A DSMS incorporates functionality for allowing its users to investigate the behavior of data streams. Posing queries on a stream of data, instead of data in a persistent database however requires that many challenges are solved. One important challenge is to restrict the consumption of memory, as the data volumes of streams grow large. Providing a query language fit to perform the stream operations is also important. Another notable challenge is to achieve low response times by processing the data as fast as possible, as it is received by the DSMS. As for measuring the Internet in real-time, several challenges are the same as those met by a DSMS. As many DSMSs are multipurpose, meaning they can be adapted to perform queries on almost any form of streaming data, many DSMSs are able to operate on the tasks of a network monitor. We need to investigate what network loads they can handle, and what types of network monitoringtasks they can solve.
[CFPR00]. The goal is to find interesting customer behavior patterns, identify sus- picious spending behavior that could indicate fraud, and forecast future data values [GÖ03]. A transactional data stream is a sequence of records that logs interactions between entities. For example, a stream of credit card transactions contains records of purchases by consumers from merchants. Data mining techniques are needed to exploit such transactional data streams since these streams contain a huge volume of simple records, any one of which is rather uninformative unless it is part of a total overview [CFPR00]. However, when the records related to a single entity are ag- gregated over time, the aggregate can yield a detailed picture of evolving behavior, in effect capturing the "signature" of that entity. A signature for a phone number might contain directly measurable features such as when most telephone calls are placed from that number, to what regions the calls are placed, and when the last call was placed. Queries investigating these matters may be quite similar to those de- tecting anomalous activity in the Internet. It might also contain derived information such as the degree to which the calling pattern from the number is "business-like" [CFPR00]. Such information is useful for target marketing and for developing new service offerings. Other examples of transaction log analyzing tasks, which are adapted from [GÖ03], are:
Integrated monitoring software evaluated historical data was proposed for shortening operation times. Well- organized historical data before the application service managers’ requests could shorten the transition times which surveys on current monitoringtasks for over many companies showed; creating relation data for recommendations could shorten the transition times; and a grouping of the two types of processing had a stronger effect on the transition times than either of them individually. In monitoring software for application service managers recommended to implement both types of processing. The results of survey show that proposed integrated monitoring system with all of the basic data formats supports 98.16% of monitoringtasks. The reduction rates of the transition times were higher than the ones in the case of other monitoringtasks contain root cause analysis and impact analysis. The proposed integrated monitoring software results suggest particularly effective in mission-critical application services.
Monitoring of a clinical trial assures a high quality of trial conduct. The tasks of "on site" monitoring are the evaluation of individual case histories of study participants, the assessment of site adherence to the protocol, the review of the ongoing implementation of appropriate data entry and quality control procedures, and essentially to assess adherence to good clinical practices. On-site monitoring might be required at the initial site selection to confirm that the site meets all study requirements. Monitoring represents a significant cost expenditure due to travel expenses and length of study visit. Thus some monitoringtasks such as review of regulatory files can be performed electronically if an ERS with such functions is in place (see table 5). However, only a physical site visit can establish the adequacy of study staff and facilities to conduct the study, to verify source documentation of eligibility of study subjects, to review the adherence to protocol and treatment plan, to confirm the drug accountability, and to validate source documentation of all data in the ERS. While the frequency of site visits is based on the study protocol, nonetheless at least 2 visits/ site should be planned: one shortly after the enrolment of the first or second patient to confirm the adherence to study procedures and another at study closure to review all enrolled subjects since the initial visit, resolution of queries from the DMC, and preparation for study storage. Budgeting for additional as-needed visits for sites with high enrollment to identify potential problems early in the study is wise.
(i) The evaluation questionnaire was adapted to the content and set-up of the training. There were eight items on general didactic elements: practical relevance of topics, practice orientation, interesting didactic conditioning, sufficient interaction, constructive learning atmospheres, personal benefits, fulfilled expectations, and overall impressions. There were nine items on specific didactic elements: theoretical introduction, memory card, practical training with SPs, monitoringtasks, self-reflection, feedback from colleagues, feedback from SPs, feedback from the trainer, and personal feedback form. Participants were asked to complete the questionnaire after the workshop. Each item was rated on a 6-point scale from 1 ‘very good’ to 6 ‘insufficient’ , according to the academic grading system in Germany, see also Table 1. Additionally, a forced choice ranking from 1
Methods for measuring fire effects are selected from the Methods Classification Key provided in FIREMON. Read each bullet in the Methods Key then refer to each of the project objectives to see if the bullet is true, and if so, employ the suggested method. Again, this key is intended as a guide and not a prescription. Use your own intuition and experience to modify results from the key to fit your special circumstances. FIREMON has been developed using established methods. Occasionally you may find that there is not a method that will assess the success of some objective. For example, there is no water quality sampling method in FIREMON. Thus, methods may need to be developed to monitor some attributes. These should be explicitly described in the MD table so that the exact procedure can be applied at the next sampling visit. Optionally, you might be able to add fields to existing methods to meet the objectives. For instance, if the Wildlife Biologist is interested in the presence of snag cavities, a field could be added in the Tree Data (TD) table of the database. (Use caution when adding fields to the FIREMON database, as it will make it difficult to merge your data with other FIREMON data.) You will find that most of the time and money spent on field campaigns are in transporting crews to sampling areas and not on actual sampling. Therefore, it is often prudent to sample additional attributes at the FIREMON plot to strengthen monitoring analyses and to widen the scope of the monitoring project. This is especially true if the FIREMON architect is wondering whether or not to sample a particular attribute. It is much better to spend an additional 10 to 20 minutes on the plot sampling another fire effect, than it is to be frustrated because some component wasn’t measured at the end of the sampling effort. For example, measuring crown characteristics for every tree on the macroplot may seem excessive if the sampling objective is to assess tree mortality, but those crown characteristics (percent crown scorch, tree DBH, height) could be used to develop salvage guidelines from percent crown scorch or predict crown fire potential using NEXUS (Scott 1999).
A parent’s interpretation or understanding of a child’s medical situation and changes in that situation may or may not be conscious, yet it provides the foundation from which a parent executes particular tasks. Essen- tially, the meaning that parents give to their child ’ s situation defines the WOC in important ways, and is likely to be related (in critical ways) to their beliefs about the large existential questions their child ’ s illness presents . For example, parents may question and try to understand their situation, and struggle to accept their child’s condition [19,22,25,43]. Or parents may question their child’s diagnosis and have their own interpretation of the illness’s meaning [25,28,38]. Parents may also evaluate their situation on the basis of how ‘in control’ they feel, and become involved in tasks of care in such a way as to increase their level of con- trol [18,19,41]. Another example of a depiction is when parents stress the positive, and try to stay optimistic about their situation [19,39,41]. The process by which families identify difficulties or problems and how they choose to focus on certain aspects while at the same time ignoring others, depends on the families ’ overall depiction of the situation.
Mozilla has applied quantitative analytics through two ini- tiatives: first, to gain insights on the evolution of the com- munity contributions (Community Management Metrics)  and second, to analyze the project’s performance (Bugzilla Anthropology) . These initiatives have developed a series of dashboards that use historical information to monitor commu- nity contributions (Figures 2–4) or to measure and track trends in bug fixing efforts (Figure 6). These dashboards support tasks such as monitoring patch contributions and identifying bug trends (status- or priority-based). However, they are tailored towards project managers and their activities; they do not provide developers with any useful support for their typical daily tasks. We now describe the four tasks current dashboards provide support for and, based on them, we motivate the need for a complementary qualitative approach that is geared towards helping developers with the decisions they must make on a daily basis.
Prediction is an approach that is widely used in data mining field that helps to identify events which have not yet occurred. This approach is getting more and more interest for the healthcare providers in the medical domain since it helps to prevent further chronic problems  and could lead to a decision about prognosis . The role of the predictive data mining considering wearable sensors is nontrivial due to requirement of modeling sequential patterns acquired from vital signs. This approach is also known as supervised learning models  where it includes feature extraction, training and testing steps while performing the prediction of the data behavior. As the common examples of the predictive models, authors in [38,39] presented a method which predicts the further stress levels of a subject. A similar example of using predictive models in healthcare are: blood glucose level prediction , mortality prediction by clustering electronic health data , and a predictive decision making system for dialysis patients . For the sake of the unexpected situations and conditions in environmental health monitoring (e.g., home), the difficulty of using predictive models is higher than controlled positions such as clinical units. However, there are several new prediction works which have used experimental wearable sensor data to perform non clinical health monitoring [26,42].
There is one major limit to using a 3D FPS game engine to perform visualisation tasks. As noted in , much of the 3D world itself is static - activities usually focus on players interacting with each other or with speciﬁc objects in the 3D game world. The 3D worlds can change, but only in predeﬁned ways known before runtime. For example, a game can have a map that the user navigates, interacts with by killing enemies within it, ﬂips switches to open further parts of the map and may even be able to ﬁnd a ‘secret room’ in which there is a number of beneﬁts to the player in the form of health and ammunition - and all of this can be done in any order. This can seem highly interactive to the player, but for sake of game design and simplicity, all of the world and the items within it have been mapped out before game start time.
In this paper, we propose a hierarchical architecture that supports models and methodologies for the efficient resource management of an on-line control enforcement mechanism operating on a private cloud- based infrastructure. Our design addresses the scalability challenge (related to the huge number of monitored information available from monitors) in several ways. The system is logically divided into several subsystems according to the different management time spans (short, medium, long term). A significant subset of the data processed by the monitoring framework at shorter time spans is made available to the runtime management modules operating at longer time spans. In this way, the duplication of preliminary operations such as data filtering is avoided. Furthermore, the modules operating at longer time scales identify from the myriad of available measures those that are really critical for the system. The goal is avoid the unnecessary and often computationally infeasible of storing, monitoring and processing all the measures available from shorter time spans. Hierarchical architectures for distributed monitoring of large systems have already proposed in literature (Newman, H.B. et al, 2003; Massie, M. L. and Chun, B. N. and Culler, D. E, 2004). However, these architectures are not capable of enforcing service orchestration. Our proposal represent a further step in this direction.
The SAM system should assist the agency to perform its core asset management tasks including asset identification, control, and licence compliance monitoring. This includes enabling the agency to complete software licence optimisation, rationalising its software portfolio by reallocating, re-harvesting and deploying software licences where appropriate. The system should enable the agency to readily and reliably plan for its future software needs, including identifying opportunities for retirement, portability or pooling for planned projects.
Two physicians reported writing their notes on patient face sheets - one page printed patient summaries. One doctor noted that “Typically these are just reminders of the plan that we have developed and I use this when writing my plan and completing my tasks for them after I leave the room.” Although face sheets originated from paper-based medical records, they are continuing to be used to supplement the EMR.
Do have well-documented procedures for the command center’s role in monitoring, change management, disaster recovery, escalation, incident management, security management, event management, and problem management. Those procedures must be followed by everyone. There should be no exceptions, unless specifically accounted for in the procedures.
a Digital-Signal-Processor, which is explored to use the powerful computation function. The DSP completes most of tasks such as real-time communication with PC, motion profile generator, velocity and position control of servo motor, monitoring condition of servo motor and external signal detection. In addition, this motion con- troller also includes Digital-Analog-Converter, Dual-Port-RAM, Synchronous-DRAM, Flash, FPGA, PCI-in- terface-chip and other peripheral circuits. Whilst DPRAM not only holds the communication interfaces between controller and host but also supports sufficient buffer, FPGA is responsible as address decoder. Furthermore, PCI-interface-chip is a 32-bit PCI-bus target interface chip to ensure the PCI industry standard. In order to sup- port memory spaces, RAM and Flash are designed to store data (position, velocity, sensor signal, parameters of controller, etc.).
Compared with other developed countries such the United States and countries in Europe, the average num- ber of bed disability days in Japan hospitals is at least twice as high (approximate average, 19 days) as indicated by OECD health data . This indicates that Japan hos- pitals have a tendency toward long-term care and evoke strong caution regarding hospital-acquired infections. Thus, the concept of hospital cleanliness to control hospital-acquired infection is well understood in Japan and other countries. However, in Japanese hospitals, daily hospital cleanliness has been limited to visual as- sessment with wiping, ignoring the hospital character- istic of long-term care, and more importantly has not been sufficiently supported by evidence-based studies on monitoring hospital cleanliness with a large amount of data.