Large amounts of data ( big data ) are readily available and collected daily by global networks worldwide. However, much of the real-time utility of this data is not realized, as data analysis tools for very large datasets, particularly time series data are cumbersome. A methodology for data cleaning and preparation needed to support big data analysis is presented, along with a comparative examination of three widely available data mining tools. This methodology and offered tools are used for analysis of a large-scale time series dataset of environmental data. The case study of environmental data analysis is presented as visualization, providing future direction for data mining on massive data sets gathered from global networks, and an illu...
Massive data sets are not unlike small to large data sets in at least one respect, namely it is esse...
The term `big data analytics' emerged in order to engage in the ever increasing amount of scientific...
Data Mining is an analytic process designed to explore data in search of consistent patterns and/or ...
The amount of relevant published data sets available in the environmental sciences is rapidly increa...
Large amounts of data are readily available and collected daily by global networks worldwide. Howeve...
Abstract: Data mining is the application of specific algorithms for extracting patterns from data. B...
More and more data is gathered every day and time series are a major part of it. Due to the usefulne...
We are now in Big Data era, and there is a growing demand for tools which can process and analyze it...
Visualization of massively large datasets presents two significant problems. First, the dataset must...
In today’s era of digitization, we work on the variety of data. Huge amount of data will be processe...
Today, real world time series data sets can take a size up to a trillion observations and even more....
This paper proposes a comprehensive approach for supporting clustering-based spatio-temporal analysi...
In the last few years, there are enormous amounts of structured, unstructured and semi-structured da...
In this paper, we provide principles, models, and main architecture of an innovative framework for s...
Numeric time series is a class of data consisting of chronologically ordered observations represente...
Massive data sets are not unlike small to large data sets in at least one respect, namely it is esse...
The term `big data analytics' emerged in order to engage in the ever increasing amount of scientific...
Data Mining is an analytic process designed to explore data in search of consistent patterns and/or ...
The amount of relevant published data sets available in the environmental sciences is rapidly increa...
Large amounts of data are readily available and collected daily by global networks worldwide. Howeve...
Abstract: Data mining is the application of specific algorithms for extracting patterns from data. B...
More and more data is gathered every day and time series are a major part of it. Due to the usefulne...
We are now in Big Data era, and there is a growing demand for tools which can process and analyze it...
Visualization of massively large datasets presents two significant problems. First, the dataset must...
In today’s era of digitization, we work on the variety of data. Huge amount of data will be processe...
Today, real world time series data sets can take a size up to a trillion observations and even more....
This paper proposes a comprehensive approach for supporting clustering-based spatio-temporal analysi...
In the last few years, there are enormous amounts of structured, unstructured and semi-structured da...
In this paper, we provide principles, models, and main architecture of an innovative framework for s...
Numeric time series is a class of data consisting of chronologically ordered observations represente...
Massive data sets are not unlike small to large data sets in at least one respect, namely it is esse...
The term `big data analytics' emerged in order to engage in the ever increasing amount of scientific...
Data Mining is an analytic process designed to explore data in search of consistent patterns and/or ...