Efficient implementation of lazy suffix trees

Giegerich R, Kurtz S, Stoye J. Efficient implementation of lazy suffix trees. SOFTWARE-PRACTICE & EXPERIENCE. 2003;33(11):1035-1049.We present an efficient implementation of a write-only top-down construction for suffix trees. Our implementation is based on a new, space-efficient representation of suffix trees that requires only 12 bytes per input character in the worst case, and 8.5 bytes per input character on average for a collection of files of different type. We show how to efficiently implement the lazy evaluation of suffix trees such that a subtree is evaluated only when it is traversed for the first time. Our experiments show that for the problem of searching many exact patterns in a fixed input string, the lazy top-down construction is often faster and more space efficient than other methods. Copyright (C) 2003 John Wiley Sons, Ltd

A disk-resident suffix tree index and generic framework for managing tunable indexes

This thesis introduces two related technologies. The first is a disk-resident index for biological sequence data, and the second is a framework and toolkit for the management of operational parameters for applications of which this index is typical. The Top-Compressed Suffix Tree is a novel data structure that can be used to provide a scalable, disk-resident index for large sequences. This data structure is based on the suffix tree, but has been designed to overcome the problems associated with using such structures on secondary memory. Top-Compressed Suffix Trees can be constructed incrementally, allowing indexes to be created that are larger than the amount of available main memory. Correspondingly, querying such an index only requires part of the data structure to be resident in main memory, thus allowing support for on-demand faulting and eviction of index sections during search. Such an index may be of great benefit to scientists requiring efficient access to vast repositories of genomic data. The Generic Index Development and Operation Framework (GIDOF) is a framework and toolkit that supports various tasks relating to the management of operational parameters. The performance of an index's implementation is typically influenced by several operational parameters parameters that must be tuned carefully if optimum performance is to be obtained. Indexes implemented using GIDOF can be structured in such a way that values of selected operational parameters can be adjusted; resulting in an index implementation that can be tuned to suit a given workload or system environment. This thesis presents a detailed description of the design of both the Top-Compressed Suffix Tree and the algorithms that operate over it. Extensive performance measurements are then presented and discussed, covering such aspects of index performance as construction time, average query performance and the size of the completed index. An overview of the GIDOF parameter model and toolkit is then given together with examples of how this framework can be used to manage tunable indexes, such as the Top-Compressed Suffix Tree