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For today's data savvy information systems, it becomes inevitable to adapt stream-processing techniques where sequence of data items arrives continuously over the period. Streaming data analytics is thus an emerging area of research where standard data mining is insufficient to produce desired efficacy. This paper aims to focus on streaming data classification and presents a comprehensive spectrum of various ensemble approaches, through systematic experimental work taking the stream data both from artificial and real time data. The paper also contributes to provide performance evaluation of existing novel and adapted algorithms applied to artificial and real time streams, with the intension to provide the research community with the classifier ensemble techniques tuned for artificial and Real time data separately. Authors of this paper would like to address real time streaming analytics majorly, as it needs more attention from research community since there is still scarcity of established benchmarks and standardized frameworks. The paper concludes with summarization of major observations in evaluation experimentation and discussion of open research directions for future work. [ABSTRACT FROM AUTHOR]

Our goal is to include a brief overview of the WSN system and its potential to smooth the path to link the real and virtual environment to a global network centered on the Internet. Thus, in these modern technologies, sensor nodes play a significant role in transmitting information from a connection node to another defined node within its full scope. According to the ideal condition of WSN, the issue of radiofrequency network localization is of considerable significance in many technological applications such as military hardware for the identification of local points of attack or protection, environmental science besides, in general, sensor nodes. The most advanced equipment for acquiring a direct Global Positioning System location. We look at the primary structure of WSNs for kernel functions and methods to identify the most suitable and great access value based on the localization action after a brief history of the introduction part. Then, to put in place, the suitable approach for accessing terminals in the best obtainable state in WSNs, we restate the algorithms and techniques to build. As a result, we conclude on the points raised to compare and make a conclusion. Therefore, the final aim of this analysis is to clarify the position's quality and effectiveness based on diverse viewpoints. The results show that optimum placement could modify this immense technology to find new ways to find nodes based on the unexpected structure of WSNs in the best nodes. [ABSTRACT FROM AUTHOR]

The processing strategy based on measurement metadata is a data stream engine running on Apache Storm, who is able to process measures in real-time. In the data stream context, the data have no an associated limit, they are al-ways arriving. The Attribute-Relation File Format (ARFF) is used by popular software like Weka, allowing offline analysis in the machine learning and data mining area. However, the ARFF file has a finite size. The CincamimisConversor library allows exporting from the data streams organized under a measurement interchange schema to a columnar-data organization in real-time. Here, an extension to the library is introduced for supporting the real-time translating and storing from the heterogeneous data streams to the ARFF file format. This is very useful, because through the library now is possible to collect data from heterogeneous data sources (e.g. Internet-of-Thing -IoT- devices) and export them in real-time for offline analysis in Weka. Even, this could foster a lot of educational applications among IoT, the measurement process with heterogeneous sources, data stream processing strategy, and Weka. A discrete simulation was carried out, obtaining promising results. It is just required at most 0.2387 ms for translating 5000 measures, while the storing operation for them consumed less than 0.2028 ms on a Solid-State disk. [ABSTRACT FROM AUTHOR]