66,917 research outputs found
An automated ETL for online datasets
While using online datasets for machine learning is commonplace today, the quality of these datasets impacts on the performance
of prediction algorithms. One method for improving the semantics of new data sources is to map these sources to a common
data model or ontology. While semantic and structural heterogeneities must still be resolved, this provides a well established
approach to providing clean datasets, suitable for machine learning and analysis. However, when there is a requirement for a
close to real time usage of online data, a method for dynamic Extract-Transform-Load of new sources data must be developed.
In this work, we present a framework for integrating online and enterprise data sources, in close to real time, to provide
datasets for machine learning and predictive algorithms. An exhaustive evaluation compares a human built data transformation
process with our systemâs machine generated ETL process, with very favourable results, illustrating the value and impact of
an automated approach
An automated ETL for online datasets
While using online datasets for machine learning is commonplace today, the quality of these datasets impacts on the performance
of prediction algorithms. One method for improving the semantics of new data sources is to map these sources to a common
data model or ontology. While semantic and structural heterogeneities must still be resolved, this provides a well established
approach to providing clean datasets, suitable for machine learning and analysis. However, when there is a requirement for a
close to real time usage of online data, a method for dynamic Extract-Transform-Load of new sources data must be developed.
In this work, we present a framework for integrating online and enterprise data sources, in close to real time, to provide
datasets for machine learning and predictive algorithms. An exhaustive evaluation compares a human built data transformation
process with our systemâs machine generated ETL process, with very favourable results, illustrating the value and impact of
an automated approach
An automated ETL for online datasets
While using online datasets for machine learning is commonplace today, the quality of these datasets impacts on the performance
of prediction algorithms. One method for improving the semantics of new data sources is to map these sources to a common
data model or ontology. While semantic and structural heterogeneities must still be resolved, this provides a well established
approach to providing clean datasets, suitable for machine learning and analysis. However, when there is a requirement for a
close to real time usage of online data, a method for dynamic Extract-Transform-Load of new sources data must be developed.
In this work, we present a framework for integrating online and enterprise data sources, in close to real time, to provide
datasets for machine learning and predictive algorithms. An exhaustive evaluation compares a human built data transformation
process with our systemâs machine generated ETL process, with very favourable results, illustrating the value and impact of
an automated approach
Crime Mapping In Law Enforcement: Identifying Analytical Tools, Methods, And Outputs
Law enforcement continues to improve with the constant changes in policies, social trends, and advancements â in their respective jurisdictions and others. Current trends in law enforcement improvements include the implications of evidence-based practices, problem-oriented policing, and intelligence-led practices. Much of the mentioned practices require practitioners to be savvy in criminological theory. Current research in criminology examine the environmental factors that influence crime, which reinforces law enforcement agencies to engage in crime analysis and crime mapping. Crime mapping is the principle method behind examining environment factors and situations that influence crime; Geographic Information Systems (GIS) create automated maps with attribute data to examine spatial, temporal, and other aspect influences on crime.
The market for GIS software for law enforcement is extensive in tools, analytical methods, and report outputs â not one software product is fit for all law enforcement agencies. This study examines the variety of GIS software used by law enforcement agencies, and compare the results to a previous study.
The results of the study is compared with a similar survey conducted in 1999, and assesses agency choices and uses of GIS software within the department. Findings from the study reveal that crime mapping continues to be an integral attribute in law enforcement practices; as well as similarities and variations in practices. The study concludes with a discussion as to why agencies vary with crime mapping practices, assess and explain crime mapping trend differences, and proposes recommendations for future crime mapping research in other areas of criminal justice and public policy
Conflict Detection for Edits on Extended Feature Models using Symbolic Graph Transformation
Feature models are used to specify variability of user-configurable systems
as appearing, e.g., in software product lines. Software product lines are
supposed to be long-living and, therefore, have to continuously evolve over
time to meet ever-changing requirements. Evolution imposes changes to feature
models in terms of edit operations. Ensuring consistency of concurrent edits
requires appropriate conflict detection techniques. However, recent approaches
fail to handle crucial subtleties of extended feature models, namely
constraints mixing feature-tree patterns with first-order logic formulas over
non-Boolean feature attributes with potentially infinite value domains. In this
paper, we propose a novel conflict detection approach based on symbolic graph
transformation to facilitate concurrent edits on extended feature models. We
describe extended feature models formally with symbolic graphs and edit
operations with symbolic graph transformation rules combining graph patterns
with first-order logic formulas. The approach is implemented by combining
eMoflon with an SMT solver, and evaluated with respect to applicability.Comment: In Proceedings FMSPLE 2016, arXiv:1603.0857
Automated schema matching techniques: an exploratory study
Manual schema matching is a problem for many database applications that use multiple data sources including data warehousing and e-commerce applications. Current research attempts to address this problem by developing algorithms to automate aspects of the schema-matching task. In this paper, an approach using an external dictionary facilitates automated discovery of the semantic meaning of database schema terms. An experimental study was conducted to evaluate the performance and accuracy of five schema-matching techniques with the proposed approach, called SemMA. The proposed approach and results are compared with two existing semi-automated schema-matching approaches and suggestions for future research are made
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