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.

Python code generated in the context of the dissertation 'Improving the semantic quality of conceptual models through text mining. A proof of concept' (Postgraduate studies Big Data & Analytics for Business and Management, KU Leuven Faculty of Economics and Business, 2018)

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

Technical notes and documentation on the common data model of the project CONCEPT-DM2.

This publication corresponds to the Common Data Model (CDM) specification of the CONCEPT-DM2 project for the implementation of a federated network analysis of the healthcare pathway of type 2 diabetes.

Aims of the CONCEPT-DM2 project:

General aim: To analyse chronic care effectiveness and efficiency of care pathways in diabetes, assuming the relevance of care pathways as independent factors of health outcomes using data from real life world (RWD) from five Spanish Regional Health Systems.

Main specific aims:

• To characterize the care pathways in patients with diabetes through the whole care system in terms of process indicators and pharmacologic recommendations
• To compare these observed care pathways with the theoretical clinical pathways derived from the clinical practice guidelines
• To assess if the adherence to clinical guidelines influence on important health outcomes, such as cardiovascular hospitalizations.
• To compare the traditional analytical methods with process mining methods in terms of modeling quality, prediction performance and information provided.

Study Design: It is a population-based retrospective observational study centered on all T2D patients diagnosed in five Regional Health Services within the Spanish National Health Service. We will include all the contacts of these patients with the health services using the electronic medical record systems including Primary Care data, Specialized Care data, Hospitalizations, Urgent Care data, Pharmacy Claims, and also other registers such as the mortality and the population register.

Cohort definition: All patients with code of Type 2 Diabetes in the clinical health records

• Inclusion criteria: patients that, at 01/01/2017 or during the follow-up from 01/01/2017 to 31/12/2022 had active health card (active TIS - tarjeta sanitaria activa) and code of type 2 diabetes (T2D, DM2 in spanish) in the clinical records of primary care (CIAP2 T90 in case of using CIAP code system)
• Exclusion criteria:
  • patients with no contact with the health system from 01/01/2017 to 31/12/2022
  • patients that had a T1D (DM1) code opened after the T2D code during the follow-up.
• Study period. From 01/01/2017 to 31/12/2022

Files included in this publication:

• Datamodel_CONCEPT_DM2_diagram.png
• Common data model specification (Datamodel_CONCEPT_DM2_v.0.1.0.xlsx)
• Synthetic datasets (Datamodel_CONCEPT_DM2_sample_data)
  • sample_data1_dm_patient.csv
  • sample_data2_dm_param.csv
  • sample_data3_dm_patient.csv
  • sample_data4_dm_param.csv
  • sample_data5_dm_patient.csv
  • sample_data6_dm_param.csv
  • sample_data7_dm_param.csv
  • sample_data8_dm_param.csv
• Datamodel_CONCEPT_DM2_explanation.pptx

Question Paper Solutions of chapter Overview and Concepts of Data Warehousing of Data Warehousing & Data Mining, 7th Semester , Information Technology

Data and model checkpoints for paper "Weakly Supervised Concept Map Generation through Task-Guided Graph Translation" by Jiaying Lu, Xiangjue Dong, and Carl Yang. The paper has been accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE).

GT-D2G-*.tar.gz are model checkpoints for GT-D2G variants. These models are trained by seed=27. nyt/dblp/yelp.*.win5.pickle.gz are initial graphs generated by NLP pipelines. glove.840B.restaurant.400d.vec.gz is the pre-trained embedding for the Yelp dataset.

For more instructions, please refer to our GitHub repo.

The SPHERE is students' performance in physics education research dataset. It is presented as a multi-domain learning dataset of students’ performance on physics that has been collected through several research-based assessments (RBAs) established by the physics education research (PER) community. A total of 497 eleventh-grade students were involved from three large and a small public high school located in a suburban district of a high-populated province in Indonesia. Some variables related to demographics, accessibility to literature resources, and students’ physics identity are also investigated. Some RBAs utilized in this data were selected based on concepts learned by the students in the Indonesian physics curriculum. We commenced the survey of students’ understanding on Newtonian mechanics at the end of the first semester using Force Concept Inventory (FCI) and Force and Motion Conceptual Evaluation (FMCE). In the second semester, we assessed the students’ scientific abilities and learning attitude through Scientific Abilities Assessment Rubrics (SAAR) and the Colorado Learning Attitudes about Science Survey (CLASS) respectively. The conceptual assessments were continued at the second semester measured through Rotational and Rolling Motion Conceptual Survey (RRMCS), Fluid Mechanics Concept Inventory (FMCI), Mechanical Waves Conceptual Survey (MWCS), Thermal Concept Evaluation (TCE), and Survey of Thermodynamic Processes and First and Second Laws (STPFaSL). We expect SPHERE could be a valuable dataset for supporting the advancement of the PER field particularly in quantitative studies. For example, there is a need to help advance research on using machine learning and data mining techniques in PER that might face challenges due to the unavailable dataset for the specific purpose of PER studies. SPHERE can be reused as a students’ performance dataset on physics specifically dedicated for PER scholars which might be willing to implement machine learning techniques in physics education.

The detailed datum of the Experiment C.

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 longitudinal nature of the data motivated temporal trend identification in the pediatric EHR datatypes. Over the past three decades (1980-2018), we identified and quantified the temporal trend of 16,460 EHR concepts across measurement, visit, diagnosis, drug, and procedure datatypes.

A database of de-identified supermarket customer transactions. This large simulated dataset was created based on a real data sample.

The 11 Critical Attributes.

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

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

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

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

Technological advances in mass spectrometry (MS) toward more accurate and faster data acquisition result in highly informative but also more complex data sets. Especially the hyphenation of liquid chromatography (LC) and MS yields large data files containing a high amount of compound specific information. Using electrospray-ionization for compounds such as polymers enables highly sensitive detection, yet results in very complex spectra, containing multiply charged ions and adducts. Recent years have seen the development of novel or updated data mining strategies to reduce the MS spectra complexity and to ultimately simplify the data analysis workflow. Among other techniques, the Kendrick mass defect analysis, which graphically highlights compounds containing a given repeating unit, has been revitalized with applications in multiple fields of study, such as lipids and polymers. Especially for the latter, various data mining concepts have been developed, which extend regular Kendrick mass defect analysis to multiply charged ion series. The aim of this work is to collect and subsequently implement these concepts in one of the most popular open-source MS data mining software, i.e., MZmine 2, to make them rapidly available for different MS based measurement techniques and various vendor formats, with a special focus on hyphenated techniques such as LC–MS. In combination with already existing data mining modules, an example data set was processed and simplified, enabling an ever faster evaluation and polymer characterization.

Electronic health records (EHR) represent a rich and relatively untapped resource for characterizing the true nature of clinical practice and for quantifying the degree of inter-relatedness of medical entities such as drugs, diseases, procedures and devices. We provide a unique set of co-occurrence matrices, quantifying the pairwise mentions of 3 million terms mapped onto 1 million clinical concepts, calculated from the raw text of 20 million clinical notes spanning 19 years of data. Co-frequencies were computed by means of a parallelized annotation, hashing, and counting pipeline that was applied over clinical notes from Stanford Hospitals and Clinics. The co-occurrence matrix quantifies the relatedness among medical concepts which can serve as the basis for many statistical tests, and can be used to directly compute Bayesian conditional probabilities, association rules, as well as a range of test statistics such as relative risks and odds ratios. This dataset can be leveraged to quantitatively assess comorbidity, drug-drug, and drug-disease patterns for a range of clinical, epidemiological, and financial applications.

We propose an LDA-based behavior-topic model (B-LDA) which jointly models user topic interests and behavioral patterns. We focus the study of the model on on-line social network settings such as microblogs like Twitter where the textual content is relatively short but user interactions on them are rich.Related Publication: Qiu, M., Zhu, F., & Jiang, J. (2013). It is not just what we say, but how we say them: LDA-based behavior-topic model. In 2013 SIAM International Conference on Data Mining (SDM’13): 2-4 May, Austin, Texas (pp. 794-802). Philadelphia: SIAM. http://doi.org/10.1137/1.9781611972832.88

Top five keyword counts by month.

The 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDGs) represent the world’s plan of action for social inclusion, environmental sustainability and economic development. The mining industry has an unprecedented opportunity to mobilize significant human, physical, technological and financial resources to advance the SDGs.

Mining is a global industry and is often located in remote, ecologically sensitive and less-developed areas that include many indigenous lands and territories. When managed appropriately, it can create jobs, spur innovation and bring investment and infrastructure at a game-changing scale over long time horizons. Yet, if managed poorly, mining can also lead to environmental degradation, displaced populations, inequality and increased conflict, among other challenges.

By mapping the linkages between mining and the SDGs, the aim of this Atlas is to encourage mining companies of all sizes to incorporate relevant SDGs into their business and operations, validate their current efforts and spark new ideas.

The email numbers of the four months.

This report is concerned with an evaluation of mining technology and the development of preliminary mining concepts for tar sand resources. The principal area of investigation in this volume concerns the development of feasible mining methods for recovering tar sand resources in Alabama, Missouri, New Mexico, and Utah. Using information gathered for each region, conceptual mining systems were developed, equipment and labor requirements were specified, and cost evaluations were prepared for each of ten sites. Surface mining, underground mining, and mine-assisted in situ recovery scenarios were developed, based on deposit characteristics at these sites.