The Data Mining Tools Market size was valued at USD 1.01 USD billion in 2023 and is projected to reach USD 1.99 USD billion by 2032, exhibiting a CAGR of 10.2 % during the forecast period. The growing adoption of data-driven decision-making and the increasing need for business intelligence are major factors driving market growth. Data mining refers to filtering, sorting, and classifying data from larger datasets to reveal subtle patterns and relationships, which helps enterprises identify and solve complex business problems through data analysis. Data mining software tools and techniques allow organizations to foresee future market trends and make business-critical decisions at crucial times. Data mining is an essential component of data science that employs advanced data analytics to derive insightful information from large volumes of data. Businesses rely heavily on data mining to undertake analytics initiatives in the organizational setup. The analyzed data sourced from data mining is used for varied analytics and business intelligence (BI) applications, which consider real-time data analysis along with some historical pieces of information. Recent developments include: May 2023 – WiMi Hologram Cloud Inc. introduced a new data interaction system developed by combining neural network technology and data mining. Using real-time interaction, the system can offer reliable and safe information transmission., May 2023 – U.S. Data Mining Group, Inc., operating in bitcoin mining site, announced a hosting contract to deploy 150,000 bitcoins in partnership with major companies such as TeslaWatt, Sphere 3D, Marathon Digital, and more. The company is offering industry turn-key solutions for curtailment, accounting, and customer relations., April 2023 – Artificial intelligence and single-cell biotech analytics firm, One Biosciences, launched a single cell data mining algorithm called ‘MAYA’. The algorithm is for cancer patients to detect therapeutic vulnerabilities., May 2022 – Europe-based Solarisbank, a banking-as-a-service provider, announced its partnership with Snowflake to boost its cloud data strategy. Using the advanced cloud infrastructure, the company can enhance data mining efficiency and strengthen its banking position.. Key drivers for this market are: Increasing Focus on Customer Satisfaction to Drive Market Growth. Potential restraints include: Requirement of Skilled Technical Resources Likely to Hamper Market Growth. Notable trends are: Incorporation of Data Mining and Machine Learning Solutions to Propel Market Growth.

Abstract The objective of this work is to improve the quality of the information that belongs to the database CubaCiencia, of the Institute of Scientific and Technological Information. This database has bibliographic information referring to four segments of science and is the main database of the Library Management System. The applied methodology was based on the Decision Trees, the Correlation Matrix, the 3D Scatter Plot, etc., which are techniques used by data mining, for the study of large volumes of information. The results achieved not only made it possible to improve the information in the database, but also provided truly useful patterns in the solution of the proposed objectives.

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.

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

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.

Due to increasing use of technology-enhanced educational assessment, data mining methods have been explored to analyse process data in log files from such assessment. However, most studies were limited to one data mining technique under one specific scenario. The current study demonstrates the usage of four frequently used supervised techniques, including Classification and Regression Trees (CART), gradient boosting, random forest, support vector machine (SVM), and two unsupervised methods, Self-organizing Map (SOM) and k-means, fitted to one assessment data. The USA sample (N = 426) from the 2012 Program for International Student Assessment (PISA) responding to problem-solving items is extracted to demonstrate the methods. After concrete feature generation and feature selection, classifier development procedures are implemented using the illustrated techniques. Results show satisfactory classification accuracy for all the techniques. Suggestions for the selection of classifiers are presented based on the research questions, the interpretability and the simplicity of the classifiers. Interpretations for the results from both supervised and unsupervised learning methods are provided.

Global Data Mining Tools market size is expected to reach $2.13 billion by 2029 at 12.9%, segmented as by tools, data mining software, data visualization tools, data preparation tools, predictive analytics tools, reporting tools

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.

The worldwide civilian aviation system is one of the most complex dynamical systems created. Most modern commercial aircraft have onboard flight data recorders that record several hundred discrete and continuous parameters at approximately 1Hz for the entire duration of the flight. These data contain information about the flight control systems, actuators, engines, landing gear, avionics, and pilot commands. In this paper, recent advances in the development of a novel knowledge discovery process consisting of a suite of data mining techniques for identifying precursors to aviation safety incidents are discussed. The data mining techniques include scalable multiple-kernel learning for large-scale distributed anomaly detection. A novel multivariate time-series search algorithm is used to search for signatures of discovered anomalies on massive datasets. The process can identify operationally significant events due to environmental, mechanical, and human factors issues in the high-dimensional flight operations quality assurance data. All discovered anomalies are validated by a team of independent domain experts. This novel automated knowledge discovery process is aimed at complementing the state-of-the-art human-generated exceedance-based analysis that fails to discover previously unknown aviation safety incidents. In this paper, the discovery pipeline, the methods used, and some of the significant anomalies detected on real-world commercial aviation data are discussed.

Data supporting the Master thesis "Monitoring von Open Data Praktiken - Herausforderungen beim Auffinden von Datenpublikationen am Beispiel der Publikationen von Forschenden der TU Dresden" (Monitoring open data practices - challenges in finding data publications using the example of publications by researchers at TU Dresden) - Katharina Zinke, Institut für Bibliotheks- und Informationswissenschaften, Humboldt-Universität Berlin, 2023

This ZIP-File contains the data the thesis is based on, interim exports of the results and the R script with all pre-processing, data merging and analyses carried out. The documentation of the additional, explorative analysis is also available. The actual PDFs and text files of the scientific papers used are not included as they are published open access.

The folder structure is shown below with the file names and a brief description of the contents of each file. For details concerning the analyses approach, please refer to the master's thesis (publication following soon).

## Data sources

Folder 01_SourceData/

- PLOS-Dataset_v2_Mar23.csv (PLOS-OSI dataset)

- ScopusSearch_ExportResults.csv (export of Scopus search results from Scopus)

- ScopusSearch_ExportResults.ris (export of Scopus search results from Scopus)

- Zotero_Export_ScopusSearch.csv (export of the file names and DOIs of the Scopus search results from Zotero)

## Automatic classification

Folder 02_AutomaticClassification/

- (NOT INCLUDED) PDFs folder (Folder for PDFs of all publications identified by the Scopus search, named AuthorLastName_Year_PublicationTitle_Title)

- (NOT INCLUDED) PDFs_to_text folder (Folder for all texts extracted from the PDFs by ODDPub, named AuthorLastName_Year_PublicationTitle_Title)

- PLOS_ScopusSearch_matched.csv (merge of the Scopus search results with the PLOS_OSI dataset for the files contained in both)

- oddpub_results_wDOIs.csv (results file of the ODDPub classification)

- PLOS_ODDPub.csv (merge of the results file of the ODDPub classification with the PLOS-OSI dataset for the publications contained in both)

## Manual coding

Folder 03_ManualCheck/

- CodeSheet_ManualCheck.txt (Code sheet with descriptions of the variables for manual coding)

- ManualCheck_2023-06-08.csv (Manual coding results file)

- PLOS_ODDPub_Manual.csv (Merge of the results file of the ODDPub and PLOS-OSI classification with the results file of the manual coding)

## Explorative analysis for the discoverability of open data

Folder04_FurtherAnalyses

Proof_of_of_Concept_Open_Data_Monitoring.pdf (Description of the explorative analysis of the discoverability of open data publications using the example of a researcher) - in German

## R-Script

Analyses_MA_OpenDataMonitoring.R (R-Script for preparing, merging and analyzing the data and for performing the ODDPub algorithm)

Feature selection is an important technique for data mining before a machine learning algorithm is applied. Despite its importance, most studies of feature selection are restricted to batch learning. Unlike traditional batch learning methods, online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale applications. Most existing studies of online learning require accessing all the attributes/features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or it is expensive to acquire the full set of attributes/features. To address this limitation, we investigate the problem of Online Feature Selection (OFS) in which an online learner is only allowed to maintain a classifier involved only a small and fixed number of features. The key challenge of Online Feature Selection is how to make accurate prediction using a small and fixed number of active features. This is in contrast to the classical setup of online learning where all the features can be used for prediction. We attempt to tackle this challenge by studying sparsity regularization and truncation techniques. Specifically, this article addresses two different tasks of online feature selection: (1) learning with full input where an learner is allowed to access all the features to decide the subset of active features, and (2) learning with partial input where only a limited number of features is allowed to be accessed for each instance by the learner. We present novel algorithms to solve each of the two problems and give their performance analysis. We evaluate the performance of the proposed algorithms for online feature selection on several public datasets, and demonstrate their applications to real-world problems including image classification in computer vision and microarray gene expression analysis in bioinformatics. The encouraging results of our experiments validate the efficacy and efficiency of the proposed techniques.Related Publication: Hoi, S. C., Wang, J., Zhao, P., & Jin, R. (2012). Online feature selection for mining big data. In Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (pp. 93-100). ACM. http://dx.doi.org/10.1145/2351316.2351329 Full text available in InK: http://ink.library.smu.edu.sg/sis_research/2402/ Wang, J., Zhao, P., Hoi, S. C., & Jin, R. (2014). Online feature selection and its applications. IEEE Transactions on Knowledge and Data Engineering, 26(3), 698-710. http://dx.doi.org/10.1109/TKDE.2013.32 Full text available in InK: http://ink.library.smu.edu.sg/sis_research/2277/

This data collection is associated with the publications: Merdith, A. S., Landgrebe, T. C. W., Dutkiewicz, A., & Müller, R. D. (2013). Towards a predictive model for opal exploration using a spatio-temporal data mining approach. Australian Journal of Earth Sciences, 60(2), 217-229. doi: 10.1080/08120099.2012.754793

and

Landgrebe, T. C. W., Merdith, A., Dutkiewicz, A., & Müller, R. D. (2013). Relationships between palaeogeography and opal occurrence in Australia: A data-mining approach. Computers & Geosciences, 56(0), 76-82. doi: 10.1016/j.cageo.2013.02.002

Publication Abstract - Merdith et al. (2013)

Opal is Australia's national gemstone, however most significant opal discoveries were made in the early 1900's - more than 100 years ago - until recently. Currently there is no formal exploration model for opal, meaning there are no widely accepted concepts or methodologies available to suggest where new opal fields may be found. As a consequence opal mining in Australia is a cottage industry with the majority of opal exploration focused around old opal fields. The EarthByte Group has developed a new opal exploration methodology for the Great Artesian Basin. The work is based on the concept of applying “big data mining” approaches to data sets relevant for identifying regions that are prospective for opal. The group combined a multitude of geological and geophysical data sets that were jointly analysed to establish associations between particular features in the data with known opal mining sites. A “training set” of known opal localities (1036 opal mines) was assembled, using those localities, which were featured in published reports and on maps. The data used include rock types, soil type, regolith type, topography, radiometric data and a stack of digital palaeogeographic maps. The different data layers were analysed via spatio-temporal data mining combining the GPlates PaleoGIS software (www.gplates.org) with the Orange data mining software (orange.biolab.si) to produce the first opal prospectivity map for the Great Artesian Basin. One of the main results of the study is that the geological conditions favourable for opal were found to be related to a particular sequence of surface environments over geological time. These conditions involved alternating shallow seas and river systems followed by uplift and erosion. The approach reduces the entire area of the Great Artesian Basin to a mere 6% that is deemed to be prospective for opal exploration. The work is described in two companion papers in the Australian Journal of Earth Sciences and Computers and Geosciences.

Publication Abstract - Landgrebe et al. (2013)

Age-coded multi-layered geological datasets are becoming increasingly prevalent with the surge in open-access geodata, yet there are few methodologies for extracting geological information and knowledge from these data. We present a novel methodology, based on the open-source GPlates software in which age-coded digital palaeogeographic maps are used to “data-mine” spatio-temporal patterns related to the occurrence of Australian opal. Our aim is to test the concept that only a particular sequence of depositional/erosional environments may lead to conditions suitable for the formation of gem quality sedimentary opal. Time-varying geographic environment properties are extracted from a digital palaeogeographic dataset of the eastern Australian Great Artesian Basin (GAB) at 1036 opal localities. We obtain a total of 52 independent ordinal sequences sampling 19 time slices from the Early Cretaceous to the present-day. We find that 95% of the known opal deposits are tied to only 27 sequences all comprising fluvial and shallow marine depositional sequences followed by a prolonged phase of erosion. We then map the total area of the GAB that matches these 27 opal-specific sequences, resulting in an opal-prospective region of only about 10% of the total area of the basin. The key patterns underlying this association involve only a small number of key environmental transitions. We demonstrate that these key associations are generally absent at arbitrary locations in the basin. This new methodology allows for the simplification of a complex time-varying geological dataset into a single map view, enabling straightforward application for opal exploration and for future co-assessment with other datasets/geological criteria. This approach may help unravel the poorly understood opal formation process using an empirical spatio-temporal data-mining methodology and readily available datasets to aid hypothesis testing.

Authors and Institutions

Andrew Merdith - EarthByte Research Group, School of Geosciences, The University of Sydney, Australia. ORCID: 0000-0002-7564-8149

Thomas Landgrebe - EarthByte Research Group, School of Geosciences, The University of Sydney, Australia

Adriana Dutkiewicz - EarthByte Research Group, School of Geosciences, The University of Sydney, Australia

R. Dietmar Müller - EarthByte Research Group, School of Geosciences, The University of Sydney, Australia. ORCID: 0000-0002-3334-5764

Overview of Resources Contained

This collection contains geological data from Australia used for data mining in the publications Merdith et al. (2013) and Landgrebe et al. (2013). The resulting maps of opal prospectivity are also included.

List of Resources

Note: For details on the files included in this data collection, see “Description_of_Resources.txt”.

Note: For information on file formats and what programs to use to interact with various file formats, see “File_Formats_and_Recommended_Programs.txt”.

• Map of Barfield region, Australia (.jpg, 270 KB)
• Map overviewing the Great Artesian basins and main opal mining camps (.png, 82 KB)
• Maps showing opal prospectivity data mining results for different geological datasets (.tif, 23.1 MB)
• Map of opal prospectivity from palaeogeography data mining (.pdf, 2.6 MB)
• Raster of palaeogeography target regions for viewing in Google Earth (.jpg, 418 KB)
• Opal mine locations (.gpml, .txt, .kmz, .shp, total 15.6 MB)
• Map of opal prospectivity from all data mining results as a Google Earth overlay (.kmz, 12 KB)
• Map of probability of opal occurrence in prospective regions from all data mining results (.tif, 5.9 MB)
• Paleogeography of Australia (.gpml, .txt, .shp, total 114.2 MB)
• Radiometric data showing potassium concentration contrasts (.tif, .kmz, total 311.3 MB)
• Regolith data (.gpml, .txt, .kml, .shp, total 7.1 MB)
• Soil type data (.gpml, .txt, .kml, .shp, total 7.1 MB)

For more information on this data collection, and links to other datasets from the EarthByte Research Group please visit EarthByte

For more information about using GPlates, including tutorials and a user manual please visit GPlates or EarthByte

Hotel customer dataset with 31 variables describing a total of 83,590 instances (customers). It comprehends three full years of customer behavioral data. In addition to personal and behavioral information, the dataset also contains demographic and geographical information. This dataset contributes to reducing the lack of real-world business data that can be used for educational and research purposes. The dataset can be used in data mining, machine learning, and other analytical field problems in the scope of data science. Due to its unit of analysis, it is a dataset especially suitable for building customer segmentation models, including clustering and RFM (Recency, Frequency, and Monetary value) models, but also be used in classification and regression problems.

In this research, we have generated student retention alerts. The alerts are classified into two types: preventive and corrective. This classification varies according to the level of maturity of the data systematization process. Therefore, to systematize the data, data mining techniques have been applied. The experimental analytical method has been used, with a population of 13,715 students with 62 sociological, academic, family, personal, economic, psychological, and institutional variables, and factors such as academic follow-up and performance, financial situation, and personal information. In particular, information is collected on each of the problems or a combination of problems that could affect dropout rates. Following the methodology, the information has been generated through an abstract data model to reflect the profile of the dropout student. As advancement from previous research, this proposal will create preventive and corrective alternatives to avoid dropout higher education. Also, in contrast to previous work, we generated corrective warnings with the application of data mining techniques such as neural networks until reaching a precision of 97% and losses of 0.1052. In conclusion, this study pretends to analyze the behavior of students who drop out the university through the evaluation of predictive patterns. The overall objective is to predict the profile of student dropout, considering reasons such as admission to higher education and career changes. Consequently, using a data systematization process promotes the permanence of students in higher education. Once the profile of the dropout has been identified, student retention strategies have been approached, according to the time of its appearance and the point of view of the institution.

High-throughput screening (HTS) experiments provide a valuable resource that reports biological activity of numerous chemical compounds relative to their molecular targets. Building computational models that accurately predict such activity status (active vs. inactive) in specific assays is a challenging task given the large volume of data and frequently small proportion of active compounds relative to the inactive ones. We developed a method, DRAMOTE, to predict activity status of chemical compounds in HTP activity assays. For a class of HTP assays, our method achieves considerably better results than the current state-of-the-art-solutions. We achieved this by modification of a minority oversampling technique. To demonstrate that DRAMOTE is performing better than the other methods, we performed a comprehensive comparison analysis with several other methods and evaluated them on data from 11 PubChem assays through 1,350 experiments that involved approximately 500,000 interactions between chemicals and their target proteins. As an example of potential use, we applied DRAMOTE to develop robust models for predicting FDA approved drugs that have high probability to interact with the thyroid stimulating hormone receptor (TSHR) in humans. Our findings are further partially and indirectly supported by 3D docking results and literature information. The results based on approximately 500,000 interactions suggest that DRAMOTE has performed the best and that it can be used for developing robust virtual screening models. The datasets and implementation of all solutions are available as a MATLAB toolbox online at www.cbrc.kaust.edu.sa/dramote

This research used data mining approaches to better understand factors affecting the formation of secondary organic aerosol (SOA). Although numerous laboratory and computational studies have been completed on SOA formation, it is still challenging to determine factors that most influence SOA formation. Experimental data were based on previous work described by Offenberg et al. (2017), where volume concentrations of SOA were measured in 139 laboratory experiments involving the oxidation of single hydrocarbons under different operating conditions. Three different data mining methods were used, including nearest neighbor, decision tree, and pattern mining. Both decision tree and pattern mining approaches identified similar chemical and experimental conditions that were important to SOA formation. Among these important factors included the number of methyl groups, the number of rings and the presence of dinitrogen pentoxide (N2O5).

This dataset is associated with the following publication: Olson, D., J. Offenberg, M. Lewandowski, T. Kleindienst, K. Docherty, M. Jaoui, J.D. Krug, and T. Riedel. Data mining approaches to understanding the formation of secondary organic aerosol. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 252: 118345, (2021).

This file contains the life cycle inventories (LCIs) developed for an associated journal article. Potential users of the data are referred to the journal article for a full description of the modeling methodology. LCIs were developed for cumene and sodium hydroxide manufacturing using data mining with metadata-based data preprocessing. The inventory data were collected from US EPA's 2012 Chemical Data Reporting database, 2011 National Emissions Inventory, 2011 Toxics Release Inventory, 2011 Electronic Greenhouse Gas Reporting Tool, 2011 Discharge Monitoring Report, and the 2011 Biennial Report generated from the RCRAinfo hazardous waste tracking system. The U.S. average cumene gate-to-gate inventories are provided without (baseline) and with process allocation applied using metadata-based filtering. In 2011, there were 8 facilities reporting public production volumes of cumene in the U.S., totaling to 2,609,309,687 kilograms of cumene produced that year. The U.S. average sodium hydroxide gate-to-gate inventories are also provided without (baseline) and with process allocation applied using metadata-based filtering. In 2011, there were 24 facilities reporting public production volumes of sodium hydroxide in the U.S., totaling to 3,878,021,614 kilograms of sodium hydroxide produced that year. Process allocation was only conducted for the top 12 facilities producing sodium hydroxide, which represents 97% of the public production of sodium hydroxide. The data have not been compiled in the formal Federal Commons LCI Template to avoid users interpreting the template to mean the data have been fully reviewed according to LCA standards and can be directly applied to all types of assessments and decision needs without additional review by industry and potential stakeholders. This dataset is associated with the following publication: Meyer, D.E., S. Cashman, and A. Gaglione. Improving the reliability of chemical manufacturing life cycle inventory constructed using secondary data. JOURNAL OF INDUSTRIAL ECOLOGY. Berkeley Electronic Press, Berkeley, CA, USA, 25(1): 20-35, (2021).

Data used for research "Structure of information content of Reddit subfora -- preliminary explorations"

This data set belongs to the paper "Video-to-Model: Unsupervised Trace Extraction from Videos for Process Discovery and Conformance Checking in Manual Assembly", submitted on March 24, 2020, to the 18th International Conference on Business Process Management (BPM).Abstract: Manual activities are often hidden deep down in discrete manufacturing processes. For the elicitation and optimization of process behavior, complete information about the execution of Manual activities are required. Thus, an approach is presented on how execution level information can be extracted from videos in manual assembly. The goal is the generation of a log that can be used in state-of-the-art process mining tools. The test bed for the system was lightweight and scalable consisting of an assembly workstation equipped with a single RGB camera recording only the hand movements of the worker from top. A neural network based real-time object classifier was trained to detect the worker’s hands. The hand detector delivers the input for an algorithm, which generates trajectories reflecting the movement paths of the hands. Those trajectories are automatically assigned to work steps using the position of material boxes on the assembly shelf as reference points and hierarchical clustering of similar behaviors with dynamic time warping. The system has been evaluated in a task-based study with ten participants in a laboratory, but under realistic conditions. The generated logs have been loaded into the process mining toolkit ProM to discover the underlying process model and to detect deviations from both, instructions and ground truth, using conformance checking. The results show that process mining delivers insights about the assembly process and the system’s precision.The data set contains the generated and the annotated logs based on the video material gathered during the user study. In addition, the petri nets from the process discovery and conformance checking conducted with ProM (http://www.promtools.org) and the reference nets modeled with Yasper (http://www.yasper.org/) are provided.

Dataset with 72000 pins from 117 users in Pinterest. Each pin contains a short raw text and an image. The images are processed using a pretrained Convolutional Neural Network and transformed into a vector of 4096 features.

This dataset was used in the paper "User Identification in Pinterest Through the Refinement of a Cascade Fusion of Text and Images" to idenfity specific users given their comments. The dataset includes the splits used in the paper.

There are nine files. text_test, text_train and text_val, contain the raw text of each pin in the corresponding split of the data. imag_test, imag_train and imag_val contain the image features of each pin in the corresponding split of the data. train_user and val_test_users contain the index of the user of each pin (between 0 and 116). There is a correspondance one-to-one among the test, train and validation files for images, text and users. There are 400 pins per user in the train set, and 100 pins per user in the validation and test sets each one.

If you have questions regarding the data, write to: jc dot gomez at ugto dot mx