Real-time, load-adaptive processing of continuous queries over data streams

Processing Data Streams

Novel Approaches to Information Systems Design - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-7998-2975-1.ch002 ◽

2020 ◽

pp. 20-39

Author(s):

Parimala N.

Keyword(s):

Data Processing ◽

Real Time ◽

Data Streams ◽

Data Stream ◽

Continuous Queries ◽

Stream Data ◽

Continuous Sequence ◽

Static Data ◽

Transactional Data ◽

Events Data

A data stream is a real-time continuous sequence that may be comprised of data or events. Data stream processing is different from static data processing which resides in a database. The data stream data is seen only once. It is too voluminous to store statically. A small portion of data called a window is considered at a time for querying, computing aggregates, etc. In this chapter, the authors explain the different types of window movement over incoming data. A query on a stream is repeatedly executed on the new data created by the movement of the window. SQL extensions to handle continuous queries is addressed in this chapter. Streams that contain transactional data as well as those that contain events are considered.

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Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i10.400 ◽

2017 ◽

Vol 7 (10) ◽

pp. 52

Author(s):

LAKSHMI PRANEETHA

Keyword(s):

Real Time ◽

Data Streams ◽

Bloom Filter ◽

Scientific Applications ◽

Pruning Algorithm ◽

Density Data ◽

Data Points ◽

Short Time ◽

Information Streams

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.

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Real-Time Compression for Tactile Internet Data Streams

Sensors ◽

10.3390/s21051924 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1924

Author(s):

Patrick Seeling ◽

Martin Reisslein ◽

Frank H. P. Fitzek

Keyword(s):

Real Time ◽

Data Streams ◽

Control Loop ◽

Real World Data ◽

Perceptual Coding ◽

Great Flexibility ◽

Broad Application ◽

Time Compression ◽

Perceptual Threshold ◽

Selection Of

The Tactile Internet will require ultra-low latencies for combining machines and humans in systems where humans are in the control loop. Real-time and perceptual coding in these systems commonly require content-specific approaches. We present a generic approach based on deliberately reduced number accuracy and evaluate the trade-off between savings achieved and errors introduced with real-world data for kinesthetic movement and tele-surgery. Our combination of bitplane-level accuracy adaptability with perceptual threshold-based limits allows for great flexibility in broad application scenarios. Combining the attainable savings with the relatively small introduced errors enables the optimal selection of a working point for the method in actual implementations.

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Multimodal analysis of body sensor network data streams for real-time healthcare

Proceedings of the international conference on Multimedia information retrieval - MIR '10 ◽

10.1145/1743384.1743467 ◽

2010 ◽

Cited By ~ 22

Author(s):

Manoj K. Garg ◽

Duk-Jin Kim ◽

Deepak S. Turaga ◽

Balakrishnan Prabhakaran

Keyword(s):

Real Time ◽

Sensor Network ◽

Data Streams ◽

Network Data ◽

Body Sensor Network ◽

Multimodal Analysis

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A novel energy-based online sequential extreme learning machine to detect anomalies over real-time data streams

Neural Computing and Applications ◽

10.1007/s00521-021-05731-2 ◽

2021 ◽

Author(s):

Xiaoping Wang ◽

Shanshan Tu ◽

Wei Zhao ◽

Chengjie Shi

Keyword(s):

Real Time ◽

Extreme Learning Machine ◽

Data Streams ◽

Time Data ◽

Real Time Data ◽

Learning Machine

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Measuring the Effectiveness of Adaptive Random Forest for Handling Concept Drift in Big Data Streams

Entropy ◽

10.3390/e23070859 ◽

2021 ◽

Vol 23 (7) ◽

pp. 859

Author(s):

Abdulaziz O. AlQabbany ◽

Aqil M. Azmi

Keyword(s):

Big Data ◽

Random Forest ◽

Real Time ◽

Data Streams ◽

Learning Algorithm ◽

Concept Drift ◽

The United States ◽

Careful Consideration ◽

Data Sets ◽

Stream Data

We are living in the age of big data, a majority of which is stream data. The real-time processing of this data requires careful consideration from different perspectives. Concept drift is a change in the data’s underlying distribution, a significant issue, especially when learning from data streams. It requires learners to be adaptive to dynamic changes. Random forest is an ensemble approach that is widely used in classical non-streaming settings of machine learning applications. At the same time, the Adaptive Random Forest (ARF) is a stream learning algorithm that showed promising results in terms of its accuracy and ability to deal with various types of drift. The incoming instances’ continuity allows for their binomial distribution to be approximated to a Poisson(1) distribution. In this study, we propose a mechanism to increase such streaming algorithms’ efficiency by focusing on resampling. Our measure, resampling effectiveness (ρ), fuses the two most essential aspects in online learning; accuracy and execution time. We use six different synthetic data sets, each having a different type of drift, to empirically select the parameter λ of the Poisson distribution that yields the best value for ρ. By comparing the standard ARF with its tuned variations, we show that ARF performance can be enhanced by tackling this important aspect. Finally, we present three case studies from different contexts to test our proposed enhancement method and demonstrate its effectiveness in processing large data sets: (a) Amazon customer reviews (written in English), (b) hotel reviews (in Arabic), and (c) real-time aspect-based sentiment analysis of COVID-19-related tweets in the United States during April 2020. Results indicate that our proposed method of enhancement exhibited considerable improvement in most of the situations.

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A comparison of select trigger algorithms for automated global seismic phase and event detection

Bulletin of the Seismological Society of America ◽

10.1785/bssa0880010095 ◽

1998 ◽

Vol 88 (1) ◽

pp. 95-106 ◽

Cited By ~ 12

Author(s):

Mitchell Withers ◽

Richard Aster ◽

Christopher Young ◽

Judy Beiriger ◽

Mark Harris ◽

...

Keyword(s):

Real Time ◽

Data Streams ◽

Event Detection ◽

Spectral Characteristics ◽

Digital Data ◽

Robust Detection ◽

Location System ◽

Global Correlation ◽

Seismic Coda ◽

Wide Range

Abstract Digital algorithms for robust detection of phase arrivals in the presence of stationary and nonstationary noise have a long history in seismology and have been exploited primarily to reduce the amount of data recorded by data logging systems to manageable levels. In the present era of inexpensive digital storage, however, such algorithms are increasingly being used to flag signal segments in continuously recorded digital data streams for subsequent processing by automatic and/or expert interpretation systems. In the course of our development of an automated, near-real-time, waveform correlation event-detection and location system (WCEDS), we have surveyed the abilities of such algorithms to enhance seismic phase arrivals in teleseismic data streams. Specifically, we have considered envelopes generated by energy transient (STA/LTA), Z-statistic, frequency transient, and polarization algorithms. The WCEDS system requires a set of input data streams that have a smooth, low-amplitude response to background noise and seismic coda and that contain peaks at times corresponding to phase arrivals. The algorithm used to generate these input streams from raw seismograms must perform well under a wide range of source, path, receiver, and noise scenarios. Present computational capabilities allow the application of considerably more robust algorithms than have been historically used in real time. However, highly complex calculations can still be computationally prohibitive for current workstations when the number of data streams become large. While no algorithm was clearly optimal under all source, receiver, path, and noise conditions tested, an STA/LTA algorithm incorporating adaptive window lengths controlled by nonstationary seismogram spectral characteristics was found to provide an output that best met the requirements of a global correlation-based event-detection and location system.

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