22,881 research outputs found
A Survey on Array Storage, Query Languages, and Systems
Since scientific investigation is one of the most important providers of
massive amounts of ordered data, there is a renewed interest in array data
processing in the context of Big Data. To the best of our knowledge, a unified
resource that summarizes and analyzes array processing research over its long
existence is currently missing. In this survey, we provide a guide for past,
present, and future research in array processing. The survey is organized along
three main topics. Array storage discusses all the aspects related to array
partitioning into chunks. The identification of a reduced set of array
operators to form the foundation for an array query language is analyzed across
multiple such proposals. Lastly, we survey real systems for array processing.
The result is a thorough survey on array data storage and processing that
should be consulted by anyone interested in this research topic, independent of
experience level. The survey is not complete though. We greatly appreciate
pointers towards any work we might have forgotten to mention.Comment: 44 page
AiiDA: Automated Interactive Infrastructure and Database for Computational Science
Computational science has seen in the last decades a spectacular rise in the
scope, breadth, and depth of its efforts. Notwithstanding this prevalence and
impact, it is often still performed using the renaissance model of individual
artisans gathered in a workshop, under the guidance of an established
practitioner. Great benefits could follow instead from adopting concepts and
tools coming from computer science to manage, preserve, and share these
computational efforts. We illustrate here our paradigm sustaining such vision,
based around the four pillars of Automation, Data, Environment, and Sharing. We
then discuss its implementation in the open-source AiiDA platform
(http://www.aiida.net), that has been tuned first to the demands of
computational materials science. AiiDA's design is based on directed acyclic
graphs to track the provenance of data and calculations, and ensure
preservation and searchability. Remote computational resources are managed
transparently, and automation is coupled with data storage to ensure
reproducibility. Last, complex sequences of calculations can be encoded into
scientific workflows. We believe that AiiDA's design and its sharing
capabilities will encourage the creation of social ecosystems to disseminate
codes, data, and scientific workflows.Comment: 30 pages, 7 figure
Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks
Sensors are present in various forms all around the world such as mobile
phones, surveillance cameras, smart televisions, intelligent refrigerators and
blood pressure monitors. Usually, most of the sensors are a part of some other
system with similar sensors that compose a network. One of such networks is
composed of millions of sensors connect to the Internet which is called
Internet of things (IoT). With the advances in wireless communication
technologies, multimedia sensors and their networks are expected to be major
components in IoT. Many studies have already been done on wireless multimedia
sensor networks in diverse domains like fire detection, city surveillance,
early warning systems, etc. All those applications position sensor nodes and
collect their data for a long time period with real-time data flow, which is
considered as big data. Big data may be structured or unstructured and needs to
be stored for further processing and analyzing. Analyzing multimedia big data
is a challenging task requiring a high-level modeling to efficiently extract
valuable information/knowledge from data. In this study, we propose a big
database model based on graph database model for handling data generated by
wireless multimedia sensor networks. We introduce a simulator to generate
synthetic data and store and query big data using graph model as a big
database. For this purpose, we evaluate the well-known graph-based NoSQL
databases, Neo4j and OrientDB, and a relational database, MySQL.We have run a
number of query experiments on our implemented simulator to show that which
database system(s) for surveillance in wireless multimedia sensor networks is
efficient and scalable
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