Real-time interpolation of streaming data

One of the key elements of real-time $C^1$-continuous cubic spline interpolation of streaming data is an estimator of the first derivative of the interpolated function that is more accurate than the ones based on finite difference schemas.Two such greedy look-ahead heuristic estimators (denoted as MinBE and MinAJ2) based on Calculus of Variations are formally defined (in closed form) together with the corresponding cubic splines they generate, and then comparatively evaluated in a series of numerical experiments involving different types of performance measures. The results presented show that the cubic Hermite splines generated by heuristic MinAJ2 significantly outperformed these based on finite difference schemas in terms of all tested performance measures (including convergence).The proposed approach is quite general. It can be directly applied to streams of univariate functional data like time-series. Multidimensional curves defined parametrically, after splitting, can be handled as well. The streaming character of the algorithm means that it can also be useful in processing data sets that are too large to fit in memory (e.g., edge computing devices, embedded time-series databases).

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Improved Real-Time Natural Hazard Monitoring Using Automated DInSAR Time Series

Remote Sensing ◽

10.3390/rs13050867 ◽

2021 ◽

Vol 13 (5) ◽

pp. 867

Author(s):

Krisztina Kelevitz ◽

Kristy F. Tiampo ◽

Brianna D. Corsa

Keyword(s):

Time Series ◽

Real Time ◽

Early Warning ◽

Atmospheric Correction ◽

Warning System ◽

Streaming Data ◽

Test Case ◽

Data Sets ◽

Volcanic Area ◽

Gravity Measurements

As part of the collaborative GeoSciFramework project, we are establising a monitoring system for the Yellowstone volcanic area that integrates multiple geodetic and seismic data sets into an advanced cyber-infrastructure framework that will enable real-time streaming data analytics and machine learning and allow us to better characterize associated long- and short-term hazards. The goal is to continuously ingest both remote sensing (GNSS, DInSAR) and ground-based (seismic, thermal and gas observations, strainmeter, tiltmeter and gravity measurements) data and query and analyse them in near-real time. In this study, we focus on DInSAR data processing and the effects from using various atmospheric corrections and real-time orbits on the automated processing and results. We find that the atmospheric correction provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) is currently the most optimal for automated DInSAR processing and that the use of real-time orbits is sufficient for the early-warning application in question. We show analysis of atmospheric corrections and using real-time orbits in a test case over the Kilauea volcanic area in Hawaii. Finally, using these findings, we present results of displacement time series in the Yellowstone area between May 2018 and October 2019, which are in good agreement with GNSS data where available. These results will contribute to a baseline model that will be the basis of a future early-warning system that will be continuously updated with new DInSAR data acquisitions.

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Innovative Web Service Grammar for Streaming Computing in Real-Time Technology

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b7306.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 1589-1595

Keyword(s):

Real Time ◽

Research Work ◽

Parallel Flow ◽

Streaming Data ◽

Advanced Technologies ◽

Query Result ◽

Different Types ◽

Pros And Cons ◽

Event Flow ◽

Streaming Computing

The purpose of this work is to develop a UJSON web technology with C# application to analyze the student data in real-ime. Execute continuous requests on JSON streaming data based on advanced technologies for parallel streaming computing, suitable for solving analytic problems and calculation of metrics in real-time. The developed management information system in this research work designed to filtering event flow, building an event flow as a query result, grouping and aggregation of events, and creating window semantics. For testing the proposed work, several queries were selected that implement aggregation with different types of semantic windows (Steps, Slides). Testing was done locally and on education moodle clusters. It was used 4 types of configurations 2, 4, 8, and 16 computing nodes. Based on the obtained results, scalability is noticeable with an increase in the number of nodes. The updated functions of the proposed UJSON could improve the construction of parallel flow systems and data processing. The developed approach based on modern and advanced parallel flow technologies for output calculations considering the pros and cons of various approaches found in the current era.

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FRACTAL ANALYSIS OF DIFFERENT EASTERN AND WESTERN MUSICAL INSTRUMENTS

Fractals ◽

10.1142/s0218348x06003192 ◽

2006 ◽

Vol 14 (03) ◽

pp. 165-170 ◽

Cited By ~ 7

Author(s):

ATIN DAS ◽

PRITHA DAS

Keyword(s):

Time Series ◽

Spectral Analysis ◽

Fractal Dimension ◽

Time Series Data ◽

Musical Instruments ◽

Series Data ◽

Data Sets ◽

Different Types ◽

Frequency Regime ◽

Musical Sounds

In this paper, we attempt musical analysis by measuring fractal dimension (D) of musical pieces played by several musical instruments. We collected solo performances of popular instruments of Western and Eastern origin as samples. We attempted usual spectral analysis of the selected clips to observe peaks of fundamental and harmonics in frequency regime. After appropriate processing, we converted them into time series data sets and computed their fractal dimension. Based on our results, we conclude that instrumental musical sounds may have higher Ds than those computed from vocal performances of different types of Indian songs.

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PRESEE: An MDL/MML Algorithm to Time-Series Stream Segmenting

The Scientific World JOURNAL ◽

10.1155/2013/386180 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Kaikuo Xu ◽

Yexi Jiang ◽

Mingjie Tang ◽

Changan Yuan ◽

Changjie Tang

Keyword(s):

Time Series ◽

Real Time ◽

Processing Speed ◽

Data Stream ◽

Minimum Description Length ◽

Data Types ◽

Minimum Message Length ◽

Message Length ◽

Different Types ◽

State Of Art

Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we proposePRESEE(parameter-free, real-time, and scalable time-series stream segmenting algorithm), which greatly improves the efficiency of time-series stream segmenting. PRESEE is based on both MDL (minimum description length) and MML (minimum message length) methods, which could segment the data automatically. To evaluate the performance of PRESEE, we conduct several experiments on time-series streams of different types and compare it with the state-of-art algorithm. The empirical results show that PRESEE is very efficient for real-time stream datasets by improving segmenting speed nearly ten times. The novelty of this algorithm is further demonstrated by the application of PRESEE in segmenting real-time stream datasets from ChinaFLUX sensor networks data stream.

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Nonlinearity Tests Using the Extrema of a Time Series

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127498001546 ◽

1998 ◽

Vol 08 (09) ◽

pp. 1831-1838 ◽

Cited By ~ 8

Author(s):

A. di Garbo ◽

R. Balocchi ◽

S. Chillemi

Keyword(s):

Time Series ◽

Real Time ◽

Dynamical Behavior ◽

Data Sets ◽

Complex Solution ◽

Time Axis ◽

The Real ◽

Starting Point ◽

Analytical Properties ◽

New Algorithms

The analytical properties of the solution of a system of ODEs in the complex time plane influence its dynamical behavior on the real time axis. In particular, the extrema of the real time solution can be associated to the singularities of the complex solution falling close to the real time axis. Moreover for a twice differentiable stochastic process, the expected value of the number of extrema for unit time can be determined. These two results are used here as the starting point to introduce two new algorithms to test for time series nonlinearity. They do not require the phase space reconstruction protocol and seem to work well also for short data sets.

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Temporal Geo-Social Personalized Keyword Search Over Streaming Data

ACM Transactions on Spatial Algorithms and Systems ◽

10.1145/3473006 ◽

2021 ◽

Vol 7 (4) ◽

pp. 1-28

Author(s):

Abdulaziz Almaslukh ◽

Yunfan Kang ◽

Amr Magdy

Keyword(s):

Real Time ◽

Data Structures ◽

Data Stream ◽

Keyword Search ◽

Streaming Data ◽

Time Data ◽

Social Data ◽

Location Aware ◽

Different Types ◽

Social Media Platforms

The unprecedented rise of social media platforms, combined with location-aware technologies, has led to continuously producing a significant amount of geo-social data that flows as a user-generated data stream. This data has been exploited in several important use cases in various application domains. This article supports geo-social personalized queries in streaming data environments. We define temporal geo-social queries that provide users with real-time personalized answers based on their social graph. The new queries allow incorporating keyword search to get personalized results that are relevant to certain topics. To efficiently support these queries, we propose an indexing framework that provides lightweight and effective real-time indexing to digest geo-social data in real time. The framework distinguishes highly dynamic data from relatively stable data and uses appropriate data structures and a storage tier for each. Based on this framework, we propose a novel geo-social index and adopt two baseline indexes to support the addressed queries. The query processor then employs different types of pruning to efficiently access the index content and provide a real-time query response. The extensive experimental evaluation based on real datasets has shown the superiority of our proposed techniques to index real-time data and provide low-latency queries compared to existing competitors.

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Model of multicomponent narrow-band periodical non-stationary random signal

Information extraction and processing ◽

10.15407/vidbir2020.48.017 ◽

2020 ◽

Vol 2020 (48) ◽

pp. 17-24

Author(s):

I.M. Javorskyj ◽

◽

R.M. Yuzefovych ◽

P.R. Kurapov ◽

◽

...

Keyword(s):

Time Series ◽

Real Time ◽

Hilbert Transform ◽

Spectral Properties ◽

Narrow Band ◽

Analytic Signal ◽

Random Signal ◽

Hilbert Transformation ◽

The Hilbert Transform

The correlation and spectral properties of a multicomponent narrowband periodical non-stationary random signal (PNRS) and its Hilbert transformation are considered. It is shown that multicomponent narrowband PNRS differ from the monocomponent signal. This difference is caused by correlation of the quadratures for the different carrier harmonics. Such features of the analytic signal must be taken into account when we use the Hilbert transform for the analysis of real time series.

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