Yochan Research Group consists of Subbarao Kambhampati and his students at Arizona State University. The group's research interests fall broadly in the areas of Artificial Intelligence and Databases.
Yochana means a plot or a plan in Sanskrit, and was the original source of the group's name. We have since found that Yochan also has meanings in Hebrew and Japanese. In Hebrew, Yochanan means "God is Gracious," and is apparently the root of such names as Johanna. We don't yet know what it means in Japanese.
Yochanites formed a strong contingent at the recently concluded International Conference on Automated Planning and Scheduling (ICAPS) held in Providence, RI.
From L to R, top row first: Menkes, Will, Terry, J., Sungwook, Dan, Minh, Rao, Kartik
Bhaumik Chokshi will conduct his thesis defense on Tuesday, 11th Sept in BYENG 455. Details are available below:
Comparing Offline and Online Statistics Estimation for Text Retrieval from Overlapped Collections Student Defense Date: September 11, 2007 Time: 10:30 AM - 12:00 PM
Contact Person: Bhaumik Chokshi Contact Email: bhaumik.chokshi@asu.edu Location: BYENG 455 Defense Type: Master's Thesis Defense Committee Members Dr. Subbarao Kambhampati Dr. Yi Chen Dr. Hasan Davulcu
In an environment of distributed text collections, the first step in the information retrieval process is to identify which of all available collections are more relevant to a given query and should thus be accessed to answer the query. Collection selection is difficult due to the varying relevance of sources as well as the overlap between these sources. Some of the previous collection selection methods have considered relevance of the collections but have ignored overlap among collections. They thus make the unrealistic assumption that the collections are all effectively disjoint. Overlap estimation can be done in two ways - offline or online. In this thesis, the main objective is to compare these two approaches for estimating statistics. One of the existing approaches(e.g., COSCO) uses offline approach to store the statistics for frequent item sets. It uses these statistics to estimate statistics for the user query. In this thesis, ROSCO is presented, which uses sample based online approach to estimate the overlap among collections for a given query. In addition to that, COSCO and ROSCO are compared with ReDDE(which does not consider overlap) under a variety of scenarios. The experiments show that ROSCO is able to outperform existing methods by 8-10% in presence of overlap among collections.