A total quality management methodology for universities

This research document is motivated by the need for a systemic, efficient quality improvement methodology at universities. There exists no methodology designed for a total quality management (TQM) program in a university. The main objective of this study is to develop a TQM Methodology that enables a university to efficiently develop an integral total quality improvement (TQM) Plan. ^ Current research focuses on the need of improving the quality of universities, the study of the perceived best quality universities, and the measurement of the quality of universities through rankings. There is no evidence of research on how to plan for an integral quality improvement initiative for the university as a whole, which is the main contribution of this study. ^ This research is built on various reference TQM models and criteria provided by ISO 9000, Baldrige and Six Sigma; and educational accreditation criteria found in ABET and SACS. The TQM methodology is proposed by following a seven-step metamethodology. The proposed methodology guides the user to develop a TQM plan in five sequential phases: initiation, assessment, analysis, preparation and acceptance. Each phase defines for the user its purpose, key activities, input requirements, controls, deliverables, and tools to use. The application of quality concepts in education and higher education is particular; since there are unique factors in education which ought to be considered. These factors shape the quality dimensions in a university and are the main inputs to the methodology. ^ The proposed TQM Methodology is used to guide the user to collect and transform appropriate inputs to a holistic TQM Plan, ready to be implemented by the university. Different input data will lead to a unique TQM plan for the specific university at the time. It may not necessarily transform the university into a world-class institution, but aims to strive for stakeholder-oriented improvements, leading to a better alignment with its mission and total quality advancement. ^ The proposed TQM methodology is validated in three steps. First, it is verified by going through a test activity as part of the meta-methodology. Secondly, the methodology is applied to a case university to develop a TQM plan. Lastly, the methodology and the TQM plan both are verified by an expert group consisting of TQM specialists and university administrators. The proposed TQM methodology is applicable to any university at all levels of advancement, regardless of changes in its long-term vision and short-term needs. It helps to assure the quality of a TQM plan, while making the process more systemic, efficient, and cost effective. This research establishes a framework with a solid foundation for extending the proposed TQM methodology into other industries. ^

Benchmarking Quality Management in Hotels

In the early 1980s many hotels in the United States adopted quality assurance as a business strategy. By the late 1980s independent and chain hotels realized that total quality management (TQM) was a more powerful process and they began utilizing many of its components. For over 10 years, hotels have flirted with a variety of tools, processes, and theories to improve service to the guest

Improving resource management in virtualized data centers using application performance models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity.^ We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. ^ This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.^

Information systems to support decision-making in *construction owner organizations: A data warehousing approach

Construction organizations typically deal with large volumes of project data containing valuable information. It is found that these organizations do not use these data effectively for planning and decision-making. There are two reasons. First, the information systems in construction organizations are designed to support day-to-day construction operations. The data stored in these systems are often non-validated, non-integrated and are available in a format that makes it difficult for decision makers to use in order to make timely decisions. Second, the organizational structure and the IT infrastructure are often not compatible with the information systems thereby resulting in higher operational costs and lower productivity. These two issues have been investigated in this research with the objective of developing systems that are structured for effective decision-making. ^ A framework was developed to guide storage and retrieval of validated and integrated data for timely decision-making and to enable construction organizations to redesign their organizational structure and IT infrastructure matched with information system capabilities. The research was focused on construction owner organizations that were continuously involved in multiple construction projects. Action research and Data warehousing techniques were used to develop the framework. ^ One hundred and sixty-three construction owner organizations were surveyed in order to assess their data needs, data management practices and extent of use of information systems in planning and decision-making. For in-depth analysis, Miami-Dade Transit (MDT) was selected which is in-charge of all transportation-related construction projects in the Miami-Dade county. A functional model and a prototype system were developed to test the framework. The results revealed significant improvements in data management and decision-support operations that were examined through various qualitative (ease in data access, data quality, response time, productivity improvement, etc.) and quantitative (time savings and operational cost savings) measures. The research results were first validated by MDT and then by a representative group of twenty construction owner organizations involved in various types of construction projects. ^

Information systems to support decision-making in construction owner organizations : a data warehousing approach

Construction organizations typically deal with large volumes of project data containing valuable information. It is found that these organizations do not use these data effectively for planning and decision-making. There are two reasons. First, the information systems in construction organizations are designed to support day-to-day construction operations. The data stored in these systems are often non-validated, nonintegrated and are available in a format that makes it difficult for decision makers to use in order to make timely decisions. Second, the organizational structure and the IT infrastructure are often not compatible with the information systems thereby resulting in higher operational costs and lower productivity. These two issues have been investigated in this research with the objective of developing systems that are structured for effective decision-making. A framework was developed to guide storage and retrieval of validated and integrated data for timely decision-making and to enable construction organizations to redesign their organizational structure and IT infrastructure matched with information system capabilities. The research was focused on construction owner organizations that were continuously involved in multiple construction projects. Action research and Data warehousing techniques were used to develop the framework. One hundred and sixty-three construction owner organizations were surveyed in order to assess their data needs, data management practices and extent of use of information systems in planning and decision-making. For in-depth analysis, Miami-Dade Transit (MDT) was selected which is in-charge of all transportation-related construction projects in the Miami-Dade county. A functional model and a prototype system were developed to test the framework. The results revealed significant improvements in data management and decision-support operations that were examined through various qualitative (ease in data access, data quality, response time, productivity improvement, etc.) and quantitative (time savings and operational cost savings) measures. The research results were first validated by MDT and then by a representative group of twenty construction owner organizations involved in various types of construction projects.

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.

E=MC3 Energy Equals Management's Continued Cost Concerns

E=MC³ Energy Equals Management's Continued Cost Concern, is an essay written by Fritz G. Hagenmeyer, Associate Professor, School of Hospitality Management at Florida International University. In the writing, Hagenmeyer initially tenders: “Energy problems in the hospitality industry can be contained or reduced, yielding elevated profits as a result of applied, quality management principles. The concepts, processes and procedures presented in this article are intended to aid present and future managers to become more effective with a sharpened focus on profitability.” This article is an overview of energy efficiency and the management of such. In an expanding energy consumption market with its escalating costs, energy management has become an ever increasing concern and component of responsible hospitality management, Hagenmeyer will have you know. “In endeavoring to "manage" on a day-to-day basis a functioning hospitality building's energy system, the person in charge must take on the role of Justice with her scales, attempting to balance the often varying comfort needs of guests and occupants with the invariable rising costs of energy utilized to generate and maintain such comfort conditions, since comfort is seen as an integral part of the "service," "product," or "price/value” perception of patrons,” says Hagenmeyer. In contrast to what was thought in the mid point of this century - that energy would be abundant and cheap - the reality has set-in that this is not the case; not by a long shot. The author wants you to be aware that energy costs in buildings are a force to be reckoned with; a major expense to be sure. “Since 1973, "energy-conscious design" has begun to become part of the repertoire of architects, design engineers, and construction companies,” Hagenmeyer states. “For instance, whereas office buildings of the early 1970s might have used 400,000 British Thermal Units (BTUs) per square foot year, new buildings are going up that use 55,000 to 65,000 BTUs per square foot year,” Hagenmeyer, like an incandescent bulb, illuminates you. Hagenmeyer references Robert E. Aulbach’s article - Energy Management – when informing you that the hospitality manager should not become complacent in addressing the energy cost issue, but should and must maintain a diligent focus on the problem. Hagenmeyer also makes reference to the Middle East War and to OPEC, and their influence on energy prices. In closing, Hagenmeyer suggests an - Energy Management Action Plan – which he outlines for you.

Managing Quality in the Hospitality Industry:

In - Managing Quality In the Hospitality Industry – an observation by W. Gerald Glover, Associate Professor, Hospitality Management Program, Appalachian State University, initially Glover establishes: “Quality is a primary concern in the hospitality industry. The author sees problems in the nature of the way businesses are managed and discusses approaches to ensuring quality in corporate cultures.” As the title suggests, the author wants to point out certain discrepancies in hospitality quality control, as well as enlighten you as to how to address some of these concerns. “A discussion of quality presents some interesting dilemmas. Quality is something that almost everyone wants,” Assistant Professor Glover notes. “Service businesses will never admit that they don't provide it to their customers, and few people actually understand what it takes to make it happen,” he further maintains. Glover wants you to know that in a dynamic industry such as hospitality, quality is the common denominator. Whether it be hotel, restaurant, airline, et al., quality is the raison d’être of the industry. “Quality involves the consistent delivery of a product or service according to the expected standards,” Glover provides. Many, if not all quality deficiencies can be traced back to management, Glover declares. He bullet points some of the operational and guest service problems managers’ face on a daily basis. One important point of note is the measuring and managing of quality. “Standards management is another critical area in people and product management that is seldom effective in corporations,” says Glover. “Typically, this area involves performance documentation, performance evaluation and appraisal, coaching, discipline, and team-building.” “To be effective at managing standards, an organization must establish communication in realms where it is currently non-existent or ineffective,” Glover goes on to say. “Coaching, training, and performance appraisal are methods to manage individuals who are expected to do what's expected.” He alludes to the benefit quality circles supply as well. In addressing American organizational behavior, Glover postures, “…a realization must develop that people and product management are the primary influences on generating revenues and eventually influencing the bottom line in all American organizations.” Glover introduces the concept of pro-activity. “Most recently, quality assurance and quality management have become the means used to develop and maintain proactive corporate cultures. When prevention is the focus, quality is most consistent and expectations are usually met,” he offers. Much of the article is dedicated to, “Appendix A-Table 1-Characteristics of Corporate Cultures (Reactive and Proactive. In it, Glover measures the impact of proactive management as opposed to the reactive management intrinsic to many elements of corporate culture mentality.

Context -aware data caching for mobile computing environments

The deployment of wireless communications coupled with the popularity of portable devices has led to significant research in the area of mobile data caching. Prior research has focused on the development of solutions that allow applications to run in wireless environments using proxy based techniques. Most of these approaches are semantic based and do not provide adequate support for representing the context of a user (i.e., the interpreted human intention.). Although the context may be treated implicitly it is still crucial to data management. In order to address this challenge this dissertation focuses on two characteristics: how to predict (i) the future location of the user and (ii) locations of the fetched data where the queried data item has valid answers. Using this approach, more complete information about the dynamics of an application environment is maintained. ^ The contribution of this dissertation is a novel data caching mechanism for pervasive computing environments that can adapt dynamically to a mobile user's context. In this dissertation, we design and develop a conceptual model and context aware protocols for wireless data caching management. Our replacement policy uses the validity of the data fetched from the server and the neighboring locations to decide which of the cache entries is less likely to be needed in the future, and therefore a good candidate for eviction when cache space is needed. The context aware driven prefetching algorithm exploits the query context to effectively guide the prefetching process. The query context is defined using a mobile user's movement pattern and requested information context. Numerical results and simulations show that the proposed prefetching and replacement policies significantly outperform conventional ones. ^ Anticipated applications of these solutions include biomedical engineering, tele-health, medical information systems and business. ^

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.