11467593
Generating Multidimensional Schemata from Relational Aggregation Queries
Queries on operational databases can be viewed as a form of business rules on data warehouse schema design. We propose to use such queries to automatically generate measures, dimensions and dimension hierarchies and their representation in a star schema. The schema produced with our approach has good properties such as non-redundant dimensional attributes and orthogonality among dimensions and can answer many more queries than just those it was generated from.
717712
Efficient Computation of Iceberg Cubes by Bounding Aggregate Functions
The iceberg cubing problem is to compute the multidimensional group-by partitions that satisfy given aggregation constraints. Pruning unproductive computation for iceberg cubing when nonantimonotone constraints are present is a great challenge because the aggregate functions do not increase or decrease monotonically along the subset relationship between partitions. In this paper, we propose a novel bound prune cubing (BP-Cubing) approach for iceberg cubing with nonantimonotone aggregation constraints. Given a cube over n dimensions, an aggregate for any group-by partition can be computed from aggregates for the most specific n--dimensional partitions (MSPs). The largest and smallest aggregate values computed this way become the bounds for all partitions in the cube. We provide efficient methods to compute tight bounds for base aggregate functions and, more interestingly, arithmetic expressions thereof, from bounds of aggregates over the MSPs. Our methods produce tighter bounds than those obtained by previous approaches. We present iceberg cubing algorithms that combine bounding with efficient aggregation strategies. Our experiments on real-world and artificial benchmark data sets demonstrate that BP-Cubing algorithms achieve more effective pruning and are several times faster than state-of-the-art iceberg cubing algorithms and that BP-Cubing achieves the best performance with the top-down cubing approach.
35361263
Reducing Overfitting in Predicting Intrinsically Unstructured Proteins
Intrinsically unstructured or disordered proteins are proteins that lack fixed 3-D structure globally or contain long disordered regions. Predicting disordered regions has attracted significant research recently. In developing a decision tree based disordered region predictor, we note that many previous predictors applying 20 amino acid compositions as training parameter tend to overfit the data. In this paper we propose to alleviate overfitting in prediction of intrinsically unstructured proteins by reducing input parameters. We also compare this approach with the random forest model, which is inherently tolerant to overfitting. Our experiments suggest that reducing 20 amino acid compositions into 4 groups according to amino acid property can reduce the overfitting in decision tree model. Alternatively, ensemble-learning techniques like random forest is inherently more tolerant to this kind of overfitting and can be a promising candidate in disordered region prediction.
8280671
Information-Based Classification by Aggregating Emerging Patterns
Emerging patterns (EPs) are knowledge patterns capturing contrasts between data classes. In this paper, we propose an information-based approach for classification by aggregating emerging patterns. The constraint-based EP mining algorithm enables the system to learn from large-volume and high-dimensional data; the new approach for selecting representative EPs and efficient algorithm for finding the EPs renders the system high predictive accuracy and short classification time. Experiments on many benchmark datasets show that the resulting classifiers have good overall predictive accuracy, and are often also superior to other state-of-the-art classification systems such as C4.5, CBA and LB.
29730920
Translating update operations from relational to object-oriented databases
Abstract In migrating a legacy relational database system to the object-oriented (OO) platform, when database migration completes, application modules are to be migrated, where embedded relational database operations are mapped into their OO correspondents. In this paper we study mapping relational update operations to their OO equivalents, which include UPDATE 1 , INSERT and DELETE operations. Relational update operation translation from relational to OO faces the touchy problem of transformation from a value-based relationship model to a reference-based model and maintaining the relational integrity constraints. Moreover, with a relational database where inheritance is expressed as attribute value subset relationship, changing of some attribute values may lead to the change of the position of an object in the class inheritance hierarchy, which we call object migration. Considering all these aspects, algorithms are given mapping relational UPDATE, INSERT and DELETE operations to their OO correspondents. Our work emphasize in examining the differences in the representation of the source schema's semantics resulting from the translation process, as well as differences in the inherent semantics of the two models.
30931074
A Distributed Object Database's Basic Design Issues
8353152
An Efficient Algorithm for Mining Coherent Patterns from Heterogeneous Microarrays
DNA microarray techniques present a novel way for geneticists to monitor interactions among tens of thousands of genes simultaneously, and have become standard lab routines in gene discovery, disease diagnosis, and drug design. There has been extensive research on coherent subspace clustering of gene expressions measured under consistent experimental settings. This implies that all experiments are run using the same batch of microarray chips with similar characteristics of noise. Algorithms developed under this assumption may not be applicable for analyzing data collected from heterogeneous settings, where the set of genes being monitored may be different and expression levels may be not directly comparable even for the same gene. In this paper, we propose a model, F-cluster, for mining subspace coherent patterns from heterogeneous gene expression data, which is shown effective for revealing truthful patterns and reducing spurious ones. We also develop an efficient and scalable hybrid approach that combines gene-pair based and sample-pair based pruning to generate F-clusters from multiple gene expression matrices simultaneously. The experimental results demonstrate that our model can discover significant clusters that may not be identified by previous models.
519875
Virtual Clusters for Grid Communities
A challenging issue facing Grid communities is that while Grids can provide access to many heterogeneous resources, the resources to which access is provided often do not match the needs of a specific application or service. In an environment in which both resource availability and software requirements evolve rapidly, this disconnect can lead to resource underutilization, user frustration, and much wasted effort spent on bridging the gap between applications and resources. We show here how these issues can be overcome by allowing authorized Grid clients to negotiate the creation of virtual clusters made up of virtual machines configured to suit client requirements for software environment and hardware allocation. We introduce descriptions and methods that allow us to deploy flexibly configured virtual cluster workspaces. We describe their configuration, implementation, and evaluate them in the context of a virtual cluster representing the environment in production use by the Open Science Grid. Our performance evaluation results show that virtual clusters representing current Grid production environments can be deployed and managed efficiently, and thus can provide an acceptable platform for Grid applications.
6359793
Integration of cluster ensemble and EM based text mining for microarray gene cluster identification and annotation
In this paper, we design and develop a unified system GE-Miner (Gene Expression Miner) to integrate cluster ensemble, text clustering and multi document summarization and provide an environment for comprehensive gene expression data analysis. We present a novel cluster ensemble approach to generate high quality gene cluster. In our text summarization module, given a gene cluster, our Expectation Maximization (EM) based algorithm can automatically identify subtopics and extract most probable terms for each topic. Then, the extracted top k topical terms from each subtopic are combined to form the biological explanation of each gene cluster. Experimental results demonstrate that our system can obtain high quality clusters and provide informative key terms for the gene clusters.
7135142
Precise dynamic slicing algorithms
Dynamic slicing algorithms can greatly reduce the debugging effort by focusing the attention of the user on a relevant subset of program statements. In this paper we present the design and evaluation of three precise dynamic slicing algorithms called the full preprocessing (FP), no preprocessing (NP) and limited preprocessing (LP) algorithms. The algorithms differ in the relative timing of constructing the dynamic data dependence graph and its traversal for computing requested dynamic slices. Our experiments show that the LP algorithm is a fast and practical precise slicing algorithm. In fact we show that while precise slices can be orders of magnitude smaller than imprecise dynamic slices, for small number of slicing requests, the LP algorithm is faster than an imprecise dynamic slicing algorithm proposed by Agrawal and Horgan.
15604810
Fault-tolerant grid services using primary-backup: feasibility and performance
The combination of grid technology and Web services has produced an attractive platform for deploying distributed applications: grid services, as represented by the Open Grid Services Infrastructure (OGSI) and its Globus toolkit implementation. As the use of grid services grows in popularity, tolerating failures becomes increasingly important. This work addresses the problem of building a reliable and highly-available grid service by replicating the service on two or more hosts using the primary-backup approach. The primary goal is to evaluate the ease and efficiency with which this can be done, by first designing a primary-backup protocol using OGSI, and then implementing it using Globus to evaluate performance implications and tradeoffs. We compared three implementations: one that makes heavy use of the notification interface defined in OGSI, one that uses standard grid service requests instead of notification, and one that uses low-level socket primitives. The overall conclusion is that, while the performance penalty of using Globus primitives - especially notification - for replica coordination can be significant, the OGSI model is suitable for building highly-available services and it makes the task of engineering such services easier.