels by visibility layers
• Distribution
• Based on formal mathematical definition
• Metamodeling of domain-specific aspects
• Efficient implementation & convenient APIs
• Available soon at jgralab.uni-koblenz.de under GPL
23