5990724
Cost-Conscious Cleaning of Massive RFID Data Sets
Efficient and accurate data cleaning is an essential task for the successful deployment of RFID systems. Although important advances have been made in tag detection rates, it is still common to see a large number of lost readings due to radio frequency (RF) interference and tag-reader configurations. Existing cleaning techniques have focused on the development of accurate methods that work well under a wide set of conditions, but have disregarded the very high cost of cleaning in a real application that may have thousands of readers and millions of tags. In this paper, we propose a cleaning framework that takes an RFID data set and a collection of cleaning methods, with associated costs, and induces a cleaning plan that optimizes the overall accuracy-adjusted cleaning costs by determining the conditions under which inexpensive methods are appropriate, and those under which more expensive methods are absolutely necessary.
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.
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.
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.
10284885
ACES: a contextual engine for search
In web information retrieval, users often have to modify their queries several times before they can reach a page that meets their information need. During these interactions with the search engine, the user provides a lot of useful information to the search engine, which can be exploited to infer the user information need. For instance, if a user types “lemur” as the query, most of the pages returned by the search engine are about the animal lemur or lemur information retrieval toolkit. On the other hand, if the search engine incorporates the previous query submitted by the same user, say, “information retrieval”, the search engine can disambiguate the meaning of “lemur” and present pages that are related to lemur information retrieval toolkit. We design and implement a sessionbased search engine ACES(A Contextual Engine for Search), which puts the search in context[2]. Although there have been several attempts at building a personalized or contextual search engine [3] or session based search engines [1], our search engine has the following new features, which will be demonstrated:
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.
20765051
Call Admission Control in Wideband CDMA Cellular Networks by Using Fuzzy Logic
In this paper, a novel call admission control (CAC) scheme using fuzzy logic is proposed for the reverse link transmission in wideband code division multiple access (CDMA) cellular communications. The fuzzy CAC scheme first estimates the effective bandwidths of the call request from a mobile station (MS) and its mobility information, then makes a decision to accept or reject the connection request based on the estimation and system resource availability. Numerical results are given to demonstrate the effectiveness of the proposed fuzzy CAC scheme in terms of new call blocking probability/handoff call dropping probability, outage probability, and resource utilization.
302118
A Hierarchical Model of Reference Affinity
To improve performance, data reorganization needs locality models to identify groups of data that have reference affinity. Much past work is based on access frequency and does not consider accessing time directly. In this paper, we propose a new model of reference affinity. This model considers the distance between data accesses in addition to the frequency. Affinity groups defined by this model are consistent and have a hierarchical structure. The former property ensures the profitability of data packing, while the latter supports data packing for storage units of different sizes. We then present a statistical clustering method that identifies affinity groups among structure fields and data arrays by analyzing training runs of a program. When used by structure splitting and array regrouping, the new method improves the performance of two test programs by up to 31%. The new data layout is significantly better than that produced by the programmer or by static compiler analysis.
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), $1 \leq k \leq \log n$.
41177176
Call admission control for heterogeneous services in wireless networks
A distributed call admission control (CAC) scheme for heterogeneous services is proposed. The scheme is based on a non-preemptive priority polling scheme and takes advantages of statistically multiplexing user mobility information. It is shown that the scheme can achieve both high resource utilization and low handoff dropping probability.
735556
Lightweight reference affinity analysis
Previous studies have shown that array regrouping and structure splitting significantly improve data locality. The most effective technique relies on profiling every access to every data element. The high overhead impedes its adoption in a general compiler, In this paper, we show that for array regrouping in scientific programs, the overhead is not needed since the same benefit can be obtained by pure program analysis.We present an interprocedural analysis technique for array regrouping. For each global array, the analysis summarizes the access pattern by access-frequency vectors and then groups arrays with similar vectors. The analysis is context sensitive, so it tracks the exact array access. For each loop or function call, it uses two methods to estimate the frequency of the execution. The first is symbolic analysis in the compiler. The second is lightweight profiling of the code. The same interprocedural analysis is used to cumulate the overall execution frequency by considering the calling context. We implemented a prototype of both the compiler and the profiling analysis in the IBM® compiler, evaluated array regrouping on the entire set of SPEC CPU2000 FORTRAN benchmarks, and compared different analysis methods. The pure compiler-based array regrouping improves the performance for the majority of programs, leaving little room for improvement by code or data profiling.