Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
C4.5: programs for machine learning
C4.5: programs for machine learning
High-level optimization via automated statistical modeling
PPOPP '95 Proceedings of the fifth ACM SIGPLAN symposium on Principles and practice of parallel programming
Machine Learning
Data mining: concepts and techniques
Data mining: concepts and techniques
Machine Learning
Introduction to Algorithms
Algorithms for Constructing of Decision Trees
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
A Dynamically Tuned Sorting Library
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
A framework for adaptive algorithm selection in STAPL
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
Language constructs for context-oriented programming: an overview of ContextL
DLS '05 Proceedings of the 2005 symposium on Dynamic languages
An Adaptive Algorithm Selection Framework for Reduction Parallelization
IEEE Transactions on Parallel and Distributed Systems
Context-oriented programming: beyond layers
ICDL '07 Proceedings of the 2007 international conference on Dynamic languages: in conjunction with the 15th International Smalltalk Joint Conference 2007
PetaBricks: a language and compiler for algorithmic choice
Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation
Fast support vector machines for continuous data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Proceedings of the ACM SIGPLAN/SIGBED 2010 conference on Languages, compilers, and tools for embedded systems
SC'08 Proceedings of the 7th international conference on Software composition
Data Mining with Decision Trees: Theroy and Applications
Data Mining with Decision Trees: Theroy and Applications
Optimized composition of performance-aware parallel components
Concurrency and Computation: Practice & Experience
Adaptation of legacy codes to context-aware composition using aspect-oriented programming
SC'12 Proceedings of the 11th international conference on Software Composition
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Context-Aware Composition allows to automatically select optimal variants of algorithms, data-structures, and schedules at runtime using generalized dynamic Dispatch Tables. These tables grow exponentially with the number of significant context attributes. To make Context-Aware Composition scale, we suggest four alternative implementations to Dispatch Tables, all well-known in the field of machine learning: Decision Trees, Decision Diagrams, Naive Bayes and Support Vector Machines classifiers. We assess their decision overhead and memory consumption theoretically and practically in a number of experiments on different hardware platforms. Decision Diagrams turn out to be more compact compared to Dispatch Tables, almost as accurate, and faster in decision making. Using Decision Diagrams in Context-Aware Composition leads to a better scalability, i.e., Context-Aware Composition can be applied at more program points and regard more context attributes than before.