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Internet Of Things (Iot) Data Management Market Size 2024-2028

The internet of things (iot) data management market size is valued to increase USD 90.3 billion, at a CAGR of 15.72% from 2023 to 2028. Growth in industrial automation will drive the internet of things (iot) data management market.

Major Market Trends & Insights

North America dominated the market and accounted for a 35% growth during the forecast period.
By Component - Solutions segment was valued at USD 34.60 billion in 2022
By Deployment - Private/hybrid segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 301.61 billion
Market Future Opportunities: USD 90.30 billion
CAGR from 2023 to 2028 : 15.72%

Market Summary

The market is a dynamic and evolving landscape, driven by the increasing adoption of IoT technologies in various industries. Core technologies, such as edge computing and machine learning, are enabling the collection, processing, and analysis of vast amounts of data generated by interconnected devices. This data is fueling innovative applications, from predictive maintenance in manufacturing to real-time supply chain optimization. However, managing IoT data effectively remains a challenge for many organizations. A recent survey revealed that over 50% of companies struggle with efficiently managing their IoT initiatives and investments. Despite this, the market continues to grow, with industrial automation being a significant driver. In fact, it's estimated that by 2025, over 50% of industrial companies will have implemented IoT solutions for predictive maintenance. Regulations, such as GDPR and HIPAA, also play a crucial role in shaping the market. Regional differences in regulatory frameworks and data privacy laws add complexity to the market landscape. As the IoT Data Management Market continues to unfold, stakeholders must stay informed about the latest trends, technologies, and regulations to remain competitive.

What will be the Size of the Internet Of Things (Iot) Data Management Market during the forecast period?

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How is the Internet Of Things (Iot) Data Management Market Segmented ?

The internet of things (iot) data management 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. ComponentSolutionsServicesDeploymentPrivate/hybridPublicGeographyNorth AmericaUSCanadaEuropeGermanyUKAPACChinaRest of World (ROW)

By Component Insights

The solutions segment is estimated to witness significant growth during the forecast period.

In the dynamic and expanding IoT data management market, software solutions, encompassing both software and hardware offerings, hold a significant market share. This dominance is driven by the increasing globalization and IT expansion of industries, particularly in emerging economies like China, India, Brazil, Indonesia, and Mexico. The surge in SMEs in these regions necessitates business-centric insights, leading to a rising demand for software-based IoT data management solutions. companies catering to the global IoT data management market offer software tools to various end-user industries. These solutions facilitate data collection and analysis, enabling organizations to derive valuable insights from their operations. Metadata management systems, data modeling techniques, and IoT device integration are integral components of these software solutions. Edge computing deployments, data versioning strategies, and data visualization dashboards further enhance their functionality. Compliance regulations adherence, time series databases, data streaming technologies, data mining procedures, data cleansing techniques, data aggregation platforms, machine learning algorithms, remote data acquisition, data transformation pipelines, data quality monitoring, data lifecycle management, data encryption methods, predictive maintenance models, and IoT sensor networks are essential features of advanced software solutions. Data warehousing techniques, real-time data processing, access control mechanisms, data schema design, deep learning applications, scalable data infrastructure, NoSQL database systems, security protocols implementation, anomaly detection algorithms, data governance frameworks, API integration methods, and network bandwidth optimization are additional capabilities that add value to these offerings. Statistical modeling techniques play a crucial role in deriving actionable insights from the vast amounts of data generated by IoT devices. By 2026, it is projected that the market for public IoT data management solutions will grow by approximately 25%, as organizations increasingly recognize the

Vector Database for Time‑Series IoT Market Outlook

According to our latest research, the global Vector Database for Time-Series IoT market size in 2024 stands at USD 1.65 billion. The market is experiencing robust expansion, driven by the increasing adoption of IoT devices and the need for efficient real-time data processing. The market is projected to grow at a CAGR of 20.8% during the forecast period of 2025 to 2033, reaching an estimated USD 10.95 billion by 2033. Key growth factors include the proliferation of connected devices, advancements in edge computing, and the critical requirement for high-performance databases that can handle massive volumes of time-series data generated by IoT ecosystems.

One of the primary drivers propelling the growth of the Vector Database for Time-Series IoT market is the exponential rise in IoT deployments across diverse industries. With billions of sensors and devices now interconnected, organizations face unprecedented volumes of streaming data. Traditional relational databases often struggle with the velocity and variety of time-series data, leading to the adoption of vector databases specifically designed for such workloads. These databases offer high-speed ingestion, efficient storage, and rapid querying capabilities, making them indispensable for industries such as manufacturing, energy, and smart cities. Furthermore, the increasing complexity of IoT applications, such as predictive maintenance and anomaly detection, demands solutions that can not only store but also analyze data in real time, further fueling market growth.

Technological advancements in artificial intelligence (AI) and machine learning (ML) are also significantly influencing the evolution of the Vector Database for Time-Series IoT market. Modern vector databases are now being integrated with advanced analytics engines, enabling organizations to perform sophisticated analyses on time-series data streams. These integrations empower businesses to extract deeper insights, automate decision-making, and optimize operational efficiency. For example, in predictive maintenance applications, AI-driven vector databases can identify subtle patterns and predict equipment failures before they occur, minimizing downtime and reducing costs. The synergy between AI, ML, and vector databases is expected to remain a key growth catalyst throughout the forecast period.

Another crucial growth factor is the shift towards edge computing, which is transforming the way data is processed and analyzed in IoT environments. As more devices generate data at the edge, organizations require database solutions capable of operating in distributed and resource-constrained environments. Vector databases, with their ability to handle high-throughput time-series data and support real-time analytics at the edge, are becoming the preferred choice for next-generation IoT architectures. This trend is especially pronounced in sectors such as transportation, logistics, and utilities, where real-time decision-making is critical. The increasing demand for decentralized data processing and analytics is expected to drive further adoption of vector databases in the coming years.

From a regional perspective, North America currently holds the largest share of the Vector Database for Time-Series IoT market, driven by significant investments in IoT infrastructure, the presence of major technology vendors, and a strong focus on digital transformation across industries. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid industrialization, urbanization, and government initiatives to develop smart cities. Europe also demonstrates substantial growth potential, particularly in the manufacturing and energy sectors. The regional landscape is characterized by varying levels of IoT maturity and regulatory frameworks, influencing adoption rates and market dynamics in each geography.

Component Analysis

The Component</

The dataset is a compilation of information collected by a DHT sensor over nearly a year of storage. This information can be valuable for time series analysis and exploring correlations between temperature and humidity.

I plan to clean and enhance the dataset with more comprehensive information in the future. I hope it proves useful to anyone interested in IoT-related topics.

Vector Database for Time-Series IoT Market Outlook

According to our latest research, the Global Vector Database for Time-Series IoT market size was valued at $1.2 billion in 2024 and is projected to reach $7.8 billion by 2033, expanding at a robust CAGR of 23.1% during 2024–2033. The primary growth driver for this market is the exponential surge in connected IoT devices generating high-velocity time-series data, which demands scalable, high-performance vector databases for real-time analytics and decision-making. Organizations across industries are increasingly recognizing the value of time-series IoT data in driving operational efficiency, predictive maintenance, and intelligent automation, thus fueling adoption of advanced database solutions tailored for time-series workloads. Furthermore, advancements in AI and machine learning are amplifying the need for specialized vector databases capable of handling complex, multidimensional data streams, positioning this market for sustained global expansion.

Regional Outlook

North America currently dominates the Vector Database for Time-Series IoT market, accounting for the largest market share, estimated at 38% of the global revenue in 2024. This leadership is attributed to the region’s mature digital infrastructure, early adoption of IoT technologies, and significant investments in AI-driven analytics across sectors such as manufacturing, healthcare, and smart cities. The United States stands out as a key contributor, with major enterprises and cloud service providers integrating vector database solutions to enhance real-time data processing and predictive analytics capabilities. Regulatory frameworks supporting digital transformation, combined with a robust ecosystem of technology vendors and research institutions, further reinforce North America’s preeminence in this market. The presence of leading vector database providers and a strong focus on innovation continue to drive market penetration and technological advancements in the region.

The Asia Pacific region is projected to be the fastest-growing market for Vector Database for Time-Series IoT, with an anticipated CAGR of 27.2% from 2024 to 2033. This rapid growth is fueled by the accelerating adoption of IoT and smart manufacturing initiatives across China, Japan, South Korea, and India. Governments in these countries are actively promoting Industry 4.0 and smart city projects, leading to a surge in deployment of connected sensors and devices that generate vast volumes of time-series data. Increased investments from both public and private sectors, coupled with the expansion of cloud infrastructure and the proliferation of 5G networks, are catalyzing the demand for advanced vector database solutions. Additionally, the presence of a burgeoning startup ecosystem focused on industrial automation and AI-powered analytics is further propelling market growth in Asia Pacific.

Emerging economies in Latin America and the Middle East & Africa are gradually embracing Vector Database for Time-Series IoT technologies, though adoption remains challenged by infrastructure limitations, skills gaps, and budgetary constraints. In these regions, localized demand is primarily driven by the energy and utilities, transportation, and smart city sectors, which are beginning to realize the benefits of real-time analytics for asset management and anomaly detection. Policy reforms aimed at digital transformation and the gradual rollout of IoT-friendly regulations are expected to stimulate future growth. However, the pace of adoption is moderated by the need for capacity building, technology transfer, and the development of localized solutions tailored to region-specific requirements. As international vendors and local players collaborate to address these challenges, these regions are poised for incremental but steady market expansion.

Report Scope

The IoT Data Management Market Report is Segmented by Solution (Integration, Migration, Analytics, and More), Deployment Model (Cloud, On-Premise, Hybrid), Data Type (Structured, Semi-Structured, Unstructured, Time-Series), End-User Industry (Agriculture, BFSI, and More), Application (Predictive Maintenance, Smart Metering, Smart Grid Analytics, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

The global Cloud-Based Time Series Database market is poised for substantial growth, projected to reach an estimated USD 12,500 million by 2025 and expand at a Compound Annual Growth Rate (CAGR) of 22% through 2033. This robust expansion is primarily fueled by the escalating demand for real-time data analytics across diverse industries. Key drivers include the proliferation of IoT devices generating massive volumes of time-stamped data, the increasing adoption of cloud infrastructure for scalability and cost-efficiency, and the critical need for efficient data management and analysis in sectors like BFSI, manufacturing, and telecommunications. The ability of cloud-based time series databases to ingest, store, and query vast amounts of temporal data at high velocity makes them indispensable for applications such as predictive maintenance, anomaly detection, and performance monitoring. The market is further stimulated by advancements in database technologies, offering enhanced query performance, data compression, and integration capabilities with other cloud services. The market landscape is characterized by a dynamic interplay of public, private, and hybrid cloud models, with hybrid cloud solutions gaining traction due to their flexibility and ability to address specific data governance and security requirements. Major players like Amazon (AWS), Microsoft, Google, and IBM are heavily investing in R&D to offer sophisticated, feature-rich time series database solutions, driving innovation and competition. Emerging trends include the integration of AI and machine learning for advanced analytics on time-series data, the development of specialized time series databases optimized for specific workloads, and a growing emphasis on data security and compliance. While the market benefits from strong growth drivers, potential restraints such as data migration complexities, vendor lock-in concerns, and the need for skilled personnel to manage and operate these systems will require strategic consideration by market participants. The Asia Pacific region, led by China and India, is expected to witness the fastest growth, driven by rapid industrialization and digital transformation initiatives. Here is a unique report description on Cloud-Based Time Series Databases, structured as requested:

binary classification (room occupancy) from Temperature,Humidity,Light and CO2. occupancy was obtained from time stamped pictures that were taken every minute.

source

Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Luis M. Candanedo, Véronique Feldheim. Energy and Buildings. Volume 112, 15 January 2016, Pages 28-39.

Time-Series Database for IoT Market Outlook

According to our latest research, the Global Time-Series Database for IoT market size was valued at $1.7 billion in 2024 and is projected to reach $8.3 billion by 2033, expanding at a robust CAGR of 19.2% during 2024–2033. The exponential increase in connected devices and the proliferation of IoT applications across sectors such as manufacturing, energy, healthcare, and smart cities are major drivers fueling this rapid growth. As organizations increasingly rely on real-time data insights for operational efficiency, the need for scalable and high-performance time-series databases becomes paramount, positioning this market for sustained expansion throughout the forecast period.

Regional Outlook

North America currently holds the largest share of the global Time-Series Database for IoT market, accounting for approximately 38% of the total market value in 2024. This dominance is attributed to the region’s mature IoT ecosystem, advanced cloud infrastructure, and high adoption rates of digital transformation initiatives across various industries. The presence of major technology players and a robust startup landscape further accelerate market penetration. Supportive government policies, such as the US’s push for smart manufacturing and infrastructure modernization, have also created a fertile environment for the deployment of advanced time-series database solutions. As a result, North America continues to set the pace in terms of innovation, deployment, and integration of IoT analytics platforms.

Asia Pacific is projected to be the fastest-growing region, with a forecasted CAGR of 23.5% from 2024 to 2033. Countries such as China, Japan, South Korea, and India are witnessing significant investments in industrial automation, smart city projects, and digital healthcare. The rapid expansion of 5G networks and the proliferation of affordable IoT devices have catalyzed market growth, enabling enterprises to leverage real-time data for predictive analytics and operational optimization. Government-led initiatives, like China’s “Made in China 2025” and India’s “Digital India,” are further driving the adoption of IoT technologies, fueling demand for scalable and efficient time-series databases. This surge in digital infrastructure investment is expected to continue propelling the region’s market share upward.

Emerging economies in Latin America and the Middle East & Africa are experiencing a gradual uptick in adoption, albeit with unique challenges. While digitalization efforts are underway, these regions face hurdles such as limited legacy infrastructure, skills shortages, and regulatory complexities. Nevertheless, the growing focus on smart city development, energy management, and supply chain optimization is creating localized demand for time-series database solutions. Policy reforms and public-private partnerships are beginning to address connectivity and data management gaps, paving the way for incremental market growth. As these markets mature, tailored solutions that address regional constraints and compliance requirements will be crucial for broader adoption.

Report Scope

By 2025, forecasts suggest that there will be more than ** billion Internet of Things (IoT) connected devices in use. This would be a nearly threefold increase from the IoT installed base in 2019. What is the Internet of Things? The IoT refers to a network of devices that are connected to the internet and can “communicate” with each other. Such devices include daily tech gadgets such as the smartphones and the wearables, smart home devices such as smart meters, as well as industrial devices like smart machines. These smart connected devices are able to gather, share, and analyze information and create actions accordingly. By 2023, global spending on IoT will reach *** trillion U.S. dollars. How does Internet of Things work? IoT devices make use of sensors and processors to collect and analyze data acquired from their environments. The data collected from the sensors will be shared by being sent to a gateway or to other IoT devices. It will then be either sent to and analyzed in the cloud or analyzed locally. By 2025, the data volume created by IoT connections is projected to reach a massive total of **** zettabytes. Privacy and security concerns   Given the amount of data generated by IoT devices, it is no wonder that data privacy and security are among the major concerns with regard to IoT adoption. Once devices are connected to the Internet, they become vulnerable to possible security breaches in the form of hacking, phishing, etc. Frequent data leaks from social media raise earnest concerns about information security standards in today’s world; were the IoT to become the next new reality, serious efforts to create strict security stands need to be prioritized.

This dataset represents the baseline benign and attack traffic for IoT (Internet of Things) consumer devices that may be representative of a smart-home network. The purpose of this dataset, in comparison to other IoT datasets, is to simplify the input data in terms of size and its ability to be interpreted under different scenarios.

A wireshark column template is provided to add extra columns of interest beyond the default view (view bottom right profile area in wireshark, right-click and "import" zip file below) - wiresharkprofile_template.zip

The dataset is provided in PCAP format (readable by Wireshark or other platforms) and is categorized as follows:

IoT SETUP (real network traffic patterns to represent setup exchanges for common IoT devices) - iot_setup_plug1_an.pcapng - iot_setup_bulb1_an.pcapng

IoT BENIGN IDLE (Network traffic associated with IoT devices on the network that are on, but only in a standby state) - all_idle_1Hrs_an.pcapng - all_idle_5Hrs_an.pcapng - all_idle_10Hrs_part1_an.pcapng - all_idle_10Hrs_part2_an.pcapng

IoT BENIGN ACTIVE (Network traffic associated with IoT devices on the network that are active and in use) - all_active_1Hrs_an.pcapng - all_active_5Hrs_an.pcapng - all_active_10Hrs_part1_an.pcapng - all_active_10Hrs_part2_an.pcapng

IoT ATTACK TRAFFIC (Kali Linux HPING3 from 192.168.100.240 attack machine using ICMP Floods and SYN Floods as Attacks for four (4) IoT device targets in use)
- ICMP flood of IoT Camera 1 (192.168.100.11) - two separate segments of flood attack within five minute session - ICMP flood of IoT EchoShow (192.168.100.21) - two separate segments of flood attack within five minute session - ICMP flood of IoT plug1 (192.168.100.31) - two separate segments of flood attack within five minute session - ICMP flood of IoT lightbulb1 (192.168.100.41) - two separate segments of flood attack within five minute session - SYN flood of IoT Camera 1 (192.168.100.11) - SYN flood of IoT EchoShow (192.168.100.21)
- SYN flood of IoT plug1 (192.168.100.31)
- SYN flood of IoT lightbulb1 (192.168.100.41)

This academic work is part of ongoing dissertation research at Colorado State University. All credit should reference the authors David Weissman (PhD Candidate) and Dr. Anura Jayasumana (Professor) - copyright (c) 2023-2024.

Datasets are subject to revisions or enhancements over time.

This web map gives access to all published data from the City of Fredericton IoT sensors. The features represent static locations for each of the sensors from different manufacturers. There are parking sensors (named Parking) and climate sensors (named Elsys, Decent Labs, and Sensus). With the IoT Sensors - All Data layer, selecting a feature shows basic attributes of the sensor and there is also the ability to show related records to gain access to the historical sensor information.Several views have been created off of this layer called IoT to

To whom intent the use of the Bot-IoT dataset, the authors have to cite the following papers that has the dataset’s details: .

Koroniotis, Nickolaos, Nour Moustafa, Elena Sitnikova, and Benjamin Turnbull. "Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset." Future Generation Computer Systems 100 (2019): 779-796. Public Access Here. Koroniotis, Nickolaos, Nour Moustafa, Elena Sitnikova, and Jill Slay. "Towards developing network forensic mechanism for botnet activities in the iot based on machine learning techniques." In International Conference on Mobile Networks and Management, pp. 30-44. Springer, Cham, 2017. Koroniotis, Nickolaos, Nour Moustafa, and Elena Sitnikova. "A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework." Future Generation Computer Systems 110 (2020): 91-106. Koroniotis, Nickolaos, and Nour Moustafa. "Enhancing network forensics with particle swarm and deep learning: The particle deep framework." arXiv preprint arXiv:2005.00722 (2020). Koroniotis, Nickolaos, Nour Moustafa, Francesco Schiliro, Praveen Gauravaram, and Helge Janicke. "A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports." IEEE Access (2020). Koroniotis, Nickolaos. "Designing an effective network forensic framework for the investigation of botnets in the Internet of Things." PhD diss., The University of New South Wales Australia, 2020.

DESCRIPTION: The authors constructed the BoT-IoT dataset by emulating a realistic network scenario in the Cyber Range Lab of UNSW Canberra. The scenario involved a mixture of normal and botnet traffic. They provide the source files of the dataset in csv. They also classified the files according to attack type and subtype for facilitating the labeling process.

The flow traffic extracted in a previous set of csvs that occupied 16.7 GB of data. The dataset comprised DDoS, DoS, OS and Service Scan, Keylogging and Data exfiltration attacks, with further categorization of DDoS and DoS attacks based on protocol.

For convenience, they then sampled 5% of the original dataset using MySQL queries. The sample consisted of 4 files with a total size of approximately 1.07 GB and about 3 million records that is provided here.

The booming IoT Data Management market is projected to reach [estimated market size in 2033] by 2033, exhibiting a robust CAGR of 16.58%. This comprehensive analysis explores key drivers, trends, and restraints, segmenting the market by solution, end-user, and region. Discover insights into leading companies and future growth opportunities within this rapidly evolving sector. Key drivers for this market are: , Proliferation of Connected Devices and Snowballing Growth in Data Volumes; Need for Data Security and Data Traffic Management. Potential restraints include: , Lack of End-to-end Solutions; Lack of Techniques that allow Seamless IT systems and Application Integration. Notable trends are: Security Solutions to Witness the Fastest Growth.

Dataset

This dataset was created by Bisma Sajjad

Released under CC0: Public Domain

Contents

Explore the rapidly expanding Real-time Database System market, valued at $20,000 million in 2025, driven by IoT, big data analytics, and edge computing. Discover key trends, drivers, restraints, and leading companies shaping the future of instant data processing.

This dataset presents the IoT network traffic generated by connected objects. In order to understand and characterise the legitimate behaviour of network traffic, a platform is created to generate IoT traffic under realistic conditions. This platform contains different IoT devices: voice assistants, smart cameras, connected printers, connected light bulbs, motion sensors, etc. Then, a set of interactions with these objects is performed to allow the generation of real traffic. This data is used to identify anomalies and intrusions using machine learning algorithms and to improve existing detection models. Our dataset is available in two formats: PCAP and csv and was created as part of the EU CEF Variot project https://variot.eu. To download the data in pcap format and for more information, our database is available on this web portal: https://www.variot.telecom-sudparis.eu/

The IoT Data Management market is booming, projected to reach $83.99B in 2025, with a 15.72% CAGR. Learn about key drivers, trends, and leading companies shaping this explosive sector. Discover regional market share and future growth projections in our comprehensive analysis.

The global Internet of Things (IoT) Data Management market is projected to reach a valuation of approximately USD 15 billion by 2033, growing at a robust compound annual growth rate (CAGR) of 18% from 2025 to 2033.

The dataset is designed to facilitate the development and evaluation of machine learning models for managing and distributing traditional cultural and educational resources, leveraging IoT data and deep learning techniques. The goal is to provide a rich set of features for exploring user interactions with cultural artifacts and historical sites, with the support of IoT-enabled sensors and virtual learning experiences.

Columns Overview: User_ID: A unique identifier for each user interacting with the system. This helps track individual behavior patterns and interactions.

Artifact_ID: The unique identifier for each artifact (e.g., a cultural artifact or historical item) that is part of the learning experience.

Artifact_Name: The name of the artifact. This could range from physical objects like sculptures, to digital representations of ancient items or sites.

Artifact_Type: A categorical field indicating the type of artifact, such as sculpture, musical instrument, religious artifact, etc.

Artifact_Location: The physical or virtual location of the artifact. This could be a museum, historical site, or an online platform.

Artifact_Historical_Significance: A textual description of the cultural and historical importance of the artifact. This can be used to provide more context for the artifact’s significance.

Digital_Representation_URL: A URL pointing to the digital representation of the artifact, which could be an image, video, or audio file related to the artifact.

Artifact_Tags: Tags that describe the artifact in terms of categories like "Ancient", "Religious", "Musical", etc. These can be used for classification or recommendations.

Site_ID: The unique identifier for each site (e.g., the location where a cultural artifact is located or a historical site).

Site_Name: The name of the site, such as "Great Wall of China" or "Colosseum".

Site_Location: The geographical coordinates (latitude and longitude) of the site.

Site_Cultural_Importance: A description of the cultural importance of the site.

Virtual_Tour_URL: A URL linking to a virtual tour of the site or artifact, offering an interactive learning experience.

IoT_Sensor_ID: The identifier of the IoT sensor collecting data from the artifact or site. These sensors track various environmental factors such as temperature, humidity, and motion.

IoT_Sensor_Type: The type of sensor used to collect data, such as "Temperature", "Humidity", "Motion", or "Light".

IoT_Sensor_Location: The location of the IoT sensor in relation to the artifact or site.

Sensor_Timestamp: The date and time when the sensor data was recorded.

Sensor_Reading: The actual value recorded by the IoT sensor. This could represent temperature, humidity, motion detection, or other environmental factors.

User_Interaction_Type: The type of interaction the user has with the artifact or site. This can include actions like "View", "Rate", or "Click".

User_Interaction_Timestamp: The timestamp of when the user interaction took place. This allows us to track the time of day and the frequency of interactions.

Interaction_Duration: The duration of the user’s interaction with the artifact or site, measured in minutes. This helps in analyzing user engagement levels.

User_Feedback: The feedback provided by the user after interacting with the artifact or site, such as "Liked", "Not Interested", or "Neutral".

Target_Column: The target variable for machine learning models. This column indicates whether the user had a positive interaction (1) or a negative interaction (0). A positive interaction is defined as a "view" action, while negative interactions include other actions like "rate" or "click".