Screening large data sets and real‐time data streams for bioacoustic signals.

2009 ◽  
Vol 125 (4) ◽  
pp. 2587-2587
Author(s):  
Holger Klinck ◽  
Lars Kindermann ◽  
David K. Mellinger ◽  
Olaf Boebel
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Time Data ◽  
Real Time Data

Development of an Intelligent Patrol Routing System Using GIS and Computer Simulations

2011 ◽  
pp. 339-351 ◽  
Author(s):  
Joseph Szakas ◽  
Christian Trefftz ◽  
Raul Ramirez ◽  
Eric Jefferis
Keyword(s):  
Information System ◽  
Real Time ◽  
Data Sets ◽  
Time Data ◽  
Map Algebra ◽  
Positioning Systems ◽  
Global Positioning ◽  
Real Time Data ◽  
Patrol Routing ◽  
Patrol Strategies

Patrolling in a nonrandom, but focused manner is an important activity in law enforcement. The use of geographic information systems, the emerging real-time data sets (spatial and nonspatial) and the ability via global positioning systems to identify locations of patrol units provide the environment to discuss the concept and requirements of an intelligent patrol routing system. This intelligent patrol routing system will combine available data utilizing Map Algebra and a data structure known as a Voronoi diagram to create a real-time updatable raster surface over the patrolling area to identify destination locations and routes for all patrol units. This information system will allow all patrol units to function “in concert” under a coordinated plan, and make good use of limited patrolling resources, and provide the means of evaluating current patrol strategies. This chapter discusses the algorithmic foundation, implications, requirements, and simulation of a GIS based intelligent patrol routing system.

Real-time visualization of large data sets on NLR's NEC SX-4

1998 ◽  
pp. 397-402 ◽  
Author(s):  
Harmen van der Ven ◽  
Bert Schultheiss ◽  
Shun Doi ◽  
Hideki Matsumoto ◽  
Kouta Sugihara ◽  
...  
Keyword(s):  
Real Time ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Real Time Visualization

scholarly journals Applying Formal Methods to Modeling and Analysis of Real-time Data Streams

2011 ◽  
Vol 5 (1) ◽  
pp. 85-110 ◽  
Author(s):  
Krasimira Kapitanova ◽  
Yuan Wei ◽  
Woo-Chul Kang ◽  
Sang-H. Son
Keyword(s):  
Real Time ◽  
Formal Methods ◽  
Data Streams ◽  
Time Data ◽  
Modeling And Analysis ◽  
Real Time Data

scholarly journals Design of smart agriculture based on big data and Internet of things

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772091706 ◽  
Author(s):  
Chunling Li ◽  
Ben Niu
Keyword(s):  
Data Mining ◽  
Information Processing ◽  
Internet Of Things ◽  
Real Time ◽  
Data Communication ◽  
Large Data ◽  
Time Data ◽  
Real Time Data ◽  
Smart Agriculture ◽  
Internet Of Things Technology

With the wide application of Internet of things technology and era of large data in agriculture, smart agricultural design based on Internet of things technology can efficiently realize the function of real-time data communication and information processing and improve the development of smart agriculture. In the process of analyzing and processing a large amount of planting and environmental data, how to extract effective information from these massive agricultural data, that is, how to analyze and mine the needs of these large amounts of data, is a pressing problem to be solved. According to the needs of agricultural owners, this article studies and optimizes the data storage, data processing, and data mining of large data generated in the agricultural production process, and it uses the k-means algorithm based on the maximum distance to study the data mining. The crop growth curve is simulated and compared with improved K-means algorithm and the original k-means algorithm in the experimental analysis. The experimental results show that the improved K-means clustering method has an average reduction of 0.23 s in total time and an average increase of 7.67% in the F metric value. The algorithm in this article can realize the functions of real-time data communication and information processing more efficiently, and has a significant role in promoting agricultural informatization and improving the level of agricultural modernization.

In the land of data streams where synopses are missing, one framework to bring them all

2021 ◽  
Vol 14 (10) ◽  
pp. 1818-1831
Author(s):  
Rudi Poepsel-Lemaitre ◽  
Martin Kiefer ◽  
Joscha von Hein ◽  
Jorge-Arnulfo Quiané-Ruiz ◽  
Volker Markl
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Data Streams ◽  
High Performance ◽  
Stream Processing ◽  
Divide And Conquer ◽  
Time Data ◽  
Real Time Data ◽  
Internal Processing ◽  
Aggregate Functions

In pursuit of real-time data analysis, approximate summarization structures, i.e., synopses, have gained importance over the years. However, existing stream processing systems, such as Flink, Spark, and Storm, do not support synopses as first class citizens, i.e., as pipeline operators. Synopses' implementation is upon users. This is mainly because of the diversity of synopses, which makes a unified implementation difficult. We present Condor, a framework that supports synopses as first class citizens. Condor facilitates the specification and processing of synopsis-based streaming jobs while hiding all internal processing details. Condor's key component is its model that represents synopses as a particular case of windowed aggregate functions. An inherent divide and conquer strategy allows Condor to efficiently distribute the computation, allowing for high-performance and linear scalability. Our evaluation shows that Condor outperforms existing approaches by up to a factor of 75x and that it scales linearly with the number of cores.

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