Spatial big data for disaster management
نویسندگان
چکیده
منابع مشابه
BSDM: Big Spatial Data Management
We are living in the era of Big Data. Spatial and Spatiotemporal Data are not an exception. Mobile apps, cars, GPS devices, UAVs, ships, airplanes, space telescopes, medical devices and IoT devices are generating explosive amounts of data with spatial characteristics. Web apps and social networking systems also store vast amounts of geo-located information, like geo-located tweets, or captured ...
متن کاملBenchmarking Spatial Big Data
Spatial computing is a set of ideas and technologies that facilitate understanding the geo-physical world, knowing and communicating relations to places in that world, and navigating through those places. The transformational potential of mobility services is already evident. From Google Maps [17] to consumer Global Positioning System (GPS) devices, society has benefited immensely from spatial ...
متن کاملReal-Time Data Management for Big Data
Users have come to expect reactivity from mobile and web applications, i.e. they assume that changes made by other users become visible immediately. However, developers are challenged with building reactive applications on top of traditional pulloriented databases, because they are ill-equipped to push new information to the client. Systems for data stream management and processing, on the othe...
متن کاملMultimedia Data Management for Disaster Situation Awareness
To raise awareness in disaster situations, the quality and analysis of disaster-related big data are essential. Recent developments in the collection, analysis, and visualization of multimedia data have led to a significant enhancement in disaster management systems. Crowdsourcing tools, for instance, allow citizens to perform an active role in reporting information relevant to disaster events ...
متن کاملLocationSpark: A Distributed In-Memory Data Management System for Big Spatial Data
We present LocationSpark, a spatial data processing system built on top of Apache Spark, a widely used distributed data processing system. LocationSpark offers a rich set of spatial query operators, e.g., range search, kNN, spatio-textual operation, spatial-join, and kNN-join. To achieve high performance, LocationSpark employs various spatial indexes for in-memory data, and guarantees that immu...
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ژورنال
عنوان ژورنال: IOP Conference Series: Materials Science and Engineering
سال: 2017
ISSN: 1757-8981,1757-899X
DOI: 10.1088/1757-899x/263/4/042008