A dynamic attribute-based data filtering and recovery scheme for web information processing

Web data being transmitted over a network channel on the Internet with excessive amount of data causes data processing problems, which include selectively choosing useful information to be retained for various data applications. In this paper, we present an approach for filtering less-informative attribute data from a source Website. A scheme for filtering attributes, instead of tuples (records), from a Website becomes imperative, since filtering a complete tuple would lead to filtering some informative, as well as less-informative, attribute data in the tuple. Since filtered data at the source Website may be of interest to the user at the destination Website, we design a data recovery approach that maintains the minimal amount of information for data recovery purpose while imposing minimal overhead for data recovery at the source Website. Our data filtering and recovery approach (1) handles a wide range of Web data in different application domains (such as weather, stock exchanges, Internet traffic, etc.), (2) is dynamic in nature, since each filtering scheme adjusts the amount of data to be filtered as needed, and (3) is adaptive, which is appealing in an ever-changing Internet environment.     

Development of a web-based insulin decision aid for the elderly: usability barriers and guidelines

Universal Access in the Information Society - In recent years, researchers have attempted to shift patient decision aids (PDAs) from paper-based to web-based to increase its accessibility. Insulin...     

Customer Retention via Data Mining

``Customer Retention' is an increasingly pressing issue intoday's ever-competitive commercial arena. This is especially relevantand important for sales and services related industries. Motivated by areal-world problem faced by a large company, we proposed a solution thatintegrates various techniques of data mining, such as featureselection via induction, deviation analysis, and mining multipleconcept-level association rules to form an intuitive and novel approachto gauging customer loyalty and predicting their likelihood ofdefection. Immediate action triggered by these ``early-warnings'resulting from data mining is often the key to eventual customerretention.     

Identification and sizing of the entirely visible rocks from a 3D surface data segmentation of laboratory rock piles

Once segmentation of 3D surface data of a rock pile has been performed, the next task is to determine the visibility of the surface rocks. A region boundary-following algorithm that accommodates irregularly spaced 3D coordinate data is presented for determining this visibility. We examine 3D surface segmentations of laboratory rock piles and determine which regions in the segmentation correspond to entirely visible rocks, and which correspond to overlapped or partially visible rocks. This is a significant distinction as it allows accurate size determination of entirely visible rocks, separate handling of partially visible rocks, and prevents erroneous bias resulting from mishandling partially visible rocks as smaller entirely visible rocks. Literature review indicates that other rock pile sizing techniques fail to make this distinction. The rock visibility results are quantified by comparison to manual surface classifications of the laboratory piles and the size results are quantified by comparison to the sieve size.     

One-dimensional and multi-dimensional substring selectivity estimation

With the increasing importance of XML, LDAP directories, and text-based information sources on the Internet, there is an ever-greater need to evaluate queries involving (sub)string matching. In many cases, matches need to be on multiple attributes/dimensions, with correlations between the multiple dimensions. Effective query optimization in this context requires good selectivity estimates. In this paper, we use pruned count-suffix trees (PSTs) as the basic data structure for substring selectivity estimation. For the 1-D problem, we present a novel technique called MO (Maximal Overlap). We then develop and analyze two 1-D estimation algorithms, MOC and MOLC, based on MO and a constraint-based characterization of all possible completions of a given PST. For the k-D problem, we first generalize PSTs to multiple dimensions and develop a space- and time-efficient probabilistic algorithm to construct k-D PSTs directly. We then show how to extend MO to multiple dimensions. Finally, we demonstrate, both analytically and experimentally, that MO is both practical and substantially superior to competing algorithms. Received April 28, 2000 / Accepted July 11, 2000     

Note: A method to isolate and detect a large number of single molecules by microdroplet fluorescence spectroscopy

A laser induced fluorescence system, in combination with a glass-frit nebulizer and a photo-voltaic cell detector, is described for single molecule detection. The glass-frit nebulizer continuously generates a large number of droplets with an average droplet size of three micrometers in diameter. Rhodamine 6G molecules were detected at the 10(-12) M level. Concentrations 10(-12)-10(-10) M would provide mostly single molecules (0, 1, 2, 3, ...) in the individual droplets, as determined by Poisson distribution.     

Can we teach digital natives digital literacy?

In recent years, there has been much debate about the concept of digital natives, in particular the differences between the digital natives' knowledge and adoption of digital technologies in informal versus formal educational contexts. This paper investigates the knowledge about educational technologies of a group of undergraduate students studying the course Introduction to eLearning at a university in Australia and how they adopt unfamiliar technologies into their learning. The study explores the 'digital nativeness' of these students by investigating their degree of digital literacy and the ease with which they learn to make use of unfamiliar technologies. The findings show that the undergraduates were generally able to use unfamiliar technologies easily in their learning to create useful artefacts. They need, however to be made aware of what constitutes educational technologies and be provided with the opportunity to use them for meaningful purposes. The self-perception measures of the study indicated that digital natives can be taught digital literacy.     

Human and program factors affecting the maintenance of programs with deployed design patterns

Context

Practitioners may use design patterns to organize program code. Various empirical studies have investigated the effects of pattern deployment and work experience on the effectiveness and efficiency of program maintenance. However, results from these studies are not all consistent. Moreover, these studies have not considered some interesting factors, such as a maintainer’s prior exposure to the program under maintenance.

Objective

This paper aims at identifying what factors may contribute to the productivity of maintainers in the context of making correct software changes when they work on programs with deployed design patterns.

Method

We performed an empirical study involving 118 human subjects with three change tasks on a medium-sized program to explore the possible effects of a suite of six human and program factors on the productivity of maintainers, measured by the time taken to produce a correctly revised program in a course-based setting. The factors we studied include the deployment of design patterns and the presence of pattern-unaware solutions, as well as the maintainer’s prior exposure to design patterns, the subject program and the programming language, and prior work experience.

Results

Among the factors under examination, we find that the deployment of design patterns, prior exposure to the program and the presence of pattern-unaware solutions are strongly correlated with the time taken to correctly complete maintenance tasks. We also report some interesting observations from the experiment.

Conclusion

A new factor, namely, the presence of pattern-unaware solutions, contributes to the efficient completion of maintenance tasks of programs with deployed design patterns. Moreover, we conclude from the study that neither prior exposure to design patterns nor prior exposure to the programming language is supported by sufficient evidences to be significant factors, whereas the subjects’ exposure to the program under maintenance is notably more important.     

Disturbance decoupled fault reconstruction using cascaded sliding mode observers

This paper presents a disturbance decoupled fault reconstruction (DDFR) scheme using cascaded sliding mode observers (SMOs). The processed signals from a SMO are found to be the output of a fictitious system which treats the faults and disturbances as inputs; the ‘outputs’ are then fed into the next SMO. This process is repeated until the attainment of a fictitious system which satisfies the conditions that guarantee DDFR. It is found that this scheme is less restrictive and enables DDFR for a wider class of systems compared to previous work when only one or two SMOs were used. This paper also presents a systematic routine to check for the feasibility of the scheme and to calculate the required number of SMOs from the outset and also to design the DDFR scheme. A design example verifies its effectiveness.