scholarly journals Methodology for identifying activities from GPS data streams

2017 ◽  
Vol 109 ◽  
pp. 10-17 ◽  
Author(s):  
Vladimir Usyukov
Keyword(s):  
Data Streams ◽  
Gps Data

scholarly journals A KNOWLEDGE-BASED METHOD FOR GENERATING SUMMARIES OF SPATIAL MOVEMENT IN GEOGRAPHIC AREAS

2010 ◽  
Vol 19 (04) ◽  
pp. 393-415 ◽  
Author(s):  
MARTIN MOLINA ◽  
AMANDA STENT
Keyword(s):  
Mobile Phones ◽  
Data Streams ◽  
Large Data ◽  
Gps Data ◽  
Knowledge Representations ◽  
Knowledge Based ◽  
Spatial Movement ◽  
Present Evaluation ◽  
Main Components

In this article we describe a method for automatically generating text summaries of data corresponding to traces of spatial movement in geographical areas. The method can help humans to understand large data streams, such as the amounts of GPS data recorded by a variety of sensors in mobile phones, cars, etc. We describe the knowledge representations we designed for our method and the main components of our method for generating the summaries: a discourse planner, an abstraction module and a text generator. We also present evaluation results that show the ability of our method to generate certain types of geospatial and temporal descriptions.

scholarly journals Time-evolving O-D matrix estimation using high-speed GPS data streams

2016 ◽  
Vol 44 ◽  
pp. 275-288 ◽  
Author(s):  
Luís Moreira-Matias ◽  
João Gama ◽  
Michel Ferreira ◽  
João Mendes-Moreira ◽  
Luis Damas
Keyword(s):  
Data Streams ◽  
High Speed ◽  
Gps Data ◽  
Matrix Estimation

Deriving Truck Route Choice from Large GPS Data Streams

2016 ◽  
Vol 2563 (1) ◽  
pp. 62-70 ◽  
Author(s):  
Mohammadreza Kamali ◽  
Alireza Ermagun ◽  
Krishnan Viswanathan ◽  
Abdul R. Pinjari
Keyword(s):  
Data Streams ◽  
Route Choice ◽  
Gps Data

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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