46516744
Efficient Keyword Search over Data-Centric XML Documents
We in this paper investigate keyword search over data-centric XML documents. We first present a novel method to divide an XML document into self-integrated subtrees, which are connected subtrees and can capture different structural information of the XML document. We then propose the meaningful self-integrated trees, which contain all the keywords and describe how the keywords are interrelated, to answer keyword search over XML documents. In addition, we introduce the B+-tree index to accelerate the retrieval of those meaningful self-integrated trees. Moreover, to further enhance the performance of keyword search, we present Bloom Filter to improve the efficiency of generating those meaningful self-integrated trees. Finally, we conducted extensive experiments to evaluate the performance of our method, and the experimental results demonstrate that our method achieves high efficiency and outperforms the existing approaches significantly.
1032115
Incremental Mining of Frequent Query Patterns from XML Queries for Caching
Existing studies for mining frequent XML query patterns mainly introduce a straightforward candidate generate-and-test strategy and compute frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XML query patterns transformed from user queries. However, it is nontrivial to maintain such discovered frequent patterns in real XML databases because there may incur frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent ones. Accordingly, existing proposals are inefficient for the evolution of the transaction database. To address these problems, this paper presents an efficient algorithm IPS-FXQPMiner for mining frequent XML query patterns without candidate maintenance and costly tree-containment checking. We transform XML queries into sequences through a one- to-one mapping and then mine the frequent sequences to generate frequent XML query patterns. More importantly, based on IPS-FXQPMiner, an efficient incremental algorithm, Incre-FXQPMiner is proposed to incrementally mine frequent XML query patterns, which can minimize the I/O and computation requirements for handling incremental updates. Our experimental study on various real-life datasets demonstrates the efficiency and scalability of our algorithms over previous known alternatives.
44653881
DMT: A Flexible and Versatile Selectivity Estimation Approach for Graph Query
Efficient and accurate selectivity estimation in graph-structured data, specifically for complex branching path query, is becoming a challenging and all-important problem for query performance optimization. Precise and flexible statistics summarization about graph-structured data plays a crucial role for graph query selectivity estimation. We propose DMT, Dynamic Markov Table, which is a dynamic graph summarization based on Markov Table by applying flexible combination of 4 Optimized Rules which investigate local forward and backward inclusions. The efficient DMT construction algorithm DMTBuilder and DMT-based statistical methods are introduced for selectivity estimations of various graph queries. Our extensive experiments demonstrate the advantages in accuracy and scalability of DMT by comparing with previously known alternative, as well as the preferred Optimized Rules that would favor different situations.
14226296
Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences
Translation initiation sites (TISs) are important signals in cDNA sequences. Many research efforts have tried to predict TISs in cDNA sequences. In this paper, we propose to use mixture Gaussian models for TIS prediction. Using both local features and some features generated from global measures, the proposed method predicts TISs with a sensitivity of 98 percent and a specificity of 93.6 percent. Our method outperforms many other existing methods in sensitivity while keeping specificity high. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models.
43356203
Robust transforms for channel estimator in clustered OFDM for high rate wireless data
Clustered OFDM can provide in-band diversity gain for wideband wireless channels. It is a promising technique for high-rate wireless packet data access, such as mobile multimedia communications. Due to smaller size of each cluster for clustered OFDM than for classical(non-clustered) OFDM, edge effects can be very large. We present new transforms for channel estimators in clustered OFDM systems. The new transforms are independent of the channel delay profiles and can effectively mitigate the edge effects. Computer simulation shows that the performance of clustered OFDM with the estimator using the new transforms is very close to the performance with the optimum estimator that depends on the channel delay profile. For the typical-urban or hilly-terrain delay profiles, clustered OFDM using the new transform based estimator is almost as good as classical OFDM with transmitter diversity.
7761968
A PTAS for Semiconductor Burn-in Scheduling
In this paper a polynomial time approximation scheme, PTAS for short, is presented for the problem of scheduling jobs in a batch processing system. Each job has a pre-defined release date, which indicates when the job is available, and a pre-defined burn-in time, which is the least time needed for processing the job. At one time, at most B jobs can be processed together, where B is a pre-given number. No preemption is permitted.
2186483
Improvements for the Symbolic Verification of Timed Automata
Based on the equivalence relation for location based reachability between continuous and integer semantics of closed timed automata, Beyer et al. have implemented the verifier Rabbit, with the uniform representation of reachable configurations. However, the growth of maximal constant of clock variables will decline the performance of Rabbit. The paper proposes an improved symbolic method, using binary decision diagrams (BDDs) to store the symbolic representation of discretized states, for the verification of timed systems. Compared with Rabbit, experiments demonstrate that besides the memory reduction, our implementation is also less sensitive to the size of clock domain.
17312614
Secrecy of Signals by Typing in Signal Transduction
We discuss secrecy of signals in signal transduction. As we have developed a basic concurrent language with interferencial coefficients, Iπ-calculus, to describe aberrance in biological models, a typing system for Iπ-calculus is proposed for achieving secrecy of signals in signal transduction. We show that this typing system guarantees that, if signal transduction typechecks, then it does not leak aberrance of signals.