7524839
A session-based search engine
In this poster, we describe a novel session-based search engine, which puts the search in context. The search engine has a number of session-based features including expansion of the current query with user query history and clickthrough data (title and summary of clicked web pages) in the same search session and the session boundary recognition through temporal closeness and probabilistic similarity between query terms. In addition, the search engine visualizes the rank change of web pages as different queries are submitted in the same search session to help the user reformulate the query.
6496359
Implicit user modeling for personalized search
Information retrieval systems (e.g., web search engines) are critical for overcoming information overload. A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users, resulting in inherently non-optimal retrieval performance. For example, a tourist and a programmer may use the same word "java" to search for different information, but the current search systems would return the same results. In this paper, we study how to infer a user's interest from the user's search context and use the inferred implicit user model for personalized search. We present a decision theoretic framework and develop techniques for implicit user modeling in information retrieval. We develop an intelligent client-side web search agent (UCAIR) that can perform eager implicit feedback, e.g., query expansion based on previous queries and immediate result reranking based on clickthrough information. Experiments on web search show that our search agent can improve search accuracy over the popular Google search engine.
6579321
Discovering interesting patterns through user's interactive feedback
In this paper, we study the problem of discovering interesting patterns through user's interactive feedback. We assume a set of candidate patterns (ie, frequent patterns) has already been mined. Our goal is to help a particular user effectively discover interesting patterns according to his specific interest. Without requiring a user to explicitly construct a prior knowledge to measure the interestingness of patterns, we learn the user's prior knowledge from his interactive feedback. We propose two models to represent a user's prior: the log linear model and biased belief model. The former is designed for item-set patterns, whereas the latter is also applicable to sequential and structural patterns. To learn these models, we present a two-stage approach, progressive shrinking and clustering, to select sample patterns for feedback. The experimental results on real and synthetic data sets demonstrate the effectiveness of our approach.
14429022
Mining long-term search history to improve search accuracy
Long-term search history contains rich information about a user's search preferences, which can be used as search context to improve retrieval performance. In this paper, we study statistical language modeling based methods to mine contextual information from long-term search history and exploit it for a more accurate estimate of the query language model. Experiments on real web search data show that the algorithms are effective in improving search accuracy for both fresh and recurring queries. The best performance is achieved when using clickthrough data of past searches that are related to the current query.
410513
Active Feedback - UIUC TREC-2003 HARD Experiments
In this paper, we report our experiments on the HARD (High Accuracy Retrieval from Documents) Track in TREC 2003. We focus on active feedback, i.e., how to intelligently propose questions for relevance feedback in order to maximize accuracy improvement in the second run. We proposed and empirically evaluated three different methods, i.e., top-k, gapped top-k, and k-cluster centroid, to extract a fixed number of text units (e.g. passage or document) for feedback. The results show that presenting the top k documents for user feedback is often not as beneficial for learning as presenting more diversified documents.
6914367
Context-sensitive information retrieval using implicit feedback
A major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collection; information about the actual user and search context is largely ignored. In this paper, we study how to exploit implicit feedback information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several context-sensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents. We use the TREC AP data to create a test collection with search context information, and quantitatively evaluate our models using this test set. Experiment results show that using implicit feedback, especially the clicked document summaries, can improve retrieval performance substantially.
33412339
Conservative Algorithms for Parallel and Sequential Integer Sorting
We present a conservative CRCW parallel algorithm for integer sorting. This algorithm sorts n integers from {0, 1, ..., m−1} in time O(n log log min(m,n)/p + log n) using p processors. The simulation of our parallel algorithm on the sequential machine yields a sequential algorithm for integer sorting which sorts n integers from {0, 1, ..., m−1} in time O(n min(log log n, log log m/log n)).
415324
Mobility information for resource management in wireless ATM networks
User mobility poses a significant technical challenge to network resource management in wireless ATM (Asynchronous Transfer Mode) networks. In order to guarantee quality of service (QoS) to mobile users and to achieve a high efficiency in network resource management, the information of mobile users' handoff at a future moment is essential for statistical multiplexing. This paper develops a novel fuzzy logic inference system to estimate the user mobility information for a wireless ATM network which uses a direct sequence code division multiple access (DS/CDMA) protocol. The estimation is based on measured pilot signal strengths from a number of the nearest base stations by the mobile user. Numerical results are presented to demonstrate the performance of the proposed technique under various path losses and channel shadowing conditions. The proposed technique can achieve simplicity, accuracy and low cost.
36077313
Developing High-Performance Parallel Applications Using EPAS
In spite of the advent of high performance parallel computers and commodity clusters, complexity of parallel application development remains one of the major obstacles towards the mainstream adoption of parallel computing. Researchers are constantly investigating different approaches to reduce parallel application development time and increase productivity. As re-usable components, patterns have gained popularity in the sequential programming domain. Subsequently, several pattern-based parallel programming environments (PPEs) have been proposed to facilitate parallel application development procedure. Unfortunately, most of these PPEs lack the required flexibility in order to develop real-life parallel applications. In this paper, we describe the features of the EPAS (Extended Parallel Architectural Skeleton) PPE that enables development of complex parallel applications. We investigate and design the required patterns, and then use them to develop a parallel data cube computing application. Finally, we present the performance of the developed applications and discuss the results.
6649930
Parallel integer sorting is more efficient than parallel comparison sorting on exclusive write PRAMs
We present a significant improvement for parallel integer sorting. On the EREW (exclusive read exclusive write) PRAM our algorithm sorts n integers in the range {0,1, . . . ,m-1 } in time O(log n) with $O(n \sqrt{\frac{\log n}{k}})$ operations using word length $k \log (m+n)$, where $1 \leq k \leq \log n$. In this paper we present the following four variants of our algorithm. 
\noindent (1) The first variant sorts integers in $\{ 0, 1, \ldots, m-1\}$ in time O(log n) and in linear space with O(n) operations using word length log m log n. 
(2) The second variant sorts integers in {0, 1, . . , n-1} in time O(log n) and in linear space with $O(n\sqrt{\log n})$ operations using word length log n. 
(3) The third variant sorts integers in {0, 1, . . . , m-1} in time O(log3/2 n) and in linear space with $O(n\sqrt{\log n})$ operations using word length log (m+n). 
(4) The fourth variant sorts integers in {0, 1, . . . ,m-1} in time O(log n) and space $O(nm^\epsilon )$ with $O(n\sqrt{\log n})$ operations using word length log (m+n). 
Our algorithms can then be generalized to the situation where the word length is k log (m+n), $1 \leq k \leq \log n$.
6045910
Parallelization of Utility Programs Based on Behavior Phase Analysis
With the fast development of multi-core processors, automatic parallelization becomes increasingly important. In this work, we focus on the parallelization of utility programs, a class of commonly used applications including compilers, transcoding utilities, file compressions, and databases. They take a series of requests as inputs and serve them one by one. Their high input dependence poses a challenge to parallelization. 
 
We use active profiling to find behavior phase boundaries and then automatically detect run-time dependences through profiling. Using a unified framework, we manually parallelize programs at phase boundaries. We show that for two programs, the technique enables parallelization at large granularity, which may span many loops and subroutines. The parallelized programs show significant speedup on multi-processor machines.