By a News Reporter-Staff News Editor at Medical Verdicts & Law Weekly -- Investigators publish new report on Information Technology - Information and Data Loss and Recovery. According to news reporting originating in Gaithersburg, Maryland, by NewsRx journalists, research stated, “Today’s cities generate tremendous amounts of data, thanks to a boom in affordable smart devices and sensors. The resulting big data creates opportunities to develop diverse sets of context-aware services and systems, ensuring smart city services are optimized to the dynamic city environment.”
Financial support for this research came from Information Technology Laboratory, National Institute of Standards and Technology.
The news reporters obtained a quote from the research from the National Institute of Standards and Technology, “Critical resources in these smart cities will be more rapidly deployed to regions in need, and those regions predicted to have an imminent or prospective need. For example, crime data analytics may be used to optimize the distribution of police, medical, and emergency services. However, as smart city services become dependent on data, they also become susceptible to disruptions in data streams, such as data loss due to signal quality reduction or due to power loss during data collection. This paper presents a dynamic network model for improving service resilience to data loss. The network model identifies statistically significant shared temporal trends across multivariate spatiotemporal data streams and utilizes these trends to improve data prediction performance in the case of data loss. Dynamics also allow the system to respond to changes in the data streams such as the loss or addition of new information flows. The network model is demonstrated by city-based crime rates reported in Montgomery County, MD, USA. A resilient network is developed utilizing shared temporal trends between cities to provide improved crime rate prediction and robustness to data loss, compared with the use of single city-based auto-regression. A maximum improvement in performance of 7.8% for Silver Spring is found and an average improvement of 5.6% among cities with high crime rates. The model also correctly identifies all the optimal network connections, according to prediction error minimization.”
According to the news reporters, the research concluded: “City-to-city distance is designated as a predictor of shared temporal trends in crime and weather is shown to be a strong predictor of crime in Montgomery County.”
For more information on this research see: Dynamic Network Model for Smart City Data-Loss Resilience Case Study: City-to-City Network for Crime Analytics. Ieee Access , 2017;5():20524-20535.
Our news correspondents report that additional information may be obtained by contacting O. Kotevska, National Institute of Standards and Technology, Gaithersburg, MD 20899, United States. Additional authors for this research include A.G. Kusne, D.V. Samarov, A. Lbath and A. Battou.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/ACCESS.2017.2757841. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-01-04), New Findings from National Institute of Standards and Technology Yields New Data on Information and Data Loss and Recovery (Dynamic Network Model for Smart City Data-Loss Resilience Case Study: City-to-City Network for Crime Analytics), Medical Verdicts & Law Weekly, 3, ISSN: 1551-5567, BUTTER® ID: 014910551
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