Jonathan E Cook

Automating process discovery through event-data analysis

Many software process methods and tools presuppose the existence of a formal model of a process. Unfortunately, developing a formal model for an on-going, complex process can be dificult, costly, and error prone. This presents a practical barrier to the adoption of process technologies. The barrier would be lowered by automatmg the creation of formal models. We are currently exploring techniques that can use basic event data captured from an on-going process to generate a formal model of process behavior. We term this kind of data analysis process discovery. Thts paper descrbes and illustrates three methods with whzch we have been experimenting: algorithmic grammar inference, Markov models, and neural networks.

Partition selection policies in object database garbage collection

The automatic reclamation of storage for unreferenced objects is very important in object databases. Existing language system algorithms for automatic storage reclamation have been shown to be inappropriate. In this paper, we investigate methods to improve the performance of algorithms for automatic for automatic storage reclamation of object databases. These algorithms are based on a technique called partitioned garbage collection, in which a subset of the entire database is collected independently of the rest. Specifically, we investigate the policy that is used to select what partition in the database should be collected. The policies that we propose and investigate are based on the intuition that the values of overwritten pointers provide good hints about where to find garbage. Using trace-driven simulation, we show that one of our policies requires less I/O to collect more garbage than any existing implementable policy and performs close to a near-optimal policy over a wide range of database sizes and object connectivities.

Semi-automatic, self-adaptive control of garbage collection rates in object databases

A fundamental problem in automating object database storage reclamation is determining how often to perform garbage collection. We show that the choice of collection rate can have a significant impact on application performance and that the best rate depends on the dynamic behavior of the application, tempered by the particular performance goals of the user. We describe two semi-automatic, self-adaptive policies for controlling collection rate that we have developed to address the problem. Using trace-driven simulations, we evaluate the performance of the policies on a test database application that demonstrates two distinct reclustering behaviors. Our results show that the policies are effective at achieving user-specified levels of I/O operations and database garbage percentage. We also investigate the sensitivity of the policies over a range of object connectivities. The evaluation demonstrates that semi-automatic, self-adaptive policies are a practical means for flexibly controlling garbage collection rate.

Lowering the entry barrier and raising the confidence in large process models

Industrial software managers have been hesitant to adopt the results of software process research in part because the costs of developing formal process models are too high and because there is insufficient support for maintaining and evolving these models. We believe that methods alleviating these shortcomings will spur the adoption of process technologies. In this paper, we describe our work towards this goal.