19,053 research outputs found
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
Semantic data mining and linked data for a recommender system in the AEC industry
Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations
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