Abstract. Walking in Place (WIP) is an important locomotion technique used in virtual environments. This paper proposes a new approach to WIP, called Speed-Amplitude-Supported Walking-in-Place (SAS-WIP), which allows people, when walking along linear paths, to control their virtual speed based on footstep amplitude and speed metrics. We argue that our approach allows users to better control the virtual distance covered by the footsteps, achieve higher average speeds and experience less fatigue than when using state-of-the-art methods based on footstep frequency, called GUD-WIP. An in-depth user evaluation with twenty participants compared our approach to GUD-WIP on common travel tasks over a range of short, medium and long distances. We measured task performance using four distinct criteria: effectiveness, precision, efficiency and speed. The results show that SAS-WIP is both more efficient and faster than GUD-WIP when walking long distances while being more effective and precise over short distances. When asked their opinion via a post-test questionnaire, participants preferred SAS-WIP to GUD-WIP and reported experiencing less fatigue, having more fun and having a greater level of control when using our approach.
Abstract. Transforming data is a fundamental operation in application scenarios involving data integration, legacy data migration, data cleaning, and extract-transform-load processes. Data transformations are often implemented as relational queries that aim at leveraging the optimization capabilities of most RDBMSs. However, relational query languages like SQL are not expressive enough to specify an important class of data transformations that produce several output tuples for a single input tuple. This class of data transformations is required for solving the data heterogeneities that occur when source data represents an aggregation of target data. In this paper, we propose and formally define the data mapper operator as an extension of the relational algebra to address one-to-many data transformations. We supply an algebraic rewriting technique that enables the optimization of data transformation expressions that combine filters expressed as standard relational operators with mappers. Furthermore, we identify the two main factors that influence the expected optimization gains.
The optimization capabilities of RDBMSs are turning them attractive for executing data transformations. However, despite the fact that many useful data transformations can be expressed as relational queries, an important class of data transformations that produce several output tuples for a single input tuple cannot be expressed in that way.To overcome this limitation, we propose to extend Relational Algebra with a new operator named data mapper. In this paper, we formalize the data mapper operator and investigate some of its properties. We then propose a set of algebraic rewriting rules that enable the logical optimization of expressions with mappers and prove their correctness. Finally, we validate experimentally the proposed optimizations and identify the key factors that influence the optimization gains.
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