The discovery of unknown lemmas, case-splits and other so called eureka steps are challenging problems for automated theorem proving and have generally been assumed to require user intervention. This thesis is mainly concerned with the automated discovery of inductive lemmas. We have explored two approaches based on failure recovery and theory formation, with the aim of improving automation of firstand higher-order inductive proofs in the IsaPlanner system. We have implemented a lemma speculation critic which attempts to find a missing lemma using information from a failed proof-attempt. However, we found few proofs for which this critic was applicable and successful. We have also developed a program for inductive theory formation, which we call IsaCoSy. IsaCoSy was evaluated on different inductive theories about natural numbers, lists and binary trees, and found to successfully produce many relevant theorems and lemmas. Using a background theory produced by IsaCoSy, it was possible for IsaPlanner to automatically prove more new theorems than with lemma speculation. In addition to the lemma discovery techniques, we also implemented an automated technique for case-analysis. This allows IsaPlanner to deal with proofs involving conditionals, expressed as if- or case-statements.

The World Wide Web is transitioning from being a mere collection of documents that contain useful information toward providing a collection of services that perform useful tasks. The emerging Web service technology has been envisioned as the next technological wave and is expected to play an important role in this recent transformation of the Web. By providing interoperable interface standards for application-to-application communication, Web services can be combined with component-based software development to promote application interaction and integration within and across enterprises. To make Web services for service-oriented computing operational, it is important that Web services repositories not only be well-structured but also provide efficient tools for an environment supporting reusable software components for both service providers and consumers. As the potential of Web services for service-oriented computing is becoming widely recognized, the demand for an integrated framework that facilitates service discovery and publishing is concomitantly growing.In our research, we propose a framework that facilitates Web service discovery and publishing by combining clustering techniques and leveraging the semantics of the XML-based service specification in WSDL files. We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the Web service domain. Our proposed approach has several appealing features: (1) It minimizes the requirements of prior knowledge from both service providers and consumers, (2) It avoids exploiting domain-dependent ontologies,(3) It is able to visualize the information space of Web services by providing a category map that depicts the semantic relationships among them,(4) It is able to semi-automatically generate Web service taxonomies that reflect both capability and geographic context, and(5) It allows service consumers to combine multiple search strategies in a flexible manner.We have developed a Web service discovery tool based on the proposed approach using an unsupervised artificial neural network and empirically evaluated the proposed approach and tool using real Web service descriptions drawn from operational Web services repositories. We believe that both service providers and consumers in a service-oriented computing environment can benefit from our Web service discovery approach.

Data mining at enterprise level operates on huge amount of data such as government transactions, banks, insurance companies and so on. Inevitably, these businesses produce complex data that might be distributed in nature. When mining is made on such data with a single-step, it produces business intelligence as a particular aspect. However, this is not sufficient in enterprise where different aspects and standpoints are to be considered before taking business decisions. It is required that the enterprises perform mining based on multiple features, data sources and methods. This is known as combined mining. The combined mining can produce patterns that reflect all aspects of the enterprise. Thus the derived intelligence can be used to take business decisions that lead to profits. This kind of knowledge is known as actionable knowledge. / Data mining is a process of obtaining trends or patterns in historical data. Such trends form business intelligence that in turn leads to taking well informed decisions. However, data mining with a single technique does not yield actionable knowledge. This is because enterprises have huge databases and heterogeneous in nature. They also have complex data and mining such data needs multi-step mining instead of single step mining. When multiple approaches are involved, they provide business intelligence in all aspects. That kind of information can lead to actionable knowledge. Recently data mining has got tremendous usage in the real world. The drawback of existing approaches is that insufficient business intelligence in case of huge enterprises. This paper presents the combination of existing works and algorithms. We work on multiple data sources, multiple methods and multiple features. The combined patterns thus obtained from complex business data provide actionable knowledge. A prototype application has been built to test the efficiency of the proposed framework which combines multiple data sources, multiple methods and multiple features in mining process. The empirical results revealed that the proposed approach is effective and can be used in the real world.

The success of the internet over last few decades has mainly depended on various infrastructure technologies to run distributed applications. Due to diversification and multi-provider nature of the internet, radical architectural improvements which require mutual agreement between infrastructure providers have become highly impractical. This escalating resistance towards the further growth has created a rising demand for new approaches to address this challenge. Network virtualization is regarded as a prominent solution to surmount these limitations. It decouples the conventional Internet service provider’s role into infrastructure provider (InP) and service provider (SP) and introduce a third player known as virtual network Provider (VNP) which creates virtual networks (VNs). Resource discovery aims to assist the VNP in selecting the best InP that has the best matching resources for a particular VN request. In the current literature, resource discovery focuses mainly on static attributes of network resources highlighting the fact that utilization on dynamic attributes imposes significant overhead on the network itself. In this thesis we propose a resource discovery approach that is capable of utilizing the dynamic resource attributes to enhance the resource discovery and increase the overall efficiency of VN creation. We realize that recourse discovery techniques should be fast and cost efficient, enough to not to impose any significant load. Hence our proposed scheme calculates aggregation values of the dynamic attributes of the substrate resources. By comparing aggregation values to VN requirements, a set of potential InPs is selected. The potential InPs satisfy basic VN embedding requirements. Moreover, we propose further enhancements to the dynamic attribute monitoring process using a vector based aggregation approach.

This study approaches ‘discovery writing’ in relation to genre, investigating whether different genres of writing might be associated with different kinds of writing processes. Discovery writing can be thought of as writing to find out what you think, and represents a reversal of the more usual sense that ideas precede writing, or that planning should precede writing. Discovery writing has previously been approached in terms of writers’ orientations, such as whether writers are Planners or Discoverers. This study engages with these previous theories, but places an emphasis on genres of writing, and on textual features, such as how writers write fictional characters, or how writers generate arguments when writing essays. The two main types of writing investigated are fiction writing and academic writing. Particular genres include short stories, crime novels, academic articles, and student essays. 11 writers were interviewed, ranging from professional fiction authors to undergraduate students. Interviews were based on a recent piece of a writer’s own writing. Most of the writers came from a literary background, being either fiction writers or Literature students. Interviews were based on set questions, but also allowed writers to describe their writing largely in their own terms and to describe aspects of their writing that interested them. A key aspect of this approach was that of engaging writers in their own interests, from where interview questions could provide a basis for discussion. Fiction writing seemed characterized by emergent processes, where writers experienced real life events and channelled their experiences and feelings into stories. The writing of characters was often associated with discovery. A key finding for fiction writing was that even writers who planned heavily and identified themselves somewhat as Planners, also tended to discover more about their characters when writing. Academic writing was characterized by difficulty, where discovery was often described in relation to struggling to summarize arguments or with finding key words. A key conclusion from this study is that writers may be Planners or Discoverers by orientation, as previous theory has recognised. However, the things that writers plan and discover, such as plots and characters, also play an important role in their writing processes.

Ingeniero Civil Industrial / La industria del acero a nivel mundial está pasando momentos difíciles, y se hace cada vez más relevante para los actores del mercado acercarse a sus clientes empresa y conocerlos de mejor forma, cambiando el paradigma histórico en el cual las siderúrgicas se dedican únicamente a producir acero, y luego esperar a que se venda, estrategia que solía funcionar principalmente debido a la escasa competencia, pero dado el contexto actual descrito en el presente trabajo ya no basta con simplemente producir el acero, sino que hay que acercarse al cliente del producto y conocerlo en profundidad. La problemática planteada ha sido abordada desde la metodología Discovery Teams, la cual consiste en crear equipos multidisciplinarios para visitar a los distintos eslabones de una cadena industrial, con un fuerte foco en el cliente final, buscando ideas revolucionarias para beneficiarlos mediante nuevos y/o mejores productos. La metodología ha sido adaptada al contexto de Gerdau Chile, principal proveedor de acero de la industria nacional en la actualidad. El hito principal de la metodología es la visita a terreno, y el principal foco de interés del trabajo presentado consiste en el levantamiento de los procesos necesarios para una correcta aplicación de la metodología, para así abarcar toda la estructura de lo que debe suceder tanto antes como después del hito principal, tanto para corroborar que la visita está bien planificada, como para también asegurar la continuidad en el tiempo de la metodología. El trabajo contempla la descripción detallada del programa Discovery Teams, siendo los programas de desarrollo e introducción de producto una tarea a futuro al interior de la organización, pero haciendo mención a la labor del equipo de exploración en ambas etapas. El trabajo presentado también contempla un capítulo dedicado exclusivamente a analizar las posibles barreras al interior de una organización que podrían dificultar la implementación, donde es posible destacar que existe un miedo al cambio y un escepticismo con respecto a los resultados de algo tan desconocido, pero también hay elementos facilitadores que permiten desarrollar la metodología adecuadamente, como una fuerte red de contactos y una gran reputación y reconocimiento a la calidad del trabajo hecho.

The purpose of this study was to ascertain the value, as determined by student achievement, of using a discovery method of teaching mathematics in a college freshman mathematics course for non-mathematics and non-science majors.

The discovery of transcription factor binding site (TFBS) motifs remains an important and challenging problem in computational biology. This thesis presents MITSU, a novel algorithm for TFBS motif discovery which exploits stochastic methods as a means of both overcoming optimality limitations in current algorithms and as a framework for incorporating relevant prior knowledge in order to improve results. The current state of the TFBS motif discovery field is surveyed, with a focus on probabilistic algorithms that typically take the promoter regions of coregulated genes as input. A case is made for an approach based on the stochastic Expectation-Maximisation (sEM) algorithm; its position amongst existing probabilistic algorithms for motif discovery is shown. The algorithm developed in this thesis is unique amongst existing motif discovery algorithms in that it combines the sEM algorithm with a derived data set which leads to an improved approximation to the likelihood function. This likelihood function is unconstrained with regard to the distribution of motif occurrences within the input dataset. MITSU also incorporates a novel heuristic to automatically determine TFBS motif width. This heuristic, known as MCOIN, is shown to outperform current methods for determining motif width. MITSU is implemented in Java and an executable is available for download. MITSU is evaluated quantitatively using realistic synthetic data and several collections of previously characterised prokaryotic TFBS motifs. The evaluation demonstrates that MITSU improves on a deterministic EM-based motif discovery algorithm and an alternative sEM-based algorithm, in terms of previously established metrics. The ability of the sEM algorithm to escape stable fixed points of the EM algorithm, which trap deterministic motif discovery algorithms and the ability of MITSU to discover multiple motif occurrences within a single input sequence are also demonstrated. MITSU is validated using previously characterised Alphaproteobacterial motifs, before being applied to motif discovery in uncharacterised Alphaproteobacterial data. A number of novel results from this analysis are presented and motivate two extensions of MITSU: a strategy for the discovery of multiple different motifs within a single dataset and a higher order Markov background model. The effects of incorporating these extensions within MITSU are evaluated quantitatively using previously characterised prokaryotic TFBS motifs and demonstrated using Alphaproteobacterial motifs. Finally, an information-theoretic measure of motif palindromicity is presented and its advantages over existing approaches for discovering palindromic motifs discussed.

<p>An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions. </p><p>This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods. </p><p> </p><p>On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.</p><p>In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.</p><p>We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,</p><p>and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy. </p><p>In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.</p> / Dissertation

Purpose - The purpose of this paper is to observe how undergraduate students approach open-ended searching for a research assignment, specifically as it affected their use of the discovery interface Primo. Design/methodology/approach - In total, 30 undergraduate students were provided with a sample research assignment and instructed to find resources for it using web tools of their choice, followed by the Primo discovery tool. Students were observed for 30 minutes. A survey was provided at the end to solicit additional feedback. Sources students found were evaluated for relevance and utility. Findings - Students expressed a high level of satisfaction with Primo despite some difficulty navigating through more complicated tasks. Despite their interest in the tool and previous exposure to it, it was usually not the first discovery tool students used when given the research assignment. Students approached the open-ended search environment much like they would with a commercial search engine. Originality/value - This paper focused on an open-ended search environment as opposed to a known- item scenario in order to assess students' preferences for web search tools and how a library discovery layer such as Primo was a part of that situation. Evaluation of the resources students found relevant were also analyzed to determine to what degree the students understood the level of quality they exhibited and from which tool they were obtained.