This dataset is about books and is filtered where the book series is Advances in data mining and database management (ADMDM) book series, featuring 9 columns including author, BNB id, book, book publisher, and book series. The preview is ordered by publication date (descending).

Data Analytics Market Valuation – 2024-2031

Data Analytics Market was valued at USD 68.83 Billion in 2024 and is projected to reach USD 482.73 Billion by 2031, growing at a CAGR of 30.41% from 2024 to 2031.

Data Analytics Market Drivers

Data Explosion: The proliferation of digital devices and the internet has led to an exponential increase in data generation. Businesses are increasingly recognizing the value of harnessing this data to gain competitive insights.

Advancements in Technology: Advancements in data storage, processing power, and analytics tools have made it easier and more cost-effective for organizations to analyze large datasets.

Increased Business Demand: Businesses across various industries are seeking data-driven insights to improve decision-making, optimize operations, and enhance customer experiences.

Data Analytics Market Restraints

Data Quality and Integrity: Ensuring the accuracy, completeness, and consistency of data is crucial for effective analytics. Poor data quality can hinder insights and lead to erroneous conclusions.

Data Privacy and Security Concerns: As organizations collect and analyze sensitive data, concerns about data privacy and security are becoming increasingly important. Breaches can have significant financial and reputational consequences.

Smart Mining Solution Market size was valued at USD 20.88 Billion in 2024 and is projected to reach USD 64.74 Billion by 2031, growing at a CAGR of 16.76% from 2024 to 2031.

Global Smart Mining Solution Market Drivers

The market drivers for the Smart Mining Solution Market can be influenced by various factors. These may include:

Growing Demand for Operational Efficiency: The mining sector is under pressure to maximize resource usage, cut costs, and increase operational efficiency. The use of smart mining solutions, such as automation, Internet of Things (IoT) sensors, and real-time monitoring systems, is fueled by the ability of mining businesses to improve productivity, limit downtime, and streamline operations.
Growing Apprehensions About Health and Safety: Given the numerous risks and hazards that miners face, safety and health issues are still of the first importance. The industry’s safety concerns are addressed by smart mining solutions, which make use of technology like wearables, predictive analytics, and remote monitoring to improve safety protocols, reduce hazards, and guarantee legal compliance.
Growing Need for Sustainable Practices: Mining corporations are being forced to implement ecologically and socially responsible practices by sustainability programs, environmental restrictions, and community expectations. Energy optimization, water management, waste reduction, and emissions monitoring are made easier by smart mining technologies, which promote environmentally friendly mining practices and lessen the sector’s impact on the environment.
Increasing Attention to Digital Transformation: Technological, data analytics, and networking breakthroughs are driving a digital transformation in the mining sector. With real-time visibility, data-driven insights, and decision support tools for enhanced productivity, resource management, and performance optimization, smart mining systems facilitate the digitization of mining operations.
Depletion of High-Grade Mineral resources: More effective and sustainable mining techniques are required due to the depletion of high-grade mineral resources and the growing complexity of ore bodies. Smart mining solutions allow mining businesses to extract resources from difficult areas, extend mine life, and preserve profitability. Examples of these solutions include automated drilling, autonomous vehicles, and improved geological modeling.
Technological Developments in AI and Machine Learning: The creation of intelligent mining solutions with autonomous operations, predictive analytics, and predictive maintenance is made possible by developments in AI, machine learning, and data analytics. The mining industry is adopting these technologies because they maximize equipment performance, predict maintenance needs, and streamline production operations.
Remote and Tough Mining areas: There are operational hazards and logistical difficulties while conducting mining operations in remote and harsh areas. Smart mining solutions allow mining businesses to operate efficiently in difficult situations while guaranteeing the safety of staff and equipment. These solutions include autonomous vehicles, drone-based inspections, and remote monitoring and control capabilities.
Governmental initiatives, industry alliances, and industry collaborations all encourage the use of smart mining technologies and stimulate innovation in the mining industry. Mining businesses are encouraged to invest in technical breakthroughs and use smart mining solutions to increase sustainability and competitiveness through funding programs, regulatory incentives, and knowledge-sharing platforms.

The size and share of the market is categorized based on Type (Data extraction tools, Predictive analytics software, Text mining tools, Web mining tools, Data clustering tools) and Application (Customer insights, Market research, Trend analysis, Risk management, Pattern recognition) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

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Mining Automation Market Size 2024-2028

The mining automation market size is forecast to increase by USD 1.87 billion at a CAGR of 7.92% between 2023 and 2028.

The market is experiencing significant growth due to the expansion of the mining industry and the increasing adoption of mobile-based technologies. The mining sector's growth is driven by factors such as increasing demand for minerals and metals, rising investment in infrastructure, and advancements in mining techniques. In addition, the use of mobile-based technologies, including autonomous vehicles and drones, is becoming increasingly popular in mining operations to improve efficiency and productivity.
However, the market also faces challenges, particularly in the area of cybersecurity. With the increasing use of automation and digital technologies in mining, there is a growing risk of cyber attacks, which could result in significant financial and operational losses. Therefore, mining companies must prioritize cybersecurity measures to protect their assets and maintain the trust of their stakeholders. Overall, the market is expected to continue growing, driven by these trends and challenges.

What will be the Size of the Mining Automation Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing adoption of advanced technologies such as remote operations, mine planning software, predictive maintenance, data management, and digital mine transformation. These innovations enable increased safety in open pit and underground mining operations, reducing hazardous environments for workers. Robotics and autonomous equipment are key components of this trend, driving efficiency, cost reduction, and optimization of production levels. Sustainability is a critical focus area, with mining companies investing in sustainable practices, safety regulations, and workforce development. Mine safety training and governance are essential for ensuring compliance with evolving legislation.
Data analytics and digital mine transformation are essential for improving business strategies, enhancing mine site security, and minimizing environmental impact. Investment opportunities In the mining automation industry are abundant, with ongoing research and development leading to continuous innovation. The economic impact of these advancements is significant, as mining companies seek to stay competitive in a rapidly changing market. Overall, the market is poised for continued growth, with a strong emphasis on safety, optimization, and sustainability.

How is this Mining Automation Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Equipment
  Software
  Communication system


Type

  Underground mining automation
  Surface mining automation


Geography

  APAC

    China
    Japan


  North America

    US


  Europe

    Germany


  South America



  Middle East and Africa

By Component Insights

The equipment segment is estimated to witness significant growth during the forecast period. The market encompasses the use of advanced technologies, including artificial intelligence (AI), robotization, wireless sensors, RFID, data communication, and visualization tools, to automate mining operations. This market caters to various mining activities, such as base metals exploration and extraction, drilling in oil sands and underground mines, and waste management. Automated solutions employ autonomous technology to operate equipment, including trucks, drillers, and loaders, in real-time, enhancing production efficiency and safety. Safety integrity level is a crucial aspect, ensuring the safety of workers in hazardous conditions. Hardware automation technology, such as wireless networks and asset management strategies, streamlines operations and minimizes human error.

Mining automation technologies also facilitate predictive maintenance and resource extraction through the integration of IoT and data analytics. Key mining sectors include coal, metals, and mineral processing, with applications in drilling, material handling, and materials processing. Safety standards are paramount, addressing equipment failures and hazardous working conditions.

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The equipment segment was valued at USD 1.27 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 42% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forec

Fleet Management Tool For Mining Market size was valued at USD 3.5 Billion in 2023 and is projected to reach USD 6.8 Billion by 2031, growing at a CAGR of 9.5% during the forecasted period 2024 to 2031.
Global Fleet Management Tool For Mining Market Drivers
The market drivers for the Fleet Management Tool For Mining Market can be influenced by various factors. These may include:

• Increased Demand for Operational Efficiency: Mining companies are seeking to improve efficiency and productivity in their operations. Fleet management tools help optimize fleet performance, reduce downtime, and ensure timely maintenance, leading to cost savings and improved operational efficiency.
• Technological Advancements: The development of advanced technologies such as IoT, GPS, and real-time data analytics has significantly enhanced fleet management capabilities. These technologies enable better tracking, monitoring, and management of mining fleets, driving the adoption of fleet management tools.

Global Fleet Management Tool For Mining Market Restraints
Several factors can act as restraints or challenges for the Fleet Management Tool For Mining Market. These may include:

• High Initial Investment: The cost of implementing advanced fleet management tools can be significant, including expenses for software, hardware, and integration with existing systems. This high upfront investment may deter smaller mining companies from adopting these technologies.
• Complexity of Integration: Integrating fleet management tools with existing mining operations and equipment can be complex and time-consuming. This complexity may lead to resistance from companies accustomed to their current systems.

Data mining and analytics in healthcare management : applications and tools is a book. It was written by David L. Olson and published by : Springer in 2023.

This chapter presents theoretical and practical aspects associated to the implementation of a combined model-based/data-driven approach for failure prognostics based on particle filtering algorithms, in which the current esti- mate of the state PDF is used to determine the operating condition of the system and predict the progression of a fault indicator, given a dynamic state model and a set of process measurements. In this approach, the task of es- timating the current value of the fault indicator, as well as other important changing parameters in the environment, involves two basic steps: the predic- tion step, based on the process model, and an update step, which incorporates the new measurement into the a priori state estimate. This framework allows to estimate of the probability of failure at future time instants (RUL PDF) in real-time, providing information about time-to- failure (TTF) expectations, statistical confidence intervals, long-term predic- tions; using for this purpose empirical knowledge about critical conditions for the system (also referred to as the hazard zones). This information is of paramount significance for the improvement of the system reliability and cost-effective operation of critical assets, as it has been shown in a case study where feedback correction strategies (based on uncertainty measures) have been implemented to lengthen the RUL of a rotorcraft transmission system with propagating fatigue cracks on a critical component. Although the feed- back loop is implemented using simple linear relationships, it is helpful to provide a quick insight into the manner that the system reacts to changes on its input signals, in terms of its predicted RUL. The method is able to manage non-Gaussian pdf’s since it includes concepts such as nonlinear state estimation and confidence intervals in its formulation. Real data from a fault seeded test showed that the proposed framework was able to anticipate modifications on the system input to lengthen its RUL. Results of this test indicate that the method was able to successfully suggest the correction that the system required. In this sense, future work will be focused on the development and testing of similar strategies using different input-output uncertainty metrics.

We discuss a statistical framework that underlies envelope detection schemes as well as dynamical models based on Hidden Markov Models (HMM) that can encompass both discrete and continuous sensor measurements for use in Integrated System Health Management (ISHM) applications. The HMM allows for the rapid assimilation, analysis, and discovery of system anomalies. We motivate our work with a discussion of an aviation problem where the identification of anomalous sequences is essential for safety reasons. The data in this application are discrete and continuous sensor measurements and can be dealt with seamlessly using the methods described here to discover anomalous flights. We specifically treat the problem of discovering anomalous features in the time series that may be hidden from the sensor suite and compare those methods to standard envelope detection methods on test data designed to accentuate the differences between the two methods. Identification of these hidden anomalies is crucial to building stable, reusable, and cost-efficient systems. We also discuss a data mining framework for the analysis and discovery of anomalies in high-dimensional time series of sensor measurements that would be found in an ISHM system. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.

NASA has some of the largest and most complex data sources in the world, with data sources ranging from the earth sciences, space sciences, and massive distributed engineering data sets from commercial aircraft and spacecraft. This talk will discuss some of the issues and algorithms developed to analyze and discover patterns in these data sets. We will also provide an overview of a large research program in Integrated Vehicle Health Management. The goal of this program is to develop advanced technologies to automatically detect, diagnose, predict, and mitigate adverse events during the flight of an aircraft. A case study will be presented on a recent data mining analysis performed to support the Flight Readiness Review of the Space Shuttle Mission STS-119.

The global smart mining technology market is experiencing robust growth, driven by the increasing need for enhanced efficiency, safety, and sustainability in mining operations. The market size in 2025 is estimated at $4,117.4 million. While the exact CAGR is not provided, considering the adoption of advanced technologies like AI/ML, blockchain, and IoT in the mining sector, a conservative estimate of the Compound Annual Growth Rate (CAGR) for the forecast period (2025-2033) would be around 8-10%. This growth is fueled by several key factors. Firstly, the rising demand for minerals and metals, coupled with depleting reserves, necessitates optimized extraction and processing methods. Secondly, increasing regulatory pressure on environmental impact and safety protocols is pushing mining companies to adopt smart technologies for emissions management and risk mitigation. Finally, the integration of AI/ML in supply chain management offers significant opportunities for cost reduction and improved resource allocation. The market is segmented by technology (AI/ML-enabled Supply Chain Management, Mining Analytics Platforms, Blockchain-based Metal Trading Platforms, Emissions Management Software, and Others) and application (Risk & Compliance Management, Mining Operations & Process Control, Mining Data Warehousing, and Others). Major players like Rockwell Automation, Caterpillar Inc., and IBM are actively investing in developing and deploying these technologies, further contributing to market expansion. The continued technological advancements in areas such as sensor technology, data analytics, and automation will be crucial drivers for future growth. The adoption of cloud-based solutions and the increasing connectivity of mining equipment are expected to enhance data accessibility and facilitate real-time decision-making. However, challenges like high initial investment costs, data security concerns, and the need for skilled personnel to operate and maintain these advanced systems might hinder market growth to some extent. Despite these challenges, the long-term outlook for the smart mining technology market remains exceptionally positive, driven by the industry's imperative for increased efficiency, sustainability, and profitability. The continuous development and integration of innovative technologies will propel the market towards significant expansion in the coming years.

The global Mine Management System (MMS) market is experiencing robust growth, projected to reach a market size of $450.6 million in 2025. While the CAGR isn't provided, considering the technological advancements driving automation and optimization in mining operations, a conservative estimate of 8-10% CAGR for the forecast period (2025-2033) is reasonable. This growth is fueled by several key drivers. Increased demand for enhanced safety, productivity, and efficiency in mining operations is prompting wider adoption of MMS solutions. The integration of technologies such as IoT, AI, and machine learning is enabling real-time data analysis, predictive maintenance, and optimized resource allocation, significantly improving operational effectiveness. Furthermore, stringent regulatory requirements related to environmental protection and worker safety are compelling mining companies to invest in advanced MMS technologies. The market is segmented by type (Fleet Management, Blasting Management, Production Optimization, Other) and application (Metal Mine, Coal Mine, Others), with Fleet Management and Metal Mine segments currently dominating. Companies like ABB, Hexagon AB, and Rockwell Automation are leading players, continually innovating to offer comprehensive and integrated solutions. However, high initial investment costs and the need for skilled personnel to implement and manage these systems remain potential restraints. Looking forward, the MMS market is poised for continued expansion. The rising adoption of cloud-based solutions is expected to streamline data management and improve accessibility. Furthermore, the increasing focus on sustainable mining practices will drive demand for MMS solutions capable of optimizing resource utilization and minimizing environmental impact. Geographic expansion, particularly in emerging economies with significant mining activities, presents another significant growth opportunity. The market is likely to witness increased competition and strategic partnerships among vendors as they strive to consolidate market share and cater to the evolving needs of the mining industry. The integration of advanced analytics and automation technologies within MMS is likely to further enhance their capabilities and drive market expansion beyond 2033.

Mining Software Market size was valued at USD 10.9 Billion in 2024 and is projected to reach USD 20.7 Billion by 2032, growing at a CAGR of 8.3% from 2025 to 2032.

Global Mining Software Market Drivers

Growing Mining Industry Digitalization: The growing digitalization of the mining industry is a major driver of the mining software market. The World Economic Forum believes that digital transformation in mining could provide $425 billion in value by 2025, while ICMM claims that 75% of mining businesses boosted digital investments in 2023, primarily in mining software. This spike is being driven by the demand for automation, AI-powered analytics, IoT integration, and sustainability solutions, which will help businesses improve efficiency, cut costs, and improve safety.

Autonomous Mining Operations: The mining software market is being driven by a shift towards autonomous mining operations. Autonomous haulage systems, which are monitored by specialized software, have enhanced production by 35% in active mining sites. In Australia, 86% of major mining enterprises want to deploy or extend autonomous systems by 2025 (Australian Government). This increased use drives up demand for AI-powered fleet management, predictive maintenance, and real-time analytics software, which improves efficiency, safety, and cost savings.

Real-time Data Analytics and Production Optimization: Real-time data analytics and production optimization are significant drivers in the Mining Software Market. The demand for real-time analytics is driving mining software usage, since it improves decision-making and efficiency. According to the USGS, miners using sophisticated analytics software have increased resource recovery rates by 23% when compared to traditional approaches. Canadian Mining Innovation Council claims that predictive maintenance software has decreased equipment downtime by 35% and maintenance expenses by 28%, making operations more cost-effective and dependable.

The global Mine Management System (MMS) market is experiencing robust growth, projected to reach $335.4 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 4.3% from 2025 to 2033. This expansion is driven by several key factors. Increasing demand for enhanced operational efficiency and safety within mining operations is a primary driver. The integration of advanced technologies such as AI, IoT, and cloud computing is revolutionizing mine management, leading to improved productivity, reduced operational costs, and minimized environmental impact. Furthermore, stringent government regulations concerning mine safety and environmental sustainability are pushing mining companies to adopt sophisticated MMS solutions. The market is segmented by application (metal mines, coal mines, and others) and type (fleet management, blasting management, and production optimization). Metal mines currently dominate the application segment due to higher investments in technology and automation. Fleet management is the leading type segment due to the critical need for real-time tracking and optimization of mining equipment. Growth across regions is expected to vary, with North America and Asia Pacific projected as leading markets due to extensive mining activities and substantial technological advancements in these regions. The competitive landscape is characterized by a mix of established players like ABB, Hexagon AB, and Rockwell Automation, alongside specialized technology providers such as Insig Technologies and Zyfra OpenMine. These companies are focusing on developing innovative solutions tailored to specific mining needs, fostering collaborations, and expanding their global reach through strategic partnerships and acquisitions. Future growth will likely be influenced by the adoption of autonomous mining technologies, improved data analytics capabilities, and the integration of MMS with other enterprise resource planning systems. The continuous evolution of technologies such as advanced sensor networks, predictive maintenance, and digital twin modeling will significantly impact the market trajectory over the forecast period. Continued advancements in data security and cyber resilience will also be crucial in shaping the market's future.

Question Paper Solutions of chapter Database Management Systems (DBMS) of Management Information System, 2nd Semester , Master of Business Administration (2023-24)

This real-life event log contains events of sepsis cases from a hospital. Sepsis is a life threatening condition typically caused by an infection. One case represents the pathway through the hospital. The events were recorded by the ERP (Enterprise Resource Planning) system of the hospital. There are about 1000 cases with in total 15,000 events that were recorded for 16 different activities. Moreover, 39 data attributes are recorded, e.g., the group responsible for the activity, the results of tests and information from checklists. Events and attribute values have been anonymized. The time stamps of events have been randomized, but the time between events within a trace has not been altered.

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 organization and data mining represents one of the main challenges for modern high throughput technologies in pharmaceutical chemistry and medical chemistry. The presented open source documentation and analysis system provides an integrated solution (tutorial, setup protocol, sources, executables) aimed at substituting the traditionally used lab-book. The data management solution provided incorporates detailed information about the processing of the gels and the experimental conditions used and includes basic data analysis facilities which can be easily extended. The sample database and User-Interface are available free of charge under the GNU license from http://webber.physik.uni-freiburg.de/∼fallerd/tutorial.htm.

The market for Mine Management Information Systems (MMIS) is projected to grow from XXX million in 2025 to XXX million by 2033, at a CAGR of XX%. This growth is attributed to the increasing demand for efficient and cost-effective mining operations, coupled with the growing adoption of digital technologies in the mining industry. Key market drivers include the need to improve productivity, enhance safety, and optimize resource utilization. The MMIS market is segmented by type, application, and region. By type, the market is classified into underlying data type, safety investigation type, and statistical analysis type. By application, the market is divided into mining, smelting, and others. By region, the market is segmented into North America, South America, Europe, Middle East & Africa, and Asia Pacific. Major players in the MMIS market include CSM Technologies, DHC Software, Pulse Mining Systems, AspenTech, Huawei, Lantrack, Longruan Technology, Mingchuang Huiyuan Technology, Siyuan Technology, Taohuadao Information Technology, and others. These companies offer a range of MMIS solutions to meet the specific needs of mining operations. The competitive landscape of the MMIS market is expected to remain fragmented, with key players focusing on innovation and partnerships to gain market share. Strategic alliances, mergers, and acquisitions are likely to shape the future of the MMIS market as companies seek to expand their product offerings and geographical reach.

The company which provided the dataset is the world leader in manufacturing of construction and mining equipment, diesel and natural gas engines, industrial gas turbines and diesel-electric locomotives. The current revenue of the company is estimated to be on the order of tens of billions and they sell products and parts via a worldwide dealer network. The company sells more than 3 million products and 700,000 parts in more than 20 countries around the world every year. They operate with more than 3,000 suppliers and 3,000 dealerships and their logistics operations alone are worth more than 60 million dollars per year. The dataset provided is one example of supply chain problem for one product of the company - a medium size excavator. In the current dataset, the number of dealers, production facilities and shipping ports is the same as in the original problem; it is only the demand figures, the production capacities, the transportation times and costs and the sale prices that have been randomly generated. The figures have been randomly generated in an interval between 0 and an upper limit which is a random increase over the maximum value in the original data, according to a negative exponential distribution.