Jorma Tarhio

Multipattern string matching with q -grams

We present three algorithms for exact string matching of multiple patterns. Our algorithms are filtering methods, which apply q-grams and bit parallelism. We ran extensive experiments with them and compared them with various versions of earlier algorithms, e.g., different trie implementations of the Aho--Corasick algorithm. All of our algorithms appeared to be substantially faster than earlier solutions for sets of 1,000--10,000 patterns and the good performance of two of them continues to 100,000 patterns. The gain is because of the improved filtering efficiency caused by q-grams.

Perspectives on Program Animation with Jeliot

The Jeliot family consists of three program animation environments which are based on a self-animation paradigm. A student can visualize her Java code without inserting additional calls to animation primitives. The design of the animation environments has been guided by the analysis of feedback from high school and university students. Evaluation studies indicate the benefit of dedicated animation environments for different user groups like novice programmers. Based on the results of these studies, we present plans for a future work on Jeliot.

Resources, tools, and techniques for problem based learning in computing

In a problem-based learning (PBL) environment, students work in groups on real-life problems and have the opportunity to determine for themselves what they need to learn in the relevant subject area(s). A common feature of problem-based learning is to provide students with a range of resources that assist them in solving the problems. This paper attempts to classify resources according to the educational functions and activities for which they will be used. It also discusses communication patterns, assessment, and evaluation with respect to problem-based learning. Finally, it presents two case studies, one that illustrates a progression of problem development within the PBL environment and one that describes a practical application of PBL, with appropriate use of resources, for a first-year course in Java.

Automated Animation of Algorithms with Eliot

Abstract Eliot is an interactive animation environment, which visualizes algorithms written in the C programming language. Eliot provides a library of visual data types which are ordinary data types with a set of pre-defined visualizations. The user selects one visualization for each data object he wants to animate. Based on these selections, Eliot constructs an animation where the objects as well as their operations are animated. Eliot can be used in algorithm design, visual debugging and learning programming.

Mobile collaborative concept mapping - combining classroom activity with simultaneous field exploration

Mobile technologies like mobile phones allow a learning community to combine classroom activities with simultaneous field explorations in an authentic environment. The collaboration between the two groups can be enhanced by a technique called collaborative concept maps, based on SMSs (short message service). This means that mobile technology is used not just as a mediator (of learning activity/collaboration) but as a trigger and platform that includes guidance and support for learning methods and the learning process. A formative evaluation indicates that it is possible to construct collaborative concept maps by mobile phones (using SMSs) and combine simultaneous classroom learning activity with mobile learning. In the conducted case study, the students in the forest sent their observations, represented as concept maps, as structured SMSs to the students in the classroom. Our experience shows that even light and easy techniques can make a difference in a learning setting, if they are carefully designed to fit the real users context.

Using the WWW as the delivery mechanism for interactive, visualization-based instructional modules (report of the ITiCSE '97 working group on visualization)

Visualization has long been an important pedagogical tool in CS education. The widespread use of the Web and the introduction of Java, with its ability to present interactive animated applets and other types of animation, all provide opportunities to expand the availability of visualization-based teaching and learning tools. In addition, the Web introduces new opportunities not available in traditional settings.We start by identifying the types of learning objectives that can be supported by visualizations and the Web environment. Next we look at specific areas where the use of the Web enhances learning beyond the usual visualization, as well as at new learning and teaching paradigms supported by the Web. We then discuss a number of different mechanisms that can be used to deliver visualizations over the Web and new ways of managing displays in the Web-based environment. We point out both advantages and disadvantages of using the Web. A look into the future follows. We consider what changes and improvements we can expect and what specific activities we would like the CS community to undertake. We end with a brief survey of currently available Web-based visualization teaching tools and a commitment to maintain a list of links to these and other sites.

Using reads to annotate the genome: influence of length, background distribution, and sequence errors on prediction capacity

Ultra high-throughput sequencing is used to analyse the transcriptome or interactome at unprecedented depth on a genome-wide scale. These techniques yield short sequence reads that are then mapped on a genome sequence to predict putatively transcribed or protein-interacting regions. We argue that factors such as background distribution, sequence errors, and read length impact on the prediction capacity of sequence census experiments. Here we suggest a computational approach to measure these factors and analyse their influence on both transcriptomic and epigenomic assays. This investigation provides new clues on both methodological and biological issues. For instance, by analysing chromatin immunoprecipitation read sets, we estimate that 4.6% of reads are affected by SNPs. We show that, although the nucleotide error probability is low, it significantly increases with the position in the sequence. Choosing a read length above 19 bp practically eliminates the risk of finding irrelevant positions, while above 20 bp the number of uniquely mapped reads decreases. With our procedure, we obtain 0.6% false positives among genomic locations. Hence, even rare signatures should identify biologically relevant regions, if they are mapped on the genome. This indicates that digital transcriptomics may help to characterize the wealth of yet undiscovered, low-abundance transcripts.

Teaching to identify problems in a creative way

Identification and specification belong to the initial stages of problem management. Traditionally, creative methods in these phases have not been considered a part of a computer scientists expertise or skills. For innovative systems, however, it is crucial that computer experts have a stronger contribution in formulating and even recognizing problems. Managing a problem up to its solution as a software product is a long process that needs creativity and flexibility. Thus, in addition to the basic elements of computer science curriculum, namely knowledge and skills, also training at attitudinal level is required. A dedicated course in creative problem management as a combination of these elements. Preliminary feedback of a pilot course indicates that the course increased students motivation and supported their self-image as creative agents.

Easy Algorithm Animation on the Web

The animation environment Jeliot automates visualization of algorithms over the Internet. The user can visualize algorithms of his own, not merely the selection supplied by a service provider. Jeliot is based on self-animating data types: the user selects the data objects of the source code to be visualized, and Jeliot produces the animation based on operations performed on those types. We present the design of Jeliot and the principles of its implementation. In addition, we discuss the educational applications of Jeliot.

Fast parameterized matching with q-grams

Two strings parameterize match if there is a bijection defined on the alphabet that transforms the first string character by character into the second string. The problem of finding all parameterized matches of a pattern in a text has been studied in both one and two dimensions but the research has been centered on developing algorithms with good worst-case performance. We present algorithms that solve this problem in sublinear time on average for moderately repetitive patterns.