6029798
Efficient Holistic Twig Joins in Leaf-to-Root Combining with Root-to-Leaf Way
Finding all the occurrences of a twig pattern on multiple elements in an XML document is a core operation for efficient evaluation of XML queries. Holistic twig join algorithms, TwigStack and TSGeneric, have been recognized as optimal solutions when the twig pattern only involves A-D(ancestordescendant) relationships, while iTwigJoin can be optimal for partial twig patterns that contain A-D only or P-C (parent-child) only relationships. However, existing algorithms involve unnecessary computations and CPU cost of them can be further improved, and we in this paper mainly address this problem. We first propose three effective optimization rules to avoid those unnecessary computations, and then present two algorithms incorporated with these optimization rules to effectively answer twig patterns in leaf-to-root combining with root-to-leaf way. Experimental results on various datasets indicate that our algorithms perform significantly better than existing proposals.
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.
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.
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.
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.
10816332
Statistically quantitative volume visualization
Visualization users are increasingly in need of techniques for assessing quantitative uncertainty and error in the images produced. Statistical segmentation algorithms compute these quantitative results, yet volume rendering tools typically produce only qualitative imagery via transfer function-based classification. This paper presents a visualization technique that allows users to interactively explore the uncertainty, risk, and probabilistic decision of surface boundaries. Our approach makes it possible to directly visualize the combined "fuzzy" classification results from multiple segmentations by combining these data into a unified probabilistic data space. We represent this unified space, the combination of scalar volumes from numerous segmentations, using a novel graph-based dimensionality reduction scheme. The scheme both dramatically reduces the dataset size and is suitable for efficient, high quality, quantitative visualization. Lastly, we show that the statistical risk arising from overlapping segmentations is a robust measure for visualizing features and assigning optical properties.
5926837
Composite Subscriptions in Content-Based Publish/Subscribe Systems
Distributed publish/subscribe systems are naturally suited for processing events in distributed systems. However, support for expressing patterns about distributed events and algorithms for detecting correlations among these events are still largely unexplored. Inspired from the requirements of decentralized, event-driven workflow processing, we design a subscription language for expressing correlations among distributed events. We illustrate the potential of our approach with a workflow management case study. The language is validated and implemented in PADRES. In this paper we present an overview of PADRES, highlighting some of its novel features, including the composite subscription language, the coordination patterns, the composite event detection algorithms, the rule-based router design, and a detailed case study illustrating the decentralized processing of workflows. Our experimental evaluation shows that rule-based brokers are a viable and powerful alternative to existing, special-purpose, content-based routing algorithms. The experiments also show that the use of composite subscriptions in PADRES significantly reduces the load on the network. Complex workflows can be processed in a decentralized fashion with a gain of 40% in message dissemination cost. All processing is realized entirely in the publish/subscribe paradigm.
29927487
Optimization of antenna configuration for MIMO systems
In this letter, we investigate the issue of selecting the number of antennas at the base station and at the mobile to optimize ergodic capacity of a multiple-input/multiple-output system, when the costs of implementing antennas at the base station and at the mobile are unequal. Total system capacity, defined as a linear combination of the uplink and downlink ergodic capacity, is used as the objective function to be maximized. The asymptotic expression for the ergodic capacity is used as an approximation. The limiting case gives some insight on how the ratio of the number of antennas at the base station to the number of antennas at the mobile may change with signal-to-noise ratio and cost ratio when the total system capacity is maximized.
16653782
Genetic Algorithm Based on Independent Component Analysis for Global Optimization
We design a new Genetic Algorithm based on Independent Component Analysis for unconstrained global optimization of continuous function. We use Independent Component Analysis to linearly transform the original dimensions of the problem into new components which are independent from each other with respect to the fitness. We project the population on the independent components and obtain corresponding sub-populations. We apply genetic operators on the sub-populations to generate new sub-populations, and combine them as a new population. In other words, we use Genetic Algorithm to find the optima on the independent components, and combine the optima as the global optimum for the problem. As we actually reduce the original high-dimensional problem into sub-problems of much fewer dimensions, the solution space decreases exponentially and thus the problem becomes easier for Genetic Algorithm to solve. The experiment results verified that our algorithm produced optimal or close-to-optimal solutions better than or comparable to those produced by some of other Genetic Algorithms and it required much less fitness evaluations of individuals.