67,054 research outputs found
Engineering Top-Down Weight-Balanced Trees
Weight-balanced trees are a popular form of self-balancing binary search
trees. Their popularity is due to desirable guarantees, for example regarding
the required work to balance annotated trees.
While usual weight-balanced trees perform their balancing operations in a
bottom-up fashion after a modification to the tree is completed, there exists a
top-down variant which performs these balancing operations during descend. This
variant has so far received only little attention. We provide an in-depth
analysis and engineering of these top-down weight-balanced trees, demonstrating
their superior performance. We also gaining insights into how the balancing
parameters necessary for a weight-balanced tree should be chosen - with the
surprising observation that it is often beneficial to choose parameters which
are not feasible in the sense of the correctness proofs for the rebalancing
algorithm.Comment: Accepted for publication at ALENEX 202
Rank, select and access in grammar-compressed strings
Given a string of length on a fixed alphabet of symbols, a
grammar compressor produces a context-free grammar of size that
generates and only . In this paper we describe data structures to
support the following operations on a grammar-compressed string:
\mbox{rank}_c(S,i) (return the number of occurrences of symbol before
position in ); \mbox{select}_c(S,i) (return the position of the th
occurrence of in ); and \mbox{access}(S,i,j) (return substring
). For rank and select we describe data structures of size
bits that support the two operations in time. We
propose another structure that uses
bits and that supports the two queries in , where
is an arbitrary constant. To our knowledge, we are the first to
study the asymptotic complexity of rank and select in the grammar-compressed
setting, and we provide a hardness result showing that significantly improving
the bounds we achieve would imply a major breakthrough on a hard
graph-theoretical problem. Our main result for access is a method that requires
bits of space and time to extract
consecutive symbols from . Alternatively, we can achieve query time using bits of space. This matches a lower bound stated by Verbin
and Yu for strings where is polynomially related to .Comment: 16 page
Customer churn prediction in telecom using machine learning and social network analysis in big data platform
Customer churn is a major problem and one of the most important concerns for
large companies. Due to the direct effect on the revenues of the companies,
especially in the telecom field, companies are seeking to develop means to
predict potential customer to churn. Therefore, finding factors that increase
customer churn is important to take necessary actions to reduce this churn. The
main contribution of our work is to develop a churn prediction model which
assists telecom operators to predict customers who are most likely subject to
churn. The model developed in this work uses machine learning techniques on big
data platform and builds a new way of features' engineering and selection. In
order to measure the performance of the model, the Area Under Curve (AUC)
standard measure is adopted, and the AUC value obtained is 93.3%. Another main
contribution is to use customer social network in the prediction model by
extracting Social Network Analysis (SNA) features. The use of SNA enhanced the
performance of the model from 84 to 93.3% against AUC standard. The model was
prepared and tested through Spark environment by working on a large dataset
created by transforming big raw data provided by SyriaTel telecom company. The
dataset contained all customers' information over 9 months, and was used to
train, test, and evaluate the system at SyriaTel. The model experimented four
algorithms: Decision Tree, Random Forest, Gradient Boosted Machine Tree "GBM"
and Extreme Gradient Boosting "XGBOOST". However, the best results were
obtained by applying XGBOOST algorithm. This algorithm was used for
classification in this churn predictive model.Comment: 24 pages, 14 figures. PDF https://rdcu.be/budK
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