An advertising analytics framework using social network big data

Summary form only given. Big data analytics is the process of examining large amounts of data of a variety of types (big data) to uncover hidden patterns, unknown correlations and other useful information. Its revolutionary potential is now universally recognized. Data complexity, heterogeneity, scale, and timeliness make data analysis a clear bottleneck in many biomedical applications, due to the complexity of the patterns and lack of scalability of the underlying algorithms. Advanced machine learning and data mining algorithms are being developed to address one or more challenges listed above. It is typical that the complexity of potential patterns may grow exponentially with respect to the data complexity, and so is the size of the pattern space. To avoid an exhaustive search through the pattern space, machine learning and data mining algorithms usually employ a greedy approach to search for a local optimum in the solution space, or use a branch-and-bound approach to seek optimal solutions, and consequently, are often implemented as iterative or recursive procedures. To improve efficiency, these algorithms often exploit the dependencies between potential patterns to maximize in-memory computation and/or leverage special hardware (such as GPU and FPGA) for acceleration. These lead to strong data dependency, operation dependency, and hardware dependency, and sometimes ad hoc solutions that cannot be generalized to a broader scope. In this talk, I will present some open challenges faced by data scientist in biomedical fields and the current approaches taken to tackle these challenges.

Big Data Analytics (BDA) is going to be discussed using data mining algorithms in this paper. Develop security in BDA fields for data security to achieve confidential storage. Big Data refers to data sets that are very vast, very complicated, and very dynamic and that have many independent sources. Big data is quickly implemented in all research and engineering fields, including physical, biological, and biomedical science, thanks to rapid developments in networking, data storage, and data gathering capacities. From a data mining point of view, this study gives a theorem characterizing the aspects of the Big Data revolution and suggests a Big Data processing model. This data-driven paradigm considers security and privacy concerns, data mining and analysis, and the modeling of user interests. Here, takes a look at a few of the knottier aspects of big data and the data-driven approach, and calculate the precisions, recalls, and measures, which are 90% of different data mining algorithms.

As we are living in the era of big data, huge volumes of a wide variety of complex data-which can be of different levels of veracity-are generated or collected at a high velocity from rich sources of data in various real-life applications. A rich source of these big data sources is the Internet of Things (IoT), which include a collection of sensors, smartphones and other mobile devices, wearable devices, as well as other "things" that are capable to operate within the existing Internet infrastructure. Embedded in these big data are valuable knowledge and useful information. Hence, the research problem of data mining from big IoT data have drawn attention of many researchers as it aims to discover implicit, previously unknown and potentially useful information and knowledge from the data. For instance, frequent pattern mining finds sets of frequently co-occurring items in the IoT domains. Associative classification discovers rules revealing relationships among items within the frequent patterns and their associations with the corresponding class labels. Induction based classification uses decision tree or random forest to learn from old big IoT for classifying or making predictions on new data. Over the past quarter of a century, many serial, distributed, parallel, and MapReduce-based (Hadook-based and Spark-based) big data mining algorithms have been proposed. These algorithms are run in local computers, distributed and parallel environments, clusters, grids, clouds and/or data centers. In this paper, we review some of these algorithms, discuss issues and research prospective in mining classification patterns from these big IoT data in fog. Our case study on a real-life application shows the feasibility on classifying real-life big IoT data over fog for urban analytics.

Addressing the policy challenges and opportunities of “Big data”

This book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs—Volume, Velocity, Variety, Veracity and Variability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data. Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries.

This report focuses on safety aspects of connected and automated vehicles (CAVs). The fundamental question to be answered is how can CAVs improve road users' safety? Using advanced data mining and thematic text analytics tools, the goal is to systematically synthesize studies related to Big Data for safety monitoring and improvement. Within this domain, the report systematically compares Big Data initiatives related to transportation initiatives nationally and internationally and provides insights regarding the evolution of Big Data science applications related to CAVs and new challenges. The objectives addressed are: 1-Creating a database of Big Data efforts by acquiring reports, white papers, and journal publications; 2-Applying text analytics tools to extract key concepts, and spot patterns and trends in Big Data initiatives; 3-Understanding the evolution of CAV Big Data in the context of safety by quantifying granular taxonomies and modeling entity relations among contents in CAV Big Data research initiatives, and 4-Developing a foundation for exploring new approaches to tracking and analyzing CAV Big Data and related innovations. The study synthesizes and derives high-quality information from innovative research activities undertaken by various research entities through Big Data initiatives. The results can provide a conceptual foundation for developing new approaches for guiding and tracking the safety implications of Big Data and related innovations.

GraphChi is the first reported disk-based graph engine that can handle billion-scale graphs on a single PC efficiently. GraphChi is able to execute several advanced data mining, graph mining and machine learning algorithms on very large graphs. With the novel technique of parallel sliding windows (PSW) to load subgraph from disk to memory for vertices and edges updating, it can achieve data processing performance close to and even better than those of mainstream distributed graph engines. GraphChi mentioned that its memory is not effectively utilized with large dataset, which leads to suboptimal computation performances. In this paper we are motivated by the concepts of "pin " from TurboGraph and "ghost" from GraphLab to propose a new memory utilization mode for GraphChi, which is called Part-in-memory mode, to improve the GraphChi algorithm performance. The main idea is to pin a fixed part of data inside the memory during the whole computing process. Part-in-memory mode is successfully implemented with only about 40 additional lines of code to the original GraphChi engine. Extensive experiments are performed with large real datasets (including Twitter graph with 1.4 billion edges). The preliminary results show that Part-in-memory mode memory management approach effectively reduces the GraphChi running time by up to 60% in PageRank algorithm. Interestingly it is found that a larger portion of data pinned in memory does not always lead to better performance in the case that the whole dataset cannot be fitted in memory. There exists an optimal portion of data which should be kept in the memory to achieve the best computational performance.

Massive amounts of building operational data are collected and stored in modern buildings, which provide rich information for in-depth investigation and assessment of actual building operational performance. However, the current utilization of big building operational data is far from being effective due to the gaps between building engineering and advanced big data analytics. Data mining is a promising technology for extracting previously unknown yet potentially useful insights from big data. This paper aims to explore the potential application of advanced data mining techniques for effective utilization of big building operational data. A case study of mining the operational data of an educational building for performance improvement is presented. Decision tree, clustering analysis, and association rule mining are adopted to analyze the operational data. The results show that useful knowledge can be extracted for identifying typical building operation patterns, detecting operation deficiencies, and spotting energy conservation opportunities. Practical application:The current utilization of big building operational data in the building industry is rather limited due to the lack in experience of using advanced big data analytics. This study presents a data mining-based method for analyzing massive building operational data. The case study results validate the efficiency and effectiveness of the method proposed. It can help building professionals to discover valuable insights into building operation patterns and thereby developing strategies for improving building energy efficiency. The method can be fully realized using the open-source software R, which provides great flexibilities in its integration with building automation systems.

The rapid evolution of machine learning (ML) and big data analytics has modernized medical decision-making procedure, offering promising path for improving classification precision and ultimately, patient outcomes. This research inspects methodologies for enhancing the classification accuracy of medical decision systems by leveraging ML algorithms and big data analytics procedure. In this study, a broad evaluation of existing literature on ML applications in healthcare and medical decision-making is carried out to discover current challenges and potential areas for improvement. The research explores the integration of diverse data sources, including electronic health records (EHRs), medical imaging, genomic data, and patient-generated data, to build robust predictive models. Moreover, the research emphasizes the importance of interpretability and transparency in ML models for medical decision-making, particularly in critical healthcare settings where the rationale behind predictions is crucial. Techniques for model explainability, such as feature importance analysis and model-agnostic interpretability methods, are explored to enhance trust and adoption of ML-driven decision systems by healthcare professionals. Furthermore, the study investigates advanced ML algorithms such as deep learning, ensemble methods, and feature engineering techniques to extract meaningful patterns from large and complex medical datasets. Through experimentation with real-world medical datasets, the efficacy of these algorithms in improving classification accuracy is evaluated and compared against traditional methods. The result of this research contributes to the advancement of ML-driven medical decision systems by providing insights into strategies for improving classification accuracy, thereby facilitating more exact diagnosis, prognosis, and treatment recommendations. Ultimately, the integration of ML and big data analytics holds immense potential for revolutionizing healthcare delivery and improving patient outcomes.

Mining big graph data is an important problem in the graph mining research area. Although cloud computing is effective at solving traditional algorithm problems, mining frequent patterns of a massive graph with cloud computing still faces the three challenges: 1) the graph partition problem, 2) asymmetry of information, and 3) pattern-preservation merging. Therefore, this paper presents a new approach, the cloud-based SpiderMine (c-SpiderMine), which exploits cloud computing to process the mining of large patterns on big graph data. The proposed method addresses the above issues for implementing a big graph data mining algorithm in the cloud. We conduct the experiments with three real data sets, and the experimental results demonstrate that c-SpiderMine can significantly reduce execution time with high scalability in dealing with big data in the cloud.

The Industrial Revolution 4.0 era has changed the landscape of the world of manufacturing and technology with the integration of digital technology, automation and sophisticated data communications. The fashion industry in Indonesia is also not immune from the major impact of these changes, especially in marketing decision making. Big data analysis is crucial in optimizing marketing strategies by understanding consumer preferences, market trends and competition. However, the use of big data also brings challenges, especially regarding the security and privacy of consumer data. This research adopts a qualitative method with a literature review approach to investigate the use of big data in marketing decision making in the Indonesian fashion industry. The research results show that big data plays an important role in customer segmentation, consumer behavior analysis, trend prediction, and product innovation. The benefits companies gain from implementing big data include deep understanding of consumer behavior, fast decision making, operational efficiency, better risk management, product and service innovation, improved customer experience, and dynamic price adjustments. However, the challenges faced include data security risks, personal identification risks, and compliance with global privacy regulations. Assessment of the readiness of Indonesian fashion industry companies to adopt big data involves evaluating technology infrastructure, data analysis skills, awareness of big data concepts, data security and privacy, understanding customer behavior, technology adoption, ability to process real-time data, budget availability, readiness for innovation, and acceptance of organizational culture. By considering these aspects, companies can evaluate the extent to which they are ready to integrate big data in marketing decision making to achieve success in the Industrial Revolution 4.0 era.

Based on big data information technology, this paper analyzes the opportunities and challenges of business development strategies. The clustering algorithm and tree model algorithm in data mining is analyzed. In order to effectively solve the problem of big data classification in consumer-oriented enterprises, the Kmeans clustering algorithm and XGBoost algorithm in the two previous models are integrated to effectively avoid the problem of over-fitting when the models are used alone. The opportunities and challenges in the current stage of business development strategy are analyzed separately. The Kmeans-XGBoost algorithm is used to analyze the pricing and output of the enterprise for prediction. It is shown that the prediction curves of the Kmeans-XGBoost model basically match the actual values, and the confidence interval range is expanding from [3694.879,7202.897] to [2211.819,8406.462]. Meanwhile, the errors of enterprise output prediction under different algorithms are analyzed. The error rate weighted by Kmeans-XGBoost mean is 42.63, which is lower than the traditional model prediction error in 4.

Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining and learning algorithms. Data mining algorithms are modified to accept the aggregated data as input. Hierarchical data aggregation serves as a paradigm under which novel data representations and algorithms work together for analysis and mining of big data. To evaluate its performance, we have implemented a multi-resolution Naive Bayes Classifier on the data aggregation structure. Experimental results show that the proposed structure helps the classifier to reduce computation time to 25% on average and reduce the memory usage while preserving the accuracy of results.

As the advance of the Internet of Things (IoT), more M2M sensors and devices are connected to the Internet. These sensors and devices generate sensor-based big data and bring new business opportunities and demands for creating and developing sensor-oriented big data infrastructures, platforms and analytics service applications. Big data sensing is becoming a new concept and next technology trend based on a connected sensor world because of IoT. It brings a strong impact on many sensor-oriented applications, including smart city, disaster control and monitor, healthcare services, and environment protection and climate change study. This paper is written as a tutorial paper by providing the informative concepts and taxonomy on big data sensing and services. The paper not only discusses the motivation, research scope, and features of big data sensing and services, but also exams the required services in big data sensing based on the state-of-the-art research work. Moreover, the paper discusses big data sensing challenges, issues, and needs.

Using the advantages of web crawlers in data collection and distributed storage technologies, we accessed to a wealth of forestry-related data. Combined with the mature big data technology at its present stage, Hadoop’s distributed system was selected to solve the storage problem of massive forestry big data and the memory-based Spark computing framework to realize real-time and fast processing of data. The forestry data contains a wealth of information, and mining this information is of great significance for guiding the development of forestry. We conducts co-word and cluster analyses on the keywords of forestry data, extracts the rules hidden in the data, analyzes the research hotspots more accurately, grasps the evolution trend of subject topics, and plays an important role in promoting the research and development of subject areas. The co-word analysis and clustering algorithm have important practical significance for the topic structure, research hotspot or development trend in the field of forestry research. Distributed storage framework and parallel computing have greatly improved the performance of data mining algorithms. Therefore, the forestry big data mining system by big data technology has important practical significance for promoting the development of intelligent forestry.