Actionable pattern analysis in computing and business intelligence systems

With advances in science and technology, computing and business intelligence (BI) systems are steadily becoming more complex with an increasing variety of heterogeneous software and hardware components. They are thus becoming progressively more difficult to monitor, manage and maintain. Traditional approaches to system management have largely relied on domain experts through a knowledge acquisition process that translates domain knowledge into operating rules and policies. It is widely acknowledged as a cumbersome, labor intensive, and error prone process, besides being difficult to keep up with the rapidly changing environments. In addition, many traditional business systems deliver primarily pre-defined historic metrics for a long-term strategic or mid-term tactical analysis, and lack the necessary flexibility to support evolving metrics or data collection for real-time operational analysis. There is thus a pressing need for automatic and efficient approaches to monitor and manage complex computing and BI systems. To realize the goal of autonomic management and enable self-management capabilities, we propose to mine system historical log data generated by computing and BI systems, and automatically extract actionable patterns from this data. This dissertation focuses on the development of different data mining techniques to extract actionable patterns from various types of log data in computing and BI systems. Four key problems—Log data categorization and event summarization, Leading indicator identification , Pattern prioritization by exploring the link structures , and Tensor model for three-way log data are studied. Case studies and comprehensive experiments on real application scenarios and datasets are conducted to show the effectiveness of our proposed approaches.

Cultural influence and learning to teach: Beginning teachers' perceptions of teacher preparation programs and school support systems

Beginning teachers in the field of English Language Arts and Reading are responsible for providing literacy instruction to students. Teachers need a broad background in teaching reading, writing, listening, speaking, and viewing, as well as critical thinking. In secondary schools in particular, beginning English Language Arts and Reading teachers are also faced with the challenge of preparing students to be proficient enough readers and writers to meet required State standards. Beginning teachers must navigate compelling challenges that exist during the first years of teaching. The school support systems available to new teachers are an integral part of their educational development. ^ This qualitative study was conceptualized as an in-depth examination of the experiences and perceptions of eight beginning teachers. They represented different racial/ethnic groups, attended different teacher preparation programs, and taught in different school cultures. The data were collected through formal and informal interviews and classroom observations. A qualitative system of data analysis was used to examine the patterns relating to the interrelationship between teacher preparation programs and school support systems. ^ The experiences of the beginning teachers in this study indicated that teacher education programs should provide preservice teachers with a critical knowledge base for teaching literature, language, and composition. A liberal arts background in English, followed by an extensive program focusing on pedagogy, seems to provide a thorough level of curriculum and instructional practices needed for teaching in 21st century classrooms. The data further suggested that a school support system should pair beginning teachers with mentor teachers and provide a caring, professional environment that seeks to nurture the teacher as she/he develops during the first years of teaching. ^

Color-based surface reflectance separation for scene illumination estimation and rendering

Given the importance of color processing in computer vision and computer graphics, estimating and rendering illumination spectral reflectance of image scenes is important to advance the capability of a large class of applications such as scene reconstruction, rendering, surface segmentation, object recognition, and reflectance estimation. Consequently, this dissertation proposes effective methods for reflection components separation and rendering in single scene images. Based on the dichromatic reflectance model, a novel decomposition technique, named the Mean-Shift Decomposition (MSD) method, is introduced to separate the specular from diffuse reflectance components. This technique provides a direct access to surface shape information through diffuse shading pixel isolation. More importantly, this process does not require any local color segmentation process, which differs from the traditional methods that operate by aggregating color information along each image plane. ^ Exploiting the merits of the MSD method, a scene illumination rendering technique is designed to estimate the relative contributing specular reflectance attributes of a scene image. The image feature subset targeted provides a direct access to the surface illumination information, while a newly introduced efficient rendering method reshapes the dynamic range distribution of the specular reflectance components over each image color channel. This image enhancement technique renders the scene illumination reflection effectively without altering the scene’s surface diffuse attributes contributing to realistic rendering effects. ^ As an ancillary contribution, an effective color constancy algorithm based on the dichromatic reflectance model was also developed. This algorithm selects image highlights in order to extract the prominent surface reflectance that reproduces the exact illumination chromaticity. This evaluation is presented using a novel voting scheme technique based on histogram analysis. ^ In each of the three main contributions, empirical evaluations were performed on synthetic and real-world image scenes taken from three different color image datasets. The experimental results show over 90% accuracy in illumination estimation contributing to near real world illumination rendering effects. ^

Analysis of fractured terrain using remote sensing and geographic information systems : establishing a correlation between fracture network properties and vegetation

This research analyzed the spatial relationship between a mega-scale fracture network and the occurrence of vegetation in an arid region. High-resolution aerial photographs of Arches National Park, Utah were used for digital image processing. Four sets of large-scale joints were digitized from the rectified color photograph in order to characterize the geospatial properties of the fracture network with the aid of a Geographic Information System. An unsupervised landcover classification was carried out to identify the spatial distribution of vegetation on the fractured outcrop. Results of this study confirm that the WNW-ESE alignment of vegetation is dominantly controlled by the spatial distribution of the systematic joint set, which in turn parallels the regional fold axis. This research provides insight into the spatial heterogeneity inherent to fracture networks, as well as the effects of jointing on the distribution of surface vegetation in desert environments.

Computer program for the analysis of non-prismatic beams

One of the major problems in the analysis of beams with Moment of Inertia varying along their length, is to find the Fixed End Moments, Stiffness, and Carry-Over Factors. In order to determine Fixed End Moments, it is necessary to consider the non-prismatic member as integrated by a large number of small sections with constant Moment of Inertia, and to find the M/EI values for each individual section. This process takes a lot of time from Designers and Structural Engineers. The object of this thesis is to design a computer program to simplify this repetitive process, obtaining rapidly and effectively the Final Moments and Shears in continuous non-prismatic Beams. For this purpose the Column Analogy and the Moment Distribution Methods of Professor Hardy Cross have been utilized as the principles toward the methodical computer solutions. The program has been specifically designed to analyze continuous beams of a maximum of four spans of any length, integrated by symmetrical members with rectangular cross sections and with rectilinear variation of the Moment of Inertia. Any load or combination of uniform and concentrated loads must be considered. Finally sample problems will be solved with the new Computer Program and with traditional systems, to determine the accuracy and applicability of the Program.

Shopping for Computer Restaurant Management Systems

A myriad of computer management systems are available for the restaurant business. The author discusses all aspects of evaluating, purchasing, and using such systems for a restaurant operation.

Near Term Computer Management Strategy For Hospitality Managers and Computer System Vendors

In his dialogue - Near Term Computer Management Strategy For Hospitality Managers and Computer System Vendors - by William O'Brien, Associate Professor, School of Hospitality Management at Florida International University, Associate Professor O’Brien initially states: “The computer revolution has only just begun. Rapid improvement in hardware will continue into the foreseeable future; over the last five years it has set the stage for more significant improvements in software technology still to come. John Naisbitt's information electronics economy¹ based on the creation and distribution of information has already arrived and as computer devices improve, hospitality managers will increasingly do at least a portion of their work with software tools.” At the time of this writing Assistant Professor O’Brien will have you know, contrary to what some people might think, the computer revolution is not over, it’s just beginning; it’s just an embryo. Computer technology will only continue to develop and expand, says O’Brien with citation. “A complacent few of us who feel “we have survived the computer revolution” will miss opportunities as a new wave of technology moves through the hospitality industry,” says ‘Professor O’Brien. “Both managers who buy technology and vendors who sell it can profit from strategy based on understanding the wave of technological innovation,” is his informed opinion. Property managers who embrace rather than eschew innovation, in this case computer technology, will benefit greatly from this new science in hospitality management, O’Brien says. “The manager who is not alert to or misunderstands the nature of this wave of innovation will be the constant victim of technology,” he advises. On the vendor side of the equation, O’Brien observes, “Computer-wise hospitality managers want systems which are easier and more profitable to operate. Some view their own industry as being somewhat behind the times… They plan to pay significantly less for better computer devices. Their high expectations are fed by vendor marketing efforts…” he says. O’Brien warns against taking a gamble on a risky computer system by falling victim to un-substantiated claims and pie-in-the-sky promises. He recommends affiliating with turn-key vendors who provide hardware, software, and training, or soliciting the help of large mainstream vendors such as IBM, NCR, or Apple. Many experts agree that the computer revolution has merely and genuinely morphed into the software revolution, informs O’Brien; “…recognizing that a computer is nothing but a box in which programs run.” Yes, some of the empirical data in this article is dated by now, but the core philosophy of advancing technology, and properties continually tapping current knowledge is sound.

Off-line and on-line affective recognition of a computer user through a biosignal processing approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. ^ In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment ("relaxation" vs. "stress") are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. ^ For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). ^ In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the "relaxation" vs. "stress" states.^

Off-line and On-line Affective Recognition of a Computer User through A Biosignal Processing Approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.