14,607 research outputs found
Large Graph Analysis in the GMine System
Current applications have produced graphs on the order of hundreds of
thousands of nodes and millions of edges. To take advantage of such graphs, one
must be able to find patterns, outliers and communities. These tasks are better
performed in an interactive environment, where human expertise can guide the
process. For large graphs, though, there are some challenges: the excessive
processing requirements are prohibitive, and drawing hundred-thousand nodes
results in cluttered images hard to comprehend. To cope with these problems, we
propose an innovative framework suited for any kind of tree-like graph visual
design. GMine integrates (a) a representation for graphs organized as
hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b)
a graph summarization methodology - CEPS. Our graph representation deals with
the problem of tracing the connection aspects of a graph hierarchy with sub
linear complexity, allowing one to grasp the neighborhood of a single node or
of a group of nodes in a single click. As a proof of concept, the visual
environment of GMine is instantiated as a system in which large graphs can be
investigated globally and locally
Building multi-layer social knowledge maps with google maps API
Google Maps is an intuitive online-map service which changes people's way of navigation on Geo-maps. People can explore the maps in a multi-layer fashion in order to avoid information overloading. This paper reports an innovative approach to extend the "power" of Google Maps to adaptive learning. We have designed and implemented a navigator for multi-layer social knowledge maps, namely ProgressiveZoom, with Google Maps API. In our demonstration, the knowledge maps are built from the Interactive System Design (ISD) course at the School of Information Science, University of Pittsburgh. Students can read the textbooks and reflect their individual and social learning progress in a context of pedagogical hierarchical structure
A semi-automatic semantic method for mapping SNOMED CT concepts to VCM Icons
VCM (Visualization of Concept in Medicine) is an iconic language for
representing key medical concepts by icons. However, the use of this language
with reference terminologies, such as SNOMED CT, will require the mapping of
its icons to the terms of these terminologies. Here, we present and evaluate a
semi-automatic semantic method for the mapping of SNOMED CT concepts to VCM
icons. Both SNOMED CT and VCM are compositional in nature; SNOMED CT is
expressed in description logic and VCM semantics are formalized in an OWL
ontology. The proposed method involves the manual mapping of a limited number
of underlying concepts from the VCM ontology, followed by automatic generation
of the rest of the mapping. We applied this method to the clinical findings of
the SNOMED CT CORE subset, and 100 randomly-selected mappings were evaluated by
three experts. The results obtained were promising, with 82 of the SNOMED CT
concepts correctly linked to VCM icons according to the experts. Most of the
errors were easy to fix
Rich Counter-Examples for Temporal-Epistemic Logic Model Checking
Model checking verifies that a model of a system satisfies a given property,
and otherwise produces a counter-example explaining the violation. The verified
properties are formally expressed in temporal logics. Some temporal logics,
such as CTL, are branching: they allow to express facts about the whole
computation tree of the model, rather than on each single linear computation.
This branching aspect is even more critical when dealing with multi-modal
logics, i.e. logics expressing facts about systems with several transition
relations. A prominent example is CTLK, a logic that reasons about temporal and
epistemic properties of multi-agent systems. In general, model checkers produce
linear counter-examples for failed properties, composed of a single computation
path of the model. But some branching properties are only poorly and partially
explained by a linear counter-example.
This paper proposes richer counter-example structures called tree-like
annotated counter-examples (TLACEs), for properties in Action-Restricted CTL
(ARCTL), an extension of CTL quantifying paths restricted in terms of actions
labeling transitions of the model. These counter-examples have a branching
structure that supports more complete description of property violations.
Elements of these counter-examples are annotated with parts of the property to
give a better understanding of their structure. Visualization and browsing of
these richer counter-examples become a critical issue, as the number of
branches and states can grow exponentially for deeply-nested properties.
This paper formally defines the structure of TLACEs, characterizes adequate
counter-examples w.r.t. models and failed properties, and gives a generation
algorithm for ARCTL properties. It also illustrates the approach with examples
in CTLK, using a reduction of CTLK to ARCTL. The proposed approach has been
implemented, first by extending the NuSMV model checker to generate and export
branching counter-examples, secondly by providing an interactive graphical
interface to visualize and browse them.Comment: In Proceedings IWIGP 2012, arXiv:1202.422
Solving the TTC 2011 Reengineering Case with GrGen.NET
The challenge of the Reengineering Case is to extract a state machine model
out of the abstract syntax graph of a Java program. The extracted state machine
offers a reduced view on the full program graph and thus helps to understand
the program regarding the question of interest. We tackle this task employing
the general purpose graph rewrite system GrGen.NET (www.grgen.net).Comment: In Proceedings TTC 2011, arXiv:1111.440
- …