4,263 research outputs found
Trip Planner: A Big Data Analytics Based Recommendation System for Tourism Planning
Foreign tourism has gained immense popularity in the recent past. To make a rational decision about the destination to be visited one has to go through variety of social media sources with very large number of reviews, which is a tedious task. Automated analysis of these reviews is quite complex as it involves non structured text data having slang terms also. Moreover, these reviews are pouring in continuously. To overcome this problem, this paper provides a Big Data analytics-based framework to make appropriate selection of the destination on the basis of automated analysis of social media contents based upon the adaptation and augmentation of various tools and technologies. The framework has been implemented using Apache Spark and Bidirectional Encoder Representation Transformers (BERT) deep learning models through which raw text review are analysed and a final score based on five metrics is obtained to recommend destination for visit
Building Near-Real-Time Processing Pipelines with the Spark-MPI Platform
Advances in detectors and computational technologies provide new
opportunities for applied research and the fundamental sciences. Concurrently,
dramatic increases in the three Vs (Volume, Velocity, and Variety) of
experimental data and the scale of computational tasks produced the demand for
new real-time processing systems at experimental facilities. Recently, this
demand was addressed by the Spark-MPI approach connecting the Spark
data-intensive platform with the MPI high-performance framework. In contrast
with existing data management and analytics systems, Spark introduced a new
middleware based on resilient distributed datasets (RDDs), which decoupled
various data sources from high-level processing algorithms. The RDD middleware
significantly advanced the scope of data-intensive applications, spreading from
SQL queries to machine learning to graph processing. Spark-MPI further extended
the Spark ecosystem with the MPI applications using the Process Management
Interface. The paper explores this integrated platform within the context of
online ptychographic and tomographic reconstruction pipelines.Comment: New York Scientific Data Summit, August 6-9, 201
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