Data Mining - Concepts and Techniques.

Internet of things: A survey on enabling technologies, application and standardization

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Simulation of the biochemical and chemical oxygen demand and total suspended solids in wastewater treatment plants: Data-mining approach

Internet of Things (IoT) is a growing technology in all fields of science and engineering. The amount of data emitted by the sensors used in the various fields is high. Therefore, efficient knowledge from such large datasets is a clear requirement of many users. This large data is far from perfect; it has many defects (such as noise, missing values, outliers etc.) and is not suitable for analysis because it can lead to incorrect conclusions. So, data preprocessing is a required technique for such data. Data preprocessing is an important and essential step, the main goal of which is to dedicate techniques to clean, refine, repair and improve that raw data. This paper proposes a survey on IoT data preprocessing and its techniques. This paper discusses exiting research on data preprocessing in the IoT context and, introduces the background of IoT data preprocessing and present literature reviews of the advanced research on data preprocessing techniques. The classification of various preprocessing approaches with techniques is clearly depicted in the figure. Various approaches of preprocessing cleaning, transformation, reduction and integration are described. In addition, methods for such approaches in IoT data preprocessing are also discussed. IoT Data preprocessing techniques on various applications are tabulated. Finally, issues and challenges, most useful in future work, are discussed.

Survey on IoT Data Preprocessing

In recent years immense growth of data i.e. big data is observed resulting in a brighter and more optimized future. Big Data demands large computational infrastructure with high–performance processing capabilities. Preparing big data for mining and analysis is a challenging task and requires data to be preprocessed to improve the quality of raw data. The data instance representation and quality are foremost. Data preprocessing is preliminary data mining practice in which raw data is transformed into a format suitable for another processing procedure. Data preprocessing improves the data quality by cleaning, normalizing, transforming and extracting relevant feature from raw data. Data preprocessing significantly improve the performance of machine learning algorithms which in turn leads to accurate data mining. Knowledge discovery from noisy, irrelevant and redundant data is a difficult task therefore precise identification of extreme values and outlier, filling up missing values poses challenges. This paper discusses various big data pre–processing techniques in order to prepare it for mining and analysis tasks.

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Data Preprocessing: A preliminary step for web data mining

The Internet of Things (IoT) and Big Data have made the leap to turn into a mainstream issue and maintains board level priorities. Both IoT and big data are continuously creating headlines all over, drawing a huge amount of research interest and highlighting unique challenges. This growing recognition is due the intersection of both technologies with tremendous scope for business analytics and the prospective that still remains unexploited. Every day, industrial machine, health monitoring systems, sensors, and devices etc. connect to the Internet and exchange information. The future IoT will be extremely populated by huge quantity of heterogeneous networked embedded devices, which will be producing deluge of data. As businesses gets on new IoT project and tries to extract valuable information from enormous data volumes, a novel data management approach is called for. Conventional database management techniques and analytics methods fail to provide precise facilities to handle diverse data constantly flooding from numerous numbers of sources which are untold. This paper inspects the complex and fast moving data of IoT, and the existing position of data management techniques and challenges in storing and analyzing it.

Big Data Management in Connected World of Internet of Things

Cellular automata have proved many of its capabilities and have bestowed a lot in many fields. With the emergence of CA, fabric pattern production has increased in less amount of time. For weaving, cellular automata start with some pattern, then continues with a sequence of steps to produce a new pattern or to change the colour of the cell in a lattice, by using particular transition rules. The main purpose of using cellular automata algorithm as it provides the superlative edge maps and the outstanding quality with one pixel wide edge, with edges having no breaks. We assessed cellular automata capabilities and CA a lot contributions in pattern generation, transformation, and data processing. This research paper reflects the integration of cellular automata across different disciplines. In this paper, we assess the computationally enriched CA rules facilitating spot detection in the study of medical images for cancer diagnosis. The dynamic behaviour of CA increases the scope of transformation and makes it practical for morphing. CA provides prediction of protein structural class and processes dynamic simulation of protein. Flexibility of CA facilitates parallel processing using VLSI, colour graph modelling the linear rules, and its exercise in fabric weaving.

Huma Jamshed

Papers

Data Preprocessing: A preliminary step for web data mining

Big Data Management in Connected World of Internet of Things

Realization of Aggregate Applications using Dynamic Behaviour of Cellular Automata