The k‐nearest neighbors algorithm is characterized as a simple yet effective data mining technique. The main drawback of this technique appears when massive amounts of data—likely to contain noise and imperfections—are involved, turning this algorithm into an imprecise and especially inefficient technique. These disadvantages have been subject of research for many years, and among others approaches, data preprocessing techniques such as instance reduction or missing values imputation have targeted these weaknesses. As a result, these issues have turned out as strengths and the k‐nearest neighbors rule has become a core algorithm to identify and correct imperfect data, removing noisy and redundant samples, or imputing missing values, transforming Big Data into Smart Data—which is data of sufficient quality to expect a good outcome from any data mining algorithm. The role of this smart data gleaning algorithm in a supervised learning context are investigated. This includes a brief overview of Smart Data, current and future trends for the k‐nearest neighbor algorithm in the Big Data context, and the existing data preprocessing techniques based on this algorithm. We present the emerging big data‐ready versions of these algorithms and develop some new methods to cope with Big Data. We carry out a thorough experimental analysis in a series of big datasets that provide guidelines as to how to use the k‐nearest neighbor algorithm to obtain Smart/Quality Data for a high‐quality data mining process. Moreover, multiple Spark Packages have been developed including all the Smart Data algorithms analyzed. This article is categorized under: Technologies > Data Preprocessing Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Classification

In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used. Big Data problems, generated by massive growth in the scale of data observed in recent years, also follow the same dictate. A common problem affecting data quality is the presence of noise, particularly in classification problems, where label noise refers to the incorrect labeling of training instances, and is known to be a very disruptive feature of data. However, in this Big Data era, the massive growth in the scale of the data poses a challenge to traditional proposals created to tackle noise, as they have difficulties coping with such a large amount of data. New algorithms need to be proposed to treat the noise in Big Data problems, providing high quality and clean data, also known as Smart Data. In this paper, two Big Data preprocessing approaches to remove noisy examples are proposed: an homogeneous ensemble and an heterogeneous ensemble filter, with special emphasis in their scalability and performance traits. The obtained results show that these proposals enable the practitioner to efficiently obtain a Smart Dataset from any Big Data classification problem.accepted that we have entered the Big Data era [31]. Big Data is the set of technologies that make processing such large amounts of data possible [7], while most of the classic knowledge extraction methods cannot work in a Big Data environment because they were not conceived for it.Big Data as concept is defined around five aspects: data volume, data velocity, data variety, data veracity and data value [24]. While the volume, variety and velocity aspects refer to the data generation process and how to capture and store the data, veracity and value aspects deal with the quality and the usefulness of the data. These two last aspects become crucial in any Big Data process, where the extraction of useful and valuable knowledge is strongly influenced by the quality of the used data.In Big Data, the usage of traditional preprocessing techniques [16,34,18] to enhance the data is even more time consuming and resource demanding, being unfeasible in most cases. The lack of efficient and affordable preprocessing techniques implies that the problems in the data will affect the models extracted. Among all the problems that may appear in the data, the presence of noise in the dataset is one of the most frequent. Noise can be defined as the partial or complete alteration of the information gathered for a data item, caused by an exogenous factor not related to the distribution that generates the data. Learning from noisy data is an important topic in machine learning, data mining and pattern recognition, as real world data sets may suffer from imperfections in data acquisition, transmission, storage, integration and categorization. Noise will lead to excessively complex models with deteriorated performance [49], resulting in even larger computing times for less value.The impact of noise in Big Data, among other pernicious traits, has not been disrega...

The large amounts of data have created a need for new frameworks for processing. The MapReduce model is a framework for processing and generating large-scale datasets with parallel and distributed algorithms. Apache Spark is a fast and general engine for large-scale data processing based on the MapReduce model. The main feature of Spark is the in-memory computation. Recently a novel framework called Apache Flink has emerged, focused on distributed stream and batch data processing. In this paper we perform a comparative study on the scalability of these two frameworks using the corresponding Machine Learning libraries for batch data processing. Additionally we analyze the performance of the two Machine Learning libraries that Spark currently has, MLlib and ML. For the experiments, the same algorithms and the same dataset are being used. Experimental results show that Spark MLlib has better perfomance and overall lower runtimes than Flink.

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