Big Data Analytics Framework Research Articles

Spatially gap free analysis of aerosol type grids in China: First retrieval via satellite remote sensing and big data analytics

Spatially contiguous aerosol type grids were rarely available for air quality management in the past. To bridge the gap, we developed an integrated remote sensing and big data analytics framework to generate spatially gap-free aerosol type grids between 2000 and 2020 in China. The effect of emission control via environmental management on haze reduction was fully realized for the first time with the aid of satellite-based gap-free aerosol type data. Daily gap-free aerosol fine mode fraction (FMF) data were first derived via a data-driven regression model based on remote sensing big data. According to empirically determined FMF probability distributions over regions with typical emission sources, aerosols in China were classified into eight major types, including typical/atypical/mixed anthropogenic aerosols, typical/atypical/mixed dust, and typical mixed and multiple modes. The results indicated that the gridded FMF estimates derived in this study agreed well with FMF retrievals from AERONET, with correlation coefficient of 0.81 and root mean square error of 0.13. The long-term variations in major aerosol types showed that in China the territory covered by typical anthropogenic aerosols was reduced from 21.38% to 11.76% over the past two decades, while dust aerosols decreased from 6.99% to 2.15%. The declining trend in anthropogenic aerosols could be attributed to reduced coal consumption and/or favorable dispersion conditions, whereas decreasing dust aerosols were largely associated with increased vegetation cover and/or weakened wind speed in the west. Overall, such advancements provide fresh evidence to improve our understanding of the emission control effect on haze pollution variations in China.

Mechanisms and techniques to enhance the security of big data analytic framework with MongoDB and Linux Containers

The frequency and scale of unauthorized access cases and misuses of data access privileges are a growing concern of many organizations. The protection of confidential data, such as social security numbers, financial information, etc., of the customers and/or employees is among the key responsibilities of any organization, and damage to such sensitive data can easily pose a threat to the future of a business and the security of the customers. Therefore, this paper proposes and implements some security mechanisms and techniques, such as secure authentication, secure authorization, and encryption, to assure the overall security of a big data analytic framework with MongoDB free community edition. This paper presents the fourth phase of our continuous research where in the first phase we proposed a data analytic framework with MongoDB and Linux Containers (LXCs) with basic security requirements. Next, in the second phase we proposed a vulnerability analysis testbed to find vulnerabilities associated with the system. Finally, in the third phase we discussed in detail root causes and some prevention techniques of vulnerabilities found in the system. In addition, this paper introduces a new security mechanism for privacy preserving data handling with MongoDB to ensure the privacy of the data before being processed. Our results show, with our initial model of the analytic framework, how well our newly introduced security mechanisms work and how these security mechanisms and techniques can be used to assure the confidentiality, integrity, and availability (CIA) of any data science project conducted on our proposed analytic framework. In addition, these security mechanisms and techniques help us to strengthen the current system against zero-day attacks where attacks on vulnerabilities that have not been patched or made public yet. Therefore, our vulnerability analysis testbed which is proposed in the second phase of this research will not be able to finds vulnerabilities related to zero-day attacks.

Big data analytics frameworks for the influence of gut microbiota on the development of tic disorder.

The association between gut microbiota and psychiatric disorders has received increasing research attention. Meanwhile, big data analysis has been utilized in many filed including business, human healthcare analysis, etc. The primary objective of this article was to provide insights into Big Data Analytics (BDA) to clarify the association between gut microbiota and TD (Tic disorder). Specifically, we investigated the recent studies related to gut microbiota composition differences in patients with TD compared to health people. We searched on PubMed and Embase (Ovid) databases for relevant published articles until June 15, 2021. A total of 78 TD and 62 health control stool samples were examined. Case-control design was applied in all the studies. No consensus was evident in α-diversity and β-diversity. The abundance of phyla Bacteroidetes and Firmicutes was predominant at the taxa level. Gut microbiota taxonomic differences were found between TD cases and controls, though inconsistently across studies. Further studies are needed to reveal the underlying pathophysiology of TD and correlation between TD and gut microbiota composition.

A plant disease image using convolutional recurrent neural network procedure intended for big data plant classification

The recent advancement of big data technology causes the data from agriculture domain to enter into the big data. They are not conventional techniques in existence to process such a large volume of data. The processing of large datasets involves parallel computation and analysis model. Hence, it is necessary to use big data analytics framework to process a large image datasets. In this paper, an automated big data framework is presented to classify the plant disease condition. This framework consists of a series operations that leads into a final step. When the classification is carried out using novel image classifier. The image classifier is designed using a Convolutional Recurrent Neural Network Classifier (CRNN) algorithm. The classifier is designed in such a way that it provides classification between a normal leaf and an abnormal leaf. The classification of plant images over large datasets that includes banana plant, pepper, potato, and tomato plant. Which is compared with other existing big data plant classification techniques like convolutional neural network, recurrent neural network, and deep neural network, artificial neural network with forward and backward propagation. The result shows that the proposed method obtains improved detection and classification of diseased plants compared to other the convolutional neural network (94.14%), recurrent neural network (94.07%), deep neural network (94%), artificial neural network with forward (93.96%), and backward propagation method (93.66%).

A Practical Guide to an Open-Source Map-Matching Approach for Big GPS Data

This work shows how map-matching helps to minimize errors in GPS data by finding the most probable corresponding points of the recorded waypoints of a trajectory on a road network. We investigate an open-source alternative for map-matching trajectories called Valhalla, which could replace limited and costly commercial map-matching services. Valhalla is an open-source routing engine, which provides different services, such as path-finding, map-matching, and generating maneuvers based on a path. We build a cloud-based big data analytics framework on Amazon Web Services (AWS) platform for map-matching. This well-established framework is scalable and could process millions of trajectories. Using an example GPS dataset, it is demonstrated how Valhalla can be used for map-matching at scale. The dataset consists of about 18 million trips in the year 2019 that have at least one recorded point in a bounding box surrounding Frankfurt am Main. The map-matching results confirm an adequate performance of Valhalla map-matching, show a reduction of errors by distance calculation, and allow for further street-segment-based analysis.

Big Data Analytics Framework for Effective Higher Education Institutions

There has been an increased dependency on Information and Communication Technologies (ICTs) in undertaking various activities in Higher Education Institutions (HEIs) ecosystems. Because of that, huge volumes of data have increasingly been generated. There have been, for instance, considerable amounts of data generated through electronic platforms involved in students’ admission and registration process, students’ academic records management, teaching and learning data, curriculum related data, and several other administrative data. Analysis of data generated from these platforms stands to give students, lecturers, HEIs Management, policy makers and implementers, and other stakeholders useful insights that would help in improving HEIs’ effectiveness. Unfortunately, literature have identified several challenges associated with existing big data analytics frameworks in HEIs. It was on this line that the present study, which was based on desk research, was carried out to propose an effective big data framework for analytics of such data. The proposed framework is composed of five stages; data collection, data pre-processing, data storage, data analytics, and data visualization. The stages were arranged systematically to address the identified challenges in the existing frameworks. Effective implementation of this framework will help HEIs to make a productive use of various data they generate. This will ultimately be beneficial to not only HEIs but also to aspired students, labour market, the government and the public at large.

Performance Tuning Of Apache Spark Framework In Big Data Processing with Respect To Block Size And Replication Factor

Apache Spark has recently become the most popular big data analytics framework. Default configurations are provided by Spark. HDFS stands for Hadoop Distributed File System. It means the large files will be physically stored on multiple nodes in a distributed fashion. The block size determines how large files are distributed, while the replication factor determines how reliable the files are. If there is just one copy of each block for a given file and the node fails, the data in the files become unreadable. The block size and replication factor are configurable per file. The results and analysis of the experimental study to determine the efficiency of adjusting the settings of tuning Apache Spark for minimizing application execution time as compared to standard values are described in this paper. Based on a vast number of studies, we employed a trial-anderror strategy to fine-tune these values. We chose two workloads to test the Apache framework for comparative analysis: Wordcount and Terasort. We used the elapsed time to evaluate the same.

Big Data Analysis Framework for Water Quality Indicators with Assimilation of IoT and ML

According to the United Nations, the Sustainable Development Goal ‘6’ seeks to ensure the availability and sustainable management of water for all. Digital technologies, such as big data, Internet of Things (IoT), and machine learning (ML) have a significant role and capability to meet the goal. Water quality analysis in any region is critical to identify and understand the standard of water quality and the quality of water is analyzed based on water quality parameters (WQP). Currently, water pollution and the scarcity of water are two major concerns in the region of Uttarakhand, and the analysis of water before it is supplied for human consumption has gained attention. In this study, a big data analytics framework is proposed to analyze the water quality parameters of 13 districts of Uttarakhand and find the correlation among the parameters with the assimilation of IoT and ML. During the analysis, statistical and fractal methods are implemented to understand the anomalies between the water quality parameters in 13 districts of Uttarakhand. The variation in WQP is analyzed using a random forest (RF) model, and the dataset is segmented location wise and the mean, mode, standard deviation, median, kurtosis, and skewness of time series datasets are examined. The mean of the parameters is adjusted with the coefficient of variation based on the standard values of each parameter. The turbidity in almost all the experimental sites has a normal distribution, with the lowest mean value (0.352 mg/L) and highest (11.9 mg/L) in the Pauri Garhwal and Almora districts, respectively. The pH of the water samples is observed to be in the standard range in all the experimental sites, with average and median values being nearly identical, at 7.189 and 7.20, respectively. However, the pH mode is 0.25. The Cl− concentration varies with mean values from the lowest (0.46 mg/L) to the highest (35.2 mg/L) over the experimental sites, i.e., the Bageshwar and Rudraprayag districts, respectively. Based on the analysis, it was concluded that the water samples were found to be safe to drink and in healthy condition in almost all the districts of the state Uttarakhand, except for the Haridwar district, where some increase in contaminants was observed.

Renewable energy management in smart grids by using big data analytics and machine learning

The application of big data in the energy sector is considered as one of the main elements of Energy Internet. Crucial and promising challenges exist especially with the integration of renewable energy sources and smart grids. The ability to collect data and to properly use it for better decision-making is a key feature; in this work, the benefits and challenges of implementing big data analytics for renewable energy power stations are addressed. A framework was developed for the potential implementation of big data analytics for smart grids and renewable energy power utilities. A five-step approach is proposed for predicting the smart grid stability by using five different machine learning methods. Data from a decentralized smart grid data system consisting of 60,000 instances and 12 attributes was used to predict the stability of the system through three different machine learning methods. The results of fitting the penalized linear regression model show an accuracy of 96% for the model implemented using 70% of the data as a training set. Using the random forest tree model has shown 84% accuracy, and the decision tree model has shown 78% accuracy. Both the convolutional neural network model and the gradient boosted decision tree model yielded 87% for the classification model.The main limitation of this work is that the amount of data available in the dataset is considered relatively small for big data analytics; however the cloud computing and real-time event analysis provided was suitable for big data analytics framework. Future research should include bigger datasets with variety of renewable energy sources and demand across more countries.

Wellness Forecasting by External and Internal Workloads in Elite Soccer Players: A Machine Learning Approach.

Training for success has increasingly become a balance between maintaining high performance standards and avoiding the negative consequences of accumulated fatigue. The aim of this study is to develop a big data analytics framework to predict players’ wellness according to the external and internal workloads performed in previous days. Such a framework is useful for coaches and staff to simulate the players’ response to scheduled training in order to adapt the training stimulus to the players’ fatigue response. 17 players competing in the Italian championship (Serie A) were recruited for this study. Players’ Global Position System (GPS) data was recorded during each training and match. Moreover, every morning each player has filled in a questionnaire about their perceived wellness (WI) that consists of a 7-point Likert scale for 4 items (fatigue, sleep, stress, and muscle soreness). Finally, the rate of perceived exertion (RPE) was used to assess the effort performed by the players after each training or match. The main findings of this study are that it is possible to accurately estimate players’ WI considering their workload history as input. The machine learning framework proposed in this study is useful for sports scientists, athletic trainers, and coaches to maximise the periodization of the training based on the physiological requests of a specific period of the season.

Exploring Coronavirus Disease 2019 Vaccine Hesitancy on Twitter Using Sentiment Analysis and Natural Language Processing Algorithms.

Vaccination can help control the coronavirus disease 2019 (COVID-19) pandemic but is undermined by vaccine hesitancy. Social media disseminates information and misinformation regarding vaccination. Tracking and analyzing social media vaccine sentiment could better prepare health professionals for vaccination conversations and campaigns. A real-time big data analytics framework was developed using natural language processing sentiment analysis, a form of artificial intelligence. The framework ingests, processes, and analyzes tweets for sentiment and content themes, such as natural health or personal freedom, in real time. A later dataset evaluated the relationship between Twitter sentiment scores and vaccination rates in the United States. The real-time analytics framework showed a widening gap in sentiment with more negative sentiment after vaccine rollout. After rollout, using a static dataset, an increase in positive sentiment was followed by an increase in vaccination. Lag cross-correlation analysis across US regions showed evidence that once all adults were eligible for vaccination, the sentiment score consistently correlated with vaccination rate with a lag of around 1 week. The Granger causality test further demonstrated that tweet sentiment scores may help predict vaccination rates. Social media has influenced the COVID-19 response through valuable information and misinformation and distrust. This tool was used to collect and analyze tweets at scale in real time to study sentiment and key terms of interest. Separate tweet analysis showed that vaccination rates tracked regionally with Twitter vaccine sentiment and might forecast changes in vaccine uptake and/or guide targeted social media and vaccination strategies. Further work is needed to analyze the interplay between specific populations, vaccine sentiment, and vaccination rates.

Towards Secure Big Data Analysis via Fully Homomorphic Encryption Algorithms.

Privacy-preserving techniques allow private information to be used without compromising privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm, cannot perform computational operations on encrypted data without first applying the decryption process. Homomorphic encryption algorithms provide innovative solutions to support computations on encrypted data while preserving the content of private information. However, these algorithms have some limitations, such as computational cost as well as the need for modifications for each case study. In this paper, we present a comprehensive overview of various homomorphic encryption tools for Big Data analysis and their applications. We also discuss a security framework for Big Data analysis while preserving privacy using homomorphic encryption algorithms. We highlight the fundamental features and tradeoffs that should be considered when choosing the right approach for Big Data applications in practice. We then present a comparison of popular current homomorphic encryption tools with respect to these identified characteristics. We examine the implementation results of various homomorphic encryption toolkits and compare their performances. Finally, we highlight some important issues and research opportunities. We aim to anticipate how homomorphic encryption technology will be useful for secure Big Data processing, especially to improve the utility and performance of privacy-preserving machine learning.

An innovative big data framework for exploring the impact on decision-making in the European Mediterranean healthcare sector

PurposeThe purpose of this paper is to propose a new conceptual framework for big data analytics (BDA) in the healthcare sector for the European Mediterranean region. The objective of this new conceptual framework is to improve the health conditions in a dynamic region characterized by the appearance of new diseases.Design/methodology/approachThis study presents a new conceptual framework that could be employed in the European Mediterranean healthcare sector. Practically, this study can enhance medical services, taking smart decisions based on accurate data for healthcare and, finally, reducing the medical treatment costs, thanks to data quality control.FindingsThis research proposes a new conceptual framework for BDA in the healthcare sector that could be integrated in the European Mediterranean region. This framework introduces the big data quality (BDQ) module to filter and clean data that are provided from different European data sources. The BDQ module acts in a loop mode where bad data are redirected to their data source (e.g. European Centre for Disease Prevention and Control, university hospitals) to be corrected to improve the overall data quality in the proposed framework. Finally, clean data are directed to the BDA to take quick efficient decisions involving all the concerned stakeholders.Practical implicationsThis study proposes a new conceptual framework for executives in the healthcare sector to improve the decision-making process, decrease operational costs, enhance management performance and save human lives.Originality/valueThis study focused on big data management and BDQ in the European Mediterranean healthcare sector as a broadly considered fundamental condition for the quality of medical services and conditions.

LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion

Abstract. Developing a big data analytics framework for generating the Long-term Gap-free High-resolution Air Pollutant concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and Earth system science analysis. By synergistically integrating multimodal aerosol data acquired from diverse sources via a tensor-flow-based data fusion method, a gap-free aerosol optical depth (AOD) dataset with a daily 1 km resolution covering the period of 2000–2020 in China was generated. Specifically, data gaps in daily AOD imageries from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra were reconstructed based on a set of AOD data tensors acquired from diverse satellites, numerical analysis, and in situ air quality measurements via integrative efforts of spatial pattern recognition for high-dimensional gridded image analysis and knowledge transfer in statistical data mining. To our knowledge, this is the first long-term gap-free high-resolution AOD dataset in China, from which spatially contiguous PM2.5 and PM10 concentrations were then estimated using an ensemble learning approach. Ground validation results indicate that the LGHAP AOD data are in good agreement with in situ AOD observations from the Aerosol Robotic Network (AERONET), with an R of 0.91 and RMSE equaling 0.21. Meanwhile, PM2.5 and PM10 estimations also agreed well with ground measurements, with R values of 0.95 and 0.94 and RMSEs of 12.03 and 19.56 µg m−3, respectively. The LGHAP provides a suite of long-term gap-free gridded maps with a high resolution to better examine aerosol changes in China over the past 2 decades, from which three major variation periods of haze pollution in China were revealed. Additionally, the proportion of the population exposed to unhealthy PM2.5 increased from 50.60 % in 2000 to 63.81 % in 2014 across China, which was then reduced drastically to 34.03 % in 2020. Overall, the generated LGHAP dataset has great potential to trigger multidisciplinary applications in Earth observations, climate change, public health, ecosystem assessment, and environmental management. The daily resolution AOD, PM2.5, and PM10 datasets are publicly available at https://doi.org/10.5281/zenodo.5652257 (Bai et al., 2021a), https://doi.org/10.5281/zenodo.5652265 (Bai et al., 2021b), and https://doi.org/10.5281/zenodo.5652263 (Bai et al., 2021c), respectively. Monthly and annual datasets can be acquired from https://doi.org/10.5281/zenodo.5655797 (Bai et al., 2021d) and https://doi.org/10.5281/zenodo.5655807 (Bai et al., 2021e), respectively. Python, MATLAB, R, and IDL codes are also provided to help users read and visualize these data.

Applying empirical method for performance metrics measurement of a nuclear security system

A nuclear security system is often developed based on a particular framework. In our case, a nuclear security system is developed based on the big data analytics framework with the purpose of integrating all nuclear security systems within a nuclear facility. Nuclear security can be defined as the prevention and detection of, and response to, theft, sabotage, unauthorized access, illegal transfer, or other malicious acts involving nuclear material, other radioactive substances, or their associated facilities. Whereas, big data analytics is denoted as a new way of collecting, analyzing large amounts of data, finding the appropriate patterns to support decision making, hence improving the action taken in solving the problems. The system-based evaluations methodology is widely used to measure the performance metrics of nuclear security systems. However, the system-based evaluation methodology consumes time and demands a lot of effort because in applying the methodology, a complete nuclear security system that is based on the particular framework needs to be designed, developed, and implemented in the nuclear facilities environment before the performance metrics could be measured. In this paper, we present an empirical method of measuring the performance metrics by utilizing the Rasch Measurement Model. The methodology is formulated to reduce the time and efforts in measuring the performance metrics of a nuclear system that is developed based on a framework…

Predictive Big Data Analytics for Service Requests: A Framework

Nowadays, valuable big data are generated and collected rapidly from numerous rich data sources. Following the initiatives of open data, many organizations including municipal governments are willing to share their data such as open big data regarding non-emergency city service requests from their residents. Big data analytics on these open big data can be for social good. Hence, in this article, we present a framework for predictive big data analytics for service requests. The framework mines historical open big data to discover patterns about service requests, and predicts the demands for these services in the future. The discovered knowledge and predictions may provide policy makers deeper understanding of their data and requested services so that appropriate actions could take place. Evaluation on open big data from the City of Winnipeg demonstrates the usefulness of our framework for conducting predictive big data analytics for service requests.

Chukonu

Apache Spark is a widely deployed big data analytics framework that offers such attractive features as resiliency, load-balancing, and a rich ecosystem. However, there is still plenty of room for improvement in its performance. Although a data-parallel system in a native programming language significantly improves performance, it may require re-implementing many functionalities of Spark to become a full-featured system. It is desirable for native big data systems to just write a compute engine in native languages to ensure high efficiency, and reuse other mature features provided by Spark rather than re-implement everything. But the interaction between the JVM and the native world risks becoming a bottleneck. This paper proposes Chukonu, a native big data framework that re-uses critical big data features provided by Spark. Owing to our novel DAG-splitting approach, the potential Spark integration overhead is alleviated, and its even outperforms existing pure native big data frameworks. Chukonu splits DAG programs into run-time parts and compile-time parts: The run-time parts are delegated to Spark to offload the complexities due to feature implementations. The compile-time parts are natively compiled. We propose a series of optimization techniques to be applied to the compile-time parts, such as operator fusion, vectorization, and compaction, to significantly reduce the Spark integration overhead. The results of evaluation show that Chukonu has a speedup of up to 71.58X (geometric mean 6.09X) over Apache Spark, and up to 7.20X (geometric mean 2.30X) over pure-native frameworks on six commonly-used big data applications. By translating the physical plan produced by SparkSQL into Chukonu programs, Chukonu accelerates Spark-SQL's TPC-DS performance by 2.29X.

An Intelligent Cognitive-Inspired Computing with Big Data Analytics Framework for Sentiment Analysis and Classification

Advancements in recent networking and information technology have always been a natural phenomenon. The exponential amount of data generated by the people in their day-to-day lives results in the rise of Big Data Analytics (BDA). Cognitive computing is an Artificial Intelligence (AI) based system that can reduce the issues faced during BDA. On the other hand, Sentiment Analysis (SA) is employed to understand such linguistic based tweets, feature extraction, compute subjectivity and sentimental texts placed in these tweets. The application of SA on big data finds it useful for businesses to take commercial benefits insight from text-oriented content. In this view, this paper presents new cognitive computing with the big data analysis tool for SA. The proposed model involves various process such as pre-processing, feature extraction, feature selection and classification. For handling big data, Hadoop Map Reduce tool is used. The proposed model initially undergoes pre-processing to remove the unwanted words. Then, Term Frequency-Inverse Document Frequency (TF-IDF) is utilized as a feature extraction technique to extract the set of feature vectors. Besides, a Binary Brain Storm Optimization (BBSO) algorithm is being used for the Feature Selection (FS) process and thereby achieving improved classification performance. Moreover, Fuzzy Cognitive Maps (FCMs) are used as a classifier to classify the incidence of positive or negative sentiments. A comprehensive experimental results analysis ensures the better performance of the presented BBSO-FCM model on the benchmark dataset. The obtained experimental values highlights the improved classification performance of the proposed BBSO-FCM model in terms of different measures.

Towards logistics 4.0: an edge-cloud software framework for big data analytics in logistics processes

Logistics 4.0 aims at enabling the sustainable satisfaction of customer demands with optimised costs of services with the use of emerging technologies, such as Internet of Things, streaming analytics, and optimised decision making. The availability of massive sensor data streams over time opens new perspectives for extracting meaningful and timely insights from data-in-motion through streaming analytics. Logistics 4.0 is a relatively new field of research which demands the development of scalable and efficient software solutions and their deployment to successful real-life case studies. In this paper, we propose a software framework for streaming analytics in an edge-cloud computational environment aiming at covering the whole data analytics lifecycle in logistics processes and thus, advancing the evolution and realisation of the Logistics 4.0 concept. The proposed framework takes advantage of edge computing technologies, streaming analytics and proactive decision making in order to monitor, analyse and support decision making in the frame of Logistics 4.0. It is applied and evaluated in a maintenance service logistics use case from the aerospace industry.

Local Food: Charting Meaning Emergence Through the Dynamics of Discourse, Rhetoric and Framing

This study seeks to understand how new meanings emerge in the context of institutional change. Existing research seeking to understand shifts in meaning has primarily accessed meaning, across numerous contexts, via the three key constructs of discourse, rhetoric, or framing. Within the context of the emergence of the local food movement in Canada, I employ a mixed methods approach using term frequencies, topic modelling and qualitative content analysis, within a computational grounded theory framework for Big Data analysis. My data consists of all articles containing any mention of the term “local food” in popular Canadian press over 37 years from 1978-2014, a database totalling 31,421 articles. My results show that firstly, new meanings pertaining to local food emerged rapidly over the 37-year period. Secondly, unexpected actors were found to significantly impact the propulsion of meaning change, by establishing new vocabularies surrounding the term “local food”. Finally, I find evidence that supports a strong role for both micro-processes of rhetoric and framing and macro-processes of discourse. I demonstrate how these processes occur through the dynamic intersection of rhetoric, framing and discourse. I propose an emergent process model of meaning change.