Ric Jentzsch

A time series data management framework

In the real world there are thousands of time series data that coexists with other data. Every day tons of data is collected in the form of time series. Time series is a collection of observations that is recorded or measured over time on a regular or irregular basis generally sequentially. Time series arise in financial, economic, and scientific applications. Typical examples are the recording of different values of stock prices, bank transactions, consumer price index, electricity and telecommunication data, etc. In theory, such data is processed, analyzed, disseminated, and presented. However, many institutions are facing some difficult issues in organizing such a vast amount of data. Therefore, the need for data management tools has become more and more important. This paper addresses this issue by proposing a framework for Time Series Data Management (TSDM). The central abstraction for the proposed domain specific framework is the notion of Business Sections, Group of Time Series, and Time Series itself. The framework integrates minimum specification regarding structural and functional aspects for time series data management.

A Framework for Teaching Mobile and Wireless Technology

Introduction Teaching the use of technology or the technology itself comes with a certain amount of inherent risks and drawbacks. Most technology is upgraded every 18 to 24 months. During any year the technology may have, patches, service packs, add-ons, etc ... New technology standards and related standards that affect the use of technology are being implemented and approved each year. In some technology areas new vendors are entering the market while some are leaving the market, and others are being amalgamated. Each of these modifications, changes, and standards, can slightly too dynamically affect the technology. These modifications and changes effect the way the technology works, can be used, is used, and its role as a business tool. The modifications potentially affect the way interactions between the human and the technology are dealt with, interactions between existing technologies, those begin used and considering to be used, and the user of the technology. In a teaching environment this means that the semester changes to the subject material might require some minor adjustments to the possibility that a massive overhaul of the teaching material is required. One technology area that is dynamically changing is mobile and wireless technology. The teaching of wireless and mobile technology brings about some inherent challenges and risks. This paper presents a framework for the teaching of mobile and wireless technology. The framework is based on the development of a subject where the teaching of the technology and feedback from a workshop presented at the International Conference on Intelligent Agents, Web Technology and Internet Commerce in Vienna, Austria February 2003. The framework is at a high level that will need to be reviewed for revisions on a periodic basis. The framework takes the perspective of the stakeholders in todays organizations that will have to and do deal with this technology on a day to day basis. These stakeholders require not only an understanding of the terminology, but how it is being used by modern organizations, and the potential to which mobile and wireless technology might be applied in an organizational and business context. Teaching Environment The teaching of mobile and wireless technology requires an environment that few universities have or are willing to commit to. This is understandable with the number of ongoing changes that continues to drive the mobile and wireless industry and the expense that is required in order to be technologically up to date in this area. However, it is not understandable from the market potential for the future skills and knowledge needed by many employees. Many universities have implemented wireless systems on their campuses providing an extensible service to the educational activities offered to students (Monash, 2002; UniWide, 2003; UTORcwn, 2003). These implementations are directed at teaching supporting not at the teaching of mobile and wireless technology. The objective of the development of a teaching framework for mobile and wireless technology is to first teach from an organizational perspective, particularly business organizations, and then from a technological perspective. The teaching needs to include the concepts and issues relating to the understanding of the use of mobile and wireless technology. The framework needs to consider how to react, within a reasonable timeframe, to the upgrades, patches, service packs, add-ons, standards, and business issues as they are discovered. The framework needs to provide a way to identify when each of these occurs and to be able to evaluate the potential affect that each could possible have on the business operations. A flow on affect is the ability to provide up to date information that would be integrated into the teaching material and the delivery method. For example an organization has recently installed a Wi-Fi local area network (WLAN) based on the 802. …

Fuzzy Cognitive Map for Software Testing Using Artificial Intelligence Techniques

This paper discusses a framework to assist test managers to evaluate the use of AI techniques as a potential tool in software testing. Fuzzy Cognitive Maps (FCMs) are employed to evaluate the framework and make decision analysis easier. A what-if analysis is presented that explores the general application of the framework. Simulations are performed to show the effectiveness of the proposed method. The framework proposed is innovative and it assists managers in making efficient decisions.

General Application of a Decision Support Framework for Software Testing Using Artificial Intelligence Techniques

The use of artificial intelligent (AI) techniques for testing software applications has been investigated for over a decade. This paper proposes a framework to assist test managers to evaluate the use of AI techniques as a potential tool to test software. The framework is designed to facilitate decision making and provoke the decision maker into a better understanding of the use of AI techniques as a testing tool. We provide an overview of the framework and its components. Fuzzy Cognitive Maps (FCMs) are employed to evaluate the framework and make decision analysis easier, and therefore help the decision making process about the use of AI techniques to test software. What-if analysis is used to explore and illustrate the general application of the framework.

Software Testing – Factor Contribution Analysis in a Decision Support Framework

A decision support framework has been developed to guide software test managers in their planning and risk management for successful software testing. Total factor contribution analysis for risk management is applied to the decision support framework. Total factor contribution analysis (FCA) is a tool that can be used to analyse risk before and during software testing. This paper illustrates how software test managers can apply FCA to the decision support framework to assess risk management issues, and interpret the results for their implications on successful software testing.

Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

Radio frequency identification (RFID) is a promising technology for improving services and reduction of cost in health care. Accurate almost real time data acquisition and analysis of patient data and the ability to update such a data is a way to improve patient’s care and reduce cost in health care systems. This article employs wireless radio frequency identification technology to acquire patient data and integrates wireless technology for fast data acquisition and transmission, while maintaining the security and privacy issues. An intelligent agent framework is proposed to assist in managing patients’ health care data in a hospital environment. A data classification method based on fuzzy logic is proposed and developed to improve the data security and privacy of data collected and propagated.

Electricity load prediction using hierarchical fuzzy logic systems

Electricity load forecasting has been the subject of research over the past several years by researchers and practitioners in academia and industry. This is due to its very important role for effective and economic operation of power stations. In this paper an intelligent hierarchical fuzzy logic system using genetic algorithms for the prediction and modelling of electricity consumption is developed. A hierarchical fuzzy logic system is developed to model and predict daily electricity load fluctuations. The system is further trained to model and predict electricity consumption for daily peak.