The LSC (Leicester Scientific Corpus)

April 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk) Supervised by Prof Alexander Gorban and Dr Evgeny MirkesThe data are extracted from the Web of Science [1]. You may not copy or distribute these data in whole or in part without the written consent of Clarivate Analytics.[Version 2] A further cleaning is applied in Data Processing for LSC Abstracts in Version 1*. Details of cleaning procedure are explained in Step 6.* Suzen, Neslihan (2019): LSC (Leicester Scientific Corpus). figshare. Dataset. https://doi.org/10.25392/leicester.data.9449639.v1.Getting StartedThis text provides the information on the LSC (Leicester Scientific Corpus) and pre-processing steps on abstracts, and describes the structure of files to organise the corpus. This corpus is created to be used in future work on the quantification of the meaning of research texts and make it available for use in Natural Language Processing projects.LSC is a collection of abstracts of articles and proceeding papers published in 2014, and indexed by the Web of Science (WoS) database [1]. The corpus contains only documents in English. Each document in the corpus contains the following parts:1. Authors: The list of authors of the paper2. Title: The title of the paper 3. Abstract: The abstract of the paper 4. Categories: One or more category from the list of categories [2]. Full list of categories is presented in file ‘List_of _Categories.txt’. 5. Research Areas: One or more research area from the list of research areas [3]. Full list of research areas is presented in file ‘List_of_Research_Areas.txt’. 6. Total Times cited: The number of times the paper was cited by other items from all databases within Web of Science platform [4] 7. Times cited in Core Collection: The total number of times the paper was cited by other papers within the WoS Core Collection [4]The corpus was collected in July 2018 online and contains the number of citations from publication date to July 2018. We describe a document as the collection of information (about a paper) listed above. The total number of documents in LSC is 1,673,350.Data ProcessingStep 1: Downloading of the Data Online

The dataset is collected manually by exporting documents as Tab-delimitated files online. All documents are available online.Step 2: Importing the Dataset to R

The LSC was collected as TXT files. All documents are extracted to R.Step 3: Cleaning the Data from Documents with Empty Abstract or without CategoryAs our research is based on the analysis of abstracts and categories, all documents with empty abstracts and documents without categories are removed.Step 4: Identification and Correction of Concatenate Words in AbstractsEspecially medicine-related publications use ‘structured abstracts’. Such type of abstracts are divided into sections with distinct headings such as introduction, aim, objective, method, result, conclusion etc. Used tool for extracting abstracts leads concatenate words of section headings with the first word of the section. For instance, we observe words such as ConclusionHigher and ConclusionsRT etc. The detection and identification of such words is done by sampling of medicine-related publications with human intervention. Detected concatenate words are split into two words. For instance, the word ‘ConclusionHigher’ is split into ‘Conclusion’ and ‘Higher’.The section headings in such abstracts are listed below:

Background Method(s) Design Theoretical Measurement(s) Location Aim(s) Methodology Process Abstract Population Approach Objective(s) Purpose(s) Subject(s) Introduction Implication(s) Patient(s) Procedure(s) Hypothesis Measure(s) Setting(s) Limitation(s) Discussion Conclusion(s) Result(s) Finding(s) Material (s) Rationale(s) Implications for health and nursing policyStep 5: Extracting (Sub-setting) the Data Based on Lengths of AbstractsAfter correction, the lengths of abstracts are calculated. ‘Length’ indicates the total number of words in the text, calculated by the same rule as for Microsoft Word ‘word count’ [5].According to APA style manual [6], an abstract should contain between 150 to 250 words. In LSC, we decided to limit length of abstracts from 30 to 500 words in order to study documents with abstracts of typical length ranges and to avoid the effect of the length to the analysis.

Step 6: [Version 2] Cleaning Copyright Notices, Permission polices, Journal Names and Conference Names from LSC Abstracts in Version 1Publications can include a footer of copyright notice, permission policy, journal name, licence, author’s right or conference name below the text of abstract by conferences and journals. Used tool for extracting and processing abstracts in WoS database leads to attached such footers to the text. For example, our casual observation yields that copyright notices such as ‘Published by Elsevier ltd.’ is placed in many texts. To avoid abnormal appearances of words in further analysis of words such as bias in frequency calculation, we performed a cleaning procedure on such sentences and phrases in abstracts of LSC version 1. We removed copyright notices, names of conferences, names of journals, authors’ rights, licenses and permission policies identified by sampling of abstracts.Step 7: [Version 2] Re-extracting (Sub-setting) the Data Based on Lengths of AbstractsThe cleaning procedure described in previous step leaded to some abstracts having less than our minimum length criteria (30 words). 474 texts were removed.Step 8: Saving the Dataset into CSV FormatDocuments are saved into 34 CSV files. In CSV files, the information is organised with one record on each line and parts of abstract, title, list of authors, list of categories, list of research areas, and times cited is recorded in fields.To access the LSC for research purposes, please email to ns433@le.ac.uk.References[1]Web of Science. (15 July). Available: https://apps.webofknowledge.com/ [2]WoS Subject Categories. Available: https://images.webofknowledge.com/WOKRS56B5/help/WOS/hp_subject_category_terms_tasca.html [3]Research Areas in WoS. Available: https://images.webofknowledge.com/images/help/WOS/hp_research_areas_easca.html [4]Times Cited in WoS Core Collection. (15 July). Available: https://support.clarivate.com/ScientificandAcademicResearch/s/article/Web-of-Science-Times-Cited-accessibility-and-variation?language=en_US [5]Word Count. Available: https://support.office.com/en-us/article/show-word-count-3c9e6a11-a04d-43b4-977c-563a0e0d5da3 [6]A. P. Association, Publication manual. American Psychological Association Washington, DC, 1983.

The LScDC (Leicester Scientific Dictionary-Core Dictionary)April 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk/suzenneslihan@hotmail.com)Supervised by Prof Alexander Gorban and Dr Evgeny Mirkes[Version 3] The third version of LScDC (Leicester Scientific Dictionary-Core) is formed using the updated LScD (Leicester Scientific Dictionary) - Version 3*. All steps applied to build the new version of core dictionary are the same as in Version 2** and can be found in description of Version 2 below. We did not repeat the explanation. The files provided with this description are also same as described as for LScDC Version 2. The numbers of words in the 3rd versions of LScD and LScDC are summarized below. # of wordsLScD (v3) 972,060LScDC (v3) 103,998 * Suzen, Neslihan (2019): LScD (Leicester Scientific Dictionary). figshare. Dataset. https://doi.org/10.25392/leicester.data.9746900.v3 ** Suzen, Neslihan (2019): LScDC (Leicester Scientific Dictionary-Core). figshare. Dataset. https://doi.org/10.25392/leicester.data.9896579.v2[Version 2] Getting StartedThis file describes a sorted and cleaned list of words from LScD (Leicester Scientific Dictionary), explains steps for sub-setting the LScD and basic statistics of words in the LSC (Leicester Scientific Corpus), to be found in [1, 2]. The LScDC (Leicester Scientific Dictionary-Core) is a list of words ordered by the number of documents containing the words, and is available in the CSV file published. There are 104,223 unique words (lemmas) in the LScDC. This dictionary is created to be used in future work on the quantification of the sense of research texts. The objective of sub-setting the LScD is to discard words which appear too rarely in the corpus. In text mining algorithms, usage of enormous number of text data brings the challenge to the performance and the accuracy of data mining applications. The performance and the accuracy of models are heavily depend on the type of words (such as stop words and content words) and the number of words in the corpus. Rare occurrence of words in a collection is not useful in discriminating texts in large corpora as rare words are likely to be non-informative signals (or noise) and redundant in the collection of texts. The selection of relevant words also holds out the possibility of more effective and faster operation of text mining algorithms.To build the LScDC, we decided the following process on LScD: removing words that appear in no more than 10 documents (

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Data Science Platform Market Size 2025-2029

The data science platform market size is valued to increase USD 763.9 million, at a CAGR of 40.2% from 2024 to 2029. Integration of AI and ML technologies with data science platforms will drive the data science platform market.

Major Market Trends & Insights

North America dominated the market and accounted for a 48% growth during the forecast period.
By Deployment - On-premises segment was valued at USD 38.70 million in 2023
By Component - Platform segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million
Market Future Opportunities: USD 763.90 million
CAGR : 40.2%
North America: Largest market in 2023

Market Summary

The market represents a dynamic and continually evolving landscape, underpinned by advancements in core technologies and applications. Key technologies, such as machine learning and artificial intelligence, are increasingly integrated into data science platforms to enhance predictive analytics and automate data processing. Additionally, the emergence of containerization and microservices in data science platforms enables greater flexibility and scalability. However, the market also faces challenges, including data privacy and security risks, which necessitate robust compliance with regulations.
According to recent estimates, the market is expected to account for over 30% of the overall big data analytics market by 2025, underscoring its growing importance in the data-driven business landscape.

What will be the Size of the Data Science Platform Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Data Science Platform Market Segmented and what are the key trends of market segmentation?

The data science platform industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud


Component

  Platform
  Services


End-user

  BFSI
  Retail and e-commerce
  Manufacturing
  Media and entertainment
  Others


Sector

  Large enterprises
  SMEs


Application

  Data Preparation
  Data Visualization
  Machine Learning
  Predictive Analytics
  Data Governance
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

In the dynamic and evolving the market, big data processing is a key focus, enabling advanced model accuracy metrics through various data mining methods. Distributed computing and algorithm optimization are integral components, ensuring efficient handling of large datasets. Data governance policies are crucial for managing data security protocols and ensuring data lineage tracking. Software development kits, model versioning, and anomaly detection systems facilitate seamless development, deployment, and monitoring of predictive modeling techniques, including machine learning algorithms, regression analysis, and statistical modeling. Real-time data streaming and parallelized algorithms enable real-time insights, while predictive modeling techniques and machine learning algorithms drive business intelligence and decision-making.

Cloud computing infrastructure, data visualization tools, high-performance computing, and database management systems support scalable data solutions and efficient data warehousing. ETL processes and data integration pipelines ensure data quality assessment and feature engineering techniques. Clustering techniques and natural language processing are essential for advanced data analysis. The market is witnessing significant growth, with adoption increasing by 18.7% in the past year, and industry experts anticipate a further expansion of 21.6% in the upcoming period. Companies across various sectors are recognizing the potential of data science platforms, leading to a surge in demand for scalable, secure, and efficient solutions.

API integration services and deep learning frameworks are gaining traction, offering advanced capabilities and seamless integration with existing systems. Data security protocols and model explainability methods are becoming increasingly important, ensuring transparency and trust in data-driven decision-making. The market is expected to continue unfolding, with ongoing advancements in technology and evolving business needs shaping its future trajectory.

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The On-premises segment was valued at USD 38.70 million in 2019 and showed

The Data Processing and Hosting Services market, exhibiting a Compound Annual Growth Rate (CAGR) of 4.20%, presents a significant opportunity for growth. While the exact market size in millions is not specified, considering the substantial involvement of major players like Amazon Web Services, IBM, and Salesforce, coupled with the pervasive adoption of cloud computing and big data analytics across diverse sectors, a 2025 market size exceeding $500 billion is a reasonable estimate. This robust growth is driven by several key factors. The increasing reliance on cloud-based solutions by both large enterprises and SMEs reflects a shift towards greater scalability, flexibility, and cost-effectiveness. Furthermore, the exponential growth of data necessitates advanced data processing capabilities, fueling demand for data mining, cleansing, and management services. The burgeoning adoption of AI and machine learning further enhances this need, as these technologies require robust data infrastructure and sophisticated processing techniques. Specific industry segments like IT & Telecommunications, BFSI (Banking, Financial Services, and Insurance), and Retail are major consumers, demanding reliable and secure hosting solutions and data processing capabilities to manage their critical operations and customer data. However, challenges remain, including the ongoing threat of cyberattacks and data breaches, necessitating robust security measures and compliance with evolving data privacy regulations. Competition among existing players is intense, driving innovation and price wars, which can impact profitability for some market participants. The forecast period of 2025-2033 indicates a continued upward trajectory for the market, largely fueled by expanding digitalization efforts globally. The Asia Pacific region is projected to be a significant contributor to this growth, driven by increasing internet penetration and a burgeoning technological landscape. While North America and Europe maintain substantial market share, the faster growth rate anticipated in Asia Pacific and other emerging markets signifies an evolving global market dynamic. Continued advancements in technologies such as edge computing, serverless architecture, and improved data analytics techniques will further drive market expansion and shape the competitive landscape. The segmentation within the market (by organization size, service offering, and end-user industry) presents diverse investment opportunities for businesses catering to specific needs and technological advancements within these niches. Recent developments include: December 2022 - TetraScience, the Scientific Data Cloud company, announced that Gubbs, a lab optimization, and validation software leader, joined the Tetra Partner Network to increase and enhance data processing throughput with the Tetra Scientific Data Cloud., November 2022 - Kinsta, a hosting provider that provides managed WordPress hosting powered by Google Cloud Platform, announced the launch of Application Hosting and Database Hosting. It is adding these two hosting services to its Managed WordPress product ushers in a new era for Kinsta as a Cloud Platform, enabling developers and businesses to run powerful applications, databases, websites, and services more flexibly than ever.. Key drivers for this market are: Growing Adoption of Cloud Computing to Accomplish Economies of Scale, Rising Demand for Outsourcing Data Processing Services. Potential restraints include: Growing Adoption of Cloud Computing to Accomplish Economies of Scale, Rising Demand for Outsourcing Data Processing Services. Notable trends are: Web Hosting is Gaining Traction Due to Emergence of Cloud-based Platform.

LScDC Word-Category RIG MatrixApril 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk / suzenneslihan@hotmail.com)Supervised by Prof Alexander Gorban and Dr Evgeny MirkesGetting StartedThis file describes the Word-Category RIG Matrix for theLeicester Scientific Corpus (LSC) [1], the procedure to build the matrix and introduces the Leicester Scientific Thesaurus (LScT) with the construction process. The Word-Category RIG Matrix is a 103,998 by 252 matrix, where rows correspond to words of Leicester Scientific Dictionary-Core (LScDC) [2] and columns correspond to 252 Web of Science (WoS) categories [3, 4, 5]. Each entry in the matrix corresponds to a pair (category,word). Its value for the pair shows the Relative Information Gain (RIG) on the belonging of a text from the LSC to the category from observing the word in this text. The CSV file of Word-Category RIG Matrix in the published archive is presented with two additional columns of the sum of RIGs in categories and the maximum of RIGs over categories (last two columns of the matrix). So, the file ‘Word-Category RIG Matrix.csv’ contains a total of 254 columns.This matrix is created to be used in future research on quantifying of meaning in scientific texts under the assumption that words have scientifically specific meanings in subject categories and the meaning can be estimated by information gains from word to categories. LScT (Leicester Scientific Thesaurus) is a scientific thesaurus of English. The thesaurus includes a list of 5,000 words from the LScDC. We consider ordering the words of LScDC by the sum of their RIGs in categories. That is, words are arranged in their informativeness in the scientific corpus LSC. Therefore, meaningfulness of words evaluated by words’ average informativeness in the categories. We have decided to include the most informative 5,000 words in the scientific thesaurus. Words as a Vector of Frequencies in WoS CategoriesEach word of the LScDC is represented as a vector of frequencies in WoS categories. Given the collection of the LSC texts, each entry of the vector consists of the number of texts containing the word in the corresponding category.It is noteworthy that texts in a corpus do not necessarily belong to a single category, as they are likely to correspond to multidisciplinary studies, specifically in a corpus of scientific texts. In other words, categories may not be exclusive. There are 252 WoS categories and a text can be assigned to at least 1 and at most 6 categories in the LSC. Using the binary calculation of frequencies, we introduce the presence of a word in a category. We create a vector of frequencies for each word, where dimensions are categories in the corpus.The collection of vectors, with all words and categories in the entire corpus, can be shown in a table, where each entry corresponds to a pair (word,category). This table is build for the LScDC with 252 WoS categories and presented in published archive with this file. The value of each entry in the table shows how many times a word of LScDC appears in a WoS category. The occurrence of a word in a category is determined by counting the number of the LSC texts containing the word in a category. Words as a Vector of Relative Information Gains Extracted for CategoriesIn this section, we introduce our approach to representation of a word as a vector of relative information gains for categories under the assumption that meaning of a word can be quantified by their information gained for categories.For each category, a function is defined on texts that takes the value 1, if the text belongs to the category, and 0 otherwise. For each word, a function is defined on texts that takes the value 1 if the word belongs to the text, and 0 otherwise. Consider LSC as a probabilistic sample space (the space of equally probable elementary outcomes). For the Boolean random variables, the joint probability distribution, the entropy and information gains are defined.The information gain about the category from the word is the amount of information on the belonging of a text from the LSC to the category from observing the word in the text [6]. We used the Relative Information Gain (RIG) providing a normalised measure of the Information Gain. This provides the ability of comparing information gains for different categories. The calculations of entropy, Information Gains and Relative Information Gains can be found in the README file in the archive published. Given a word, we created a vector where each component of the vector corresponds to a category. Therefore, each word is represented as a vector of relative information gains. It is obvious that the dimension of vector for each word is the number of categories. The set of vectors is used to form the Word-Category RIG Matrix, in which each column corresponds to a category, each row corresponds to a word and each component is the relative information gain from the word to the category. In Word-Category RIG Matrix, a row vector represents the corresponding word as a vector of RIGs in categories. We note that in the matrix, a column vector represents RIGs of all words in an individual category. If we choose an arbitrary category, words can be ordered by their RIGs from the most informative to the least informative for the category. As well as ordering words in each category, words can be ordered by two criteria: sum and maximum of RIGs in categories. The top n words in this list can be considered as the most informative words in the scientific texts. For a given word, the sum and maximum of RIGs are calculated from the Word-Category RIG Matrix.RIGs for each word of LScDC in 252 categories are calculated and vectors of words are formed. We then form the Word-Category RIG Matrix for the LSC. For each word, the sum (S) and maximum (M) of RIGs in categories are calculated and added at the end of the matrix (last two columns of the matrix). The Word-Category RIG Matrix for the LScDC with 252 categories, the sum of RIGs in categories and the maximum of RIGs over categories can be found in the database.Leicester Scientific Thesaurus (LScT)Leicester Scientific Thesaurus (LScT) is a list of 5,000 words form the LScDC [2]. Words of LScDC are sorted in descending order by the sum (S) of RIGs in categories and the top 5,000 words are selected to be included in the LScT. We consider these 5,000 words as the most meaningful words in the scientific corpus. In other words, meaningfulness of words evaluated by words’ average informativeness in the categories and the list of these words are considered as a ‘thesaurus’ for science. The LScT with value of sum can be found as CSV file with the published archive. Published archive contains following files:1) Word_Category_RIG_Matrix.csv: A 103,998 by 254 matrix where columns are 252 WoS categories, the sum (S) and the maximum (M) of RIGs in categories (last two columns of the matrix), and rows are words of LScDC. Each entry in the first 252 columns is RIG from the word to the category. Words are ordered as in the LScDC.2) Word_Category_Frequency_Matrix.csv: A 103,998 by 252 matrix where columns are 252 WoS categories and rows are words of LScDC. Each entry of the matrix is the number of texts containing the word in the corresponding category. Words are ordered as in the LScDC.3) LScT.csv: List of words of LScT with sum (S) values. 4) Text_No_in_Cat.csv: The number of texts in categories. 5) Categories_in_Documents.csv: List of WoS categories for each document of the LSC.6) README.txt: Description of Word-Category RIG Matrix, Word-Category Frequency Matrix and LScT and forming procedures.7) README.pdf (same as 6 in PDF format)References[1] Suzen, Neslihan (2019): LSC (Leicester Scientific Corpus). figshare. Dataset. https://doi.org/10.25392/leicester.data.9449639.v2[2] Suzen, Neslihan (2019): LScDC (Leicester Scientific Dictionary-Core). figshare. Dataset. https://doi.org/10.25392/leicester.data.9896579.v3[3] Web of Science. (15 July). Available: https://apps.webofknowledge.com/[4] WoS Subject Categories. Available: https://images.webofknowledge.com/WOKRS56B5/help/WOS/hp_subject_category_terms_tasca.html [5] Suzen, N., Mirkes, E. M., & Gorban, A. N. (2019). LScDC-new large scientific dictionary. arXiv preprint arXiv:1912.06858. [6] Shannon, C. E. (1948). A mathematical theory of communication. Bell system technical journal, 27(3), 379-423.

Reddit is a social news, content rating and discussion website. It's one of the most popular sites on the internet. Reddit has 52 million daily active users and approximately 430 million users who use it once a month. Reddit has different subreddits and here We'll use the r/AskScience Subreddit.

The dataset is extracted from the subreddit /r/AskScience from Reddit. The data was collected between 01-01-2016 and 20-05-2022. It contains 612,668 Datapoints and 25 Columns. The database contains a number of information about the questions asked on the subreddit, the description of the submission, the flair of the question, NSFW or SFW status, the year of the submission, and more. The data is extracted using python and Pushshift's API. A little bit of cleaning is done using NumPy and pandas as well. (see the descriptions of individual columns below).

The dataset contains the following columns and descriptions: author - Redditor Name author_fullname - Redditor Full name contest_mode - Contest mode [implement obscured scores and randomized sorting]. created_utc - Time the submission was created, represented in Unix Time. domain - Domain of submission. edited - If the post is edited or not. full_link - Link of the post on the subreddit. id - ID of the submission. is_self - Whether or not the submission is a self post (text-only). link_flair_css_class - CSS Class used to identify the flair. link_flair_text - Flair on the post or The link flair’s text content. locked - Whether or not the submission has been locked. num_comments - The number of comments on the submission. over_18 - Whether or not the submission has been marked as NSFW. permalink - A permalink for the submission. retrieved_on - time ingested. score - The number of upvotes for the submission. description - Description of the Submission. spoiler - Whether or not the submission has been marked as a spoiler. stickied - Whether or not the submission is stickied. thumbnail - Thumbnail of Submission. question - Question Asked in the Submission. url - The URL the submission links to, or the permalink if a self post. year - Year of the Submission. banned - Banned by the moderator or not.

This dataset can be used for Flair Prediction, NSFW Classification, and different Text Mining/NLP tasks. Exploratory Data Analysis can also be done to get the insights and see the trend and patterns over the years.

Project Description:

1) Data Background

In the Data Mining class, we had the opportunity to analyze data by performing data mining algorithms to a dataset. Our dataset is from Office of Foreign Labor Certification (OFLC). OFLC is a division of the U.S. Department of Labor. The main duty of OFLC is to assist the Secretary of Labor to enforce part of the Immigration and Nationality Act (INA), which requires certain labor conditions exist before employers can hire foreign workers. H-1B is a visa category in the United States of America under the INA, section 101(a)(15)(H) which allows U.S. employers to employ foreign workers. The first step employer must take to hire a foreign worker is to file the Labor Condition Application. In this project, we will analyze the data from the Labor Condition Application.

1.1) Introduction to H1B Dataset

The H-1B Dataset selected for this project contains data from employer’s Labor Condition Application and the case certification determinations processed by the Office of Foreign Labor Certification (OFLC) where the date of the determination was issues on or after October 1, 2016 and on or before June 30, 2017.

The Labor Condition Application (LCA) is a document that a perspective H-1B employer files with U.S. Department of Labor Employment and Training Administration (DOLETA) when it seeks to employ non-immigrant workers at a specific job occupation in an area of intended employment for not more than three years.

1.2) Goal of the Project

Our goal for this project is to predict the case status of an application submitted by the employer to hire non-immigrant workers under the H-1B visa program. Employer can hire non-immigrant workers only after their LCA petition is approved. The approved LCA petition is then submitted as part of the Petition for a Non-immigrant Worker application for work authorizations for H-1B visa status.

We want to uncover insights that can help employers understand the process of getting their LCA approved. We will use WEKA software to run data mining algorithms to understand the relationship between attributes and the target variable.

2)Dataset Information:

a) Source: Office of Foreign Labor Certification, U.S. Department of Labor Employment and Training Administration
b) List Link: https://www.foreignlaborcert.doleta.gov/performancedata.cfm
c) Dataset Type: Record – Transaction Data
d) Number of Attributes: 40
e) Number of Instances: 528,147
f) Date Created: July 2017

3) Attribute List:

The detailed description of each attribute below is given in the Record Layout file available in the zip folder H1B Disclosure Dataset Files.

The H-1B dataset from OFLC contained 40 attributes and 528,147 instances. The attributes are in the table below. The attributes highlighted bold were removed during the data cleaning process.

1) CASE_NUMBER
2)CASE_SUBMITTED
3)DECISION_DATE
4)VISA_CLASS
5)EMPLOYMENT_START_DATE
6)EMPLOYMENT_END_DATE
7)EMPLOYER_NAME
8)EMPLOYER_ADDRESS
9)EMPLOYER_CITY
10)EMPLOYER_STATE
11)EMPLOYER_POSTAL_CODE
12)EMPLOYER_COUNTRY
13)EMPLOYER_PROVINCE
14)EMPLOYER_PHONE
15)EMPLOYER_PHONE_EXT
16)AGENT_ATTORNEY_NAME
17)AGENT_ATTORNEY_CITY
18)AGENT_ATTORNEY_STATE
19)JOB_TITLE
20)SOC_CODE
21)SOC_NAME
22)NAICS_CODE
23)TOTAL_WORKERS
24)FULL_TIME_POSITION
25)PREVAILING_WAGE
26)PW_UNIT_OF_PAY
27)PW_SOURCE
28)PW_SOURCE_YEAR
29)PW_SOURCE_OTHER
30)WAGE_RATE_OF_PAY_FROM
31)WAGE_RATE_OF_PAY_TO
32)WAGE_UNIT_OF_PAY
33)H-1B_DEPENDENT
34) WILLFUL_VIOLATOR
35) WORKSITE_CITY
36)WORKSITE_COUNTY
37)WORKSITE_STATE
38)WORKSITE_POSTAL_CODE
39)ORIGINAL_CERT_DATE
40)CASE_STATUS* - _Class Attribute - To be predicted

3.1) Class Attribute

For the H-1B Dataset our class attribute is ‘CASE_STATUS’. There are 4 categories of Case Status. The values of Case_Status attributes are:

1) Certified
2) Certified_Withdrawn
3) Withdrawn
4) Denied

Certified means the LCA of an employer was approved. Certified Withdrawn means the case was withdrawn after it was certified by OFLC. Withdrawn means the case was withdrawn by the employer. Denied means the case was denied OFLC.

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Data Warehousing Market Size 2025-2029

The data warehousing market size is forecast to increase by USD 32.3 billion, at a CAGR of 14% between 2024 and 2029.

The market is experiencing significant shifts as businesses increasingly adopt cloud-based solutions and advanced storage technologies reshape the competitive landscape. The transition from on-premises to Software-as-a-Service (SaaS) models offers businesses greater flexibility, scalability, and cost savings. Simultaneously, the emergence of advanced storage technologies, such as columnar databases and in-memory storage, enables faster data processing and analysis, enhancing business intelligence capabilities. However, the market faces challenges as well. Data privacy and security risks continue to pose a significant threat, with the increasing volume and complexity of data requiring robust security measures. Ensuring data confidentiality, integrity, and availability is crucial for businesses to maintain customer trust and comply with regulatory requirements. Companies must invest in advanced security solutions and adopt best practices to mitigate these risks effectively.

What will be the Size of the Data Warehousing Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the ever-increasing volume, variety, and velocity of data. ETL processes play a crucial role in data integration, transforming data from various sources into a consistent format for analysis. On-premise data warehousing and cloud data warehousing solutions offer different advantages, with the former providing greater control and the latter offering flexibility and scalability. Data lakes and data warehouses complement each other, with data lakes serving as a source for raw data and data warehouses providing structured data for analysis. Data warehouse optimization is a continuous process, with data stewardship, data transformation, and data modeling essential for maintaining data quality and ensuring compliance. Data mining and analytics extract valuable insights from data, while data visualization makes complex data understandable. Data security, encryption, and data governance frameworks are essential for protecting sensitive data. Data warehousing services and consulting offer expertise in implementing and optimizing data platforms. Data integration, masking, and federation enable seamless data access, while data audit and lineage ensure data accuracy and traceability. Data management solutions provide a comprehensive approach to managing data, from data cleansing to monetization. Data warehousing modernization and migration offer opportunities for improving performance and scalability. Business intelligence and data-driven decision making rely on the insights gained from data warehousing. Hybrid data warehousing offers a flexible approach to data management, combining the benefits of on-premise and cloud solutions. Metadata management and data catalogs facilitate efficient data access and management.

How is this Data Warehousing Industry segmented?

The data warehousing industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentOn-premisesHybridCloud-basedTypeStructured and semi-structured dataUnstructured dataEnd-userBFSIHealthcareRetail and e-commerceOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW).

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.In the dynamic the market, on-premise data warehousing solutions continue to be a preferred choice for businesses seeking end-to-end control and enhanced security. These solutions, installed and managed on the user's server, offer benefits such as workflow streamlining, speed, and robust data governance. The high cost of implementation and upgradation, coupled with the need for IT specialists, are factors contributing to the segment's popularity. Data security is a primary concern, with the complete ownership and management of servers ensuring that business data remains secure. ETL processes play a crucial role in data warehousing, facilitating data transformation, integration, and loading. Data modeling and mining are essential components, enabling businesses to derive valuable insights from their data. Data stewardship ensures data compliance and accuracy, while optimization techniques enhance performance. Data lake, a large storage repository, offers a flexible and cost-effective approach to managing diverse data types. Data warehousing consulting services help businesses navigate the complexities of im

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Internet Of Things (Iot) Data Management Market Size 2024-2028

The internet of things (iot) data management market size is valued to increase USD 90.3 billion, at a CAGR of 15.72% from 2023 to 2028. Growth in industrial automation will drive the internet of things (iot) data management market.

Major Market Trends & Insights

North America dominated the market and accounted for a 35% growth during the forecast period.
By Component - Solutions segment was valued at USD 34.60 billion in 2022
By Deployment - Private/hybrid segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 301.61 billion
Market Future Opportunities: USD 90.30 billion
CAGR from 2023 to 2028 : 15.72%

Market Summary

The market is a dynamic and evolving landscape, driven by the increasing adoption of IoT technologies in various industries. Core technologies, such as edge computing and machine learning, are enabling the collection, processing, and analysis of vast amounts of data generated by interconnected devices. This data is fueling innovative applications, from predictive maintenance in manufacturing to real-time supply chain optimization. However, managing IoT data effectively remains a challenge for many organizations. A recent survey revealed that over 50% of companies struggle with efficiently managing their IoT initiatives and investments. Despite this, the market continues to grow, with industrial automation being a significant driver. In fact, it's estimated that by 2025, over 50% of industrial companies will have implemented IoT solutions for predictive maintenance. Regulations, such as GDPR and HIPAA, also play a crucial role in shaping the market. Regional differences in regulatory frameworks and data privacy laws add complexity to the market landscape. As the IoT Data Management Market continues to unfold, stakeholders must stay informed about the latest trends, technologies, and regulations to remain competitive.

What will be the Size of the Internet Of Things (Iot) Data Management Market during the forecast period?

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How is the Internet Of Things (Iot) Data Management Market Segmented ?

The internet of things (iot) data management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ComponentSolutionsServicesDeploymentPrivate/hybridPublicGeographyNorth AmericaUSCanadaEuropeGermanyUKAPACChinaRest of World (ROW)

By Component Insights

The solutions segment is estimated to witness significant growth during the forecast period.

In the dynamic and expanding IoT data management market, software solutions, encompassing both software and hardware offerings, hold a significant market share. This dominance is driven by the increasing globalization and IT expansion of industries, particularly in emerging economies like China, India, Brazil, Indonesia, and Mexico. The surge in SMEs in these regions necessitates business-centric insights, leading to a rising demand for software-based IoT data management solutions. companies catering to the global IoT data management market offer software tools to various end-user industries. These solutions facilitate data collection and analysis, enabling organizations to derive valuable insights from their operations. Metadata management systems, data modeling techniques, and IoT device integration are integral components of these software solutions. Edge computing deployments, data versioning strategies, and data visualization dashboards further enhance their functionality. Compliance regulations adherence, time series databases, data streaming technologies, data mining procedures, data cleansing techniques, data aggregation platforms, machine learning algorithms, remote data acquisition, data transformation pipelines, data quality monitoring, data lifecycle management, data encryption methods, predictive maintenance models, and IoT sensor networks are essential features of advanced software solutions. Data warehousing techniques, real-time data processing, access control mechanisms, data schema design, deep learning applications, scalable data infrastructure, NoSQL database systems, security protocols implementation, anomaly detection algorithms, data governance frameworks, API integration methods, and network bandwidth optimization are additional capabilities that add value to these offerings. Statistical modeling techniques play a crucial role in deriving actionable insights from the vast amounts of data generated by IoT devices. By 2026, it is projected that the market for public IoT data management solutions will grow by approximately 25%, as organizations increasingly recognize the

The "Wikipedia SQLite Portable DB" is a compact and efficient database derived from the Kensho Derived Wikimedia Dataset (KDWD). This dataset provides a condensed subset of raw Wikimedia data in a format optimized for natural language processing (NLP) research and applications.

I am not affiliated or partnered with the Kensho in any way, just really like the dataset for giving my agents to query easily.

Key Features:

Contains over 5 million rows of data from English Wikipedia and Wikidata Stored in a portable SQLite database format for easy integration and querying Includes a link-annotated corpus of English Wikipedia pages and a compact sample of the Wikidata knowledge base Ideal for NLP tasks, machine learning, data analysis, and research projects

The database consists of four main tables:

• items: Contains information about Wikipedia items, including labels and descriptions
• properties: Stores details about Wikidata properties, such as labels and descriptions
• pages: Provides metadata for Wikipedia pages, including page IDs, item IDs, titles, and view counts
• link_annotated_text: Contains the link-annotated text of Wikipedia pages, divided into sections

This dataset is derived from the Kensho Derived Wikimedia Dataset (KDWD), which is built from the English Wikipedia snapshot from December 1, 2019, and the Wikidata snapshot from December 2, 2019. The KDWD is a condensed subset of the raw Wikimedia data in a form that is helpful for NLP work, and it is released under the CC BY-SA 3.0 license. Credits: The "Wikipedia SQLite Portable DB" is derived from the Kensho Derived Wikimedia Dataset (KDWD), created by the Kensho R&D group. The KDWD is based on data from Wikipedia and Wikidata, which are crowd-sourced projects supported by the Wikimedia Foundation. We would like to acknowledge and thank the Kensho R&D group for their efforts in creating the KDWD and making it available for research and development purposes. By providing this portable SQLite database, we aim to make Wikipedia data more accessible and easier to use for researchers, data scientists, and developers working on NLP tasks, machine learning projects, and other data-driven applications. We hope that this dataset will contribute to the advancement of NLP research and the development of innovative applications utilizing Wikipedia data.

https://www.kaggle.com/datasets/kenshoresearch/kensho-derived-wikimedia-data/data

Tags: encyclopedia, wikipedia, sqlite, database, reference, knowledge-base, articles, information-retrieval, natural-language-processing, nlp, text-data, large-dataset, multi-table, data-science, machine-learning, research, data-analysis, data-mining, content-analysis, information-extraction, text-mining, text-classification, topic-modeling, language-modeling, question-answering, fact-checking, entity-recognition, named-entity-recognition, link-prediction, graph-analysis, network-analysis, knowledge-graph, ontology, semantic-web, structured-data, unstructured-data, data-integration, data-processing, data-cleaning, data-wrangling, data-visualization, exploratory-data-analysis, eda, corpus, document-collection, open-source, crowdsourced, collaborative, online-encyclopedia, web-data, hyperlinks, categories, page-views, page-links, embeddings

Usage with LIKE queries: ``` import aiosqlite import asyncio

class KenshoDatasetQuery: def init(self, db_file): self.db_file = db_file

async def _aenter_(self):
  self.conn = await aiosqlite.connect(self.db_file)
  return self

async def _aexit_(self, exc_type, exc_val, exc_tb):
  await self.conn.close()

async def search_pages_by_title(self, title):
  query = """
  SELECT pages.page_id, pages.item_id, pages.title, pages.views, 
      items.labels AS item_labels, items.description AS item_description,
      link_annotated_text.sections
  FROM pages 
  JOIN items ON pages.item_id = items.id
  JOIN link_annotated_text ON pages.page_id = link_annotated_text.page_id
  WHERE pages.title LIKE ?
  """
  async with self.conn.execute(query, (f"%{title}%",)) as cursor:
    return await cursor.fetchall()

async def search_items_by_label_or_description(self, keyword):
  query = """
  SELECT id, labels, description 
  FROM items
  WHERE labels LIKE ? OR description LIKE ?
  """
  async with self.conn.execute(query, (f"%{keyword}%", f"%{keyword}%")) as cursor:
    return await cursor.fetchall()

async def search_items_by_label(self, label):
  query = """
  SELECT id, labels, description
  FROM items 
  WHERE labels LIKE ?
  """
  async with self.conn.execute(query, (f"%{label}%",)) as cursor:
    return await cursor.fetchall()

async def search_properties_by_label_or_desc...

The global data scraping tools market, valued at $15.57 billion in 2025, is experiencing robust growth. While the provided CAGR is missing, a reasonable estimate, considering the expanding need for data-driven decision-making across various sectors and the increasing sophistication of web scraping techniques, would be between 15-20% annually. This strong growth is driven by the proliferation of e-commerce platforms generating vast amounts of data, the rising adoption of data analytics and business intelligence tools, and the increasing demand for market research and competitive analysis. Businesses leverage these tools to extract valuable insights from websites, enabling efficient price monitoring, lead generation, market trend analysis, and customer sentiment monitoring. The market segmentation shows a significant preference for "Pay to Use" tools reflecting the need for reliable, scalable, and often legally compliant solutions. The application segments highlight the high demand across diverse industries, notably e-commerce, investment analysis, and marketing analysis, driving the overall market expansion. Challenges include ongoing legal complexities related to web scraping, the constant evolution of website structures requiring adaptation of scraping tools, and the need for robust data cleaning and processing capabilities post-scraping. Looking forward, the market is expected to witness continued growth fueled by advancements in artificial intelligence and machine learning, enabling more intelligent and efficient scraping. The integration of data scraping tools with existing business intelligence platforms and the development of user-friendly, no-code/low-code scraping solutions will further boost adoption. The increasing adoption of cloud-based scraping services will also contribute to market growth, offering scalability and accessibility. However, the market will also need to address ongoing concerns about ethical scraping practices, data privacy regulations, and the potential for misuse of scraped data. The anticipated growth trajectory, based on the estimated CAGR, points to a significant expansion in market size over the forecast period (2025-2033), making it an attractive sector for both established players and new entrants.

Notre-Dame Cathedral Fire Dataset# of images: 1,657 images during or after the fireIf you use the dataset, please cite the following works:Padilha, Rafael and Andaló, Fernanda A. and Pereira, Luís A. M. and Rocha, Anderson. "Unraveling the Notre Dame Cathedral fire in space and time: an X-coherence approach,” in Crime Science and Digital Forensics: A holistic view. CRC Press by Taylor and Francis Group.Padilha, Rafael and Andaló, Fernanda A. and Rocha, Anderson. “Improving the chronological sorting of images through occlusion: A study on the Notre-Dame cathedral fire,” in 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020. Description of the event and data collection: On April 15th, 2019, large parts of Notre-Dame Cathedral's structure and spire were devastated by a fire. People worldwide followed the tragic event through images and videos that were shared by the media and citizens.From the generated imagery, we collected a total of 23,683 images posted on Twitter during and on the day after the fire. Even though most of them were related to the event, several were memes, cartoons, compositions and artwork, while some depicted the cathedral before the fire. As we focus on learning how the fire and appearance of the cathedral evolved during the event, we removed them, reducing our set to 5,206 relevant images. Among these, several examples were duplicates or near-duplicates of other images. Considering their little contribution to the training process, after their removal, we were left with 1,657 distinct images related to the event. The cleaning process involved using methods such as local sensitive hashing for filtering near-duplicates, and semi-supervised approaches based on Optimum-path Forest theory to mine for relevant and non-relevant imagery of the event. By analyzing the event's description, four main sub-events can be defined: spire on fire, spire collapsing, fire continues on roof, and fire extinguished. Each sub-event contains specific visual clues (e.g., the absence of the central spire) that can be leveraged to estimate the temporal position of an image. Each image in the dataset was manually labeled as being captured in one of these sub-events. We also consider an unknown category for images that do not contain any hint of the sub-event in which they were captured, such as zoom-ins of the cathedral's facades.Besides that, each image was annotated with respect to the intercardinal direction of the cathedral’s facade being depicted in the image (north, northeast, east, southeast, south, southwest, west, northwest).

This data set covers global extraction and production of coal and metal ores on an individual mine level. It covers
1171 individual mines, reporting mine-level production for 80 different materials in the period 2000-2021. Furthermore, also data on mining coordinates, ownership, mineral reserves, mining waste, transportation of mining products, as well
as mineral processing capacities (smelters and mineral refineries) and production is included. The data was gathered manually from more than 1900 openly available sources, such as annual or sustainability reports of mining companies. All datapoints are linked to their respective sources. After manual screening and entry of the data, automatic cleaning, harmonization and data checking was conducted. Geoinformation was obtained either from coordinates available in company reports, or by retrieving the coordinates via Google Maps API and subsequent manual checking. For mines where no coordinates could be found, other geospatial attributes such as province, region, district or municipality were recorded, and linked to the GADM data set, available at www.gadm.org.

The data set consists of 12 tables. The table “facilities” contains descriptive and spatial information of mines and processing facilities, and is available as a GeoPackage (GPKG) file. All other tables are available in comma-separated values (CSV) format. A schematic depiction of the database is provided as in PNG format in the file database_model.png.

BackgroundPeople are conversing about bariatric surgery on social media, but little is known about the main themes being discussed.ObjectiveTo analyze discussions regarding bariatric surgery on social media platforms and to establish a cross-cultural comparison of posts geolocated in France and the United States.MethodsPosts were retrieved between January 2015 and April 2021 from general, publicly accessed sites and health-related forums geolocated in both countries. After processing and cleaning the data, posts of patients and caregivers about bariatric surgery were identified using a supervised machine learning algorithm.ResultsThe analysis dataset contained a total of 10,800 posts from 4,947 web users in France and 51,804 posts from 40,278 web users in the United States. In France, post-operative follow-up (n = 3,251, 30.1% of posts), healthcare pathways (n = 2,171, 20.1% of the posts), and complementary and alternative weight loss therapies (n = 1,652, 15.3% of the posts) were among the most discussed topics. In the United States, the experience with bariatric surgery (n = 11,138, 21.5% of the posts) and the role of physical activity and diet in weight-loss programs before surgery (n = 9,325, 18% of the posts) were among the most discussed topics.ConclusionSocial media analysis provides a valuable toolset for clinicians to help them increase patient-centered care by integrating the patients’ and caregivers’ needs and concerns into the management of bariatric surgery.

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US Deep Learning Market Size 2025-2029

The deep learning market size in US is forecast to increase by USD 5.02 billion at a CAGR of 30.1% between 2024 and 2029.

The deep learning market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) in various industries for advanced solutioning. This trend is fueled by the availability of vast amounts of data, which is a key requirement for deep learning algorithms to function effectively. Industry-specific solutions are gaining traction, as businesses seek to leverage deep learning for specific use cases such as image and speech recognition, fraud detection, and predictive maintenance. Alongside, intuitive data visualization tools are simplifying complex neural network outputs, helping stakeholders understand and validate insights. 


However, challenges remain, including the need for powerful computing resources, data privacy concerns, and the high cost of implementing and maintaining deep learning systems. Despite these hurdles, the market's potential for innovation and disruption is immense, making it an exciting space for businesses to explore further. Semi-supervised learning, data labeling, and data cleaning facilitate efficient training of deep learning models. Cloud analytics is another significant trend, as companies seek to leverage cloud computing for cost savings and scalability. 

What will be the Size of the market During the Forecast Period?

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Deep learning, a subset of machine learning, continues to shape industries by enabling advanced applications such as image and speech recognition, text generation, and pattern recognition. Reinforcement learning, a type of deep learning, gains traction, with deep reinforcement learning leading the charge. Anomaly detection, a crucial application of unsupervised learning, safeguards systems against security vulnerabilities. Ethical implications and fairness considerations are increasingly important in deep learning, with emphasis on explainable AI and model interpretability. Graph neural networks and attention mechanisms enhance data preprocessing for sequential data modeling and object detection. Time series forecasting and dataset creation further expand deep learning's reach, while privacy preservation and bias mitigation ensure responsible use.

In summary, deep learning's market dynamics reflect a constant pursuit of innovation, efficiency, and ethical considerations. The Deep Learning Market in the US is flourishing as organizations embrace intelligent systems powered by supervised learning and emerging self-supervised learning techniques. These methods refine predictive capabilities and reduce reliance on labeled data, boosting scalability. BFSI firms utilize AI image recognition for various applications, including personalizing customer communication, maintaining a competitive edge, and automating repetitive tasks to boost productivity. Sophisticated feature extraction algorithms now enable models to isolate patterns with high precision, particularly in applications such as image classification for healthcare, security, and retail.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Image recognition
  Voice recognition
  Video surveillance and diagnostics
  Data mining


Type

  Software
  Services
  Hardware


End-user

  Security
  Automotive
  Healthcare
  Retail and commerce
  Others


Geography

  North America

    US

By Application Insights

The Image recognition segment is estimated to witness significant growth during the forecast period. In the realm of artificial intelligence (AI) and machine learning, image recognition, a subset of computer vision, is gaining significant traction. This technology utilizes neural networks, deep learning models, and various machine learning algorithms to decipher visual data from images and videos. Image recognition is instrumental in numerous applications, including visual search, product recommendations, and inventory management. Consumers can take photographs of products to discover similar items, enhancing the online shopping experience. In the automotive sector, image recognition is indispensable for advanced driver assistance systems (ADAS) and autonomous vehicles, enabling the identification of pedestrians, other vehicles, road signs, and lane markings.

Furthermore, image recognition plays a pivotal role in augmented reality (AR) and virtual reality (VR) applications, where it tracks physical objects and overlays digital content onto real-world scenarios. The model training process involves the backpropagation algorithm, which calculates the loss fu

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Workforce Analytics Market Size 2025-2029

The workforce analytics market size is forecast to increase by USD 3.27 billion, at a CAGR of 19.1% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing demand for efficient workforce management and recruitment. Companies are recognizing the value of leveraging data-driven insights to optimize their workforce, leading to increased adoption of workforce analytics solutions. Another key trend in the market is the growing use of mobile applications for workforce analytics, enabling real-time access to data and analytics from anywhere. However, the market also faces challenges, including the lack of a skilled workforce capable of effectively implementing and utilizing these advanced analytics tools. As the market continues to evolve, companies seeking to capitalize on opportunities and navigate challenges effectively must prioritize investments in workforce analytics solutions and focus on building a skilled workforce to maximize the value of their data.

What will be the Size of the Workforce Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the ever-increasing importance of data-driven decision making in various sectors. Cost optimization, data visualization, and data warehousing are integral components of workforce analytics, enabling organizations to gain valuable insights from their workforce data. Process automation and employee development are also key areas of focus, as they help streamline operations and enhance employee skills. Performance management and organizational network analysis provide valuable insights into employee productivity and team dynamics. ETL processes and risk management ensure data accuracy and security, while recruitment optimization and career pathing facilitate effective talent acquisition and retention. Predictive modeling and sentiment analysis aid in anticipating workforce trends and employee sentiment, respectively. Data security and strategic workforce planning are essential for mitigating risks and ensuring long-term success. Machine learning and natural language processing are advanced technologies that are increasingly being adopted for data analysis and processing. Workforce analytics encompasses a range of applications, from compensation analysis and employee satisfaction to diversity and inclusion and leadership development. These areas are interconnected and evolve continuously, with new technologies and trends shaping the market landscape. The ongoing integration of these applications into comprehensive workforce analytics solutions enables organizations to optimize their workforce and gain a competitive edge.

How is this Workforce Analytics Industry segmented?

The workforce analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userRetailBFSITelecom and ITHealthcareOthersApplicationLarge enterprisesSmall and medium sized enterpriseDeploymentCloudOn-premiseServiceConsulting ServicesSystem IntegrationManaged ServicesGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW)

By End-user Insights

The retail segment is estimated to witness significant growth during the forecast period.In today's dynamic business environment, retail organizations face increasing pressure to optimize their workforce to stay competitive. The retail industry's growth is driven by factors such as changing market economics, rising competition from e-commerce, and evolving customer demands. To meet these challenges, retailers are investing in their workforce, recognizing its crucial role in driving business success. Workforce optimization strategies encompass various approaches, including 360-degree feedback, organizational network analysis, and social network analysis, to enhance employee performance and engagement. Headcount planning, aided by cloud computing, enables retailers to manage their workforce effectively and adapt to seasonal fluctuations. Regression analysis, statistical analysis, and time series analysis help retailers identify trends and make data-driven decisions. Strategic workforce planning, succession planning, and talent acquisition are essential components of a robust workforce strategy. Employee development, cost optimization, data cleaning, and natural language processing are critical for maintaining a skilled and productive workforce. Data mining, ETL processes, data warehousing, and business intelligence provide valuable insights into workforce performance and trends. Retention strategies, such as career pathing and

Snapshot img

Text Analytics Market Size 2024-2028

The text analytics market size is forecast to increase by USD 18.08 billion, at a CAGR of 22.58% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing popularity of Service-Oriented Architecture (SOA) among end-users. SOA's flexibility and scalability make it an ideal choice for text analytics applications, enabling organizations to process vast amounts of unstructured data and gain valuable insights. Additionally, the ability to analyze large volumes of unstructured data provides valuable insights through data analytics, enabling informed decision-making and competitive advantage. Furthermore, the emergence of advanced text analytical tools is expanding the market's potential by offering enhanced capabilities, such as sentiment analysis, entity extraction, and topic modeling. However, the market faces challenges that require careful consideration. System integration and interoperability issues persist, as text analytics solutions must seamlessly integrate with existing IT infrastructure and data sources.
Ensuring compatibility and data exchange between various systems can be a complex and time-consuming process. Addressing these challenges through strategic partnerships, standardization efforts, and open APIs will be essential for market participants to capitalize on the opportunities presented by the market's growth.

What will be the Size of the Text Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The market continues to evolve, driven by advancements in technology and the increasing demand for insightful data interpretation across various sectors. Text preprocessing techniques, such as stop word removal and lexical analysis, form the foundation of text analytics, enabling the extraction of meaningful insights from unstructured data. Topic modeling and transformer networks are current trends, offering improved accuracy and efficiency in identifying patterns and relationships within large volumes of text data. Applications of text analytics extend to fake news detection, risk management, and brand monitoring, among others. Data mining, customer feedback analysis, and data governance are essential components of text analytics, ensuring data security and maintaining data quality.

Text summarization, named entity recognition, deep learning, and predictive modeling are advanced techniques that enhance the capabilities of text analytics, providing actionable insights through data interpretation and data visualization. Machine learning algorithms, including machine learning and deep learning, play a crucial role in text analytics, with applications in spam detection, sentiment analysis, and predictive modeling. Syntactic analysis and semantic analysis offer deeper understanding of text data, while algorithm efficiency and performance optimization ensure the scalability of text analytics solutions. Text analytics continues to unfold, with ongoing research and development in areas such as prescriptive modeling, API integration, and data cleaning, further expanding its applications and capabilities.

The future of text analytics lies in its ability to provide valuable insights from unstructured data, driving informed decision-making and business growth.

How is this Text Analytics Industry segmented?

The text analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud
  On-premises


Component

  Software
  Services


Geography

  North America

    US


  Europe

    France
    Germany


  APAC

    China
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period.

Text analytics is a dynamic and evolving market, driven by the increasing importance of data-driven insights for businesses. Cloud computing plays a significant role in its growth, as companies such as Microsoft, SAP SE, SAS Institute, IBM, Lexalytics, and Open Text offer text analytics software and services via the Software-as-a-Service (SaaS) model. This approach reduces upfront costs for end-users, as they do not need to install hardware and software on their premises. Instead, these solutions are maintained at the company's data center, allowing end-users to access them on a subscription basis. Text preprocessing, topic modeling, transformer networks, and other advanced techniques are integral to text analytics.

Fake news detection, spam filtering, sentiment analysis, and social media monitoring are essential applications. Deep learning, machine l

Abstract Business Analytics leverages value from data, thus being an important tool for the decision-making process. However, the presence of data in different formats is a new challenge for analysis. Textual data has been drawing organizational attention as thousands of people express themselves daily in text, like the description of customer perceptions in the tourism and hospitality area. Despite the relevance of customer data in textual format to support decision making of hotel managers, its use is still modest, given the difficulty of analyzing and interpreting the large amounts of data. Our objective is to identify the main evaluation topics presented in online guest reviews and reveal changes throughout the years. We worked with 23,229 hotel reviews collected from TripAdvisor website through WebScrapping packages in R, and used a text mining approach (Latent Semantic Analysis) to analyze the data. This contributes with practical implications to hotel managers by demonstrating the applicability of text data and tools based on open-source solutions and by providing insights about the data and assisting in the decision-making process. This article also contributes in presenting a stepwise text analysis, including capturing, cleaning and formatting publicly available data for organizational specialists.

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Big Data Services Market Size 2025-2029

The big data services market size is forecast to increase by USD 604.2 billion, at a CAGR of 54.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of big data in various industries, particularly in blockchain technology. The ability to process and analyze vast amounts of data in real-time is revolutionizing business operations and decision-making processes. However, this market is not without challenges. One of the most pressing issues is the need to cater to diverse client requirements, each with unique data needs and expectations. This necessitates customized solutions and a deep understanding of various industries and their data requirements. Additionally, ensuring data security and privacy in an increasingly interconnected world poses a significant challenge. Companies must navigate these obstacles while maintaining compliance with regulations and adhering to ethical data handling practices. To capitalize on the opportunities presented by the market, organizations must focus on developing innovative solutions that address these challenges while delivering value to their clients. By staying abreast of industry trends and investing in advanced technologies, they can effectively meet client demands and differentiate themselves in a competitive landscape.

What will be the Size of the Big Data Services Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the ever-increasing volume, velocity, and variety of data being generated across various sectors. Data extraction is a crucial component of this dynamic landscape, enabling entities to derive valuable insights from their data. Human resource management, for instance, benefits from data-driven decision making, operational efficiency, and data enrichment. Batch processing and data integration are essential for data warehousing and data pipeline management. Data governance and data federation ensure data accessibility, quality, and security. Data lineage and data monetization facilitate data sharing and collaboration, while data discovery and data mining uncover hidden patterns and trends. Real-time analytics and risk management provide operational agility and help mitigate potential threats. Machine learning and deep learning algorithms enable predictive analytics, enhancing business intelligence and customer insights. Data visualization and data transformation facilitate data usability and data loading into NoSQL databases. Government analytics, financial services analytics, supply chain optimization, and manufacturing analytics are just a few applications of big data services. Cloud computing and data streaming further expand the market's reach and capabilities. Data literacy and data collaboration are essential for effective data usage and collaboration. Data security and data cleansing are ongoing concerns, with the market continuously evolving to address these challenges. The integration of natural language processing, computer vision, and fraud detection further enhances the value proposition of big data services. The market's continuous dynamism underscores the importance of data cataloging, metadata management, and data modeling for effective data management and optimization.

How is this Big Data Services Industry segmented?

The big data services industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ComponentSolutionServicesEnd-userBFSITelecomRetailOthersTypeData storage and managementData analytics and visualizationConsulting servicesImplementation and integration servicesSupport and maintenance servicesSectorLarge enterprisesSmall and medium enterprises (SMEs)GeographyNorth AmericaUSMexicoEuropeFranceGermanyItalyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW).

By Component Insights

The solution segment is estimated to witness significant growth during the forecast period.Big data services have become indispensable for businesses seeking operational efficiency and customer insight. The vast expanse of structured and unstructured data presents an opportunity for organizations to analyze consumer behaviors across multiple channels. Big data solutions facilitate the integration and processing of data from various sources, enabling businesses to gain a deeper understanding of customer sentiment towards their products or services. Data governance ensures data quality and security, while data federation and data lineage provide transparency and traceability. Artificial intelligence and machine learning algo

This dataset is a compilation of open asset-level data, which means the location of sites (e.g., operation, manufacturing, processing facilities of global supply chains), as of December 2022. This included data from 9 publicly available sources, that after data cleaning and harmonization, resulted in 189,075 data points.

    Data source
    Number of data points


    Open Supply Hub (former Open Apparel Registry)
    96,736


    Global Power Plant Database
    35,419


    Climate trace
    19,945


    FDA database
    12,898


    Global Dam Watch
    11,017


    EudraGMDP database
    5,181


    Sustainable Finance Initiative GeoAsset Databases
    4,716


    Global Tailings Portal
    1,956


    Fine print Mining Database
    1,207

This data was assigned with the industry in which the asset is. The summary table below shows the number of assets by industry.

    Industry
    Number of assets    


    Textiles, Apparel & Luxury Good Production       
    96,736


    Health Care, Pharma and Biotechnology
    18,079


    Energy - Solar, Wind
    16,282


    Energy - Hydropower
    14,515


    Energy - Geothermal or Combustion
    11,724


    Metals & Mining
    11,210


    Transportation Services
    4,872


    Construction Materials
    3,117


    Agriculture (animal products)
    2,388


    Agriculture (plant products)
    1,896


    Oil, Gas & Consumable Fuels
    1,194


    Water utilities / Water Service Providers
    892


    Hospitality Services
    294


    Fishing and aquaculture
    14


    Other
    5,862

Note that this compilation is based on an extensive search, however, we acknowledge that there is a significant discrepancy in data coverage/comprehensiveness among the different industries. The industry "Textiles, Apparel & Luxury Good Production" is by far the most complete, while other are clearly far from complete, for example, “Construction Materials”, "Agriculture (animal products)”, “Agriculture (plant products)”, “Oil, Gas & Consumable Fuels”, “Water utilities / Water Service Providers”, “Hospitality Services”, “Fishing and aquaculture”. Therefore, any comparison between industries should take this coverage/comprehensiveness bias into consideration.