Memory Management and Garbage Collection Algorithms for Java-Based Prolog

Memory Management and Garbage Collection Algorithms for Java-Based Prolog

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Date: August 2001
Creator: Zhou, Qinan
Description: Implementing a Prolog Runtime System in a language like Java which provides its own automatic memory management and safety features such as built--in index checking and array initialization requires a consistent approach to memory management based on a simple ultimate goal: minimizing total memory management time and extra space involved. The total memory management time for Jinni is made up of garbage collection time both for Java and Jinni itself. Extra space is usually requested at Jinni's garbage collection. This goal motivates us to find a simple and practical garbage collection algorithm and implementation for our Prolog engine. In this thesis we survey various algorithms already proposed and offer our own contribution to the study of garbage collection by improvements and optimizations for some classic algorithms. We implemented these algorithms based on the dynamic array algorithm for an all--dynamic Prolog engine (JINNI 2000). The comparisons of our implementations versus the originally proposed algorithm allow us to draw informative conclusions on their theoretical complexity model and their empirical effectiveness.
Contributing Partner: UNT Libraries
Clustering Algorithms for Time Series Gene Expression in Microarray Data

Clustering Algorithms for Time Series Gene Expression in Microarray Data

Date: August 2012
Creator: Zhang, Guilin
Description: Clustering techniques are important for gene expression data analysis. However, efficient computational algorithms for clustering time-series data are still lacking. This work documents two improvements on an existing profile-based greedy algorithm for short time-series data; the first one is implementation of a scaling method on the pre-processing of the raw data to handle some extreme cases; the second improvement is modifying the strategy to generate better clusters. Simulation data and real microarray data were used to evaluate these improvements; this approach could efficiently generate more accurate clusters. A new feature-based algorithm was also developed in which steady state value; overshoot, rise time, settling time and peak time are generated by the 2nd order control system for the clustering purpose. This feature-based approach is much faster and more accurate than the existing profile-based algorithm for long time-series data.
Contributing Partner: UNT Libraries
A Novel Space Partitioning Algorithm to Improve Current Practices in Facility Placement

A Novel Space Partitioning Algorithm to Improve Current Practices in Facility Placement

Date: March 2011
Creator: Jimenez, Tamara; Mikler, Armin R. & Tiwari, Chetan
Description: This article discusses a novel space partitioning algorithm to improve current practices in facility placement.
Contributing Partner: UNT College of Engineering