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.

Peer-to-peer (P2P) networks are gaining popularity in many applications such as file sharing, e-commerce, and social networking, many of which deal with rich, distributed data sources that can benefit from data mining. P2P networks are, in fact,well-suited to distributed data mining (DDM), which deals with the problem of data analysis in environments with distributed data,computing nodes,and users. This article offers an overview of DDM applications and algorithms for P2P environments,focusing particularly on local algorithms that perform data analysis by using computing primitives with limited communication overhead. The authors describe both exact and approximate local P2P data mining algorithms that work in a decentralized and communication-efficient manner.

Data mining-Handbooks, manuals, etc is a book subject. It includes 4 books, written by 4 different authors.

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

This paper proposes a scalable, local privacy preserving algorithm for distributed Peer-to-Peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more. Unlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions and it is highly scalable. It particularly deals with the distributed computation of the sum of a set of numbers stored at different peers in a P2P network in the context of a P2P web mining application. The proposed optimization based privacy-preserving technique for computing the sum allows different peers to specify different privacy requirements without having to adhere to a global set of parameters for the chosen privacy model. Since distributed sum computation is a frequently used primitive, the proposed approach is likely to have significant impact on many data mining tasks such as multi-party privacy-preserving clustering, frequent itemset mining, and statistical aggregate computation.

The purpose of data mining analysis is always to find patterns of the data using certain kind of techiques such as classification or regression. It is not always feasible to apply classification algorithms directly to dataset. Before doing any work on the data, the data has to be pre-processed and this process normally involves feature selection and dimensionality reduction. We tried to use clustering as a way to reduce the dimension of the data and create new features. Based on our project, after using clustering prior to classification, the performance has not improved much. The reason why it has not improved could be the features we selected to perform clustering are not well suited for it. Because of the nature of the data, classification tasks are going to provide more information to work with in terms of improving knowledge and overall performance metrics. From the dimensionality reduction perspective: It is different from Principle Component Analysis which guarantees finding the best linear transformation that reduces the number of dimensions with a minimum loss of information. Using clusters as a technique of reducing the data dimension will lose a lot of information since clustering techniques are based a metric of 'distance'. At high dimensions euclidean distance loses pretty much all meaning. Therefore using clustering as a "Reducing" dimensionality by mapping data points to cluster numbers is not always good since you may lose almost all the information. From the creating new features perspective: Clustering analysis creates labels based on the patterns of the data, it brings uncertainties into the data. By using clustering prior to classification, the decision on the number of clusters will highly affect the performance of the clustering, then affect the performance of classification. If the part of features we use clustering techniques on is very suited for it, it might increase the overall performance on classification. For example, if the features we use k-means on are numerical and the dimension is small, the overall classification performance may be better. We did not lock in the clustering outputs using a random_state in the effort to see if they were stable. Our assumption was that if the results vary highly from run to run which they definitely did, maybe the data just does not cluster well with the methods selected at all. Basically, the ramification we saw was that our results are not much better than random when applying clustering to the data preprocessing. Finally, it is important to ensure a feedback loop is in place to continuously collect the same data in the same format from which the models were created. This feedback loop can be used to measure the model real world effectiveness and also to continue to revise the models from time to time as things change.

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.

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.

Privacy is as a critical issue in the age of data. Organizations and corporations who publicly share their data always have a major concern that their sensitive information may be leaked or extracted by rivals or attackers using data miners. High-utility itemset mining (HUIM) is an extension to frequent itemset mining (FIM) which deals with business data in the form of transaction databases, data that is also in danger of being stolen. To deal with this, a number of privacy-preserving data mining (PPDM) techniques have been introduced. An important topic in PPDM in the recent years is privacy-preserving utility mining (PPUM). The goal of PPUM is to protect the sensitive information, such as sensitive high-utility itemsets, in transaction databases, and make them undiscoverable for data mining techniques. However, available PPUM methods do not consider the generalization of items in databases (categories, classes, groups, etc.). These algorithms only consider the items at a specialized level, leaving the item combinations at a higher level vulnerable to attacks. The insights gained from higher abstraction levels are somewhat more valuable than those from lower levels since they contain the outlines of the data. To address this issue, this work suggests two PPUM algorithms, namely MLHProtector and FMLHProtector, to operate at all abstraction levels in a transaction database to protect them from data mining algorithms. Empirical experiments showed that both algorithms successfully protect the itemsets from being compromised by attackers.

This dataset is about books and is filtered where the book subjects is Data mining-Social aspects, featuring 9 columns including author, BNB id, book, book publisher, and book subjects. The preview is ordered by publication date (descending).

In response to NASA SBIR topic A1.05, "Data Mining for Integrated Vehicle Health Management", Michigan Aerospace Corporation (MAC) asserts that our unique SPADE (Sparse Processing Applied to Data Exploitation) technology meets a significant fraction of the stated criteria and has functionality that enables it to handle many applications within the aircraft lifecycle. SPADE distills input data into highly quantized features and uses MAC's novel techniques for constructing Ensembles of Decision Trees to develop extremely accurate diagnostic/prognostic models for classification, regression, clustering, anomaly detection and semi-supervised learning tasks. These techniques are currently being employed to do Threat Assessment for satellites in conjunction with researchers at the Air Force Research Lab. Significant advantages to this approach include: 1) completely data driven; 2) training and evaluation are faster than conventional methods; 3) operates effectively on huge datasets (> billion samples X > million features), 4) proven to be as accurate as state-of-the-art techniques in many significant real-world applications. The specific goals for Phase 1 will be to work with domain experts at NASA and with our partners Boeing, SpaceX and GMV Space Systems to delineate a subset of problems that are particularly well-suited to this approach and to determine requirements for deploying algorithms on platforms of opportunity.

This paper proposes a scalable, local privacy preserving algorithm for distributed Peer-to-Peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more. Unlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions and it is highly scalable. It particularly deals with the distributed computation of the sum of a set of numbers stored at different peers in a P2P network in the context of a P2P web mining application. The proposed optimization based privacy-preserving technique for computing the sum allows different peers to specify different privacy requirements without having to adhere to a global set of parameters for the chosen privacy model. Since distributed sum computation is a frequently used primitive, the proposed approach is likely to have significant impact on many data mining tasks such as multi-party privacy-preserving clustering, frequent itemset mining, and statistical aggregate computation.

Privacy is as a critical issue in the age of data. Organizations and corporations who publicly share their data always have a major concern that their sensitive information may be leaked or extracted by rivals or attackers using data miners. High-utility itemset mining (HUIM) is an extension to frequent itemset mining (FIM) which deals with business data in the form of transaction databases, data that is also in danger of being stolen. To deal with this, a number of privacy-preserving data mining (PPDM) techniques have been introduced. An important topic in PPDM in the recent years is privacy-preserving utility mining (PPUM). The goal of PPUM is to protect the sensitive information, such as sensitive high-utility itemsets, in transaction databases, and make them undiscoverable for data mining techniques. However, available PPUM methods do not consider the generalization of items in databases (categories, classes, groups, etc.). These algorithms only consider the items at a specialized level, leaving the item combinations at a higher level vulnerable to attacks. The insights gained from higher abstraction levels are somewhat more valuable than those from lower levels since they contain the outlines of the data. To address this issue, this work suggests two PPUM algorithms, namely MLHProtector and FMLHProtector, to operate at all abstraction levels in a transaction database to protect them from data mining algorithms. Empirical experiments showed that both algorithms successfully protect the itemsets from being compromised by attackers.

Data mining with R : learning with case studies is a book. It was written by Luís Torgo and published by C R C in 2011.

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

United States Number of Job Postings: New: Mining data was reported at 4,396.000 Unit in 17 Mar 2025. This records a decrease from the previous number of 6,113.000 Unit for 10 Mar 2025. United States Number of Job Postings: New: Mining data is updated weekly, averaging 1,113.500 Unit from Jan 2008 (Median) to 17 Mar 2025, with 898 observations. The data reached an all-time high of 16,507.000 Unit in 16 May 2022 and a record low of 75.000 Unit in 28 Apr 2008. United States Number of Job Postings: New: Mining data remains active status in CEIC and is reported by Revelio Labs, Inc.. The data is categorized under Global Database’s United States – Table US.RL.JP: Number of Job Postings: New: by Industry.

Serbia Construction Work Value: CE: IS: Mining or Extraction data was reported at 1,168,060.000 RSD th in 2016. This records an increase from the previous number of 871,462.000 RSD th for 2015. Serbia Construction Work Value: CE: IS: Mining or Extraction data is updated yearly, averaging 380,207.000 RSD th from Dec 1994 (Median) to 2016, with 23 observations. The data reached an all-time high of 1,221,953.000 RSD th in 2008 and a record low of 22,757.000 RSD th in 1995. Serbia Construction Work Value: CE: IS: Mining or Extraction data remains active status in CEIC and is reported by Statistical Office of the Republic of Serbia. The data is categorized under Global Database’s Serbia – Table RS.EA002: Construction Work Value.

Please cite the following paper when using this dataset:N. Thakur, "Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research Questions", Journal of Analytics, Volume 1, Issue 2, 2022, pp. 72-97, DOI: https://doi.org/10.3390/analytics1020007AbstractThe exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to increase by multiple times its current value within the next two years. Therefore, it is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction, towards exoskeletons, for which the availability of Big Data of conversations about exoskeletons is necessary. The Internet of Everything style of today’s living, characterized by people spending more time on the internet than ever before, with a specific focus on social media platforms, holds the potential for the development of such a dataset by the mining of relevant social media conversations. Twitter, one such social media platform, is highly popular amongst all age groups, where the topics found in the conversation paradigms include emerging technologies such as exoskeletons. To address this research challenge, this work makes two scientific contributions to this field. First, it presents an open-access dataset of about 140,000 Tweets about exoskeletons that were posted in a 5-year period from 21 May 2017 to 21 May 2022. Second, based on a comprehensive review of the recent works in the fields of Big Data, Natural Language Processing, Information Retrieval, Data Mining, Pattern Recognition, and Artificial Intelligence that may be applied to relevant Twitter data for advancing research, innovation, and discovery in the field of exoskeleton research, a total of 100 Research Questions are presented for researchers to study, analyze, evaluate, ideate, and investigate based on this dataset.

In 2019 there were 81,491 thousand people employed in the U.S. coal mining industry, of which only nine percent were women. By 2023, U.S. coal employment shrank by approximately 13,000 jobs, to 68,623. The share of women employees in U.S. coal mining amounted to seven percent in 2023.

This dataset is about books and is filtered where the book is Data mining for bioinformatics applications, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).