Exploratory sequential data analysis of user interaction in contemporary BIM applications

Creation oriented software allows the user to work according to their own vision and rules. From the perspective of software analysis, this is challenging because there is no certainty as to how the users are using the software and what kinds of workflows emerge among different users. The aim of this thesis was to study and identify the potential of sequential event pattern data extraction analysis from expert field creation oriented software in the field of Building Information Modeling (BIM). The thesis additionally introduces a concept evaluation model for detecting repetition based usability disruption. Finally, the work presents an implementation of sequential pattern mining based user behaviour analysis and machine learning predictive application using state of the art algorithms. The thesis introduces a data analysis implementation that is built upon inspections of Sequential or Exploratory Sequential Data Analysis (SDA or ESDA) based theory in usability studies. The study implements a test application specific workflow sequence detection and database transfer approach. The paper uses comparative modern mining algorithms known as BIDE and TKS for sequential pattern discovery. Finally, the thesis utilizes the created sequence database to create user detailing workflow predictions using a CPT+ algorithm. The main contribution of the thesis outcome is to open scalable options for both software usability and product development to automatically recognize and predict usability and workflow related information, deficiencies and repetitive workflow. By doing this, more quantifiable metrics can be revealed in relation to software user interface behavior analytics.Luomiseen perustuva ohjelmisto mahdollistaa käyttäjän työskentelyn oman visionsa ja sääntöjensä mukaisesti. Ohjelmien analysoinnin kannalta tämä on haastavaa, koska ei ole varmuutta siitä, kuinka ohjelmistoa tarkalleen käytetään ja millaisia työskentelytapoja ohjelmiston eri käyttäjäryhmille voi syntyä. Opinnäytetyön tavoitteena oli tutkia ja identifioida toistuvien käyttäjätapahtumasekvenssien analyysipotentiaalia tietomallinnukseen keskittyvässä luomispoh jaisessa ohjelmistossa. Opinnäyte esittelee myös evaluointimallikonseptin, jonka avulla on mahdollista tunnistaa toistuvasta käyttäytymisestä aiheutuvia käytettävyysongelmia. Lopuksi työssä esitellään sekvenssianalyysiin perustuva ohjelmiston käyttäjän toiminta-analyysi sekä ennustava koneoppimisen sovellus. Opinnäytetyössä esitellään data-analyysisovellus, joka perustuu käytettävyystutkimuksessa esiintyvien toistuvien sekvenssien tai kokeellisesti toistuvien sekvenssien analyysiteorian tarkasteluun. Sovelluksen toteutus on tehty eritoten työssä käytetylle ohjelmistolle, jossa käyttäjän detaljointitapahtumista muodostetaan sekvenssejä sekvenssitietokannan luomiseksi. Työssä käytetään sekvenssien toistuvuusanalyysiin moderneja louhintamenetelmiä nimeltään BIDE ja TKS. Lopuksi työssä hyödynnetään luotua sekvenssitietokantaa myös käyttäjän detaljointityön ennustamista varten käyttämällä CPT+ algoritmia. Opinnäytetyön tulosten pohjalta pyritään löytämään vaihtoehtoja käytettävyyden ja tuotekehityksen päätöksenteon tietopohjaiseksi tueksi tunnistamalla ja ennusta malla käyttäjien toimintaa ohjelmistossa. Löydetyn informaation avulla on mahdollista ilmaista käytettävyyteen liittyviä ongelmia kvantitatiivisen tiedon valossa

The productivity of polymorphic stream equations and the composition of circular traversals

This thesis has two independent parts concerned with different aspects of laziness in functional programs. The first part is a theoretical study of productivity for very restricted stream programs. In the second part we define a programming abstraction over a recursive pattern for defining circular traversals modularly. Productivity is in general undecidable. By restricting ourselves to mutually recursive polymorphic stream equations having only three basic operations, namely "head", "tail", and "cons", we aim to prove interesting properties about productivity. Still undecidable for this restricted class of programs, productivity of polymorphic stream functions is equivalent to the totality of their indexing function, which characterise their behaviour in terms of operations on indices. We prove that our equations generate all possible polymorphic stream functions, and therefore their indexing functions are all the computable functions, whose totality problem is indeed undecidable. We then further restrict our language by reducing the numbers of equations and parameters, but despite those constraints the equations retain their expressiveness. In the end we establish that even two non-mutually recursive equations on unary stream functions are undecidable with complexity $Π_2^0$. However, the productivity of a single unary equation is decidable. Circular traversals have been used in the eighties as an optimisation to combine multiple traversals in a single traversal. In particular they provide more opportunities for applying deforestation techniques since it is the case that an intermediate datastructure can only be eliminated if it is consumed only once. Another use of circular programs is in the implementation of attribute grammars in lazy functional languages. There is a systematic transformation to define a circular traversal equivalent to multiple traversals. Programming with this technique is not modular since the individual traversals are merged together. Some tools exist to transform programs automatically and attribute grammars have been suggested as a way to describe the circular traversals modularly. Going to the root of the problem, we identify a recursive pattern that allows us to define circular programs modularly in a functional style. We give two successive implementations, the first one is based on algebras and has limited scope: not all circular traversals can be defined this way. We show that the recursive scheme underlying attribute grammars computation rules is essential to combine circular programs. We implement a generic recursive operation on a novel attribute grammar abstraction, using containers as a parametric generic representation of recursive datatypes. The abstraction makes attribute grammars first-class objects. Such a strongly typed implementation is novel and make it possible to implement a high level embedded language for defining attribute grammars, with many interesting new features promoting modularity

The productivity of polymorphic stream equations and the composition of circular traversals

This thesis has two independent parts concerned with different aspects of laziness in functional programs. The first part is a theoretical study of productivity for very restricted stream programs. In the second part we define a programming abstraction over a recursive pattern for defining circular traversals modularly. Productivity is in general undecidable. By restricting ourselves to mutually recursive polymorphic stream equations having only three basic operations, namely "head", "tail", and "cons", we aim to prove interesting properties about productivity. Still undecidable for this restricted class of programs, productivity of polymorphic stream functions is equivalent to the totality of their indexing function, which characterise their behaviour in terms of operations on indices. We prove that our equations generate all possible polymorphic stream functions, and therefore their indexing functions are all the computable functions, whose totality problem is indeed undecidable. We then further restrict our language by reducing the numbers of equations and parameters, but despite those constraints the equations retain their expressiveness. In the end we establish that even two non-mutually recursive equations on unary stream functions are undecidable with complexity $Π_2^0$. However, the productivity of a single unary equation is decidable. Circular traversals have been used in the eighties as an optimisation to combine multiple traversals in a single traversal. In particular they provide more opportunities for applying deforestation techniques since it is the case that an intermediate datastructure can only be eliminated if it is consumed only once. Another use of circular programs is in the implementation of attribute grammars in lazy functional languages. There is a systematic transformation to define a circular traversal equivalent to multiple traversals. Programming with this technique is not modular since the individual traversals are merged together. Some tools exist to transform programs automatically and attribute grammars have been suggested as a way to describe the circular traversals modularly. Going to the root of the problem, we identify a recursive pattern that allows us to define circular programs modularly in a functional style. We give two successive implementations, the first one is based on algebras and has limited scope: not all circular traversals can be defined this way. We show that the recursive scheme underlying attribute grammars computation rules is essential to combine circular programs. We implement a generic recursive operation on a novel attribute grammar abstraction, using containers as a parametric generic representation of recursive datatypes. The abstraction makes attribute grammars first-class objects. Such a strongly typed implementation is novel and make it possible to implement a high level embedded language for defining attribute grammars, with many interesting new features promoting modularity

Developments in multiscale ONIOM and fragment methods for complex chemical systems

Multiskalenprobleme werden in der Computerchemie immer allgegenwärtiger und bestimmte Klassen solcher Probleme entziehen sich einer effizienten Beschreibung mit den verfügbaren Berechnungsansätzen. In dieser Arbeit wurden effiziente Erweiterungen der Multilayer-Methode ONIOM und von Fragmentmethoden als Lösungsansätze für derartige Probleme entwickelt. Dabei wurde die Kombination von ONIOM und Fragmentmethoden im Rahmen der Multi-Centre Generalised ONIOM entwickelt sowie die eine Multilayer-Variante der Fragment Combinatio Ranges. Außerdem wurden Schemata für elektronische Einbettung derartiger Multilayer-Systeme entwickelt. Der zweite Teil der Arbeit beschreibt die Implementierung im Haskell-Programm "Spicy" und demonstriert Anwendungen derartiger Multiskalen-Methoden

Online Mapping and Perception Algorithms for Multi-robot Teams Operating in Urban Environments.

This thesis investigates some of the sensing and perception challenges faced by multi-robot teams equipped with LIDAR and camera sensors. Multi-robot teams are ideal for deployment in large, real-world environments due to their ability to parallelize exploration, reconnaissance or mapping tasks. However, such domains also impose additional requirements, including the need for a) online algorithms (to eliminate stopping and waiting for processing to finish before proceeding) and b) scalability (to handle data from many robots distributed over a large area). These general requirements give rise to specific algorithmic challenges, including 1) online maintenance of large, coherent maps covering the explored area, 2) online estimation of communication properties in the presence of buildings and other interfering structure, and 3) online fusion and segmentation of multiple sensors to aid in object detection. The contribution of this thesis is the introduction of novel approaches that leverage grid-maps and sparse multi-variate gaussian inference to augment the capability of multi-robot teams operating in urban, indoor-outdoor environments by improving the state of the art of map rasterization, signal strength prediction, colored point cloud segmentation, and reliable camera calibration. In particular, we introduce a map rasterization technique for large LIDAR-based occupancy grids that makes online updates possible when data is arriving from many robots at once. We also introduce new online techniques for robots to predict the signal strength to their teammates by combining LIDAR measurements with signal strength measurements from their radios. Processing fused LIDAR+camera point clouds is also important for many object-detection pipelines. We demonstrate a near linear-time online segmentation algorithm to this domain. However, maintaining the calibration of a fleet of 14 robots made this approach difficult to employ in practice. Therefore we introduced a robust and repeatable camera calibration process that grounds the camera model uncertainty in pixel error, allowing the system to guide novices and experts alike to reliably produce accurate calibrations.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113516/1/jhstrom_1.pd

Fine-grained complexity and algorithm engineering of geometric similarity measures

Point sets and sequences are fundamental geometric objects that arise in any application that considers movement data, geometric shapes, and many more. A crucial task on these objects is to measure their similarity. Therefore, this thesis presents results on algorithms, complexity lower bounds, and algorithm engineering of the most important point set and sequence similarity measures like the Fréchet distance, the Fréchet distance under translation, and the Hausdorff distance under translation. As an extension to the mere computation of similarity, also the approximate near neighbor problem for the continuous Fréchet distance on time series is considered and matching upper and lower bounds are shown.Punktmengen und Sequenzen sind fundamentale geometrische Objekte, welche in vielen Anwendungen auftauchen, insbesondere in solchen die Bewegungsdaten, geometrische Formen, und ähnliche Daten verarbeiten. Ein wichtiger Bestandteil dieser Anwendungen ist die Berechnung der Ähnlichkeit von Objekten. Diese Dissertation präsentiert Resultate, genauer gesagt Algorithmen, untere Komplexitätsschranken und Algorithm Engineering der wichtigsten Ähnlichkeitsmaße für Punktmengen und Sequenzen, wie zum Beispiel Fréchetdistanz, Fréchetdistanz unter Translation und Hausdorffdistanz unter Translation. Als eine Erweiterung der bloßen Berechnung von Ähnlichkeit betrachten wir auch das Near Neighbor Problem für die kontinuierliche Fréchetdistanz auf Zeitfolgen und zeigen obere und untere Schranken dafür

Efficient pac-learning for episodic tasks with acyclic state spaces and the optimal node visitation problem in acyclic stochastic digaphs.

The first part of this research program concerns the development of customized and easily implementable Probably Approximately Correct (PAC)-learning algorithms for episodic tasks over acyclic state spaces. The defining characteristic of our algorithms is that they take explicitly into consideration the acyclic structure of the underlying state space and the episodic nature of the considered learning task. The first of the above two attributes enables a very straightforward and efficient resolution of the ``exploration vs exploitation' dilemma, while the second provides a natural regenerating mechanism that is instrumental in the dynamics of our algorithms. Some additional characteristics that distinguish our algorithms from those developed in the past literature are (i) their direct nature, that eliminates the need of a complete specification of the underlying MDP model and reduces their execution to a very simple computation, and (ii) the unique emphasis that they place in the efficient implementation of the sampling process that is defined by their PAC property. More specifically, the aforementioned PAC-learning algorithms complete their learning task by implementing a systematic episodic sampling schedule on the underlying acyclic state space. This sampling schedule combined with the stochastic nature of the transitions taking place, define the need for efficient routing policies that will help the algorithms complete their exploration program while minimizing, in expectation, the number of executed episodes. The design of an optimal policy that will satisfy a specified pattern of arc visitation requirements in an acyclic stochastic graph, while minimizing the expected number of required episodes, is a challenging problem, even under the assumption that all the branching probabilities involved are known a priori. Hence, the sampling process that takes place in the proposed PAC-learning algorithms gives rise to a novel, very interesting stochastic control/scheduling problem, that is characterized as the problem of the Optimal Node Visitation (ONV) in acyclic stochastic digraphs. The second part of the work presented herein seeks the systematic modelling and analysis of the ONV problem. The last part of this research program explores the computational merits obtained by heuristical implementations that result from the integration of the ONV problem developments into the PAC-algorithms developed in the first part of this work. We study, through numerical experimentation, the relative performance of these resulting heuristical implementations in comparison to (i) the initial version of the PAC-learning algorithms, presented in the first part of the research program, and (ii) standard Q-learning algorithm variations provided in the RL literature. The work presented in this last part reinforces and confirms the driving assumption of this research, i.e., that one can design customized RL algorithms of enhanced performance if the underlying problem structure is taken into account.Ph.D.Committee Chair: Reveliotis, Spyros; Committee Member: Ayhan, Hayriye; Committee Member: Goldsman, Dave; Committee Member: Shamma, Jeff; Committee Member: Zwart, Ber

Pseudoedge--a hierarchical skeletal modeler for the computer aided design of structural components

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1991.Includes bibliographical references (p. 121-122).by David Leo Bonner.M.S

Parallel algorithms for direct blood flow simulations

Fluid mechanics of blood can be well approximated by a mixture model of a Newtonian fluid and deformable particles representing the red blood cells. Experimental and theoretical evidence suggests that the deformation and rheology of red blood cells is similar to that of phospholipid vesicles. Vesicles and red blood cells are both area preserving closed membranes that resist bending. Beyond red blood cells, vesicles can be used to investigate the behavior of cell membranes, intracellular organelles, and viral particles. Given the importance of vesicle flows, in this thesis we focus in efficient numerical methods for such problems: we present computationally scalable algorithms for the simulation of dilute suspension of deformable vesicles in two and three dimensions. Our method is based on the boundary integral formulation of Stokes flow. We present new schemes for simulating the three-dimensional hydrodynamic interactions of large number of vesicles with viscosity contrast. The algorithms incorporate a stable time-stepping scheme, high-order spatiotemporal discretizations, spectral preconditioners, and a reparametrization scheme capable of resolving extreme mesh distortions in dynamic simulations. The associated linear systems are solved in optimal time using spectral preconditioners. The highlights of our numerical scheme are that (i) the physics of vesicles is faithfully represented by using nonlinear solid mechanics to capture the deformations of each cell, (ii) the long-range, N-body, hydrodynamic interactions between vesicles are accurately resolved using the fast multipole method (FMM), and (iii) our time stepping scheme is unconditionally stable for the flow of single and multiple vesicles with viscosity contrast and its computational cost-per-simulation-unit-time is comparable to or less than that of an explicit scheme. We report scaling of our algorithms to simulations with millions of vesicles on thousands of computational cores.PhDCommittee Chair: Biros, George; Committee Member: Alben, Silas; Committee Member: Fernandez-Nieves, Alberto; Committee Member: Hu, David; Committee Member: Vuduc, Richar

Tight Bounds for Approximate Near Neighbor Searching for Time Series under the {F}r\'{e}chet Distance

We study the $c$-approximate near neighbor problem under the continuous Fr\'echet distance: Given a set of $n$ polygonal curves with $m$ vertices, a radius $\delta > 0$, and a parameter $k \leq m$, we want to preprocess the curves into a data structure that, given a query curve $q$ with $k$ vertices, either returns an input curve with Fr\'echet distance at most $c\cdot \delta$ to $q$, or returns that there exists no input curve with Fr\'echet distance at most $\delta$ to $q$. We focus on the case where the input and the queries are one-dimensional polygonal curves -- also called time series -- and we give a comprehensive analysis for this case. We obtain new upper bounds that provide different tradeoffs between approximation factor, preprocessing time, and query time. Our data structures improve upon the state of the art in several ways. We show that for any $0 < \varepsilon \leq 1$ an approximation factor of $(1+\varepsilon)$ can be achieved within the same asymptotic time bounds as the previously best result for $(2+\varepsilon)$. Moreover, we show that an approximation factor of $(2+\varepsilon)$ can be obtained by using preprocessing time and space $O(nm)$, which is linear in the input size, and query time in $O(\frac{1}{\varepsilon})^{k+2}$, where the previously best result used preprocessing time in $n \cdot O(\frac{m}{\varepsilon k})^k$ and query time in $O(1)^k$. We complement our upper bounds with matching conditional lower bounds based on the Orthogonal Vectors Hypothesis. Interestingly, some of our lower bounds already hold for any super-constant value of $k$. This is achieved by proving hardness of a one-sided sparse version of the Orthogonal Vectors problem as an intermediate problem, which we believe to be of independent interest