Becoming to Know: Essays on Extended Epistemology of Knowledge Creation

This qualitative, explorative study, which comprises four essays, focuses on knowledge management (KM). It seeks to answer the question: How can the knowledge creation theory of KM benefit from social learning theories? While studying the five development phases of knowledge creation theory of KM through 1995-2008 and applying some social learning theories in essays, the concepts of knowing, learning and becoming have emerged. Drawing on these three concepts and on becoming ontology and extended epistemology as research philosophies the study suggests the ‘becoming epistemology’ concept and develops the ‘becoming to know’ framework. The framework proposes becoming as phronesis of dialectic interactions between learning and knowing. It shows how becoming to know evolves as an interplay between concrete experience and logical thinking in the present and in a living context. The proposed framework could be considered a contribution to the current development phase of the knowledge creation theory of KM because it illustrates how ontological and epistemological knowledge spirals come together, which is the essence of the knowledge creation theory of KM.

Systems between information and knowledge : In a memory management model of an extended enterprise

The research question of this thesis was how knowledge can be managed with information systems. Information systems can support but not replace knowledge management. Systems can mainly store epistemic organisational knowledge included in content, and process data and information. Certain value can be achieved by adding communication technology to systems. All communication, however, can not be managed. A new layer between communication and manageable information was named as knowformation. Knowledge management literature was surveyed, together with information species from philosophy, physics, communication theory, and information system science. Positivism, post-positivism, and critical theory were studied, but knowformation in extended organisational memory seemed to be socially constructed. A memory management model of an extended enterprise (M3.exe) and knowformation concept were findings from iterative case studies, covering data, information and knowledge management systems. The cases varied from groups towards extended organisation. Systems were investigated, and administrators, users (knowledge workers) and managers interviewed. The model building required alternative sets of data, information and knowledge, instead of using the traditional pyramid. Also the explicit-tacit dichotomy was reconsidered. As human knowledge is the final aim of all data and information in the systems, the distinction between management of information vs. management of people was harmonised. Information systems were classified as the core of organisational memory. The content of the systems is in practice between communication and presentation. Firstly, the epistemic criterion of knowledge is not required neither in the knowledge management literature, nor from the content of the systems. Secondly, systems deal mostly with containers, and the knowledge management literature with applied knowledge. Also the construction of reality based on the system content and communication supports the knowformation concept. Knowformation belongs to memory management model of an extended enterprise (M3.exe) that is divided into horizontal and vertical key dimensions. Vertically, processes deal with content that can be managed, whereas communication can be supported, mainly by infrastructure. Horizontally, the right hand side of the model contains systems, and the left hand side content, which should be independent from each other. A strategy based on the model was defined.

Kalman Filter Algorithm for Rating and Prediction in Basketball

The Thesis presents a state-space model for a basketball league and a Kalman filter algorithm for the estimation of the state of the league. In the state-space model, each of the basketball teams is associated with a rating that represents its strength compared to the other teams. The ratings are assumed to evolve in time following a stochastic process with independent Gaussian increments. The estimation of the team ratings is based on the observed game scores that are assumed to depend linearly on the true strengths of the teams and independent Gaussian noise. The team ratings are estimated using a recursive Kalman filter algorithm that produces least squares optimal estimates for the team strengths and predictions for the scores of the future games. Additionally, if the Gaussianity assumption holds, the predictions given by the Kalman filter maximize the likelihood of the observed scores. The team ratings allow probabilistic inference about the ranking of the teams and their relative strengths as well as about the teams’ winning probabilities in future games. The predictions about the winners of the games are correct 65-70% of the time. The team ratings explain 16% of the random variation observed in the game scores. Furthermore, the winning probabilities given by the model are concurrent with the observed scores. The state-space model includes four independent parameters that involve the variances of noise terms and the home court advantage observed in the scores. The Thesis presents the estimation of these parameters using the maximum likelihood method as well as using other techniques. The Thesis also gives various example analyses related to the American professional basketball league, i.e., National Basketball Association (NBA), and regular seasons played in year 2005 through 2010. Additionally, the season 2009-2010 is discussed in full detail, including the playoffs.

Almost Stable Matchings by Truncating the Gale–Shapley Algorithm

We show that the ratio of matched individuals to blocking pairs grows linearly with the number of propose–accept rounds executed by the Gale–Shapley algorithm for the stable marriage problem. Consequently, the participants can arrive at an almost stable matching even without full information about the problem instance; for each participant, knowing only its local neighbourhood is enough. In distributed-systems parlance, this means that if each person has only a constant number of acceptable partners, an almost stable matching emerges after a constant number of synchronous communication rounds. We apply our results to give a distributed (2 + ε)-approximation algorithm for maximum-weight matching in bicoloured graphs and a centralised randomised constant-time approximation scheme for estimating the size of a stable matching.

A local 2-approximation algorithm for the vertex cover problem

We present a distributed 2-approximation algorithm for the minimum vertex cover problem. The algorithm is deterministic, and it runs in (Δ + 1)2 synchronous communication rounds, where Δ is the maximum degree of the graph. For Δ = 3, we give a 2-approximation algorithm also for the weighted version of the problem.

A simple local 3-approximation algorithm for vertex cover

We present a local algorithm (constant-time distributed algorithm) for finding a 3-approximate vertex cover in bounded-degree graphs. The algorithm is deterministic, and no auxiliary information besides port numbering is required. (c) 2009 Elsevier B.V. All rights reserved.

A local 2-approximation algorithm for the vertex cover problem

We present a distributed 2-approximation algorithm for the minimum vertex cover problem. The algorithm is deterministic, and it runs in (Δ + 1)2 synchronous communication rounds, where Δ is the maximum degree of the graph. For Δ = 3, we give a 2-approximation algorithm also for the weighted version of the problem.

An optimal local approximation algorithm for max-min linear programs

In a max-min LP, the objective is to maximise ω subject to Ax ≤ 1, Cx ≥ ω1, and x ≥ 0 for nonnegative matrices A and C. We present a local algorithm (constant-time distributed algorithm) for approximating max-min LPs. The approximation ratio of our algorithm is the best possible for any local algorithm; there is a matching unconditional lower bound.

Algorithms for Exact Structure Discovery in Bayesian Networks

Bayesian networks are compact, flexible, and interpretable representations of a joint distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure. This is called structure discovery. This thesis contributes to two areas of structure discovery in Bayesian networks: space--time tradeoffs and learning ancestor relations. The fastest exact algorithms for structure discovery in Bayesian networks are based on dynamic programming and use excessive amounts of space. Motivated by the space usage, several schemes for trading space against time are presented. These schemes are presented in a general setting for a class of computational problems called permutation problems; structure discovery in Bayesian networks is seen as a challenging variant of the permutation problems. The main contribution in the area of the space--time tradeoffs is the partial order approach, in which the standard dynamic programming algorithm is extended to run over partial orders. In particular, a certain family of partial orders called parallel bucket orders is considered. A partial order scheme that provably yields an optimal space--time tradeoff within parallel bucket orders is presented. Also practical issues concerning parallel bucket orders are discussed. Learning ancestor relations, that is, directed paths between nodes, is motivated by the need for robust summaries of the network structures when there are unobserved nodes at work. Ancestor relations are nonmodular features and hence learning them is more difficult than modular features. A dynamic programming algorithm is presented for computing posterior probabilities of ancestor relations exactly. Empirical tests suggest that ancestor relations can be learned from observational data almost as accurately as arcs even in the presence of unobserved nodes.