199 research outputs found
Classification algorithms for Big Data with applications in the urban security domain
A classification algorithm is a versatile tool, that can serve as a predictor for the
future or as an analytical tool to understand the past. Several obstacles prevent
classification from scaling to a large Volume, Velocity, Variety or Value. The aim
of this thesis is to scale distributed classification algorithms beyond current limits,
assess the state-of-practice of Big Data machine learning frameworks and validate
the effectiveness of a data science process in improving urban safety.
We found in massive datasets with a number of large-domain categorical features
a difficult challenge for existing classification algorithms. We propose associative
classification as a possible answer, and develop several novel techniques to distribute
the training of an associative classifier among parallel workers and improve the final
quality of the model. The experiments, run on a real large-scale dataset with more
than 4 billion records, confirmed the quality of the approach.
To assess the state-of-practice of Big Data machine learning frameworks and
streamline the process of integration and fine-tuning of the building blocks, we
developed a generic, self-tuning tool to extract knowledge from network traffic
measurements. The result is a system that offers human-readable models of the data
with minimal user intervention, validated by experiments on large collections of
real-world passive network measurements.
A good portion of this dissertation is dedicated to the study of a data science
process to improve urban safety. First, we shed some light on the feasibility of a
system to monitor social messages from a city for emergency relief. We then propose
a methodology to mine temporal patterns in social issues, like crimes. Finally,
we propose a system to integrate the findings of Data Science on the citizenry’s
perception of safety and communicate its results to decision makers in a timely
manner. We applied and tested the system in a real Smart City scenario, set in Turin,
Italy
Scaling associative classification for very large datasets
Supervised learning algorithms are nowadays successfully scaling up to
datasets that are very large in volume, leveraging the potential of in-memory
cluster-computing Big Data frameworks. Still, massive datasets with a number of
large-domain categorical features are a difficult challenge for any classifier.
Most off-the-shelf solutions cannot cope with this problem. In this work we
introduce DAC, a Distributed Associative Classifier. DAC exploits ensemble
learning to distribute the training of an associative classifier among parallel
workers and improve the final quality of the model. Furthermore, it adopts
several novel techniques to reach high scalability without sacrificing quality,
among which a preventive pruning of classification rules in the extraction
phase based on Gini impurity. We ran experiments on Apache Spark, on a real
large-scale dataset with more than 4 billion records and 800 million distinct
categories. The results showed that DAC improves on a state-of-the-art solution
in both prediction quality and execution time. Since the generated model is
human-readable, it can not only classify new records, but also allow
understanding both the logic behind the prediction and the properties of the
model, becoming a useful aid for decision makers
A Fast Minimal Infrequent Itemset Mining Algorithm
A novel fast algorithm for finding quasi identifiers in large datasets is
presented. Performance measurements on a broad range of datasets demonstrate
substantial reductions in run-time relative to the state of the art and the
scalability of the algorithm to realistically-sized datasets up to several
million records
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