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  • Excel Help: Data Input Help

    - by B-Ballerl
    Everyday I download data from a site that will have rows each filled with individual data for clients. I'm able to input the data into excel as a whole but after that I'm having trouble figuring out how to put it into a chart. For example Web visits time. So say Client 1 stayed for 5 min increasing his total time on the site to 20 min and Client 2 stayed for 0 min keeping his time of 10 min and they were both registered on new years eve, and R1's last login was today and R2's was yesterday. (R for some reason repersents Client, no idea why...). Client 3 hasn't been on since he registered keeping his total at 4 min So my data would look something like this for Today (20110104) R1,20101231,20110104,20 R2,20101231,20110103,10 R3,20101231,20101231,4 And this for the day before (201101030), R1,20101231,20110102,15 R2,20101231,20110103,10 R3,20101231,20101231,4 I get about 200+ client rows each day where even the names of the Client list are changing. Is it possible to import the data each day and fill it in a excel sheet where the Client number is off on the left hand side in a table, and the amount of time (Whole Number ex. 4) each day it spends on the site extend to the right under it's specific date see Picture? I've manage to create a manual sheet but have been unsucessful at getting excel to do any of it for me. Here are two pictures:

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  • Recover data from hard drive with partitions (but not most data) overwritten

    - by Macha
    I have a 500GB hard drive I've been keeping around to recover data from that I removed from a failing NAS drive that got sort of... erratic at the end. I finally got rid of the NAS when during a firmware update it removed the partition table. Fast forward to a week ago, when I was building a new PC, and a mixup resulted in me placing the hard drive in question in the new PC and installing Windows XP on the first 100GB. I'm presuming any data on that first 100GB is now gone, but for the rest of it, is there any way I can recover it at home, as professional data recovery is currently too expensive? I have a blank 1TB HDD if I can store any images of that hard drive on. The problem was definitely with the NAS and not the hard drive, as the hard drive had a successful install of Windows when mistakenly place in the new PC, and there were capacitors in the NAS's circuitry clearly broken. The data I want to recover (in order of priority) is: High: Some jpgs of family photos. Medium: Some RAW files. (There are also jpg versions of all of these) Low: Some mp3s, avis and ISOs, I can re-rip most of these if need be, but it'd be handy not to have to. (I don't need a backup lecture, and if you can hold it in from nagging Jeff Atwood for it, you can hold it in from nagging me for it) In short: The partition tables are gone and overwritten. The data is not overwritten, except for an amount equal to the size of a Windows XP SP3 installation.

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  • Why Oracle Delivers More Value than IBM in Data Integration Solutions

    - by irem.radzik(at)oracle.com
    For data integration projects, IT organization look for a robust but an easy-to-use solution, which simplifies enterprise data architecture while providing exceptional value-- not one that adds complexity and costs. This is a major challenge today for customers who are using IBM InfoSphere products like DataStage or Change Data Capture. Whereas, Oracle consistently delivers higher level value with its data integration products such as Oracle Data Integrator, Oracle GoldenGate. There are many differentiators for Oracle's Data Integration offering in comparison to IBM. Here are the top five: Lower cost of ownership Higher performance in both real-time and bulk data movement Ease of use and flexibility Reliability Complete, Open, and Integrated Middleware Offering Architectural differences between products contribute a great deal to these differences. First of all, Oracle's ETL architecture does not require a middle-tier transformation server, something IBM does require. Not only it costs more to manage an additional transformation server including energy costs, but it adds a performance bottleneck as well. In addition, IBM's data integration products are complex and often require lengthy professional services engagements to integrate. This translates to higher costs and delayed time to market. Then there's the reliability factor. Our customers choose Oracle GoldenGate over IBM's InfoSphere Change Data Capture product because Oracle GoldenGate is designed for mission-critical systems that require guaranteed data delivery and automatic recovery in case of process interruptions. On Thursday we will discuss these key differentiators in detail and provide customer examples that chose Oracle over IBM in data integration projects. Join us on Thursday Feb 10th at 11am PT to learn how Oracle delivers more value than IBM in data integration solutions.

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  • Go import error while trying to import web.go package after using goinstall

    - by Metropolis
    With halfdans advice, I was successfully able to use goinstall github.com/hoisie/web.go without any errors after installing git first. However, now when I try to compile the sample code given, go is not finding the web package. I get the error, main.go:4: can't find import: web On this code package main import ( "web" ) func hello(val string) string { return "hello " + val } func main() { web.Get("/(.*)", hello) web.Run("0.0.0.0:9999") } Is there something special I need to do in order for it to recognize the package? I found the package source at $GOROOT/src/pkg/github.com/hoisie/web.go/web. I tried that path as the import and it still did not like that.

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  • how to import the parent model on gae-python

    - by zjm1126
    main:. +-a ¦ +-__init__.py ¦ +-aa.py +-b ¦ +-__init__.py ¦ +-bb.py +-cc.py if i am in aa.py , how to import cc.py ? this is my code ,but it is error : from main import cc what should i do . thanks updated in normal python file (not on gae),i can use this code : import os,sys dirname=os.path.dirname path=os.path.join(dirname(dirname(__file__))) sys.path.insert(0,path) import cc print cc.c but on gae , it show error : ImportError: No module named cc

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  • how to import a.py not a folder

    - by zjm1126
    zjm_code |-----a.py |-----a |----- __init__.py |-----b.py in a.py is : c='ccc' in b.py is : import a print dir(a) when i execute b.py ,it show (it import 'a' folder): ['__builtins__', '__doc__', '__file__', '__name__', '__path__'] and when i delete a folder, it show ,(it import a.py): ['__builtins__', '__doc__', '__file__', '__name__', 'c'] so my question is : how to import a.py via not delete a folder thanks

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  • How to import your own non-packaged Java classes in Jython

    - by thepandaatemyface
    I know in Jython you can do import java.util.Random as Random Random().nextInt() But if I have a class I wrote myself, how can I import it into Jython without putting the class itself in a package? If I have a testclass Test: public class Test { public void foo() { System.out.println("bar"); } } that's not inside a package. Can I even import that into jython by using something along the lines offrom Test import Test?

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  • Programmatically import CSV data to Access

    - by FrustratedWithFormsDesigner
    I have an Access database and the source of data comes from generated CSV files. I'd like to have an easy way for the users to simply select the data file and import it. Import should append the existing data to the data already in the data table. Is there a way in Access to create a file selector and import using saved CSV import settings that are already in the file?

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  • Big GRC: Turning Data into Actionable GRC Intelligence

    - by Jenna Danko
    While it’s no longer headline news that Governments have carried out large scale data-mining programmes aimed at terrorism detection and identifying other patterns of interest across a wide range of digital data sources, the debate over the ethics and justification over this action, will clearly continue for some time to come. What is becoming clear is that these programmes are a framework for the collation and aggregation of massive amounts of unstructured data and from this, the creation of actionable intelligence from analyses that allowed the analysts to explore and extract a variety of patterns and then direct resources. This data included audio and video chats, phone calls, photographs, e-mails, documents, internet searches, social media posts and mobile phone logs and connections. Although Governance, Risk and Compliance (GRC) professionals are not looking at the implementation of such programmes, there are many similar GRC “Big data” challenges to be faced and potential lessons to be learned from these high profile government programmes that can be applied a lot closer to home. For example, how can GRC professionals collect, manage and analyze an enormous and disparate volume of data to create and manage their own actionable intelligence covering hidden signs and patterns of criminal activity, the early or retrospective, violation of regulations/laws/corporate policies and procedures, emerging risks and weakening controls etc. Not exactly the stuff of James Bond to be sure, but it is certainly more applicable to most GRC professional’s day to day challenges. So what is Big Data and how can it benefit the GRC process? Although it often varies, the definition of Big Data largely refers to the following types of data: Traditional Enterprise Data – includes customer information from CRM systems, transactional ERP data, web store transactions, and general ledger data. Machine-Generated /Sensor Data – includes Call Detail Records (“CDR”), weblogs and trading systems data. Social Data – includes customer feedback streams, micro-blogging sites like Twitter, and social media platforms like Facebook. The McKinsey Global Institute estimates that data volume is growing 40% per year, and will grow 44x between 2009 and 2020. But while it’s often the most visible parameter, volume of data is not the only characteristic that matters. In fact, according to sources such as Forrester there are four key characteristics that define big data: Volume. Machine-generated data is produced in much larger quantities than non-traditional data. This is all the data generated by IT systems that power the enterprise. This includes live data from packaged and custom applications – for example, app servers, Web servers, databases, networks, virtual machines, telecom equipment, and much more. Velocity. Social media data streams – while not as massive as machine-generated data – produce a large influx of opinions and relationships valuable to customer relationship management as well as offering early insight into potential reputational risk issues. Even at 140 characters per tweet, the high velocity (or frequency) of Twitter data ensures large volumes (over 8 TB per day) need to be managed. Variety. Traditional data formats tend to be relatively well defined by a data schema and change slowly. In contrast, non-traditional data formats exhibit a dizzying rate of change. Without question, all GRC professionals work in a dynamic environment and as new services, new products, new business lines are added or new marketing campaigns executed for example, new data types are needed to capture the resultant information.  Value. The economic value of data varies significantly. Typically, there is good information hidden amongst a larger body of non-traditional data that GRC professionals can use to add real value to the organisation; the greater challenge is identifying what is valuable and then transforming and extracting that data for analysis and action. For example, customer service calls and emails have millions of useful data points and have long been a source of information to GRC professionals. Those calls and emails are critical in helping GRC professionals better identify hidden patterns and implement new policies that can reduce the amount of customer complaints.   Now on a scale and depth far beyond those in place today, all that unstructured call and email data can be captured, stored and analyzed to reveal the reasons for the contact, perhaps with the aggregated customer results cross referenced against what is being said about the organization or a similar peer organization on social media. The organization can then take positive actions, communicating to the market in advance of issues reaching the press, strengthening controls, adjusting risk profiles, changing policy and procedures and completely minimizing, if not eliminating, complaints and compensation for that specific reason in the future. In this one example of many similar ones, the GRC team(s) has demonstrated real and tangible business value. Big Challenges - Big Opportunities As pointed out by recent Forrester research, high performing companies (those that are growing 15% or more year-on-year compared to their peers) are taking a selective approach to investing in Big Data.  "Tomorrow's winners understand this, and they are making selective investments aimed at specific opportunities with tangible benefits where big data offers a more economical solution to meet a need." (Forrsights Strategy Spotlight: Business Intelligence and Big Data, Q4 2012) As pointed out earlier, with the ever increasing volume of regulatory demands and fines for getting it wrong, limited resource availability and out of date or inadequate GRC systems all contributing to a higher cost of compliance and/or higher risk profile than desired – a big data investment in GRC clearly falls into this category. However, to make the most of big data organizations must evolve both their business and IT procedures, processes, people and infrastructures to handle these new high-volume, high-velocity, high-variety sources of data and be able integrate them with the pre-existing company data to be analyzed. GRC big data clearly allows the organization access to and management over a huge amount of often very sensitive information that although can help create a more risk intelligent organization, also presents numerous data governance challenges, including regulatory compliance and information security. In addition to client and regulatory demands over better information security and data protection the sheer amount of information organizations deal with the need to quickly access, classify, protect and manage that information can quickly become a key issue  from a legal, as well as technical or operational standpoint. However, by making information governance processes a bigger part of everyday operations, organizations can make sure data remains readily available and protected. The Right GRC & Big Data Partnership Becomes Key  The "getting it right first time" mantra used in so many companies remains essential for any GRC team that is sponsoring, helping kick start, or even overseeing a big data project. To make a big data GRC initiative work and get the desired value, partnerships with companies, who have a long history of success in delivering successful GRC solutions as well as being at the very forefront of technology innovation, becomes key. Clearly solutions can be built in-house more cheaply than through vendor, but as has been proven time and time again, when it comes to self built solutions covering AML and Fraud for example, few have able to scale or adapt appropriately to meet the changing regulations or challenges that the GRC teams face on a daily basis. This has led to the creation of GRC silo’s that are causing so many headaches today. The solutions that stand out and should be explored are the ones that can seamlessly merge the traditional world of well-known data, analytics and visualization with the new world of seemingly innumerable data sources, utilizing Big Data technologies to generate new GRC insights right across the enterprise.Ultimately, Big Data is here to stay, and organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be the ones that are well positioned to make the most of it. A Blueprint and Roadmap Service for Big Data Big data adoption is first and foremost a business decision. As such it is essential that your partner can align your strategies, goals, and objectives with an architecture vision and roadmap to accelerate adoption of big data for your environment, as well as establish practical, effective governance that will maintain a well managed environment going forward. Key Activities: While your initiatives will clearly vary, there are some generic starting points the team and organization will need to complete: Clearly define your drivers, strategies, goals, objectives and requirements as it relates to big data Conduct a big data readiness and Information Architecture maturity assessment Develop future state big data architecture, including views across all relevant architecture domains; business, applications, information, and technology Provide initial guidance on big data candidate selection for migrations or implementation Develop a strategic roadmap and implementation plan that reflects a prioritization of initiatives based on business impact and technology dependency, and an incremental integration approach for evolving your current state to the target future state in a manner that represents the least amount of risk and impact of change on the business Provide recommendations for practical, effective Data Governance, Data Quality Management, and Information Lifecycle Management to maintain a well-managed environment Conduct an executive workshop with recommendations and next steps There is little debate that managing risk and data are the two biggest obstacles encountered by financial institutions.  Big data is here to stay and risk management certainly is not going anywhere, and ultimately financial services industry organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be best positioned to make the most of it. Matthew Long is a Financial Crime Specialist for Oracle Financial Services. He can be reached at matthew.long AT oracle.com.

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  • Introducing Data Annotations Extensions

    - by srkirkland
    Validation of user input is integral to building a modern web application, and ASP.NET MVC offers us a way to enforce business rules on both the client and server using Model Validation.  The recent release of ASP.NET MVC 3 has improved these offerings on the client side by introducing an unobtrusive validation library built on top of jquery.validation.  Out of the box MVC comes with support for Data Annotations (that is, System.ComponentModel.DataAnnotations) and can be extended to support other frameworks.  Data Annotations Validation is becoming more popular and is being baked in to many other Microsoft offerings, including Entity Framework, though with MVC it only contains four validators: Range, Required, StringLength and Regular Expression.  The Data Annotations Extensions project attempts to augment these validators with additional attributes while maintaining the clean integration Data Annotations provides. A Quick Word About Data Annotations Extensions The Data Annotations Extensions project can be found at http://dataannotationsextensions.org/, and currently provides 11 additional validation attributes (ex: Email, EqualTo, Min/Max) on top of Data Annotations’ original 4.  You can find a current list of the validation attributes on the afore mentioned website. The core library provides server-side validation attributes that can be used in any .NET 4.0 project (no MVC dependency). There is also an easily pluggable client-side validation library which can be used in ASP.NET MVC 3 projects using unobtrusive jquery validation (only MVC3 included javascript files are required). On to the Preview Let’s say you had the following “Customer” domain model (or view model, depending on your project structure) in an MVC 3 project: public class Customer { public string Email { get; set; } public int Age { get; set; } public string ProfilePictureLocation { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } When it comes time to create/edit this Customer, you will probably have a CustomerController and a simple form that just uses one of the Html.EditorFor() methods that the ASP.NET MVC tooling generates for you (or you can write yourself).  It should look something like this: With no validation, the customer can enter nonsense for an email address, and then can even report their age as a negative number!  With the built-in Data Annotations validation, I could do a bit better by adding a Range to the age, adding a RegularExpression for email (yuck!), and adding some required attributes.  However, I’d still be able to report my age as 10.75 years old, and my profile picture could still be any string.  Let’s use Data Annotations along with this project, Data Annotations Extensions, and see what we can get: public class Customer { [Email] [Required] public string Email { get; set; }   [Integer] [Min(1, ErrorMessage="Unless you are benjamin button you are lying.")] [Required] public int Age { get; set; }   [FileExtensions("png|jpg|jpeg|gif")] public string ProfilePictureLocation { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Now let’s try to put in some invalid values and see what happens: That is very nice validation, all done on the client side (will also be validated on the server).  Also, the Customer class validation attributes are very easy to read and understand. Another bonus: Since Data Annotations Extensions can integrate with MVC 3’s unobtrusive validation, no additional scripts are required! Now that we’ve seen our target, let’s take a look at how to get there within a new MVC 3 project. Adding Data Annotations Extensions To Your Project First we will File->New Project and create an ASP.NET MVC 3 project.  I am going to use Razor for these examples, but any view engine can be used in practice.  Now go into the NuGet Extension Manager (right click on references and select add Library Package Reference) and search for “DataAnnotationsExtensions.”  You should see the following two packages: The first package is for server-side validation scenarios, but since we are using MVC 3 and would like comprehensive sever and client validation support, click on the DataAnnotationsExtensions.MVC3 project and then click Install.  This will install the Data Annotations Extensions server and client validation DLLs along with David Ebbo’s web activator (which enables the validation attributes to be registered with MVC 3). Now that Data Annotations Extensions is installed you have all you need to start doing advanced model validation.  If you are already using Data Annotations in your project, just making use of the additional validation attributes will provide client and server validation automatically.  However, assuming you are starting with a blank project I’ll walk you through setting up a controller and model to test with. Creating Your Model In the Models folder, create a new User.cs file with a User class that you can use as a model.  To start with, I’ll use the following class: public class User { public string Email { get; set; } public string Password { get; set; } public string PasswordConfirm { get; set; } public string HomePage { get; set; } public int Age { get; set; } } Next, create a simple controller with at least a Create method, and then a matching Create view (note, you can do all of this via the MVC built-in tooling).  Your files will look something like this: UserController.cs: public class UserController : Controller { public ActionResult Create() { return View(new User()); }   [HttpPost] public ActionResult Create(User user) { if (!ModelState.IsValid) { return View(user); }   return Content("User valid!"); } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Create.cshtml: @model NuGetValidationTester.Models.User   @{ ViewBag.Title = "Create"; }   <h2>Create</h2>   <script src="@Url.Content("~/Scripts/jquery.validate.min.js")" type="text/javascript"></script> <script src="@Url.Content("~/Scripts/jquery.validate.unobtrusive.min.js")" type="text/javascript"></script>   @using (Html.BeginForm()) { @Html.ValidationSummary(true) <fieldset> <legend>User</legend> @Html.EditorForModel() <p> <input type="submit" value="Create" /> </p> </fieldset> } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } In the Create.cshtml view, note that we are referencing jquery validation and jquery unobtrusive (jquery is referenced in the layout page).  These MVC 3 included scripts are the only ones you need to enjoy both the basic Data Annotations validation as well as the validation additions available in Data Annotations Extensions.  These references are added by default when you use the MVC 3 “Add View” dialog on a modification template type. Now when we go to /User/Create we should see a form for editing a User Since we haven’t yet added any validation attributes, this form is valid as shown (including no password, email and an age of 0).  With the built-in Data Annotations attributes we can make some of the fields required, and we could use a range validator of maybe 1 to 110 on Age (of course we don’t want to leave out supercentenarians) but let’s go further and validate our input comprehensively using Data Annotations Extensions.  The new and improved User.cs model class. { [Required] [Email] public string Email { get; set; }   [Required] public string Password { get; set; }   [Required] [EqualTo("Password")] public string PasswordConfirm { get; set; }   [Url] public string HomePage { get; set; }   [Integer] [Min(1)] public int Age { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Now let’s re-run our form and try to use some invalid values: All of the validation errors you see above occurred on the client, without ever even hitting submit.  The validation is also checked on the server, which is a good practice since client validation is easily bypassed. That’s all you need to do to start a new project and include Data Annotations Extensions, and of course you can integrate it into an existing project just as easily. Nitpickers Corner ASP.NET MVC 3 futures defines four new data annotations attributes which this project has as well: CreditCard, Email, Url and EqualTo.  Unfortunately referencing MVC 3 futures necessitates taking an dependency on MVC 3 in your model layer, which may be unadvisable in a multi-tiered project.  Data Annotations Extensions keeps the server and client side libraries separate so using the project’s validation attributes don’t require you to take any additional dependencies in your model layer which still allowing for the rich client validation experience if you are using MVC 3. Custom Error Message and Globalization: Since the Data Annotations Extensions are build on top of Data Annotations, you have the ability to define your own static error messages and even to use resource files for very customizable error messages. Available Validators: Please see the project site at http://dataannotationsextensions.org/ for an up-to-date list of the new validators included in this project.  As of this post, the following validators are available: CreditCard Date Digits Email EqualTo FileExtensions Integer Max Min Numeric Url Conclusion Hopefully I’ve illustrated how easy it is to add server and client validation to your MVC 3 projects, and how to easily you can extend the available validation options to meet real world needs. The Data Annotations Extensions project is fully open source under the BSD license.  Any feedback would be greatly appreciated.  More information than you require, along with links to the source code, is available at http://dataannotationsextensions.org/. Enjoy!

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  • Why does Python's __import__ require fromlist?

    - by ieure
    In Python, if you want to programmatically import a module, you can do: module = __import__('module_name') If you want to import a submodule, you would think it would be a simple matter of: module = __import__('module_name.submodule') Of course, this doesn't work; you just get module_name again. You have to do: module = __import__('module_name.submodule', fromlist=['blah']) Why? The actual value of fromlist don't seem to matter at all, as long as it's non-empty. What is the point of requiring an argument, then ignoring its values? Most stuff in Python seems to be done for good reason, but for the life of me, I can't come up with any reasonable explanation for this behavior to exist.

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  • Export/Import Windows XP wireless configs

    - by blunders
    About to rebuild my XP install and figured I'd see if there was a file or interface for collecting the configs for the built in Windows XP wireless manager. I've looked under the "advance settings" tab and within the properties GUI for each connection and I'm not seeing a way to export the configs. Clearly if I'm exporting these I'd like to be able to import or override the default config with the backup.

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  • Import orders file on magento enterprise or community (product with customs options)

    - by wil
    Hello, We need to import some orders file on magento enterprise. In our file, products contains customs Options. We tried to make an extension but we have some problems to import Customs options. The import of standard product is successful but not for the product with customs options. For customs option, missing "info_buyRequest" valu in database. The technical support of magento we responded "the import process currently can't handle importing products with custom options". Magento use custom options when ordering a product with customs options on website. What features do magento use to fill in the fields "info_buyRequest" and "Product_options" when ordering? Have you see a extension pack for import file order with product contains customs options? Thanks for your help.

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  • phpmyadmin import database table from google spreadsheet

    - by phunehehe
    I'm managing a small website, which allows people to register as members. Previously I used a google form to manage member registration, but now as the number of users becomes quite big I am switching to mysql. Currently I have around 500 members in the database, saved in a google spreadsheet. How can I do a bulk import from a google spreadsheet to a table in mysql? BTW I'm using phpmyadmin, so a solution for phpmyadmin is preferable :) Thanks.

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  • Excel 2007 save import steps on csv file?

    - by Chris Marisic
    I have a csv file that constantly needs opened into Excel and then have the data copied over to a separate workbook. I find the process of having to click through all of the dialogs, setting the text identifier, setting the columns to all be text extremely tedious. In many actions with data like this in regards to MSSQL or Access the program will ask you if you wish to save these steps however Excel doesn't readily ask that. Is there any way to get a comparable usage with Excel?

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  • Import de-normalized relational data from Excel into SQL Server

    - by roryf
    I need to import data from an Excel spreadsheet into SQL Server, but the data isn't in a relational/normalized format so the import wizard isn't going to cut it (as far as I know). The data is in this format: Category SubCategory Name Description Category#1 SubCategory#1 Product#1 Description#1 Category#1 SubCategory#1 Product#2 Description#2 Category#1 SubCategory#2 Product#3 Description#3 Category#1 SubCategory#2 Product#4 Description#4 Category#2 SubCategory#3 Product#5 Description#5 (apologies I'm lacking the inventiveness to come up with 'real' data at this time in the morning...) Each row contains a unique product, but the cateogry structure is duplicated. I want to import this data into three tables: Category SubCategory Product (I know SubCategory should really be contained within Category, DB was not my design) I need a way to import unique rows based on the Category and then SubCategory columns, and then when importing the other columns into Product, obtain a reference to the SubCategory based on name. Short of scripting this, is there any way to do it using the import wizard or some other tool?

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  • Import orders file on magento enterprise 1.7 (product with customs options)

    - by wil
    Hello, We need to import some orders file on magento enterprise. In our file, products contains customs Options. We tried to make an extension but we have some problems to import Customs options. The import of standard product is successful but not for the product with customs options. For customs option, missing "info_buyRequest" valu in database. The technical support of magento we responded "the import process currently can't handle importing products with custom options". Magento use custom options when ordering a product with customs options on website. What features do magento use to fill in the fields "info_buyRequest" and "Product_options" when ordering? Have you see a extension pack for import file order with product contains customs options? Thanks for your help.

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  • Import python module NOT on path

    - by Vort3x
    I have read all the questions I could find on it on SO, but none answers my question. I have a module foo, containing util.py and bar.py. I want to import it in IDLE or python session. How do I go about this? I could find no documentation on how to import modules not in the current directory or the default python PATH. After trying import "<full path>/foo/util.py", and from "<full path>" import util The closest I could get was import imp imp.load_source('foo.util','C:/.../dir/dir2/foo') Which gave me Permission denied on windows 7.

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  • No response in Eclipse: File ->Import->Existing Projects into Workspace

    - by Hula
    I'm trying to import one of the GWT samples into Eclipse by following the instructions below. But when I browse to the directory containing the "Hello" sample and uncheck "Copy projects into workspace", the Finish button is grayed out, preventing me from completing the import. Any ideas why? -- Option A: Import your project into Eclipse (recommended) -- If you use Eclipse, you can simply import the generated project into Eclipse. We've tested against Eclipse 3.3 and 3.4. Later versions will likely also work, earlier versions may not. In Eclipse, go to the File menu and choose: File - Import... - Existing Projects into Workspace Browse to the directory containing this file, select "Hello". Be sure to uncheck "Copy projects into workspace" if it is checked. Click Finish.

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  • Cleaning a dataset of song data - what sort of problem is this?

    - by Rob Lourens
    I have a set of data about songs. Each entry is a line of text which includes the artist name, song title, and some extra text. Some entries are only "extra text". My goal is to resolve as many of these as possible to songs on Spotify using their web API. My strategy so far has been to search for the entry via the API - if there are no results, apply a transformation such as "remove all text between ( )" and search again. I have a list of heuristics and I've had reasonable success with this but as the code gets more and more convoluted I keep thinking there must be a more generic and consistent way. I don't know where to look - any suggestions for what to try, topics to study, buzzwords to google?

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  • Augmenting your Social Efforts via Data as a Service (DaaS)

    - by Mike Stiles
    The following is the 3rd in a series of posts on the value of leveraging social data across your enterprise by Oracle VP Product Development Don Springer and Oracle Cloud Data and Insight Service Sr. Director Product Management Niraj Deo. In this post, we will discuss the approach and value of integrating additional “public” data via a cloud-based Data-as-as-Service platform (or DaaS) to augment your Socially Enabled Big Data Analytics and CX Management. Let’s assume you have a functional Social-CRM platform in place. You are now successfully and continuously listening and learning from your customers and key constituents in Social Media, you are identifying relevant posts and following up with direct engagement where warranted (both 1:1, 1:community, 1:all), and you are starting to integrate signals for communication into your appropriate Customer Experience (CX) Management systems as well as insights for analysis in your business intelligence application. What is the next step? Augmenting Social Data with other Public Data for More Advanced Analytics When we say advanced analytics, we are talking about understanding causality and correlation from a wide variety, volume and velocity of data to Key Performance Indicators (KPI) to achieve and optimize business value. And in some cases, to predict future performance to make appropriate course corrections and change the outcome to your advantage while you can. The data to acquire, process and analyze this is very nuanced: It can vary across structured, semi-structured, and unstructured data It can span across content, profile, and communities of profiles data It is increasingly public, curated and user generated The key is not just getting the data, but making it value-added data and using it to help discover the insights to connect to and improve your KPIs. As we spend time working with our larger customers on advanced analytics, we have seen a need arise for more business applications to have the ability to ingest and use “quality” curated, social, transactional reference data and corresponding insights. The challenge for the enterprise has been getting this data inline into an easily accessible system and providing the contextual integration of the underlying data enriched with insights to be exported into the enterprise’s business applications. The following diagram shows the requirements for this next generation data and insights service or (DaaS): Some quick points on these requirements: Public Data, which in this context is about Common Business Entities, such as - Customers, Suppliers, Partners, Competitors (all are organizations) Contacts, Consumers, Employees (all are people) Products, Brands This data can be broadly categorized incrementally as - Base Utility data (address, industry classification) Public Master Reference data (trade style, hierarchy) Social/Web data (News, Feeds, Graph) Transactional Data generated by enterprise process, workflows etc. This Data has traits of high-volume, variety, velocity etc., and the technology needed to efficiently integrate this data for your needs includes - Change management of Public Reference Data across all categories Applied Big Data to extract statics as well as real-time insights Knowledge Diagnostics and Data Mining As you consider how to deploy this solution, many of our customers will be using an online “cloud” service that provides quality data and insights uniformly to all their necessary applications. In addition, they are requesting a service that is: Agile and Easy to Use: Applications integrated with the service can obtain data on-demand, quickly and simply Cost-effective: Pre-integrated into applications so customers don’t have to Has High Data Quality: Single point access to reference data for data quality and linkages to transactional, curated and social data Supports Data Governance: Becomes more manageable and cost-effective since control of data privacy and compliance can be enforced in a centralized place Data-as-a-Service (DaaS) Just as the cloud has transformed and now offers a better path for how an enterprise manages its IT from their infrastructure, platform, and software (IaaS, PaaS, and SaaS), the next step is data (DaaS). Over the last 3 years, we have seen the market begin to offer a cloud-based data service and gain initial traction. On one side of the DaaS continuum, we see an “appliance” type of service that provides a single, reliable source of accurate business data plus social information about accounts, leads, contacts, etc. On the other side of the continuum we see more of an online market “exchange” approach where ISVs and Data Publishers can publish and sell premium datasets within the exchange, with the exchange providing a rich set of web interfaces to improve the ease of data integration. Why the difference? It depends on the provider’s philosophy on how fast the rate of commoditization of certain data types will occur. How do you decide the best approach? Our perspective, as shown in the diagram below, is that the enterprise should develop an elastic schema to support multi-domain applicability. This allows the enterprise to take the most flexible approach to harness the speed and breadth of public data to achieve value. The key tenet of the proposed approach is that an enterprise carefully federates common utility, master reference data end points, mobility considerations and content processing, so that they are pervasively available. One way you may already be familiar with this approach is in how you do Address Verification treatments for accounts, contacts etc. If you design and revise this service in such a way that it is also easily available to social analytic needs, you could extend this to launch geo-location based social use cases (marketing, sales etc.). Our fundamental belief is that value-added data achieved through enrichment with specialized algorithms, as well as applying business “know-how” to weight-factor KPIs based on innovative combinations across an ever-increasing variety, volume and velocity of data, will be where real value is achieved. Essentially, Data-as-a-Service becomes a single entry point for the ever-increasing richness and volume of public data, with enrichment and combined capabilities to extract and integrate the right data from the right sources with the right factoring at the right time for faster decision-making and action within your core business applications. As more data becomes available (and in many cases commoditized), this value-added data processing approach will provide you with ongoing competitive advantage. Let’s look at a quick example of creating a master reference relationship that could be used as an input for a variety of your already existing business applications. In phase 1, a simple master relationship is achieved between a company (e.g. General Motors) and a variety of car brands’ social insights. The reference data allows for easy sort, export and integration into a set of CRM use cases for analytics, sales and marketing CRM. In phase 2, as you create more data relationships (e.g. competitors, contacts, other brands) to have broader and deeper references (social profiles, social meta-data) for more use cases across CRM, HCM, SRM, etc. This is just the tip of the iceberg, as the amount of master reference relationships is constrained only by your imagination and the availability of quality curated data you have to work with. DaaS is just now emerging onto the marketplace as the next step in cloud transformation. For some of you, this may be the first you have heard about it. Let us know if you have questions, or perspectives. In the meantime, we will continue to share insights as we can.Photo: Erik Araujo, stock.xchng

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