The Experts below are selected from a list of 177 Experts worldwide ranked by ideXlab platform

He Wei-song - One of the best experts on this subject based on the ideXlab platform.

  • Network Traffic Anomaly Detection Based on Data Mining in Time-Series Graph
    Computer Science, 2009
    Co-Authors: He Wei-song
    Abstract:

    Comprehensive collection and accurate description of traffic information are core problems in network traffic anomaly detection.Aiming at the lack of traffic anomaly detection in analyzing multi-Time Series,we proposed a network traffic anomaly detection method based on Graph mining.Our method accurately and completely described the relationship among multi-Time Series which are used in traffic anomaly detection by Time-Series Graph. By mean of the support count of the patterns,our method mined all the frequent patterns,which is conducive to detecting many kinds of abnormal traffic effectively, through mining the relationship among all pattern sets,our method introduced weight coefficients of the pattern sets,which is able to solve relationship quantification issues of multi-Time Series in traffic anomaly detection.The simulation results show that the proposed method can effectively detect the network traffic anomaly and achieves a higher accuracy than the based CWT (Continuous Wavelet Transform) method in term of DDos attacks detection.

Elizabeth Buckinghamjeffery - One of the best experts on this subject based on the ideXlab platform.

  • correcting for day of the week and public holiday effects improving a national daily syndromic surveillance service for detecting public health threats
    BMC Public Health, 2017
    Co-Authors: Elizabeth Buckinghamjeffery, Roger Morbey, Thomas House, Alex J Elliot, Sally Harcourt, Gillian E Smith
    Abstract:

    As service provision and patient behaviour varies by day, healthcare data used for public health surveillance can exhibit large day of the week effects. These regular effects are further complicated by the impact of public holidays. Real-Time syndromic surveillance requires the daily analysis of a range of healthcare data sources, including family doctor consultations (called general practitioners, or GPs, in the UK). Failure to adjust for such reporting biases during analysis of syndromic GP surveillance data could lead to misinterpretations including false alarms or delays in the detection of outbreaks. The simplest smoothing method to remove a day of the week effect from daily Time Series data is a 7-day moving average. Public Health England developed the working day moving average in an attempt also to remove public holiday effects from daily GP data. However, neither of these methods adequately account for the combination of day of the week and public holiday effects. The extended working day moving average was developed. This is a further data-driven method for adding a smooth trend curve to a Time Series Graph of daily healthcare data, that aims to take both public holiday and day of the week effects into account. It is based on the assumption that the number of people seeking healthcare services is a combination of illness levels/severity and the ability or desire of patients to seek healthcare each day. The extended working day moving average was compared to the seven-day and working day moving averages through application to data from two syndromic indicators from the GP in-hours syndromic surveillance system managed by Public Health England. The extended working day moving average successfully smoothed the syndromic healthcare data by taking into account the combined day of the week and public holiday effects. In comparison, the seven-day and working day moving averages were unable to account for all these effects, which led to misleading smoothing curves. The results from this study make it possible to identify trends and unusual activity in syndromic surveillance data from GP services in real-Time independently of the effects caused by day of the week and public holidays, thereby improving the public health action resulting from the analysis of these data.

Alexander M. Gorbach - One of the best experts on this subject based on the ideXlab platform.

  • A Platform to Record Patient Events During Physiological Monitoring With Wearable Sensors: Proof-of-Concept Study.
    Interactive journal of medical research, 2019
    Co-Authors: Alexander M. Gorbach
    Abstract:

    Background Patient journals have been used as valuable resources in clinical studies. However, the full potential value of such journals can be undermined by inefficiencies and ambiguities associated with handwritten patient reports. The increasing number of mobile phones and mobile-based health care approaches presents an opportunity to improve communications from patients to clinicians and clinical researchers through the use of digital patient journals. Objective The objective of this project was to develop a smartphone-based platform that would enable patients to record events and symptoms on the same Timeline as clinical data collected by wearable sensors. Methods This platform consists of two major components: a smartphone for patients to record their journals and wireless sensors for clinical data collection. The clinical data and patient records are then exported to a clinical researcher interface, and the data and journal are processed and combined into a single Time-Series Graph for analysis. This paper gives a block diagram of the platform's principal components and compares its features to those of other methods but does not explicitly discuss the process of design or development of the system. Results As a proof of concept, body temperature data were obtained in a 4-hour span from a 22-year-old male, during which the subject simultaneously recorded relevant activities and events using the iPhone platform. After export to a clinical researcher's desktop, the digital records and temperature data were processed and fused into a single Time-Series Graph. The events were filtered based on specific keywords to facilitate data analysis. Conclusions We have developed a user-friendly patient journal platform, based on widely available smartphone technology, that gives clinicians and researchers a simple method to track and analyze patient activities and record the activities on a shared Timeline with clinical data from wearable devices.

  • A Platform to Record Patient Events During Physiological Monitoring With Wearable Sensors: Proof-of-Concept Study (Preprint)
    2018
    Co-Authors: Alexander M. Gorbach
    Abstract:

    BACKGROUND Patient journals have been used as valuable resources in clinical studies. However, the full potential value of such journals can be undermined by inefficiencies and ambiguities associated with handwritten patient reports. The increasing number of mobile phones and mobile-based health care approaches presents an opportunity to improve communications from patients to clinicians and clinical researchers through the use of digital patient journals. OBJECTIVE The objective of this project was to develop a smartphone-based platform that would enable patients to record events and symptoms on the same Timeline as clinical data collected by wearable sensors. METHODS This platform consists of two major components: a smartphone for patients to record their journals and wireless sensors for clinical data collection. The clinical data and patient records are then exported to a clinical researcher interface, and the data and journal are processed and combined into a single Time-Series Graph for analysis. This paper gives a block diagram of the platform’s principal components and compares its features to those of other methods but does not explicitly discuss the process of design or development of the system. RESULTS As a proof of concept, body temperature data were obtained in a 4-hour span from a 22-year-old male, during which the subject simultaneously recorded relevant activities and events using the iPhone platform. After export to a clinical researcher’s desktop, the digital records and temperature data were processed and fused into a single Time-Series Graph. The events were filtered based on specific keywords to facilitate data analysis. CONCLUSIONS We have developed a user-friendly patient journal platform, based on widely available smartphone technology, that gives clinicians and researchers a simple method to track and analyze patient activities and record the activities on a shared Timeline with clinical data from wearable devices.

Viktor K. Prasanna - One of the best experts on this subject based on the ideXlab platform.

  • IPDPS - Distributed Programming over Time-Series Graphs
    2015 IEEE International Parallel and Distributed Processing Symposium, 2015
    Co-Authors: Yogesh Simmhan, Neel Choudhury, Charith Wickramaarachchi, Alok Kumbhare, Marc Frincu, Cauligi S. Raghavendra, Viktor K. Prasanna
    Abstract:

    Graphs are a key form of Big Data, and performing scalable analytics over them is invaluable to many domains. There is an emerging class of inter-connected data which accumulates or varies over Time, and on which novel algorithms both over the network structure and across the Time-variant attribute values is necessary. We formalize the notion of Time-Series Graphs and propose a Temporally Iterative BSP programming abstraction to develop algorithms on such datasets using several design patterns. Our abstractions leverage a sub-Graph centric programming model and extend it to the temporal dimension. We present three Time-Series Graph algorithms based on these design patterns and abstractions, and analyze their performance using the Offish distributed platform on Amazon AWS Cloud. Our results demonstrate the efficacy of the abstractions to develop practical Time-Series Graph algorithms, and scale them on commodity hardware.

Alastair D. Jenkins - One of the best experts on this subject based on the ideXlab platform.

  • Wave Duration/Persistence Statistics, Recording Interval, and Fractal Dimension
    International Journal of Offshore and Polar Engineering, 2002
    Co-Authors: Alastair D. Jenkins
    Abstract:

    The statistics of sea state duration (persistence) have been found to be de- pendent upon the recording intervalt. Such behavior can be explained as a consequence of the fact that the Graph of a Time Series of an environ- mental parameter such as the significant wave height has an ir regular, "frac- tal" geometry. The mean duration, � can have a power-law dependence on �t ast ! 0, with an exponent equal to the fractal dimension of the level sets of the Time Series Graph. This recording interval dependence means that the mean duration is not a well defined quantity to use for mari ne opera- tional purposes. A more practical quantity may be the "useful mean dura- tion", � u , estimated from the formula ( P � 2 i )/( Pi), where each interval

  • Wave duration/persistence statistics, recording interval, and fractal dimension
    arXiv: Atmospheric and Oceanic Physics, 2001
    Co-Authors: Alastair D. Jenkins
    Abstract:

    The statistics of sea state duration (persistence) have been found to be dependent upon the recording interval \Delta t. Such behavior can be explained as a consequence of the fact that the Graph of a Time Series of an environmental parameter such as the significant wave height has an irregular, "fractal" geometry. The mean duration, \bar\tau, can have a power-law dependence on \Delta t as \Delta t -> 0, with an exponent equal to the fractal dimension of the level sets of the Time Series Graph. This recording interval dependence means that the mean duration is not a well defined quantity to use for marine operational purposes. A more practical quantity may be the "useful mean duration", \bar\tau^u, estimated from the formula (\sum\tau_i^2)/(\sum\tau_i), where each interval [t_i,t_i+\tau_i] satisfying the appropriate criterion is weighted by its duration. These results are illustrated using wave data from the Frigg gas field in the North Sea.