Category-Based Infidelity Bounded Queries over Unstructured Data Streams

2013 ◽  
Vol 25 (11) ◽  
pp. 2448-2462 ◽  
Author(s):  
Manish Bhide ◽  
Krithi Ramamritham
Keyword(s):  
Data Streams ◽  
Unstructured Data ◽  
Bounded Queries

GNOSIS- query-driven multimodal event processing for unstructured data streams

2021 ◽  
Author(s):  
Piyush Yadav ◽  
Dhaval Salwala ◽  
Bharath Sudharsan ◽  
Edward Curry
Keyword(s):  
Data Streams ◽  
Unstructured Data ◽  
Event Processing

scholarly journals Real-Time RDF Extraction from Unstructured Data Streams

2013 ◽  
pp. 135-150 ◽  
Author(s):  
Daniel Gerber ◽  
Sebastian Hellmann ◽  
Lorenz Bühmann ◽  
Tommaso Soru ◽  
Ricardo Usbeck ◽  
...  
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Unstructured Data

scholarly journals Bayesian Sentiment Analytics for Emerging Trends in Unstructured Data Streams

2019 ◽  
Vol 6 (22) ◽  
pp. 159355
Author(s):  
Najam Sahar ◽  
Muhammad Irshad ◽  
Muhammad Khan
Keyword(s):  
Data Streams ◽  
Unstructured Data ◽  
Emerging Trends

Online Feature Extraction Algorithms for Data Streams

2012 ◽  
Vol 132 (1) ◽  
pp. 6-13
Author(s):  
Seiichi Ozawa
Keyword(s):  
Feature Extraction ◽  
Data Streams

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

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.

Text Mining Approach for Unstructured Data Using SVM

2017 ◽  
Author(s):  
Roshni Kalbhore ◽  
Pravin Malviya
Keyword(s):  
Text Mining ◽  
Unstructured Data

Advanced signal processing system for demodulation, equalization and crosspol optimization of GBPS data streams

1994 ◽  
Author(s):  
Thomas Kolze ◽  
Pascal Finkenbeiner ◽  
Keith Yamashiro
Keyword(s):  
Signal Processing ◽  
Data Streams ◽  
Processing System ◽  
Signal Processing System

Retrieving Information and Discovering Knowledge from Unstructured Data Using Big Data Mining Technique: Heavy Oil Fields Example

2014 ◽  
Author(s):  
Wenkuang Wu ◽  
Xiaoguang Lu ◽  
Ben Cox ◽  
Guoqiang Li ◽  
Lihua Lin ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Heavy Oil ◽  
Oil Fields ◽  
Unstructured Data ◽  
Data Mining Technique ◽  
Big Data Mining ◽  
Mining Technique
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