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8 results for “Business Intelligence”
Artificial Intelligence and the Future of Smart Cities-Figure 6. Importance of AI for respondents business or industry by respondent's age
<p>On question 8 respondents have to indicate on a scale of 1 to 5 (1 being “not important” and 5 being “critical for survival”), how important they think the AI will be for their business or industry (or for the one they are preparing for) in the next 10 years? No statistical interaction was found between age and gender F (3, 108) = .174, p>0.05). There were statistically significant differences by gender F (1, 108) = 50.261, p<0.05 and age, F (3, 108) = 9.298, p<0.05. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=3.36, SD=.88) on question 8 about the importance of AI for the fields of activity of the participants (or for those they are preparing for) in the next 10 years. At the same question: the 26-30 age group scored the highest followed by the 18-25 age group (M=3.80, SD=1.18); the 31-40 age group scored lower than the 18-25 age group (M=3.50, SD=.87); the 31-40 age group scored also lower than the 18-25 age group (M=3.50, SD=.51) (Figure 6).</p>
NAVIGATING BUSINESS INTELLIGENCE TOOLS: STRATEGIES TO DRIVE BUSINESS GROWTH
<p><span>This study explores the evolution and current state of business intelligence (BI) tools and their strategic role in driving business growth. The research utilizes a combination of market analysis, industry case studies, and theoretical frameworks, including the DIKW hierarchy and Resource-Based View, to examine BI adoption trends. The results highlight the importance of data quality, cross-functional collaboration, and user adoption in maximizing BI effectiveness. Key findings indicate that cloud-based and self-service BI tools significantly improve data-driven decision-making, while challenges remain in data governance and integration. To address these challenges, organizations must implement robust data policies and empower users through training and self-service capabilities. The study concludes that integrating BI tools into digital transformation initiatives provides a competitive edge, enabling strategic planning, operational efficiency, and innovation. This research offers new insights into how organizations can leverage BI tools for sustained growth and enhanced decision-makingl.</span></p>
Business Process Intelligence Challenge 2019 - Processed
<p>This dataset is the pre-processed version of the BPIC19 dataset. It contains potential confounding features, the treatment indicator, and the cycle time.</p>
Business Process Intelligence Challenge 2017 - Processed
<p>This dataset is the pre-processed version of the BPIC17 dataset. It contains potential confounding features, the treatment indicator, and the cycle time.</p>
Business Intelligence com Qlik Sense aplicado ao Radar Saúde
<p>Este artigo relata a adoção do Qlik Sense no processo de construção<br> do Radar Saúde, software pelo qual o TCE-MT divulga gráficos e indicadores<br> sobre a saúde no estado do Mato-Grosso de modo a garantir a transparência<br> e a prestação de contas ao cidadão. Os resultados mostraram que o Qlik<br> Sense é de fácil utilização e apresenta vantagens em termos de simplicidade,<br> qualidade, diminuição de tempo e aumento de agilidade na implementação da<br> aplicação e gerenciamento de dados. O artigo auxilia no aumento do corpo de<br> conhecimento relacionado a Qlik Sense. Em termos práticos, o artigo é útil<br> para desenvolvedores que buscam formas mais eficientes para gerenciar<br> dados.</p>
Artificial intelligence as a company's internal communication media: Investigating employee's acceptance of chatbot for effective communication and sustainable business
<p><span><span>This research investigates Kalbe Consumer Health (KCH) employees’ acceptance of AMBER, a Generative AI Chatbot implemented at KCH. KCH, a prominent pharmaceutical company with over 27 years of experience, has been recognized as the best place to work in Asia for two consecutive years by HR Asia. Despite its success, challenges persist, by the end of 2022, it has reached a 5 percent turnover rate with 70 percent departures in leadership positions. AMBER is an internal communication tool, functioning for employees to express their sentiments and opinions honestly. </span></span><span>By fostering an environment where employees feel heard and valued, AMBER encourages proactive issue resolution within teams.</span><span><span> Drawing </span></span><span>on the Uses and Gratification Theory (UGT) and the Unified Theory of Acceptance and Use of Technology (UTAUT), this study employs regression analysis to analyze data from an anonymous online survey. </span><span><span>The study contributes theoretically by integrating UGT and UTAUT into the context of chatbot utilization in internal communication. Furthermore, it offers practical insights for businesses leveraging AI in communication strategies, facilitating sustainable business practices, and aligning with Sustainable Development Goals (SDG) 16 and 8 by fostering an inclusive, transparent, and accountable culture, and enhancing employee satisfaction and performance through effective communication.</span></span></p>
Policosanol extraction patents in six extraction routes from IP Business Intelligence of Orbit seekers (version 1.9.8)®: World and Ibero-American countries (PDF Format).
<p>These results include tables of contents in PDF format of patents, corresponding to the substance Policosanol and its six main extraction methods, in the world and in Ibero-American countries, prospected from Advanced Search and IP Business Intelligence of Orbit seekers (version 1.9.8)<sup>® </sup>(Orbit, 2013, 2015; Orbit Intelligence, 2021, 2022; Winson, 2013).</p> <p>No results were found for the Ibero-American countries, in technological route 5 - "Molecular Distillation", until June 29, 2024, reason for the absence of their respective files in PDF and CSV.</p> <p>The prospecting reports contain the following information:</p> <ol> <li>Priority numbers</li> <li>Application number</li> <li>Publication numbers</li> <li>Family numbers</li> <li>Priority dates</li> <li>Application dates</li> <li>Publication dates</li> <li>Grant date</li> <li>Expected expiry dates</li> <li>Titles</li> <li>Abstracts</li> <li>Assignees</li> <li>Inventors</li> <li>Representatives</li> <li>Key content</li> <li>Technical concepts</li> <li>Images</li> <li>Claims</li> <li>Description</li> <li>Keywords in context</li> <li>Technology domains</li> <li>Cooperative classification</li> <li>International classification</li> <li>US patent classification</li> <li>Japanese classifications</li> <li>Citing patents (forward citations)</li> <li>Citing patents - Raw information</li> <li>Cited patents (backward citations)</li> <li>Cited patents - Raw information</li> <li>Cited non-patent literature</li> <li>Legal status</li> <li>Legal actions</li> <li>Designated states</li> <li>Protected authorities</li> <li>Farliest countries</li> <li>Assignee country</li> <li>Representative country</li> <li>Inventor country</li> <li>Cited patents (backward citations) - Counts</li> <li>Citing patents (forward citations) - Counts</li> <li>Independent claims - Counts</li> <li>Dependent claims - Counts</li> <li>All claims - Counts</li> <li>Assignees - Counts</li> <li>Inventors - Counts</li> <li>Age - Counts</li> <li>Words in document - Questel semantic count</li> <li>Words in abstract - Questel semantic count</li> <li>Words in description - Questel semantic count</li> <li>WO and EP designated states - Counts</li> <li>Drawings - Counts</li> <li>US specific - Counts</li> <li>Licensing agreements - Counts</li> <li>Number of figures</li> <li>WO parent</li> <li>Number of priorities</li> <li>Licensing interest names</li> <li>Security interest names</li> <li>Referenced drug names</li> <li>WO parent</li> <li>EP parent</li> <li>Filing notes</li> <li>Filing details</li> <li>Filing language</li> <li>Litigation</li> <li>Standards</li> <li>Licence ID</li> </ol> <p>The original format of the report generated by the database was preserved.</p> <p>In order to manage the query according to specific or joint interests, the PDF files are presented separately and also gathered in a single document (with 12,200 pages). This aggregated file is organized with the results of the world appearing in first place and those of the Ibero-American countries next, both obeying the numbering order of the prospected routes, separated by the respective summaries of contents.</p> <p> </p> <p><strong>References</strong></p> <p> </p> <p>Orbit. (2013). IP Business Intelligence (Gold and Platinum Versions). <em>Orbit Help Documents</em>.</p> <p>Orbit. (2015). Orbit Work Files. <em>Orbit Help Documents</em>.</p> <p>Orbit Intelligence. (2021). <em>FullPat: worldwide collection of patents containing bibliographic information, full text & legal status</em>.</p> <p>Orbit Intelligence. (2022). <em>Fampat: worldwide collection of patents grouped by invention-based families containing bibliographic information, full text & legal status</em>.</p> <p>Winson, A. (2013). <em>Industrial Designs in Orbit.com</em>. <em>April</em>, 1–5.</p>
Policosanol extraction patents in six extraction routes from IP Business Intelligence of Orbit seekers (version 1.9.8)®: World and Ibero-American countries (CSV Format).
<p>These results include tables of contents in CSV format of patents, corresponding to the substance Policosanol and its six main extraction methods, in the world and in Ibero-American countries, prospected from Advanced Search and IP Business Intelligence of Orbit seekers (version 1.9.8)<sup>® </sup>(Orbit, 2013, 2015; Orbit Intelligence, 2021, 2022; Winson, 2013).</p> <p>No results were found for the Ibero-American countries, in technological route 5 - "Molecular Distillation", until October 1, 2024, reason for the absence of their respective files in PDF and CSV.</p> <p>The prospecting reports contain the following information:</p> <ol> <li>Priority numbers</li> <li>Application number</li> <li>Publication numbers</li> <li>Family numbers</li> <li>Priority dates</li> <li>Application dates</li> <li>Publication dates</li> <li>Grant date</li> <li>Expected expiry dates</li> <li>Titles</li> <li>Abstracts</li> <li>Assignees</li> <li>Inventors</li> <li>Representatives</li> <li>Key content</li> <li>Technical concepts</li> <li>Images</li> <li>Claims</li> <li>Description</li> <li>Keywords in context</li> <li>Technology domains</li> <li>Cooperative classification</li> <li>International classification</li> <li>US patent classification</li> <li>Japanese classifications</li> <li>Citing patents (forward citations)</li> <li>Citing patents - Raw information</li> <li>Cited patents (backward citations)</li> <li>Cited patents - Raw information</li> <li>Cited non-patent literature</li> <li>Legal status</li> <li>Legal actions</li> <li>Designated states</li> <li>Protected authorities</li> <li>Farliest countries</li> <li>Assignee country</li> <li>Representative country</li> <li>Inventor country</li> <li>Cited patents (backward citations) - Counts</li> <li>Citing patents (forward citations) - Counts</li> <li>Independent claims - Counts</li> <li>Dependent claims - Counts</li> <li>All claims - Counts</li> <li>Assignees - Counts</li> <li>Inventors - Counts</li> <li>Age - Counts</li> <li>Words in document - Questel semantic count</li> <li>Words in abstract - Questel semantic count</li> <li>Words in description - Questel semantic count</li> <li>WO and EP designated states - Counts</li> <li>Drawings - Counts</li> <li>US specific - Counts</li> <li>Licensing agreements - Counts</li> <li>Number of figures</li> <li>WO parent</li> <li>Number of priorities</li> <li>Licensing interest names</li> <li>Security interest names</li> <li>Referenced drug names</li> <li>WO parent</li> <li>EP parent</li> <li>Filing notes</li> <li>Filing details</li> <li>Filing language</li> <li>Litigation</li> <li>Standards</li> <li>Licence ID</li> </ol> <p>The original format of the report generated by the database was preserved.</p> <p>In order to manage the query according to specific or joint interests, the PDF files are presented separately and also gathered in a single document (with 12,200 pages). This aggregated file is organized with the results of the world appearing in first place and those of the Ibero-American countries next, both obeying the numbering order of the prospected routes, separated by the respective summaries of contents.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.