Background: It is important to minimize energy consumption that improves battery life, system rel... more Background: It is important to minimize energy consumption that improves battery life, system reliability and other environmental concerns and energy optimization is turning into a very important in the tiny devices in Internet of Things (IoT) research area with the increasing demand for battery operated devices. IoT need battery life improvement in tiny device, so power optimization is significant. Methods: In this paper an Experimental Design (ED) is proposed for the performance improvement in terms of energy and run time. Among the many sutras of optimizations, by using the Green computing the products are work with limited battery. This proposed technique results in reduce power conumption. Results: The result shows that the proposed Energy saving on tiny devices based on IoT are the energy consumed and run time by the code after applying optimization techniques, which is the minimum among all four. Besides reduction in energy and runtime, reduction in the number of executed ins...
Data mining used to find hidden knowledge from large amount of Databases. Periodic Pattern Mining... more Data mining used to find hidden knowledge from large amount of Databases. Periodic Pattern Mining is useful in Weather Forecasting, Fraud Detection and GIS Applications. In General, spatio-temporal pattern discovery process finds the partially ordered subsets of the event- types whose instances are located together and occur serially for a given collection of Boolean spatio-temporal event-types. Big Data concerns large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data is now rapidly expanding in all science and engineering domains, including physical, biological and bio-medical sciences. In this paper, a new framework is proposed to find spatiotemporal patterns from Big Data. Existing algorithms are well in computation of necessary patterns, but more problematic when they are applied to Big Data. Big Data is a new trend used to analyze the datasets that due to their ...
2016 IEEE 6th International Conference on Advanced Computing (IACC), 2016
Mining frequent and maximal Periodic Patterns is the challenging task for the data scientists due... more Mining frequent and maximal Periodic Patterns is the challenging task for the data scientists due to unstructured, dynamic and huge raw data generated from web. Big Data is a latest tendency used to examine the datasets from large, complex databases. Developers cannot supervise them through conventional algorithms or Knowledge discovery software tools. Big Data mining is the ability to extract valuable information either from these huge datasets or else streams of data, due to its three V's (Volume, Velocity, and Variety). The previous studies focus on how to find frequent patterns from large traffic and sensor data based applications. However, mining maximal and useful periodic patterns from spatiotemporal datasets is still an open research problem in weather conditions, Fraud recognition and forest fire prevention Applications. We propose two algorithms such as Enhanced Tree based pattern mining algorithm (ETMA) and Extended Frequent Pattern Mining algorithm (EFPMA, which generates all frequent and maximal periodic patterns from spatiotemporal databases. A novel framework is introduced to mine spatiotemporal patterns from Big Data. All Existing algorithms are fit in calculation of essential patterns, but more tedious if they apply for Big Data. The proposed framework and algorithms produce better results than UDDAG, STNR algorithms towards scalability and decreased run time compared to DBSCAN, ECLAT algorithms.
... O.Obulesu Dr.A.Rama Mohan Reddy K.Suresh ... They are mentioned given below: 1) Symbol Period... more ... O.Obulesu Dr.A.Rama Mohan Reddy K.Suresh ... They are mentioned given below: 1) Symbol Periodicity 2) Sequence Periodicity or Partial Periodic Patterns 3) Segment or Full-Cycle Periodicity B. Symbol Periodicity A Time-Series is said to be a Symbol periodicity, if at least one ...
Mining frequent and maximal Periodic Patterns is the challenging task for data scientists due to ... more Mining frequent and maximal Periodic Patterns is the challenging task for data scientists due to unstructured, dynamic and huge raw data generated from web. Big Data is a latest tendency used to examine the datasets from large, complex databases. Developers cannot supervise them through conventional algorithms or Knowledge discovery software tools. Big Data mining is the ability to extract valuable information either from these huge datasets or else streams of data, due to its three V's (Volume, Velocity, and Variety). The previous studies focus on how to find frequent patterns from large traffic and sensor data based applications. However, mining maximal and useful periodic patterns from spatiotemporal datasets is still an open research problem in weather conditions, Fraud recognition and forest fire prevention Applications. We propose two algorithms such as Enhanced Tree based pattern mining algorithm (ETMA) and Extended Frequent Pattern Mining algorithm (EFPMA, which generates all frequent and maximal periodic patterns from spatiotemporal databases. A novel framework is introduced to mine spatiotemporal patterns from Big Data. All Existing algorithms are fit in calculation of essential patterns, but more tedious if they are apply for Big Data. The proposed framework and algorithms produce better results than UDDAG, STNR algorithms towards scalability and decreased run time compared to DBSCAN, ECLAT algorithms.
— Data mining used to find hidden knowledge from large amount of Databases. Periodic Pattern Mini... more — Data mining used to find hidden knowledge from large amount of Databases. Periodic Pattern Mining is useful in Weather Forecasting, Fraud Detection and GIS Applications. In General, spatio-temporal pattern discovery process finds the partially ordered subsets of the event-types whose instances are located together and occur serially for a given collection of Boolean spatio-temporal event-types. Big Data concerns large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data is now rapidly expanding in all science and engineering domains, including physical, biological and bio-medical sciences. In this paper, a new framework is proposed to find spatiotemporal patterns from Big Data. Existing algorithms are well in computation of necessary patterns, but more problematic when they are applied to Big Data. Big Data is a new trend used to analyze the datasets that due to their large size and complexity, Developers cannot manage them with traditional current algorithms or data mining software tools. Big Data mining is the capability of extracting useful information from these large datasets or streams of data, that due to its volume, variety, and velocity, it was not possible before to do it. The Big Data challenge is becoming one of the most exciting opportunities for the next years. This Paper focuses on a broad overview of pattern mining algorithms and significance in Spatiotemporal Databases, its current status, trade-offs, and forecast to the big data pattern mining future.
In many applications that track and analyze spatiotemporal data, movements obey periodic patterns... more In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. Periodic Pattern Mining or Periodicity Detection has numerous applications such as Prediction, Forecasting, Detection of Unusual events, etc. The periodic patterns are detected in a Time-Series database depending on the time intervals. Existi ng approaches could not detect all types of periodicity such as Symbol, Sequence and S egment at a time. In this Paper, we propose an approach to detect all types of the periodicity in time series Databases. It also finds the periodicity in the subsections of the Time-Series very effectively. Actually the periodicity detection results in the redundant data. To remove redundant data there are pruning techniques to apply and to get the desired pattern as an output. The comprehensive study demonstrates the effectiveness of the proposed approach. This is very time efficient, accurate approach t han many existing approaches.
Background: It is important to minimize energy consumption that improves battery life, system rel... more Background: It is important to minimize energy consumption that improves battery life, system reliability and other environmental concerns and energy optimization is turning into a very important in the tiny devices in Internet of Things (IoT) research area with the increasing demand for battery operated devices. IoT need battery life improvement in tiny device, so power optimization is significant. Methods: In this paper an Experimental Design (ED) is proposed for the performance improvement in terms of energy and run time. Among the many sutras of optimizations, by using the Green computing the products are work with limited battery. This proposed technique results in reduce power conumption. Results: The result shows that the proposed Energy saving on tiny devices based on IoT are the energy consumed and run time by the code after applying optimization techniques, which is the minimum among all four. Besides reduction in energy and runtime, reduction in the number of executed ins...
Data mining used to find hidden knowledge from large amount of Databases. Periodic Pattern Mining... more Data mining used to find hidden knowledge from large amount of Databases. Periodic Pattern Mining is useful in Weather Forecasting, Fraud Detection and GIS Applications. In General, spatio-temporal pattern discovery process finds the partially ordered subsets of the event- types whose instances are located together and occur serially for a given collection of Boolean spatio-temporal event-types. Big Data concerns large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data is now rapidly expanding in all science and engineering domains, including physical, biological and bio-medical sciences. In this paper, a new framework is proposed to find spatiotemporal patterns from Big Data. Existing algorithms are well in computation of necessary patterns, but more problematic when they are applied to Big Data. Big Data is a new trend used to analyze the datasets that due to their ...
2016 IEEE 6th International Conference on Advanced Computing (IACC), 2016
Mining frequent and maximal Periodic Patterns is the challenging task for the data scientists due... more Mining frequent and maximal Periodic Patterns is the challenging task for the data scientists due to unstructured, dynamic and huge raw data generated from web. Big Data is a latest tendency used to examine the datasets from large, complex databases. Developers cannot supervise them through conventional algorithms or Knowledge discovery software tools. Big Data mining is the ability to extract valuable information either from these huge datasets or else streams of data, due to its three V's (Volume, Velocity, and Variety). The previous studies focus on how to find frequent patterns from large traffic and sensor data based applications. However, mining maximal and useful periodic patterns from spatiotemporal datasets is still an open research problem in weather conditions, Fraud recognition and forest fire prevention Applications. We propose two algorithms such as Enhanced Tree based pattern mining algorithm (ETMA) and Extended Frequent Pattern Mining algorithm (EFPMA, which generates all frequent and maximal periodic patterns from spatiotemporal databases. A novel framework is introduced to mine spatiotemporal patterns from Big Data. All Existing algorithms are fit in calculation of essential patterns, but more tedious if they apply for Big Data. The proposed framework and algorithms produce better results than UDDAG, STNR algorithms towards scalability and decreased run time compared to DBSCAN, ECLAT algorithms.
... O.Obulesu Dr.A.Rama Mohan Reddy K.Suresh ... They are mentioned given below: 1) Symbol Period... more ... O.Obulesu Dr.A.Rama Mohan Reddy K.Suresh ... They are mentioned given below: 1) Symbol Periodicity 2) Sequence Periodicity or Partial Periodic Patterns 3) Segment or Full-Cycle Periodicity B. Symbol Periodicity A Time-Series is said to be a Symbol periodicity, if at least one ...
Mining frequent and maximal Periodic Patterns is the challenging task for data scientists due to ... more Mining frequent and maximal Periodic Patterns is the challenging task for data scientists due to unstructured, dynamic and huge raw data generated from web. Big Data is a latest tendency used to examine the datasets from large, complex databases. Developers cannot supervise them through conventional algorithms or Knowledge discovery software tools. Big Data mining is the ability to extract valuable information either from these huge datasets or else streams of data, due to its three V's (Volume, Velocity, and Variety). The previous studies focus on how to find frequent patterns from large traffic and sensor data based applications. However, mining maximal and useful periodic patterns from spatiotemporal datasets is still an open research problem in weather conditions, Fraud recognition and forest fire prevention Applications. We propose two algorithms such as Enhanced Tree based pattern mining algorithm (ETMA) and Extended Frequent Pattern Mining algorithm (EFPMA, which generates all frequent and maximal periodic patterns from spatiotemporal databases. A novel framework is introduced to mine spatiotemporal patterns from Big Data. All Existing algorithms are fit in calculation of essential patterns, but more tedious if they are apply for Big Data. The proposed framework and algorithms produce better results than UDDAG, STNR algorithms towards scalability and decreased run time compared to DBSCAN, ECLAT algorithms.
— Data mining used to find hidden knowledge from large amount of Databases. Periodic Pattern Mini... more — Data mining used to find hidden knowledge from large amount of Databases. Periodic Pattern Mining is useful in Weather Forecasting, Fraud Detection and GIS Applications. In General, spatio-temporal pattern discovery process finds the partially ordered subsets of the event-types whose instances are located together and occur serially for a given collection of Boolean spatio-temporal event-types. Big Data concerns large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data is now rapidly expanding in all science and engineering domains, including physical, biological and bio-medical sciences. In this paper, a new framework is proposed to find spatiotemporal patterns from Big Data. Existing algorithms are well in computation of necessary patterns, but more problematic when they are applied to Big Data. Big Data is a new trend used to analyze the datasets that due to their large size and complexity, Developers cannot manage them with traditional current algorithms or data mining software tools. Big Data mining is the capability of extracting useful information from these large datasets or streams of data, that due to its volume, variety, and velocity, it was not possible before to do it. The Big Data challenge is becoming one of the most exciting opportunities for the next years. This Paper focuses on a broad overview of pattern mining algorithms and significance in Spatiotemporal Databases, its current status, trade-offs, and forecast to the big data pattern mining future.
In many applications that track and analyze spatiotemporal data, movements obey periodic patterns... more In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. Periodic Pattern Mining or Periodicity Detection has numerous applications such as Prediction, Forecasting, Detection of Unusual events, etc. The periodic patterns are detected in a Time-Series database depending on the time intervals. Existi ng approaches could not detect all types of periodicity such as Symbol, Sequence and S egment at a time. In this Paper, we propose an approach to detect all types of the periodicity in time series Databases. It also finds the periodicity in the subsections of the Time-Series very effectively. Actually the periodicity detection results in the redundant data. To remove redundant data there are pruning techniques to apply and to get the desired pattern as an output. The comprehensive study demonstrates the effectiveness of the proposed approach. This is very time efficient, accurate approach t han many existing approaches.
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