1,721,004 research outputs found
Efficient large-scale configuration via integer linear programming
No full textConfiguration of large-scale applications in an engineering context requires a modeling environment that allows the design engineer to draft the configuration problem in a natural way and efficient methods that can process the modeled setting and scale with the number of components. Existing configuration methods in artificial intelligence typically perform quite well in certain subareas but are hard to use for general-purpose modeling without mathematical or logics background (the so-called knowledge acquisition bottleneck) and/or have scalability issues. As a remedy to this important issue both in theory and in practical applications, we use a standard modeling environment like the Unified Modeling Language that has been proposed by the configuration community as a suitable object-oriented formalism for configuration problems. We provide a translation of key concepts of class diagrams to inequalities and identify relevant configuration aspects and show how they are treated as an integer linear program. Solving an integer linear program can be done efficiently, and integer linear programming scales well to large configurations consisting of several thousands components and interactions. We conduct an empirical study in the context of package management for operating systems and for the Linux kernel configuration. We evaluate our methodology by a benchmark and obtain convincing results in support for using integer linear programming for configuration applications of realistic size and complexity
A text mining framework in R and its applications
No full textText mining has become an established discipline both in research as in business intelligence. However, many existing text mining toolkits lack easy extensibility and provide only poor support for interacting with statistical computing environments. Therefore we propose a text mining framework for the statistical computing environment R which provides intelligent methods for corpora handling, meta data management, preprocessing, operations on documents, and data export. We present how well established text mining techniques can be applied in our framework and show how common text mining tasks can be performed utilizing our infrastructure. The second part in this thesis is dedicated to a set of realistic applications using our framework. The first application deals with the implementation of a sophisticated mailing list analysis, whereas the second example identifies the potential of text mining methods for business to consumer electronic commerce. The third application shows the benefits of text mining for law documents. Finally we present an application which deals with authorship attribution on the famous Wizard of Oz book series. (author's abstract
Towards hybrid techniques for efficient declarative configuration
No full textDuring the last decades configuration has been extensively
employed in a wide range of application domains, implemented
by a multitude of techniques like logics, procedural,
object-oriented, or resourced-driven approaches. Especially declarative methods provide the foundation for precise and well-understood semantics for reasoning tasks and allow for a succinct representation of the underlying knowledge base. However, a drawback in using such powerful declarative techniques lies in their computational complexity. In this paper we present a simple declarative framework for configuration in Prolog in order to show the advantages of logicbased techniques but also to identify some challenges for such formalisms. We argue for hybrid systems which combine and utilize efficient techniques from different configuration methodologies under a unified declarative interface
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Automated Coding of Qualitative Interviews with Latent Semantic Analysis
No full textCoding and analysing qualitative interviews is one of several core techniques used in marketing research. Qualitative methods offer valuable information hardly gained by standard quantitative methods since open-ended questions and interviews provide deeper insight into customer demands. The main disadvantages of qualitative methods are their inherent subjectivity and their high costs. We tackle this problem by applying latent semantic analysis (LSA) in a fully automated way on transcripts of interviews and we propose two algorithms based on LSA. We evaluate the algorithms against two separate real-life cases taken from the automobile industry and from the Austrian mobile phone market. Thereby, we compare the machine results against marketing expert judgements and show that the algorithms proposed provide perfect reliability with appropriate validity in automated coding and textual analysis
Consistency and Minimality of UML Class Specifications with Multiplicities and Uniqueness Constraints
No full textAppropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Kernel-based machine learning for fast text mining in R
No full textRecent advances in the field of kernel-based machine learning methods allow fast processing of text using string kernels utilizing suffix arrays. kernlab provides both kernel methods' infrastructure and a large collection of already implemented algorithms and includes an implementation of suffix-array-based string kernels. Along with the use of the text mining infrastructure provided by tm these packages provide R with functionality in processing, visualizing and grouping large collections of text data using kernel methods. The emphasis is on the performance of various types of string kernels at these tasks.
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