Dataset created as part of the Master Thesis "Business Intelligence – Automation of Data Marts modeling and its data processing".

Lucerne University of Applied Sciences and Arts

Master of Science in Applied Information and Data Science (MScIDS)

Autumn Semester 2022

Change log Version 1.1:

The following SQL scripts were added:

    Index
    Type
    Name


    1
    View
    pg.dictionary_table


    2
    View
    pg.dictionary_column


    3
    View
    pg.dictionary_relation


    4
    View
    pg.accesslayer_table


    5
    View
    pg.accesslayer_column


    6
    View
    pg.accesslayer_relation


    7
    View
    pg.accesslayer_fact_candidate


    8
    Stored Procedure
    pg.get_fact_candidate


    9
    Stored Procedure
    pg.get_dimension_candidate


    10
    Stored Procedure
    pg.get_columns

Scripts are based on Microsoft SQL Server Version 2017 and compatible with a data warehouse built with Datavault Builder. Data warehouse objects scripts of the sample data warehouse are restricted and cannot be shared.

Dataset

This dataset was created by Abdul Hamith

Contents

Data on approximately 2 million births occurring in NJ, OH, and PA from 2000 - 2005. Linked to PM2.5 and ozone concentration estimates from EPA CMAQ fused model. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Birth data can be acquired through application to the state health statistics departments of NJ, OH, and PA. Contact author for code. rappazzo.kristen@epa.gov. Format: No data included. This dataset is associated with the following publication: Rappazzo, K., D. Lobdell, L. Messer, C. Poole, and J. Daniels. Comparison of gestational dating methods and implications for exposure-outcome associations: an example with PM2.5 and preterm birth. JOURNAL OF OCCUPATIONAL AND ENVIRONMENTAL MEDICINE. Lippincott Williams & Wilkins, Philadelphia, PA, USA, 74(2): 138-143, (2017).

Using data sourced from Twitter, Reddit and TikTok which was synthesised from an independent sample of 133,667 data privacy officers worldwide, 12 months to 6th July 2024, we asked them a number of questions including the biggest challenges in responding to DSARs. The top reasons found were Data Protection Regulations Compliance (41.0%), Stakeholders Communication / Coordination (27.2%), Maintaining Data Privacy and Security (24.9%), Handling Sensitive or Confidential Information (3.9%), and Managing Large Volumes of Data (2.9%).

Dataset

This dataset was created by nguyenthanhktdt

Contents

Report Overview

The global Privacy Management Software market has become a vital sector in the technology landscape. With increasingly sophisticated cyber threats, organizations are investing heavily in advanced solutions. In 2023, the market value stood at USD 3.0 billion, and it is projected to soar to USD 83.7 billion by 2033, growing at an impressive CAGR of 39.50% between 2024 and 2033. This surge is fueled by the rapid adoption of digital transformation strategies, growing reliance on cloud infrastructure, and the ever-increasing risk of cyberattacks.

AI and ML are playing a pivotal role in automating privacy management processes. These technologies enable real-time data monitoring, identify compliance risks, and offer predictive insights to mitigate potential breaches. For instance, AI-based solutions can now detect anomalies in large data sets, improving compliance efficiency. By 2024, over 40% of privacy management tools will incorporate AI-driven analytics.

With regulations such as GDPR, CCPA, and China's Personal Information Protection Law (PIPL), companies are prioritizing consumer rights like data portability, the right to be forgotten, and opt-out preferences. Privacy management solutions are increasingly equipped with features to address these rights efficiently. For example, the demand for data subject access request (DSAR) management tools has surged by nearly 35% annually.

Privacy management software is being integrated with broader cybersecurity platforms to create unified solutions. This integration helps companies streamline compliance while protecting data from unauthorized access. Gartner predicts that by 2025, 60% of the privacy management software market will be bundled with cybersecurity suites to address overlapping challenges.

Industries like healthcare, finance, and e-commerce are seeing tailored privacy management solutions that cater to specific compliance needs. For example, healthcare providers are adopting tools to meet HIPAA compliance, while financial institutions are leveraging software that ensures data security in line with GDPR and PSD2 regulations.

Organizations are increasingly concerned about the data shared with third-party vendors. Privacy management tools now include third-party risk assessment capabilities to evaluate vendor compliance with privacy standards. According to a recent survey, 55% of organizations implemented third-party risk management in 2023, a figure expected to grow significantly in 2024.

As businesses migrate to cloud environments, cloud-based privacy management software is becoming a preferred choice due to its scalability and ease of integration. Currently, 67% of businesses prefer cloud-based solutions, a number anticipated to grow as remote work and digital transformation expand.

Governments worldwide are enforcing data localization rules, requiring businesses to store user data within specific geographic boundaries. Privacy management tools now offer features to ensure compliance with such laws, enabling organizations to align with region-specific data storage requirements.

To meet growing consumer expectations, organizations are deploying privacy dashboards that allow users to view, manage, and delete their data. These dashboards are becoming a standard feature, with 30% of companies globally adopting them in 2023 to improve transparency.

Organizatio...

Example data matrix for seven subjects with sums of categories (cat1, cat2, cat3) and sums of pictures (A, B, C).

Dataset

This dataset was created by VINAY CHAKRAVARTHI

Released under Apache 2.0

Contents

Data Subject Request Orchestration Market Outlook

According to our latest research, the global Data Subject Request Orchestration market size reached USD 1.34 billion in 2024. The market is experiencing robust expansion, with a recorded compound annual growth rate (CAGR) of 23.1% from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of USD 10.38 billion. This impressive growth is primarily driven by the increasing regulatory emphasis on data privacy and the need for organizations to efficiently manage and automate data subject requests (DSRs) in compliance with global privacy regulations such as GDPR, CCPA, and others.

One of the most significant growth factors for the Data Subject Request Orchestration market is the escalating complexity and volume of privacy regulations worldwide. With the proliferation of data privacy laws, organizations are under mounting pressure to respond to DSRs in a timely and compliant manner. This has heightened the demand for orchestration platforms that can automate and streamline the management of these requests across various data sources and business units. The need to avoid hefty fines and reputational damage has prompted enterprises to invest in robust DSR orchestration solutions, further propelling market growth.

Another key driver is the rapid digital transformation across industries, which has resulted in exponential growth in data generation and storage. As organizations collect and process more personal data, the likelihood and frequency of receiving DSRs from customers, employees, and partners have increased significantly. This shift necessitates advanced orchestration tools capable of handling large volumes of requests efficiently, ensuring transparency, and maintaining customer trust. Moreover, the integration of artificial intelligence and automation technologies within these platforms has further enhanced their efficiency, scalability, and accuracy, making them indispensable for modern enterprises.

The rising adoption of cloud-based solutions is also contributing to the market’s expansion. Cloud deployment offers scalability, flexibility, and cost-effectiveness, making it particularly attractive for small and medium enterprises (SMEs) with limited IT resources. The ability to rapidly deploy DSR orchestration solutions without extensive infrastructure investment has democratized access to privacy compliance tools. Furthermore, the increasing awareness among organizations about the reputational and operational risks associated with non-compliance is driving proactive investments in data subject request orchestration, fueling sustained market growth.

From a regional perspective, North America currently dominates the Data Subject Request Orchestration market, accounting for the largest revenue share in 2024. This leadership position is attributed to the region’s advanced regulatory environment, early adoption of privacy technologies, and the presence of major market players. However, Europe and Asia Pacific are rapidly emerging as high-growth regions, driven by stringent privacy regulations and increasing digitalization. The Asia Pacific region, in particular, is witnessing accelerated adoption due to rising awareness and evolving regulatory frameworks. Collectively, these regional trends are shaping the global trajectory of the Data Subject Request Orchestration market.

Component Analysis

The Data Subject Request Orchestration market is segmented by component into software and services. The software segment holds the largest share, driven by the increasing need for robust platforms that can automate and manage the end-to-end DSR process. Modern orchestration software offers advanced features such as workflow automation, real-time monitoring, comprehensive reporting, and integration with existing enterprise systems. These capabilities are crucial for organizations seeking to efficiently handle large volumes of data subject requests while maintaining com

3D skeletons UP-Fall Dataset

                      Different between Fall and Impact detection 

Overview

This dataset aims to facilitate research in fall detection, particularly focusing on the precise detection of impact moments within fall events. The 3D skeletons data accuracy and comprehensiveness make it a valuable resource for developing and benchmarking fall detection algorithms. The dataset contains 3D skeletal data extracted from fall events and daily activities of 5 subjects performing fall scenarios

Data Collection

The skeletal data was extracted using a pose estimation algorithm, which processes images frames to determine the 3D coordinates of each joint. Sequences with less than 100 frames of extracted data were excluded to ensure the quality and reliability of the dataset. As a result, some subjects may have fewer CSV files.

CSV Structure

The data is organized by subjects, and each subject contains CSV files named according to the pattern C1S1A1T1, where:

C: Camera (1 or 2)

S: Subject (1 to 5)

A: Activity (1 to N, representing different activities)

T: Trial (1 to 3)

subject1/`: Contains CSV files for Subject 1.

C1S1A1T1.csv: Data from Camera 1, Activity 1, Trial 1 for Subject 1

C1S1A2T1.csv: Data from Camera 1, Activity 2, Trial 1 for Subject 1

C1S1A3T1.csv: Data from Camera 1, Activity 3, Trial 1 for Subject 1

C2S1A1T1.csv: Data from Camera 2, Activity 1, Trial 1 for Subject 1

C2S1A2T1.csv: Data from Camera 2, Activity 2, Trial 1 for Subject 1

C2S1A3T1.csv: Data from Camera 2, Activity 3, Trial 1 for Subject 1

subject2/`: Contains CSV files for Subject 2.

C1S2A1T1.csv: Data from Camera 1, Activity 1, Trial 1 for Subject 2

C1S2A2T1.csv: Data from Camera 1, Activity 2, Trial 1 for Subject 2

C1S2A3T1.csv: Data from Camera 1, Activity 3, Trial 1 for Subject 2

C2S2A1T1.csv: Data from Camera 2, Activity 1, Trial 1 for Subject 2

C2S2A2T1.csv: Data from Camera 2, Activity 2, Trial 1 for Subject 2

C2S2A3T1.csv: Data from Camera 2, Activity 3, Trial 1 for Subject 2

subject3/, subject4/, subject5/: Similar structure as above, but may contain fewer CSV files due to the data extraction criteria mentioned above.

Column Descriptions

Each CSV file contains the following columns representing different skeletal joints and their respective coordinates in 3D space:

Column Name

Description

joint_1_x

X coordinate of joint 1

joint_1_y

Y coordinate of joint 1

joint_1_z

Z coordinate of joint 1

joint_2_x

X coordinate of joint 2

joint_2_y

Y coordinate of joint 2

joint_2_z

Z coordinate of joint 2

...

...

joint_n_x

X coordinate of joint n

joint_n_y

Y coordinate of joint n

joint_n_z

Z coordinate of joint n

LABEL

Label indicating impact (1) or non-impact (0)

Example

Here is an example of what a row in one of the CSV files might look like:

joint_1_x

joint_1_y

joint_1_z

joint_2_x

joint_2_y

joint_2_z

...

joint_n_x

joint_n_y

joint_n_33

LABEL

0.123

0.456

0.789

0.234

0.567

0.890

...

0.345

0.678

0.901

0

Usage

This data can be used for developing and benchmarking impact fall detection algorithms. It provides detailed information on human posture and movement during falls, making it suitable for machine learning and deep learning applications in impact fall detection and prevention.

Using github

1. Clone the repository:

   -bash git clone

https://github.com/Tresor-Koffi/3D_skeletons-UP-Fall-Dataset

1. Navigate to the directory:

   -bash -cd 3D_skeletons-UP-Fall-Dataset

Examples

Here's a simple example of how to load and inspect a sample data file using Python:```pythonimport pandas as pd

Load a sample data file for Subject 1, Camera 1, Activity 1, Trial 1

data = pd.read_csv('subject1/C1S1A1T1.csv')print(data.head())

This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 820820. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). Content in this publication reflects the authors' view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.

Dataset

This dataset was created by OH SEOK KIM

Contents

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..The age dependency ratio is derived by dividing the combined under-18 and 65-and-over populations by the 18-to-64 population and multiplying by 100..The old-age dependency ratio is derived by dividing the population 65 and over by the 18-to-64 population and multiplying by 100..The child dependency ratio is derived by dividing the population under 18 by the 18-to-64 population and multiplying by 100..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

These data contain the results of GC-MS, LC-MS and immunochemistry analyses of mask sample extracts. The data include tentatively identified compounds through library searches and compound abundance. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The data can not be accessed. Format: The dataset contains the identification of compounds found in the mask samples as well as the abundance of those compounds for individuals who participated in the trial. This dataset is associated with the following publication: Pleil, J., M. Wallace, J. McCord, M. Madden, J. Sobus, and G. Ferguson. How do cancer-sniffing dogs sort biological samples? Exploring case-control samples with non-targeted LC-Orbitrap, GC-MS, and immunochemistry methods. Journal of Breath Research. Institute of Physics Publishing, Bristol, UK, 14(1): 016006, (2019).

a) Defined as living with partner/family.b) Defined as studies on University level.c) Defined as employment in the open labor.d) Defined as prescribed medication taken daily.*A twin pair unaffected by schizophrenia or bipolar disorder were on antidepressants at the time of lumbar puncture.

The data set is a collection of environmental records associated with the individual events. The data set has been generated using the serdif-api wrapper (https://github.com/navarral/serdif-api) when sending a CSV file with example events for the Republic of Ireland. The serdif-api send a semantic query that (i) selects the environmental data sets within the region of the event, (ii) filters by the specific period of interest from the event, (iii) aggregates the data sets using the minimum, maximum, average or sum for each of the available variables for a specific time unit. The aggregation method and the time unit can be passed to the serdif-api through the Command Line Interface (CLI) (see example in https://github.com/navarral/serdif-api). The resulting data set format can be also specified as data table (CSV) or as graph (RDF) for analysis and publication as FAIR data. The open-ready data for research is retrieved as a zip file that contains: (i) data as csv: environmental data associated to particular events as a data table (ii) data as rdf: environmental data associated to particular events as a graph (iii) metadata for publication as rdf: metadata record with generalized information about the data that do not contain personal data as a graph; therefore, publishable. (iv) metadata for research as rdf: metadata records with detailed information about the data, such as individual dates, regions, data sets used and data lineage; which could lead to data privacy issues if published without approval from the Data Protection Officer (DPO) and data controller.

The present dataset was used in a corpus study on the diachrony of subject placement in the history of Latin, to appear in 'Catalan Journal of Linguistics'. The main file contains a set of Latin examples, which have all been annotated for a number of variables needed for the purpose of the study. A detailed description of the contents of this dataset is given in the README file. Finally there is a file with the R-code used to produce all the quantitative data mentioned in the paper. Below you can find the abstract of the article. Abstract The aim of this paper is to provide further support for one aspect of the analysis of Classical and Late Latin clause structure proposed in Danckaert (2017a), namely the diachrony of subject placement. According to the relevant proposal, one needs to distinguish an earlier grammar (‘Grammar A’, whose heyday is the period from ca. 200 BC until 200 AD), in which there is no A-movement for subjects, and a later grammar (‘Grammar B’, which is on the rise from ca. 50-100 AD, and fully productive from ca. 200 AD onwards), where subjects optionally move to the inflectional layer. Assuming the variationist acquisition model of language change developed in Yang (2000, 2002a,b), I present corpus evidence which confirms that it is only in the Late Latin period that TP-internal subjects fully establish themselves as a grammatical option.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Data about computer and Internet use were collected by asking respondents to select "Yes" or "No" to each type of computer and each type of Internet subscription. Therefore, respondents were able to select more than one type of computer and more than one type of Internet subscription..The category "Has a computer" includes those who said "Yes" to at least one of the following types of computers: Desktop or laptop; smartphone; tablet or other portable wireless computer; or some other type of computer. The category "No computer" consists of those who said "No" to all of these types of computers..An Internet "subscription" refers to a type of service that someone pays for to access the Internet such as a cellular data plan, broadband such as cable, fiber optic or DSL, or other type of service. This will normally refer to a service that someone is billed for directly for Internet alone or sometimes as part of a bundle..The category "With a broadband Internet subscription" refers to those who said "Yes" to at least one of the following types of Internet subscriptions: Broadband such as cable, fiber optic, or DSL; a cellular data plan; satellite; a fixed wireless subscription; or other non-dial up subscription types. The category "Without an Internet subscription" includes those who accessed the Internet without a subscription and also those with no Internet access at all..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing est...

Data and R code are provided for statistical analysis of approximately 39,000 corpus examples of predicate agreement in constructions with quantified subjects in Russian. The analysis indicates that these constructions constitute a network of constructions (“allostructions”) with various preferences for singular or plural agreement. Factors pull in different directions, and we observe a relatively stable situation in the face of variation. We present an analysis of a multidimensional network of allostructions in Russian, thus contributing to our understanding of allostructional relationships in Construction Grammar. With regard to historical linguistics, language stability is an understudied field. We illustrate an interplay of divergent factors that apparently resists language change. The syntax of numerals and other quantifiers represents a notoriously complex phenomenon of the Russian language. Our study sheds new light on the contributions of factors that favor singular or plural agreement in sentences with quantified subjects.

This is just an example data set. This is just an example data set. This is just an example data set. This is just an example data set. This is just an example data set. This is just an example data set. This is just an example data set. This is just an example data set. This is just an example data set. This is just an example data set. This is just an example data set. This is just an example data set. This is just an example data set. This is just an example data set.