Measuring Application Availability,Usage and Performance : Implementation of EnView System

The main objective for this degree project is to implement an Application Availability Monitoring (AAM) system named Softek EnView for Fujitsu Services. The aim of implementing the AAM system is to proactively identify end user performance problems, such as application and site performance, before the actual end users experience them. No matter how well applications and sites are designed and nomatter how well they meet business requirements, they are useless to the end users if the performance is slow and/or unreliable. It is important for the customers to find out whether the end user problems are caused by the network or application malfunction. The Softek EnView was comprised of the following EnView components: Robot, Monitor, Reporter, Collector and Repository. The implemented system, however, is designed to use only some of these EnView elements: Robot, Reporter and depository. Robots can be placed at any key user location and are dedicated to customers, which means that when the number of customers increases, at the sametime the amount of Robots will increase. To make the AAM system ideal for the company to use, it was integrated with Fujitsu Services’ centralised monitoring system, BMC PATROL Enterprise Manager (PEM). That was actually the reason for deciding to drop the EnView Monitor element. After the system was fully implemented, the AAM system was ready for production. Transactions were (and are) written and deployed on Robots to simulate typical end user actions. These transactions are configured to run with certain intervals, which are defined collectively with customers. While they are driven against customers’ applicationsautomatically, transactions collect availability data and response time data all the time. In case of a failure in transactions, the robot immediately quits the transactionand writes detailed information to a log file about what went wrong and which element failed while going through an application. Then an alert is generated by a BMC PATROL Agent based on this data and is sent to the BMC PEM. Fujitsu Services’ monitoring room receives the alert, reacts to it according to the incident management process in ITIL and by alerting system specialists on critical incidents to resolve problems. As a result of the data gathered by the Robots, weekly reports, which contain detailed statistics and trend analyses of ongoing quality of IT services, is provided for the Customers.

Implementation and role of device management Solutions to the end users

This research is based on consumer complaints with respect to recently purchased consumer electronics. This research document will investigate the instances of development and device management as a tool used to aid consumer and manage consumer’s mobile products in order to resolve issues in or before the consumers is aware one exists. The problem at the present time is that mobile devices are becoming very advanced pieces of technology, and not all manufacturers and network providers have kept up the support element of End users. As such, the subject of the research is to investigate how device management could possibly be used as a method to promote research and development of mobile devices, and provide a better experience for the consumer. The wireless world is becoming increasingly complex as revenue opportunities are driven by new and innovative data services. We can no longer expect the customer to have the knowledge or ability to configure their own device. Device Management platforms can address the challenges of device configuration and support through new enabling technologies. Leveraging these technologies will allow a network operator to reduce the cost of subscriber ownership, drive increased ARPU (Average Revenue per User) by removing barriers to adoption, reduce churn by improving the customer experience and increase customer loyalty. DM technologies provide a flexible and powerful management method but are managing the same device features that have historically been configured manually through call centers or by the end user making changes directly on the device. For this reason DM technologies must be treated as part of a wider support solution. The traditional requirement for discovery, fault finding, troubleshooting and diagnosis are still as relevant with DM as they are in the current human support environment yet the current generation of solutions do little to address this problem. In the deployment of an effective Device Management solution the network operator must consider the integration of the DM platform, interfacing with many areas of the business, supported by knowledge of the relationship between devices, applications, solutions and services maintained on an ongoing basis. Complementing the DM solution with published device information, setup guides, training material and web based tools will ensure the quality of the customer experience, ensuring that problems are completely resolved, driving data usage by focusing customer education on the use of the wireless service In this way device management becomes a tool used both internally within the network or device vendor and by the customer themselves, with each user empowered to effectively manage the device without any prior knowledge or experience, confident that changes they apply will be relevant, accurate, stable and compatible. The value offered by an effective DM solution with an expert knowledge service will become a significant differentiator for the network operator in an ever competitive wireless market. This research document is intended to highlight some of the issues the industry faces as device management technologies become more prevalent, and offers some potential solutions to simplify the increasingly complex task of managing devices on the network, where device management can be used as a tool to aid customer relations and manage customer’s mobile products in order to resolve issues before the user is aware one exists. The research is broken down into the following, Customer Relationship Management, Device management, the role of knowledge with the DM, Companies that have successfully implemented device management, and the future of device management and CRM. And it also consists of questionnaires aimed at technical support agents and mobile device users. Interview was carried out with CRM managers within support centre to further the evidence gathered. To conclude, the document is to consider the advantages and disadvantages of device management and attempt to determine the influence it will have over customer support centre, and what methods could be used to implement it.

A web application for follow-up of results from a mobile device test battery for parkinson’s disease patients

Background: A test battery consisting of self-assessments and motor tests (tapping and spiral drawing) was developed for a hand computer with touch screen in a telemedicine setting. Objectives: To develop and evaluate a web-based system that delivers decision support information to the treating clinical staff for assessing PD symptoms in their patients based on the test battery data. Methods: The test battery is currently being used in a clinical trial (DAPHNE, EudraCT No. 2005-002654-21) by sixty five patients with advanced Parkinson’s disease (PD) on 9991 test occasions (four tests per day during in all 362 week-long test periods) at nine clinics around Sweden. Test results are sent continuously from the hand unit over a mobile net to a central computer and processed with statistical methods. They are summarized into scores for different dimensions of the symptom state and an ‘overall test score’ reflecting the overall condition of the patient during a test period. The information in the web application is organized and presented graphically in a way that the general overview of the patient performance per test period is emphasized. Focus is on the overall test score, symptom dimensions and daily summaries. In a recent preliminary user evaluation, the web application was demonstrated to the fifteen study nurses who had used the test battery in the clinical trial. At least one patient per clinic was shown. Results: In general, the responses from nurses were positive. They claimed that the test results shown in the system were consistent with their own clinical observations. They could follow complications, changes and trends within their patients. Discussion: In conclusion, the system is able to summarise the various time series of motor test results and self-assessments during test periods and present them in a useful manner. Its main contribution is a novel and reliable way to capture and easily access symptom information from patients’ home environment. The convenient access to current symptom profile as well as symptom history provides a basis for individualized evaluation and adjustment of treatments.

Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease

Objective: To develop a method for objective quantification of PD motor symptoms related to Off episodes and peak dose dyskinesias, using spiral data gathered by using a touch screen telemetry device. The aim was to objectively characterize predominant motor phenotypes (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Background: A retrospective analysis was conducted on recordings from 65 patients with advanced idiopathic PD from nine different clinics in Sweden, recruited from January 2006 until August 2010. In addition to the patient group, 10 healthy elderly subjects were recruited. Upper limb movement data were collected using a touch screen telemetry device from home environments of the subjects. Measurements with the device were performed four times per day during week-long test periods. On each test occasion, the subjects were asked to trace pre-drawn Archimedean spirals, using the dominant hand. The pre-drawn spiral was shown on the screen of the device. The spiral test was repeated three times per test occasion and they were instructed to complete it within 10 seconds. The device had a sampling rate of 10Hz and measured both position and time-stamps (in milliseconds) of the pen tip. Methods: Four independent raters (FB, DH, AJ and DN) used a web interface that animated the spiral drawings and allowed them to observe different kinematic features during the drawing process and to rate task performance. Initially, a number of kinematic features were assessed including ‘impairment’, ‘speed’, ‘irregularity’ and ‘hesitation’ followed by marking the predominant motor phenotype on a 3-category scale: tremor, bradykinesia and/or choreatic dyskinesia. There were only 2 test occasions for which all the four raters either classified them as tremor or could not identify the motor phenotype. Therefore, the two main motor phenotype categories were bradykinesia and dyskinesia. ‘Impairment’ was rated on a scale from 0 (no impairment) to 10 (extremely severe) whereas ‘speed’, ‘irregularity’ and ‘hesitation’ were rated on a scale from 0 (normal) to 4 (extremely severe). The proposed data-driven method consisted of the following steps. Initially, 28 spatiotemporal features were extracted from the time series signals before being presented to a Multilayer Perceptron (MLP) classifier. The features were based on different kinematic quantities of spirals including radius, angle, speed and velocity with the aim of measuring the severity of involuntary symptoms and discriminate between PD-specific (bradykinesia) and/or treatment-induced symptoms (dyskinesia). A Principal Component Analysis was applied on the features to reduce their dimensions where 4 relevant principal components (PCs) were retained and used as inputs to the MLP classifier. Finally, the MLP classifier mapped these components to the corresponding visually assessed motor phenotype scores for automating the process of scoring the bradykinesia and dyskinesia in PD patients whilst they draw spirals using the touch screen device. For motor phenotype (bradykinesia vs. dyskinesia) classification, the stratified 10-fold cross validation technique was employed. Results: There were good agreements between the four raters when rating the individual kinematic features with intra-class correlation coefficient (ICC) of 0.88 for ‘impairment’, 0.74 for ‘speed’, 0.70 for ‘irregularity’, and moderate agreements when rating ‘hesitation’ with an ICC of 0.49. When assessing the two main motor phenotype categories (bradykinesia or dyskinesia) in animated spirals the agreements between the four raters ranged from fair to moderate. There were good correlations between mean ratings of the four raters on individual kinematic features and computed scores. The MLP classifier classified the motor phenotype that is bradykinesia or dyskinesia with an accuracy of 85% in relation to visual classifications of the four movement disorder specialists. The test-retest reliability of the four PCs across the three spiral test trials was good with Cronbach’s Alpha coefficients of 0.80, 0.82, 0.54 and 0.49, respectively. These results indicate that the computed scores are stable and consistent over time. Significant differences were found between the two groups (patients and healthy elderly subjects) in all the PCs, except for the PC3. Conclusions: The proposed method automatically assessed the severity of unwanted symptoms and could reasonably well discriminate between PD-specific and/or treatment-induced motor symptoms, in relation to visual assessments of movement disorder specialists. The objective assessments could provide a time-effect summary score that could be useful for improving decision-making during symptom evaluation of individualized treatment when the goal is to maximize functional On time for patients while minimizing their Off episodes and troublesome dyskinesias.

Measuring CO2 emissions induced by online and brick-and-mortar retailing

We develop a method for empirically measuring the difference in carbon footprint between traditional and online retailing (“e-tailing”) from entry point to a geographical area to consumer residence. The method only requires data on the locations of brick-and-mortar stores, online delivery points, and residences of the region’s population, and on the goods transportation networks in the studied region. Such data are readily available in most countries, so the method is not country or region specific. The method has been evaluated using data from the Dalecarlia region in Sweden, and is shown to be robust to all assumptions made. In our empirical example, the results indicate that the average distance from consumer residence to a brick-and-mortar retailer is 48.54 km in the studied region, while the average distance to an online delivery point is 6.7 km. The results also indicate that e-tailing increases the average distance traveled from the regional entry point to the delivery point from 47.15 km for a brick-and-mortar store to 122.75 km for the online delivery points. However, as professional carriers transport the products in bulk to stores or online delivery points, which is more efficient than consumers’ transporting the products to their residences, the results indicate that consumers switching from traditional to e-tailing on average reduce their CO2 footprints by 84% when buying standard consumer electronics products. 

Measuring CO2 emissions induced by online and brick-and-mortar retailing

We develop a method for empirically measuring the difference in carbon footprint between traditional and online retailing (“e-tailing”) from entry point to a geographical area to consumer residence. The method only requires data on the locations of brick-and-mortar stores, online delivery points, and residences of the region’s population, and on the goods transportation networks in the studied region. Such data are readily available in most countries, so the method is not country or region specific. The method has been evaluated using data from the Dalecarlia region in Sweden, and is shown to be robust to all assumptions made. In our empirical example, the results indicate that the average distance from consumer residence to a brick-and-mortar retailer is 48.54 km in the studied region, while the average distance to an online delivery point is 6.7 km. The results also indicate that e-tailing increases the average distance traveled from the regional entry point to the delivery point from 47.15 km for a brick-and-mortar store to 122.75 km for the online delivery points. However, as professional carriers transport the products in bulk to stores or online delivery points, which is more efficient than consumers’ transporting the products to their residences, the results indicate that consumers switching from traditional to e-tailing on average reduce their CO2 footprints by 84% when buying standard consumer electronics products.