28835634
A Distance-Based Algorithm for Clustering Database User Sessions
In this paper, we present a distance-based clustering algorithm for grouping database user sessions. The algorithm considers both local and global similarities between sessions and incorporates three distance metrics in the computation of the distance between two sessions. We describe the three metrics and discuss the rational for combining them. The algorithm is evaluated on two datasets. One is a clinic OLTP workload file and the other is the TPC-W benchmark. The evaluation results are reported.
9414412
Concept-based biomedical text retrieval
One challenging problem for biomedical text retrieval is to find accurate synonyms or name variants for biomedical entities. In this paper, we propose a new concept-based approach to tackle this problem. In this approach, a set of concepts instead of keywords will be extracted from a query first. Then these concepts will be used for retrieval purpose. The experiment results show that the proposed approach can boost the retrieval performance and it generates very good results on 2005 TREC Genomics data sets.
9602783
Discovery of interesting association rules from Livelink web log data
We present our experience in mining web usage patterns from a large collection of Livelink log data. Livelink is a web-based product of Open Text, which provides automatic management and retrieval of different types of information objects over an intranet or extranet. We report our experience in preprocessing raw log data and post-processing the mining results for finding interesting rules. In particular we compare and evaluate a number of rule interestingness measures and find that two of the measures that have not been used in association rule learning work very well.
668000
Applying language modeling to session identification from database trace logs
A database session is a sequence of requests presented to the database system by a user or an application to achieve a certain task. Session identification is an important step in discovering useful patterns from database trace logs. The discovered patterns can be used to improve the performance of database systems by prefetching predicted queries, rewriting the current query or conducting effective cache replacement.In this paper, we present an application of a new session identification method based on statistical language modeling to database trace logs. Several problems of the language modeling based method are revealed in the application, which include how to select values for the parameters of the language model, how to evaluate the accuracy of the session identification result and how to learn a language model without well-labeled training data. All of these issues are important in the successful application of the language modeling based method for session identification. We propose solutions to these open issues. In particular, new methods for determining an entropy threshold and the order of the language model are proposed. New performance measures are presented to better evaluate the accuracy of the identified sessions. Furthermore, three types of learning methods, namely, learning from labeled data, learning from semi-labeled data and learning from unlabeled data, are introduced to learn language models from different types of training data. Finally, we report experimental results that show the effectiveness of the language model based method for identifying sessions from the trace logs of an OLTP database application and the TPC-C Benchmark.
27390518
Session Boundary Detection for Association Rule Learning Using n-Gram Language Models
We present a statistical method using n-gram language models to identify session boundaries in a large collection of Livelink log data. The identified sessions are then used for association rule learning. Unlike the traditional ad hoc timeout method, which uses fixed time thresholds for session identification, our method uses an information theoretic approach that provides a natural technique for performing dynamic session identification. The effectiveness of our approach is evaluated with respect to 4 different interestingness measures. We find that we obtain a significant improvement in each interestingness measure, ranging from a 26.6% to 39% improvement on average over the best results obtained with standard timeout methods.
921552
Dynamic Web log session identification with statistical language models
We present a novel session identification method based on statistical language modeling. Unlike standard time-out methods, which use fixed time thresholds for session identification, we use an information theoretic approach that yields more robust results for identifying session boundaries. We evaluate our new approach by learning interesting association rules from the segmented session files. We then compare the performance of our approach to three standard session identification method--the standard timeout method, the reference length method, and the maximal forward reference method--and find that our statistical language modeling approach generally yields superior results. However, as with every method, the performance of our technique varies with changing parameter settings. Therefore, we also analyze the influence of the two key factors in our language-modeling-based approach: the choice of smoothing technique and the language model order. We find that all standard smoothing techniques, save one, perform well, and that performance is robust to language model order.
27213248
Multiresolution recognition of offline handwritten Chinese characters with wavelet transform
The authors propose a novel multiresolution recognition scheme for handwritten Chinese character recognition in which an input pattern is recognized by adopting the coefficients of the wavelet transforms. It is known that wavelet representation provides a coarse-to-fine strategy. The recognition starts from the coarse scale and moves to the finer scales. After preprocessing, the wavelet transform is applied to the kanji image. Then, we make use of the coefficients with the lowest resolution to select 50 candidates from 3755 categories. In order to enhance the statistical feature of a character, we used statistical methods to reconstruct features in fine classification. With the proposed recognition system, experiments are performed on the 863 Testing System. The correct rate reaches 80.56%, which is a promising result.
2218327
Strong computational lower bounds via parameterized complexity
We develop new techniques for deriving strong computational lower bounds for a class of well-known NP-hard problems. This class includes weighted satisfiability, dominating set, hitting set, set cover, clique, and independent set. For example, although a trivial enumeration can easily test in time O(n^k) if a given graph of n vertices has a clique of size k, we prove that unless an unlikely collapse occurs in parameterized complexity theory, the problem is not solvable in time f(k)n^o^(^k^) for any function f, even if we restrict the parameter values to be bounded by an arbitrarily small function of n. Under the same assumption, we prove that even if we restrict the parameter values k to be of the order @Q(@m(n)) for any reasonable function @m, no algorithm of running time n^o^(^k^) can test if a graph of n vertices has a clique of size k. Similar strong lower bounds on the computational complexity are also derived for other NP-hard problems in the above class. Our techniques can be further extended to derive computational lower bounds on polynomial time approximation schemes for NP-hard optimization problems. For example, we prove that the NP-hard distinguishing substring selection problem, for which a polynomial time approximation scheme has been recently developed, has no polynomial time approximation schemes of running time f(1/@e)n^o^(^1^/^@e^) for any function f unless an unlikely collapse occurs in parameterized complexity theory.
41440512
Polynomial Time Approximation Schemes and Parameterized Complexity
42418095
An Improved ALOHA Algorithm for RFID Tag Identification
One of problems that we faced for Radio frequency identification (RFID) systems is that the collision between tags which lowers the efficiency of the RFID systems. One of the popular anti-collision algorithms is ALOHA-type algorithms, which are simple and have good performance when the number of tags to be read is reasonable small. In this paper, an enhanced dynamic framed slotted ALOHA algorithm for RFID is extended to a more general close form, hence reference [9] becomes only one special case described in our paper. The improved ALOHA algorithm for RFID tag identifications has much larger capacity to handle the cases when the tag numbers increasing largely while every frame keeping the optimum system efficiency 35.5%. Simulation results show that the proposed algorithm improves the efficiency nicely in comparison with the conventional algorithms keeping >80% as the previous paper shown.
28347745
On global sequence alignment
We present a dynamic programming algorithm for computing a best global alignment of two sequences. The proposed algorithm is robust in identifying any of several global relationships between two sequences. The algorithm delivers a best alignment of two sequences in linear space and quadratic time. We also describe a multiple alignment algorithm based on the pairwise algorithm. Both algorithms have been implemented as portable C programs. Experimental results indicate that for a commonly used set of gap penalties, the new programs produce more satisfactory alignments on sequences of various lengths than some existing pairwise and multiple programs based on the dynamic programming algorithm of Needleman and Wunsch.