Question Paper Solutions of chapter Introduction to Data Mining of Data Warehousing and Data Mining, 3rd Semester , Master of Computer Applications (2 Years)

Question Paper Solutions of chapter Overview and Concepts of Data Warehousing of Data Warehousing & Data Mining, 7th Semester , Information Technology

Question Paper Solutions of chapter Overview of data mining and predictive analytics of Data Mining, 6th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

The Pharmaceutical Data Mining Solutions market has emerged as a pivotal player in enhancing decision-making processes within the pharmaceutical industry. With the increasing complexities of drug development, regulatory compliance, and market dynamics, companies now rely heavily on sophisticated data mining solution

The Data Mining Market is Segmented by Component (Tools [ETL and Data Preparation, Data-Mining Workbench, and More], Services [Professional Services, and More]), End-User Enterprise Size (Small and Medium Enterprises, Large Enterprises), Deployment (Cloud, On-Premise), End-User Industry (BFSI, IT and Telecom, Government and Defence, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Question Paper Solutions of chapter Introduction to Data Warehousing of Data Warehousing and Data Mining, 3rd Semester , Master of Computer Applications (2 Years)

Global Predictive Analytics Market size worth at USD 16.19 Billion in 2023 and projected to USD 113.8 Billion by 2032, with a CAGR of around 24.19% between 2024-2032.

Data Analysis is the process that supports decision-making and informs arguments in empirical studies. Descriptive statistics, Exploratory Data Analysis (EDA), and Confirmatory Data Analysis (CDA) are the approaches that compose Data Analysis (Xia & Gong; 2014). An Exploratory Data Analysis (EDA) comprises a set of statistical and data mining procedures to describe data. We ran EDA to provide statistical facts and inform conclusions. The mined facts allow attaining arguments that would influence the Systematic Literature Review of DL4SE.

The Systematic Literature Review of DL4SE requires formal statistical modeling to refine the answers for the proposed research questions and formulate new hypotheses to be addressed in the future. Hence, we introduce DL4SE-DA, a set of statistical processes and data mining pipelines that uncover hidden relationships among Deep Learning reported literature in Software Engineering. Such hidden relationships are collected and analyzed to illustrate the state-of-the-art of DL techniques employed in the software engineering context.

Our DL4SE-DA is a simplified version of the classical Knowledge Discovery in Databases, or KDD (Fayyad, et al; 1996). The KDD process extracts knowledge from a DL4SE structured database. This structured database was the product of multiple iterations of data gathering and collection from the inspected literature. The KDD involves five stages:

Selection. This stage was led by the taxonomy process explained in section xx of the paper. After collecting all the papers and creating the taxonomies, we organize the data into 35 features or attributes that you find in the repository. In fact, we manually engineered features from the DL4SE papers. Some of the features are venue, year published, type of paper, metrics, data-scale, type of tuning, learning algorithm, SE data, and so on.

Preprocessing. The preprocessing applied was transforming the features into the correct type (nominal), removing outliers (papers that do not belong to the DL4SE), and re-inspecting the papers to extract missing information produced by the normalization process. For instance, we normalize the feature “metrics” into “MRR”, “ROC or AUC”, “BLEU Score”, “Accuracy”, “Precision”, “Recall”, “F1 Measure”, and “Other Metrics”. “Other Metrics” refers to unconventional metrics found during the extraction. Similarly, the same normalization was applied to other features like “SE Data” and “Reproducibility Types”. This separation into more detailed classes contributes to a better understanding and classification of the paper by the data mining tasks or methods.

Transformation. In this stage, we omitted to use any data transformation method except for the clustering analysis. We performed a Principal Component Analysis to reduce 35 features into 2 components for visualization purposes. Furthermore, PCA also allowed us to identify the number of clusters that exhibit the maximum reduction in variance. In other words, it helped us to identify the number of clusters to be used when tuning the explainable models.

Data Mining. In this stage, we used three distinct data mining tasks: Correlation Analysis, Association Rule Learning, and Clustering. We decided that the goal of the KDD process should be oriented to uncover hidden relationships on the extracted features (Correlations and Association Rules) and to categorize the DL4SE papers for a better segmentation of the state-of-the-art (Clustering). A clear explanation is provided in the subsection “Data Mining Tasks for the SLR od DL4SE”. 5.Interpretation/Evaluation. We used the Knowledge Discover to automatically find patterns in our papers that resemble “actionable knowledge”. This actionable knowledge was generated by conducting a reasoning process on the data mining outcomes. This reasoning process produces an argument support analysis (see this link).

We used RapidMiner as our software tool to conduct the data analysis. The procedures and pipelines were published in our repository.

Overview of the most meaningful Association Rules. Rectangles are both Premises and Conclusions. An arrow connecting a Premise with a Conclusion implies that given some premise, the conclusion is associated. E.g., Given that an author used Supervised Learning, we can conclude that their approach is irreproducible with a certain Support and Confidence.

Support = Number of occurrences this statement is true divided by the amount of statements Confidence = The support of the statement divided by the number of occurrences of the premise

Results after applying nonparametric bootstrapping. Costs as of 20.03.2014 (1€ = 34,000 rials).

Average costs of outpatient services.

The Global Composite AI Market Size Was Worth USD 1,300 Million in 2023 and Is Expected To Reach USD 6460 Million by 2032, CAGR of 19.5%.

Global deep learning market worth at USD 2.74 Billion in 2024, is expected to surpass USD 85.99 Billion by 2034, with a CAGR of 41.3% from 2025 to 2034

Raise Boring Rig Market Outlook

According to our latest research, the global raise boring rig market size reached USD 690 million in 2024, registering a robust growth trajectory. The market is projected to expand at a CAGR of 5.2% from 2025 to 2033, culminating in a forecasted market size of USD 1,087 million by 2033. This impressive growth is driven by the increasing adoption of mechanized tunneling solutions, expanding mining and infrastructure projects, and the growing need for efficient and safe vertical shaft construction across diverse industries.

The growth of the raise boring rig market is strongly influenced by the rapid expansion of the global mining sector, particularly in regions rich in mineral resources. As the demand for minerals and metals continues to surge, mining companies are under pressure to optimize their operations and improve safety standards. Raise boring rigs are gaining traction as they offer significant advantages over traditional drilling and blasting methods, such as reduced environmental impact, enhanced worker safety, and improved operational efficiency. Additionally, the increasing focus on deep mining activities and the exploration of previously inaccessible ore bodies are further propelling the adoption of advanced raise boring technologies.

Another critical growth factor for the raise boring rig market is the boom in infrastructure development, especially in emerging economies. The construction of tunnels for transportation, hydroelectric power plants, and utility passages necessitates reliable and precise shaft boring solutions. Governments and private sector players are investing heavily in upgrading infrastructure, which includes the construction of subways, underground utilities, and hydroelectric projects. These applications require robust and efficient raise boring rigs, further stimulating market demand. The trend towards urbanization and the need for sustainable energy sources, such as hydropower, are also contributing to the market's expansion.

Technological advancements and the integration of automation in raise boring rigs are also pivotal in driving market growth. Manufacturers are focusing on developing rigs with enhanced operational capabilities, remote monitoring features, and improved safety mechanisms. The introduction of data analytics and IoT-enabled rigs allows for real-time performance monitoring, predictive maintenance, and optimized drilling processes. Such innovations not only improve the productivity of raise boring operations but also reduce downtime and operational costs, making them highly attractive to end-users in both mining and construction sectors.

From a regional perspective, the Asia Pacific region stands out as the dominant force in the raise boring rig market, accounting for the largest share in 2024. This dominance is attributed to the massive investments in mining and infrastructure projects across countries like China, India, and Australia. North America and Europe also hold significant market shares, driven by the presence of established mining industries, stringent safety regulations, and the adoption of advanced tunneling technologies. Meanwhile, Latin America and the Middle East & Africa regions are witnessing steady growth, fueled by increasing exploration activities and infrastructure modernization initiatives. The regional landscape is expected to evolve further as emerging markets continue to invest in large-scale mining and construction projects.

Type Analysis

The raise boring rig market is segmented by type into mechanized and conventional rigs, each catering to distinct operational requirements and project scales. Mechanized raise boring rigs have witnessed a surge in adoption due to their advanced automation, precision, and ability to handle complex shaft boring tasks with minimal human intervention. These rigs are particularly favored in large-scale mining and infrastructure projects where safety, speed, and efficiency are paramount. The mechanized segment is expected to experience the highest growth rate during the

Discover the booming global mining drilling services market projected to reach $70 billion by 2033. This in-depth analysis reveals key trends, growth drivers, regional insights, and leading companies shaping the future of mining exploration and resource extraction. Explore market segmentation, CAGR, and competitive landscape data for informed decision-making.

The global Data Historian market, valued at $1376.9 million in 2025, is projected to experience robust growth, driven by the increasing adoption of Industrial IoT (IIoT) and the rising demand for advanced data analytics across various sectors. The market's Compound Annual Growth Rate (CAGR) of 6.8% from 2025 to 2033 indicates a significant expansion opportunity. Key drivers include the need for improved operational efficiency, predictive maintenance capabilities, and regulatory compliance within industries like manufacturing, energy, and pharmaceuticals. The cloud-based deployment model is gaining traction due to its scalability, cost-effectiveness, and accessibility, fueling market growth. However, challenges such as data security concerns, high initial investment costs, and the complexity of integrating legacy systems with modern data historian solutions could potentially restrain market expansion. Significant regional variations are anticipated, with North America and Europe leading the market due to established industrial infrastructure and early adoption of advanced technologies. The diverse application segments—marine, chemicals and pharmaceuticals, paper and pulp, metals and mining, utilities, and data centers—each contribute significantly to the overall market size, reflecting the widespread applicability of Data Historian solutions. The competitive landscape is characterized by a mix of established players like ABB, Siemens, and Honeywell, alongside innovative startups offering niche solutions. The forecast period (2025-2033) suggests substantial market expansion, driven by ongoing technological advancements and the increasing digitization across diverse industries. The continued integration of Data Historians with advanced analytics platforms like AI and machine learning will further enhance their capabilities, providing deeper insights into operational data and enabling proactive decision-making. The growth in the adoption of digital twin technology is another key factor driving demand for data historian solutions. This allows companies to create virtual representations of their assets and processes, facilitating optimized operations and predictive maintenance strategies. Future market growth will also be shaped by the increasing availability of high-speed data networks and the growing adoption of edge computing, which enables real-time data processing and analysis at the source, reducing latency and improving responsiveness. Furthermore, the increasing focus on sustainability and environmental compliance across industries will drive demand for Data Historians in monitoring and optimizing energy consumption and emissions.

The global deep learning market size reached USD 30.9 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 423.4 Billion by 2033, exhibiting a growth rate (CAGR) of 29.92% during 2025-2033. North America currently dominates the market, holding a significant market share of over 36.5% in 2024. The increasing artificial intelligence (AI) adoption, advancements in data processing, the growing demand for image and speech recognition, investments in research and development (R&D), and the introduction of big data and cloud computing technologies are some of the major factors propelling the market.

Average costs of mastectomy and oophorectomy. (Costs as of 20.03.2014; 1€ = 34,000 rials).

According to Cognitive Market Research, the global Business Analytics market size is USD 66719.8 million in 2024 and will expand at a compound annual growth rate (CAGR) of 10.3% from 2024 to 2031. Market Dynamics of

Business Analytics Market

Key Drivers for

Business Analytics Market

Increasing Demand for Data-Driven Decision Making: Organizations are progressively depending on business analytics to transform raw data into actionable insights. This enhances strategic planning, customer targeting, and operational efficiency across various sectors.

Rising Adoption of Cloud-Based Analytics Solutions: Cloud platforms provide scalability, cost-effectiveness, and remote access to analytics tools. Companies are transitioning to cloud analytics for greater flexibility and quicker implementation.

Growing Use of Big Data Across Industries: The surge of data from digital platforms, IoT devices, and customer interactions is driving the adoption of analytics. Companies utilize these insights to comprehend trends, behaviors, and performance in real time.

Key Restraints for

Business Analytics Market

High Costs of Implementation and Maintenance: Establishing a business analytics infrastructure, which includes tools, skilled personnel, and integration, can be costly. This poses significant challenges for small and medium-sized enterprises.

Shortage of Skilled Professionals and Data Analysts: There is an increasing talent gap in advanced analytics, data science, and machine learning. A lack of expertise can impede effective analytics implementation and diminish return on investment.

Concerns Regarding Data Privacy and Security: Managing sensitive business and customer data raises issues about compliance with regulations such as GDPR and CCPA. Ensuring secure and ethical data usage remains a continual challenge.

Key Trends for

Business Analytics Market

Integration of Artificial Intelligence and Machine Learning: AI and ML are revolutionizing analytics by facilitating predictive modeling, anomaly detection, and automated insights. These technologies improve decision-making and provide a competitive edge.

Expansion of Self-Service Analytics Tools: User-friendly platforms are empowering non-technical users to run queries, generate reports, and visualize data independently. This democratization increases data literacy across organizations.

Real-Time and Embedded Analytics Capabilities: Businesses are embedding analytics directly into applications, dashboards, and workflows. Real-time data access enables quicker responses and more agile business strategies. Introduction of the Business Analytics Market

Business Analytics also referred to as experiencing rapid growth, is driven by the increasing importance of data-driven decision-making across industries. Organizations are leveraging business analytics to gain insights, enhance operational efficiency, and achieve competitive advantages. The market encompasses a wide range of solutions, including data mining, predictive analytics, and data visualization tools. Key factors fueling market expansion include the proliferation of big data, advancements in artificial intelligence and machine learning, and the growing adoption of cloud-based analytics. Additionally, the rise of IoT and the digital transformation of businesses are further propelling the demand for sophisticated analytics tools. However, challenges such as data privacy concerns and a shortage of skilled professionals may impede market growth.

The Industrial Analytics market is experiencing robust growth, projected to reach $32.60 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 16.92% from 2025 to 2033. This expansion is driven by several key factors. The increasing adoption of Industry 4.0 technologies, including the Internet of Things (IoT) and advanced sensors, generates massive amounts of data from industrial operations. Analyzing this data provides invaluable insights into optimizing processes, improving efficiency, reducing downtime, and enhancing predictive maintenance. Furthermore, the growing need for enhanced operational visibility and the pressure to improve resource allocation are strong drivers. The market is segmented across various deployment models (on-premises and cloud), components (software and services), analytics types (predictive, prescriptive, and descriptive), and end-user industries (construction, manufacturing, mining, transportation, and others). The cloud deployment model is witnessing faster growth due to its scalability, flexibility, and cost-effectiveness. Predictive analytics, offering the potential to anticipate equipment failures and optimize production schedules, is a particularly high-growth segment. The leading players in the Industrial Analytics market – including Cisco Systems, IBM, General Electric, Amazon Web Services, Oracle, and others – are continually innovating and expanding their offerings to meet the rising demand. Competition is fierce, driving continuous advancements in analytics capabilities and the development of user-friendly interfaces. While data security and integration challenges represent some restraints, the overall market outlook remains extremely positive. The North American market currently holds a significant share, driven by early adoption of Industry 4.0 technologies and strong investments in digital transformation initiatives. However, the Asia-Pacific region is expected to exhibit the fastest growth, fuelled by increasing industrialization and government support for digital initiatives. The continued maturation of AI and machine learning technologies will further accelerate market growth in the coming years, leading to more sophisticated and impactful analytics solutions. Recent developments include: November 2022-Fractal, a developer of artificial intelligence and advanced analytics solutions to Fortune 500 enterprises, announced the introduction of Asper.ai today. Asper.ai is a purpose-built linked AI solution for consumer goods, manufacturing, and retail, building on the company's existing AI capabilities., January 2022: Dunnhumby, the prominent player in Customer Data Science, announced a new strategic collaboration with SAP, the industry leader in business application software, to assist retailers in integrating sophisticated customer insights into their marketing and merchandising operations. The collaboration will enable businesses to make quicker, client-driven choices and provide a more individualized in-store and online shopping experience. As they prepare for the future of retail, retailers will be better able to transform customer data into unambiguous actions to streamline and improve routine business procedures.. Key drivers for this market are: Increasing Demand for Big-Data in Information Technology Sector, Rising Demand from the E-commerce Sector. Potential restraints include: Increasing Demand for Big-Data in Information Technology Sector, Rising Demand from the E-commerce Sector. Notable trends are: Manufacturing Sector to Dominate the Market Over the Forecast Period.

Age characteristics of study patients with HER2-positive BC.