Presents an in-depth historical reconstruction and a detailed ethnographic account of the Western Apache culture based on firsthand observations made over a span of nearly ten years in the field
The ...Social Organization of the Western Apache is still one of the most comprehensive descriptions of the social life of an American Indian tribe. Grenville Goodwin knew the Western Apache better than any other ethnographer who ever lived. And he wrote about them from the conviction that his knowledge was important—not only for specialists interested in the tribes of the Southwest, but for all anthropologists concerned with the structure and operation of primitive social systems.
This incisive ethnographic analysis of indigenous language documentation, maintenance, and revitalization focuses on linguistic heritage issues on the Native American reservation at Fort Apache and ...explores the broader social, political and religious influences on changing language practices in indigenous communities.Offers a focused ethnographic analysis of an indigenous community that also explores global issues of language endangerment and maintenance and their socio-historical contextsAddresses the complexities and conflicts in language documentation and revitalization programs, and how they articulate with localized discourse genres, education practices, religious beliefs, and politicsExamines differing evaluations of language loss, and maintenance, among members of affected communities, and their creative responses to challenges posed by encompassing socio-cultural regimes, including university accredited language expertsProvides an ethnographic analysis of speech in indigenous communities that moves beyond narrowly conceived language documentation to consider changing linguistic and social identities
The k-Nearest Neighbors classifier is a simple yet effective widely renowned method in data mining. The actual application of this model in the big data domain is not feasible due to time and memory ...restrictions. Several distributed alternatives based on MapReduce have been proposed to enable this method to handle large-scale data. However, their performance can be further improved with new designs that fit with newly arising technologies.
In this work we provide a new solution to perform an exact k-nearest neighbor classification based on Spark. We take advantage of its in-memory operations to classify big amounts of unseen cases against a big training dataset. The map phase computes the k-nearest neighbors in different training data splits. Afterwards, multiple reducers process the definitive neighbors from the list obtained in the map phase. The key point of this proposal lies on the management of the test set, keeping it in memory when possible. Otherwise, it is split into a minimum number of pieces, applying a MapReduce per chunk, using the caching skills of Spark to reuse the previously partitioned training set. In our experiments we study the differences between Hadoop and Spark implementations with datasets up to 11 million instances, showing the scaling-up capabilities of the proposed approach. As a result of this work an open-source Spark package is available.
Wisdom from the past . . . hope for the future . . .
In 1945 the hot wind from a nuclear explosion at Trinity Site on a nearby missile range raged across the Mescalero Apache Reservation in ...south-central New Mexico, killing hundreds of head of livestock and causing sickness among the descendants of some of the most famous Apache heroes in American history. In many ways, this disaster typified what these Apaches had come to expect from the federal government: attention was often accompanied by undesired results.
Four thousand Apaches of the Mescalero, Chiricahua, and Lipan bands now live on this reservation. In twelve remarkable oral history interviews, three generations of Mescalero, Chiricahua, and Lipan Apaches reflect on the trials of the past, the challenges of the present, and hope for the future. A common thread among all of the interviewees is a collective memory of their people as formidable enemies of the U.S. government in the not-too-distant past.
Author and ethnographer H. Henrietta Stockel has structured these interviews to encompass three groups of Mescalero Apache society: the elders, the “warriors” (middle-aged), and the “horseholders,” or young apprentices.
The emergence of smart cities aims at mitigating the challenges raised due to the continuous urbanization development and increasing population density in cities. To face these challenges, ...governments and decision makers undertake smart city projects targeting sustainable economic growth and better quality of life for both inhabitants and visitors. Information and Communication Technology (ICT) is a key enabling technology for city smartening. However, ICT artifacts and applications yield massive volumes of data known as big data. Extracting insights and hidden correlations from big data is a growing trend in information systems to provide better services to citizens and support the decision making processes. However, to extract valuable insights for developing city level smart information services, the generated datasets from various city domains need to be integrated and analyzed. This process usually referred to as big data analytics or big data value chain. Surveying the literature reveals an increasing interest in harnessing big data analytics applications in general and in the area of smart cities in particular. Yet, comprehensive discussions on the essential characteristics of big data analytics frameworks fitting smart cities requirements are still needed. This paper presents a novel big data analytics framework for smart cities called “Smart City Data Analytics Panel — SCDAP”. The design of SCDAP is based on answering the following research questions: what are the characteristics of big data analytics frameworks applied in smart cities in literature and what are the essential design principles that should guide the design of big data analytics frameworks have to serve smart cities purposes? In answering these questions, we adopted a systematic literature review on big data analytics frameworks in smart cities. The proposed framework introduces new functionalities to big data analytics frameworks represented in data model management and aggregation. The value of the proposed framework is discussed in comparison to traditional knowledge discovery approaches.
Heart disease is one of the first causes of death worldwide. This paper presents a real-time system for predicting heart disease from medical data streams that describe a patient’s current health ...status. The main goal of the proposed system is to find the optimal machine learning algorithm that achieves high accuracy for heart disease prediction. Two types of features selection algorithms, univariate feature selection and Relief, are used to select important features from the dataset. We compared four types of machine learning algorithms; Decision Tree, Support Vector Machine, Random Forest Classifier, and Logistic Regression Classifier with the selected features as well as full features. We apply hyperparameter tuning and cross-validation with machine learning to enhance accuracy. One core merit of the proposed system is able to handle Twitter data streams that contain patients’ data efficiently. This is done by integrating Apache Kafka with Apache Spark as the underlying infrastructure of the system. The results show the random forest classifier outperforms the other models by achieving the highest accuracy at 94.9%.
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•Developing a real-time to extract knowledge related to heart diseases from user tweets streaming.•Finding the optimal machine learning algorithm that achieves the best accuracy for heart disease.•Applying feature selection to select the most important features from the dataset to build the model.
Evaluation of Stream Processing Frameworks Van Dongen, Giselle; Van den Poel, Dirk E.
IEEE transactions on parallel and distributed systems,
08/2020, Volume:
31, Issue:
8
Journal Article
Peer reviewed
The increasing need for real-time insights in data sparked the development of multiple stream processing frameworks. Several benchmarking studies were conducted in an effort to form guidelines for ...identifying the most appropriate framework for a use case. In this work, we extend this research and present the results gathered. In addition to Spark Streaming and Flink, we also include the emerging frameworks Structured Streaming and Kafka Streams. We define four workloads with custom parameter tuning. Each of these is optimized for a certain metric or for measuring performance under specific scenarios such as bursty workloads. We analyze the relationship between latency, throughput and resource consumption and we measure the performance impact of adding different common operations to the pipeline. To ensure correct latency measurements, we use a single Kafka broker. Our results show that the latency disadvantages of using a micro-batch system are most apparent for stateless operations. With more complex pipelines, customized implementations can give event-driven frameworks a large latency advantage. Due to its micro-batch architecture, Structured Streaming can handle very high throughput at the cost of high latency. Under tight latency SLAs, Flink sustains the highest throughput. Additionally, Flink shows the least performance degradation when confronted with periodic bursts of data. When a burst of data needs to be processed right after startup, however, micro-batch systems catch up faster while event-driven systems output the first events sooner.
Grenville Goodwin was one of the leading field anthropologists during a crucial period in American Indian research-the 1930s. His letters from the field provide original source material on Western ...Apache beliefs and customs. They also reveal the attitudes and methods which made him so effective in his work. A dedicated and thorough ethnographer, Goodwin became familiar with every aspect of Western Apache culture. During this same period, Morris Opler was studying the Chiricahua and Mescalero Apache in New Mexico. In order to exchange information about their studies, Goodwin and Opler began corresponding. Both men were convinced that a long-overdue, systematic comparison of Apachean cultures would yield significant results.
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