872843
Active feedback in ad hoc information retrieval
Information retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information need. The retrieval system can actively probe a user with questions to clarify the information need instead of just passively responding to user queries. A basic question is thus how a retrieval system should propose questions to the user so that it can obtain maximum benefits from the feedback on these questions. In this paper, we study how a retrieval system can perform active feedback, i.e., how to choose documents for relevance feedback so that the system can learn most from the feedback information. We present a general framework for such an active feedback problem, and derive several practical algorithms as special cases. Empirical evaluation of these algorithms shows that the performance of traditional relevance feedback (presenting the top K documents) is consistently worse than that of presenting documents with more diversity. With a diversity-based selection algorithm, we obtain fewer relevant documents, however, these fewer documents have more learning benefits.
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.
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.
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.
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:
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.
6253791
Array regrouping and structure splitting using whole-program reference affinity
While the memory of most machines is organized as a hierarchy, program data are laid out in a uniform address space. This paper defines a model of reference affinity, which measures how close a group of data are accessed together in a reference trace. It proves that the model gives a hierarchical partition of program data. At the top is the set of all data with the weakest affinity. At the bottom is each data element with the strongest affinity. Based on the theoretical model, the paper presents k-distance analysis, a practical test for the hierarchical affinity of source-level data. When used for array regrouping and structure splitting, k-distance analysis consistently outperforms data organizations given by the programmer, compiler analysis, frequency profiling, statistical clustering, and all other methods we have tried.
16600319
Update-Based Cache Access and Replacement in Wireless Data Access
Cache has been applied for wireless data access with different replacement policies in wireless networks. Most of the current cache replacement schemes are access-based replacement policies since they are based on object access frequency/recency information. Access-based replacement policies either ignore or do not focus on update information. However, update information is extremely important since it can make access information almost useless. In this paper, we consider two fundamental and strongly consistent access algorithms: poll-per-read (PER) and call-back (CB). We propose a server-based PER (SB-PER) cache access mechanism in which the server makes replacement decisions and a client-based CB cache access mechanism in which clients make replacement decisions. Both mechanisms have been designed to be suitable for using both update frequency and access frequency. We further propose two update-based replacement policies, least access-to-update ratio (LA2U) and least access-to-update difference (LAUD). We provide a thorough performance analysis via extensive simulations for evaluating these algorithms in terms of access rate, update rate, cache size, database size, object size, etc. Our study shows that although effective hit ratio is a better metric than cache hit ratio, it is a worse metric than transmission cost, and a higher effective hit ratio does not always mean a lower cost. In addition, the proposed SB-PER mechanism is better than the original PER algorithm in terms of effective hit ratio and cost, and the update-based policies outperform access-based policies in most cases
25793995
Update-based cache replacement policies in wireless data access
Most of cache replacement schemes are access-based replacement policies since they are based on object access frequency/time information. However, update information is extremely important since an updated object makes itself invalid, and the object hit information becomes useless. In this paper, we propose two update-based replacement policies, the least access-to-update ratio (LA2U) and least access-to-update difference (LAUD) in wireless data access, based on both update frequency and access frequency. Extensive simulations have been carried out to evaluate the proposed policies. Simulation results show that the proposed update-based policies outperform access-based policies at most cases. It is concluded that considering update information in designing replacement policies can increase cache performance, especially, when updates are heavy
9442074
Generalized Latin squares II
Abstract This paper is a continuation of a study on a new class of combinatorial structures called generalized Latin squares or 〈k,l〉-Latin squares. Here, we study the existence question of perfect 〈k,l〉-Latin squares. The main result is an existence theorem for the case M = 2, where M is the multiplicity of the generalized Latin squares. Specifically, we show that for given positive integers N, k = ip, l = iq, where i = gcd(k,l), there exists a perfect 〈k,l〉-Latin square of order N with M = 2 if and only if (i) N = 4k, (ii) pq(p + q)| N, and (iii) q|2.
7313627
Voice capacity analysis of WLAN with unbalanced traffic
An analytical model to study the performance of wireless local area networks (WLANs) supporting asymmetric nonpersistent traffic using the IEEE 802.11 distributed coordination function mode for medium access control (MAC) is developed. Given the parameters of the MAC protocol and voice codecs, the voice capacity of an infrastructure-based WLAN, in terms of the maximum number of voice connections that can be supported with satisfactory user-perceived quality, is obtained. In addition, voice capacity analysis reveals how the overheads from different layers, codec rate, and voice packetization interval affect voice traffic performance in WLANs, which provides an important guideline for network planning and management. The analytical results can be used for effective call admission control to guarantee the quality of voice connections. Extensive simulations have been performed to validate the analytical results