Jussi Nikander

Taxonomy of effortless creation of algorithm visualizations

The idea of using visualization technology to enhance the understanding of abstract concepts, like data structures and algorithms, has become widely accepted. Several attempts have been made to introduce a system that levels out the burden of creating new visualizations. However, one of the main obstacles to fully taking advantage of algorithm visualization seems to be the time and effort required to design, integrate and maintain the visualizations.Effortlessness in the context of algorithm visualization is a highly subjective matter including many factors. Thus, we first introduce a taxonomy to characterize effortlessness in algorithm visualization systems. We have identified three main categories based on a survey conducted among CS educators: i) scope, i.e. how wide is the context one can apply the system to ii) integrability, i.e., how easy it is to take in use by a third party, and iii) interaction techniques, i.e., how well does the system support different use cases regularly applied by educators. We will conclude that generic and effortless visualization systems are needed. Such a system, however, needs to combine a range of characteristics implemented in many current AV systems.

Algorithm Visualization in Teaching Spatial Data Algorithms

Algorithm visualization is a widely-used tool for teaching data structures and algorithms. Spatial data algorithms are algorithms that are designed to process multidimensional data. This work introduces a spatial data extension to the successful TRAKLA2 learning environment, which includes automatically assessed visual algorithm simulation exercises. First impressions on using the visualizations in teaching are also described.

Indoor and Outdoor Mobile Navigation by Using a Combination of Floor Plans and Street Maps

Positioning and map technology integrated to smart mobile devices allows the users to locate themselves and find routes between locations. Such route finding typically works only outdoors due to reliance on the GPS system and lack of indoor map data. This work introduces a prototype for combined indoor and outdoor mobile navigation system for a university campus. An important part of the prototype implementation is the conversion of CAD floor plans to GIS data that can be used together with existing outdoor maps for locating and for finding shortest routes between locations. This work describes a semi-automatic conversion process that produces indoor map data, which is combined with OpenStreetMap and Bing map data for route finding and displaying a hybrid map. The prototype application, which uses this data, has been implemented on the iPad. The prototype uses GPS for outdoor positioning and QR codes for indoor positioning. The work is currently in process, and future prospects of the prototype are discussed.

Experiences on Using TRAKLA2 to Teach Spatial Data Algorithms

This paper reports on the results of a two year project in which visual algorithm simulation exercises were developed for a spatial data algorithms course. The success of the project is studied from several point of views, i.e., from developers, teacherss, and students perspective. The amount of work, learning outcomes, and feasibility of the system has been estimated based on the data gathered during the project. The results are encouraging, which motivates to extend the concept also for other courses in the future.

Visualization of Spatial Data Structures on Different Levels of Abstraction

Spatial data structures are used to manipulate location data. The visualization of such structures faces many challenges that are not relevant in the visualization of one-dimensional data. The visualized data can be represented using several different types of visual metaphors. These metaphors can be divided into several different levels of abstraction depending on the purpose of the visualization. This paper proposes a division of data structure visualization into four levels of abstraction, and shows how these abstractions can be taken into account in the visualization of spatial data structures.

Algorithm Visualization System for Teaching Spatial Data Algorithms

Introduction Spatial data is data that is located in a multidimensional space (Laurini & Thompson, 1992). In other words, each spatial data item is identified by a set of coordinates, which define its location in relation to other data items. Thus, each spatial data item contains spatial information (coordinates) that describes the location of the item and associated attribute data that describes what it represents. Spatial data is used in numerous disciplines, such as geographic information systems (GIS), computer graphics, robotics, virtual reality, computer-aided design, biology, VLSI design, and many others. In the context of GIS and related disciplines, the data is assumed to model geographic locations on the Earths surface and their associated properties. In this paper, we discuss spatial data in this context. In GIS, there are at least two coordinate axes (x and y, which represent geographical longitude and latitude), and two additional ones (height and time) can also be used. Spatial data algorithms (SDA) are algorithms designed to process and manipulate such data and spatial data structures are entities used to store the data. Spatial data structures are based on regular non-spatial data structures such as arrays and trees, as well as algorithms that manipulate these basic structures. However, the multidimensional nature of spatial data makes them more complex than the basic data structures. This also makes the design and implementation of efficient spatial data algorithms more difficult. For example, dictionary structures for spatial data must be able to locate data items according to their coordinates instead of using one-dimensional key values. Furthermore, the spatial nature of the data makes teaching spatial data algorithms more challenging than basic non-spatial data structures and algorithms. For example, in order to show both how data items are related to each other and how they are arranged in a data structure, typically two illustrations are required: one to show the data items and the space they occupy, and another to show the data structures. Consequently, the learner must be able to connect the illustrations in order to comprehend how the data is organized. In basic data structures, on the other hand, the relationships between the data items are typically distinguishable with just a single picture. SDA and associated data structures are an integral part of geoinformatics, a branch of science where computer science is applied to cartography and geosciences. The data geoinformatics studies is location data on the Earths surface, and therefore SDA are required for efficient storage and processing. Geoinformatics is also closely related to cartography, and therefore many different kinds of illustrations are used. Maps are the most fundamental way to represent spatial information. Visualization of maps is in itself a large, varied and important field (Slocum, McMaster, Kessler, & Howard, 2004). However, since many spatial data sets have several properties for each location, other visualization methods are also required for understanding the data. For example, multivariate visualization techniques such as parallel coordinate plots or star plots can be used in conjunction with map views. A map view shows how the data is distributed geographically, while other views show what information the data contains. Software Visualization (SV) is a branch of software engineering that aims to use graphics and animation to illustrate the different aspects of software (Stasko, Domingue, Brown, & Price, 1998). Price, Baecker, and Small (1993) divide SV into two subcategories: program visualization (PV) and algorithm visualization (AV). PV is the use of various visual techniques to enhance the human understanding of computer programs. It is typically used to illustrate actual, implemented programs. AV, on the other hand, illustrates abstractions of algorithmic concepts and is independent of any actual algorithm implementation. …

Spatial data algorithm extension to TRAKLA2 environment

In this paper an extension that brings spatial data algorithm support to the TRAKLA2 system is described. The different exercise types and the problems implementing them are discussed, and suggestions for future work and ideas for better spatial data support to the system are given.