Data Summarization Using Sampling Algorithms: Data Stream Case Study

2020 ◽  
pp. 105-124
Author(s):  
Rayane El Sibai ◽  
Jacques Bou Abdo ◽  
Yousra Chabchoub ◽  
Jacques Demerjian ◽  
Raja Chiky ◽  
...  
Keyword(s):  
Data Stream ◽  
Data Summarization ◽  
Sampling Algorithms

The Move-to-Front Rule: A Case Study for two Perfect Sampling Algorithms

1998 ◽  
Vol 12 (3) ◽  
pp. 283-302 ◽  
Author(s):  
James Allen Fill
Keyword(s):  
Finite Population ◽  
Mixing Time ◽  
Test Case ◽  
Perfect Sampling ◽  
Mcmc Algorithms ◽  
Time Result ◽  
Elementary Problem ◽  
Sampling Algorithms ◽  
Coupling Algorithm

The elementary problem of exhaustively sampling a finite population without replacement is used as a nonreversible test case for comparing two recently proposed MCMC algorithms for perfect sampling, one based on backward coupling and the other on strong stationary duality. The backward coupling algorithm runs faster in this case, but the duality-based algorithm is unbiased for user impatience. An interesting by-product of the analysis is a new and simple stochastic interpretation of a mixing-time result for the move-to-front rule.

Improvised methods for tackling big data stream mining challenges: case study of human activity recognition

2016 ◽  
Vol 72 (10) ◽  
pp. 3927-3959 ◽  
Author(s):  
Simon Fong ◽  
Kexing Liu ◽  
Kyungeun Cho ◽  
Raymond Wong ◽  
Sabah Mohammed ◽  
...  
Keyword(s):  
Big Data ◽  
Activity Recognition ◽  
Human Activity ◽  
Data Stream ◽  
Human Activity Recognition ◽  
Data Stream Mining ◽  
Stream Mining

scholarly journals A Viterbi decoder and its hardware Trojan models: an FPGA-based implementation study

2020 ◽  
Vol 6 ◽  
pp. e250
Author(s):  
Varsha Kakkara ◽  
Karthi Balasubramanian ◽  
B. Yamuna ◽  
Deepak Mishra ◽  
Karthikeyan Lingasubramanian ◽  
...  
Keyword(s):  
Integrated Circuits ◽  
Power Dissipation ◽  
Data Stream ◽  
Hardware Trojan ◽  
Viterbi Decoder ◽  
Entire Duration ◽  
Decoder Design ◽  
Coded Communication ◽  
Very High

Integrated circuits may be vulnerable to hardware Trojan attacks during its design or fabrication phases. This article is a case study of the design of a Viterbi decoder and the effect of hardware Trojans on a coded communication system employing the Viterbi decoder. Design of a Viterbi decoder and possible hardware Trojan models for the same are proposed. An FPGA-based implementation of the decoder and the associated Trojan circuits have been discussed. The noise-added encoded input data stream is stored in the block RAM of the FPGA and the decoded data stream is monitored on the PC through an universal asynchronous receiver transmitter interface. The implementation results show that there is barely any change in the LUTs used (0.5%) and power dissipation (3%) due to the insertion of the proposed Trojan circuits, thus establishing the surreptitious nature of the Trojan. In spite of the fact that the Trojans cause negligible changes in the circuit parameters, there are significant changes in the bit error rate (BER) due to the presence of Trojans. In the absence of Trojans, BER drops down to zero for signal to noise rations (SNRs) higher than 6 dB, but with the presence of Trojans, BER doesn’t reduce to zero even at a very high SNRs. This is true even with the Trojan being activated only once during the entire duration of the transmission.

scholarly journals Evaluative Review of Streaming Analytics: Tools and Technologies in Real-Time Data Processing

2021 ◽  
pp. 423-432
Author(s):  
Ms. Shailaja B. Jadhav ◽  
Dr. D. V. Kodavade
Keyword(s):  
Data Processing ◽  
Data Stream ◽  
Research Field ◽  
Streaming Data ◽  
Rail Transportation ◽  
Time Data ◽  
Data Record ◽  
Real Time Data Processing ◽  
Stream Analytics

Nowadays, big data processing systems are evolving to be more stream-oriented; where each data record is processed as it arrives by distributed and low latency computational frameworks [18]. Data streams have been extensively used in several fields of computational analytics such as data mining, business intelligence etc. [17]. In every field, the data stream can be considered as an ordered sequence of data items, as they continuously arrive over the period. Due to this characteristic, streaming data analytics is a challenging area of research [5, 11]. This paper aims to present data stream processing as a growing research field , along with streaming analytics frameworks as a rich focus area. The paper also contributes to evaluate the efficacy of available stream analytics frameworks. One of the Industry 4.0 use case - predictive maintenance rail transportation - has been illustrated here as a case study design mapped with streaming analytics framework.

scholarly journals IMPLEMENTASI TEKNIK DEEP PACKET INSPECTION DENGAN MENGGUNAKAN WIRESHARK PADA SISTEM OPERASI UBUNTU

2018 ◽  
Vol 1 (2) ◽  
pp. 79-85
Author(s):  
I Putu Agus Eka Pratama ◽  
Putu Adhika Dharmesta
Keyword(s):  
Information Technology ◽  
Network Management ◽  
Data Stream ◽  
Deep Packet Inspection ◽  
Data Packets ◽  
Slowing Down ◽  
Packet Inspection ◽  
Network Speed

Deep Packet Inspection (DPI) is a technique commonly used by network administrator to be able to monitor in detail the flow of data in the form of data packets that occur at that moment. This data stream will produce an information that can be used for network management purposes. One example of a case study that can be done with this technique is the intranet that is available in Information Technology major Udayana University’s. Deep Packet Inspection Technique is done with purpose to identifying the initial slowing down of network speed on Information Technology major Udayana University’s.

Using Data Stream Management Systems for Traffic Analysis – A Case Study –

2004 ◽  
pp. 215-226 ◽  
Author(s):  
Thomas Plagemann ◽  
Vera Goebel ◽  
Andrea Bergamini ◽  
Giacomo Tolu ◽  
Guillaume Urvoy-Keller ◽  
...  
Keyword(s):  
Data Stream ◽  
Traffic Analysis ◽  
Management Systems ◽  
Data Stream Management ◽  
Stream Management ◽  
Data Stream Management Systems ◽  
Using Data

Real-Time Analysis of Vital Signs Using Incremental Data Stream Mining Techniques with a Case Study of ARDS Under ICU Treatment

2015 ◽  
Vol 5 (5) ◽  
pp. 1108-1115 ◽  
Author(s):  
Simon Fong ◽  
Shirley W. I. Siu ◽  
Suzy Zhou ◽  
Jonathan H. Chan ◽  
Sabah Mohammed ◽  
...  
Keyword(s):  
Real Time ◽  
Data Stream ◽  
Vital Signs ◽  
Data Stream Mining ◽  
Time Analysis ◽  
Stream Mining ◽  
Real Time Analysis ◽  
Icu Treatment

scholarly journals POINTS CLOUD PRE-PROCESSING AND SAMPLING BASED ON DISTANCE ALGORITHM TECHNIQUE

2021 ◽  
Vol 25 (02) ◽  
pp. 1-8
Author(s):  
Ali M. Al-Bdairy ◽  
◽  
Ahmed A. A. Al-Duroobi ◽  
Maan A. Tawfiq ◽  
◽  
...  
Keyword(s):  
Rapid Development ◽  
Laser Scanner ◽  
Mathematical Representation ◽  
Surface Model ◽  
Sampling Process ◽  
Huge Data ◽  
Geometrical Features ◽  
Laser Scanners ◽  
Sampling Algorithms

Although the rapid development of reverse engineering techniques such as a modern 3D laser scanners, but can’t use this techniques immediately to generate a perfect surface model for the scanned parts, due to the huge data, the noisy data which associated to the scanning process, and the accuracy limitation of some scanning devices, so, the present paper present a points cloud pre-processing and sampling algorithms have been proposed based on distance calculations and statistical considerations to simplify the row points cloud which obtained using MATTER and FORM 3D laser scanner as a manner to obtain the required geometrical features and mathematical representation from the row points cloud of the scanned object through detection, isolating, and deleting the noised points. A MATLAB program has been constructed for executing the proposed algorithms implemented using a suggested case study with non-uniform shape. The results were proved the validity of the introduced distance algorithms for pre-processing and sampling process where the proficiency percent for pre-processing was (18.65%) with a single attempt, and the counted deviation value rang with the sampling process was (0.0002-0.3497mm).

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