The number of small and medium-sized enterprises in the United States was forecast to continuously decrease between 2024 and 2029 by in total 6.7 thousand enterprises (-2.24 percent). After the fourteenth consecutive decreasing year, the number is estimated to reach 291.94 thousand enterprises and therefore a new minimum in 2029. According to the OECD an enterprise is defined as the smallest combination of legal units, which is an organisational unit producing services or goods, that benefits from a degree of autonomy with regards to the allocation of resources and decision making. Shown here are small and medium-sized enterprises, which are defined as companies with 1-249 employees.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Abstract (en): Dun's Review began publishing monthly data on business failures by branch of business during the 1890s. At that time, a business failure was defined as a concern which was involved in a court proceeding or voluntary action which was likely to end in loss to creditors. Liabilities of failed businesses were defined "as all liabilities except long-term publicly-held obligations, chiefly bonds." Dun's published data on failures by branch of business from 1895 through 1935. This dataset reconstructs that series and links it to its successors. The successor series include data on business failures by division of industry, which Dun and Bradstreet's published from 1934 through 1940. This study includes six parts. Part One contains aggregate liabilities in dollars, broken down by branch, month, and year. Part Two contains aggregate numbers of business failures broken down by branch, month, and year. Part Three contains aggregate liability in dollars broken down by division, month, and year. Part Four contains aggregate numbers of business failures broken down by division, month, and year. Part Five contains aggregate liabilities broken down by sector, month, and year. Part Six contains aggregate numbers of business failures broken down by sector, month, and year. Part One and Part Two contain 36 variables and 562 cases. Part Three and Part Four contain 51 variables and 60 cases. Part Five and Part Six contain 6 variables and 562 cases. This study allows for economic analysis of business failures. It is intended to provide a resource on business failure and liabilites from 1895 to 1940. Data originally collected from court filings at municipal, county, state, and United States district court houses throughout the United States from 1895 through 1940. Data published periodically by R. G. Dun and Company, Bradstreet's Company, and their successors through 1940. From their publications, the principal investigators collected, cleaned, compiled, and computerized the current data series. Variables include monthly, unadjusted, liabilities and monthly, unadjusted, number of failures for different branches, sectors, divisons. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Businesses that failed in the United States from 1895 through 1940. Smallest Geographic Unit: United States The data consist of the aggregate number of corporations filing for bankruptcy in various industries each month in the United States and the total liabilities of those corporations. Please refer to the codebook for sampling information in the "Original P.I. Documentation" section. Additional information can be found by visiting the National Bureau of Economic Research (NBER) Web site. For additional information on these datasets please see the National Bureau of Economic Research (NBER) Web site.The dates in the Original P.I. Documentation for Business Failures by Industry in the United States range from 1895 to 1939, however, the data range from 1895 to 1940. The title for ICPSR 34016 has been changed to reflect the data.

The Los Angeles BusinessSource Centers provide startup ventures and current small business owners various cost effective tools to make their business a success. Through these tools, small businesses can grow and remain competitive within the City of Los Angeles. Startups focuses on owners of businesses with five (5) or fewer employees, one of whom owns the enterprise, and have net operating income of less than Two Hundred Thousand Dollars ($200,000). This focus is particularly important as the majority of the businesses within the City may be categorized as “survivors,” and historically, many such businesses fail in their first two years of operation. The survival and growth of such businesses is still very important to the ongoing economic vitality of the City.

Many small businesses and startups struggle to adjust their operational plans to quickly changing market and financial situations. Traditional data-driven techniques often miss possibilities and waste resources. Our unique approach, Unified Statistical Association Validation (USAV), allows dynamic and real-time data association and improvement assessment to address this essential issue. USAV classifies and validates critical data associations based on business features to improve startup incubation and innovation decision-making. USAV analyses different financial eras using federated learning to find performance inefficiencies using a Kaggle dataset on small business success and failure. USAV recommends actionable improvements during innovation using non-recurrent statistical patterns, unlike standard models that use prior financial data. The framework allows real-time flexibility with continual statistical updates without data redundancy. The proposed approach achieved an improvement assessment score of 0.98, data association accuracy of 96%, statistical update efficiency of 0.97, modification ratio of 35%, and incubation analysis time reduction of 240 units in experimental evaluation. These findings demonstrate USAV’s ability to help strategic decision-making in dynamic corporate situations.

The Global Entrepreneurship Monitor (GEM) was designed to capture various aspects of firm creation and entrepreneurship across countries. The data have been collected over a number of years (1998-2003) and include responses from 4,685 experts in over 38 countries and three subnational regions. This study seeks to measure the national attributes considered critical for new firm births and small firm growth. The dataset is a harmonized file capturing the results from all of the surveys. The expert, or key informant, questionnaire was improved and adjusted each year to increase the reliability of multi-item indices and provide for the addition of new dimensions. For each version of the questionnaire, respondents completed 70-80 standardized items that were the basis for 12-15 multi-item indices. Respondents were initially asked a series of general questions pertaining to starting a business, such as whether they were currently trying to start a new business, whether they knew anyone who had started a new business, and whether they thought it was a good time to do so. Respondents were also asked about the process of starting up a new business; whether they had done anything to start a new business in the past 12 months; whether they would own all, part, or none of the new business; how many people would be involved with the new business; what sort of business they were starting; and what they would sell. In addition, respondents identified the total start-up costs, the various sources of the start-up money, and why they were involved in the start-up. Respondents then answered a set of questions to assess the national conditions influencing entrepreneurial activity in their own country. In this respect, respondents provided their opinions on business and entrepreneurial education, the integration of new technology in businesses, the availability of financial support through government policies and programs, the availability of subcontractors, yearly changes in the economic market, and the physical infrastructure in their country. Views were also elicited from respondents about their national cultures in regard to entrepreneurial efforts and opportunities, attitudes towards entrepreneurs in general, women entrepreneurs and the resources available to them, and citizens' knowledge and experience with new businesses. They also gave their views on the Intellectual Property Rights (IPR) legislation and its enforcement in their respective countries. Respondents were then queried on the technological strengths of their country by ranking the top five sectors in which there has been development of the greatest number of technology-intensive start-up companies in the past ten years. Finally, respondents were asked the same general questions as those used in the GLOBAL ENTREPRENEURSHIP MONITOR (GEM): ADULT POPULATION SURVEY DATA SET, 1998-2003 (ICPSR 20320) in order to ascertain whether the opinions and behaviors of the current "expert" respondents differ from those of the general population. These questions included whether they were starting a new business, if there were opportunities for new businesses, funding sources for a new business, skills required to start a new business, shutting down a business, and whether a fear of failure was preventing the start of a new business. The dataset also contains variables that describe the respondent's gender, age, educational attainment, labor force status, the entrepreneurial areas in which they feel they have strong expertise, and the month and year the survey was conducted.

Annual data on births, deaths and survival of businesses in the UK, by geographical area and Standard Industrial Classification 2007: SIC 2007 groups.

A company has a fleet of devices transmitting daily sensor readings. They would like to create a predictive maintenance solution to proactively identify when maintenance should be performed. This approach promises cost savings over routine or time based preventive maintenance, because tasks are performed only when warranted.

The task is to build a predictive model using machine learning to predict the probability of a device failure. When building this model, be sure to minimize false positives and false negatives. The column you are trying to Predict is called failure with binary value 0 for non-failure and 1 for failure.

The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society.
The survey is created for both individuals and businesses.
It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.

The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)

***Description of the data in this data set: structure of the survey and pre-defined answers (if any)***
1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed}
2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high
3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question)
4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility}
5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available
6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
8. How would you assess the value of the following data categories?
8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question
10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question
11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question
12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)}
13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable
14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)}
15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company
16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company}
17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”}
18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}

***Format of the file***
.xls, .csv (for the first spreadsheet only), .odt

***Licenses or restrictions***
CC-BY

With competition getting more stiffer in telecom direct-to-home operators, complaint management process will lead to the key outcome on business survival and growth.

As per the prevailing process and business setups, to cut down on cost companies prefer outsourcing of call centre operations and complaint management process.

Process Steps for handling customer complaints

Customer logs complaint from various modes like call at the call centre, visit retailer/company showroom/ website site/ App/ Email on customer care/ Social media (Facebook / Twitter) All major operators in business give complete attention and alertness on each and every customer complaint and after customer complaint getting logged in the system , same flows to back end team through CRM workflows Dedicated Service recovery teams made available in backend or service agencies Each and every case got assigned to the backend/service team for a customer visit and complaint closure There are broadly two types of transactions for complaints (FTR) First-time resolution and (NFTR) Non-first time resolution. For FTR cases, front end team like a call centre or showroom executive do the required troubleshooting and give resolution to customer and case closed as per customer satisfaction In NFTR cases, backend operation team aligned and visit done at customer premises and closure done by rectifying hardware, product or Outdoor unit. Some operators give delight code/ Happy code to the customer on logging of NFTR complaints and same code need to be provided to the engineer if complaint got resolved as per customer satisfaction Major Challenges in handling customer complaints

During sudden technical failure or any natural calamity, there will be a high flow of complaints, which takes time to manage and close the complaint to customer expectation. These instances bring challenging time in telecom/ DTH operators as a customer not ready for any delay in resolution As per business requirements, there has been a lot of fresh hiring done for call centre advisors and a lot of efforts being put on their training but due to the initial learning curve, basic mistakes done by new hires leading to irrelevant and wrong complaints being raised in the system. This sometimes leads to delay in resolution and telecom/DTH operator undergo firefighting scenarios Managing social media errors is also one of the challenging tasks, sometimes operator’s reputation goes to stake due to small negligence of any employee or any process failure

content

This dataset consists of almost 2224 rows and 11 columns which belongs to all complaints can be raised by user

Acknowledgements

Reference : https://github.com/Kavyapriyakp/Telecome-Consumer-Complaints-Data-Analytics-PYTHON

This study is about what matters: predicting when microfinance institutions might fail, especially in places where financial stability is closely linked to economic inclusion. The challenge? Creating something practical and usable. The Adjusted Gross Granular Model (ARGM) model comes here. It combines clever techniques, such as granular computing and machine learning, to handle messy and imbalanced data, ensuring that the model is not just a theoretical concept but a practical tool that can be used in the real world.Data from 56 financial institutions in Peru was analyzed over almost a decade (2014–2023). The results were quite promising. The model detected risks with nearly 90% accuracy in detecting failures and was right more than 95% of the time in identifying safe institutions. But what does this mean in practice? It was tested and flagged six institutions (20% of the total) as high risk. This tool’s impact on emerging markets would be very significant. Financial regulators could act in advance with this model, potentially preventing financial disasters. This is not just a theoretical exercise but a practical solution to a pressing problem in these markets, where every failure has domino effects on small businesses and clients in local communities, who may see their life savings affected and lost due to the failure of these institutions. Ultimately, this research is not just about a machine learning model or using statistics to evaluate results. It is about giving regulators and supervisors of financial institutions a tool they can rely on to help them take action before it is too late when microfinance institutions get into bad financial shape and to make immediate decisions in the event of a possible collapse.

Attitudes towards entrepreneurship. Topics: preferred employment status: employed, self-employed; activities in starting a business; considerations with regard to starting a business; preference to set up new business or to take over existing one; best qualified advisors with regard to staring own business: lawyer or private consultant, bank, chamber of commerce or professional association, public support organisation for businesses, other entrepreneurs, friends or relatives, other; preferred teaching on how to run own business at: secondary school, technical secondary school, university or other tertiary level of education, specific courses for adults, nowhere, elsewhere; attitude towards the following statements on the national education system: develops a state of mind in young people which encourages them to create a firm, does not develop a state of mind in young people which encourages them to create a firm; attitude towards the following statements with regard to starting a business: is difficult due to lack of financial support, is difficult due to complex administrative procedures, readiness to invest free time in courses on how to run a business, possibility to have a second chance, less inclined to order goods from someone who has failed before, no investment in business that failed in the past, risk of failure, bad economic climate; most important risks for setting up a business; most important reasons for most of the businesses being one-person businesses; assumed time needed for administrative procedures when hiring first employee. Demography: sex; age; age at end of education; occupation; professional position; region; type of community; parents’ occupation. Additionally coded was: country; question number; weighting factor. Einstellungen zur Selbständigkeit und zum Unternehmertum. Themen: Präferierter beruflicher Status (angestellt oder selbständig); eigene Gedanken über eine Unternehmensgründung oder Übernahme eines Unternehmens sowie tatsächliche eigene unternehmerische Tätigkeiten; präferierte Berater bei Fragen zur Unternehmensgründung; Präferenz für eine Unternehmensgründung oder eine Übernahme eines Unternehmens; präferierte Ausbildungsinstitution für den Erwerb von Kenntnissen bei der Unternehmensführung; wahrgenommene Förderung des unternehmerischen Geistes durch die Bildungsinstitutionen des eigenen Landes; Einstellung zur Gründung eines eigenen Unternehmens (Skala): Schwierigkeiten beim Unternehmensstart durch mangelnde Finanzen oder aufgrund zu großen bürokratischen Aufwands, Bereitschaft an Kursen über Unternehmensgründung auf eigene Kosten teilzunehmen, Erhalten einer zweiten Chance nach einmaligem Misserfolg, Misstrauen im geschäftlichen Umgang gegenüber einmal gescheiterten Unternehmern, Verzicht auf Unternehmensgründung bei Erfolgsrisiko, ungünstiges ökonomisches Klima für Unternehmensgründungen; erwartete Risiken bei einer Unternehmensgründung; vermutete Gründe für das Betreiben von Ein-Personen-Unternehmen; Einschätzung des Zeitaufwands für administrative Vorgänge, den ein Ein-Personen-Unternehmen bei der Einstellung des ersten Arbeitnehmers einplanen muss. Demographie: Geschlecht; Alter; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Region; Urbanisierungsgrad; berufliche Stellung der Eltern. Zusätzlich verkodet wurde: Land; Fragenummer; Gewichtungsfaktor.

High Frequency Indicator: The dataset contains year-, month- and bank-wise compiled data from the year 2021 to till date on the transactions performed (responses) and failed (returns) by debit sponsor banks through National Automated Clearing House (NACH) system

Notes:

1. NACH Credit is an electronic payment service used by an institution for affording credits to a large number of beneficiaries in their bank accounts for the payment of dividend, interest, salary, pension etc. by raising a single debit to the bank account of the user institution
2. Business Declines (BD) are declined transactions due to a customer entering an invalid pin, incorrect beneficiary account etc. or due to other business reasons such as exceeding per transaction limit, exceeding permitted count of transactions per day, exceeding amount limit for the day etc.
3. Technical Declines (TD) transactions are those transactions are declined due to any technical reasons such as bank ID is empty or not in correct format or exception code not in Database or not in correct format, etc

Registered business locations in San Francisco maintained by the Office of Treasurer-Tax Collector, including business locations that have been sold, closed, or moved out of San Francisco. Each registered business can have one or many locations. Each record represents a single location.

This dataset updates weekly. It is scheduled for Tuesdays but may fail and retry the next day.

Access only for peer review. The dataset will be opened when the paper is accepted in a journal.

MACHINE LEARNING ALGORITHMS FOR THE FORECASTING OF BUSINESS FAILURE IN SPANISH COMPANIES DATASETThis is the dataset used in the research conducted as part of the study titled "Machine Learning Algorithms for the Forecasting of Business Failure in Spanish Companies", which collects the current state (State column where: 1 = unhealthy 0 = healthy) of different companies (ID_company column), and the values in previous years of the different independent variables:

Assets and liabilities- Fixed assets- Current assets- Total assets- Equity- Liquid liabilities- Total liabilities and equity- Indebtedness - Long-term creditors - Financial debts

Liquidity - Liquid assets - Debtors - Liquidity ratio- Commercial creditors - Working capital

Dynamic variables - Cash flow - Business added value

Performance variables - Net turnover - Operating income - Personnel costs - Gross profit - Operating profit - Ordinary earnings before taxes - Profit for the year - Financial result - Return on assets - Financial return - EBIT - EBITDA

Descriptive variables - Sector - Activity - Size

FOI-02559 confirmed 445 claims to the Vaccine Damage Payment Scheme had met the criteria for causation but failed to satisfy the condition of 60% disability so not considered disabled enough to receive an award. Please can you tell me of the 445 claims: 1. How many were assessed as having at least 20% disability? 2. How many were assessed as having at least 50% disability? Our response I can confirm that the NHS Business Services Authority (NHSBSA) holds the information you have requested. The information is enclosed in this response. All data as of 31 January 2025. All data relates to claims received by the NHSBSA and those transferred from the Department for Work and Pensions (DWP) on 1 November 2021. All figures provided relate to COVID-19 vaccines only. Claimants are provided with a clinical assessment report, explaining how the medical assessor reached their decision. In the medical assessment report, level of disablement is sometimes reported as a range. Where the medical assessor has reported the vaccinated person’s disability as a range, we've used the upper number reported to categorise the claim. This data must therefore be interpreted as a guide only, because it is not possible to determine the accurate number of claims in each category. Question 1 - How many were assessed as having at least 20% disability? Of the 445 claims which met the criteria for causation, 180 were assessed as having a level of disablement between 20% to 49%. Question 2 - How many were assessed as having at least 50% disability? Of the 445 claims which met the criteria for causation, 10 were assessed as having a level of disablement above 50%.

The global Data Base Management Systems market was valued at USD 50.5 billion in 2022 and is projected to reach USD 120.6 Billion by 2030, registering a CAGR of 11.5 % for the forecast period 2023-2030. Factors Affecting Data Base Management Systems Market Growth

Growing inclination of organizations towards adoption of advanced technologies like cloud-based technology favours the growth of global DBMS market

The cloud-based data base management system solutions offer the organizations with an ability to scale their database infrastructure up or down as per requirement. In a crucial business environment data volume can vary over time. Here, the cloud allows organizations to allocate resources in a dynamic and systematic manner, thereby, ensuring optimal performance without underutilization. In addition, these cloud-based solutions are cost-efficient. As, these cloud-based DBMS solutions eliminate the need for companies to maintain and invest in physical infrastructure and hardware. It helps in reducing ongoing operational costs and upfront capital expenditures. Organizations can choose pay-as-you-go pricing models, where they need to pay only for the resources they consume. Therefore, it has been a cost-efficient option for both smaller businesses and large-enterprises. Moreover, the cloud-based data base management system platforms usually come with management tools which streamline administrative tasks such as backup, provisioning, recovery, and monitoring. It allows IT teams to concentrate on more of strategic tasks rather than routine maintenance activities, thereby, enhancing operational efficiency. Whereas, these cloud-based data base management systems allow users to remote access and collaboration among teams, irrespective of their physical locations. Thus, in regards with today's work environment, which focuses on distributed and remote workforces. These cloud-based DBMS solution enables to access data and update in real-time through authorized personnel, allowing collaboration and better decision-making. Thus, owing to all the above factors, the rising adoption of advanced technologies like cloud-based DBMS is favouring the market growth.

Availability of open-source solutions is likely to restrain the global data base management systems market growth

Open-source data base management system solutions such as PostgreSQL, MongoDB, and MySQL, offer strong functionality at minimal or no licensing costs. It makes open-source solutions an attractive option for companies, especially start-ups or smaller businesses with limited budgets. As these open-source solutions offer similar capabilities to various commercial DBMS offerings, various organizations may opt for this solutions in order to save costs. The open-source solutions may benefit from active developer communities which contribute to their development, enhancement, and maintenance. This type of collaborative environment supports continuous innovation and improvement, which results into solutions that are slightly competitive with commercial offerings in terms of performance and features. Thus, the open-source solutions create competition for commercial DBMS market, they thrive in the market by offering unique value propositions, addressing needs of organizations which prioritize professional support, seamless integration into complex IT ecosystems, and advanced features. Introduction of Data Base Management Systems

A Database Management System (DBMS) is a software which is specifically designed to organize and manage data in a structured manner. This system allows users to create, modify, and query a database, and also manage the security and access controls for that particular database. The DBMS offers tools for creating and modifying data models, that define the structure and relationships of data in a database. This system is also responsible for storing and retrieving data from the database, and also provide several methods for searching and querying the data. The data base management system also offers mechanisms to control concurrent access to the database, in order to ensure that number of users may access the data. The DBMS provides tools to enforce security constraints and data integrity, such as the constraints on the value of data and access controls that restricts who can access the data. The data base management system also provides mechanisms for recovering and backing up the data when a system failure occurs....

Attitudes towards entrepreneurship. Topics: preferred employment status: employed, self-employed; reasons for preferred employment status; preference to work for selected kinds of companies: family business, not family owned business; reasons for preference to work in family business; reasons for preference to work in not family owned business; feasibility to become self-employed within the next five years; reasons for non-feasibility; desire to become self-employed within the next five years; participation in courses about entrepreneurship; attitude towards the following statements on personal school education with regard to entrepreneurship: helpful to develop entrepreneurial attitude, helpful to understand role of entrepreneurs in society, made interested in becoming entrepreneur, has given essential skills; attitude towards selected statements on entrepreneurs: create benefit for society, only think of themselves, are job creators, take advantage of other people’s work; activities in starting a business; considerations with regard to starting a business; current situation in own business; importance of each of the following issues in the decision to start a business: dissatisfaction with previous job situation, appropriate business idea, contact with appropriate business partner, getting necessary financial resources, role model, addressing special needs; reasons for starting own business; preference to set up new business or to take over existing one; most important risks for setting up a business; attitude towards selected groups of people: entrepreneurs, top managers, professions; use of an assumed heritage; importance of each of the following issues with regard to starting a business: lack of financial support, complex administrative procedures, find information on how to start a business, risk of failure, possibility to have a second chance; main sources of income; type of own business: started from scratch, take over from other owner, family business. Demography: age; sex; nationality; age at end of education; occupation; professional position; parents’ occupation; satisfaction with household income; region; type of community; own a mobile phone and fixed (landline) phone; household composition and household size. Additionally coded was: respondent ID; type of phone line; country; weighting factor. Meinung zu Selbständigkeit versus Angestelltenstatus. Erfahrungen mit einer Unternehmensgründung. Vorstellungen von Unternehmertum. Rolle von Bildung bei unternehmerischen Aktivitäten. Vermittlung von unternehmerischem Denken in der Schule. Themen: Präferenz für selbständige Erwerbstätigkeit vs. Angestelltenstatus; Gründe für die Präferenz des Angestelltenstatus; präferierte Unternehmensart (Familienunternehmen oder börsennotiertes Unternehmen); Gründe für die Wahl der präferierten Unternehmensart; Gründe für den Wunsch nach Selbstständigkeit; Einschätzung der Umsetzung der Selbständigkeit in den nächsten fünf Jahren; Gründe, die gegen eine Selbständigkeit sprechen; Stärke des Wunsches nach selbständiger Erwerbstätigkeit; Teilnahme an einem Kurs zum Thema Unternehmertum; Vermittlung von unternehmerischem Denken in der Schule (Skala); Einstellung zu Unternehmern (Skala: schaffen neue Produkte und Dienstleistungen zum Nutzen der Allgemeinheit, denken nur an den eigenen Vorteil, schaffen Arbeitsplätze, ziehen Vorteile aus der Arbeit Anderer); Erfahrung hinsichtlich der Gründung oder Übernahme eines Unternehmens; Wichtigkeit ausgewählter Gründe für die Entscheidung zur Gründung oder Übernahme eines Unternehmens; Hauptgrund für diese Entscheidung; Präferenz für die Gründung eines Unternehmens oder die Übernahme eines bestehenden Unternehmens; größte Risiken für die Gründung eines Unternehmens; Meinung zu ausgewählten Personengruppen (Unternehmer, Top-Manager und freie Berufe wie Architekten, Anwälte, Ärzte); präferierte Verwendung einer größeren Geldsumme; Risiken und Schwierigkeiten bezüglich Selbständigkeit; Selbständige wurden gefragt: Haupteinnahmequelle, Neugründung des Unternehmens, Übernahme von einem anderen Firmenbesitzer oder Familienunternehmen. Demographie: Alter; Geschlecht; Staatsangehörigkeit; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; berufliche Stellung der Eltern; Zufriedenheit mit dem Haushaltseinkommen; Region; Urbanisierungsgrad; Besitz eines Mobiltelefons; Festnetztelefon im Haushalt; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Befragten-ID; Interviewmodus (Mobiltelefon oder Festnetz); Land; Gewichtungsfaktor. Probability: MultistageProbability.Multistage Wahrscheinlichkeitsauswahl: Mehrstufige ZufallsauswahlProbability.Multistage Face-to-face interview: Computer-assisted (CAPI/CAMI)Interview.FaceToFace.CAPIorCAMI Persönliches Interview : Computerunterstützte Befragung (CAPI/CAMI)Interview.FaceToFace.CAPIorCAMI

As the fashion e-commerce markets rapidly develop, tens of thousands of products are registered daily on e-commerce platforms. Individual sellers register products after setting up a product category directly on a fashion e-commerce platform. However, many sellers fail to find a suitable category and mistakenly register their products under incorrect ones. Precise category matching is important for increasing sales through search optimization and accurate product exposure. However, manually correcting registered categories is time-consuming and costly for platform managers. To resolve this problem, this study proposes a methodology for fashion e-commerce product classification based on multi-modal deep learning and transfer learning. Through the proposed methodology, three challenges in classifying fashion e-commerce products are addressed. First, the issue of extremely biased e-commerce data is addressed through under-sampling. Second, multi-modal deep learning enables the model to simultaneously use input data in different formats, which helps mitigate the impact of noisy and low-quality e-commerce data by providing richer information.Finally, the high computational cost and long training times involved in training deep learning models with both image and text data are mitigated by leveraging transfer learning. In this study, three strategies for transfer learning to fine-tune the image and text modules are presented. In addition, five methods for fusing feature vectors extracted from a single modal into one and six strategies for fine-tuning multi-modal models are presented, featuring a total of 14 strategies. The study shows that multi-modal models outperform unimodal models based solely on text or image. It also suggests the optimal conditions for classifying e-commerce products, helping fashion e-commerce practitioners construct models tailored to their respective business environments more efficiently.

Update July 2024###\r

\r From 1 July 2024, the dataset will no longer publicly distinguish between relevant qualifications or training courses or approved qualifications or training courses.\r \r

Update March 2024###\r

\r From 1 March 2024, the dataset will be updated to include 5 new fields and 1 existing field will also be updated (see help file for details).\r \r

Update August 2023###\r

\r From 24 August 2023, the dataset will be updated to include 1 new field, ABLE_TO_PROVIDE_TFAS, (see help file for details).\r \r

Update April 2022###\r

\r We have replaced the .xlsx file resources for all our datasets. This was required due to the API and web page search functionality no longer being supported for .xlsx files on the Data.Gov platform.\r \r

Update January 2022 ###\r

\r From 10 January 2022, the field ADV_FASEA _APPROVED_QUAL will be renamed to ADV_APPROVED_QUAL.\r \r

Update November 2019 - additional fields ###\r

\r From 21 November 2019, the dataset will be updated to include 7 new fields (see help file for details)\r \r These fields are included in conjunction with the professional standards reforms for financial advisers. More information can be found on the ASIC website https://asic.gov.au/regulatory-resources/financial-services/professional-standards-for-financial-advisers-reforms/.\r \r Note: For most advisers the new fields will be unpopulated on 21 November 2019. As advisers provide this data to ASIC it will appear in the dataset.\r \r ***\r \r

Dataset summary###\r

\r ASIC is Australia’s corporate, markets and financial services regulator. ASIC contributes to Australia’s economic reputation and wellbeing by ensuring that Australia’s financial markets are fair and transparent, supported by confident and informed investors and consumers. \r \r Australian Financial Services Licensees are required to keep the details of their financial advisers up to date on ASIC's Financial Advisers Register. Information contained in the register is made available to the public to search via ASIC's Moneysmart website. \r \r Select data from the Financial Advisers Register will be uploaded each week to www.data.gov.au. The data made available will be a snapshot of the register at a point in time. Legislation prescribes the type of information ASIC is allowed to disclose to the public. \r \r The information included in the downloadable dataset is: \r \r * Adviser name\r * Adviser number\r * Adviser role\r * Adviser sub type\r * Adviser role status\r * Adviser ABN\r * Year first provided advice \r * Licence name\r * Licence number\r * Licence ABN\r * Licence controlled by\r * Adviser start date\r * Adviser end date\r * Overall registration status\r * Registration status under financial licence\r * Registration start date under financial licence\r * Registration end date under financial licence\r * Adviser CPD failure year\r * Adviser principal business address suburb\r * Adviser principal business address State/Territory\r * Adviser principal business address postcode\r * Adviser principal business address Country\r * Appointing representative name\r * Appointing representative number\r * Appointing representative ABN\r * Disciplinary action start date\r * Disciplinary action end date\r * Disciplinary action type\r * Disciplinary action description (Financial Services and Credit Panel (FSCP) decision)\r * Product authorisations (for a full list see the Financial Adviser Register – Help File)\r * Ability to provide tax financial advice\r * Qualifications and Training\r * Memberships\r * Further restrictions\r \r Additional information about financial advisers can be found via ASIC's website. Accessing some information may attract a fee. \r \r More information about searching ASIC's registers.