Table 2 Efficient data analytics methods for data mining Problem Method References Clustering BIRCH [44] DBSCAN [45] Incremental DBSCAN [46] RKM [47] TKM [42] Classification SLIQ [50] TLAESA [51] FastNN [52] SFFS [53] GPU-based SVM [43] Association rules CLOSET [54] FP-tree [32] CHARM [55] MAFIA [56] FAST [57] Sequential patterns SPADE [58] CloSpan [59] PrefixSpan [60] SPAM [61] ISE [62].
They've also made their rodent abatement program 20% more efficient using data analytics.
Another efficient big data analytics was presented in [89], called generalized linear aggregates distributed engine (GLADE).
Hence, it becomes necessary to have a good understanding of the currently available technologies to develop a framework which can support efficient data collection, storage and analytics.
If a company is planning to analyze data, having to spend a lot of time to find it in the data lake and then prepare it for analytics runs contrary to efficient data access.
The HESA/Jisc Analytics Lab collaboration is in many respects a prototype of the kind of data practices envisaged by Data Futures, emphasizing efficient data collection, removal of duplication, and capacity-building in cutting edge data analytics and visualization techniques.
With the rise in big data demands, a data lake implementation has the potential to improve analytics, create wider data availability and produce more efficient data throughput.
Traditional data management systems lack the capability to handle big data storage and analytics requirements and thus NoSQL technology is contributing to provide suitable solutions for timely data retrieval and efficient data processing.
Data clustering, a prominent analytic method of data mining, is being efficiently employed in data analytics.
Rinke's company, Celonis, focuses on process mining, a technology designed to make big data analytics more efficient by allowing companies to visualize their processes from beginning to end, in real time.
