The Data Mining Tools Market is expected to be valued at $1.24 billion in 2024, with an anticipated expansion at a CAGR of 11.63% to reach $3.73 billion by 2034.

In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system. Much of the system's functionality such as message routing, information retrieval and load sharing relies on modeling the global state. We refer to the outcome of the function (e.g., the load experienced by each peer) as the emph{model} of the system. Since the state of the system is constantly changing, it is necessary to keep the models up-to-date. Computing global data mining models e.g. decision trees, k-means clustering in large distributed systems may be very costly due to the scale of the system and due to communication cost, which may be high. The cost further increases in a dynamic scenario when the data changes rapidly. In this paper we describe a two step approach for dealing with these costs. First, we describe a highly efficient emph{local} algorithm which can be used to monitor a wide class of data mining models. Then, we use this algorithm as a feedback loop for the monitoring of complex functions of the data such as its k-means clustering. The theoretical claims are corroborated with a thorough experimental analysis.

The Data Mining Tools Market size is expected to reach a valuation of USD 3.33 billion in 2033 growing at a CAGR of 12.50%. The Data Mining Tools market research report classifies market by share, trend, demand, forecast and based on segmentation.

Lifesciences Data Mining and Visualization Market Outlook

The global market size for Lifesciences Data Mining and Visualization was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 4.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The growth of this market is driven by the increasing demand for sophisticated data analysis tools in the life sciences sector, advancements in analytical technologies, and the rising volume of complex biological data generated from research and clinical trials.

One of the primary growth factors for the Lifesciences Data Mining and Visualization market is the burgeoning amount of data generated from various life sciences applications, such as genomics, proteomics, and clinical trials. With the advent of high-throughput technologies, researchers and healthcare professionals are now capable of generating vast amounts of data, which necessitates the use of advanced data mining and visualization tools to derive actionable insights. These tools not only help in managing and interpreting large datasets but also in uncovering hidden patterns and relationships, thereby accelerating research and development processes.

Another significant driver is the increasing adoption of artificial intelligence (AI) and machine learning (ML) algorithms in the life sciences domain. These technologies have proven to be invaluable in enhancing data analysis capabilities, enabling more precise and predictive modeling of biological systems. By integrating AI and ML with data mining and visualization platforms, researchers can achieve higher accuracy in identifying potential drug targets, understanding disease mechanisms, and personalizing treatment plans. This trend is expected to continue, further propelling the market's growth.

Moreover, the rising emphasis on personalized medicine and the need for precision in healthcare is fueling the demand for data mining and visualization tools. Personalized medicine relies heavily on the analysis of individual genetic, proteomic, and metabolomic profiles to tailor treatments specifically to patients' unique characteristics. The ability to visualize these complex datasets in an understandable and actionable manner is critical for the successful implementation of personalized medicine strategies, thereby boosting the demand for advanced data analysis tools.

From a regional perspective, North America is anticipated to dominate the Lifesciences Data Mining and Visualization market, owing to the presence of a robust healthcare infrastructure, significant investments in research and development, and a high adoption rate of advanced technologies. The European market is also expected to witness substantial growth, driven by increasing government initiatives to support life sciences research and the presence of leading biopharmaceutical companies. The Asia Pacific region is projected to experience the fastest growth, attributed to the expanding healthcare sector, rising investments in biotechnology research, and the increasing adoption of data analytics solutions.

Component Analysis

The Lifesciences Data Mining and Visualization market is segmented by component into software and services. The software segment is expected to hold a significant share of the market, driven by the continuous advancements in data mining algorithms and visualization techniques. Software solutions are critical in processing large volumes of complex biological data, facilitating real-time analysis, and providing intuitive visual representations that aid in decision-making. The increasing integration of AI and ML into these software solutions is further enhancing their capabilities, making them indispensable tools in life sciences research.

The services segment, on the other hand, is projected to grow at a considerable rate, as organizations seek specialized expertise to manage and interpret their data. Services include consulting, implementation, and maintenance, as well as training and support. The demand for these services is driven by the need to ensure optimal utilization of data mining software and to keep up with the rapid pace of technological advancements. Moreover, many life sciences organizations lack the in-house expertise required to handle large-scale data analytics projects, thereby turning to external service providers for assistance.

Within the software segment, there is a growing trend towards the development of integrated platforms that combine multiple functionalities, such as data collection, pre

Data Mining Software Market Outlook

The global data mining software market size was valued at USD 7.2 billion in 2023 and is projected to reach USD 15.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.7% during the forecast period. This growth is driven primarily by the increasing adoption of big data analytics and the rising demand for business intelligence across various industries. As businesses increasingly recognize the value of data-driven decision-making, the market is expected to witness substantial growth.

One of the significant growth factors for the data mining software market is the exponential increase in data generation. With the proliferation of internet-enabled devices and the rapid advancement of technologies such as the Internet of Things (IoT), there is a massive influx of data. Organizations are now more focused than ever on harnessing this data to gain insights, improve operations, and create a competitive advantage. This has led to a surge in demand for advanced data mining tools that can process and analyze large datasets efficiently.

Another driving force is the growing need for personalized customer experiences. In industries such as retail, healthcare, and BFSI, understanding customer behavior and preferences is crucial. Data mining software enables organizations to analyze customer data, segment their audience, and deliver personalized offerings, ultimately enhancing customer satisfaction and loyalty. This drive towards personalization is further fueling the adoption of data mining solutions, contributing significantly to market growth.

The integration of artificial intelligence (AI) and machine learning (ML) technologies with data mining software is also a key growth factor. These advanced technologies enhance the capabilities of data mining tools by enabling them to learn from data patterns and make more accurate predictions. The convergence of AI and data mining is opening new avenues for businesses, allowing them to automate complex tasks, predict market trends, and make informed decisions more swiftly. The continuous advancements in AI and ML are expected to propel the data mining software market over the forecast period.

Regionally, North America holds a significant share of the data mining software market, driven by the presence of major technology companies and the early adoption of advanced analytics solutions. The Asia Pacific region is also expected to witness substantial growth due to the rapid digital transformation across various industries and the increasing investments in data infrastructure. Additionally, the growing awareness and implementation of data-driven strategies in emerging economies are contributing to the market expansion in this region.

Text Mining Software is becoming an integral part of the data mining landscape, offering unique capabilities to analyze unstructured data. As organizations generate vast amounts of textual data from various sources such as social media, emails, and customer feedback, the need for specialized tools to extract meaningful insights is growing. Text Mining Software enables businesses to process and analyze this data, uncovering patterns and trends that were previously hidden. This capability is particularly valuable in industries like marketing, customer service, and research, where understanding the nuances of language can lead to more informed decision-making. The integration of text mining with traditional data mining processes is enhancing the overall analytical capabilities of organizations, allowing them to derive comprehensive insights from both structured and unstructured data.

Component Analysis

The data mining software market is segmented by components, which primarily include software and services. The software segment encompasses various types of data mining tools that are used for analyzing and extracting valuable insights from raw data. These tools are designed to handle large volumes of data and provide advanced functionalities such as predictive analytics, data visualization, and pattern recognition. The increasing demand for sophisticated data analysis tools is driving the growth of the software segment. Enterprises are investing in these tools to enhance their data processing capabilities and derive actionable insights.

Within the software segment, the emergence of cloud-based data mining solutions is a notable trend. Cloud-based solutions offer several advantages, including s

To characterize each cognitive function per se and to understand the brain as an aggregate of those functions, it is vital to relate dozens of these functions to each other. Knowledge about the relationships among cognitive functions is informative not only for basic neuroscientific research but also for clinical applications and developments of brain-inspired artificial intelligence. In the present study, we propose an exhaustive data mining approach to reveal relationships among cognitive functions based on functional brain mapping and network analysis. We began our analysis with 109 pseudo-activation maps (cognitive function maps; CFM) that were reconstructed from a functional magnetic resonance imaging meta-analysis database, each of which corresponds to one of 109 cognitive functions such as ‘emotion,’ ‘attention,’ ‘episodic memory,’ etc. Based on the resting-state functional connectivity between the CFMs, we mapped the cognitive functions onto a two-dimensional space where the relevant functions were located close to each other, which provided a rough picture of the brain as an aggregate of cognitive functions. Then, we conducted so-called conceptual analysis of cognitive functions using clustering of voxels in each CFM connected to the other 108 CFMs with various strengths. As a result, a CFM for each cognitive function was subdivided into several parts, each of which is strongly associated with some CFMs for a subset of the other cognitive functions, which brought in sub-concepts (i.e., sub-functions) of the cognitive function. Moreover, we conducted network analysis for the network whose nodes were parcels derived from whole-brain parcellation based on the whole-brain voxel-to-CFM resting-state functional connectivities. Since each parcel is characterized by associations with the 109 cognitive functions, network analyses using them are expected to inform about relationships between cognitive and network characteristics. Indeed, we found that informational diversities of interaction between parcels and densities of local connectivity were dependent on the kinds of associated functions. In addition, we identified the homogeneous and inhomogeneous network communities about the associated functions. Altogether, we suggested the effectiveness of our approach in which we fused the large-scale meta-analysis of functional brain mapping with the methods of network neuroscience to investigate the relationships among cognitive functions.

The Big Data Tools market is experiencing robust growth, driven by the exponential increase in data volume and the rising need for advanced analytics across diverse sectors. While precise market sizing data is unavailable, considering the presence of established players like IBM and Dundas BI alongside emerging competitors like AnswerDock and ClicData, a reasonable estimate for the 2025 market size would be around $15 billion USD, exhibiting a Compound Annual Growth Rate (CAGR) of approximately 15% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the increasing adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting both large enterprises and smaller businesses. Secondly, the growing demand for real-time data processing and insights is driving investments in sophisticated analytics tools. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are seamlessly integrating with Big Data tools, enhancing their analytical capabilities and further propelling market expansion. However, the market also faces certain restraints. The complexity of Big Data tools can lead to high implementation costs and a need for specialized expertise, potentially limiting adoption amongst smaller companies with limited resources. Data security and privacy concerns also remain critical challenges, demanding robust security measures and compliance with data protection regulations. Despite these constraints, the long-term outlook remains positive, driven by the continuously increasing volume of data generated across various industries, the ongoing need for data-driven decision-making, and the continued innovation within the Big Data tools landscape. Market segmentation is likely divided across various deployment models (cloud, on-premise), industry verticals (finance, healthcare, retail), and tool functionalities (data visualization, data warehousing, data mining). Competitive analysis indicates a mix of established vendors and emerging players constantly innovating to improve offerings, leading to a dynamic and competitive market environment.

In a large network of computers, wireless sensors, or mobile devices, each of the components (hence, peers) has some data about the global status of the system. Many of the functions of the system, such as routing decisions, search strategies, data cleansing, and the assignment of mutual trust, depend on the global status. Therefore, it is essential that the system be able to detect, and react to, changes in its global status. Computing global predicates in such systems is usually very costly. Mainly because of their scale, and in some cases (e.g., sensor networks) also because of the high cost of communication. The cost further increases when the data changes rapidly (due to state changes, node failure, etc.) and computation has to follow these changes. In this paper we describe a two step approach for dealing with these costs. First, we describe a highly efficient local algorithm which detect when the L2 norm of the average data surpasses a threshold. Then, we use this algorithm as a feedback loop for the monitoring of complex predicates on the data – such as the data’s k-means clustering. The efficiency of the L2 algorithm guarantees that so long as the clustering results represent the data (i.e., the data is stationary) few resources are required. When the data undergoes an epoch change – a change in the underlying distribution – and the model no longer represents it, the feedback loop indicates this and the model is rebuilt. Furthermore, the existence of a feedback loop allows using approximate and “best-effort ” methods for constructing the model; if an ill-fit model is built the feedback loop would indicate so, and the model would be rebuilt.

To characterize each cognitive function per se and to understand the brain as an aggregate of those functions, it is vital to relate dozens of these functions to each other. Knowledge about the relationships among cognitive functions is informative not only for basic neuroscientific research but also for clinical applications and developments of brain-inspired artificial intelligence. In the present study, we propose an exhaustive data mining approach to reveal relationships among cognitive functions based on functional brain mapping and network analysis. We began our analysis with 109 pseudo-activation maps (cognitive function maps; CFM) that were reconstructed from a functional magnetic resonance imaging meta-analysis database, each of which corresponds to one of 109 cognitive functions such as ‘emotion,’ ‘attention,’ ‘episodic memory,’ etc. Based on the resting-state functional connectivity between the CFMs, we mapped the cognitive functions onto a two-dimensional space where the relevant functions were located close to each other, which provided a rough picture of the brain as an aggregate of cognitive functions. Then, we conducted so-called conceptual analysis of cognitive functions using clustering of voxels in each CFM connected to the other 108 CFMs with various strengths. As a result, a CFM for each cognitive function was subdivided into several parts, each of which is strongly associated with some CFMs for a subset of the other cognitive functions, which brought in sub-concepts (i.e., sub-functions) of the cognitive function. Moreover, we conducted network analysis for the network whose nodes were parcels derived from whole-brain parcellation based on the whole-brain voxel-to-CFM resting-state functional connectivities. Since each parcel is characterized by associations with the 109 cognitive functions, network analyses using them are expected to inform about relationships between cognitive and network characteristics. Indeed, we found that informational diversities of interaction between parcels and densities of local connectivity were dependent on the kinds of associated functions. In addition, we identified the homogeneous and inhomogeneous network communities about the associated functions. Altogether, we suggested the effectiveness of our approach in which we fused the large-scale meta-analysis of functional brain mapping with the methods of network neuroscience to investigate the relationships among cognitive functions.

We propose to develop a state-of-the-art data mining engine that extends the functionality of Virtual Observatories (VO) from data portal to science analysis resource. Our solution consists of two integrated products, IDDat and RemoteMiner:

(1) IDDat is an advanced grid-based computing infrastructure which acts as an add-on to VOs and supports processing and remote data analysis of widely distributed data in space sciences. IDDat middleware design is such as to reduce undue network traffic on the VO.

(2) RemoteMiner is a novel data mining engine that connects to the VO via the IDDat. It supports multi-users, has autonomous operation for automated systematic identification while enabling the advanced users to do their own mining and can be used by data centers for pre-mining.

These innovations will significantly enhance the science return from NASA missions by providing data centers and individual researchers alike an unprecedented capability to mine vast quantities of data. Phase I is aimed at complete definition of the design of the product and a demonstration of a prototype of the proposed major innovations. Phase II work will encompass the building of a full commercial product with associated production quality technical and user documentation.

The Enterprise Data Warehouse (EDW) market is experiencing robust growth, projected to reach a market size of $3455.2 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 5.6% from 2025 to 2033. This expansion is driven by the increasing need for organizations to consolidate data from disparate sources for improved business intelligence, enhanced decision-making, and streamlined operational efficiency. The rising adoption of cloud-based EDW solutions, fueled by scalability, cost-effectiveness, and accessibility, is a significant factor contributing to this growth. Furthermore, the expanding use of advanced analytics techniques, such as data mining and predictive modeling, within EDWs is further boosting market demand across diverse sectors including healthcare, finance, and retail. The market is segmented by deployment type (web-based and server-based) and application (information processing, data mining, and analytical processing), reflecting the diverse functionalities and deployment models available. Key players, including industry giants like Amazon Web Services, Microsoft, and Google, alongside specialized vendors like Teradata and Snowflake, are aggressively innovating to meet the evolving needs of enterprises. The competitive landscape is characterized by both established players and emerging technology providers. The ongoing trend towards data democratization, where access to data and analytics is broadened within organizations, is fostering demand for user-friendly EDW interfaces and tools. While regulatory compliance and data security remain key restraints, the overall market outlook for EDWs remains positive, with substantial growth potential driven by the continuous rise in data volumes, the growing need for real-time analytics, and increasing investments in digital transformation initiatives across industries globally. The North American market currently holds a significant share due to early adoption and technological advancements, but the Asia-Pacific region is projected to witness rapid growth in the coming years due to increased digitalization and technological infrastructure development.

This zip file contains data used to create figures and tables, describing the results of the paper "Reconstruction of magnetospheric storm-time dynamics using cylindrical basis functions and multi-mission data mining", by N. A. Tsyganenko, V. A. Andreeva, and M. I. Sitnov.

In a large network of computers, wireless sensors, or mobile devices, each of the components (hence, peers) has some data about the global status of the system. Many of the functions of the system, such as routing decisions, search strategies, data cleansing, and the assignment of mutual trust, depend on the global status. Therefore, it is essential that the system be able to detect, and react to, changes in its global status. Computing global predicates in such systems is usually very costly. Mainly because of their scale, and in some cases (e.g., sensor networks) also because of the high cost of communication. The cost further increases when the data changes rapidly (due to state changes, node failure, etc.) and computation has to follow these changes. In this paper we describe a two step approach for dealing with these costs. First, we describe a highly efficient local algorithm which detect when the L2 norm of the average data surpasses a threshold. Then, we use this algorithm as a feedback loop for the monitoring of complex predicates on the data – such as the data’s k-means clustering. The efficiency of the L2 algorithm guarantees that so long as the clustering results represent the data (i.e., the data is stationary) few resources are required. When the data undergoes an epoch change – a change in the underlying distribution – and the model no longer represents it, the feedback loop indicates this and the model is rebuilt. Furthermore, the existence of a feedback loop allows using approximate and “best-effort ” methods for constructing the model; if an ill-fit model is built the feedback loop would indicate so, and the model would be rebuilt.

Consider a scenario in which the data owner has some private/sensitive data and wants a data miner to access it for studying important patterns without revealing the sensitive information. Privacy preserving data mining aims to solve this problem by randomly transforming the data prior to its release to data miners. Previous work only considered the case of linear data perturbations — additive, multiplicative or a combination of both for studying the usefulness of the perturbed output. In this paper, we discuss nonlinear data distortion using potentially nonlinear random data transformation and show how it can be useful for privacy preserving anomaly detection from sensitive datasets. We develop bounds on the expected accuracy of the nonlinear distortion and also quantify privacy by using standard definitions. The highlight of this approach is to allow a user to control the amount of privacy by varying the degree of nonlinearity. We show how our general transformation can be used for anomaly detection in practice for two specific problem instances: a linear model and a popular nonlinear model using the sigmoid function. We also analyze the proposed nonlinear transformation in full generality and then show that for specific cases it is distance preserving. A main contribution of this paper is the discussion between the invertibility of a transformation and privacy preservation and the application of these techniques to outlier detection. Experiments conducted on real-life datasets demonstrate the effectiveness of the approach.

The Pipeline and Hazardous Materials Safety Administration has the primary responsibility for the issuance of DOT Special Permits and Approvals to the Hazardous Materials Regulations (HMR). A Special Permit or Approval is a document which authorizes a person to perform a function that is not currently authorized under the authority of the HMR. Also, in many instances, the Regulations require approvals and/or registrations prior to transportation in commerce. The Special Permits Search tool allows a user to search for active Special permits.

This dataset contains the extension of a publicly available dataset that was published initially by Ferenc et al. in their paper:

“Ferenc, R.; Hegedus, P.; Gyimesi, P.; Antal, G.; Bán, D.; Gyimóthy, T. Challenging machine learning algorithms in predicting vulnerable javascript functions. 2019 IEEE/ACM 7th InternationalWorkshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE). IEEE, 2019, pp. 8–14.”

The dataset contained software metrics for source code functions written in JavaScript (JS) programming language. Each function was labeled as vulnerable or clean. The authors gathered vulnerabilities from publicly available vulnerability databases.

In our paper entitled: “Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction” and cited as:

“Kalouptsoglou I, Siavvas M, Kehagias D, Chatzigeorgiou A, Ampatzoglou A. Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction. Entropy. 2022; 24(5):651. https://doi.org/10.3390/e24050651”

, we presented an extended version of the dataset by extracting textual features for the labeled JS functions. In particular, we got the dataset provided by Ferenc et al. in CSV format and then we gathered all the GitHub URLs of the dataset's functions (i.e., methods). Using these URLs, we collected the source code of the corresponding JS files from GitHub. Subsequently, by utilizing the start and end line information for every function, we cut off the code of the functions. Each function was then tokenized to construct a list of tokens per function.

To extract text features, we used a text mining technique called sequences of tokens. As a result, we created a repository with all methods' source code, the token sequences of each method, and their labels. To boost the generalizability of type-specific tokens, all comments were eliminated, as well as all integers and strings, which were replaced with two unique IDs.

The dataset contains 12,106 JavaScript functions, from which 1,493 are considered vulnerable.

This dataset was created and utilized during the Vulnerability Prediction Task of the Horizon2020 IoTAC Project as training and evaluation data for the construction of vulnerability prediction models. The dataset is provided in the csv format. Each row of the csv file has the following parts:

• Label: Flag with values ‘1’ for vulnerable and ‘0’ for non-vulnerable methods
• Name: The name of the JavaScript method
• Longname: The longname of the JavaScript method
• Path: The path of the file of the method in the repository
• Full_repo_path: The GitHub URL of the file of the method
• TokenX: Each next row corresponds to each token included in the method

Statistically significant hotspots of fishing activities in the Mediterranean and Atlanti Seas were identified by the application of the Getis-Ord Gi statistic (Getis and Ord 2010) though the statistical software R using the globalG.test function (spdep package). The function computes a global test for spatial autocorrelation using a Monte Carlo simulation approach. It tests the null hypothesis of no autocorrelation against the alternative hypothesis of positive spatial autocorrelation. Then the local spatial autocorrelation was tested calculating the Gi statistic, using the local_g_perm function (dfdep package), which indicates the strength of the clustering.

Categorization of hotspots was performed, according to the Gi value and the p-value of a folded permutation test obtained for each grid cell, as follows:

• Gi>0 and p_value <=0.01 as Very hot
• Gi>0 and p_value <=0.05 as Hot
• Gi>0 and p_value <=0.1 as Somewhat hot
• Gi<0 and p_value <=0.1 as Somewhat cold
• Gi<0 and p_value <=0.05 as Cold
• Gi<0 and p_value <=0.01 à Very cold

Grid cells with a p-value > 0.1 were categorized as Insignificant.

The analyses were performed on cumulative fishing activity data at 0.5° resolution of seven different gears separately for the two macroareas.

The dataset presented includes for each area maps of each gear hotspot and spatial layers of the gears hotspots (.shp; .csv)

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 6,2023. Many Laboratories chose to design and print their own microarrays. At present, the choice of the genes to include on a certain microarray is a very laborious process requiring a high level of expertise. Onto-Design database is able to assist the designers of custom microarrays by providing the means to select genes based on their experiment. Design custom microarrays based on GO terms of interest. User account required. Platform: Online tool