<p>The RISS (Relational Inquiry and Storage System) data base management system was first implemented at Forest Hospital in Des Plaines, Illinois. It was originally written in BASIC-PLUS to run under the RSTS/E operating system on a DEC PDP-II minicomputer. The RISS system used the relational data base structure because of its basic simplicity and because of the ease with which new relations may be added to the data base without disturbing existing applications.</p> <p>The aim of this project was to convert the existing RISS software from BASIC-PLUS to CBASIC-2 to run on the Dynabyte microcomputer under the CP/M operating system, for use in the McMaster University Faculty of Business. A simple application program was also developed to demonstrate the converted RISS system.</p> / Master of Science (MSc)

<p>This report is concerned with an investigation into a software system designed to allow effect utilization of FORTRAN application programs from a library. The components of this system consist of an interpreter program to manipulate character strings and provide overall control, an evaluator program to carry out operations on numeric data and to provide for the calling of library programs, and an associative memory to store and retrieve facts about the environment or field of study in which the system is being used. Details involving how to use each component and how each component works are discussed. Possible improvements to the system and the relationship of the system to the field of control structures are also considered. The implementation of the system is discussed and this leads to an examination of the algorithms used in the operation of the system. Control is easily maintained so systems constructed from the components may be modified or extended by any user. Thus, these components form a basis for a class of extendable systems.</p> / Master of Science (MSc)

This project is an extension of i2MapReduce: Incremental MapReduce for Mining Evolving Big Data . i2MapReduce is used for incremental big data processing, which uses a fine-grained incremental engine, a general purpose iterative model that includes iteration algorithms such as PageRank, Fuzzy-C-Means(FCM), Generalized Iterated Matrix-Vector Multiplication(GIM-V), Single Source Shortest Path(SSSP). The main purpose of this project is to reduce input/output overhead, to avoid incurring the cost of re-computation and avoid stale data mining results. Finally, the performance of i2MapReduce is analyzed by comparing the resultant graphs.

As more people seek the benefits of going online, more people are exposed to privacy risks from their time online. With a largely unregulated Internet, self-determination about privacy risks must be feasible for people from all walks of life. Yet in many cases decisions are either not obvious or not accessible. As one example, privacy policies are written beyond most adults reading comprehension level, and few people read policies let alone act based on the information policies contain. In my thesis I examine decisions made about threats from website data collection. In the course of multiple studies I use a variety of tools including lab-based studies, online studies, mental models interviews, economic analysis, and analysis of cookies used for tracking. Privacy literature is full of apparent conflicts between people saying they care very much about their privacy, yet not taking the steps required to protect their privacy. By using multiple approaches and crossing multiple disciplines I am able to contribute to a more coherent picture of whether people are able to make choices about protecting their online privacy.

<p>This thesis discusses and describes empirical comparisons of execution times of three programs for computing runs in strings. Since two of the pro- grams were thought to be of O(n log n) algorithms (crochB and crochB7) and the third is an implementation of a linear algorithm (runFinder), it was ex- pected that for larger strings runFinder() will strongly outperform the other two programs in the processing of long strings. The aim of this study is thus manifold. We establish the upper limits of lengths of strings for which the performances of crochB and crochB7 are faster or comparable to the perfor- mance of runFinder; we also investigate what kind of penalty in performance crochB7 incurs for the memory saving implementation; furthermore, we wish to explore the relative trade-offs of using one technique (represented through the programs with which experimentation was gone about) over another: within what context would it be advantageous to utilize one program over another of those that are being investigated.</p> <p>The motivation for this work is the continuation of work of Franek, Jiang, Smyth, Weng, and Xiao, who implemented a space efficient version of Crochemore’s repetition algorithm [6], and then extended it to compute runs [4, 5]. The three programs tested are:</p> <p>1. crochB – a direct C++ implementation of the extension of Crochemore’s algorithm for runs by Franek, Jiang, and Weng without any space savings techniques;</p> <p>2. crochB7 – a space efficient version of crochB by the same authors, 3. runFinder – an efficient C++ implementation by Hideo Bannai from the</p> <p>iiiDepartment of Informatics at Kyushu University in Japan. His imple- mentation utilizes the linear-time strategy of computing the suffix array of the string; using the suffix array it then computes the LCP array; us- ing the suffix and LCP arrays it computes the Lempel-Ziv factorization; from the Lempel-Ziv factorization all leftmost runs are computed using Main’s algorithm; and the rest of the runs are computed using Kolpakov- Kucherov’s algorithm.</p> <p>In this thesis, the three programs are discussed, the experimental setup for the performance measurements is described, the measurements are presented and a brief analysis of the results follows. It will be shown that although an expectation of O(n log n) performance can be expected in the case of processing of one category of investigated data by the latest version of the implementation of the Crochemore program, in some circumstances (discussed), a performance expectation of order n2, and in others one between this and one of order n log n will be encountered.</p> / Master of Science (MSc)

Knowing when and how to seek academic help is crucial to the success of undergraduate computing students. While individual help-seeking resources have been studied, little is understood about the factors influencing students to use or avoid certain re- sources. Understanding students’ patterns of help-seeking can help identify factors contributing to utilization or avoidance of help resources by different groups, an important step toward improving the quality and accessibility of resources. We present a mixed-methods study investigating the help-seeking behavior of undergraduate computing students. We collected survey data (n = 138) about students’ frequency of using several resources followed by one-on-one student interviews (n = 15) to better understand why they use those resources. Several notable patterns were found. Women sought help in office hours more frequently than men did and computing majors sought help from their peers more often than non-computing majors. Additionally, interview data revealed a common progression in which students started from easily accessible but low utility resources (online sources and peers) before moving on to less easily accessible, high utility resources (like instructor office hours). Finally, while no differences between racial groups was observed, the lack of diversity in our sample limits these findings.

Malware is computer software written by someone with mischievous or, more usually, malicious and/or criminal intent and specifically designed to damage data, hosts or networks. The variety of malware is increasing proportionally with the increase in computers and we are not aware of newly emerging malware. Tools are needed to categorize families of malware, so that analysts can compare new malware samples to ones that have been previously analyzed and determine steps to detect and prevent malware infections. In this thesis, I developed a technique to catalog and characterize the behavior of malware, so that malware families, the level of potential threat, and the effects of malware can be identified. Combinations of complementary techniques, including third-party tools, are integrated to scan and illustrate how malware may harm a target machine, search for related malware behavior, and organize malware into families, based on a number of characteristics.

Microsoft’s series of Windows operating systems represents some of the most commonly encountered technologies in the field of digital forensics. It is then fair to say that Microsoft’s design decisions greatly affect forensic efforts. Because of this, it is exceptionally important for the forensics community to keep abreast of new developments in the Windows product line. With each new release, the Windows operating system may present investigators with significant new artifacts to explore. Described by some as the heart of the Windows operating system, the Windows registry has been proven to contain many of these forensically interesting artifacts. Given the weight of Microsoft’s influence on digital forensics and the role of the registry within Windows operating systems, this thesis delves into the Windows 8 registry in the hopes of developing new Windows forensics utilities.

The focus of this dissertation has been on improving the non-negative tensor factorization technique of text mining. The improvements have been made in both pre-processing and post-processing stages, with the goal of making the non-negative tensor factorization algorithm accessible to the casual user. The improved implementation allows the user to construct and modify the contents of the tensor, experiment with relative term weights and trust measures, and experiment with the total number of algorithm output features. Non-negative tensor factorization output feature production is closely integrated with a visual post-processing tool, FutureLens, that allows the user to perform in depth analysis and has a great potential for discovery of interesting and novel patterns within a large collection of textual data. This dissertation necessitated a number of significant modifications and additions to FutureLens in order to facilitate its integration into the analysis environment.

Computer Game Playing has been an active area of research since Samuel’s first Checkers player (Samuel 1959). Recently interest beyond the classic games of Chess and Checkers has led to competitions such as the General Game Playing competition, in which players have no beforehand knowledge of the games they are to play, and the Computer Poker Competition which force players to reason about imperfect information under conditions of uncertainty. The purpose of this dissertation is to explore the area of General Game Playing both specifically and generally. On the specific side, we describe the design and implementation of our General Game Playing system OGRE. This system includes an innovative method for feature extraction that helped it to achieve second and fourth place in two international General Game Playing competitions. On the more general side, we also introduce the Regular Game Language, which goes beyond current works to provide support for both stochastic and imperfect information games as well as the more traditional games.