دانلود و نمایش مقالات مرتبط با Corporate data::صفحه 1
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نتیجه جستجو - Corporate data

تعداد مقالات یافته شده: 7
ردیف عنوان نوع
1 Research on the dilemma and improvement of legal regulation for unfair competition related to corporate data in China
تحقیق در مورد معضل و بهبود مقررات قانونی برای رقابت ناعادلانه مربوط به داده های شرکتی در چین-2020
Corporate data disputes have been rising rapidly in recent years in China. Courts typically apply the trade secret clause, the Internet clause, and the general clause under the Anti- Unfair Competition Law of PRC to the disputes. However, there are some limitations and problems, including the limited scope of the trade secret clause, the difficulty in interpretation of the Internet clause, and short of sufficient demonstration of the general clause, all leading to the lack of clear rules and guidelines for solving corporate data competition issue. The property nature of corporate data and the business operators’ factual control of the data necessarily require standard legal protection. Corporate data is not the property right, but the property interest protected under the Anti-Unfair Competition Law. For further legal positioning of the corporate data, the paper refers to the trade secret clause’s legislative principles. The paper also learns from the United States and Japan that both in- formation misappropriation rule and newly established ‘shared data with limited access’ provision protect corporate data under their anti-unfair competition law. The paper concludes by providing judicial and legislative suggestions to pave the way for corporate data protection in China. At the judicial level, Chinese courts should clarify the specific application conditions of the general clause. At the legislative level, enacting new legislation ‘data clause’ into the Anti-Unfair Competition Law is necessary to regulate unfair competition behaviors related to corporate data.© 2021 Huaiyin Zhang, Yanhong Lou, Kui Cai. Published by Elsevier Ltd. All rights reserved.
Keywords: Corporate data | Unfair competition | Trade secrets | Property interests | Data clause
مقاله انگلیسی
2 Big data analytics sentiment: US-China reaction to data collection by business and government
احساس تجزیه و تحلیل داده های بزرگ: واکنش ایالات متحده و چین به جمع آوری اطلاعات توسط کسب و کار و دولت-2018
As society continues its rapid change to a digitized individual, corporate, and government environment it is prudent for researchers to investigate the zeitgeist of the global citizenry. The technological changes brought about by big data analytics are changing the way we gather and view data. This big data analytics sentiment research examines how Chinese and American respondents may view big data collection and analytics differ ently. The paper follows with an analysis of reported attitudes toward possible viewpoints from each country on various big data analytics topics ranging from individual to business and governmental foci. Hofstedes cultural dimensions are used to inform and frame our research hypotheses. Findings suggest that Chinese and American perspectives differ on individual data values, with the Chinese being more open to data collection and analytic techniques targeted toward individuals. Furthermore, support is found that US respondents have a more fa vorable view of businesses use of data analytics. Finally, there is a strong difference in the attitudes toward governmental use of data, where US respondents do not favor governmental big data analytics usage and the Chinese respondents indicated a greater acceptance of governmental data usage. These findings are helpful in better understanding appropriate technological change and adoption from a societal perspective. Specifically, this research provides insights for corporate business and government entities suggesting how they might adjust their approach to big data collection and management in order to better support and sustain their organizations services and products.
Keywords: Big data ethics ، Business data usage ، Corporate data collection ، Government data usage ، Technology ethics ، US-China similarities ، US-China differences
مقاله انگلیسی
3 Data governance case at KrauseMcMahon LLP in an era of self-service BI and Big Data
مورد مدیریت داده در KrauseMcMahon LLP در عصر خود سرویس هوش تجاری و داده های بزرگ-2017
This case increases your understanding of data governance in an era of sophisticated ana lytics and Big Data where corporate data integrity and data quality may be at risk. KrauseMcMahon, a large certified public accounting and business consulting firm, faces a tradeoff of increasing control of the company’s data assets versus unleashing end user innovation due to the proliferation of self-service business intelligence tools. You are required to analyze the issues in the case from organizational, financial, and technical per spectives to propose alternatives the organization should consider and make specific rec ommendations on how the company should proceed. By completing this case, you will demonstrate cross-disciplinary abilities related to foundational business, accounting, and broad management competencies. By addressing such competencies, the case requires your use of accounting, MIS, and upper-level business skills.
Keywords:Big Data|Data governance|Self-service business intelligence
مقاله انگلیسی
4 Integration of gel-based and gel-free proteomic data for functional analysis of proteins through Soybean Proteome Database
ادغام داده های مبتنی بر ژل و عاری از ژل برای تحلیل عملکرد پروتئین ها از طریق پایگاه پروتئوم سویا-2017
The Soybean Proteome Database (SPD) stores data on soybean proteins obtained with gel-based and gel-free pro teomic techniques. The database was constructed to provide information on proteins for functional analyses. The majority of the data is focused on soybean (Glycine max ‘Enrei’). The growth and yield of soybean are strongly affected by environmental stresses such as flooding. The database was originally constructed using data on soy bean proteins separated by two-dimensional polyacrylamide gel electrophoresis, which is a gel-based proteomic technique. Since 2015, the database has been expanded to incorporate data obtained by label-free mass spectrometry-based quantitative proteomics, which is a gel-free proteomic technique. Here, the portions of the database consisting of gel-free proteomic data are described. The gel-free proteomic database contains 39,212 proteins identified in 63 sample sets, such as temporal and organ-specific samples of soybean plants grown under flooding stress or non-stressed conditions. In addition, data on organellar proteins identified in mitochon dria, nuclei, and endoplasmic reticulum are stored. Furthermore, the database integrates multiple omics data such as genomics, transcriptomics, metabolomics, and proteomics. The SPD database is accessible at http:// proteome.dc.affrc.go.jp/Soybean/. Biological significance: The Soybean Proteome Database stores data obtained from both gel-based and gel-free proteomic techniques. The gel-free proteomic database comprises 39,212 proteins identified in 63 sample sets, such as different organs of soybean plants grown under flooding stress or non-stressed conditions in a time dependent manner. In addition, organellar proteins identified in mitochondria, nuclei, and endoplasmic reticu lum are stored in the gel-free proteomics database. A total of 44,704 proteins, including 5490 proteins identified using a gel-based proteomic technique, are stored in the SPD. It accounts for approximately 80% of all predicted proteins from genome sequences, though there are over lapped proteins. Based on the demonstrated application of data stored in the database for functional analyses, it is suggested that these data will be useful for analyses of biological mechanisms in soybean. Furthermore, coupled with recent advances in information and communica tion technology, the usefulness of this database would increase in the analyses of biological mechanisms.
Keywords: Soybean | Database | Temporal-specific protein profile | Organ-specific protein profile | Proteomics | Abiotic stress
مقاله انگلیسی
5 Data governance case at KrauseMcMahon LLP in an era of self-service BI and Big Data
مورد دولت داده ها در KrauseMcMahon LLP در عصر هوش تجاری خود سرویس و داده های بزرگ-2017
This case increases your understanding of data governance in an era of sophisticated ana lytics and Big Data where corporate data integrity and data quality may be at risk. KrauseMcMahon, a large certified public accounting and business consulting firm, faces a tradeoff of increasing control of the company’s data assets versus unleashing end user innovation due to the proliferation of self-service business intelligence tools. You are required to analyze the issues in the case from organizational, financial, and technical per spectives to propose alternatives the organization should consider and make specific rec ommendations on how the company should proceed. By completing this case, you will demonstrate cross-disciplinary abilities related to foundational business, accounting, and broad management competencies. By addressing such competencies, the case requires your use of accounting, MIS, and upper-level business skills.
Keywords:Big Data|Data governance|Self-service business intelligence
مقاله انگلیسی
6 Advanced topic modeling for social business intelligence
مدل سازی موضوعات پیشرفته برای هوش تجاری اجتماعی-2015
Social business intelligence combines corporate data with user-generated content (UGC) to make decision-makers aware of the trends perceived from the environment. A key role in the analysis of textual UGC is played by topics, meant as specific concepts of interest within a subject area. To enable aggregations of topics at different levels, a topic hierarchy has to be defined. Some attempts have been made to address the peculiarities of topic hierarchies, but no comprehensive solution has been found so far. The approach we propose to model topic hierarchies in ROLAP systems is called meta-stars. Its basic idea is to use metamodeling coupled with navigation tables and with dimension tables: navigation tables support hierarchy instances with different lengths and with non-leaf facts, and allow different roll-up semantics to be explicitly annotated; meta-modeling enables hierarchy heterogeneity and dynamics to be accommodated; dimension tables are easily integrated with standard business hierarchies. After outlining a reference architecture for social business intelligence and describing the metastar approach, we formalize its querying expressiveness and give a cost model for the main query execution plans. Then, we evaluate meta-stars by presenting experimental results for query performances and disk space. Keywords: business intelligence, social media, user-generated content, multidimensional modeling
مقاله انگلیسی
7 ‌راه‌حل داده‌کاوی دانشجویان – سیستم مدیریت دانش مرتبط با مؤسسات آموزش عالی
سال انتشار: 2014 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 24
مؤسسات آموزش عالی (HEI) اغلب کنجکاوند بدانند دانش آموزان حین تحصیل شان موفق‌اند یا خیر. ‌مؤسسات دانشگاهی پیش از دوره و در حین آن تلاش می‌کنند تا درصد دانشجویان موفق را برآورد کنند. اما آیا می‌توان درصد موفقیت دانش آموزانی که در این دوره‌ها نام نویسی کرده‌اند پیش بینی کرد؟ آیا خصوصیات دانشجویی خاصی وجود دارد که بتوان آن را با درصد موفقیت دانشجویان ربط داد؟ آیا داده‌های قابل دسترسی مربوط به دانشجویان برای ‌مؤسسات آموزش عالی وجود دارد که براساس آنها بتوانند درصد موفقیت دانشجویان را پیش بینی کنند؟ پاسخ سوالات تحقیقاتی فوق را عموماً می‌توان با بکارگیری روش‌های داده‌کاوی پیدا کرد. متأسفانه، الگوریتم‌های داده‌کاوی با مجموعه داده‌های بزرگ بهترین عملکرد را نتیجه می‌دهند، درحالیکه داده‌های قابل دسترس برای ‌مؤسسات و مرتبط با این دوره‌ها محدودند و در دسته مجموعه داده‌های کوچک قرار می‌گیرند. به همین دلیل، محوریت این مقاله داده‌کاوی برای مجموعه داده‌های کوچک دانشجویی است و درصدد است که به سوالات مطرح شده با مقایسه دو روش متفاوت داده‌کاوی پاسخ دهد. نتیجه گیری‌های این مطالعه بسیار نویدبخش‌اند و ‌مؤسسات آموزش عالی را تشویق می‌کنند تا روش‌های داده‌کاوی را به عنوان بخش مهمی از سیستم‌های مدیریت دانش آموزش عالی خود بکار گیرند.
کلیدواژه‌ها: داده‌کاوی | سیستم مدیریت دانش | درصد موفقیت دانشجو | داده‌کاوی برای مجموعه داده‌های کوچک | موسسه آموزش عالی | داده‌کاوی آموزشی
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