Mining and Modeling Database User Access Patterns

Yao, Qingsong; An, Aijun; Huang, Xiangji

doi:10.1007/11875604_56

Cited by 3 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, the authors in [9] use Markov models to estimate the next transaction a user will execute based on what transaction the DBMS is executing now. The work described in [27] does use Markov models based on queries much like ours, but their models are designed to identify user sessions across transactional boundaries and to extract additional usage patterns for off-line analysis purposes.…”

Section: Related Workmentioning

confidence: 99%

On predictive modeling for optimizing transaction execution in parallel OLTP systems

Pavlo

Jones²,

Zdonik

2011

Proc. VLDB Endow.

View full text Add to dashboard Cite

A new emerging class of parallel database management systems (DBMS) is designed to take advantage of the partitionable workloads of on-line transaction processing (OLTP) applications [23,20]. Transactions in these systems are optimized to execute to completion on a single node in a shared-nothing cluster without needing to coordinate with other nodes or use expensive concurrency control measures [18]. But some OLTP applications cannot be partitioned such that all of their transactions execute within a singlepartition in this manner. These distributed transactions access data not stored within their local partitions and subsequently require more heavy-weight concurrency control protocols. Further difficulties arise when the transaction's execution properties, such as the number of partitions it may need to access or whether it will abort, are not known beforehand. The DBMS could mitigate these performance issues if it is provided with additional information about transactions. Thus, in this paper we present a Markov model-based approach for automatically selecting which optimizations a DBMS could use, namely (1) more efficient concurrency control schemes, (2) intelligent scheduling, (3) reduced undo logging, and (4) speculative execution. To evaluate our techniques, we implemented our models and integrated them into a parallel, main-memory OLTP DBMS to show that we can improve the performance of applications with diverse workloads.

show abstract

Section: Related Workmentioning

confidence: 99%

On predictive modeling for optimizing transaction execution in parallel OLTP systems

Pavlo

Jones²,

Zdonik

2011

Proc. VLDB Endow.

View full text Add to dashboard Cite

show abstract

“…From the perspective of improving system performance, optimizing web application deployment and achieving load balance rely on knowledge of system utilization and user request patterns [4][5]. From the perspective of security and privacy assurance, system usage patterns can be analyzed to learn profiles for anomaly detection systems, defend against outsider intrusions, and identify insider threats [6][7].…”

Section: Introductionmentioning

confidence: 99%

Towards understanding the usage pattern of web-based electronic medical record systems

Malin

2011

2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks

View full text Add to dashboard Cite

The benefits and importance of electronic medical record (EMR) systems have been well recognized in the healthcare industry. Yet, their wide adoption still face significant barriers in providing on-demand secure medical information access while preserving patients’ privacy. Understanding the usage pattern of an EMR system is the first essential step towards building such environment. This paper conducts an in-depth trace analysis of a large-scale EMR system that has been in operation for more than a decade at the Vanderbilt Medical Center. Our study demonstrates several important characteristics of EMR system usage from the perspective of user-initiated sessions. First, the workload of the EMR system is highly stable and consistent with a weekly pattern. Second, EMR behavior varies between users, but each user’s behavior tends to be consistent with a slow rate of migration across sessions. Finally, the degree of access between users and medical records is sparse, echoing the limits of patient-caregiver relationships that manifest in real healthcare operations. We believe these observations can assist in the development of system security measures, such as EMR-specific anomaly detection systems, and facilitate system performance optimization.

show abstract

Towards workflow-driven database system workload modeling

Yao

Wang

2009

Proceedings of the Second International Workshop on Testing Database Systems

View full text Add to dashboard Cite

Mining and Modeling Database User Access Patterns

Cited by 3 publications

References 9 publications

On predictive modeling for optimizing transaction execution in parallel OLTP systems

On predictive modeling for optimizing transaction execution in parallel OLTP systems

Towards understanding the usage pattern of web-based electronic medical record systems

Towards workflow-driven database system workload modeling

Contact Info

Product

Resources

About