Data Disc control: Graphics 8,

"COO-1469-0152. File no. 818."

Ex ante evaluations of alternate data structures for end user queries: Theory and experimental test

The data structure of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. This research develops a methodology for evaluating, ex ante, the relative desirability of alternative data structures for end user queries. This research theorizes that the data structure that yields the lowest weighted average complexity for a representative sample of information requests is the most desirable data structure for end user queries. The theory was tested in an experiment that compared queries from two different relational database schemas. As theorized, end users querying the data structure associated with the less complex queries performed better Complexity was measured using three different Halstead metrics. Each of the three metrics provided excellent predictions of end user performance. This research supplies strong evidence that organizations can use complexity metrics to evaluate, ex ante, the desirability of alternate data structures. Organizations can use these evaluations to enhance the efficient and effective retrieval of information by creating data structures that minimize end user query complexity.

The design and implementation of a repository for the management of spatial data integrity constraints

This paper reviews the key features of an environment to support domain users in spatial information system (SIS) development. It presents a full design and prototype implementation of a repository system for the storage and management of metadata, focusing on a subset of spatial data integrity constraint classes. The system is designed to support spatial system development and customization by users within the domain that the system will operate.

Approximate processing of massive continuous quantile queries over high-speed data streams

Quantile computation has many applications including data mining and financial data analysis. It has been shown that an is an element of-approximate summary can be maintained so that, given a quantile query d (phi, is an element of), the data item at rank [phi N] may be approximately obtained within the rank error precision is an element of N over all N data items in a data stream or in a sliding window. However, scalable online processing of massive continuous quantile queries with different phi and is an element of poses a new challenge because the summary is continuously updated with new arrivals of data items. In this paper, first we aim to dramatically reduce the number of distinct query results by grouping a set of different queries into a cluster so that they can be processed virtually as a single query while the precision requirements from users can be retained. Second, we aim to minimize the total query processing costs. Efficient algorithms are developed to minimize the total number of times for reprocessing clusters and to produce the minimum number of clusters, respectively. The techniques are extended to maintain near-optimal clustering when queries are registered and removed in an arbitrary fashion against whole data streams or sliding windows. In addition to theoretical analysis, our performance study indicates that the proposed techniques are indeed scalable with respect to the number of input queries as well as the number of items and the item arrival rate in a data stream.

Molecular crowding effects of linear polymers in protein solutions

Measurement of protein-polymer second virial coefficients (B-AP) by sedimentation equilibrium studies of carbonic anhydrase and cytochrome c in the presence of dextrans (T10-T80) has revealed an inverse dependence of B-AP upon dextran molecular mass that conforms well with the behaviour predicted for the excluded-volume interaction between a spherical protein solute A and a random-flight representation of the polymeric cosolute P. That model of the protein-polymer interaction is also shown to provide a reasonable description of published gel chromatographic and equilibrium dialysis data on the effect of polymer molecular mass on BAP for human serum albumin in the presence of polyethylene glycols, a contrary finding from analysis of albumin solubility measurements being rejected on theoretical grounds. Inverse dependence upon polymer chainlength is also the predicted excluded-volume effect on the strength of several types of macromolecular equilibria-protein isomerization, protein dimerization, and 1 : 1 complex formation between dissimilar protein reactants. It is therefore concluded that published experimental observations of the reverse dependence, preferential reaction enhancement within DNA replication complexes by larger polyethylene glycols, must reflect the consequences of cosolute chemical interactions that outweigh those of thermodynamic nonideality arising from excluded-volume effects. (c) 2005 Elsevier B.V. All rights reserved.

Optimum conditions for adsorptive storage

The storage of gases in porous adsorbents, such as activated carbon and carbon nanotubes, is examined here thermodynamically from a systems viewpoint, considering the entire adsorption-desorption cycle. The results provide concrete objective criteria to guide the search for the Holy Grail adsorbent, for which the adsorptive delivery is maximized. It is shown that, for ambient temperature storage of hydrogen and delivery between 30 and 1.5 bar pressure, for the optimum adsorbent the adsorption enthalpy change is 15.1 kJ/mol. For carbons, for which the average enthalpy change is typically 5.8 kJ/mol, an optimum operating temperature of about 115 K is predicted. For methane, an optimum enthalpy change of 18.8 kJ/mol is found, with the optimum temperature for carbons being 254 K. It is also demonstrated that for maximum delivery of the gas the optimum adsorbent must be homogeneous, and that introduction of heterogeneity, such as by ball milling, irradiation, and other means, can only provide small increases in physisorption-related delivery for hydrogen. For methane, heterogeneity is always detrimental, at any value of average adsorption enthalpy change. These results are confirmed with the help of experimental data from the literature, as well as extensive Monte Carlo simulations conducted here using slit pore models of activated carbons as well as atomistic models of carbon nanotubes. The simulations also demonstrate that carbon nanotubes offer little or no advantage over activated carbons in terms of enhanced delivery, when used as storage media for either hydrogen or methane.

IJDWM Special Issue: Advances in Data Mining Applications

This special issue is a collection of the selected papers published on the proceedings of the First International Conference on Advanced Data Mining and Applications (ADMA) held in Wuhan, China in 2005. The articles focus on the innovative applications of data mining approaches to the problems that involve large data sets, incomplete and noise data, or demand optimal solutions.

A scaleless data model for direct and progressive spatial query processing

A progressive spatial query retrieves spatial data based on previous queries (e.g., to fetch data in a more restricted area with higher resolution). A direct query, on the other side, is defined as an isolated window query. A multi-resolution spatial database system should support both progressive queries and traditional direct queries. It is conceptually challenging to support both types of query at the same time, as direct queries favour location-based data clustering, whereas progressive queries require fragmented data clustered by resolutions. Two new scaleless data structures are proposed in this paper. Experimental results using both synthetic and real world datasets demonstrate that the query processing time based on the new multiresolution approaches is comparable and often better than multi-representation data structures for both types of queries.

Space efficient quantile summary for constrained sliding windows on a data stream

In many online applications, we need to maintain quantile statistics for a sliding window on a data stream. The sliding windows in natural form are defined as the most recent N data items. In this paper, we study the problem of estimating quantiles over other types of sliding windows. We present a uniform framework to process quantile queries for time constrained and filter based sliding windows. Our algorithm makes one pass on the data stream and maintains an E-approximate summary. It uses O((1)/(epsilon2) log(2) epsilonN) space where N is the number of data items in the window. We extend this framework to further process generalized constrained sliding window queries and proved that our technique is applicable for flexible window settings. Our performance study indicates that the space required in practice is much less than the given theoretical bound and the algorithm supports high speed data streams.

Improving support vector solutions by selecting a sequence of training subsets

In this paper we demonstrate that it is possible to gradually improve the performance of support vector machine (SVM) classifiers by using a genetic algorithm to select a sequence of training subsets from the available data. Performance improvement is possible because the SVM solution generally lies some distance away from the Bayes optimal in the space of learning parameters. We illustrate performance improvements on a number of benchmark data sets.