Structured Data Archiving And Application Retirement Market size was valued at USD 6.43 Billion in 2023 and is projected to reach USD 14.413 Billion by 2030, growing at a CAGR of 9.5% from 2024 to 2030.

Structured Data Archiving And Application Retirement Market Drivers

Regulatory Compliance Requirements: Organizations in a variety of sectors must adhere to legal requirements pertaining to data archiving and preservation. Structured data must be kept on file for legal, auditing, and compliance reasons, according to regulations. Data from defunct or decommissioned applications must be archived by organizations in order to comply with laws like Sarbanes-Oxley (SOX), GDPR, HIPAA, and others. The demand for application retirement and structured data archiving solutions is driven by the necessity to comply with regulations.

Cost Optimization and Efficiency: By retiring old programs that are no longer in active use, businesses aim to reduce IT expenses and streamline processes. Updating out-of-date apps requires resources for infrastructure, upkeep, and license. Organizations can enhance operational efficiency, save storage costs, and decommission outdated applications by using structured data archiving and application retirement solutions. These services also free up resources for more strategic projects.

Data Governance and Risk Management: Organizations must manage data at every stage of its lifespan, including the archiving and retirement procedures, in order to implement effective data governance standards. Solutions for structured data archiving make it easier to manage structured data assets by offering features like data classification, audit trails, retention policies, and access controls. Through the implementation of application retirement and organized data archiving methods, organizations can reduce the risks associated with data loss, security breaches, and unauthorized access.

Most machine learning, data science, and artificial intelligence (AI) developers work with unstructured text data of the size between 50 MB and 1 GB, with a combined 51 percent of respondents indicating as such. Twelve percent of respondents work with unstructured video data with a size larger than 1 TB.

FILS, Douglas, Ocean Leadership, 1201 New York Ave, NW, 4th Floor, Washington, DC 20005, SHEPHERD, Adam, Woods Hole Oceangraphic Inst, 266 Woods Hole Road, Woods Hole, MA 02543-1050 and LINGERFELT, Eric, Earth Science Support Office, Boulder, CO 80304

The growth in the amount of geoscience data on the internet is paralleled by the need to address issues of data citation, access and reuse. Additionally, new research tools are driving a demand for machine accessible data as part of researcher workflows. In the commercial sector, elements of this have been addressed by the use of the Schema.org vocabulary encoded via JSON-LD and coupled with web publishing patterns. Adaptable publishing approaches are already in use by many data facilities as they work to address publishing and FAIR patterns. While these often lack the structured data elements these workflows could be leveraged to additionally implement schema.org style publishing patterns.

This presentation will report on work that grew out of the EarthCube Council of Data Facilities known as, Project 418. Project 418 was a proof of concept funded by the EarthCube Science Support Office for exploring the approach of publishing JSON-LD with schema.org and extensions by a set of NSF data facilities. The goal was focused on using this approach to describe data set resources and evaluate the use of this structured metadata to address discovery. Additionally, we will discuss growing interest by Google and others in leveraging this approach to data set discovery.

The work scoped 47,650 datasets from 10 NSF-funded data facilities. Across these datasets, the harvester found 54,665 data download URLs, and approximately 560K dataset variables and 35k unique identifiers (DOIs, IGSNs or ORCIDs).

The various publishing workflows used by the involved data facilities will be presented along with the harvesting and interface developments. Details on how resources were indexed into text, spatial and graph systems and used for search interfaces will be presented along with future directions underway building on this foundation.

➡️ DOCS With just the School LinkedIn Profile URL, you can get the list of the students in a school, including their LinkedIn profile URL, which then you can use our People Profile API to enrich all profiles with structured data Check out our API Docs at ➡ nubela.co/proxycurl/docs

➡️ PRICING MODEL Get the data using our API at just $0.01/credit, with each successful request using up only 1 credit. If you need more advanced data points, use more credits for each API request.

➡️ COVERAGE Our Student Listing API covers profiles globally.

➡️ FRESHNESS 88% of our data is fetched in real time, and the API takes 2-3 seconds to complete. If freshness is not a priority, you can choose cached results, which returns immediately.

➡️ LEGAL COMPLIANCE All our data and procedures are in place that meet major legal compliance requirements such as GDPR, CCPA. We help you be compliant too.

Internet search engines have remodeled the use of the internet, making it easy to find the content we are interested in. The Web was originally designed to exchange natural language documents. It is difficult for machines to interpret this type of data. Structured data placed on websites solves this problem by allowing search engines to "understand" the content better. This can also be applied to chemical data.

We have developed three tools to convert chemical data into structured data. SDFEater allows to convert SDF files, Molstruct converts CSV files and MEgen is a web application that allows entering data in a form. Using our tools, we generated 10 datasets including 5 main datasets (DS1, DS2, DS3, DS4, and DS5) and 5 small datasets (DS1s, DS2s, DS3s, DS4s, and DS5s) consisting of 10 files with one molecule each. They are based on well-known chemical databases (ChEBI, DrugBank, PubChem) as well as other data (WikiData). We make them available in JSON-LD HTML, JSON-LD, RDFa, and Microdata structured data formats.

More details about the inputs and outputs as well as how the data is generated can be found in README.txt.

Structured Data Archiving Or Application Retirement Market size was valued at USD 6.43 Billion in 2023 and is projected to reach USD 14.413 Billion by 2030, growing at a CAGR von 9.5% starting 2024 to 2030.

Structured Data Archiving And Application Retirement Market Drivers

Regulatory Corporate Requirements: Organizations in a variety of sectors should adhere at legal requirements pertaining to data archive and preservation. Structured data must be kept on file for legal, accounting, and compliance reasons, according to requirements. Data from defunct or decommissioned applications must be archived from organizations in order to complies on laws like Sarbanes-Oxley (SOX), GDPR, HIPAA, and others. One requirement forward application retirement and structured data document solutions is driven by the necessity to comply with regulations.

Cost Optimization and Efficiency: By retiring old programs that are no long in activated uses, businesses aim to reduce E expenses and streamline processes. Updating out-of-date apps requires resources on infrastructure, care, and license. Organizations can enhance operational performance, store storage costs, and decommission outdated applications by using structured data archiving and application retirement solutions. Like benefit see get up our for more strategic projects.

Data Governance additionally Risk Betriebsleitung: Organization must manage data at every stage starting you lifespan, including the archiving and retirement processes, includes order go implement effective data governance setting. Solutions for structured data archiving make items easier to manage structured evidence assets by offering features like data classification, audit trails, retention policies, and admittance controls. Through the implementation away application retirement and organizes date archiving methods, organizations can reduce and risks associated with data loss, security violate, plus unauthorized access.

We introduce methods for visualization of data structured along trees, especially hierarchically structured collections of time series. To this end, we identify questions that often emerge when working with hierarchical data and provide an R package to simplify their investigation. Our key contribution is the adaptation of the visualization principles of focus-plus-context and linking to the study of tree-structured data. Our motivating application is to the analysis of bacterial time series, where an evolutionary tree relating bacteria is available a priori. However, we have identified common problem types where, if a tree is not directly available, it can be constructed from data and then studied using our techniques. We perform detailed case studies to describe the alternative use cases, interpretations, and utility of the proposed visualization methods.

This article provides an introduction to the field of datasets, including their types, characteristics, and applications. Datasets refer to collections of data that have been organized for specific purposes. They can come in various forms, including structured data, unstructured data, and semi-structured data. Each type of dataset has its own unique characteristics and uses. For example, structured data typically includes datasets that have been organized into tables and rows, such as spreadsheets or databases, while unstructured data typically includes text, images, and videos. Semi-structured data, on the other hand, combines elements of structured and unstructured data and typically includes datasets that have some organization but are not in a traditional table format. Applications of datasets span a wide range of fields, including machine learning, artificial intelligence, marketing, social science research, and more. By understanding the different types of datasets and their characteristics, users can choose the appropriate datasets for their specific projects and goals.

➡️ DOCS With just the persons' LinkedIn profile URL, you can get tons of data points of an individual, up to a whooping 44 data points. Check out our API Docs at ➡ nubela.co/proxycurl/docs

➡️ PRICING MODEL Get the data using our API at just $0.01/credit, with each successful request using up only 1 credit. If you need more advanced data points, use more credits for each API request.

➡️ COVERAGE Our People Profile API covers profiles globally.

➡️ FRESHNESS 88% of our data is fetched in real time, and the API takes 2-3 seconds to complete. If freshness is not a priority, you can choose cached results, which returns immediately.

➡️ LEGAL COMPLIANCE All our data and procedures are in place that meet major legal compliance requirements such as GDPR, CCPA. We help you be compliant too.

Snapshot img

Intelligent Document Processing Market Forecast 2023-2027

The intelligent document processing market size is projected to reach a value of USD 3.34 billion, with an accelerated CAGR of 29.69% between 2022 and 2027. The growth of the market depends on several factors, including the growing use of big data analytics, the reduction of document management costs, and the introduction of cloud-based deployment solutions. This market analysis and report also includes an in-depth analysis of drivers, trends, and challenges. Furthermore, the market research and growth report includes historic market data from 2017 to 2021.

What will be the size of the Intelligent Document Processing Market During the Forecast Period?

To learn more about this report, View Report Sample

Market Overview

Key Driver

One of the key factors driving market growth is the growing use of big data analytics. There has been an increasing adoption of big data analytics among enterprises as it offers many business benefits, including improving customer service and operational efficiency, devising effective marketing strategies, identifying new revenue opportunities, and gaining competitive advantages over rivals.

As several enterprises generate structured, unstructured, and semi-structured data, it can be directly integrated with the analytics solution. Hence, this data can be harnessed and analyzed using IDP software solutions after digitization. With the help of IDP software provider solutions, it enables organizations to utilize the information stored in physical documents with the help of digital transformation. Therefore, the growing use of big data analytics is leading to an increase in demand for IDP software provider solutions which will drive the growth of the market focus during the forecast period.

Latest Trend

Organizations have started to realize the importance of data as an asset to their business operations, decision-making process, and, ultimately, their revenues and profits. The convergence of software with machine learning (ML) marks a paradigm shift in document processing. This not only enhances efficiency but also significantly impacts the market.

Automation in document processing reduces manual intervention, minimizing errors and time-consuming tasks associated. It not only increases productivity but also allows enterprises to allocate resources more strategically, focusing on core business operations and innovation through automation in documents.
Document Recognition Technology significantly augments the capabilities of software by improving efficiency, accuracy, automation, and versatility. It ensures compliance with regulations by accurately capturing and storing data safely.
Ephesoft, a pioneer in this field, introduces Ephesoft Insight—a groundbreaking ML document mining platform. It meticulously analyzes files from content repositories on a large scale, unraveling business meaning and extracting substantial value from these previously untapped resources.
Document recognition technology stands at the forefront of this transformative change, driving operational efficiency and unlocking invaluable insights for businesses worldwide marking as future of document automation.

Aligning with market trends and analysis, as enterprises embrace this integrated approach, the growth trajectory of the future of the document automation market during the forecast period appears promising and poised for substantial expansion.

Market Segmentation By Component

The market share growth by the solution segment will be significant during the forecast period. There is a rise in demand for the solutions segment of the market due to its ability to automate document processing tasks, streamline workflow, and improve accuracy. Due to an increasing amount of data generated every day, businesses are facing challenges in processing and managing data efficiently, leading to high demand for IDP software provider solutions.

Get a glance at the market contribution of various segments View Free PDF Sample

The solution segment was valued at USD 419.30 million in 2017. Some of the key benefits of IDP software include the ability to process unstructured data, reduce the time required to process large amounts of data and eliminate errors caused by human intervention. Thus, it has resulted in the rise in adoption in industries across healthcare, finance, legal, and insurance. For example, in the healthcare industry, there are wide applications of IDP solutions as they are being used to streamline the processing of medical records, insurance claims, and patient information. Hence, such benefits are expected to drive the growth of this segment which in turn will drive the market growth during the forecast period.

Regional Overview

For more insights on the market share of various regions Download PDF Sample now!

North America is estimated to contr

Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.

Snapshot img

The big data services market is estimated to grow at a CAGR of 35.68% between 2022 and 2027 and the size of the market is forecast to increase by USD 153.75 billion. The growth of the market depends on several factors, including the growing amount of data, the increase in the adoption of big data services in industries, and the increased importance of big data in social media marketing.

This report extensively covers market segmentation by component (solution and services), end-user (BFSI, telecom, retail, and others), and geography (North America, Europe, APAC, South America, and Middle East and Africa). It also includes an in-depth analysis of drivers, trends, and challenges. Furthermore, the report includes historic market data from 2017 to 2021.

What will be the size of the Big Data Services Market During the Forecast Period?

To learn more about this report, Download Report Sample

Big Data Services Market: Key Drivers, Trends, Challenges, and Customer Landscape

Our researchers analyzed the data with 2022 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

Key Big Data Services Market Driver

The growing amount of data is one of the key factors driving the growth of the big data as a service market. Enterprise applications are generating large volumes of data, and this will keep continuing throughout the forecast period and beyond. With the growing volume, variety, veracity, and velocity of data, which is referred to as 4Vs, organizations are facing difficulties to analyze and manage large databases efficiently. The increasing volume of data generated in organizations through various channels and sources has compelled organizations to implement big data analytics and save a significant amount of cost for the organizations.

Big data analytics has helped organizations transform unstructured and semi-structured data into structured and meaningful data. Big data analytics retrieved and analyzed data to discover significant weaknesses, develop indicator patterns to identify opportunities and threats and optimize business decisions. Since the demand for big data analytics is growing, there is a need for big services such as big data project-based services and big data outsourcing services to manage big data analytics applications to ensure security, agility, and performance.

Key Big Data Services Market Trends

Big data in blockchain technology is a primary trend for the global data analytics market growth. Blockchain technology is gaining popularity in the financial sector. JPMorgan, Citi, Wells Fargo, US Bancorp, PNC, Fifth Third Bank, and Signature Bank are some of the banks that use blockchain technology. It is replacing the current centralized business model of financial services. Blockchain technology, also called a ledger framework, is a distributed network of digital databases that maintains records and manages transactions. It uses advanced cryptography to keep transactions secure and manage approvals. All transaction details are then registered in the database. Moreover, the big data blockchain technology has databases that are scalable, have query languages, and accurate blockchains. The big data blockchain database is decentralized; hence, the control can be shared with appropriate authorities.

Big data blockchain technology helps in collecting and interpreting huge amounts of information and supports organizations in decision-making processes. This increases operational efficiency and improves security. The technology also overhauls and improves security and highlights where action needs to be taken before and after a hack. However, shifting to a decentralized database network will require educating end-users and operators and integrating it into the current working process.

Key Big Data Services Market Challenge

Adhering to diverse client requirements is a major challenge for global big data as a service market growth. Several industries lack policies or frameworks to store the high volume of data, which leads to difficulties in the effective performance of big data. This, in turn, affects the market penetration of big data service providers as their quality of service deteriorates. Big data service providers need to continuously develop and offer innovative solutions in step with changing customer requirements. This is a complex and cost-consuming process as it involves high degrees of uncertainty, and failure in understanding the requirements of customers leads to potential loss of time and money. Most of the clients expect business results but are wary about spending.

The lack of a forward-looking policy makes it difficult for vendors to calculate and track ROI. This may hinder the value additions from service providers challenging the development of the market. Therefore, it is important for vendor

These files were used with participants in a study of the experience of using YARRRML and SPARQL Anything to map structured data, e.g. CSV, JSON and XML, to RDF. YARRRML is a mapping language, whereas SPARQL Anything is an extension of SPARQL which uses the CONSTRUCT query to define the mapping.

The study was a between-participants study, i.e. some participants were asked to use YARRRML and other participants were asked to use SPARQL Anything. Participants were observed undertaking a number of mapping tasks; the same mapping tasks were used for both conditions. Participants took part remotely, using Microsoft Teams, and their screen activity was recorded, along with any comments they made. Subsequent analysis of the recordings enabled a qualitative study of their experiences.

The files are:: * YARRRML tutorial * Information for YARRRML study participants, including questions * SPARQL Anything tutorial * Information for SPARQL Anything study participants, including questions * YARRRML.zip * SPARQL Anything.zip

Please note that the two tutorials are not comprehensive, but contain only the information required by participants in the study.

The two zip files were as sent out to participants immediately prior to the study. They contain: * The data files, i.e. artist_data.csv, artist38.csv; artwork.json, artwork.xml, artworkAttributes.xml. * For the YARRRML participants, 1.yml, 2.yml etc, which contain the YARRRML code requiring completion; and similarly for the SPARQL Anything participants, 1.sparql, 2.sparql etc, which contain the SPARQL Anything code requiring completion. * 1.ont, 2.ont etc., which contain the required output for each question. * A 'bak' folder which contain either 1.yml, 2.yml etc., or 1.sparql, 2.sparql etc., as appropriate. These were provided in case a participant wished to start a question again with a 'clean slate'.

The .yml, .sparql and .ont files in the two zip files are numbered as in the paper. Some participants were sent alternative zip files with the order of questions 3, 4, 5 and the order of questions 6, 7, 8 permuted.

This dataset is a real-world web page collection used for research on the automatic extraction of structured data (e.g., attribute-value pairs of entities) from the Web. We hope it could serve as a useful benchmark for evaluating and comparing different methods for structured web data extraction.

This is a dataset about the usage of properties and datatypes in the Web Data Commons RDFa, Microdata, Embedded JSON-LD, and Microformats Data Sets (November 2018) based on the Common Crawl November 2018 archive. The dataset has been produced using the RDF Property and Datatype Usage Scanner v2.1.1, which is based on the Apache Jena framework. Only RDFa and embedded JSON-LD data were considered, as Microdata and Microformats do not incorporate explicit datatypes.

Dataset Properties

• Size: 22.2 MiB compressed, 569.6 MiB uncompressed, 2 608 325 rows plus 1 head line determined using gunzip -c measurements.csv.gz | wc -l
• Parsing Failures: The scanner failed to parse 4 135 842 triples (~0.077 %) of the source dataset (containing 5 367 569 192 triples).
• Content:
  • CATEGORY: The category (html-embedded-jsonld or html-rdfa) of the Web Data Commons file that has been measured.
  • FILE_URL: The URL of the Web Data Commons file that has been measured.
  • MEASUREMENT: The applied measurement with specific conditions, one of:
    • UnpreciseRepresentableInDouble: The number of lexicals that are in the lexical space but not in the value space of xsd:double.
    • UnpreciseRepresentableInFloat: The number of lexicals that are in the lexical space but not in the value space of xsd:float.
    • UsedAsDatatype: The total number of literals with the datatype.
    • UsedAsPropertyRange: The number of statements that specify the datatype as range of the property.
    • ValidDateNotation: The number of lexicals that are in the lexical space of xsd:date.
    • ValidDateTimeNotation: The number of lexicals that are in the lexical space of xsd:dateTime.
    • ValidDecimalNotation: The number of lexicals that represent a number with decimal notation and whose lexical representation is thereby in the lexical space of xsd:decimal, xsd:float, and xsd:double.
    • ValidExponentialNotation: The number of lexicals that represent a number with exponential notation and whose lexical representation is thereby in the lexical space of xsd:float, and xsd:double.
    • ValidInfOrNaNNotation: The number of lexicals that equals either INF, +INF, -INF or NaN and whose lexical representation is thereby in the lexical space of xsd:float, and xsd:double.
    • ValidIntegerNotation: The number of lexicals that represent an integer number and whose lexical representation is thereby in the lexical space of xsd:integer, xsd:decimal, xsd:float, and xsd:double.
    • ValidTimeNotation: The number of lexicals that are in the lexical space of xsd:time.
    • ValidTrueOrFalseNotation: The number of lexicals that equal either true or false and whose lexical representation is thereby in the lexical space of xsd:boolean.
    • ValidZeroOrOneNotation: The number of lexicals that equal either 0 or 1 and whose lexical representation is thereby in the lexical space of xsd:boolean, and xsd:integer, xsd:decimal, xsd:float, and xsd:double.
    Note: Lexical representation of xsd:double values in embedded JSON-LD got normalized to always use exponential notation with up to 16 fractional digits (see related code). Be careful by drawing conclusions from according Valid… and Unprecise… measures.
  • PROPERTY: The property that has been measured.
  • DATATYPE: The datatype that has been measured.
  • QUANTITY: The count of statements that fulfill the condition specified by the measurement per file, property and datatype.

Preview

"CATEGORY","FILE_URL","MEASUREMENT","PROPERTY","DATATYPE","QUANTITY"
"html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","https://www.w3.org/2006/vcard/ns#longitude","https://www.w3.org/2001/XMLSchema#float","4"
"html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","https://www.w3.org/2006/vcard/ns#latitude","https://www.w3.org/2001/XMLSchema#float","4"
"html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","https://purl.org/goodrelations/v1#hasCurrencyValue","https://www.w3.org/2001/XMLSchema#float","6"
"html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","http://purl.org/goodrelations/v1#hasCurrencyValue","http://www.w3.org/2001/XMLSchema#floatfloat","8"
"html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","https://opengraphprotocol.org/schema/latitude","http://www.w3.org/2001/XMLSchema#string","30"
…
"html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-embedded-jsonld.nq-00734.gz","ValidZeroOrOneNotation","http://schema.org/numberOfItems","http://www.w3.org/2001/XMLSchema#integer","40"
"html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-embedded-jsonld.nq-00734.gz","ValidZeroOrOneNotation","http://schema.org/ratingValue","http://www.w3.org/2001/XMLSchema#integer","431"
"html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-embedded-jsonld.nq-00734.gz","ValidZeroOrOneNotation","http://schema.org/width","http://www.w3.org/2001/XMLSchema#integer","122"
"html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-embedded-jsonld.nq-00734.gz","ValidZeroOrOneNotation","http://schema.org/minValue","http://www.w3.org/2001/XMLSchema#integer","63"
"html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-embedded-jsonld.nq-00734.gz","ValidZeroOrOneNotation","http://schema.org/pageEnd","http://www.w3.org/2001/XMLSchema#integer","139"

Note: The data contain malformed IRIs, like "xsd:dateTime" (instead of probably "http://www.w3.org/2001/XMLSchema#dateTime"), which are caused by missing namespace definitions in the original source website.

Reproduce

To reproduce this dataset checkout the RDF Property and Datatype Usage Scanner v2.1.1 and execute:

mvn clean package
java -jar target/Scanner.jar --category html-rdfa --list http://webdatacommons.org/structureddata/2018-12/files/html-rdfa.list November2018
java -jar target/Scanner.jar --category html-embedded-jsonld --list http://webdatacommons.org/structureddata/2018-12/files/html-embedded-jsonld.list November2018
./measure.sh November2018
# Wait until the scan has completed. This will take a few days
java -jar target/Scanner.jar --results ./November2018/measurements.csv.gz November2018

• Driving Factors
• Restraining Factors
• Data Discovery Market Segment Analysis
• Growth Opportunities
• Regional Analysis
• Data Discovery Industry by Region
• Key Player Overview: Data Discovery Market
• Recent Developments

The data discovery market was valued at USD 17.9 billion in 2022. It is expected to reach USD 31.9 billion in 2032, at a CAGR of 18.2% during the forecast period from 2023 to 2032.The rise in demand for structured and unstructured information is the main driving factor of the data discovery market. Structure data includes the names, addresses, and dates that can be searched easily by comparison.Whereas, unstructured data includes everything starting from social media posts to video & audio files, emails, and images. Unstructured data generates about 90% of all information globally. Though structured data contributes a small amount of percentage to existing data, it is still considered more valuable as it is much easier to handle and extract the information.Structure data in the data discovery market plays an important role in various industries from business and finance to healthcare and education. It is used in data analysis to help organizations get valuable insights from their information.This information is used to make decision-making processes, enhance operations, and predict market trends. For example, in the healthcare sector, structured data helps in maintaining patient health records that allow medical experts to access the information and standardize it. It also improves patient safety and helps in diagnosis and other medical research.Moreover, in the retail business, it helps to maintain inventory records. In many businesses structured data helps to track product sales, and consumer preferences and improve the shopping experience. Whereas, banks depend on these structured data to keep accurate records of customer’s money, and transactions and control regulatory compliance.Additionally, data discovery does not require the creation of complex models by the business users. Several businesses depend upon data discovery for their business intelligence software, which provides them with a complete overview of their business in a visual pattern.It helps the business to understand the market trends and other sorts of information and also data discovery uses structural diagrams, text, and visual storytelling methods to analyze the business. Non-IT individuals can decode multiple amounts of information and obtain the data they want instantly. By following this method data discovery can democratize data analysis for all the working employees in the organization. This market is rapidly expanding and is likely to surge during the forecast period.

Driving Factors

Growing Need to Discover Sensitive Structured and Unstructured Data Drives Market Growth

The growing need to discover sensitive structured and unstructured data is a primary driver for the expansion of the data discovery market. In the digital age, organizations are inundated with vast volumes of data, both in traditional databases and unstructured formats like emails, documents, and social media. Identifying and managing sensitive information within this data is crucial for compliance, risk management, and decision-making.As businesses recognize the importance of understanding what data they possess and its potential risks or values, the demand for data discovery tools that can efficiently scan, categorize, and analyze this information grows. This trend is likely to persist, driven by the increasing complexity and volume of data generated, necessitating advanced solutions for data discovery and management.

Increasing Investments in Data Privacy with Evolving Regulations Boosts Market

Increasing investments in data privacy, spurred by evolving global regulations like GDPR, are significantly boosting the data discovery market. Compliance with these regulations requires organizations to have a clear understanding and control over the data they hold, particularly personal and sensitive information. This has led to increased demand for data discovery tools that enable businesses to locate, classify, and monitor data in line with regulatory requirements.The emphasis on data privacy and security is not just a compliance issue but also a matter of corporate responsibility and trust. As regulations continue to evolve and more regions implement their data protection laws, the necessity for robust data discovery solutions becomes more pronounced, promising continued market growth.

Growing Adoption of AI and Machine Learning Propels Market Innovation

The growing adoption of artificial intelligence (AI) and machine learning (ML) is propelling the data discovery market to new heights. AI and ML technologies enhance the capabilities of data discovery tools by enabling more efficient processing, pattern recognition, and predictive analytics. This integration allows for more sophisticated analysis and understanding of large data sets, making the discovery process faster and more insightful.The incorporation of AI and ML in data discovery tools is not just an enhancement but a transformation, shifting the market towards more intelligent and autonomous solutions. This trend is anticipated to continue, with AI and ML driving innovation and expanding the possibilities of what data discovery platforms can achieve.

Growing Adoption of Big Data Analytics Enhances Market Scope

The growing adoption of big data analytics is significantly enhancing the scope of the data discovery market. As organizations increasingly leverage big data for strategic insights, the need to efficiently navigate and make sense of this data becomes critical. Data discovery tools play a vital role in this context, providing the means to extract actionable insights from vast and varied data sources.The synergy between big data analytics and data discovery is creating new opportunities for market growth. As big data technologies evolve, so does the need for advanced data discovery solutions capable of handling the complexity and scale of big data.

Restraining Factors

Lack of Awareness About Data Discovery Tools Restrains Market Growth

The growth of the data discovery market is hindered by a general lack of awareness about these tools and their benefits. Many organizations, especially small to medium-sized businesses, may not fully understand how data discovery solutions can enhance decision-making and operational efficiency. This lack of awareness results in slow adoption rates as potential users may not recognize the value these tools bring or how they could be integrated into their existing systems. Increasing awareness and demonstrating the tangible benefits of data discovery tools are crucial to expanding their market presence.

High Implementation Costs of Data Discovery Solutions Restrain Market Growth

High implementation costs significantly limit the expansion of the data discovery market. The development, deployment, and maintenance of effective data discovery solutions often require substantial investment, particularly for comprehensive and advanced systems. This financial barrier can be prohibitive for smaller organizations or those with limited IT budgets, leading them to opt for more basic, less costly alternatives. Overcoming cost concerns, possibly through scalable solutions or as-a-service models, is essential for broader market adoption.

Data Discovery Market Segment Analysis

By Component

The software segment dominates the data discovery market. This predominance is attributed to the increasing need for organizations to gain insights from large volumes of data. Data discovery software provides tools for data aggregation, processing, visualization, and analysis, helping businesses make informed decisions. The growth is propelled by advancements in AI and machine learning, enabling more sophisticated and automated data analysis.While services are less dominant, they play a crucial role in supporting the software segment. Services include consulting, implementation, and ongoing support, which are essential for the successful deployment and utilization of data discovery solutions.

By Deployment

Cloud-based deployment leads the market, offering scalability, flexibility, and cost-effectiveness. The growth of cloud computing has made data discovery tools more accessible to a broader range of businesses, including SMEs. Cloud deployment also facilitates real-time data analysis and collaboration across locations.On-premise solutions, while not as prevalent, are important for organizations requiring enhanced data security and control, particularly in sectors like BFSI and government.<img class='alignnone wp-image-42077' src='https://marketresearch.biz/wp-content/uploads/2021/08/data-discovery-market-deployment.jpg' alt='data discovery market deployment' width='744'

This is a dataset about the usage of properties and datatypes in the Web Data Commons RDFa, Microdata, Embedded JSON-LD, and Microformats Data Sets (October 2016) based on the Common Crawl October 2016 archive. The dataset has been produced using the RDF Property and Datatype Usage Scanner v2.1.1, which is based on the Apache Jena framework. Only RDFa and embedded JSON-LD data were considered, as Microdata and Microformats do not incorporate explicit datatypes.

Dataset Properties

• Size: 17.4 MiB compressed, 351.1 MiB uncompressed, 1 612 479 rows plus 1 head line determined using gunzip -c measurements.csv.gz | wc -l
• Parsing Failures: The scanner failed to parse 28 326 152 triples (~0.69 %) of the source dataset (containing 4 097 655 302 triples).
• Content:
  • CATEGORY: The category (html-embedded-jsonld or html-rdfa) of the Web Data Commons file that has been measured.
  • FILE_URL: The URL of the Web Data Commons file that has been measured.
  • MEASUREMENT: The applied measurement with specific conditions, one of:
    • UnpreciseRepresentableInDouble: The number of lexicals that are in the lexical space but not in the value space of xsd:double.
    • UnpreciseRepresentableInFloat: The number of lexicals that are in the lexical space but not in the value space of xsd:float.
    • UsedAsDatatype: The total number of literals with the datatype.
    • UsedAsPropertyRange: The number of statements that specify the datatype as range of the property.
    • ValidDateNotation: The number of lexicals that are in the lexical space of xsd:date.
    • ValidDateTimeNotation: The number of lexicals that are in the lexical space of xsd:dateTime.
    • ValidDecimalNotation: The number of lexicals that represent a number with decimal notation and whose lexical representation is thereby in the lexical space of xsd:decimal, xsd:float, and xsd:double.
    • ValidExponentialNotation: The number of lexicals that represent a number with exponential notation and whose lexical representation is thereby in the lexical space of xsd:float, and xsd:double.
    • ValidInfOrNaNNotation: The number of lexicals that equals either INF, +INF, -INF or NaN and whose lexical representation is thereby in the lexical space of xsd:float, and xsd:double.
    • ValidIntegerNotation: The number of lexicals that represent an integer number and whose lexical representation is thereby in the lexical space of xsd:integer, xsd:decimal, xsd:float, and xsd:double.
    • ValidTimeNotation: The number of lexicals that are in the lexical space of xsd:time.
    • ValidTrueOrFalseNotation: The number of lexicals that equal either true or false and whose lexical representation is thereby in the lexical space of xsd:boolean.
    • ValidZeroOrOneNotation: The number of lexicals that equal either 0 or 1 and whose lexical representation is thereby in the lexical space of xsd:boolean, and xsd:integer, xsd:decimal, xsd:float, and xsd:double.
    Note: Lexical representation of xsd:double values in embedded JSON-LD got normalized to always use exponential notation with up to 16 fractional digits (see related code). Be careful by drawing conclusions from according Valid… and Unprecise… measures.
  • PROPERTY: The property that has been measured.
  • DATATYPE: The datatype that has been measured.
  • QUANTITY: The count of statements that fulfill the condition specified by the measurement per file, property and datatype.

Preview

"CATEGORY","FILE_URL","MEASUREMENT","PROPERTY","DATATYPE","QUANTITY"
"html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2016-10/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","http://schema.org/aggregateRating","http://www.w3.org/2001/XMLSchema#string","36"
"html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2016-10/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","http://opengraphprotocol.org/schema/longitude","http://www.w3.org/2001/XMLSchema#string","1137"
"html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2016-10/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","http://ogp.me/ns#title","http://www.w3.org/2001/XMLSchema#string","3"
"html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2016-10/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","http://ogp.me/nslongitude","http://www.w3.org/2001/XMLSchema#string","1"
"html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2016-10/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","http://ogp.me/ns#latitude","http://www.w3.org/2001/XMLSchema#string","884"
[…]
"html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2016-10/quads/dpef.html-embedded-jsonld.nq-00294.gz","ValidZeroOrOneNotation","http://schema.org/minPrice","http://www.w3.org/2001/XMLSchema#integer","12"
"html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2016-10/quads/dpef.html-embedded-jsonld.nq-00294.gz","ValidZeroOrOneNotation","http://schema.org/highPrice","http://www.w3.org/2001/XMLSchema#integer","1"
"html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2016-10/quads/dpef.html-embedded-jsonld.nq-00294.gz","ValidZeroOrOneNotation","http://schema.org/numberOfItems","http://www.w3.org/2001/XMLSchema#integer","44"
"html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2016-10/quads/dpef.html-embedded-jsonld.nq-00294.gz","ValidZeroOrOneNotation","http://schema.org/ratingValue","http://www.w3.org/2001/XMLSchema#integer","139"
"html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2016-10/quads/dpef.html-embedded-jsonld.nq-00294.gz","ValidZeroOrOneNotation","http://schema.org/width","http://www.w3.org/2001/XMLSchema#integer","76"

Note: The data contain malformed IRIs, like "xsd:dateTime" (instead of probably "http://www.w3.org/2001/XMLSchema#dateTime"), which are caused by missing namespace definitions in the original source website.

Reproduce

To reproduce this dataset checkout the RDF Property and Datatype Usage Scanner v2.1.1 and execute:

mvn clean package
java -jar target/Scanner.jar --category html-rdfa --list http://webdatacommons.org/structureddata/2016-10/files/rdfa.list October2016
java -jar target/Scanner.jar --category html-embedded-jsonld --list http://webdatacommons.org/structureddata/2016-10/files/html-embedded-jsonld.list October2016
./measure.sh October2016
# Wait until the scan has completed. This will take a few days
java -jar target/Scanner.jar --results ./October2016/measurements.csv.gz October2016

The Business Structure Database (BSD) contains a small number of variables for almost all business organisations in the UK. The BSD is derived primarily from the Inter-Departmental Business Register (IDBR), which is a live register of data collected by HM Revenue and Customs via VAT and Pay As You Earn (PAYE) records. The IDBR data are complimented with data from ONS business surveys. If a business is liable for VAT (turnover exceeds the VAT threshold) and/or has at least one member of staff registered for the PAYE tax collection system, then the business will appear on the IDBR (and hence in the BSD). In 2004 it was estimated that the businesses listed on the IDBR accounted for almost 99 per cent of economic activity in the UK. Only very small businesses, such as the self-employed were not found on the IDBR.

The IDBR is frequently updated, and contains confidential information that cannot be accessed by non-civil servants without special permission. However, the ONS Virtual Micro-data Laboratory (VML) created and developed the BSD, which is a 'snapshot' in time of the IDBR, in order to provide a version of the IDBR for research use, taking full account of changes in ownership and restructuring of businesses. The 'snapshot' is taken around April, and the captured point-in-time data are supplied to the VML by the following September. The reporting period is generally the financial year. For example, the 2000 BSD file is produced in September 2000, using data captured from the IDBR in April 2000. The data will reflect the financial year of April 1999 to March 2000. However, the ONS may, during this time, update the IDBR with data on companies from its own business surveys, such as the Annual Business Survey (SN 7451).

The data are divided into 'enterprises' and 'local units'. An enterprise is the overall business organisation. A local unit is a 'plant', such as a factory, shop, branch, etc. In some cases, an enterprise will only have one local unit, and in other cases (such as a bank or supermarket), an enterprise will own many local units.

For each company, data are available on employment, turnover, foreign ownership, and industrial activity based on Standard Industrial Classification (SIC)92, SIC 2003 or SIC 2007. Year of 'birth' (company start-up date) and 'death' (termination date) are also included, as well as postcodes for both enterprises and their local units. Previously only pseudo-anonymised postcodes were available but now all postcodes are real.

The ONS is continually developing the BSD, and so researchers are strongly recommended to read all documentation pertaining to this dataset before using the data.

Linking to Other Business Studies
These data contain IDBR reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Latest Edition Information
For the sixteenth edition (March 2024), data files and a variable catalogue document for 2023 have been added.

A DataSet of Supply Chains used by the company DataCo Global was used for the analysis. Dataset of Supply Chain , which allows the use of Machine Learning Algorithms and R Software. Areas of important registered activities : Provisioning , Production , Sales , Commercial Distribution.It also allows the correlation of Structured Data with Unstructured Data for knowledge generation. Type Data : Structured Data : DataCoSupplyChainDataset.csv Unstructured Data : tokenized_access_logs.csv (Clickstream) Types of Products : Clothing , Sports , and Electronic Supplies Additionally it is attached in another file called DescriptionDataCoSupplyChain.csv, the description of each of the variables of the DataCoSupplyChainDatasetc.csv.