Application of XML for Telemetry Data Management

2013 ◽  
Vol 756-759 ◽  
pp. 3250-3253
Author(s):  
Zhi Guo Zhang ◽  
Lei Gong ◽  
Hong Ping Wang
Keyword(s):  
Data Structure ◽  
Data Management ◽  
Information Sharing ◽  
Large Scale ◽  
Text File ◽  
Telemetry Data ◽  
Binary File ◽  
Practical System ◽  
Management Method

At present, the telemetry data is stored and released in the form of binary file or text file, and the user cant find out the data structure from the file itself, this bring difficulties in query and the information sharing. To deal with the problem, a XML-based telemetry data management method was proposed by taking the advantages of extensible makeup language (XML). Large scale of telemetry data can be stored in sections using this method, index efficiency of telemetry data is increased obviously in the operation of practical system. The distribution ability of telemetry data is increased too.

scholarly journals BIOTAS: BIOTelemetry Analysis Software, for the semi-automated removal of false positives from radio telemetry data

2022 ◽  
Vol 10 (1) ◽  
Author(s):  
K. Nebiolo ◽  
T. Castro-Santos
Keyword(s):  
Data Management ◽  
False Positive ◽  
Large Scale ◽  
Cross Validation ◽  
Radio Telemetry ◽  
Wide Band ◽  
False Positives ◽  
Training Data ◽  
Analysis Software ◽  
Telemetry Data

Abstract Introduction Radio telemetry, one of the most widely used techniques for tracking wildlife and fisheries populations, has a false-positive problem. Bias from false-positive detections can affect many important derived metrics, such as home range estimation, site occupation, survival, and migration timing. False-positive removal processes have relied upon simple filters and personal opinion. To overcome these shortcomings, we have developed BIOTAS (BIOTelemetry Analysis Software) to assist with false-positive identification, removal, and data management for large-scale radio telemetry projects. Methods BIOTAS uses a naïve Bayes classifier to identify and remove false-positive detections from radio telemetry data. The semi-supervised classifier uses spurious detections from unknown tags and study tags as training data. We tested BIOTAS on four scenarios: wide-band receiver with a single Yagi antenna, wide-band receiver that switched between two Yagi antennas, wide-band receiver with a single dipole antenna, and single-band receiver that switched between five frequencies. BIOTAS has a built in a k-fold cross-validation and assesses model quality with sensitivity, specificity, positive and negative predictive value, false-positive rate, and precision-recall area under the curve. BIOTAS also assesses concordance with a traditional consecutive detection filter using Cohen’s $$\kappa$$ κ . Results Overall BIOTAS performed equally well in all scenarios and was able to discriminate between known false-positive detections and valid study tag detections with low false-positive rates (< 0.001) as determined through cross-validation, even as receivers switched between antennas and frequencies. BIOTAS classified between 94 and 99% of study tag detections as valid. Conclusion As part of a robust data management plan, BIOTAS is able to discriminate between detections from study tags and known false positives. BIOTAS works with multiple manufacturers and accounts for receivers that switch between antennas and frequencies. BIOTAS provides the framework for transparent, objective, and repeatable telemetry projects for wildlife conservation surveys, and increases the efficiency of data processing.

Current State of (Dis)Integration: Public Health and Fusion Centers

2020 ◽  
Vol 17 (2) ◽  
Author(s):  
Cody Minks ◽  
Anke Richter
Keyword(s):  
Public Health ◽  
Law Enforcement ◽  
Information Sharing ◽  
Homeland Security ◽  
Large Scale ◽  
Ad Hoc ◽  
Limiting Factor ◽  
Fusion Centers ◽  
Public Health Emergencies ◽  
Public Health Information

AbstractObjectiveResponding to large-scale public health emergencies relies heavily on planning and collaboration between law enforcement and public health officials. This study examines the current level of information sharing and integration between these domains by measuring the inclusion of public health in the law enforcement functions of fusion centers.MethodsSurvey of all fusion centers, with a 29.9% response rate.ResultsOnly one of the 23 responding fusion centers had true public health inclusion, a decrease from research conducted in 2007. Information sharing is primarily limited to information flowing out of the fusion center, with little public health information coming in. Most of the collaboration is done on a personal, informal, ad-hoc basis. There remains a large misunderstanding of roles, capabilities, and regulations by all parties (fusion centers and public health). The majority of the parties appear to be willing to work together, but there but there is no forward momentum to make these desires a reality. Funding and staffing issues seem to be the limiting factor for integration.ConclusionThese problems need to be urgently addressed to increase public health preparedness and enable a decisive and beneficial response to public health emergencies involving a homeland security response.

scholarly journals PowerSystems.jl — A power system data management package for large scale modeling

SoftwareX ◽  
2021 ◽  
Vol 15 ◽  
pp. 100747
Author(s):  
José Daniel Lara ◽  
Clayton Barrows ◽  
Daniel Thom ◽  
Dheepak Krishnamurthy ◽  
Duncan Callaway
Keyword(s):  
Power System ◽  
Data Management ◽  
Large Scale ◽  
Scale Modeling ◽  
System Data ◽  
Large Scale Modeling
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