Search Results

Search found 108959 results on 4359 pages for 'ado net data services'.

Page 605/4359 | < Previous Page | 601 602 603 604 605 606 607 608 609 610 611 612  | Next Page >

  • Do you need all that data?

    - by BuckWoody
    I read an amazing post over on ars technica (link: http://arstechnica.com/science/news/2010/03/the-software-brains-behind-the-particle-colliders.ars?utm_source=rss&utm_medium=rss&utm_campaign=rss) abvout the LHC, or as they are also known, the "particle colliders". Beyond just the pure scientific geek awesomeness, these instruments have the potential to collect more data than you can (or possibly should) store. Actually, this problem has a lot in common with a BI system. There's so much granular detail available in the source systems that a designer has to decide how, and how much, to roll up the data. Whenver you do that, you lose fidelity, but in many cases that's OK. Take, for example, your car's speedometer. You don't actually need to track each and every point of speed as it happens. You only need to know that you're hovering around the speed limit at a certain point in time. Since this is the way that humans percieve data, is there some lesson we should take in the design of data "flows" - and what implications does this have for new technologies like StreamInsight? Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

    Read the article

  • Accessing Server-Side Data from Client Script: Accessing JSON Data From an ASP.NET Page Using jQuery

    When building a web application, we must decide how and when the browser will communicate with the web server. The ASP.NET WebForms model greatly simplifies web development by providing a straightforward mechanism for exchanging data between the browser and the server. With WebForms, each ASP.NET page's rendered output includes a <form> element that performs a postback to the same page whenever a Button control within the form is clicked, or whenever the user modifies a control whose AutoPostBack property is set to True. On postback, the server sends the entire contents of the web page back to the browser, which then displays this new content. With WebForms we don't need to spend much time or effort thinking about how or when the browser will communicate with the server or how that returned information will be processed by the browser. It just works. While this approach certainly works and has its advantages, it's not without its drawbacks. The primary concern with postback forms is that they require a large amount of information to be exchanged between the browser and the server. Specifically, the browser sends back all of its form fields (including hidden ones, like view state, which may be quite large) and then the server sends back the entire contents of the web page. Granted, there are scenarios where this large quantity of data needs to be exchanged, but in many cases we can use techniques that exchange much less information. However, these techniques necessitate spending more time and effort thinking about how and when to have the browser communicate with the server and intelligently deciding on what information needs to be exchanged. This article, the first in a multi-part series, examines different techniques for accessing server-side data from a browser using client-side script. Throughout this series we will explore alternative ways to expose data on the server so that it can be accessed from the browser using script; we will also examine various tools for communicating with the server from JavaScript, including jQuery and the ASP.NET AJAX library. Read on to learn more! Read More >

    Read the article

  • SQL SERVER – Standards Support, Protocol, Data Portability – 3 Important SQL Server Documentations for Downloads

    - by pinaldave
    I have been working with SQL Server for more than 8 years now continuously and I like to read a lot. Some time I read easy things and sometime I read stuff which are not so easy.  Here are few recently released article which I referred and read. They are not easy read but indeed very important read if you are the one who like to read things which are more advanced. SQL Server Standards Support Documentation The SQL Server standards support documentation provides detailed support information for certain standards that are implemented in Microsoft SQL Server. Microsoft SQL Server Protocol Documentation The Microsoft SQL Server protocol documentation provides technical specifications for Microsoft proprietary protocols that are implemented and used in Microsoft SQL Server 2008. Microsoft SQL Server Data Portability Documentation The SQL Server data portability documentation explains various mechanisms by which user-created data in SQL Server can be extracted for use in other software products. These mechanisms include import/export functionality, documented APIs, industry standard formats, or documented data structures/file formats. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Documentation, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • Fixing up Configurations in BizTalk Solution Files

    - by Elton Stoneman
    Just a quick one this, but useful for mature BizTalk solutions, where over time the configuration settings can get confused, meaning Debug configurations building in Release mode, or Deployment configurations building in Development mode. That can cause issues in the build which aren't obvious, so it's good to fix up the configurations. It's time-consuming in VS or in a text editor, so this bit of PowerShell may come in useful - just substitute your own solution path in the $path variable: $path = 'C:\x\y\z\x.y.z.Integration.sln' $backupPath = [System.String]::Format('{0}.bak', $path) [System.IO.File]::Copy($path, $backupPath, $True) $sln = [System.IO.File]::ReadAllText($path)   $sln = $sln.Replace('.Debug|.NET.Build.0 = Deployment|.NET', '.Debug|.NET.Build.0 = Development|.NET') $sln = $sln.Replace('.Debug|.NET.Deploy.0 = Deployment|.NET', '.Debug|.NET.Deploy.0 = Development|.NET') $sln = $sln.Replace('.Debug|Any CPU.ActiveCfg = Deployment|.NET', '.Debug|Any CPU.ActiveCfg = Development|.NET') $sln = $sln.Replace('.Deployment|.NET.ActiveCfg = Debug|Any CPU', '.Deployment|.NET.ActiveCfg = Release|Any CPU') $sln = $sln.Replace('.Deployment|Any CPU.ActiveCfg = Debug|Any CPU', '.Deployment|Any CPU.ActiveCfg = Release|Any CPU') $sln = $sln.Replace('.Deployment|Any CPU.Build.0 = Debug|Any CPU', '.Deployment|Any CPU.Build.0 = Release|Any CPU') $sln = $sln.Replace('.Deployment|Mixed Platforms.ActiveCfg = Debug|Any CPU', '.Deployment|Mixed Platforms.ActiveCfg = Release|Any CPU') $sln = $sln.Replace('.Deployment|Mixed Platforms.Build.0 = Debug|Any CPU', '.Deployment|Mixed Platforms.Build.0 = Release|Any CPU') $sln = $sln.Replace('.Deployment|.NET.ActiveCfg = Debug|Any CPU', '.Deployment|.NET.ActiveCfg = Release|Any CPU') $sln = $sln.Replace('.Debug|.NET.ActiveCfg = Deployment|.NET', '.Debug|.NET.ActiveCfg = Development|.NET')   [System.IO.File]::WriteAllText($path, $sln) The script creates a backup of the solution file first, and then fixes up all the configs to use the correct builds. It's a simple search and replace list, so if there are any patterns that need to be added let me know and I'll update the script. A RegEx replace would be neater, but when it comes to hacking solution files, I prefer the conservative approach of knowing exactly what you're changing.

    Read the article

  • Filtering a Grid of Data in ASP.NET MVC

    This article is the fourth installment in an ongoing series on displaying a grid of data in an ASP.NET MVC application. The previous two articles in this series - Sorting a Grid of Data in ASP.NET MVC and Displaying a Paged Grid of Data in ASP.NET MVC - showed how to sort and page data in a grid. This article explores how to present a filtering interface to the user and then only show those records that conform to the filtering criteria. In particular, the demo we examine in this installment presents an interface with three filtering criteria: the category, minimum price, and whether to omit discontinued products. Using this interface the user can apply one or more of these criteria, allowing a variety of filtered displays. For example, the user could opt to view: all products in the Condiments category; those products in the Confections category that cost $50.00 or more; all products that cost $25.00 or more and are not discontinued; or any other such combination. Like with its predecessors, this article offers step-by-step instructions and includes a complete, working demo available for download at the end of the article. Read on to learn more! Read More >

    Read the article

  • Convert ddply {plyr} to Oracle R Enterprise, or use with Embedded R Execution

    - by Mark Hornick
    The plyr package contains a set of tools for partitioning a problem into smaller sub-problems that can be more easily processed. One function within {plyr} is ddply, which allows you to specify subsets of a data.frame and then apply a function to each subset. The result is gathered into a single data.frame. Such a capability is very convenient. The function ddply also has a parallel option that if TRUE, will apply the function in parallel, using the backend provided by foreach. This type of functionality is available through Oracle R Enterprise using the ore.groupApply function. In this blog post, we show a few examples from Sean Anderson's "A quick introduction to plyr" to illustrate the correpsonding functionality using ore.groupApply. To get started, we'll create a demo data set and load the plyr package. set.seed(1) d <- data.frame(year = rep(2000:2014, each = 3),         count = round(runif(45, 0, 20))) dim(d) library(plyr) This first example takes the data frame, partitions it by year, and calculates the coefficient of variation of the count, returning a data frame. # Example 1 res <- ddply(d, "year", function(x) {   mean.count <- mean(x$count)   sd.count <- sd(x$count)   cv <- sd.count/mean.count   data.frame(cv.count = cv)   }) To illustrate the equivalent functionality in Oracle R Enterprise, using embedded R execution, we use the ore.groupApply function on the same data, but pushed to the database, creating an ore.frame. The function ore.push creates a temporary table in the database, returning a proxy object, the ore.frame. D <- ore.push(d) res <- ore.groupApply (D, D$year, function(x) {   mean.count <- mean(x$count)   sd.count <- sd(x$count)   cv <- sd.count/mean.count   data.frame(year=x$year[1], cv.count = cv)   }, FUN.VALUE=data.frame(year=1, cv.count=1)) You'll notice the similarities in the first three arguments. With ore.groupApply, we augment the function to return the specific data.frame we want. We also specify the argument FUN.VALUE, which describes the resulting data.frame. From our previous blog posts, you may recall that by default, ore.groupApply returns an ore.list containing the results of each function invocation. To get a data.frame, we specify the structure of the result. The results in both cases are the same, however the ore.groupApply result is an ore.frame. In this case the data stays in the database until it's actually required. This can result in significant memory and time savings whe data is large. R> class(res) [1] "ore.frame" attr(,"package") [1] "OREbase" R> head(res)    year cv.count 1 2000 0.3984848 2 2001 0.6062178 3 2002 0.2309401 4 2003 0.5773503 5 2004 0.3069680 6 2005 0.3431743 To make the ore.groupApply execute in parallel, you can specify the argument parallel with either TRUE, to use default database parallelism, or to a specific number, which serves as a hint to the database as to how many parallel R engines should be used. The next ddply example uses the summarise function, which creates a new data.frame. In ore.groupApply, the year column is passed in with the data. Since no automatic creation of columns takes place, we explicitly set the year column in the data.frame result to the value of the first row, since all rows received by the function have the same year. # Example 2 ddply(d, "year", summarise, mean.count = mean(count)) res <- ore.groupApply (D, D$year, function(x) {   mean.count <- mean(x$count)   data.frame(year=x$year[1], mean.count = mean.count)   }, FUN.VALUE=data.frame(year=1, mean.count=1)) R> head(res)    year mean.count 1 2000 7.666667 2 2001 13.333333 3 2002 15.000000 4 2003 3.000000 5 2004 12.333333 6 2005 14.666667 Example 3 uses the transform function with ddply, which modifies the existing data.frame. With ore.groupApply, we again construct the data.frame explicilty, which is returned as an ore.frame. # Example 3 ddply(d, "year", transform, total.count = sum(count)) res <- ore.groupApply (D, D$year, function(x) {   total.count <- sum(x$count)   data.frame(year=x$year[1], count=x$count, total.count = total.count)   }, FUN.VALUE=data.frame(year=1, count=1, total.count=1)) > head(res)    year count total.count 1 2000 5 23 2 2000 7 23 3 2000 11 23 4 2001 18 40 5 2001 4 40 6 2001 18 40 In Example 4, the mutate function with ddply enables you to define new columns that build on columns just defined. Since the construction of the data.frame using ore.groupApply is explicit, you always have complete control over when and how to use columns. # Example 4 ddply(d, "year", mutate, mu = mean(count), sigma = sd(count),       cv = sigma/mu) res <- ore.groupApply (D, D$year, function(x) {   mu <- mean(x$count)   sigma <- sd(x$count)   cv <- sigma/mu   data.frame(year=x$year[1], count=x$count, mu=mu, sigma=sigma, cv=cv)   }, FUN.VALUE=data.frame(year=1, count=1, mu=1,sigma=1,cv=1)) R> head(res)    year count mu sigma cv 1 2000 5 7.666667 3.055050 0.3984848 2 2000 7 7.666667 3.055050 0.3984848 3 2000 11 7.666667 3.055050 0.3984848 4 2001 18 13.333333 8.082904 0.6062178 5 2001 4 13.333333 8.082904 0.6062178 6 2001 18 13.333333 8.082904 0.6062178 In Example 5, ddply is used to partition data on multiple columns before constructing the result. Realizing this with ore.groupApply involves creating an index column out of the concatenation of the columns used for partitioning. This example also allows us to illustrate using the ORE transparency layer to subset the data. # Example 5 baseball.dat <- subset(baseball, year > 2000) # data from the plyr package x <- ddply(baseball.dat, c("year", "team"), summarize,            homeruns = sum(hr)) We first push the data set to the database to get an ore.frame. We then add the composite column and perform the subset, using the transparency layer. Since the results from database execution are unordered, we will explicitly sort these results and view the first 6 rows. BB.DAT <- ore.push(baseball) BB.DAT$index <- with(BB.DAT, paste(year, team, sep="+")) BB.DAT2 <- subset(BB.DAT, year > 2000) X <- ore.groupApply (BB.DAT2, BB.DAT2$index, function(x) {   data.frame(year=x$year[1], team=x$team[1], homeruns=sum(x$hr))   }, FUN.VALUE=data.frame(year=1, team="A", homeruns=1), parallel=FALSE) res <- ore.sort(X, by=c("year","team")) R> head(res)    year team homeruns 1 2001 ANA 4 2 2001 ARI 155 3 2001 ATL 63 4 2001 BAL 58 5 2001 BOS 77 6 2001 CHA 63 Our next example is derived from the ggplot function documentation. This illustrates the use of ddply within using the ggplot2 package. We first create a data.frame with demo data and use ddply to create some statistics for each group (gp). We then use ggplot to produce the graph. We can take this same code, push the data.frame df to the database and invoke this on the database server. The graph will be returned to the client window, as depicted below. # Example 6 with ggplot2 library(ggplot2) df <- data.frame(gp = factor(rep(letters[1:3], each = 10)),                  y = rnorm(30)) # Compute sample mean and standard deviation in each group library(plyr) ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y)) # Set up a skeleton ggplot object and add layers: ggplot() +   geom_point(data = df, aes(x = gp, y = y)) +   geom_point(data = ds, aes(x = gp, y = mean),              colour = 'red', size = 3) +   geom_errorbar(data = ds, aes(x = gp, y = mean,                                ymin = mean - sd, ymax = mean + sd),              colour = 'red', width = 0.4) DF <- ore.push(df) ore.tableApply(DF, function(df) {   library(ggplot2)   library(plyr)   ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y))   ggplot() +     geom_point(data = df, aes(x = gp, y = y)) +     geom_point(data = ds, aes(x = gp, y = mean),                colour = 'red', size = 3) +     geom_errorbar(data = ds, aes(x = gp, y = mean,                                  ymin = mean - sd, ymax = mean + sd),                   colour = 'red', width = 0.4) }) But let's take this one step further. Suppose we wanted to produce multiple graphs, partitioned on some index column. We replicate the data three times and add some noise to the y values, just to make the graphs a little different. We also create an index column to form our three partitions. Note that we've also specified that this should be executed in parallel, allowing Oracle Database to control and manage the server-side R engines. The result of ore.groupApply is an ore.list that contains the three graphs. Each graph can be viewed by printing the list element. df2 <- rbind(df,df,df) df2$y <- df2$y + rnorm(nrow(df2)) df2$index <- c(rep(1,300), rep(2,300), rep(3,300)) DF2 <- ore.push(df2) res <- ore.groupApply(DF2, DF2$index, function(df) {   df <- df[,1:2]   library(ggplot2)   library(plyr)   ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y))   ggplot() +     geom_point(data = df, aes(x = gp, y = y)) +     geom_point(data = ds, aes(x = gp, y = mean),                colour = 'red', size = 3) +     geom_errorbar(data = ds, aes(x = gp, y = mean,                                  ymin = mean - sd, ymax = mean + sd),                   colour = 'red', width = 0.4)   }, parallel=TRUE) res[[1]] res[[2]] res[[3]] To recap, we've illustrated how various uses of ddply from the plyr package can be realized in ore.groupApply, which affords the user explicit control over the contents of the data.frame result in a straightforward manner. We've also highlighted how ddply can be used within an ore.groupApply call.

    Read the article

  • LibGdx efficient data saving/loading?

    - by grimrader22
    Currently, my LibGDX game consists of a 512 x 512 map of Tiles and entities such as players and monsters. I am wondering how to efficiently save and load the data of my levels. At the moment I am using JSON serialization for each class I want to save. I implement the Json.Serializable interface for all of these classes and write only the variables that are necessary. So my map consists of 512 x 512 tiles, that's 260,000 tiles. Each tile on the map consists of a Tile object, which points to some final Tile object like a GRASS_TILE or a STONE_TILE. When I serialize each level tile, the final Tile that it points to is re-serialized over and over again, so if I have 100 Tiles all pointing to GRASS_TILE, the data of GRASS_TILE is written 100 times over. When I go to load/deserialize my objects, 100 GrassTile objects are created, but they are each their own object. They no longer point to the final tile object. I feel like this reading/writing files very slow. If I were to abandon JSON serialization, to my knowledge my next best option would be saving the level data to a sql database. Unless there is a way to speed up serializing/deserializing 260,000 tiles I may have to do this. Is this a good idea? Could I really write that many tiles to the database efficiently? To sum all this up, I am trying to save my levels using JSON serialization, but it is VERY slow. What other options do I have for saving the data of so many tiles. I also must note that the JSON serialization is not slow on a PC, it is only VERY slow on a mobile device. Since file writing/reading is so slow on mobile devices, what can I do?

    Read the article

  • MVVM - child windows and data contexts

    - by GlenH7
    Should a child window have it's own data context (View-Model) or use the data context of the parent? More broadly, should each View have its own View-Model? Are there are any rules to guide making that decision? What if the various View-Models will be accessing the same Model? I haven't been able to find any consistent guidance on my question. The MS definition of MVVM appears to be silent on child windows. For one example, I have created a warning message notification View. It really didn't need a data context since it was passed the message to display. But if I needed to fancy it up a bit, I would have tapped the parent's data context. I have run into another scenario that needs a child window and is more complicated than the notification box. The parent's View-Model is already getting cluttered, so I had planned on generating a dedicated VM for the child window. But I can't find any guidance on whether this is a good idea or what the potential consequences may be. FWIW, I happen to be working in Silverlight, but I don't know that this question is strictly a Silverlight issue.

    Read the article

  • Best Persistence choice for J2EE-App with frequently changing Data Model

    - by Ben-G
    Whenever I develop a J2EE-Application, I at some point decide to switch from my dummy Persistence (Simply Using Lists and other Data Structures) to some Sort of Database Persistence. Mostly when I hope the Data Model is more or less complete. From this point on, changes to the data model become exhausting, but unluckily they occur rather often. I've used different Object-Relational-Mappers (iBatis, Hibernate) for my projects. They definitely reduce the pain coming with Data Model changes, but they anyway let me adjust code/configuration at 3 or 4 places for every single change. To me, that's cumbersome and error prone. I made a better experience with DB4O, which simply persists Java Objects as they are, but I believe it's performance does not scale for huge applications. Is there anyway to maintain performance while letting out all the ugly configuration work? I'm seeking a performant framework which really hides persistence from my code. Wish for thinking? Or am I missing out THE technology? Hope you can help.

    Read the article

  • Organising data access for dependency injection

    - by IanAWP
    In our company we have a relatively long history of database backed applications, but have only just begun experimenting with dependency injection. I am looking for advice about how to convert our existing data access pattern into one more suited for dependency injection. Some specific questions: Do you create one access object per table (Given that a table represents an entity collection)? One interface per table? All of these would need the low level Data Access object to be injected, right? What about if there are dozens of tables, wouldn't that make the composition root into a nightmare? Would you instead have a single interface that defines things like GetCustomer(), GetOrder(), etc? If I took the example of EntityFramework, then I would have one Container that exposes an object for each table, but that container doesn't conform to any interface itself, so doesn't seem like it's compatible with DI. What we do now, in case it helps: The way we normally manage data access is through a generic data layer which exposes CRUD/Transaction capabilities and has provider specific subclasses which handle the creation of IDbConnection, IDbCommand, etc. Actual table access uses Table classes that perform the CRUD operations associated with a particular table and accept/return domain objects that the rest of the system deals with. These table classes expose only static methods, and utilise a static DataAccess singleton instantiated from a config file.

    Read the article

  • The Oracle MDM Portfolio & Strategy Session - It All Comes Down to Master Data

    - by Mala Narasimharajan
     By Narayana Machiraju We are less than a week now from the start of Oracle Open World 2012 and I would like to introduce you all to one of the most awaited MDM strategy sessions this year titled “What’s there to Know about Oracle’s Master Data Management Portfolio and Roadmap?”. Manouj Tahiliani, Senior Director of MDM Product Strategy provides you a complete picture of the Oracle MDM Portfolio, the Product releases, the Strategy and the Roadmaps. Manoj will be discussing Oracle Fusion MDM applications, the first enterprise-grade SaaS MDM product suite. You’ll hear strategies for leveraging MDM and data quality in the enterprise and how you can derive business value by deploying an MDM foundation for strategic initiatives such as customer experience management, product innovation, and financial transformation. And as a bonus, he is also going to discuss the confluence of MDM with emerging technologies such as big data, social, and mobile. The session is co-presented by GEHC and Westpac. Tony Craddock from Westpac is going to share the insights of their MDM Implementation in the lines of Business drivers, data governance, ROI and other important implementation considerations. A reprsentative from GEHC is going to talk about their MDM journey and the multi-domain MDM story. I strongly recommend yo not miss this important session The MDM track at Oracle Open World covers variety of topics related to MDM. In addition to the product management team presenting product updates and roadmap, we have several Customer Panels, Conference sessions and Customer round table sessions featuring a lot of marquee Customers. You can see an overview of MDM sessions here. 

    Read the article

  • SQLAuthority News – Download Whitepaper – Power View Infrastructure Configuration and Installation: Step-by-Step and Scripts

    - by pinaldave
    Power View, a feature of SQL Server 2012 Reporting Services Add-in for Microsoft SharePoint Server 2010 Enterprise Edition, is an interactive data exploration, visualization, and presentation experience. It provides intuitive ad-hoc reporting for business users such as data analysts, business decision makers, and information workers. Microsoft has recently released very interesting whitepaper which covers a sample scenario that validates the connectivity of the Power View reports to both PowerPivot workbooks and tabular models. This white paper talks about following important concepts about Power View: Understanding the hardware and software requirements and their download locations Installing and configuring the required infrastructure when Power View and its data models are on the same computer and on different computer Installing and configuring a computer used for client access to Power View reports, models, Sharepoint 2012 and Power View in a workgroup Configuring single sign-on access for double-hop scenarios with and without Kerberos You can download the whitepaper from here. This whitepaper talks about many interesting scenarios. It would be really interesting to know if you are using Power View in your production environment. If yes, would you please share your experience over here. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Business Intelligence, Data Warehousing, PostADay, SQL, SQL Authority, SQL Download, SQL Query, SQL Server, SQL Tips and Tricks, SQL White Papers, T SQL, Technology

    Read the article

  • Data Pump: Consistent Export?

    - by Mike Dietrich
    Ouch ... I have to admit as I did say in several workshops in the past weeks that a data pump export with expdp is per se consistent. Well ... I thought it is ... but it's not. Thanks to a customer who is doing a large unicode migration at the moment. We were discussing parameters in the expdp's par file. And I did ask my colleagues after doing some research on MOS. And here are the results of my "research": MOS Note 377218.1 has a nice example showing a data pump export of a partitioned table with DELETEs on that table as inconsistent Background:Back in the old 9i days when Data Pump was designed flashback technology wasn't as popular and well known as today - and UNDO usage was the major concern as a consistent per default export would have heavily relied on UNDO. That's why - similar to good ol' exp - the export won't operate per default in consistency mode To get a consistent data pump export with expdp you'll have to set: FLASHBACK_TIME=SYSTIMESTAMPin your parameter file. Then it will be consistent according to the timestamp when the process has been started. You could use FLASHBACK_SCN instead and determine the SCN beforehand if you'd like to be exact. So sorry if I had proclaimed a feature which unfortunately is not there by default - Mike

    Read the article

  • Hack a Linksys Router into a Ambient Data Monitor

    - by Jason Fitzpatrick
    If you have a data source (like a weather report, bus schedule, or other changing data set) you can pull it and display it with an ambient data monitor; this fun build combines a hacked Linksys router and a modified toy bus to display transit arrival times. John Graham-Cumming wanted to keep an eye on the current bus arrival time tables without constantly visiting the web site to check them. His workaround turns a hacked Linksys router, a display, a modified London city bus (you could hack apart a more project-specific enclosure, of course), and a simple bit code that polls the bus schedule’s API, into a cool ambient data monitor that displays the arrival time, in minutes, of the next two buses that will pass by his stop. The whole thing could easily be adapted to another API to display anything from stock prices to weather temps. Hit up the link below for more information on the project. Ambient Bus Arrival Monitor Hacked from Linksys Router [via Make] Make Your Own Windows 8 Start Button with Zero Memory Usage Reader Request: How To Repair Blurry Photos HTG Explains: What Can You Find in an Email Header?

    Read the article

  • Updating an ADF Web Service Data Control When Service Structure or Location Change

    - by Shay Shmeltzer
    The web service data control in Oracle ADF gives you a simplified approach to consuming services in ADF applications, and now with ADF Mobile the usage of this service seems to be growing. A frequent question we get is what happens if the service that I'm consuming changes - how do I update my data control? Well, first we should mention that if you do a good design of your application before you actually code - then things like Web service method signature shouldn't change. The signature is the contract between the publisher and the consumer, and contracts shouldn't be broken. But in reality things do change during development stages, so here is how you can update both method signatures and service location with the Web service data control: After watching this video you might be tempted to not copy the WSDLs to your project - which lets you use the right click update on a data control. However there is a reason why the copy is on by default, it reduces network traffic when you are actually running your application since ADF doesn't need to go to the server to find out the service structure. So for runtime performance, you probably should keep the WSDL local.  I encourage you to further look into both the connections.xml file where your service location is saved, and the datacontrols.dcx file where its definition is kept to get an even deeper understanding of how ADF works underneath the declarative layers.

    Read the article

  • Flashback Data Archives: Ein gutes Gedächtnis für DBA und Entwickler

    - by Heinz-Wilhelm Fabry (DBA Community)
    Daten werden gespeichert und zum Teil lange aufbewahrt. Mitunter werden Daten nach ihrer ersten Speicherung geändert, vielleicht sogar mehrfach. Je nach gesetzlicher oder betrieblicher Vorgabe müssen die Veränderungen sogar nachverfolgbar sein. Damit sind zugleich Mechanismen gefordert, die sicherstellen, dass die Folge der Versionen lückenlos ist. Und implizit bedeutet das zusätzlich, dass die Versionen auch vor Löschen und Verändern geschützt sein müssen. Das Versionieren kann über die Anwendung, mit der die Daten auch erfasst werden, erfolgen, über Trigger oder über besondere Werkzeuge. Jede dieser Lösungen hat ihre eigenen Schwächen. Zusätzlich steht die Frage nach dem Schutz vor unerlaubtem Löschen oder Ändern versionierter Daten im Raum. Flashback Data Archives lösen diese Frage, denn sie bieten nicht nur einen wirksamen Mechanismus zum Versionieren von Datensätzen, sondern sie schützen diese Versionen auch vor Veränderung und löschen sie schließlich sogar automatisch nach Ablauf ihrer Aufbewahrungsfrist.Ursprünglich wurden die Archive als eigenständige Option zur Enterprise Edition der Oracle Database 11g unter dem Namen Total Recall eingeführt. Ende Juni 2012 verloren die Flashback Data Archives ihren Status als eigenständige Option. Weil die Archive aber grundsätzlich komprimiert wurden, hat Oracle sie stattdessen zu einem Feature der Advanced Compression Option der Enterprise Edition (ACO) gemacht. Seit der Version 11.2.0.4 der Datenbank ist das Komprimieren aber für die Archive nicht mehr zwangsläufig, sondern optional. Damit gibt es lizenzrechtlich erneut eine Änderung: Wer die Kompression verwendet, der muss nach wie vor ACO lizensieren. Wer die Flashback Data Archives dagegen ohne Kompression verwendet - also zum Beispiel Entwickler -, dem stehen sie ab 11.2.0.4 aufwärts im Lieferumfang aller Editionen der Datenbank zur Verfügung. Diese Änderung ist in den Handbüchern zur Lizensierung der Versionen 11.2 und 12.1 der Datenbank dokumentiert. Im Rahmen der DBA Community ist bereits über die Flashback Data Archives berichtet worden. Der hier vorliegende Artikel ersetzt alle vorangegangenen Beiträge zum Thema.

    Read the article

  • Data indexing frameworks fit for large E-Commerce applications

    - by Dabu
    we wrote and still maintain a large E-Commerce application. Our feature list resembles what you would expect from most shops. We'd like to improve some of our features, and now the search/suggestion list functionality (enter some letters, a JScripted suggestion list appears) has caught our eye. Currently, we use http://xapian.org/. It has some drawbacks. Firstly, it's not actually the right solution. It has been created to index documents, not ever-changing data in a granularity that an E-Commerce application would need. Secondly, the load on the database is significant when we reindex all data every night. We'd like a framework that has been designed for indexing database data, which can add to the index easily and without much load, which can supply data changes in the backoffice quickly to the frontend without much load and delay. I'm aware of the fact that Xapian is Open Source and even Free Software, so we could adapt it to our needs if we decided to invest the time and manpower. But taking a quick look around for a solution more suited seems fair, right? Oh, and commercial applications are fine, too. FOSS is not required. Thanks a bunch.

    Read the article

  • Non use of persisted data – Part deux

    - by Dave Ballantyne
    In my last blog I showed how persisted data may not be used if you have used the base data on an include on an index. That wasn't the only problem ive had that showed the same symptom.  Using the same code as before,  I was executing similar to the below : select BillToAddressID,SOD.SalesOrderDetailID,SOH.CleanedGuid from sales.salesorderheader SOH join Sales.SalesOrderDetail SOD on SOH.SalesOrderID = SOD.SalesOrderID But,  due to a distribution error in statistics i found it necessary to use a table hint.  In this case, I wanted to force a loop join select BillToAddressID,SOD.SalesOrderDetailID,SOH.CleanedGuid from sales.salesorderheader SOH inner loop join Sales.SalesOrderDetail SOD on SOH.SalesOrderID = SOD.SalesOrderID   But, being the diligent  TSQL developer that I am ,looking at the execution plan I noticed that the ‘compute scalar’ operator was again calling the function.  Again,  profiler is a more graphic way to view this…..   All very odd,  just because ive forced a join , that has NOTHING, to do with my persisted data then something is causing the data to be re-evaluated. Not sure if there is any easy fix you can do to the TSQL here, but again its a lesson learned (or rather reinforced) examine the execution plan of every query you write to ensure that it is operating as you thought it would.

    Read the article

  • How to parse JSON data from web more faster [closed]

    - by Kaidul Islam Sazal
    I have json inventory inventory.json on the server like this: [ { "body" : "SUV", "color" : { "ext" : "White diamond pearl", "int" : "Taupe" }, "id" : "276181", "make" : "Acura", "miles" : 35949, "model" : "RDX", "pic" : [ { "full" : "http://images1.dealercp.com/90961/000JNBD/001_0292.jpg" } ], "power" : { "drive" : "Front wheel drive", "eng" : "2.3L DOHC PGM-FI 16-VALVE", "trans" : "Automatic" }, "price" : { "net" : 29488 }, "stock" : "6942", "trim" : "AWD 4dr Tech Pkg SUV", "vin" : "5J8TB2H53BA000334", "year" : 2011 }, { "body" : "Sedan", "color" : { "ext" : "Premium white pearl", "int" : "Taupe" }, "id" : "275622", "make" : "Acura", "miles" : 40923, "model" : "TSX", "pic" : [ { "full" : "http://images1.dealercp.com/90961/000JMC6/001_1765.jpg" } ], "power" : { "drive" : "Front wheel drive", "eng" : "2.4L L4 MPI DOHC 16V", "trans" : "Automatic" }, "price" : { "net" : 22288 }, "stock" : "6945", "trim" : "4dr Sdn I4 Auto Sedan", "vin" : "JH4CU2F66AC011933", "year" : 2010 } ] here are two index, There are almost 5000 index like this. I parsed this json like this: var url = "inventory/inventory.json"; $.getJSON(url, function(data){ $.each(data, function(index, item){ //straight-forward loop if(item.year == 2012) { $('#desc').append(item.make + ' ' + item.model + ' ' + '<br/>' + item.price.net + '<br/>' + item.pic[0].full); } }); }); This is working fine.But the problem is that, this searching and fetching process is little bit slow as there are 5000 indexes already and it's increasing day by day. It seems that, it is a straight-forward loop to parse the data and a normal brute-force method. Now I want to know if there any time efiicient way to parse more faster.Any faster method to parse instead of straight-forward loop ?

    Read the article

  • Compressing 2D level data

    - by Lucius
    So, I'm developing a 2D, tile based game and a map maker thingy - all in Java. The problem is that recently I've been having some memory issues when about 4 maps are loaded. Each one of these maps are composed of 128x128 tiles and have 4 layers (for details and stuff). I already spent a good amount of time searching for solutions and the best thing I found was run-length enconding (RLE). It seems easy enough to use with static data, but is there a way to use it with data that is constantly changing, without a big drop in performance? In my maps, supposing I'm compressing the columns, I would have 128 rows, each with some amount of data (hopefully less than it would be without RLE). Whenever I change a tile, that whole row would have to be checked and I'm affraid that would slow down too much the production (and I'm in a somewhat tight schedule). Well, worst case scenario I work on each map individually, and save them using RLE, but it would be really nice if I could avoind that. EDIT: What I'm currently using to store the data for the tiles is a 2D array of HashMaps that use the layer as key and store the id of the tile in that position - like this: private HashMap< Integer, Integer [][]

    Read the article

  • PASS Summit Preconference and Sessions

    - by Davide Mauri
    I’m very pleased to announce that I’ll be delivering a Pre-Conference at PASS Summit 2012. I’ll speak about Business Intelligence again (as I did in 2010) but this time I’ll focus only on Data Warehouse, since it’s big topic even alone. I’ll discuss not only what is a Data Warehouse, how it can be modeled and built, but also how it’s development can be approached using and Agile approach, bringing the experience I gathered in this field. Building the Agile Data Warehouse with SQL Server 2012 http://www.sqlpass.org/summit/2012/Sessions/SessionDetails.aspx?sid=2821 I’m sure you’ll like it, especially if you’re starting to create a BI Solution and you’re wondering what is a Data Warehouse, if it is still useful nowadays that everyone talks about Self-Service BI and In-Memory databases, and what’s the correct path to follow in order to have a successful project up and running. Beside this Preconference, I’ll also deliver a regular session, this time related to database administration, monitoring and tuning: DMVs: Power in Your Hands http://www.sqlpass.org/summit/2012/Sessions/SessionDetails.aspx?sid=3204 Here we’ll dive into the most useful DMVs, so that you’ll see how that can help in everyday management in order to discover, understand and optimze you SQL Server installation, from the server itself to the single query. See you there!!!!!

    Read the article

  • Keeping a domain model consistent with actual data

    - by fstuijt
    Recently domain driven design got my attention, and while thinking about how this approach could help us I came across the following problem. In DDD the common approach is to retrieve entities (or better, aggregate roots) from a repository which acts as a in-memory collection of these entities. After these entities have been retrieved, they can be updated or deleted by the user, however after retrieval they are essentially disconnected from the data source and one must actively inform the repository to update the data source and make is consistent again with our in-memory representation. What is the DDD approach to retrieving entities that should remain connected to the data source? For example, in our situation we retrieve a series of sensors that have a specific measurement during retrieval. Over time, these measurement values may change and our business logic in the domain model should respond to these changes properly. E.g., domain events may be raised if a sensor value exceeds a predefined threshold. However, using the repository approach, these sensor values are just snapshots, and are disconnected from the data source. Does any of you have an idea on how to solve this following the DDD approach?

    Read the article

  • PASS Summit Preconference and Sessions

    - by Davide Mauri
    I’m very pleased to announce that I’ll be delivering a Pre-Conference at PASS Summit 2012. I’ll speak about Business Intelligence again (as I did in 2010) but this time I’ll focus only on Data Warehouse, since it’s big topic even alone. I’ll discuss not only what is a Data Warehouse, how it can be modeled and built, but also how it’s development can be approached using and Agile approach, bringing the experience I gathered in this field. Building the Agile Data Warehouse with SQL Server 2012 http://www.sqlpass.org/summit/2012/Sessions/SessionDetails.aspx?sid=2821 I’m sure you’ll like it, especially if you’re starting to create a BI Solution and you’re wondering what is a Data Warehouse, if it is still useful nowadays that everyone talks about Self-Service BI and In-Memory databases, and what’s the correct path to follow in order to have a successful project up and running. Beside this Preconference, I’ll also deliver a regular session, this time related to database administration, monitoring and tuning: DMVs: Power in Your Hands http://www.sqlpass.org/summit/2012/Sessions/SessionDetails.aspx?sid=3204 Here we’ll dive into the most useful DMVs, so that you’ll see how that can help in everyday management in order to discover, understand and optimze you SQL Server installation, from the server itself to the single query. See you there!!!!!

    Read the article

  • Service Pack 1 for Telerik Extensions for ASP.NET MVC just released

    We just released the first service pack for the Q1 2010 release of Telerik Extensions for ASP.NET MVC. As you may have guessed this is mostly a maintenance release addressing all reported bugfixes. It is important to note that the service pack will be available only to licensed users. We will update the open source version only for major releases. However if a critical bug has been found we will publish builds in the forum so no worries.   Whats new Everything is described in the release notes. There are a few breaking changes in the TreeView and Grid. Check here to see if you are affected: Grid changes and backwards compatibility TreeView changes and backwards compatibility We have also tested the extensions with Visual Studio 2010 to confirm we fully support it. The source and samples will continue to ship in Visual Studio 2008 projects though. Opening ...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

    Read the article

  • Allowing client to select data to return via REST interface

    - by CMP
    I have a rest service that is essentially a proxy to a variety of other services. So if I call GET /users/{id} It will get their user profile, as well as order history, and contact info, etc... all from various services, and aggregates them into one nice object. My problem is that each call to a different service has the potential to add time to the original request, so we would rather not get ALL the data ALL of the time if a particular client does not care about all of the pieces. A solution I have arrived at is to do something like this: GET /users/{id}?includeOrders=true&includeX=true&includeY=true... That works, and it allow me to do only what I need to, but it is cumbersome. We have added enough different data sources that there are too many parameters for that style to be useful. I could do something similar with a single integer and a bitmask or something, but that only makes it harder to read, and it does not feel very Restful. I could break it down into multiple calls so they would need to call /users/{id}/orders and /users/{id}/profile separately, but that sort of defeats the purpose of an aggregating proxy, who's purpose is to make clients jobs easier. Are there any good patterns that can help me return just enough data for each client, without making it too difficult for them to filter and select what they want?

    Read the article

< Previous Page | 601 602 603 604 605 606 607 608 609 610 611 612  | Next Page >