Privacy‑Preserving Data Mining Tools Market Outlook

According to our latest research, the global Privacy‑Preserving Data Mining Tools market size reached USD 1.42 billion in 2024, reflecting robust adoption across diverse industries. The market is expected to exhibit a CAGR of 22.8% during the forecast period, propelling the market to USD 10.98 billion by 2033. This remarkable growth is driven by the increasing need for secure data analytics, stringent data protection regulations, and the rising frequency of data breaches, all of which are pushing organizations to adopt advanced privacy solutions.

One of the primary growth factors for the Privacy‑Preserving Data Mining Tools market is the exponential rise in data generation and the parallel escalation of privacy concerns. As organizations collect vast amounts of sensitive information, especially in sectors like healthcare and BFSI, the risk of data exposure and misuse grows. Governments worldwide are enacting stricter data protection laws, such as the GDPR in Europe and CCPA in California, compelling enterprises to integrate privacy‑preserving technologies into their analytics workflows. These regulations not only mandate compliance but also foster consumer trust, making privacy‑preserving data mining tools a strategic investment for businesses aiming to maintain a competitive edge while safeguarding user data.

Another significant driver is the rapid digital transformation across industries, which necessitates the extraction of actionable insights from large, distributed data sets without compromising privacy. Privacy‑preserving techniques, such as federated learning, homomorphic encryption, and differential privacy, are gaining traction as they allow organizations to collaborate and analyze data securely. The advent of cloud computing and the proliferation of connected devices further amplify the demand for scalable and secure data mining solutions. As enterprises embrace cloud-based analytics, the need for robust privacy-preserving mechanisms becomes paramount, fueling the adoption of advanced tools that can operate seamlessly in both on-premises and cloud environments.

Moreover, the increasing sophistication of cyber threats and the growing awareness of the potential reputational and financial damage caused by data breaches are prompting organizations to prioritize data privacy. High-profile security incidents have underscored the vulnerabilities inherent in traditional data mining approaches, accelerating the shift towards privacy-preserving alternatives. The integration of artificial intelligence and machine learning with privacy-preserving technologies is also opening new avenues for innovation, enabling more granular and context-aware data analytics. This technological convergence is expected to further catalyze market growth, as organizations seek to harness the full potential of their data assets while maintaining stringent privacy standards.

From a regional perspective, North America currently commands the largest share of the Privacy‑Preserving Data Mining Tools market, driven by the presence of leading technology vendors, high awareness levels, and a robust regulatory framework. Europe follows closely, propelled by stringent data privacy laws and increasing investments in secure analytics infrastructure. The Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, expanding IT ecosystems, and rising cybersecurity concerns in emerging economies such as China and India. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as organizations in these regions increasingly recognize the importance of privacy in data-driven decision-making.

Component Analysis

The Privacy‑Preserving Data Mining Tools market is segmented by component into software and services, each playing a pivotal role in shaping the industry landscape. The software segment dominates the market, accounting for the majority of revenue in 2024. Organizations are increasingly investing in advanced software so

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.

From Dryad entry:

"Abstract
Neuroendocrine neoplasms (NENs) are clinically diverse and incompletely characterized cancers that are challenging to classify. MicroRNAs (miRNAs) are small regulatory RNAs that can be used to classify cancers. Recently, a morphology-based classification framework for evaluating NENs from different anatomic sites was proposed by experts, with the requirement of improved molecular data integration. Here, we compiled 378 miRNA expression profiles to examine NEN classification through comprehensive miRNA profiling and data mining. Following data preprocessing, our final study cohort included 221 NEN and 114 non-NEN samples, representing 15 NEN pathological types and five site-matched non-NEN control groups. Unsupervised hierarchical clustering of miRNA expression profiles clearly separated NENs from non-NENs. Comparative analyses showed that miR-375 and miR-7 expression is substantially higher in NEN cases than non-NEN controls. Correlation analyses showed that NENs from diverse anatomic sites have convergent miRNA expression programs, likely reflecting morphologic and functional similarities. Using machine learning approaches, we identified 17 miRNAs to discriminate 15 NEN pathological types and subsequently constructed a multi-layer classifier, correctly identifying 217 (98%) of 221 samples and overturning one histologic diagnosis. Through our research, we have identified common and type-specific miRNA tissue markers and constructed an accurate miRNA-based classifier, advancing our understanding of NEN diversity.

Methods
Sequencing-based miRNA expression profiles from 378 clinical samples, comprising 239 neuroendocrine neoplasm (NEN) cases and 139 site-matched non-NEN controls, were used in this study. Expression profiles were either compiled from published studies (n=149) or generated through small RNA sequencing (n=229). Prior to sequencing, total RNA was isolated from formalin-fixed paraffin-embedded (FFPE) tissue blocks or fresh-frozen (FF) tissue samples. Small RNA cDNA libraries were sequenced on HiSeq 2500 Illumina platforms using an established small RNA sequencing (Hafner et al., 2012 Methods) and sequence annotation pipeline (Brown et al., 2013 Front Genet) to generate miRNA expression profiles. Scaling our existing approach to miRNA-based NEN classification (Panarelli et al., 2019 Endocr Relat Cancer; Ren et al., 2017 Oncotarget), we constructed and cross-validated a multi-layer classifier for discriminating NEN pathological types based on selected miRNAs.

Usage notes
Diagnostic histopathology and small RNA cDNA library preparation information for all samples are presented in Table S1 of the associated manuscript."

The Exploratory Data Analysis (EDA) tools market is experiencing robust growth, driven by the increasing volume and complexity of data across various industries. The market, estimated at $1.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $5 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of big data analytics and business intelligence initiatives across large enterprises and SMEs is creating a significant demand for efficient EDA tools. Secondly, the growing need for faster, more insightful data analysis to support better decision-making is driving the preference for user-friendly graphical EDA tools over traditional non-graphical methods. Furthermore, advancements in artificial intelligence and machine learning are seamlessly integrating into EDA tools, enhancing their capabilities and broadening their appeal. The market segmentation reveals a significant portion held by large enterprises, reflecting their greater resources and data handling needs. However, the SME segment is rapidly gaining traction, driven by the increasing affordability and accessibility of cloud-based EDA solutions. Geographically, North America currently dominates the market, but regions like Asia-Pacific are exhibiting high growth potential due to increasing digitalization and technological advancements. Despite this positive outlook, certain restraints remain. The high initial investment cost associated with implementing advanced EDA solutions can be a barrier for some SMEs. Additionally, the need for skilled professionals to effectively utilize these tools can create a challenge for organizations. However, the ongoing development of user-friendly interfaces and the availability of training resources are actively mitigating these limitations. The competitive landscape is characterized by a mix of established players like IBM and emerging innovative companies offering specialized solutions. Continuous innovation in areas like automated data preparation and advanced visualization techniques will further shape the future of the EDA tools market, ensuring its sustained growth trajectory.

Numbers of detected logic relationships by Logicome Profiler.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The Exploratory Data Analysis (EDA) tools market is experiencing robust growth, driven by the increasing volume and complexity of data across industries. The rising need for data-driven decision-making, coupled with the expanding adoption of cloud-based analytics solutions, is fueling market expansion. While precise figures for market size and CAGR are not provided, a reasonable estimation, based on the prevalent growth in the broader analytics market and the crucial role of EDA in the data science workflow, would place the 2025 market size at approximately $3 billion, with a projected Compound Annual Growth Rate (CAGR) of 15% through 2033. This growth is segmented across various applications, with large enterprises leading the adoption due to their higher investment capacity and complex data needs. However, SMEs are witnessing rapid growth in EDA tool adoption, driven by the increasing availability of user-friendly and cost-effective solutions. Further segmentation by tool type reveals a strong preference for graphical EDA tools, which offer intuitive visualizations facilitating better data understanding and communication of findings. Geographic regions, such as North America and Europe, currently hold a significant market share, but the Asia-Pacific region shows promising potential for future growth owing to increasing digitalization and data generation. Key restraints to market growth include the need for specialized skills to effectively utilize these tools and the potential for data bias if not handled appropriately. The competitive landscape is dynamic, with both established players like IBM and emerging companies specializing in niche areas vying for market share. Established players benefit from brand recognition and comprehensive enterprise solutions, while specialized vendors provide innovative features and agile development cycles. Open-source options like KNIME and R packages (Rattle, Pandas Profiling) offer cost-effective alternatives, particularly attracting academic institutions and smaller businesses. The ongoing development of advanced analytics functionalities, such as automated machine learning integration within EDA platforms, will be a significant driver of future market growth. Further, the integration of EDA tools within broader data science platforms is streamlining the overall analytical workflow, contributing to increased adoption and reduced complexity. The market's evolution hinges on enhanced user experience, more robust automation features, and seamless integration with other data management and analytics tools.

The Globalization of Personal Data (GPD) was an international, multi-disciplinary and collaborative research initiative drawing mainly on the social sciences but also including information, computing, technology studies, and law, that explored the implications of processing personal and population data in electronic format from 2004 to 2008. Such data included everything from census statistics to surveillance camera images, from biometric passports to supermarket loyalty cards. The project ma intained a strong concern for ethics, politics and policy development around personal data. The project, funded by the Social Sciences and Humanities Research Council of Canada (SSHRCC) under its Initiative on the New Economy program, conducted research on why surveillance occurs, how it operates, and what this means for people's everyday lives (See http://www.sscqueens.org/projects/gpd). The unique aspect of the GPD included a major international survey on citizens' attitudes to issues of surveillance and privacy. The GPD project was conducted in nine countries: Canada, U.S.A., France, Spain, Hungary, Mexico, Brazil, China, and Japan. Three data files were produced: a Seven-Country file (Canada, U.S.A., France, Spain, Hungary, Mexico, and Brazil), a China file, and a Japan file. Country Report are available for download from QSpace (Queen's University Research and Learning Repository).

The list of frequently occurred genes in the detected logic relationships in the KEGG OC ortholog dataset.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset contains the scripts and dataset used in the study reported at Mining the Technical Roles of GitHub Users paper. The files are described in more detailed below:

• processed_ground_truth.csv: A CSV file with the information of the developers considered in the study. Due to privacy issues, we already preprocessed the dataset to remove identification clues. Please contact the authors in case you need the original one.
• processed_ground_truth_fullstack.csv: Same CSV file but with fullstack developers.
• script.ipynb, utils.py: Source code of the script used in our study.
• Dockerfile, docker-compose.yml, requirements.txt: Files to replicate the code environment used in this study.
• BoW-tuning.csv: List of classifications results for different bag of words parameters.

Reported is the list of 596 protein kinase pairs, consisting of 141 kinases and selectivity profiles of 10,060 multi-kinase inhibitors found in ChEMBL23 high-confidence data. For each of the reported protein kinase pairs, UniProt IDs defining the kinase forming a pair is provided, as well as the shared inhibitors and their selectivity profiles. For each target within the pair, potency value for each compound is reported as pIC50 value, as well as the absolute potency difference used to assess the selectivity profiles.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

According to Cognitive Market Research, the global Lifescience Data Mining And Visualization market size is USD 5815.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 9.60% from 2023 to 2030.

North America held the major market of more than 40% of the global revenue with a market size of USD 2326.08 million in 2023 and will grow at a compound annual growth rate (CAGR) of 7.8% from 2023 to 2030
Europe held the major market of more than 40% of the global revenue with a market size of USD 1744.56 million in 2023 and will grow at a compound annual growth rate (CAGR) of 8.1% from 2023 to 2030. 
Asia Pacific held the fastest growing market of more than 23% of the global revenue with a market size of USD 1337.50 million in 2023 and will grow at a compound annual growth rate (CAGR) of 11.6% from 2023 to 2030
Latin America market held of more than 5% of the global revenue with a market size of USD 290.76 million in 2023 and will grow at a compound annual growth rate (CAGR) of 9.0% from 2023 to 2030
Middle East and Africa market held of more than 2.00% of the global revenue with a market size of USD 116.30 million in 2023 and will grow at a compound annual growth rate (CAGR) of 9.3% from 2023 to 2030
The demand for Lifescience Data Mining And Visualizations is rising due to rapid growth in biological data and increasing emphasis on personalized medicine.
Demand for On-Demand remains higher in the Lifescience Data Mining And Visualization market.
The Pharmaceuticals category held the highest Lifescience Data Mining And Visualization market revenue share in 2023.

Market Dynamics of Lifescience Data Mining And Visualization

Key Drivers of Lifescience Data Mining And Visualization

Advancements in Healthcare Informatics to Provide Viable Market Output

The Lifescience Data Mining and Visualization market are driven by continuous advancements in healthcare informatics. As the life sciences industry generates vast volumes of complex data, sophisticated data mining and visualization tools are increasingly crucial. Advancements in healthcare informatics, including electronic health records (EHRs), genomics, and clinical trial data, provide a wealth of information. Data mining and visualization technologies empower researchers and healthcare professionals to extract meaningful insights, aiding in personalized medicine, drug discovery, and treatment optimization.

August 2020: Johnson & Johnson and Regeneron Pharmaceuticals announced a strategic collaboration to develop and commercialize cancer immunotherapies.

(Source:investor.regeneron.com/news-releases/news-release-details/regeneron-and-cytomx-announce-strategic-research-collaboration)

Rising Focus on Precision Medicine Propel Market Growth

A key driver in the Lifescience Data Mining and Visualization market is the growing focus on precision medicine. As healthcare shifts towards personalized treatment strategies, there is an increasing need to analyze diverse datasets, including genetic, clinical, and lifestyle information. Data mining and visualization tools facilitate the identification of patterns and correlations within this multidimensional data, enabling the development of tailored treatment approaches. The emphasis on precision medicine, driven by advancements in genomics and molecular profiling, positions data mining and visualization as essential components in deciphering the intricate relationships between biological factors and individual health, thereby fostering innovation in life science research and healthcare practices.

In June 2022, SAS Institute Inc. (US) entered into an agreement with Gunvatta (US) to expedite clinical trials and FDA reporting through the SAS Life Science Analytics Framework on Azure.

(Source:www.prnewswire.com/news-releases/clinical-research-and-drug-development-accelerated-via-analytics-301571580.html)

Increasing adoption of artificial intelligence (AI) and machine learning (ML) algorithms is propelling the market growth of life science data mining and visualization

These technologies have revolutionized the ability to analyze and interpret vast, complex datasets in fields such as drug discovery and personalized medicine. For instance, companies like Insitro are utilizing AI-driven models to analyze biological and chemical data, dramatically accelerating drug discovery timelines and optimizing the identification of new therape...

An experiment in web-database access to large multi-dimensional data sets using a standardized experimental platform to determine if the larger scientific community can be given simple, intuitive, and user-friendly web-based access to large microarray data sets. All data in PEPR is also available via NCBI GEO. The structure and goals of PEPR differ from other mRNA expression profiling databases in a number of important ways. * The experimental platform in PEPR is standardized, and is an Affymetrix - only database. All microarrays available in the PEPR web database should ascribe to quality control and standard operating procedures. A recent publication has described the QC/SOP criteria utilized in PEPR profiles ( The Tumor Analysis Best Practices Working Group 2004 ). * PEPR permits gene-based queries of large Affymetrix array data sets without any specialized software. For example, a number of large time series projects are available within PEPR, containing 40-60 microarrays, yet these can be simply queried via a dynamic web interface with no prior knowledge of microarray data analysis. * Projects in PEPR originate from scientists world-wide, but all data has been generated by the Research Center for Genetic Medicine, Children''''s National Medical Center, Washington DC. Future developments of PEPR will allow remote entry of Affymetrix data ascribing to the same QC/SOP protocols. They have previously described an initial implementation of PEPR, and a dynamic web-queried time series graphical interface ( Chen et al. 2004 ). A publication showing the utility of PEPR for pharmacodynamic data has recently been published ( Almon et al. 2003 ).

Motivation

This repository contains the data-set used as a basis for our MSR'2021 paper Mining API Interactions to Analyze Software Revisions for the Evolution of Energy Consumption.

Description of the dataset

The dataset is stored in a file msr_2021_dataset.csv and contains the following data:

id - an individual identifier

sampleNr - a number identifying the group this sample relates to

name - the name of the library examined

className - the class name as an abbreviation

method - the name of the executed method

duration - duration of method execution

durationAdjusted - duration after alignment between method trace and energy profile

energyConsumption - computed energy consumption

watts - recorded wattage

package-names - per package uAPI profile

uApi - the computed uAPI profile value

The files joule_anova_posthoc_result.csv and uAPI_anova_posthoc_result.csv contain the results of the ANOVA and Tukey HSD posthoc analysis to determine accuracy and F1-score of the presented approach.

License

Creative Commons CC-BY

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival rate of 10-15% due to late-stage diagnosis and limited efficacy of existing treatments. This study utilized proteomics-based system modelling to generate multimodal datasets from various research models, including PDAC cells, spheroids, organoids, and tissues derived from murine and human samples. Identical mass spectrometry-based proteomics was applied across the different models. Preparation and validation of the research models and the proteomics were described in detail. The assembly datasets we present here contribute to the data collection on PDAC, which will be useful for systems modeling, data mining, knowledge discovery in databases, and bioinformatics of individual models. Further data analysis may lead to generation of research hypotheses, predictions of targets for diagnosis and treatment and relationships between data variables.

Task

Fake news has become one of the main threats of our society. Although fake news is not a new phenomenon, the exponential growth of social media has offered an easy platform for their fast propagation. A great amount of fake news, and rumors are propagated in online social networks with the aim, usually, to deceive users and formulate specific opinions. Users play a critical role in the creation and propagation of fake news online by consuming and sharing articles with inaccurate information either intentionally or unintentionally. To this end, in this task, we aim at identifying possible fake news spreaders on social media as a first step towards preventing fake news from being propagated among online users.

After having addressed several aspects of author profiling in social media from 2013 to 2019 (bot detection, age and gender, also together with personality, gender and language variety, and gender from a multimodality perspective), this year we aim at investigating if it is possbile to discriminate authors that have shared some fake news in the past from those that, to the best of our knowledge, have never done it.

As in previous years, we propose the task from a multilingual perspective:

• English
• Spanish

NOTE: Although we recommend to participate in both languages (English and Spanish), it is possible to address the problem just for one language.

Data

Input

The uncompressed dataset consists in a folder per language (en, es). Each folder contains:

• A XML file per author (Twitter user) with 100 tweets. The name of the XML file correspond to the unique author id.
• A truth.txt file with the list of authors and the ground truth.

The format of the XML files is:

The format of the truth.txt file is as follows. The first column corresponds to the author id. The second column contains the truth label.

  b2d5748083d6fdffec6c2d68d4d4442d:::0
  2bed15d46872169dc7deaf8d2b43a56:::0
  8234ac5cca1aed3f9029277b2cb851b:::1
  5ccd228e21485568016b4ee82deb0d28:::0
  60d068f9cafb656431e62a6542de2dc0:::1
  ...
  

Output

Your software must take as input the absolute path to an unpacked dataset, and has to output for each document of the dataset a corresponding XML file that looks like this:

The naming of the output files is up to you. However, we recommend to use the author-id as filename and "xml" as extension.

IMPORTANT! Languages should not be mixed. A folder should be created for each language and place inside only the files with the prediction for this language.

Evaluation

The performance of your system will be ranked by accuracy. For each language, we will calculate individual accuracies in discriminating between the two classes. Finally, we will average the accuracy values per language to obtain the final ranking.

Submission

Once you finished tuning your approach on the validation set, your software will be tested on the test set. During the competition, the test set will not be released publicly. Instead, we ask you to submit your software for evaluation at our site as described below.

We ask you to prepare your software so that it can be executed via command line calls. The command shall take as input (i) an absolute path to the directory of the test corpus and (ii) an absolute path to an empty output directory:

mySoftware -i INPUT-DIRECTORY -o OUTPUT-DIRECTORY

Within OUTPUT-DIRECTORY, we require two subfolders: en and es, one folder per language, respectively. As the provided output directory is guaranteed to be empty, your software needs to create those subfolders. Within each of these subfolders, you need to create one xml file per author. The xml file looks like this:

The naming of the output files is up to you. However, we recommend to use the author-id as filename and "xml" as extension.

Note: By submitting your software you retain full copyrights. You agree to grant us usage rights only for the purpose of the PAN competition. We agree not to share your software with a third party or use it for other purposes than the PAN competition.

Related Work

• Bilal Ghanem, Paolo Rosso, Francisco Rangel. An Emotional Analysis of False Information in Social Media and News Articles. arXiv preprint arXiv:1908.09951 (2019). ACM Transactions on Internet Technology (TOIT). In Press.
• Anastasia Giachanou, Paolo Rosso, Fabio Crestani. Leveraging Emotional Signals for Credibility Detection. Proceedings of the 42nd International ACM Conference on Research and Development in Information Retrieval (SIGIR). pp 877–880. (2019)
• Andre Guess, Jonathan Nagler, and Joshua Tucker. Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science Advances vol. 5 (2019)
• Andrew Hall, Loren Terveen, Aaron Halfaker. Bot Detection in Wikidata Using Behavioral and Other Informal Cues. Proceedings of the ACM on Human-Computer Interaction. 2018 Nov 1;2(CSCW):64.
• Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, Gerhard Weikum. DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp 22-32. (2018)
• Francisco Rangel and Paolo Rosso. Overview of the 7th Author Profiling Task at PAN 2019: Bots and Gender Profiling in Twitter. In: L. Cappellato, N. Ferro, D. E. Losada and H. Müller (eds.) CLEF 2019 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings.CEUR-WS.org, vol. 2380
• Francisco Rangel, Paolo Rosso, Martin Potthast, Benno Stein. Overview of the 6th author profiling task at pan 2018: multimodal gender identification in Twitter. In: CLEF 2018 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 2125.
• Francisco Rangel, Paolo Rosso, Martin Potthast, Benno Stein. Overview of the 5th Author Profiling Task at PAN 2017: Gender and Language Variety Identification in Twitter. In: Cappellato L., Ferro N., Goeuriot L, Mandl T. (Eds.) CLEF 2017 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1866.
• Francisco Rangel, Paolo Rosso, Ben Verhoeven, Walter Daelemans, Martin Pottast, Benno Stein. Overview of the 4th Author Profiling Task at PAN 2016: Cross-Genre Evaluations. In: Balog K., Capellato L., Ferro N., Macdonald C. (Eds.) CLEF 2016 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1609, pp. 750-784
• Francisco Rangel, Fabio Celli, Paolo Rosso, Martin Pottast, Benno Stein, Walter Daelemans. Overview of the 3rd Author Profiling Task at PAN 2015.In: Linda Cappelato and Nicola Ferro and Gareth Jones and Eric San Juan (Eds.): CLEF 2015 Labs and Workshops, Notebook Papers, 8-11 September, Toulouse, France. CEUR Workshop Proceedings. ISSN 1613-0073, http://ceur-ws.org/Vol-1391/,2015.
• Francisco Rangel, Paolo Rosso, Irina Chugur, Martin Potthast, Martin Trenkmann, Benno Stein, Ben Verhoeven, Walter Daelemans. Overview of the 2nd Author Profiling Task at PAN 2014. In: Cappellato L., Ferro N., Halvey M., Kraaij W. (Eds.) CLEF 2014 Labs and Workshops, Notebook Papers. CEUR-WS.org, vol. 1180, pp. 898-827.
• Francisco Rangel, Paolo Rosso, Moshe Koppel, Efstatios Stamatatos, Giacomo Inches. Overview of the Author Profiling Task at PAN 2013. In: Forner P., Navigli R., Tufis D. (Eds.)Notebook Papers of CLEF 2013 LABs and Workshops. CEUR-WS.org, vol. 1179
• Francisco Rangel and Paolo Rosso On the Implications of the General Data Protection Regulation on the Organisation of Evaluation Tasks. In: Language and Law / Linguagem e Direito, Vol. 5(2), pp. 80-102
• Kai Shu, Suhang Wang, and Huan Liu. Understanding user profiles on social media for fake news detection. Proceedings of the IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 430--435 (2018)
• Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter. (2017)

Vertical seismic profiling (VSP) surveys done by the Geological Survey of Canada for research into downhole seismic imaging techniques for mineral exploration.

A large-scale dataset on the dynamic profiles based on function calls of 35,974 benign and malicious Android apps from 10 historical years (2010 through 2019). Function calls are a commonly used means to model program behaviors, which may contribute to various code analysis approaches to assuring software correctness, reliability, and security. In particular, our dataset includes dynamic profiles of each app resulting from the same-length of time (10 mins) of being exercised by randomly generated inputs on both emulator and real device, enabling interesting and useful app analysis that reason about app behaviors in an evolutionary perspective while informing the differences of app behaviors on different run-time hardware platforms. Since we have 20 yearly datasets associated with 35,974 unique Android apps across the 10 years, profiling these apps took 12,000 hours. Considering the costs of filtering out apps that were originally sampled but that we were unable to profile (due to various reasons such as broken APKs, not being executable because of incompatibility issues, not instrumentable, etc.), we took over two years to produce all these traces. We hope to save future researchers' time in producing such a set of dynamic data to enable their empirical and technical work. ================== Thanks for your interest in our dataset. Collecting this dataset took tremendous computational and human effort. Thus, please observe the following restrictions in using our dataset: - Do not redistribute this dataset without our consent. - Do not make commercial usage of this dataset. - Get a faculty, or someone in a permanent position, to agree and commit to these conditions. - When publishing your work that uses our dataset, please cite the following MSR 2021 data paper. @inproceedings{AndroidCT, title = {AndroCT: Ten Years of App Call Traces in Android}, author = {Wen Li, Xiaoqin Fu, and Haipeng Cai}, booktitle = {The 18th International Conference on Mining Software Repositories (MSR 2021), Data Showcase Track}, year = {2021}, }