This huge amount of data is referred to as Big Data and the task of handling it comes in Big Data Analytics. The various data mining techniques proposed till date serve as an aid to the problem of efficiently a
Big Data analytics are recently coming up as prominent research area in the field of data science. Apache Spark is an open source distributed data processing platform that uses distributed memory...doi:10.1007/978-3-319-74690-6_41Omar Hesham Mohamed...
Discover the latest articles and news from researchers in related subjects, suggested using machine learning. Big Data Data Analytics Data Mining Data processing Functional clustering Time Series Analysis References Akaike, H. (1981). Likelihood of a model and information criteria. Journal of ...
In this respect, the need to support advanced analytics on Big Data is driving data scientist’ interest toward massively parallel distributed systems and software platforms, such as Map-Reduce and Spark, that make possible their scalable utilization. However, when complex data mining algorithms are ...
Commonly utilized techniques include data compression, machine learning, correlation analysis and clustering for data processing and analytics in IoT [[3], [5]]. As one of the most leading big data mining approaches for drilling smart data, clustering attempts to divide the raw objects into ...
In the recent literature, axiomatic frameworks have been proposed for clustering and its quality. But none of the proposed frameworks has concentrated on the computational aspects of clustering, which is essential in current big data analytics. In this paper, we propose an axiomatic framework for ...
Electric load forecasting is crucial in the planning and operating electric power companies. It has evolved from statistical methods to artificial intelligence-based techniques that use machine learning models. In this study, we investigate short-term lo
Big data has become popular for processing, storing and managing massive volumes of data. The clustering of datasets has become a challenging issue in the field of big data analytics. The K-means algorithm is best suited for finding similarities between entities based on distance measures with sma...
In the modern field of data analytics, proper data management is the only way to maximize performance while minimizing costs.Google BigQuery, one of the leading cloud-based data warehouses, shows great skills in managing huge datasets by partitioning and clustering. Understanding the differences betwe...
Groundwater is a vital global resource. However, mapping aquifers remains challenging, particularly in developing nations. This study proposes a novel methodology for aquifer delineation using time-series clustering of groundwater-level data. The modular