The data for case studies.

PADMINI: A PEER-TO-PEER DISTRIBUTED ASTRONOMY DATA MINING SYSTEM AND A CASE STUDY TUSHAR MAHULE, KIRK BORNE, SANDIPAN DEY, SUGANDHA ARORA, AND HILLOL KARGUPTA** Abstract. Peer-to-Peer (P2P) networks are appealing for astronomy data mining from virtual observatories because of the large volume of the data, compute-intensive tasks, potentially large number of users, and distributed nature of the data analysis process. This paper offers a brief overview of PADMINI—a Peer-to-Peer Astronomy Data MINIng system. It also presents a case study on PADMINI for distributed outlier detection using astronomy data. PADMINI is a webbased system powered by Google Sky and distributed data mining algorithms that run on a collection of computing nodes. This paper offers a case study of the PADMINI evaluating the architecture and the performance of the overall system. Detailed experimental results are presented in order to document the utility and scalability of the system.

The data for all uncertain parameters.

NASA has some of the largest and most complex data sources in the world, with data sources ranging from the earth sciences, space sciences, and massive distributed engineering data sets from commercial aircraft and spacecraft. This talk will discuss some of the issues and algorithms developed to analyze and discover patterns in these data sets. We will also provide an overview of a large research program in Integrated Vehicle Health Management. The goal of this program is to develop advanced technologies to automatically detect, diagnose, predict, and mitigate adverse events during the flight of an aircraft. A case study will be presented on a recent data mining analysis performed to support the Flight Readiness Review of the Space Shuttle Mission STS-119.

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.

With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner’s Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research.

Yearly citation counts for the publication titled "Data mining approach to monitoring the requirements of the job market: A case study".

Yearly citation counts for the publication titled "Handling missing values in data mining - A case study of heart failure dataset".

These are artificially made beginner data mining datasets for learning purposes.

Case study:

• FEELS LIKE HOME is an interior design company, which has about 100 000 registered customers and provide services for more than 200 000 clients annually.
• The range of the products can be divided in 5 major classes: Decor accessories, Furniture, Textiles, Lighting and Art with an option to purchase Limited Edition versions for an extra charge. These goods can be distributed by 3 channels: Physical stores, yearly catalogs and the companies’ website.
• FEELS LIKE HOME has been doing a great job during recent years, achieving decent profits and revenues, but the future remains volatile. In order to solve the problem of instability the company is planning to launch new marketing program, especially to improve the accuracy of marketing campaigns.

The aim of FeelsLikeHome_Campaign dataset is to create project is in which you build a predictive model (using a sample of 2500 clients’ data) forecasting the highest profit from the next marketing campaign, which will indicate the customers who will be the most likely to accept the offer.

The aim of FeelsLikeHome_Cluster dataset is to create project in which you split company’s customer base on homogenous clusters (using 5000 clients’ data) and propose draft marketing strategies for these groups based on customer behavior and information about their profile.

FeelsLikeHome_Score dataset can be used to calculate total profit from marketing campaign and for producing a list of sorted customers by the probability of the dependent variable in predictive model problem.

Results of running KHC on our case study.

The coupling values of the classes in our case study.

About Dataset

This case study is a part of Google Data Analytics course. Cyclistic is a fictional bike-sharing company, however, the data is real. It encompasses information about bike-sharing stations in Chicago and total rides with rented bikes during more than 10 years, from 2013 until February 2023.

The business task is to help design the marketing strategy. The project owner aims at converting casual riders into annual members. To achieve that goal the marketing team needs to better understand how annual members and casual riders differ in using rented bikes.

My specific task was to analyze the available data of rides and provide 3 main recommendation for the marketing strategy, based on the data analysis.

The requirement was to analyze the data for the last 12 months. However, I decided to use the whole dataset, since it was openly available for the whole period of operations.

Data License Agreement

Lyft Bikes and Scooters, LLC (“Bikeshare”) operates the City of Chicago’s (“City”) Divvy bicycle sharing service. Bikeshare and the City are committed to supporting bicycling as an alternative transportation option. As part of that commitment, the City permits Bikeshare to make certain Divvy system data owned by the City (“Data”) available to the public, subject to the terms and conditions of this License Agreement (“Agreement”). By accessing or using any of the Data, you agree to all of the terms and conditions of this Agreement.

License. Bikeshare hereby grants to you a non-exclusive, royalty-free, limited, perpetual license to access, reproduce, analyze, copy, modify, distribute in your product or service and use the Data for any lawful purpose (“License”). Prohibited Conduct. The License does not authorize you to do, and you will not do or assist others in doing, any of the following

Use the Data in any unlawful manner or for any unlawful purpose; Host, stream, publish, distribute, sublicense, or sell the Data as a stand-alone dataset; provided, however, you may include the Data as source material, as applicable, in analyses, reports, or studies published or distributed for non-commercial purposes; Access the Data by means other than the interface Bikeshare provides or authorizes for that purpose; Circumvent any access restrictions relating to the Data; Use data mining or other extraction methods in connection with Bikeshare's website or the Data; Attempt to correlate the Data with names, addresses, or other information of customers or Members of Bikeshare; and State or imply that you are affiliated, approved, endorsed, or sponsored by Bikeshare. Use or authorize others to use, without the written permission of the applicable owners, the trademarks or trade names of Lyft Bikes and Scooters, LLC, the City of Chicago or any sponsor of the Divvy service. These marks include, but are not limited to DIVVY, and the DIVVY logo, which are owned by the City of Chicago. No Warranty. THE DATA IS PROVIDED “AS IS,” AS AVAILABLE (AT BIKESHARE’S SOLE DISCRETION) AND AT YOUR SOLE RISK. TO THE MAXIMUM EXTENT PROVIDED BY LAW BIKESHARE DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. BIKESHARE FURTHER DISCLAIMS ANY WARRANTY THAT THE DATA WILL MEET YOUR NEEDS OR WILL BE OR CONTINUE TO BE AVAILABLE, COMPLETE, ACCURATE, TIMELY, SECURE, OR ERROR FREE.

Limitation of Liability and Covenant Not to Sue. Bikeshare, its parent, affiliates and sponsors, and their respective directors, officers, employees, or agents will not be liable to you or anyone else for any loss or damage, including any direct, indirect, incidental, and consequential damages, whether foreseeable or not, based on any theory of liability, resulting in whole or in part from your access to or use of the Data. You will not bring any claim for damages against any of those persons or entities in any court or otherwise arising out of or relating to this Agreement, the Data, or your use of the Data. In any event, if you were to bring and prevail on such a claim, your maximum recovery is limited to $100 in the aggregate even if you or they had been advised of the possibility of liability exceeding that amount. Ownership and Provision of Data. The City of Chicago owns all right, title, and interest in the Data. Bikeshare may modify or cease providing any or all of the Data at any time, without notice, in its sole discretion. No Waiver. Nothing in this Agreement is or implies a waiver of any rights Bikeshare or the City of Chicago has in the Data or in any copyrights, patents, or trademarks owned or licensed by Bikeshare, its parent, affiliates or sponsors. The DIVVY trademarks are owned by the City of Chicago. Termination of Agreement. Bikeshare may terminate this Agreement at any time and for any reason in its sole discretion. Termination will be effective ...

Yearly citation counts for the publication titled "The Potential of Text Mining in Data Integration and Network Biology for Plant Research: A Case Study onArabidopsis".

Data present in references cited are in graphs, tables, and/or text. This dataset is not publicly accessible because: Data are publicly available in the references cited. It can be accessed through the following means: Accessing the references. Format: Journal articles (references for the review article) contain data on which percent efficiencies (when presented in the manuscript) were calculated. This dataset is associated with the following publication: Butler, B.A., and L.E. Brase. Critical Review of Field Studies of Chemical Surface Coatings to Mitigate Leaching from Mining Wastes. Mine Water and the Environment. Springer-Verlag, BERLIN-HEIDELBERG, GERMANY, 43: 03-15, (2024).

Data from a study to critically examine some of the issues of using data from ToxRefDB, a database largely composed of guideline studies for pesticidal active ingredients, using a case study focusing on chemically-induced anemia. This dataset is associated with the following publication: Judson, R.S., M. Martin, G. Patlewicz, and C.E. Wood. (Reg. Tox. Pharm.) Retrospective Mining of Toxicology Data to Discover Multispecies and Chemical Class Effects: Anemia as a Case Study. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 86: 74-92, (2017).

Results of running KSA for Extract Message Refactoring on our case study.

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.

Summary of literature review.

This collection comprises the manifest and the two generated XES logs from the case study in [1]. The manifest specifies which data related to the execution of the CryptoKitties smart contracts was retrieved from the transaction log of the public Ethereum blockchain. The manifest also specifies how this data was transformed into the two XES logs. Details regarding the approach to extracting XES logs from the Ethereum blockchain and the case study are provided in [1]. For more information on the XES standard see http://www.xes-standard.org.

[1] Klinkmüller, C., Ponomarev, A., Tran, A., Weber, I., and van der Aalst, W.: "Mining Blockchain Processes: Extracting Process Mining Data from Blockchain Applications", Blockchain Forum at BPM 2019.

Qualitative data gathered from interviews that were conducted with case organisations. The data is analysed using a qualitative data analysis tool (AtlasTi) to code and generate network diagrams. Software such as Atlas.ti 8 Windows will be a great advantage to use in order to view these results. Interviews were conducted with four case organisations. The details of the responses from the respondents from case organisations are captured. The data gathered during the interview sessions is captured in a tabular form and graphs were also created to identify trends. Also in this study is desktop review of the case organisations that formed part of the study. The desktop study was done using published annual reports over a period of more than seven years. The analysis was done given the scope of the project and its constructs.