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  • Casse-têtes en C#, un article de Jon Skeet sur les pièges du langage, traduit par Jean-Michel Ormes

    Cette discussion est destinée à recueillir vos commentaires sur l'article Casse-têtes en C# (traduction de l'article C# Brainteasers de Jon Skeet) Citation: Régulièrement, je tombe sur une situation intéressante en C# qui donne des résultats surprenants. Cette page contient une liste d'exemples. Dans les exemples où il n'y a qu'un bout de code, nous supposerons que celui-ci est dans la m...

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  • Predicting Likelihood of Click with Multiple Presentations

    - by Michel Adar
    When using predictive models to predict the likelihood of an ad or a banner to be clicked on it is common to ignore the fact that the same content may have been presented in the past to the same visitor. While the error may be small if the visitors do not often see repeated content, it may be very significant for sites where visitors come repeatedly. This is a well recognized problem that usually gets handled with presentation thresholds – do not present the same content more than 6 times. Observations and measurements of visitor behavior provide evidence that something better is needed. Observations For a specific visitor, during a single session, for a banner in a not too prominent space, the second presentation of the same content is more likely to be clicked on than the first presentation. The difference can be 30% to 100% higher likelihood for the second presentation when compared to the first. That is, for example, if the first presentation has an average click rate of 1%, the second presentation may have an average CTR of between 1.3% and 2%. After the second presentation the CTR stays more or less the same for a few more presentations. The number of presentations in this plateau seems to vary by the location of the content in the page and by the visual attraction of the content. After these few presentations the CTR starts decaying with a curve that is very well approximated by an exponential decay. For example, the 13th presentation may have 90% the likelihood of the 12th, and the 14th has 90% the likelihood of the 13th. The decay constant seems also to depend on the visibility of the content. Modeling Options Now that we know the empirical data, we can propose modeling techniques that will correctly predict the likelihood of a click. Use presentation number as an input to the predictive model Probably the most straight forward approach is to add the presentation number as an input to the predictive model. While this is certainly a simple solution, it carries with it several problems, among them: If the model learns on each case, repeated non-clicks for the same content will reinforce the belief of the model on the non-clicker disproportionately. That is, the weight of a person that does not click for 200 presentations of an offer may be the same as 100 other people that on average click on the second presentation. The effect of the presentation number is not a customer characteristic or a piece of contextual data about the interaction with the customer, but it is contextual data about the content presented. Models tend to underestimate the effect of the presentation number. For these reasons it is not advisable to use this approach when the average number of presentations of the same content to the same person is above 3, or when there are cases of having the presentation number be very large, in the tens or hundreds. Use presentation number as a partitioning attribute to the predictive model In this approach we essentially build a separate predictive model for each presentation number. This approach overcomes all of the problems in the previous approach, nevertheless, it can be applied only when the volume of data is large enough to have these very specific sub-models converge.

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  • The softer side of BPM

    - by [email protected]
    BPM and RTD are great complementary technologies that together provide a much higher benefit than each of them separately. BPM covers the need for automating processes, making sure that there is uniformity, that rules and regulations are complied with and that the process runs smoothly and quickly processes the units flowing through it. By nature, this automation and unification can lead to a stricter, less flexible process. To avoid this problem it is common to encounter process definition that include multiple conditional branches and human input to help direct processing in the direction that best applies to the current situation. This is where RTD comes into play. The selection of branches and conditions and the optimization of decisions is better left in the hands of a system that can measure the results of its decisions in a closed loop fashion and make decisions based on the empirical knowledge accumulated through observing the running of the process.When designing a business process there are key places in which it may be beneficial to introduce RTD decisions. These are:Thresholds - whenever a threshold is used to determine the processing of a unit, there may be an opportunity to make the threshold "softer" by introducing an RTD decision based on predicted results. For example an insurance company process may have a total claim threshold to initiate an investigation. Instead of having that threshold, RTD could be used to help determine what claims to investigate based on the likelihood they are fraudulent, cost of investigation and effect on processing time.Human decisions - sometimes a process will let the human participants make decisions of flow. For example, a call center process may leave the escalation decision to the agent. While this has flexibility, it may produce undesired results and asymetry in customer treatment that is not based on objective functions but subjective reasoning by the agent. Instead, an RTD decision may be introduced to recommend escalation or other kinds of treatments.Content Selection - a process may include the use of messaging with customers. The selection of the most appropriate message to the customer given the content can be optimized with RTD.A/B Testing - a process may have optional paths for which it is not clear what populations they work better for. Rather than making the arbitrary selection or selection by committee of the option deeped the best, RTD can be introduced to dynamically determine the best path for each unit.In summary, RTD can be used to make BPM based process automation more dynamic and adaptable to the different situations encountered in processing. Effectively making the automation softer, less rigid in its processing.

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  • Ignoring Robots - Or Better Yet, Counting Them Separately

    - by [email protected]
    It is quite common to have web sessions that are undesirable from the point of view of analytics. For example, when there are either internal or external robots that check the site's health, index it or just extract information from it. These robotic session do not behave like humans and if their volume is high enough they can sway the statistics and models.One easy way to deal with these sessions is to define a partitioning variable for all the models that is a flag indicating whether the session is "Normal" or "Robot". Then all the reports and the predictions can use the "Normal" partition, while the counts and statistics for Robots are still available.In order for this to work, though, it is necessary to have two conditions:1. It is possible to identify the Robotic sessions.2. No learning happens before the identification of the session as a robot.The first point is obvious, but the second may require some explanation. While the default in RTD is to learn at the end of the session, it is possible to learn in any entry point. This is a setting for each model. There are various reasons to learn in a specific entry point, for example if there is a desire to capture exactly and precisely the data in the session at the time the event happened as opposed to including changes to the end of the session.In any case, if RTD has already learned on the session before the identification of a robot was done there is no way to retract this learning.Identifying the robotic sessions can be done through the use of rules and heuristics. For example we may use some of the following:Maintain a list of known robotic IPs or domainsDetect very long sessions, lasting more than a few hours or visiting more than 500 pagesDetect "robotic" behaviors like a methodic click on all the link of every pageDetect a session with 10 pages clicked at exactly 20 second intervalsDetect extensive non-linear navigationNow, an interesting experiment would be to use the flag above as an output of a model to see if there are more subtle characteristics of robots such that a model can be used to detect robots, even if they fall through the cracks of rules and heuristics.In any case, the basic and simple technique of partitioning the models by the type of session is simple to implement and provides a lot of advantages.

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  • Is RTD Stateless or Stateful?

    - by [email protected]
    Yes.   A stateless service is one where each request is an independent transaction that can be processed by any of the servers in a cluster.  A stateful service is one where state is kept in a server's memory from transaction to transaction, thus necessitating the proper routing of requests to the right server. The main advantage of stateless systems is simplicity of design. The main advantage of stateful systems is performance. I'm often asked whether RTD is a stateless or stateful service, so I wanted to clarify this issue in depth so that RTD's architecture will be properly understood. The short answer is: "RTD can be configured as a stateless or stateful service." The performance difference between stateless and stateful systems can be very significant, and while in a call center implementation it may be reasonable to use a pure stateless configuration, a web implementation that produces thousands of requests per second is practically impossible with a stateless configuration. RTD's performance is orders of magnitude better than most competing systems. RTD was architected from the ground up to achieve this performance. Features like automatic and dynamic compression of prediction models, automatic translation of metadata to machine code, lack of interpreted languages, and separation of model building from decisioning contribute to achieving this performance level. Because  of this focus on performance we decided to have RTD's default configuration work in a stateful manner. By being stateful RTD requests are typically handled in a few milliseconds when repeated requests come to the same session. Now, those readers that have participated in implementations of RTD know that RTD's architecture is also focused on reducing Total Cost of Ownership (TCO) with features like automatic model building, automatic time windows, automatic maintenance of database tables, automatic evaluation of data mining models, automatic management of models partitioned by channel, geography, etcetera, and hot swapping of configurations. How do you reconcile the need for a low TCO and the need for performance? How do you get the performance of a stateful system with the simplicity of a stateless system? The answer is that you make the system behave like a stateless system to the exterior, but you let it automatically take advantage of situations where being stateful is better. For example, one of the advantages of stateless systems is that you can route a message to any server in a cluster, without worrying about sending it to the same server that was handling the session in previous messages. With an RTD stateful configuration you can still route the message to any server in the cluster, so from the point of view of the configuration of other systems, it is the same as a stateless service. The difference though comes in performance, because if the message arrives to the right server, RTD can serve it without any external access to the session's state, thus tremendously reducing processing time. In typical implementations it is not rare to have high percentages of messages routed directly to the right server, while those that are not, are easily handled by forwarding the messages to the right server. This architecture usually provides the best of both worlds with performance and simplicity of configuration.   Configuring RTD as a pure stateless service A pure stateless configuration requires session data to be persisted at the end of handling each and every message and reloading that data at the beginning of handling any new message. This is of course, the root of the inefficiency of these configurations. This is also the reason why many "stateless" implementations actually do keep state to take advantage of a request coming back to the same server. Nevertheless, if the implementation requires a pure stateless decision service, this is easy to configure in RTD. The way to do it is: Mark every Integration Point to Close the session at the end of processing the message In the Session entity persist the session data on closing the session In the session entity check if a persisted version exists and load it An excellent solution for persisting the session data is Oracle Coherence, which provides a high performance, distributed cache that minimizes the performance impact of persisting and reloading the session. Alternatively, the session can be persisted to a local database. An interesting feature of the RTD stateless configuration is that it can cope with serializing concurrent requests for the same session. For example, if a web page produces two requests to the decision service, these requests could come concurrently to the decision services and be handled by different servers. Most stateless implementation would have the two requests step onto each other when saving the state, or fail one of the messages. When properly configured, RTD will make one message wait for the other before processing.   A Word on Context Using the context of a customer interaction typically significantly increases lift. For example, offer success in a call center could double if the context of the call is taken into account. For this reason, it is important to utilize the contextual information in decision making. To make the contextual information available throughout a session it needs to be persisted. When there is a well defined owner for the information then there is no problem because in case of a session restart, the information can be easily retrieved. If there is no official owner of the information, then RTD can be configured to persist this information.   Once again, RTD provides flexibility to ensure high performance when it is adequate to allow for some loss of state in the rare cases of server failure. For example, in a heavy use web site that serves 1000 pages per second the navigation history may be stored in the in memory session. In such sites it is typical that there is no OLTP that stores all the navigation events, therefore if an RTD server were to fail, it would be possible for the navigation to that point to be lost (note that a new session would be immediately established in one of the other servers). In most cases the loss of this navigation information would be acceptable as it would happen rarely. If it is desired to save this information, RTD would persist it every time the visitor navigates to a new page. Note that this practice is preferred whether RTD is configured in a stateless or stateful manner.  

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  • How Do Nautilus Album Art Thumbnails work?

    - by Amir Adar
    There's something for which I've been searching an answer for a while now, but to no avail, and it's strange to me, as it seems like a thing that people would talk about: one of those nice little nonsense that enhance the computing experience a little bit. Anyway. I have a fair music collection. I save all the songs as ogg files. All is fine, and I can listen to the files, but there's something weird with the files in Nautilus: some have icons displaying their album art, while others don't, and I just can't understand WHY. I read on this site today that it's a matter of embedding the album art to the file, but that's not true, as I embedded the album art to the files I wanted several times, to no avail. Furthermore, removing an embedded album art from a file didn't have any effect on those that ARE displaying the icons. So my question is: How does it work? Where does Nautilus (or Ubuntu, I don't know) get the picture from? How do I edit it? Thanks in advance! -Amir

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  • Tips on ensuring Model Quality

    - by [email protected]
    Given enough data that represents well the domain and models that reflect exactly the decision being optimized, models usually provide good predictions that ensure lift. Nevertheless, sometimes the modeling situation is less than ideal. In this blog entry we explore the problems found in a few such situations and how to avoid them.1 - The Model does not reflect the problem you are trying to solveFor example, you may be trying to solve the problem: "What product should I recommend to this customer" but your model learns on the problem: "Given that a customer has acquired our products, what is the likelihood for each product". In this case the model you built may be too far of a proxy for the problem you are really trying to solve. What you could do in this case is try to build a model based on the result from recommendations of products to customers. If there is not enough data from actual recommendations, you could use a hybrid approach in which you would use the [bad] proxy model until the recommendation model converges.2 - Data is not predictive enoughIf the inputs are not correlated with the output then the models may be unable to provide good predictions. For example, if the input is the phase of the moon and the weather and the output is what car did the customer buy, there may be no correlations found. In this case you should see a low quality model.The solution in this case is to include more relevant inputs.3 - Not enough cases seenIf the data learned does not include enough cases, at least 200 positive examples for each output, then the quality of recommendations may be low. The obvious solution is to include more data records. If this is not possible, then it may be possible to build a model based on the characteristics of the output choices rather than the choices themselves. For example, instead of using products as output, use the product category, price and brand name, and then combine these models.4 - Output leaking into input giving the false impression of good quality modelsIf the input data in the training includes values that have changed or are available only because the output happened, then you will find some strong correlations between the input and the output, but these strong correlations do not reflect the data that you will have available at decision (prediction) time. For example, if you are building a model to predict whether a web site visitor will succeed in registering, and the input includes the variable DaysSinceRegistration, and you learn when this variable has already been set, you will probably see a big correlation between having a Zero (or one) in this variable and the fact that registration was successful.The solution is to remove these variables from the input or make sure they reflect the value as of the time of decision and not after the result is known. 

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  • Short Season, Long Models - Dealing with Seasonality

    - by Michel Adar
    Accounting for seasonality presents a challenge for the accurate prediction of events. Examples of seasonality include: ·         Boxed cosmetics sets are more popular during Christmas. They sell at other times of the year, but they rise higher than other products during the holiday season. ·         Interest in a promotion rises around the time advertising on TV airs ·         Interest in the Sports section of a newspaper rises when there is a big football match There are several ways of dealing with seasonality in predictions. Time Windows If the length of the model time windows is short enough relative to the seasonality effect, then the models will see only seasonal data, and therefore will be accurate in their predictions. For example, a model with a weekly time window may be quick enough to adapt during the holiday season. In order for time windows to be useful in dealing with seasonality it is necessary that: The time window is significantly shorter than the season changes There is enough volume of data in the short time windows to produce an accurate model An additional issue to consider is that sometimes the season may have an abrupt end, for example the day after Christmas. Input Data If available, it is possible to include the seasonality effect in the input data for the model. For example the customer record may include a list of all the promotions advertised in the area of residence. A model with these inputs will have to learn the effect of the input. It is possible to learn it specific to the promotion – and by the way learn about inter-promotion cross feeding – by leaving the list of ads as it is; or it is possible to learn the general effect by having a flag that indicates if the promotion is being advertised. For inputs to properly represent the effect in the model it is necessary that: The model sees enough events with the input present. For example, by virtue of the model lifetime (or time window) being long enough to see several “seasons” or by having enough volume for the model to learn seasonality quickly. Proportional Frequency If we create a model that ignores seasonality it is possible to use that model to predict how the specific person likelihood differs from average. If we have a divergence from average then we can transfer that divergence proportionally to the observed frequency at the time of the prediction. Definitions: Ft = trailing average frequency of the event at time “t”. The average is done over a suitable period of to achieve a statistical significant estimate. F = average frequency as seen by the model. L = likelihood predicted by the model for a specific person Lt = predicted likelihood proportionally scaled for time “t”. If the model is good at predicting deviation from average, and this holds over the interesting range of seasons, then we can estimate Lt as: Lt = L * (Ft / F) Considering that: L = (L – F) + F Substituting we get: Lt = [(L – F) + F] * (Ft / F) Which simplifies to: (i)                  Lt = (L – F) * (Ft / F)  +  Ft This latest expression can be understood as “The adjusted likelihood at time t is the average likelihood at time t plus the effect from the model, which is calculated as the difference from average time the proportion of frequencies”. The formula above assumes a linear translation of the proportion. It is possible to generalize the formula using a factor which we will call “a” as follows: (ii)                Lt = (L – F) * (Ft / F) * a  +  Ft It is also possible to use a formula that does not scale the difference, like: (iii)               Lt = (L – F) * a  +  Ft While these formulas seem reasonable, they should be taken as hypothesis to be proven with empirical data. A theoretical analysis provides the following insights: The Cumulative Gains Chart (lift) should stay the same, as at any given time the order of the likelihood for different customers is preserved If F is equal to Ft then the formula reverts to “L” If (Ft = 0) then Lt in (i) and (ii) is 0 It is possible for Lt to be above 1. If it is desired to avoid going over 1, for relatively high base frequencies it is possible to use a relative interpretation of the multiplicative factor. For example, if we say that Y is twice as likely as X, then we can interpret this sentence as: If X is 3%, then Y is 6% If X is 11%, then Y is 22% If X is 70%, then Y is 85% - in this case we interpret “twice as likely” as “half as likely to not happen” Applying this reasoning to (i) for example we would get: If (L < F) or (Ft < (1 / ((L/F) + 1)) Then  Lt = L * (Ft / F) Else Lt = 1 – (F / L) + (Ft * F / L)  

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  • External USB hard drive makes noises when the computer is turned off

    - by Amir Adar
    I have an external USB hard drive of 500GB. I can't tell what model it is exactly, as nothing specific is written on it and I don't have the box anymore. I use it as a backup disk. It works absolutely fine when the computer is turned on: no problems with writing or reading, and everything is done in dead silence. However, if I turn the computer off and the disk is still connected, it stays on and makes clicking noises. For that reason I only connect it when I need to back up or restore. Does that mean there's a problem with the disk, or with some preferences in the system itself? Or something else?

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  • Customer retention - why most companies have it wrong

    - by Michel Adar
    At least in the US market it is quite common for service companies to offer an initially discounted price to new customers. While this may attract new customers and robe customers from competitors, it is my argument that it is a bad strategy for the company. This strategy gives an incentive to change companies and a disincentive to stay with the company. From the point of view of the customer, after 6 months of being a customer the company rewards the loyalty by raising the price. A better strategy would be to reward customers for staying with the company. For example, by lowering the cost by 5% every year (compound discount so it does never get to zero). This is a very rational thing to do for the company. Acquiring new customers and setting up their service is expensive, new customers also tend to use more of the common resources like customer service channels. It is probably true for most companies that the cost of providing service to a customer of 10 years is lower than providing the same service in the first year of a customer's tenure. It is only logical to pass these savings to the customer. From the customer point of view, the competition would have to offer something very attractive, whether in terms of price or service, in order for the customer to switch. Such a policy would give an advantage to the first mover, but would probably force the competitors to follow suit. Overall, I would expect that this would reduce the mobility in the market, increase loyalty, increase the investment of companies in loyal customers and ultimately, increase competition for providing a better service. Competitors may even try to break the scheme by offering customers the porting of their tenure, but that would not work that well because it would disenchant existing customers and would be costly, assuming that it is costlier to serve a customer through installation and first year. What do you think? Is this better than using "save offers" to retain flip-floppers?

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  • Can't login to Unity 3d after enabling Xinerama for a short moment

    - by Amir Adar
    Today I connected a second monitor to my computer. I set it up using nVidia's control panel, and all was working quite well, so I figured it won't be a problem to try Xinerama, just to see the difference between that and twinview. After enabling Xinerama and restarting the X session, I saw that I was logged into a Unity 2d session. I thought it was a problem with Xinerama, so I switched back to twinview, but it still logged me into Unity 2d. I tried disconnecting the second monitor, no luck: still Unity 2d. I tried changing GPU drivers and installing drivers from a separate ppa, and still I was logged into Unity 2d. Up until this point, I didn't have any problem logging into Unity 3d. It only happened after I tried using Xinerama. I should note that I was doing all this while updates were going on in the background, so it could be something related to that, though I can't imagine what (I tried booting with another kernel, but no luck). So what exactly happened? Did changing the mode to Xinerama triggered some other changes that I'm not aware of? Did these updates cause a certain malfunction in the driver? Is it something else?

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  • Cross domain javascript form filling, reverse proxy

    - by Michel van Engelen
    I need a javascript form filler that can bypass the 'same origin policy' most modern browsers implement. I made a script that opens the desired website/form in a new browser. With the handler, returned by the window.open method, I want to retrieve the inputs with theWindowHandler.document.getElementById('inputx') and fill them (access denied). Is it possible to solve this problem by using Isapi Rewrite (official site) in IIS 6 acting like a reverse proxy? If so, how would I configure the reverse proxy? This is how far I got: RewriteEngine on RewriteLogLevel 9 LogLevel debug RewriteRule CarChecker https://the.actualcarchecker.com/CheckCar.aspx$1 [NC,P] The rewrite works, http://ourcompany.com/ourapplication/CarChecker, as evident in the logging. From within our companysite I can run the carchecker as if it was in our own domain. Except, the 'same origin policy' is still in force. Regards, Michel

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  • Allow and restrict remote sql server access

    - by Michel
    Hi, I want to expose my sql server instance via the internet. I've been programming asp.net to sql server for a long time, but for the first time i'm hosting the sql server myself instead of the clients server. So what i want to do is move my sql server from my dev machine at home to a virtual server (yet to hire). But of course i don't want anyone to just enter my sql server but just a few persons. So what i was thinking was to allow only a few ip addresses to the sql server instance. Can anyone tell me how i can expose my sql server to the internet and limit the access to the instance to only a few ip addresses? And ehm, if you know even better ways to secure it, i'd be happy, because this is the first time for me :) Michel

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  • Trace redirect loop

    - by Michel Krämer
    I have a large PHP application. After I changed some settings I get a redirection loop (i.e. the browser is redirected to the same page over and over again). The problem is that I don't know which command (which line in which PHP file) in this application causes the redirect. Is there a way to trace calls to the header() function? Or - even better - is there a way to trace redirects in PHP? Thanks in advance, Michel

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  • speed: XmlTextReader vs LinqtoXml

    - by Michel
    Hi, i'm about to read some xml (who isn't :-)) This time however it's a lot of data: about 30.000 records with 5 properties, all in one file. Till now i've always read that the XmlTextReader is the fastest way to read xml data, but now there also is the (nice sytax of) linqtoXml. Does anybody know any performance issues, or that there aren't any, with LinqToXml? Michel

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  • how to implement IOC without a global static service?

    - by Michel
    Hi, we want to use Unity for IOC. All i've seen is the implementation that there is one global static service which holds a reference to the Unity container, which registers all interface/class combinations and every class asks that object: give me an implementation for Ithis or IThat. Frequently i see a response that this pattern is not good because it leads to a dependency from ALL classes to this service. But what i don't see often, is: what is the alternative way? Michel

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  • How to keep my functions (objects/methods) 'lean and mean'

    - by Michel
    Hi, in all (Agile) articles i read about this: keep your code and functions small and easy to test. How should i do this with the 'controller' or 'coordinator' class? In my situation, i have to import data. In the end i have one object who coordinates this, and i was wondering if there is a way of keeping the coordinator lean(er) and mean(er). My coordinator now does the followling (pseudocode) //Write to the log that the import has started Log.StartImport() //Get the data in Excel sheet format result = new Downloader().GetExcelFile() //Log this step Log.LogStep(result ) //convert the data to intern objects result = new Converter().Convertdata(result); //Log this step Log.LogStep(result ) //write the data result = Repository.SaveData(result); //Log this step Log.LogStep(result ) Imho, this is one of those 'know all' classes or at least one being 'not lean and mean'? Or, am i taking this lean and mean thing to far and is it impossible to program an import without some kind of 'fat' importer/coordinator? Michel

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  • How about the Asp.net processes and threads and apppools?

    - by Michel
    Hi, as i understand, when i load a asp.net .aspx page on the (iis)server, it's processed via the w3p.exe process. But when iis gets multiple requests, are they all processed by the same w3p process? And does this process automaticly use all my processors and cores? And after that: when i start i new thread in my page, this thread still works when the pages is already served to the client. Where does this thread live? also in the w3p.exe process? And what if i assign another apppool to my site, what does that do? Michel

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  • how to implement IOC without a global static service (non-service locator solution)?

    - by Michel
    Hi, we want to use Unity for IOC. All i've seen is the implementation that there is one global static service which holds a reference to the Unity container, which registers all interface/class combinations and every class asks that object: give me an implementation for Ithis or IThat. Frequently i see a response that this pattern is not good because it leads to a dependency from ALL classes to this service. But what i don't see often, is: what is the alternative way? Michel EDIT: found out that the global static service is called the service locator, added that to the title.

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  • Entity framework (1): implement 1 foreign key to multiple tables

    - by Michel
    Hi, i've modeled this: i have an import table, and an import steps table import 1 .. N importsteps Now i have a table importparams, which hold key/value pairs to register all kind of info about the import or the importsteps. So i have modeled a FK in SqlServer which points to the PK of the import table and to the PK of the importsteps table (the ID's for both the import as the importsteps table are guids, so i can query the importparams with either the id from import or from importsteps and get the right importparams). Makes sense a bit? But how can i model this in the EF? I can see it's a bit hard for the EF to model this, because one realtion can point to multiple classes, but is there a way? The workaround normally is just to get all importparams where FK is the ID, but as you know the FK is not available in the EF version 1. I hope you can help me out, michel

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  • automatic enter login credentials while testing my app (from visual studio 2008)

    - by Michel
    Hi all, this is something that i don't want to program, but i was looking for a handy way of logging on to my web app. i'm building (and testing and running) my webapp over and over again, and here i've been provided a strong password. needless to say it's not so nice to enter my full user name + strong password 30 times a day. is there a nifty tool which lives in the background and when i open page localhost/mytestpage.aspx, it will say: "hey, let me type in michel and sdfs%^%gfhg in these two textboxes"?

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  • How to test a localized WPF application in visual studio 2012

    - by Michel Keijzers
    I am trying to create a localized application in C# / WPF in Visual Studio 2012. For that I created two resource files and changed one string in a (XAML) window to use the resource files (instead of a hardcoded string). I see the English text from the resource file, which is correct. However, I want to check if the other resource file (fr-FR) also works but I cannot find a setting or procedure how to change my 'project' to run in French. Thanks in advance.

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  • Logparser and Powershell

    - by Michel Klomp
    Logparser in powershell One of the few examples how to use logparser in powershell is from the Microsoft.com Operations blog. This script is a good base to create more advanced logparser scripts: $myQuery = new-object -com MSUtil.LogQuery $szQuery = “Select top 10 * from r:\ex07011210.log”; $recordSet = $myQuery.Execute($szQuery) for(; !$recordSet.atEnd(); $recordSet.moveNext()) {             $record=$recordSet.getRecord();             write-host ($record.GetValue(0) + “,”+ $record.GetValue(1)); } $recordSet.Close(); Logparser input formats The previous example uses the default logparser object, you can extent this with the logparser input formats. with this formats get information from the event-log, different types of logfiles, the Active Directory, the registry and XML files. Here are the different ProgId’s you can use. Input Format ProgId ADS MSUtil.LogQuery.ADSInputFormat BIN MSUtil.LogQuery.IISBINInputFormat CSV MSUtil.LogQuery.CSVInputFormat ETW MSUtil.LogQuery.ETWInputFormat EVT MSUtil.LogQuery.EventLogInputFormat FS MSUtil.LogQuery.FileSystemInputFormat HTTPERR MSUtil.LogQuery.HttpErrorInputFormat IIS MSUtil.LogQuery.IISIISInputFormat IISODBC MSUtil.LogQuery.IISODBCInputFormat IISW3C MSUtil.LogQuery.IISW3CInputFormat NCSA MSUtil.LogQuery.IISNCSAInputFormat NETMON MSUtil.LogQuery.NetMonInputFormat REG MSUtil.LogQuery.RegistryInputFormat TEXTLINE MSUtil.LogQuery.TextLineInputFormat TEXTWORD MSUtil.LogQuery.TextWordInputFormat TSV MSUtil.LogQuery.TSVInputFormat URLSCAN MSUtil.LogQuery.URLScanLogInputFormat W3C MSUtil.LogQuery.W3CInputFormat XML MSUtil.LogQuery.XMLInputFormat Using logparser to parse IIS logs if you use the IISW3CinputFormat you can use the field names instead of de row number to get the information from an IIS logfile, it also skips the comment rows in the logfile. $ObjLogparser = new-object -com MSUtil.LogQuery $objInputFormat = new-object -com MSUtil.LogQuery.IISW3CInputFormat $Query = “Select top 10 * from c:\temp\hb\ex071002.log”; $recordSet = $ObjLogparser.Execute($Query, $objInputFormat) for(; !$recordSet.atEnd(); $recordSet.moveNext()) {     $record=$recordSet.getRecord();     write-host ($record.GetValue(“s-ip”) + “,”+ $record.GetValue(“cs-uri-query”)); } $recordSet.Close();

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