The Shakespeare User

The Shakespeare User explores uses of Shakespeare in a wide variety of 21st century contexts, including business manuals, non-literary scholarship, database aggregation, social media, gaming, and creative criticism. Essays in this volume demonstrate that users’ critical and creative uses of the dramatist’s works position contemporary issues of race, power, identity, and authority in new networks that redefine Shakespeare and reconceptualize the ways in which he is processed in both scholarly and popular culture. This reticular understanding of Shakespeare use expands scholarly forays into non-academic practices, digital discourse communities, and creative critical works manifest via YouTube, Twitter, blogs, databases, websites, and popular fiction

T2{}^2K2{}^2: The Twitter Top-K Keywords Benchmark

Information retrieval from textual data focuses on the construction of vocabularies that contain weighted term tuples. Such vocabularies can then be exploited by various text analysis algorithms to extract new knowledge, e.g., top-k keywords, top-k documents, etc. Top-k keywords are casually used for various purposes, are often computed on-the-fly, and thus must be efficiently computed. To compare competing weighting schemes and database implementations, benchmarking is customary. To the best of our knowledge, no benchmark currently addresses these problems. Hence, in this paper, we present a top-k keywords benchmark, T${}^2$K${}^2$, which features a real tweet dataset and queries with various complexities and selectivities. T${}^2$K${}^2$ helps evaluate weighting schemes and database implementations in terms of computing performance. To illustrate T${}^2$K${}^2$'s relevance and genericity, we successfully performed tests on the TF-IDF and Okapi BM25 weighting schemes, on one hand, and on different relational (Oracle, PostgreSQL) and document-oriented (MongoDB) database implementations, on the other hand

Assessing impacts of CAP reform in France and Germany

The 2003 CAP Reform left EU member states much room for national implementation. The farm group model EU-FARMIS is applied to quantify the effects of the reform and the impacts of the options for national implementation. The analysis is done for France and Germany because their implementation schemes adequately reflect the broad range of options. It is found that cereal and fodder maize production is reduced both in France and Germany. In contrast, the acreage of other arable fodder crops, of set-aside and of non-food crops is expanded. While bull fattening is substantially reduced in both countries, suckler cow production is extended in France due to partial decoupling, but reduced in Germany due to full decoupling. Sectoral income effects measured in Farm Net Value Added are similar. The regional implementation of decoupling in Germany induces a significant redistribution of direct payments and therefore causes differences in income effects depending on farm type, location and size.CAP Reform, decoupling, farm group model, FADN, Agricultural and Food Policy, Land Economics/Use,

ILP Modulo Data

The vast quantity of data generated and captured every day has led to a pressing need for tools and processes to organize, analyze and interrelate this data. Automated reasoning and optimization tools with inherent support for data could enable advancements in a variety of contexts, from data-backed decision making to data-intensive scientific research. To this end, we introduce a decidable logic aimed at database analysis. Our logic extends quantifier-free Linear Integer Arithmetic with operators from Relational Algebra, like selection and cross product. We provide a scalable decision procedure that is based on the BC(T) architecture for ILP Modulo Theories. Our decision procedure makes use of database techniques. We also experimentally evaluate our approach, and discuss potential applications.Comment: FMCAD 2014 final version plus proof

Measuring Regional Economic Impacts from Wildfire: Case Study of Southeast Oregon Cattle-Ranching Business

public grazing, regional economic impact, Social Accounting Matrix, Southeast Oregon, wildfire

Speculative Approximations for Terascale Analytics

Model calibration is a major challenge faced by the plethora of statistical analytics packages that are increasingly used in Big Data applications. Identifying the optimal model parameters is a time-consuming process that has to be executed from scratch for every dataset/model combination even by experienced data scientists. We argue that the incapacity to evaluate multiple parameter configurations simultaneously and the lack of support to quickly identify sub-optimal configurations are the principal causes. In this paper, we develop two database-inspired techniques for efficient model calibration. Speculative parameter testing applies advanced parallel multi-query processing methods to evaluate several configurations concurrently. The number of configurations is determined adaptively at runtime, while the configurations themselves are extracted from a distribution that is continuously learned following a Bayesian process. Online aggregation is applied to identify sub-optimal configurations early in the processing by incrementally sampling the training dataset and estimating the objective function corresponding to each configuration. We design concurrent online aggregation estimators and define halting conditions to accurately and timely stop the execution. We apply the proposed techniques to distributed gradient descent optimization -- batch and incremental -- for support vector machines and logistic regression models. We implement the resulting solutions in GLADE PF-OLA -- a state-of-the-art Big Data analytics system -- and evaluate their performance over terascale-size synthetic and real datasets. The results confirm that as many as 32 configurations can be evaluated concurrently almost as fast as one, while sub-optimal configurations are detected accurately in as little as a $1/20^{\text{th}}$ fraction of the time