4 resultados para Mining City

em Boston University Digital Common


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This project investigates how religious music, invested with symbolic and cultural meaning, provided African Americans in border city churches with a way to negotiate conflict, assert individual values, and establish a collective identity in the post- emancipation era. In order to focus on the encounter between former slaves and free Blacks, the dissertation examines black churches that received large numbers of southern migrants during and after the Civil War. Primarily a work of history, the study also employs insights and conceptual frameworks from other disciplines including anthropology and ritual studies, African American studies, aesthetic theory, and musicology. It is a work of historical reconstruction in the tradition of scholarship that some have called "lived religion." Chapter 1 introduces the dissertation topic and explains how it contributes to scholarship. Chapter 2 examines social and religious conditions African Americans faced in Baltimore, MD, Philadelphia, PA, and Washington, DC to show why the Black Church played a key role in African Americans' adjustment to post-emancipation life. Chapter 3 compares religious slave music and free black church music to identify differences and continuities between them, as well as their functions in religious settings. Chapters 4, 5, and 6 present case studies on Bethel African Methodist Episcopal Church (Baltimore), Zoar Methodist Episcopal Church (Philadelphia), and St. Luke’s Protestant Episcopal Church (Washington, DC), respectively. Informed by fresh archival materials, the dissertation shows how each congregation used its musical life to uphold values like education and community, to come to terms with a shared experience, and to confront or avert authority when cultural priorities were threatened. By arguing over musical choices or performance practices, or agreeing on mutually appealing musical forms like the gospel songs of the Sunday school movement, African Americans forged lively faith communities and distinctive cultures in otherwise adverse environments. The study concludes that religious music was a crucial form of African American discourse and expression in the post-emancipation era. In the Black Church, it nurtured an atmosphere of exchange, gave structure and voice to conflict, helped create a public sphere, and upheld the values of black people.

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The increasing practicality of large-scale flow capture makes it possible to conceive of traffic analysis methods that detect and identify a large and diverse set of anomalies. However the challenge of effectively analyzing this massive data source for anomaly diagnosis is as yet unmet. We argue that the distributions of packet features (IP addresses and ports) observed in flow traces reveals both the presence and the structure of a wide range of anomalies. Using entropy as a summarization tool, we show that the analysis of feature distributions leads to significant advances on two fronts: (1) it enables highly sensitive detection of a wide range of anomalies, augmenting detections by volume-based methods, and (2) it enables automatic classification of anomalies via unsupervised learning. We show that using feature distributions, anomalies naturally fall into distinct and meaningful clusters. These clusters can be used to automatically classify anomalies and to uncover new anomaly types. We validate our claims on data from two backbone networks (Abilene and Geant) and conclude that feature distributions show promise as a key element of a fairly general network anomaly diagnosis framework.

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The problem of discovering frequent arrangements of temporal intervals is studied. It is assumed that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine temporal arrangements of event intervals that appear frequently in the database. The motivation of this work is the observation that in practice most events are not instantaneous but occur over a period of time and different events may occur concurrently. Thus, there are many practical applications that require mining such temporal correlations between intervals including the linguistic analysis of annotated data from American Sign Language as well as network and biological data. Two efficient methods to find frequent arrangements of temporal intervals are described; the first one is tree-based and uses depth first search to mine the set of frequent arrangements, whereas the second one is prefix-based. The above methods apply efficient pruning techniques that include a set of constraints consisting of regular expressions and gap constraints that add user-controlled focus into the mining process. Moreover, based on the extracted patterns a standard method for mining association rules is employed that applies different interestingness measures to evaluate the significance of the discovered patterns and rules. The performance of the proposed algorithms is evaluated and compared with other approaches on real (American Sign Language annotations and network data) and large synthetic datasets.