LZ77, LZ78 and other compression algorithms use derivates of the original Lempel-Ziv (LZ) parsing algorithm, which computes the LZ production complexity. The focus of this paper is on the original algorithm. Like its derivates, it progressively factors an input string s into a series of steps, each of which consists of a reference to a substring starting in the already parsed part of the s (reproduction) and a single symbol (innovation). In all LZ parsers, the input string generally ends on an incomplete step part-way through a reproduction rather than on an innovation symbol. This LZ parsing tail is usually given scant treatment as it tends to be short compared to the string length |s|, even though its length is bound only by |s|- 1. This paper examines the expected length of the tail for |s| produced by an i.i.d. Bernoulli source, and uses this to derive a new result on the statistics of subsequences of large strings.

Distributed platforms are now a de facto standard in modern software and application development. Although the ACM/IEEE Curriculum 2013 introduces Parallel and Distributed Computing as a first class knowledge area for the first time, the right level of abstraction to teach these concepts is still an important question that needs to be explored. This work presents our findings in teaching cloud computing by exposing upper-level students to testbeds in use by the distributed systems research community. The possibility of giving students practical and relevant experience was explored in the context of new course assignment objectives. Furthermore, students were able to significantly contribute to a pilot class project with medium-scale computation based on satellite data. However, the software engineering challenges in these environments proved to be daunting. In particular, these challenges were exacerbated by a lack of debugging support relative to the environments students were more familiar with---requiring development practices that out-stripped typical course experiences. Our proposed set of experiments and project provide a basis for an evaluation of the trade-offs of teaching cloud and distributed systems on the wild side. We hope that these findings provide insight into some of the possibilities to consider when preparing the next generation of computer scientists to engage with software practices and paradigms that are already fundamental in today's highly distributed systems.

This paper presents flott, a fast, low memory T-transform algorithm which can be used to compute the string complexity measure T-complexity. The algorithm uses approximately one third of the memory of its predecessor while reducing the running time by about 20 percent. The flott implementation has the same worst-case memory requirements as state of the art suffix tree construction algorithms. A suffix tree can be used to efficiently compute the Lempel-Ziv production complexity, which is another measure of string complexity. The C-implementation of flott is available as Open Source software.