Automated Construction of Relational Attributes ACORA: A Progress Report

Data mining research has not only development a large number of algorithms, but also enhanced our knowledge and understanding of their applicability and performance. However, the application of data mining technology in business environments is still no very common, despite the fact that organizations have access to large amounts of data and make decisions that could profit from data mining on a daily basis. One of the reasons is the mismatch between data representation for data storage and data analysis. Data are most commonly stored in multi-table relational databases whereas data mining methods require that the data be represented as a simple feature vector. This work presents a general framework for feature construction from multiple relational tables for data mining applications. The second part describes our prototype implementation ACORA (Automated Construction of Relational Features).Information Systems Working Papers Serie

Political economy of oil production from 1850s to 1974

A study of the oil industry in its modern development from the 1850s to 1973. During this period the industry underwent significant changes in terms of its productive expansion, the diversity of its products, its role in general production, its corporate organisation and in terms of its significance to the very reproduction of advanced societies. The examination of the oil industry focuses on a political economy of its historical expansion. The thesis uses a Marxist theoretical framework to examine issues related to oil production as well as synthesising the elemental features of oil production into a structured conceptual model of the oil industry. The thesis divides the analysis of oil between chapters dealing with economic and political concerns in the context of historic epochs. The economic components of the thesis deal with the capitalist development of oil, its relationship with other sectors of production and consumption and an assessment of its role in economic growth as a whole. This provides the basis for the subsequent politically focused analyses. The political chapters deal with two primary issues, including the state response to the monopolisation of the oil industry and the effect of the expanding importance of oil on political relations. The analysis of the monopolisation of the oil industry provides an opportunity to study the relationship between the state in a regulatory function and the subsequent constraint on oil industry autonomy. The study of interstate relations focuses in turn on the effect of expanding oil production on the economic interests of states, in their support for the reproduction of capital in their domains

Economic Impacts of Proposed Limits on Trans Fats in Canada

In response to growing concerns about coronary heart disease (CHD), the Government of Canada has recently taken policy measures to reduce Canadian trans fatty acid (TFA) consumption. The mandatory labelling of trans fat content in foods began in December 2005. The House of Commons also established a task force in November 2004 to develop a set of regulations to ban the sale of food products with a TFA content greater than 2 percent. The issue at stake is whether the mandatory content restriction has economic merit. While the mandatory TFA reductions could reduce heart disease and improve the health of Canadians, they also have the potential to increase economic costs faced by all aspects of the Canadian food oil complex, from primary producers to consumers. The goal of this article is to examine the impacts of a mandatory reduction of trans fat content by estimating the potential health benefits and potential adverse impacts on the agri-food sector.Agricultural and Food Policy, Food Consumption/Nutrition/Food Safety,

Holomorphic Semiflows and Poincaré-Steklov Semigroups

Die Arbeit untersucht einen überraschenden Zusammenhang zwischen Halbflüssen von holomorphen Selbstabbildungen auf einfach zusammenhängenden Gebieten und Halbgruppen, die von Poincaré-Steklov Operatoren erzeugt werden. Mithilfe von Erzeuger von Kompositionshalbgruppen auf Banachräumen von analytischen Funktionen werden insbesondere Dirichlet-zu-Neumann und Dirichlet-zu-Robin Operatoren konstruiert. Dieser Zugang eröffnet einen neuen Ansatz für das Studium partiellen Differentialgleichungen, die mit solchen Operatoren assoziiert sind.We study a surprising connection between semiflows of holomorphic selfmaps of a simply connected domain and semigroups generated by Poincaré-Steklov operators. In particular, by means of generators of semigroups of composition operators on Banach spaces of analytic functions, we construct Dirichlet-to-Neumann and Dirichlet-to-Robin operators. This approach gives new insights to the theory of partial differential equations associated with such operators

ACORA: Distribution-Based Aggregation for Relational Learning from Identifier Attributes

Feature construction through aggregation plays an essential role in modeling relational domains with one-to-many relationships between tables. One-to-many relationships lead to bags (multisets) of related entities, from which predictive information must be captured. This paper focuses on aggregation from categorical attributes that can take many values (e.g., object identifiers). We present a novel aggregation method as part of a relational learning system ACORA, that combines the use of vector distance and meta-data about the class-conditional distributions of attribute values. We provide a theoretical foundation for this approach deriving a "relational fixed-effect" model within a Bayesian framework, and discuss the implications of identifier aggregation on the expressive power of the induced model. One advantage of using identifier attributes is the circumvention of limitations caused either by missing/unobserved object properties or by independence assumptions. Finally, we show empirically that the novel aggregators can generalize in the presence of identi- fier (and other high-dimensional) attributes, and also explore the limitations of the applicability of the methods.Information Systems Working Papers Serie

Bound

This paper contains four major sections. The first section focuses on the historical development of the ring and how the finger ring in every era has reflected the function and aesthetics of its time. The second part delves into the different materials I have chosen to use, and why I selected them. The third section examines what influences have affected my work, and includes a detailed explanation of the eleven rings shown in my thesis project. The conclusion summarizes how my art reflects my aesthetic

Aggregation-Based Feature Invention and Relational

Due to interest in social and economic networks, relational modeling is attracting increasing attention. The field of relational data mining/learning, which traditionally was dominated by logic-based approaches, has recently been extended by adapting learning methods such as naive Bayes, Baysian networks and decision trees to relational tasks. One aspect inherent to all methods of model induction from relational data is the construction of features through the aggregation of sets. The theoretical part of this work (1) presents an ontology of relational concepts of increasing complexity, (2) derives classes of aggregation operators that are needed to learn these concepts, and (3) classifies relational domains based on relational schema characteristics such as cardinality. We then present a new class of aggregation functions, ones that are particularly well suited for relational classification and class probability estimation. The empirical part of this paper demonstrates on real domain the effects on the system performance of different aggregation methods on different relational concepts. The results suggest that more complex aggregation methods can significantly increase generalization performance and that, in particular, task-specific aggregation can simplify relational prediction tasks into well-understood propositional learning problems.Information Systems Working Papers Serie

Distribution-based aggregation for relational learning with identifier attributes

Identifier attributes—very high-dimensional categorical attributes such as particular product ids or people’s names—rarely are incorporated in statistical modeling. However, they can play an important role in relational modeling: it may be informative to have communicated with a particular set of people or to have purchased a particular set of products. A key limitation of existing relational modeling techniques is how they aggregate bags (multisets) of values from related entities. The aggregations used by existing methods are simple summaries of the distributions of features of related entities: e.g., MEAN, MODE, SUM, or COUNT. This paper’s main contribution is the introduction of aggregation operators that capture more information about the value distributions, by storing meta-data about value distributions and referencing this meta-data when aggregating—for example by computing class-conditional distributional distances. Such aggregations are particularly important for aggregating values from high-dimensional categorical attributes, for which the simple aggregates provide little information. In the first half of the paper we provide general guidelines for designing aggregation operators, introduce the new aggregators in the context of the relational learning system ACORA (Automated Construction of Relational Attributes), and provide theoretical justification.We also conjecture special properties of identifier attributes, e.g., they proxy for unobserved attributes and for information deeper in the relationship network. In the second half of the paper we provide extensive empirical evidence that the distribution-based aggregators indeed do facilitate modeling with high-dimensional categorical attributes, and in support of the aforementioned conjectures.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc