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  • What is a Data Warehouse?

    Typically Data Warehouses are considered to be non-volatile in comparison to traditional databasesdue to the fact that data within the warehouse does not change that often.  In addition, Data Warehouses typically represent data through the use of Multidimensional Conceptual Views that allow data to be extracted based on the view and the current position within the view. Common Data Warehouse Traits Relatively Non-volatile Data Supports Data Extraction and Analysis Optimized for Data Retrieval and Analysis Multidimensional Views of Data Flexible Reporting Multi User Support Generic Dimensionality Transparent Accessible Unlimited Dimensions of Data Unlimited Aggregation levels of Data Normally, Data Warehouses are much larger then there traditional database counterparts due to the fact that they store the basis data along with derived data via Multidimensional Conceptual Views. As companies store larger and larger amounts of data, they will need a way to effectively and accurately extract analysis information that can be used to aide in formulating current and future business decisions. This process can be done currently through data mining within a Data Warehouse. Data Warehouses provide access to data derived through complex analysis, knowledge discovery and decision making. Secondly, they support the demands for high performance in regards to analyzing an organization’s existing and current data. Data Warehouses provide support for an organization’s data and acquired business knowledge.  Within a Data Warehouse multiple types of operations/sub systems are supported. Common Data Warehouse Sub Systems Online Analytical Processing (OLAP) Decision –Support Systems (DSS) Online Transaction Processing (OLTP)

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  • Partner Webcast - Oracle Data Integration Competency Center (DICC): A Niche Market for services

    - by Thanos Terentes Printzios
    Market success now depends on data integration speed. This is why we collected all best practices from the most advanced IT leaders, simply to prove that a Data Integration competency center should be the primary new IT team you should establish. This is a niche market with unlimited potential for partners becoming, the much needed, data integration services provider trusted by customers. We would like to elaborate with OPN Partners on the Business Value Assessment and Total Economic Impact of the Data Integration Platform for End Users, while justifying re-organizing your IT services teams. We are happy to share our research on: The Economical impact of data integration platform/competency center. Justifying strongest reasons and differentiators, using numeric analysis and best-practice in customer case studies from specific industries Utilizing diagnostics and health-check analysis in building a business case for your customers What exactly is so special in the technology of Oracle Data Integration Impact of growing data volume and amount of data sources Analysis of usual solutions that are being implemented so far, addressing key challenges and mistakes During this partner webcast we will balance business case centric content with extensive numerical ROI analysis. Join us to find out how to build a unified approach to moving/sharing/integrating data across the enterprise and why this is an important new services opportunity for partners. Agenda: Data Integration Competency Center Oracle Data Integration Solution Overview Services Niche Market For OPN Summary Q&A Delivery Format This FREE online LIVE eSeminar will be delivered over the Web. Registrations received less than 24hours prior to start time may not receive confirmation to attend. Presenter: Milomir Vojvodic, EMEA Senior Business Development Manager for Oracle Data Integration Product Group Date: Thursday, September 4th, 10pm CEST (8am UTC/11am EEST)Duration: 1 hour Register Today For any questions please contact us at [email protected]

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  • WPF Layout algorithm woes - control will resize, but not below some arbitrary value.

    - by Quantumplation
    I'm working on an application for a client, and one of the requirements is the ability to make appointments, and display the current week's appointments in a visual format, much like in Google Calender's or Microsoft Office. I found a great (3 part) article on codeproject, in which he builds a "RangePanel", and composes one for each "period" (for example, the work day.) You can find part 1 here: http://www.codeproject.com/KB/WPF/OutlookWpfCalendarPart1.aspx The code presents, but seems to choose an arbitrary height value overall (440.04), and won't resize below that without clipping. What I mean to say, is that the window/container will resize, but it just cuts off the bottom of the control, instead of recalculating the height of the range panels, and the controls in the range panels representing the appointment. It will resize and recalculate for greater values, but not less. Code-wise, what's happening is that when you resize below that value, first the "MeasureOverride" is called with the correct "new height". However, by the time the "ArrangeOverride" method is called, it's passing the same 440.04 value as the height to arrange to. I need to find a solution/workaround, but any information that you can provide that might direct me for things to look into would also be greatly appreciated ( I understand how frustrating it is to debug code when you don't have the codebase in front of you. :) ) The code for the various Arrange and Measure functions are provided below. The "CalendarView" control has a "CalendarViewContentPresenter", which handles several periods. Then, the periods have a "CalendarPeriodContentPresenter", which handles each "block" of appointments. Finally, the "RangePanel" has it's own implementation. (To be honest, i'm still a bit hazy on how the control works, so if my explanations are a bit hazy, the article I linked probably has a more cogent explanation. :) ) CalendarViewContentPresenter: protected override Size ArrangeOverride(Size finalSize) { int columnCount = this.CalendarView.Periods.Count; Size columnSize = new Size(finalSize.Width / columnCount, finalSize.Height); double elementX = 0; foreach (UIElement element in this.visualChildren) { element.Arrange(new Rect(new Point(elementX, 0), columnSize)); elementX = elementX + columnSize.Width; } return finalSize; } protected override Size MeasureOverride(Size constraint) { this.GenerateVisualChildren(); this.GenerateListViewItemVisuals(); // If it's coming back infinity, just return some value. if (constraint.Width == Double.PositiveInfinity) constraint.Width = 10; if (constraint.Height == Double.PositiveInfinity) constraint.Height = 10; return constraint; } CalendarViewPeriodPersenter: protected override Size ArrangeOverride(Size finalSize) { foreach (UIElement element in this.visualChildren) { element.Arrange(new Rect(new Point(0, 0), finalSize)); } return finalSize; } protected override Size MeasureOverride(Size constraint) { this.GenerateVisualChildren(); return constraint; } RangePanel: protected override Size ArrangeOverride(Size finalSize) { double containerRange = (this.Maximum - this.Minimum); foreach (UIElement element in this.Children) { double begin = (double)element.GetValue(RangePanel.BeginProperty); double end = (double)element.GetValue(RangePanel.EndProperty); double elementRange = end - begin; Size size = new Size(); size.Width = (Orientation == Orientation.Vertical) ? finalSize.Width : elementRange / containerRange * finalSize.Width; size.Height = (Orientation == Orientation.Vertical) ? elementRange / containerRange * finalSize.Height : finalSize.Height; Point location = new Point(); location.X = (Orientation == Orientation.Vertical) ? 0 : (begin - this.Minimum) / containerRange * finalSize.Width; location.Y = (Orientation == Orientation.Vertical) ? (begin - this.Minimum) / containerRange * finalSize.Height : 0; element.Arrange(new Rect(location, size)); } return finalSize; } protected override Size MeasureOverride(Size availableSize) { foreach (UIElement element in this.Children) { element.Measure(availableSize); } // Constrain infinities if (availableSize.Width == double.PositiveInfinity) availableSize.Width = 10; if (availableSize.Height == double.PositiveInfinity) availableSize.Height = 10; return availableSize; }

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  • How do you solve the 15-puzzle with A-Star or Dijkstra's Algorithm?

    - by Sean
    I've read in one of my AI books that popular algorithms (A-Star, Dijkstra) for path-finding in simulation or games is also used to solve the well-known "15-puzzle". Can anyone give me some pointers on how I would reduce the 15-puzzle to a graph of nodes and edges so that I could apply one of these algorithms? If I were to treat each node in the graph as a game state then wouldn't that tree become quite large? Or is that just the way to do it?

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  • Which DHT algorithm to use (if I want to join two separate DHTs)?

    - by webdreamer
    I've been looking into some DHT systems, specially Pastry and Chord. I've read some concerns about Chord's reaction to churn, though I believe that won't be a problem for the task I have at hands. I'm implementing some sort of social network service that doesn't rely on any central servers for a course project. I need the DHT for the lookups. Now I don't know of all the servers in the network in the beginning. As I've stated, there's no main tracker server. It works this way: each client has three dedicated servers. The three servers have the profile of the client, and it's wall, it's personal info, replicated. I only get to know about other group of servers when the user adds a friend (inputing the client's address). So I would create two separate DHTs on the two groups of three servers and when they friend each other I would like to join the DHTs. I would like to this consistently. I haven't had a lot of time to get all that familiar with the protocols, so I would like to know which one is better if I want to join the two separate DHTs?

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  • If I write an algorithm to encrypt a file, are their tools available to break the encryption?

    - by Andrew
    I have an idea for encryption that I could program fairly easily to encrypt some local text file. Given that my approach is novel, and does not use any of the industry standard encryption techniques, would I be able to test the strength of my encryption using 'cracker' apps or suchlike? Or do all those tools rely on advanced knowledge of the encryption process (or intercepted 'keys'), meaning I'd have to build my own cracker for testing?

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  • Defining < for STL sort algorithm - operator overload, functor or standalone function?

    - by Andy
    I have a stl::list containing Widget class objects. They need to be sorted according to two members in the Widget class. For the sorting to work, I need to define a less-than comparator comparing two Widget objects. There seems to be a myriad of ways to do it. From what I can gather, one can either: a. Define a comparison operator overload in the class: bool Widget::operator< (const Widget &rhs) const b. Define a standalone function taking two Widgets: bool operator<(const Widget& lhs, const Widget& rhs); And then make the Widget class a friend of it: class Widget { // Various class definitions ... friend bool operator<(const Widget& lhs, const Widget& rhs); }; c. Define a functor and then include it as a parameter when calling the sort function: class Widget_Less : public binary_function<Widget, Widget, bool> { bool operator()(const Widget &lhs, const Widget& rhs) const; }; Does anybody know which method is better? In particular I am interested to know if I should do 1 or 2. I searched the book Effective STL by Scott Meyer but unfortunately it does not have anything to say about this. Thank you for your reply.

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  • What is jasper report's algorithm for using a data source?

    - by spderosso
    Hi, I have created my custom data source by implementing the interface JRDataSource. This interface looks like this: public interface JRDataSource { /** * Tries to position the cursor on the next element in the data source. * @return true if there is a next record, false otherwise * @throws JRException if any error occurs while trying to move to the next element */ public boolean next() throws JRException; /** * Gets the field value for the current position. * @return an object containing the field value. The object type must be the field object type. */ public Object getFieldValue(JRField jrField) throws JRException; } My question is the following: In what way does jasper report call this functions for obtaining the fields in the .jrxml. E.g: if( next() )){ call getFieldValue for every field present in the page header while( next() ){ call getFieldValue for every field present in detail part } call getFieldValue for every field present the footer } The previous is just an example, experimentally in fact I found out that it is actually not like that. So my question arised. Thanks!

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  • What is the best algorithm to locate a point in an image file?

    - by suugaku
    Hi all, I want to create a mark sheet recognizer. Here is the description: My system uses black and white color scheme. The mark sheet paper has a small black rectangle on each corner and an additional small black rectangle, to determine orientation, near one of the previous rectangles. The paper is scanned to yield an image (in bmp format for example). The first step is to locate these five references in image as eficient as possible. My rough idea is to trace row by row and from left to right for each row. It sounds very slow I think. Is there any better way to do that? Thank you in advance. regards, Suugaku

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  • How to specify the image scaling algorithm used by a WPF Image?

    - by mackenir
    Is there a way to specify how an image is scaled up in an Image element with LayoutTransform set to a ScaleTransform with integer values for ScaleX and ScaleY? I want to display the scaled image crisply (ie using 'nearest neighbour' scaling), with no blurring. (Imagine how you would want a bitmap editing program to behave when zooming in). I noticed the protected property VisualBitmapScalingMode on Image, so created a subclass of Image that sets this property to BitmapScalingMode.NearestNeighbor. However, this had no effect.

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  • What's a good algorithm for searching arrays N and M, in order to find elements in N that also exist

    - by GenTiradentes
    I have two arrays, N and M. they are both arbitrarily sized, though N is usually smaller than M. I want to find out what elements in N also exist in M, in the fastest way possible. To give you an example of one possible instance of the program, N is an array 12 units in size, and M is an array 1,000 units in size. I want to find which elements in N also exist in M. (There may not be any matches.) The more parallel the solution, the better. I used to use a hash map for this, but it's not quite as efficient as I'd like it to be. Typing this out, I just thought of running a binary search of M on sizeof(N) independent threads. (Using CUDA) I'll see how this works, though other suggestions are welcome.

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  • Problem solving/ Algorithm Skill is a knack or can be developed with practice?

    - by KaluSingh Gabbar
    Every time I start a hard problem and if can not figure out the exact solution or can not get started, I get into this never ending discussion with myself, as below: That problem solving/mathematics/algorithms skills are gifted (not that you can learn by practicing, by practice, you only master the kind of problems that you already have solved before) only those who went to good schools can do it, as they learned it early. What are your thoughts, can one achieve awesomeness in problem solving/algorithms just by hard work or you need to have that extra-gene in you?

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  • What is the most efficient algorithm for reversing a String in Java?

    - by Hultner
    I am wondering which way to reverse a string in Java that is most efficient. Should I use some sort of xor method? The easy way would be to put all the chars in a stack and put them back into a string again but I doubt that's a very efficient way to do it. And please do not tell me to use some built in function in Java. I am interested in learning how to do it not to use an efficient function but not knowing why it's efficient or how it's built up.

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  • What's the fastest lookup algorithm for a pair data structure (i.e, a map)?

    - by truncheon
    In the following example a std::map structure is filled with 26 values from A - Z (for key) and 0 – 26 for value. The time taken (on my system) to lookup the last entry (10000000 times) is roughly 250 ms for the vector, and 125 ms for the map. (I compiled using release mode, with O3 option turned on for g++ 4.4) But if for some odd reason I wanted better performance than the std::map, what data structures and functions would I need to consider using? I apologize if the answer seems obvious to you, but I haven't had much experience in the performance critical aspects of C++ programming. UPDATE: This example is rather trivial and hides the true complexity of what I'm trying to achieve. My real world project is a simple scripting language that uses a parser, data tree, and interpreter (instead of a VM stack system). I need to use some kind of data structure (perhaps map) to store the variables names created by script programmers. These are likely to be pretty randomly named, so I need a lookup method that can quickly find a particular key within a (probably) fairly large list of names. #include <ctime> #include <map> #include <vector> #include <iostream> struct mystruct { char key; int value; mystruct(char k = 0, int v = 0) : key(k), value(v) { } }; int find(const std::vector<mystruct>& ref, char key) { for (std::vector<mystruct>::const_iterator i = ref.begin(); i != ref.end(); ++i) if (i->key == key) return i->value; return -1; } int main() { std::map<char, int> mymap; std::vector<mystruct> myvec; for (int i = 'a'; i < 'a' + 26; ++i) { mymap[i] = i - 'a'; myvec.push_back(mystruct(i, i - 'a')); } int pre = clock(); for (int i = 0; i < 10000000; ++i) { find(myvec, 'z'); } std::cout << "linear scan: milli " << clock() - pre << "\n"; pre = clock(); for (int i = 0; i < 10000000; ++i) { mymap['z']; } std::cout << "map scan: milli " << clock() - pre << "\n"; return 0; }

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