Automated discovery and integration of semantic urban data streams: The ACEIS middleware

2017 ◽  
Vol 76 ◽  
pp. 561-581 ◽  
Author(s):  
Feng Gao ◽  
Muhammad Intizar Ali ◽  
Edward Curry ◽  
Alessandra Mileo
Keyword(s):  
Data Streams ◽  
Automated Discovery ◽  
Discovery And Integration ◽  
Urban Data

Building a Data Pipeline for the Management and Processing of Urban Data Streams

2018 ◽  
pp. 379-395
Author(s):  
Elarbi Badidi ◽  
Nouf El Neyadi ◽  
Meera Al Saeedi ◽  
Fatima Al Kaabi ◽  
Muthucumaru Maheswaran
Keyword(s):  
Data Streams ◽  
Data Pipeline ◽  
Urban Data

scholarly journals Data smashing: uncovering lurking order in data

2014 ◽  
Vol 11 (101) ◽  
pp. 20140826 ◽  
Author(s):  
Ishanu Chattopadhyay ◽  
Hod Lipson
Keyword(s):  
Data Streams ◽  
Domain Knowledge ◽  
Cardiac Activity ◽  
Epileptic Seizures ◽  
Domain Experts ◽  
Automated Discovery ◽  
Comparison Algorithms ◽  
Almost All ◽  
Compare And Contrast

From automatic speech recognition to discovering unusual stars, underlying almost all automated discovery tasks is the ability to compare and contrast data streams with each other, to identify connections and spot outliers. Despite the prevalence of data, however, automated methods are not keeping pace. A key bottleneck is that most data comparison algorithms today rely on a human expert to specify what ‘features' of the data are relevant for comparison. Here, we propose a new principle for estimating the similarity between the sources of arbitrary data streams, using neither domain knowledge nor learning. We demonstrate the application of this principle to the analysis of data from a number of real-world challenging problems, including the disambiguation of electro-encephalograph patterns pertaining to epileptic seizures, detection of anomalous cardiac activity from heart sound recordings and classification of astronomical objects from raw photometry. In all these cases and without access to any domain knowledge, we demonstrate performance on a par with the accuracy achieved by specialized algorithms and heuristics devised by domain experts. We suggest that data smashing principles may open the door to understanding increasingly complex observations, especially when experts do not know what to look for.

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.

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