Masoud Bazari Jamkhaneh

S. M. T. K. MirMostafaee

Marziye Jadidi

Annals of Data Science

Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.      ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.

The Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results, and case studies in the field of data science. Data science is recognized as an interdisciplinary domain that employs mathematics, statistics, databases, data mining, high-performance computing, knowledge management, and virtualization to extract knowledge from Big Data. However, it is essential for data science to have its own scientific content, such as axioms, laws, and rules, which are crucial for experts in various fields to explore their interests within Big Data. ADS encourages contributors to tackle these challenging issues on this exchange platform. Currently, addressing how to discover knowledge from heterogeneous data in a Big Data environment remains a significant challenge. ADS is a series of volumes edited by either the editorial office or guest editors, who are responsible for the call-for-papers and the review process to ensure high-quality contributions in their volumes.

《Annals of Data Science》(ADS) 发表数据科学领域的前沿研究成果、实验结果和案例研究。数据科学被视为一个跨学科领域，利用数学、统计学、数据库、数据挖掘、高性能计算、知识管理和虚拟化等方法，从大数据中发现知识。尽管如此，数据科学应具备自身的科学内容，如公理、定律和规则，这对不同领域的专家从大数据中探索自己的兴趣至关重要。ADS 鼓励贡献者在这一交流平台上解决这些具有挑战性的问题。目前，如何在大数据环境下从异构数据中发现知识仍需深入探讨。ADS 是一系列卷册，由编辑部或特邀编辑负责。特邀编辑将负责征集论文和审查过程，以确保其卷册中的高质量贡献。

On the xgamma k-record values and associated inference

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