Showing papers by "Anthony Tomasic published in 2008"
Proceedings Article•
13 Jul 2008TL;DR: Tests show a clear advantage for learning-enabled RADAR over all other test conditions, and as machine learning plays a central role in many system components, this paper compares versions of RADAR with and without learning.
Abstract: Email client software is widely used for personal task management, a purpose for which it was not designed and is poorly suited. Past attempts to remedy the problem have focused on adding task management features to the client UI. RADAR uses an alternative approach modeled on a trusted human assistant who reads mail, identifies task-relevant message content, and helps manage and execute tasks. This paper describes the integration of diverse AI technologies and presents results from human evaluation studies comparing RADAR user performance to unaided COTS tool users and users partnered with a human assistant. As machine learning plays a central role in many system components, we also compare versions of RADAR with and without learning. Our tests show a clear advantage for learning-enabled RADAR over all other test conditions.
71 citations
01 Aug 2008
TL;DR: This paper introduces Ferdinand, the first proxy-based cooperative query result cache with fully distributed consistency management, and implements a fully functioning Ferdinand prototype and evaluates its performance compared to several alternative query-caching approaches, showing that high cache hit rate and consistency management are both critical for Ferdinand's performance gains over existing systems.
Abstract: The backend database system is often the performance bottleneck when running web applications. A common approach to scale the database component is query result caching, but it faces the challenge of maintaining a high cache hit rate while efficiently ensuring cache consistency as the database is updated. In this paper we introduce Ferdinand, the first proxy-based cooperative query result cache with fully distributed consistency management. To maintain a high cache hit rate, Ferdinand uses both a local query result cache on each proxy server and a distributed cache. Consistency management is implemented with a highly scalable publish/subscribe system. We implement a fully functioning Ferdinand prototype and evaluate its performance compared to several alternative query-caching approaches, showing that our high cache hit rate and consistency management are both critical for Ferdinand's performance gains over existing systems.
59 citations
01 Jan 2008
TL;DR: It is argued that typical database application design enables a more holistic analysis that maintains the relationship between the database and application data and shows that this holistic analysis outperforms traditional nonholistic methods both statically and when used as part of a dynamic, distributed environment for executing Web applications using database caches.
Abstract: Current database performance optimizations stop at the border between the database application and the database system, focusing either on improving the performance of just the database system or the application’s execution in isolation of the other. We argue that typical database application design enables a more holistic analysis that maintains the relationship between the database and application data. We describe techniques to maintain this relationship and introduce several optimizations to improve the efficiency of Web application execution in a distributed environment. We show that our holistic analysis outperforms traditional nonholistic methods both statically and when used as part of a dynamic, distributed environment for executing Web applications using database caches.
3 citations
01 Jan 2008
TL;DR: This paper reduces or eliminate the effort required to create training data by automatically converting other sources of data into annotated training data and shows that the methods are effective and that the trained NER system outperforms all of the baseline results.
Abstract: : Training a named entity recognizer (NER) has always been a difficult task due to the effort required to generate a significant amount of annotated training data. In this paper, we reduce or eliminate the effort required to create training data by automatically converting other sources of data into annotated training data. The performance of this approach is tested on a gene-protein name extractor by using the mouse and fly data obtained from the BioCreAtIvE challenge. Results show that our methods are effective and that our trained NER system outperforms all of our baseline results.
3 citations