A Study of Big Data Processing for Sentiments Analysis

Data processing has become an important field in today's big data-dominated world. The data has been generating at a tremendous pace from different sources. There has been a change in the nature of data from batch-data to streaming-data, and consequently, data processing methodologies have also changed. Traditional SQL is no longer capable of dealing with this big data. This chapter describes the nature of data and various tools, techniques, and technologies to handle this big data. The chapter also describes the need of shifting big data on to cloud and the challenges in big data processing in the cloud, the migration from data processing to data analytics, tools used in data analytics, and the issues and challenges in data processing and analytics. Then the chapter touches an important application area of streaming data, sentiment analysis, and tries to explore it through some test case demonstrations and results.

Kernel Fisher Envelope Surface for Pattern Recognition

It is found that the batch process is more difficultly monitored compared with the continuous process, due to its complex features, such as nonlinearity, non-stable operation, unequal production cycles, and most variables only measured at the end of batch. Traditional methods for batch process, such as multiway FDA (Chen 2004) and multi-model FDA (He et al. 2005), cannot solve these issues well. They require complete batch data only available at the end of a batch. Therefore, the complete batch trajectory must be estimated real time, or alternatively only the measured values at the current moment are used for online diagnosis. Moreover, the above approaches do not consider the problem of inconsistent production cycles.

Elution profile of cationic and anionic adsorbate from exhausted adsorbent using solvent desorption

Present invention involves to study the elution profile of anionic and cationic compounds from exhausted adsorbents using various eluents. Batch elution studies of anionic components like Congo Red dye and Carbonate ion; and cationic compounds such as Methylene blue dye and Cadmium metal from previously used naturally prepared adsorbents i.e. Gulmohar (Delonix regia) leaf powder - GLP; and Neem (Azadirachta indica) leaf powder – NLP and their derivatives were conducted. Different eluents used for batch study were various acids and alkaline solution having various concentration and solvents having different functional groups in seven sorption-desorption cycles. The batch data were accessed by kinetic models (Pseudo First-, Pseudo Second-order, Intra-partice and Elovic equation). Column elution experiments of Congo red and Cadmium from NLP and activated charcoal from NLP (AC-NLP) respectively was performed using selected eluent. Sorption and elution process plots and parameters for seven sorption–desorption cycles were evaluated and discussed. Plots of life cycle indicating activity-indicator equations were drawn, and their parameters were calculated and mentioned. From desorption efficiencies, it revealed that desorption exploration is predominately depends upon pH factor.

Universal strategies for the two-alternative big data processing

We consider the two-alternative processing of big data in the framework of the two-armed bandit problem. We assume that there are two processing methods with different, fixed but a priori unknown efficiencies which are due to different reasons including those caused by legislation. Results of data processing are interpreted as random incomes. During control process, one has to determine the most efficient method and to provide its primary usage. The difficulty of the problem is caused by the fact that its solution essentially depends on distributions of one-step incomes corresponding to results of data processing. However, in case of big data we show that there are universal processing strategies for a wide class of distributions of one-step incomes. To this end, we consider Gaussian two-armed bandit which naturally arises when batch data processing is analyzed. Minimax risk and minimax strategy are searched for as Bayesian ones corresponding to the worst-case prior distribution. We present recursive integro-difference equation for computing Bayesian risk and Bayesian strategy with respect to the worst-case prior distribution and a second order partial differential equation into which integro-difference equation turns in the limiting case as the control horizon goes to infinity. We also show that, in case of big data, processing of data one-by-one is not more efficient than optimal batch data processing for some types of distributions of one-step incomes, e.g. for Bernoulli and Poissonian distributions. Numerical experiments are presented and show that proposed universal strategies provide high performance of two-alternative big data processing.

Assessment of the use of tropical peats as local alternative materials for the adsorption of Pb, Zn and Cd: An equilibrium study

Peat is an organic material that has been widely used as an efficient and low-cost adsorbent. As many studies tend to focus on temperate peats, there is a lack of knowledge about the adsorption mechanism of tropical peats. This paper investigates the use of two Brazilian peats (Cravinhos - C and Luis Antônio - LA) from the Mogi-Guaçu river basin for the adsorption of lead (Pb), zinc (Zn), and cadmium (Cd), in order to contribute to the use of local and easy access materials to remediate contaminated sites. The peats adsorbed a high percentage of cations, especially Pb cations (100.0-46.3%), with commercial peat C showing higher adsorption than peat LA. The removal order was Pb2+ > Cd2+ ≥ Zn2+ for C and Pb2+ > Zn2+ > Cd2+ for LA. The batch data for both peats and for all metals were better fit by the Langmuir isotherm, with adsorption capacities (qm) for Pb, Zn, and Cd of 37.3134, 29.0674 and 21.2890 mmol kg-1 in peat C and 21.7391, 14.2550 and 3.6460 mmol kg-1 in LA, respectively, values comparable to those of other peats and biosorbents. The studied peats are considered efficient, alternative and low-cost adsorptive materials for these metals. The proximity of peatlands to areas with high potential for contamination necessitates the use of local materials to reduce remediation costs.

A Study of Big Data Processing for Sentiments Analysis

Data processing has become an important field in today's big data-dominated world. The data has been generating at a tremendous pace from different sources. There has been a change in the nature of data from batch-data to streaming-data, and consequently, data processing methodologies have also changed. Traditional SQL is no longer capable of dealing with this big data. This chapter describes the nature of data and various tools, techniques, and technologies to handle this big data. The chapter also describes the need of shifting big data on to cloud and the challenges in big data processing in the cloud, the migration from data processing to data analytics, tools used in data analytics, and the issues and challenges in data processing and analytics. Then the chapter touches an important application area of streaming data, sentiment analysis, and tries to explore it through some test case demonstrations and results.

An Online Transfer Learning Approach for Identification and Predictive Control Design With Application to RCCI Engines

This paper presents a framework to refine identified artificial neural networks (ANN) based state-space linear parameter-varying (LPV-SS) models with closed-loop data using online transfer learning. An LPV-SS model is assumed to be first identified offline using inputs/outputs data and a model predictive controller (MPC) designed based on this model. Using collected closed-loop batch data, the model is further refined using online transfer learning and thus the control performance is improved. Specifically, fine-tuning, a transfer learning technique, is employed to improve the model. Furthermore, the scenario where the offline identified model and the online controlled system are “similar but not identitical” is discussed. The proposed method is verified by testing on an experimentally validated high-fidelity reactivity controlled compression ignition (RCCI) engine model. The verification results show that the new online transfer learning technique combined with an adaptive MPC law improves the engine control performance to track requested engine loads and desired combustion phasing with minimum errors.