An Evaluation of Generic Bulk Loading Techniques
Proceedings of the 27th International Conference on Very Large Data Bases
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Flow Algorithms for Parallel Query Optimization
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Parallel Evaluation of Composite Aggregate Queries
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Experiences on Processing Spatial Data with MapReduce
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
MapReduce: a flexible data processing tool
Communications of the ACM - Amir Pnueli: Ahead of His Time
Hive: a warehousing solution over a map-reduce framework
Proceedings of the VLDB Endowment
Communications of the ACM
A survey of mobile phone sensing
IEEE Communications Magazine
Voronoi-Based Geospatial Query Processing with MapReduce
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
RCFile: A fast and space-efficient data placement structure in MapReduce-based warehouse systems
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
MD-HBase: A Scalable Multi-dimensional Data Infrastructure for Location Aware Services
MDM '11 Proceedings of the 2011 IEEE 12th International Conference on Mobile Data Management - Volume 01
A Mobile Data Analysis Framework for Environmental Health Decision Support
ITNG '12 Proceedings of the 2012 Ninth International Conference on Information Technology - New Generations
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Continued advances and cost reduction in personal mobile devices such as smart phones made them widely used in daily-life practices. Mobile devices can be integrated with a growing set of cheap powerful embedded sensors that enable the emergence of mobile sensing applications, including healthcare, environmental monitoring and transportation. As the size of the sensor data continuously grows, managing the data becomes increasingly difficult using traditional database systems. This paper proposes a new framework for large-scale continuously changing mobile sensor data analysis. We discuss the emerging environmental sensing paradigms and opportunities to apply HBase and MapReduce for managing multiple sensor data in the environmental exposome domain. Moreover, we provide an architectural framework and present a concrete use case with a set of data models, spatio-temporal queries, and MapReduce functions.