649,409 research outputs found
GCG: Mining Maximal Complete Graph Patterns from Large Spatial Data
Recent research on pattern discovery has progressed from mining frequent
patterns and sequences to mining structured patterns, such as trees and graphs.
Graphs as general data structure can model complex relations among data with
wide applications in web exploration and social networks. However, the process
of mining large graph patterns is a challenge due to the existence of large
number of subgraphs. In this paper, we aim to mine only frequent complete graph
patterns. A graph g in a database is complete if every pair of distinct
vertices is connected by a unique edge. Grid Complete Graph (GCG) is a mining
algorithm developed to explore interesting pruning techniques to extract
maximal complete graphs from large spatial dataset existing in Sloan Digital
Sky Survey (SDSS) data. Using a divide and conquer strategy, GCG shows high
efficiency especially in the presence of large number of patterns. In this
paper, we describe GCG that can mine not only simple co-location spatial
patterns but also complex ones. To the best of our knowledge, this is the first
algorithm used to exploit the extraction of maximal complete graphs in the
process of mining complex co-location patterns in large spatial dataset.Comment: 1
Characterizing Driving Context from Driver Behavior
Because of the increasing availability of spatiotemporal data, a variety of
data-analytic applications have become possible. Characterizing driving
context, where context may be thought of as a combination of location and time,
is a new challenging application. An example of such a characterization is
finding the correlation between driving behavior and traffic conditions. This
contextual information enables analysts to validate observation-based
hypotheses about the driving of an individual. In this paper, we present
DriveContext, a novel framework to find the characteristics of a context, by
extracting significant driving patterns (e.g., a slow-down), and then
identifying the set of potential causes behind patterns (e.g., traffic
congestion). Our experimental results confirm the feasibility of the framework
in identifying meaningful driving patterns, with improvements in comparison
with the state-of-the-art. We also demonstrate how the framework derives
interesting characteristics for different contexts, through real-world
examples.Comment: Accepted to be published at The 25th ACM SIGSPATIAL International
Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL
2017
Mining Sequences of Developer Interactions in Visual Studio for Usage Smells
In this paper, we present a semi-automatic approach for mining a large-scale dataset of IDE interactions to extract usage smells, i.e., inefficient IDE usage patterns exhibited by developers in the field. The approach outlined in this paper first mines frequent IDE usage patterns, filtered via a set of thresholds and by the authors, that are subsequently supported (or disputed) using a developer survey, in order to form usage smells. In contrast with conventional mining of IDE usage data, our approach identifies time-ordered sequences of developer actions that are exhibited by many developers in the field. This pattern mining workflow is resilient to the ample noise present in IDE datasets due to the mix of actions and events that these datasets typically contain. We identify usage patterns and smells that contribute to the understanding of the usability of Visual Studio for debugging, code search, and active file navigation, and, more broadly, to the understanding of developer behavior during these software development activities. Among our findings is the discovery that developers are reluctant to use conditional breakpoints when debugging, due to perceived IDE performance problems as well as due to the lack of error checking in specifying the conditional
The performance of space – exploring social and spatial phenomena of interaction patterns in an organisation.
It is often proposed that the design of the physical workplace influences social interaction and therefore organisational behaviour in one way or the other. Yet there is little accordance among scholars on how exactly the relationship between the social space and the social structure of an organisation is constituted. In order to explore this relationship, we combine an interpretive, phenomenological approach with a correlational, syntactic approach. Using the example of a workplace environment studied on multiple layers as well as in detail we propose that physical space influences the formation of social structure and organisational behaviour in manifold, but analytically tractable ways. The application of qualitative and quantitative methods in tandem proves fruitful for understanding the complex phenomena that characterise the emergence of organisational culture
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