67,379 research outputs found
Problem-Solving Knowledge Mining from Users’\ud Actions in an Intelligent Tutoring System
In an intelligent tutoring system (ITS), the domain expert should provide\ud
relevant domain knowledge to the tutor so that it will be able to guide the\ud
learner during problem solving. However, in several domains, this knowledge is\ud
not predetermined and should be captured or learned from expert users as well as\ud
intermediate and novice users. Our hypothesis is that, knowledge discovery (KD)\ud
techniques can help to build this domain intelligence in ITS. This paper proposes\ud
a framework to capture problem-solving knowledge using a promising approach\ud
of data and knowledge discovery based on a combination of sequential pattern\ud
mining and association rules discovery techniques. The framework has been implemented\ud
and is used to discover new meta knowledge and rules in a given domain\ud
which then extend domain knowledge and serve as problem space allowing\ud
the intelligent tutoring system to guide learners in problem-solving situations.\ud
Preliminary experiments have been conducted using the framework as an alternative\ud
to a path-planning problem solver in CanadarmTutor
Detecting degree symmetries in networks
The surrounding of a vertex in a network can be more or less symmetric. We
derive measures of a specific kind of symmetry of a vertex which we call degree
symmetry -- the property that many paths going out from a vertex have
overlapping degree sequences. These measures are evaluated on artificial and
real networks. Specifically we consider vertices in the human metabolic
network. We also measure the average degree-symmetry coefficient for different
classes of real-world network. We find that most studied examples are weakly
positively degree-symmetric. The exceptions are an airport network (having a
negative degree-symmetry coefficient) and one-mode projections of social
affiliation networks that are rather strongly degree-symmetric
Query Stability in Monotonic Data-Aware Business Processes [Extended Version]
Organizations continuously accumulate data, often according to some business
processes. If one poses a query over such data for decision support, it is
important to know whether the query is stable, that is, whether the answers
will stay the same or may change in the future because business processes may
add further data. We investigate query stability for conjunctive queries. To
this end, we define a formalism that combines an explicit representation of the
control flow of a process with a specification of how data is read and inserted
into the database. We consider different restrictions of the process model and
the state of the system, such as negation in conditions, cyclic executions,
read access to written data, presence of pending process instances, and the
possibility to start fresh process instances. We identify for which facet
combinations stability of conjunctive queries is decidable and provide
encodings into variants of Datalog that are optimal with respect to the
worst-case complexity of the problem.Comment: This report is the extended version of a paper accepted at the 19th
International Conference on Database Theory (ICDT 2016), March 15-18, 2016 -
Bordeaux, Franc
Clear Visual Separation of Temporal Event Sequences
Extracting and visualizing informative insights from temporal event sequences
becomes increasingly difficult when data volume and variety increase. Besides
dealing with high event type cardinality and many distinct sequences, it can be
difficult to tell whether it is appropriate to combine multiple events into one
or utilize additional information about event attributes. Existing approaches
often make use of frequent sequential patterns extracted from the dataset,
however, these patterns are limited in terms of interpretability and utility.
In addition, it is difficult to assess the role of absolute and relative time
when using pattern mining techniques.
In this paper, we present methods that addresses these challenges by
automatically learning composite events which enables better aggregation of
multiple event sequences. By leveraging event sequence outcomes, we present
appropriate linked visualizations that allow domain experts to identify
critical flows, to assess validity and to understand the role of time.
Furthermore, we explore information gain and visual complexity metrics to
identify the most relevant visual patterns. We compare composite event learning
with two approaches for extracting event patterns using real world company
event data from an ongoing project with the Danish Business Authority.Comment: In Proceedings of the 3rd IEEE Symposium on Visualization in Data
Science (VDS), 201
- …