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Due to its simplicity and scalability, MapReduce has become a de facto standard computing model for big data processing. Since the original MapReduce model was only appropriate for embarrassingly parallel batch processing, many follow-up studies have focused on improving the efficiency and performance of the model. Spark follows one of these recent trends by providing in-memory processing capability...
Big Data can be defined as large data sets which are being generated from different sources like social media, audios, imaging, logging online websites etc. A need exists to process and analyze this huge amount of data to extract meaningful information. This can be a challenging task. Big data exceeds the processing capability of traditional databases to capture, manage, and process the voluminous...
Topic modeling is a widely used approach for analyzing large text collections. In particular, Latent Dirichlet Allocation (LDA) is one of the most popular topic modeling approaches to aggregate vocabulary from a document corpus to form latent "topics". However, learning meaningful topic models with massive document collections which contain millions of documents, billions of tokens is challenging,...
Spark has grown both in popularity and complexity in recent years. In order to use available resources in an efficient way, users need to understand how the behavior of their applications is affected by the size of the datasets and various configuration settings. Indeed, Spark allows users to specify many configuration parameters and understanding the impact of these choices with respect to the application...
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