The workload in large-scale data repositories involves concurrent users and contains
homogenous and heterogeneous data. The large volume of data, dynamic behavior and …

A learned system uses machine learning (ML) internally to improve performance. We can
expect such systems to be vulnerable to some adversarial-ML attacks. Often, the learned …

Data management systems are essential elements for any organization which is dealing
with large volume of data now a days. Due to increase in data volume, and its complexities …

Due to the complexity of industry-scale database systems, the total cost of ownership for
these systems is no longer dominated by hardware and software, but by administration …

The workloads of enterprise DBS often show periodic patterns, eg because there are mainly
OLTP transactions during day-time and analysis operations at night. However, current DBS …

Autonomic computing is a promising approach to the problem of effectively managing large
complex software systems such as database management systems (DBMSs). In order to be …

Free and Open Source Software (FOSS) has come to play a critical role in the global
software industry. Organizations are widely adopting FOSS and interacting with open source …

In this paper a model where an autonomic DBMS can identify and characterize the type of
workload acting upon it is developed and the most important database status variables …

针对数据库负载预测中物理资源的变化导致预测失效, 模型易对异常数据敏感和未关注序列变化
中潜在的加权隐层特征状态导致预测精度低等问题, 在长短期记忆网络模型的基础上提出一种 …

It is difficult to handle the database (DB) workload due to the huge increase in data., the
functionality demand from the user., and the rapid changes in data. It is not easy to manage …