Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Overcoming Challenges and Unlocking the Potential: Empowering Small and Medium Enterprises (SMEs) with Data Analytics Solutions

In today's data-driven business landscape, Data Analytics (DA) has emerged as a vital tool for organizations to extract insights from their existing data, enabling informed decision-making. While large enterprises have wholeheartedly embraced DA as a strategic asset for operational enhancement, SMEs have been comparatively slower in adopting these transformative solutions. To remain competitive and surpass their rivals, SMEs must recognize the significance of harnessing their data assets effectively to drive decision-making processes. This research aims to delve into the challenges hindering the adoption of DA among SMEs, particularly focusing on issues such as inadequate information infrastructure and limited awareness of the benefits that DA can offer. Furthermore, this study investigates the implementation of data analytics as a practical solution to address these challenges, providing a comprehensive analysis of both the advantages and disadvantages associated with DA adoption in the SME context. By shedding light on the untapped potential of data analytics, this research aims to empower SMEs and equip them with the necessary tools to thrive in today's digitally-driven era of business

Integrating IoT Analytics into Marketing Decision Making: A Smart Data-Driven Approach

With the advent of the Internet of Things (IoT), businesses have gained access to vast amounts of data generated by interconnected devices. Leveraging IoT analytics and marketing intelligence, organizations can extract valuable insights from this data to enhance decision-making processes. This paper presents a comprehensive methodology for data-driven decision-making in the context of IoT analytics and marketing intelligence. A real-time example is used to illustrate the application of this methodology, followed by an inference and discussion of the results. The rise of IoT has enabled real-time data collection from a wide array of interconnected devices, offering unprecedented opportunities for businesses to gain actionable insights. This paper focuses on the intersection of IoT analytics and marketing intelligence, exploring how data-driven decision-making can empower organizations to optimize their marketing strategies, customer experiences, and overall business performance

Machine Learning and AI in Business Intelligence: Trends and Opportunities

The integration of machine learning and artificial intelligence (AI) in business intelligence has brought forth a plethora of trends and opportunities. These cutting-edge technologies have revolutionized how businesses analyze data, gain insights, and make informed decisions. One prominent trend is the rise of predictive analytics. Machine learning algorithms can sift through vast amounts of historical data to identify patterns and trends, enabling businesses to make accurate predictions about future outcomes. This empowers organizations to optimize operations, anticipate customer needs, and mitigate risks.  By leveraging business intelligence, companies can uncover hidden patterns, identify opportunities for growth and improvement, optimize business processes, and ultimately make informed decisions that drive their success. Another trend is the adoption of AI-powered chatbots and virtual assistants. The opportunities presented by machine learning and AI in business intelligence are extensive. From automated data analysis and anomaly detection to demand forecasting and dynamic pricing, these technologies empower businesses to optimize processes, reduce costs, and identify new revenue streams. In conclusion, the integration of machine learning and AI in business intelligence offers promising trends and abundant opportunities. By leveraging these technologies, businesses can gain a competitive edge, drive innovation, and unlock new levels of success in the digital era

The Advantages of Artificial Intelligence in Operational Decision Making

This research paper explores the advantages of artificial intelligence (AI) in operational decision making, focusing on the analysis of production processes, supply chains, and resources. The research highlights several advantages of AI in operational decision making. It empowers organizations to make data-driven decisions, reducing reliance on human intuition and biases. AI technologies can process vast amounts of data in real-time, enabling timely decision-making and facilitating agile operations. Moreover, AI can learn from historical data and continuously improve decision-making processes, leading to enhanced performance over time. The research method employed in this study is utilizing literature review as the data collection method. The literature review involved searching for relevant theories and examining findings from previous researchers, which served as the foundation for developing the analysis to discuss the research outcomes. This research underscores the significant advantages of AI in operational decision making, specifically in the areas of production processes, supply chains, and resource management. By leveraging AI technologies, organizations can achieve improved efficiency, reduced costs, and better overall performance. The findings of this study contribute to a better understanding of the transformative potential of AI and encourage its adoption in various operational domains

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Development and implementation of the profitability risk module process

Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe main objective of this report is to outline the project carried out at Neyond, where the main goal was developing an automated E.T.L. reporting process for an international bank, one of the clients. This project played a crucial role in solidifying the knowledge gained and implementing the diverse techniques learned throughout the initial academic year. It also provided an opportunity to merge academic training with practical professional experience. This report provides an overview of the project’s goals, methodologies, tools, technologies used, and the challenges encountered during its execution. To accomplish this objective, this report describes the project and the main goals to achieve the desired result, the tools and technologies used, as well as some of the challenges and how they were surpassed

CGIAR Platform for Big Data in Agriculture - Plan of Work and Budget 2021

The CGIAR Platform for Big Data in Agriculture is a cross-cutting program of the global CGIAR consortium of non-profit research institutes looking into virtually every aspect of food security spanning: genomics, breeding, agroecology, climate science, and the socioeconomic drivers and context of food systems change. The Platform tends to data standards and data sharing, digital innovation strategy and technology transfer, and research into the intersection of digital technologies and agricultural development in emerging regions