scholarly journals Real-time user clickstream behavior analysis based on apache storm streaming

2021 ◽  
Author(s):  
Gautam Pal ◽  
Katie Atkinson ◽  
Gangmin Li
Keyword(s):  
Real Time ◽  
Pattern Mining ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
The Past ◽  
Lambda Architecture ◽  
Market Driven ◽  
Client Side ◽  
Distributed Cluster ◽  
Apache Storm

AbstractThis paper presents an approach to analyzing consumers’ e-commerce site usage and browsing motifs through pattern mining and surfing behavior. User-generated clickstream is first stored in a client site browser. We build an ingestion pipeline to capture the high-velocity data stream from a client-side browser through Apache Storm, Kafka, and Cassandra. Given the consumer’s usage pattern, we uncover the user’s browsing intent through n-grams and Collocation methods. An innovative clustering technique is constructed through the Expectation-Maximization algorithm with Gaussian Mixture Model. We discuss a framework for predicting a user’s clicks based on the past click sequences through higher order Markov Chains. We developed our model on top of a big data Lambda Architecture which combines high throughput Hadoop batch setup with low latency real-time framework over a large distributed cluster. Based on this approach, we developed an experimental setup for an optimized Storm topology and enhanced Cassandra database latency to achieve real-time responses. The theoretical claims are corroborated with several evaluations in Microsoft Azure HDInsight Apache Storm deployment and in the Datastax distribution of Cassandra. The paper demonstrates that the proposed techniques help user experience optimization, building recently viewed products list, market-driven analyses, and allocation of website resources.

scholarly journals Semantic Enrichment of Streaming Healthcare Data

2019 ◽  
Author(s):  
Daniel Cotter ◽  
V. K. Cody Bumgardner
Keyword(s):  
Real Time ◽  
Healthcare Industry ◽  
Time Data ◽  
Specialized Knowledge ◽  
Healthcare Data ◽  
The Past ◽  
Automated Inference ◽  
The Cost ◽  
Client Side ◽  
Integrate Data

AbstractIn the past decade, the healthcare industry has made significant advances in the digitization of patient information. However, a lack of interoperability among healthcare systems still imposes a high cost to patients, hospitals, and insurers. Currently, most systems pass messages using idiosyncratic messaging standards that require specialized knowledge to interpret. This increases the cost of systems integration and often puts more advanced uses of data out of reach. In this project, we demonstrate how two open standards, FHIR and RDF, can be combined both to integrate data from disparate sources in real time and make that data queryable and susceptible to automated inference. To validate the effectiveness of the semantic engine, we perform simulations of real-time data feeds and demonstrate how they can be combined and used by client-side applications with no knowledge of the underlying sources.

Video real-time imaging of artificial membranes during freeze-drying by cryo-electron microscopy

1988 ◽  
Vol 46 ◽  
pp. 16-17
Author(s):  
Alan S. Rudolph ◽  
Ronald R. Price
Keyword(s):  
Real Time ◽  
Self Assembly ◽  
Freeze Drying ◽  
Video Capture ◽  
Drying Process ◽  
Artificial Membranes ◽  
The Past ◽  
Cryo Electron Microscopy ◽  
Spherical Structures ◽  
Capture Techniques

We have employed cryoelectron microscopy to visualize events that occur during the freeze-drying of artificial membranes by employing real time video capture techniques. Artificial membranes or liposomes which are spherical structures within internal aqueous space are stabilized by water which provides the driving force for spontaneous self-assembly of these structures. Previous assays of damage to these structures which are induced by freeze drying reveal that the two principal deleterious events that occur are 1) fusion of liposomes and 2) leakage of contents trapped within the liposome [1]. In the past the only way to access these events was to examine the liposomes following the dehydration event. This technique allows the event to be monitored in real time as the liposomes destabilize and as water is sublimed at cryo temperatures in the vacuum of the microscope. The method by which liposomes are compromised by freeze-drying are largely unknown. This technique has shown that cryo-protectants such as glycerol and carbohydrates are able to maintain liposomal structure throughout the drying process.

Between Jerusalem and Athens

2018 ◽  
Author(s):  
Nurit Yaari
Keyword(s):  
Real Time ◽  
Social Conditions ◽  
Cultural Events ◽  
Greek Drama ◽  
The Past ◽  
Key Points ◽  
Mise En Scene ◽  
Dramatic Writing ◽  
Artistic Performance

How does a theatrical tradition emerge in the fields of dramatic writing and artistic performance? Can a culture, in which theatre played no part in the past, create a theatrical tradition in real time—and how? What was the contribution of classical Greek drama to the evolution of Israeli theatre? How do political and social conditions affect the encounter between cultures—and what role do they play in creating a theatre with a distinctive identity? This book, the first of its kind, attempts to answer these and other questions, by examining the reception of classical Greek drama in the Israeli theatre over the last seventy years. It deals with dramatic and aesthetic issues while analysing translations, adaptations, new writing, mise-en-scène, and ‘post dramatic’ performances of classical Greek drama that were created and staged at key points of the development of Israeli culture amidst fateful political, social, and cultural events in the country’s history.

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

scholarly journals A palace fit for the future: Desirability in temporal work

2021 ◽  
pp. 147612702110120
Author(s):  
Siavash Alimadadi ◽  
Andrew Davies ◽  
Fredrik Tell
Keyword(s):  
Real Time ◽  
Crucial Role ◽  
The Past ◽  
Renewal Program ◽  
The Future ◽  
Depth Study ◽  
Future Events ◽  
Future Outcomes ◽  
Pluralistic Contexts

Research on the strategic organization of time often assumes that collective efforts are motivated by and oriented toward achieving desirable, although not necessarily well-defined, future states. In situations surrounded by uncertainty where work has to proceed urgently to avoid an impending disaster, however, temporal work is guided by engaging with both desirable and undesirable future outcomes. Drawing on a real-time, in-depth study of the inception of the Restoration and Renewal program of the Palace of Westminster, we investigate how organizational actors develop a strategy for an uncertain and highly contested future while safeguarding ongoing operations in the present and preserving the heritage of the past. Anticipation of undesirable future events played a crucial role in mobilizing collective efforts to move forward. We develop a model of future desirability in temporal work to identify how actors construct, link, and navigate interpretations of desirable and undesirable futures in their attempts to create a viable path of action. By conceptualizing temporal work based on the phenomenological quality of the future, we advance understanding of the strategic organization of time in pluralistic contexts characterized by uncertainty and urgency.

Real-Time Web GIS Analysis Using WebGL

2012 ◽  
Vol 1 (3) ◽  
pp. 49-61 ◽  
Author(s):  
Michael Auer
Keyword(s):  
Parallel Processing ◽  
Real Time ◽  
Large Data ◽  
Web Gis ◽  
3D Graphics ◽  
Gis Analysis ◽  
User Interactions ◽  
Algorithm Performance ◽  
Gis Applications ◽  
Client Side

Parallel processing methods in Geographic Information Systems (GIS) are traditionally used to accelerate the calculation of large data volumes with sophisticated spatial algorithms. Such kinds of acceleration can also be applied to provide real-time GIS applications to improve the responsiveness of user interactions with the data. This paper presents a method to enable this approach for Web GIS applications. It uses the JavaScript 3D graphics API (WebGL) to perform client-side parallel real-time computations of 2D or 2.5D spatial raster algorithms on the graphics card. The potential of this approach is evaluated using an example implementation of a hillshade algorithm. Performance comparisons of parallel and sequential computations reveal acceleration factors between 25 and 100, mainly depending on mobile or desktop environments.

scholarly journals Data Assimilation with Gaussian Mixture Models Using the Dynamically Orthogonal Field Equations. Part I: Theory and Scheme

2013 ◽  
Vol 141 (6) ◽  
pp. 1737-1760 ◽  
Author(s):  
Thomas Sondergaard ◽  
Pierre F. J. Lermusiaux
Keyword(s):  
Data Assimilation ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Expectation Maximization Algorithm ◽  
Planck Equation ◽  
Information Criterion ◽  
Gaussian Mixture ◽  
Field Equations ◽  
Dynamical Equations ◽  
Prior Probabilities

Abstract This work introduces and derives an efficient, data-driven assimilation scheme, focused on a time-dependent stochastic subspace that respects nonlinear dynamics and captures non-Gaussian statistics as it occurs. The motivation is to obtain a filter that is applicable to realistic geophysical applications, but that also rigorously utilizes the governing dynamical equations with information theory and learning theory for efficient Bayesian data assimilation. Building on the foundations of classical filters, the underlying theory and algorithmic implementation of the new filter are developed and derived. The stochastic Dynamically Orthogonal (DO) field equations and their adaptive stochastic subspace are employed to predict prior probabilities for the full dynamical state, effectively approximating the Fokker–Planck equation. At assimilation times, the DO realizations are fit to semiparametric Gaussian Mixture Models (GMMs) using the Expectation-Maximization algorithm and the Bayesian Information Criterion. Bayes’s law is then efficiently carried out analytically within the evolving stochastic subspace. The resulting GMM-DO filter is illustrated in a very simple example. Variations of the GMM-DO filter are also provided along with comparisons with related schemes.

A Home Multimedia Control Centers of Improved Design

2011 ◽  
Vol 338 ◽  
pp. 796-799
Author(s):  
Wei Chang Feng
Keyword(s):  
Remote Control ◽  
Real Time ◽  
Multimedia System ◽  
The Internet ◽  
Tv Viewing ◽  
Server Side ◽  
Video And Audio ◽  
The Rich ◽  
Improved Design ◽  
Client Side

E-Yuan multimedia system is developed for the rich audio and video resource on the Internet and on its server side, it can automatically search and integration of network video and audio resources, and send to the client side for the user in real-time broadcast TV viewing, full use of remote control operation, Simply it’s a very easy to use multimedia system. This article introduces its infrastructure, main technical ideas and you can also see some details about server side and client side. At the same time, the improvement on how to collect and integrate video resources is comprehensively elaborated.

scholarly journals Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1126
Author(s):  
Zhentao Hu ◽  
Linlin Yang ◽  
Yong Jin ◽  
Han Wang ◽  
Shibo Yang
Keyword(s):  
Real Time ◽  
Gaussian Mixture ◽  
Measurement Noise ◽  
Tracking Accuracy ◽  
Process Noise ◽  
Probability Hypothesis Density ◽  
Measurement Noise Covariance ◽  
Multi Target Tracking ◽  
Noise Covariance ◽  
Fading Factor

Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene. To solve this problem, a strong tracking PHD filter based on Variational Bayes (VB) approximation is proposed in this paper. The measurement noise covariance is described in the linear system by the inverse Wishart (IW) distribution. Then, the fading factor in the strong tracking principle uses the optimal measurement noise covariance at the previous moment to control the state prediction covariance in real-time. The Gaussian IW (GIW) joint distribution adopts the VB approximation to jointly return the measurement noise covariance and the target state covariance. The simulation results show that, compared with the traditional Gaussian mixture PHD (GM-PHD) and the VB-adaptive PHD, the proposed algorithm has higher tracking accuracy and stronger robustness in a more reasonable calculation time.

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