Graph-RAT programming environment

Graph-RAT is a new programming environment specializing in relational data mining. It incorporates a number of different techniques into a single framework for data collection, data cleaning, propositionalization, and analysis. The language is functional where algorithms are executed over arbitrary sub-graphs of the data. Analytical results can be conducted using collaborative filtering or machine learning techniques. The example algorithms are under BSD license

MIR task and evaluation techniques

Existing tasks in MIREX have traditionally focused on low-level MIR tasks working with flat (usually DSP-only) ground-truth. These evaluation techniques, however, can not evaluate the increasing number of algorithms that utilize relational data and are not currently utilizing the state of the art in evaluating ranked or ordered output. This paper summarizes the state of the art in evaluating relational ground-truth. These components are then synthesized into novel evaluation techniques that are then applied to 14 concrete music document retrieval tasks, demonstrating how these evaluation techniques can be applied in a practical context

Linear-time graph triples census algorithm under assumptions typical of social networks

A graph triples census is a histogram of all possible sets of three vertici (called a triple) from a graph. Graph triples census have been in active use in sociology for over 50 years. The earliest paper using this approach is by Holland and Leinhardt [1]. This gives a general description of the structure of directed graphs in a fixed length vector. Since this time, this analytic tool has been widely used in social network analysis. A summary of important papers using this approach, both as end product and as a component of further analysis, are in[2]

Graph-RAT: Combining data sources in music recommendation systems

The complexity of music recommendation systems has increased rapidly in recent years, drawing upon different sources of information: content analysis, web-mining, social tagging, etc. Unfortunately, the tools to scientifically evaluate such integrated systems are not readily available; nor are the base algorithms available. This article describes Graph-RAT (Graph-based Relational Analysis Toolkit), an open source toolkit that provides a framework for developing and evaluating novel hybrid systems. While this toolkit is designed for music recommendation, it has applications outside its discipline as well. An experiment—indicative of the sort of procedure that can be configured using the toolkit—is provided to illustrate its usefulness

Sport and social media research: A review

The emergence of social media has profoundly impacted the delivery and consumption of sport. In the current review we analysed the existing body of knowledge of social media in the field of sport management from a service-dominant logic perspective, with an emphasis on relationship marketing. We reviewed 70 journal articles published in English-language sport management journals, which investigated new media technologies facilitating interactivity and co-creation that allow for the development and sharing of user-generated content among and between brands and individuals (i.e., social media). Three categories of social media research were identified: strategic, operational, and user-focussed. The findings of the review demonstrate that social media research in sport management aligns with service-dominant logic and illustrates the role of social media in cultivating relationships among and between brands and individuals. Interaction and engagement play a crucial role in cultivating these relationships. Discussion of each category, opportunities for future research as well as suggestions for theoretical approaches, research design and context are advanced

Graph-RAT Programming Environment Graph-RAT Programming Environment

Abstract Graph-RAT is a new programming environment specializing in relational data mining. It incorporates a number of different techniques into a single framework for data collection, data cleaning, propositionalization, and analysis. The language is functional where algorithms are executed over arbitrary sub-graphs of the data. Analytical results can be conducted using collaborative filtering or machine learning techniques. The example algorithms are under BSD license

On-demand metadata extraction network (OMEN)

OMEN (On-demand Metadata Extraction Network) addresses a fundamental problem in Music Information Retrieval: the lack of universal access to a large dataset containing significant amounts of copyrighted music. This thesis proposes a solution to this problem that is accomplished by utilizing the large collections of digitized music available at many libraries. Using OMEN, libraries will be able to perform on-demand feature extraction on site, returning feature values to researchers instead of providing direct access to the recordings themselves. This avoids copyright difficulties, since the underlying music never leaves the library that owns it. The analysis is performed using grid-style computation on library machines that are otherwise under-used (e.g., devoted to patron web and catalogue use)

JAUDIO: A FEATURE EXTRACTION LIBRARY ABSTRACT

cory.mcka