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.
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.
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:
45976743
Improved lightpath (wavelength) routing in large WDM networks
We address the problem of efficient circuit switching in wide area networks. The solution provided is based on finding optimal routes for lightpaths and semilightpaths. A lightpath is a fully optical transmission path, while a semilightpath is a transmission path constructed by chaining several lightpaths together, using wavelength conversion at their junctions. The problem thus is to find an optimal lightpath/semilightpath in the network in terms of the cost of wavelength conversion and the cost of using the wavelengths on links. We first present fast, efficient algorithms both for the general problem and for a natural restricted version. The new algorithms outperform earlier work, providing time improvements amounting to an almost linear time factor in most cases. Also, all our algorithms can be implemented on the network in a distributed way.
16150333
Self-healing group key distribution with time-limited node revocation for wireless sensor networks
Abstract A novel key distribution scheme with time-limited node revocation is proposed for secure group communications in wireless sensor networks. The proposed scheme offers two important security properties: the seal-healing re-keying message distribution which features periodic one-way re-keying with implicitly authentication, efficient tolerance for the lost re-keying messages, and seamless Traffic Encryption Key (TEK) switch without disrupting ongoing data transmissions; and the time-limited dynamic node attachment and detachment, so that both forward and backward secrecy is assured by dual directional hash chains. It is shown that the communication and computation overhead of the proposed protocol is light, and the protocol is robust under poor communication channel quality and frequent group node topology change.
11389576
Ultra-wideband wireless communications
Summary Ultra-wideband (UWB) communication techniques have attracted a great interest in both academia and industry in the past few years for applications in short-range wireless mobile systems. This is due to the potential advantages of UWB transmissions such as low power, high rate, immunity to multipath propagation, less complex transceiver hardware, and low interference. However, tremendous R&D efforts are required to face various technical challenges in developing UWB wireless systems, including UWB channel characterization, transceiver design, coexistence and interworking with other narrowband wireless systems, design of the link and network layers to benefit from UWB transmission characteristics. This paper is to provide an overview of UWB communications, summarize the previous research results, and identify further research issues that need to be tackled. The emphasis is placed on the commercial wireless communications. Copyright # 2003 John Wiley & Sons, Ltd.
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.
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.