882537
Change detection in hierarchically structured information
Detecting and representing changes to data is important for active databases, data warehousing, view maintenance, and version and configuration management. Most previous work in change management has dealt with flat-file and relational data; we focus on hierarchically structured data. Since in many cases changes must be computed from old and new versions of the data, we define the hierarchical change detection problem as the problem of finding a "minimum-cost edit script" that transforms one data tree to another, and we present efficient algorithms for computing such an edit script. Our algorithms make use of some key domain characteristics to achieve substantially better performance than previous, general-purpose algorithms. We study the performance of our algorithms both analytically and empirically, and we describe the application of our techniques to hierarchically structured documents.
14104611
Meaningful change detection in structured data
Detecting changes by comparing data snapshots is an important requirement for difference queries, active databases, and version and configuration management. In this paper we focus on detecting meaningful changes in hierarchically structured data, such as nested-object data. This problem is much more challenging than the corresponding one for relational or flat-file data. In order to describe changes better, we base our work not just on the traditional “atomic” insert, delete, update operations, but also on operations that move an entire sub-tree of nodes, and that copy an entire sub-tree. These operations allows us to describe changes in a semantically more meaningful way. Since this change detection problem is NP-hard, in this paper we present a heuristic change detection algorithm that yields close to “minimal” descriptions of the changes, and that has fewer restrictions than previous algorithms. Our algorithm is based on transforming the change detection problem to a problem of computing a minimum-cost edge cover of a bipartite graph. We study the quality of the solution produced by our algorithm, as well as the running time, both analytically and experimentally.
14183146
Replicated condition monitoring
A condition monitoring system tracks real-world variables and alerts users when a predefined condition becomes true, e.g., when stock price drops, or when a nuclear reactor over-heats. Replication of monitoring servers can reduce the probability that an important alert is missed. However, replicated independent servers can sometimes report “conflicting” alerts to the user, causing confusion. In this paper, we study the problem of replicated condition monitoring. We identify and formally define three desirable properties of a replicated system, namely, orderedness, consistency, and completeness. We propose new monitoring algorithms that enforce some or all of the desired properties in different scenarios.