18992033
Designing Quality XML Schemas from E-R Diagrams
XML has emerged as the standard for representing, exchanging and integrating data on the Web. To guarantee the quality of XML documents, the design of quality XML Schemas becomes essentially important. In this paper, we look into this problem by designing quality XML Schemas from given E-R diagrams. We first discuss several criteria in designing a good XML Schema. Following these criteria, transformation rules are then devised that take all constructs of an E-R diagram into account. Finally, a recursive algorithm is developed to transform an E-R diagram to a corresponding quality XML Schema.
1263346
Accelerating XML Structural Join by Partitioning
Structural join is the core part of XML queries and has a significant impact on the performance of XML queries, several classical structural join algorithms have been proposed such as Stack-tree join and XR-Tree join. In this paper, we consider to answer the problem of structural join by partitioning. We first extend the relationships between nodes to the relationships between partitions in the plane and get some observations. We then propose a new partition-based method P-Join for structural join. Based on P-Join, moreover, we present an enhanced partitioned-based spatial structural join algorithm PSSJ.
16942821
Mining the optimal class association rule set
We define an optimal class association rule set to be the minimum rule set with the same predictive power of the complete class association rule set. Using this rule set instead of the complete class association rule set we can avoid redundant computation that would otherwise be required for mining predictive association rules and hence improve the efficiency of the mining process significantly. We present an efficient algorithm for mining the optimal class association rule set using an upward closure property of pruning weak rules before they are actually generated. We have implemented the algorithm and our experimental results show that our algorithm generates the optimal class association rule set, whose size is smaller than 1/17 of the complete class association rule set on average, in significantly less rime than generating the complete class association rule set. Our proposed criterion has been shown very effective for pruning weak rules in dense databases.
5583846
Parallel computing of a quasi-Monte Carlo algorithm for valuing derivatives
Abstract The performance of the standard Monte Carlo method is compared with the performance obtained through the use of ( t , m , s )-nets in base b in the approximation of several high dimensional integral problems in valuing derivatives and other securities. The ( t , m , s )-nets are generated by a parallel algorithm, where particular considerations are given to scalability of dynamic adaptive routing and load balancing in the design and implementation of the algorithm. From the numerical evidence it appears that such nets can be powerful tools for valuing such securities.
13206070
Discovering and ranking important rules
Decision rules generated from reducts can fully describe a data set. We introduce a new method of evaluating rules by taking advantage of rough sets theory. We consider rules generated from the original data set as attributes in the new constructed decision table. Reducts generated from this new decision table contain essential attributes, which are the rules. Only important rules are contained in the reducts. Experiments on an artificial data set and a medical data set show that the "reduct rules" are more important, and this new method provides an automatic and effective way of ranking rules.
16063524
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first presentan optimal global-recoding method for the (α, k)-anonymity problem. Next we propose a local-recoding algorithm which is more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general case.
18041701
Modeling the effects of elevation data resolution on the performance of topography-based watershed runoff simulation
The spatial uncertainty of a topography based rainfall runoff model (TOPMODEL) is addressed in this study to assess its variability in simulating watershed hydrologic response with regards to the change of digital elevation model (DEM) resolution. Twelve DEM realizations of different grid sizes ranging from 30m to 3000m for each of two case watersheds are used for comparative examinations. The study shows that DEM grid size has significant influence on the topographic index distribution which represents the effect of topography on watershed hydrology in TOPMODEL. The smoothing effect of grid size increase may result in deteriorated topographic index distributions at coarse resolutions as the ratio of grid cell area to watershed area gets larger. The simulated discharges and model efficiencies using a same set of TOPMODEL parameters are sensitive to DEM grid size especially at coarse resolutions. This sensitivity, however, can be moderated by parameter calibrations as the optimization runs show that fairly equal efficiencies can be preserved by the compensation effect of transmissivity parameter T"0 within a large extent of DEM resolution for each watershed. The interaction between T"0 and the topographic index distribution with respect to TOPMDOEL model performance is also examined. It is found that both study watersheds demonstrate a similar pattern of change in model performance along with the increase of the grid-to-watershed ratio. The analysis reveals that the ratio poses an important factor in controlling the effect of DEM grid size on TOPMODEL performance. A ratio of less than 5% is suggested in DEM resolution selection for TOPMODEL applications based on the results of this study.