Layered light is a textile design project aiming to explore light as a design variable and its abilities to - in combination with layers of laser cut textiles - create dynamic patterns. The reason being to explore how light can be used to create the very pattern itself and what the textile quality can bring to the expression. Through a practice based working method, laser cutting has been used to manipulate different textile materials, that in combination with movement have resulted in range of design examples displaying various effects of the technique. Three of the found effects have been used to take forth a collection of patterns drawing inspiration from the elusive beauty of light in nature. One of which has been produced in a larger scale, exemplifying how the expression could be used in a spacial context. The result implicates the usefulness of integrating light early on in a design process and how textile and light can be combined to enhance each other all well as displaying a whole new expression for laser cut textiles.

An examination of throwing was performed in a controlled environment with the aim of identifying the control and order parameters of throwing as proposed by dynamic pattern theory. A pilot study was conducted to test the possibility that the mass of a ball, the distance thrown and the size of targets were the control parameters. Based upon the results of the pilot study, only the distance was manipulated as an independent variable in the principal study.Three-dimensional motion was recorded using three video cameras in the motion analysis laboratory and later analysed using the Peak motion analysis system (software version 5.0, 1992). Sixteen right handed adult females, aged 18 - 35 years, volunteered to participate in the principal study. Subjects were seated with their trunks secured to the back support of an adjustable chair. Ten different targets (0.6 to 6.91 m) were labelled on the floor in front of the subjects. A large area for each target was defined so that the throwing skill of subjects could be ignored as a factor in the research design. Subjects were asked to throw a 0.5 kg ball to ten different distances using their own styles which allowed them to change the pattern of throwing as the distance thrown increased.All 16 subjects selected either an overarm or an underarm throw or employed both patterns. No subject used other patterns of throwing. At the shortest distance, a greater number of subjects selected an underarm throw. As the distance thrown increased, some subjects switched to the overarm throw. At the distance of 3.36 m, there were eight subjects (50%) using each style of throwing. Alteration of the throwing pattern mainly occurred from the underarm to the overarm throw. The results suggest that the distance thrown may be one of the control parameters in the throwing movement.Furthermore, the presence of both throwing patterns for all distances ++ / thrown suggests the presence of a multiple stable state in throwing motions. Trajectories of movement become more uniform as the distance thrown increased. Variability was greatest when subjects threw to the shortest distance for both patterns. These results imply that as the distance thrown increased the stability of both throwing patterns increased. Moreover, these results also imply a phase transition within each throwing pattern, in addition to the phase shift between the pattern of throwing.No result could directly illustrate the period of the transition. This may be due to the fact that phase transition in a multistable system is the result of an external force which drives the system from one state to another. Alteration of the pattern does not occur as a result of loss of the stability of the previous state. Furthermore, the presence of both throwing patterns for all distances thrown suggests the presence of a multiple stable state in throwing motions. Trajectories of movement become more uniform as the distance thrown increased. Variability was greatest when subjects threw to the shortest distance for both patterns. These results imply that as the distance thrown increased the stability of both throwing patterns increased. Moreover, these results also imply a phase transition within each throwing pattern, in addition to the phase shift between the pattern of throwing. No result could directly illustrate the period of the transition. This may be due to the fact that phase transition in a multistable system is the result of an external force which drives the system from one state to another. Alteration of the pattern does not occur as a result of loss of the stability of the previous state. However, some of the results such as the hysteresis graph presented indirect evidence, which could imply a phase shift between throwing patterns. In addition the higher ++ / degrees of joint angle recruitment in the overarm throw suggest that the stability of the system may be better maintained in the overarm throw than in the underarm throw.Alteration of the sub-styles of throwing within the same throwing pattern seemed to occur in between the shortest and the longest distances thrown. Many of the results supported this concept, for example, data related to the relative timing, the total ROM, the releasing joint angles, the trajectories of the movement, the phase plane plots, the angle-angle plots, and relative phase. However, the presence of sub-styles in the underarm throw was not as obvious as was the case for the overarm throw.In conclusion, the changes in motor behaviour during throwing as the distance thrown increased as examined in the present study can be explained by dynamic pattern theory in some aspects. However, further investigation of the stability of the patterns and energy utilisation necessary for the execution of the underarm and overarm throw are essential to determine the most suitable order parameter and to confirm the proposed control parameter (distance thrown) identified.

We are witnessing an explosion of available data coming from a huge amount of sources and domains, which is leading to the creation of datasets larger and larger, as well as richer and richer. Understanding, processing, and extracting useful information from those datasets requires specialized algorithms that take into consideration both the dynamism and the heterogeneity of the data they contain. Although several pattern mining techniques have been proposed in the literature, most of them fall short in providing interesting structures when the data can be interpreted differently from user to user, when it can change from time to time, and when it has different representations. In this thesis, we propose novel approaches that go beyond the traditional pattern mining algorithms, and can effectively and efficiently discover relevant structures in dynamic and heterogeneous settings. In particular, we address the task of pattern mining in multi-weighted graphs, pattern mining in dynamic graphs, and pattern mining in heterogeneous temporal databases. In pattern mining in multi-weighted graphs, we consider the problem of mining patterns for a new category of graphs called emph{multi-weighted graphs}. In these graphs, nodes and edges can carry multiple weights that represent, for example, the preferences of different users or applications, and that are used to assess the relevance of the patterns. We introduce a novel family of scoring functions that assign a score to each pattern based on both the weights of its appearances and their number, and that respect the anti-monotone property, pivotal for efficient implementations. We then propose a centralized and a distributed algorithm that solve the problem both exactly and approximately. The approximate solution has better scalability in terms of the number of edge weighting functions, while achieving good accuracy in the results found. An extensive experimental study shows the advantages and disadvantages of our strategies, and proves their effectiveness. Then, in pattern mining in dynamic graphs, we focus on the particular task of discovering structures that are both well-connected and correlated over time, in graphs where nodes and edges can change over time. These structures represent edges that are topologically close and exhibit a similar behavior of appearance and disappearance in the snapshots of the graph. To this aim, we introduce two measures for computing the density of a subgraph whose edges change in time, and a measure to compute their correlation. The density measures are able to detect subgraphs that are silent in some periods of time but highly connected in the others, and thus they can detect events or anomalies happened in the network. The correlation measure can identify groups of edges that tend to co-appear together, as well as edges that are characterized by similar levels of activity. For both variants of density measure, we provide an effective solution that enumerates all the maximal subgraphs whose density and correlation exceed given minimum thresholds, but can also return a more compact subset of representative subgraphs that exhibit high levels of pairwise dissimilarity. Furthermore, we propose an approximate algorithm that scales well with the size of the network, while achieving a high accuracy. We evaluate our framework with an extensive set of experiments on both real and synthetic datasets, and compare its performance with the main competitor algorithm. The results confirm the correctness of the exact solution, the high accuracy of the approximate, and the superiority of our framework over the existing solutions. In addition, they demonstrate the scalability of the framework and its applicability to networks of different nature. Finally, we address the problem of entity resolution in heterogeneous temporal data-ba-se-s, which are datasets that contain records that give different descriptions of the status of real-world entities at different periods of time, and thus are characterized by different sets of attributes that can change over time. Detecting records that refer to the same entity in such scenario requires a record similarity measure that takes into account the temporal information and that is aware of the absence of a common fixed schema between the records. However, existing record matching approaches either ignore the dynamism in the attribute values of the records, or assume that all the records share the same set of attributes throughout time. In this thesis, we propose a novel time-aware schema-agnostic similarity measure for temporal records to find pairs of matching records, and integrate it into an exact and an approximate algorithm. The exact algorithm can find all the maximal groups of pairwise similar records in the database. The approximate algorithm, on the other hand, can achieve higher scalability with the size of the dataset and the number of attributes, by relying on a technique called meta-blocking. This algorithm can find a good-quality approximation of the actual groups of similar records, by adopting an effective and efficient clustering algorithm.