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  • TCO Comparison: Oracle Exadata vs IBM P-Series

    - by Javier Puerta
    Cost Comparison for Business Decision-makersOracle Exadata Database Machine vs. IBM Power SystemsHow to Weigh a Purchase DecisionOctober 2012 Download full report here In this research-based  white paper conducted at the request of Oracle, The FactPoint Group compares the cost of ownership of the Oracle Exadata engineered system to a traditional build-your-own (BYO) solution, in this case an IBM Power 770 (P770) with SAN storage.  The IBM P770 was chosen given it is IBM’s current most popular model, based on FactPoint primary and secondary research and IBM claims, and because at least one of the interviewed customers had specifically migrated from a P770 to Exadata, affording us a more specific data point for comparison. This research found that Oracle Exadata: Can be deployed more quickly and easily requiring 59% fewer man-hours than a traditional IBM Power Systems solution. Delivers dramatically higher performance typically up to 12X improvement, as described by customers, over their prior solution.  Requires 40% fewer systems administrator hours to maintain and operate annually, including quicker support calls because of less finger-pointing and faster service with a single vendor.  Will become even easier to operate over time as users become more proficient and organize around the benefits of integrated infrastructure. Supplies a highly available, highly scalable and robust solution that results in reserve capacity that make Exadata easier for IT to operate because IT administrators can manage proactively, not reactively.  Overall, Exadata operations and maintenance keep IT administrators from “living on the edge.”  And it’s pre-engineered for long-term growth. Finally, compared to IBM Power Systems hardware, Exadata is a bargain from a total cost of ownership perspective:  Over three years, the IBM hardware running Oracle Database cost 31% more in TCO than Exadata.

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  • Calculate travel time on road map with semaphores

    - by Ivansek
    I have a road map with intersections. At intersections there are semaphores. For each semaphore I generate a red light time and green light time which are represented with syntax [R:T1, G:T2], for example: 119 185 250 A ------- B: [R:6, G:4] ------ C: [R:5, G:5] ------ D I want to calculate a car travel time from A - D. Now I do this with this pseudo code: function get_travel_time(semaphores_configuration) { time = 0; for( i=1; i<path.length;i++) { prev_node = path[i-1]; next_node = path[i]); cost = cost_between(prev_node, next_node) time += (cost/movement_speed) // movement_speed = 50px per second light_times = get_light_times(path[i], semaphore_configurations) lights_cycle = get_lights_cycle(light_times) // Eg: [R,R,R,G,G,G,G], where [R:3, G:4] lights_sum = light_times.green_time+light_times.red_light; // Lights cycle time light = lights_cycle[cost%lights_sum]; if( light == "R" ) { time += light_times.red_light; } } return time; } So for distance 119 between A and B travel time is, 119/50 = 2.38s ( exactly mesaured time is between 2.5s and 2.6s), then we add time if we came at a red light when at B. If we came at a red light is calculated with lines: lights_cycle = get_lights_cycle(light_times) // Eg: [R,R,R,G,G,G,G], where [R:3, G:4] lights_sum = light_times.green_time+light_times.red_light light = lights_cycle[cost%lights_sum]; if( light == "R" ) { time += light_times.red_light; } This pseudo code doesn't calculate exactly the same times as they are mesaured, but the calculations are very close to them. Any idea how I would calculate this?

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  • Basics of Join Predicate Pushdown in Oracle

    - by Maria Colgan
    Happy New Year to all of our readers! We hope you all had a great holiday season. We start the new year by continuing our series on Optimizer transformations. This time it is the turn of Predicate Pushdown. I would like to thank Rafi Ahmed for the content of this blog.Normally, a view cannot be joined with an index-based nested loop (i.e., index access) join, since a view, in contrast with a base table, does not have an index defined on it. A view can only be joined with other tables using three methods: hash, nested loop, and sort-merge joins. Introduction The join predicate pushdown (JPPD) transformation allows a view to be joined with index-based nested-loop join method, which may provide a more optimal alternative. In the join predicate pushdown transformation, the view remains a separate query block, but it contains the join predicate, which is pushed down from its containing query block into the view. The view thus becomes correlated and must be evaluated for each row of the outer query block. These pushed-down join predicates, once inside the view, open up new index access paths on the base tables inside the view; this allows the view to be joined with index-based nested-loop join method, thereby enabling the optimizer to select an efficient execution plan. The join predicate pushdown transformation is not always optimal. The join predicate pushed-down view becomes correlated and it must be evaluated for each outer row; if there is a large number of outer rows, the cost of evaluating the view multiple times may make the nested-loop join suboptimal, and therefore joining the view with hash or sort-merge join method may be more efficient. The decision whether to push down join predicates into a view is determined by evaluating the costs of the outer query with and without the join predicate pushdown transformation under Oracle's cost-based query transformation framework. The join predicate pushdown transformation applies to both non-mergeable views and mergeable views and to pre-defined and inline views as well as to views generated internally by the optimizer during various transformations. The following shows the types of views on which join predicate pushdown is currently supported. UNION ALL/UNION view Outer-joined view Anti-joined view Semi-joined view DISTINCT view GROUP-BY view Examples Consider query A, which has an outer-joined view V. The view cannot be merged, as it contains two tables, and the join between these two tables must be performed before the join between the view and the outer table T4. A: SELECT T4.unique1, V.unique3 FROM T_4K T4,            (SELECT T10.unique3, T10.hundred, T10.ten             FROM T_5K T5, T_10K T10             WHERE T5.unique3 = T10.unique3) VWHERE T4.unique3 = V.hundred(+) AND       T4.ten = V.ten(+) AND       T4.thousand = 5; The following shows the non-default plan for query A generated by disabling join predicate pushdown. When query A undergoes join predicate pushdown, it yields query B. Note that query B is expressed in a non-standard SQL and shows an internal representation of the query. B: SELECT T4.unique1, V.unique3 FROM T_4K T4,           (SELECT T10.unique3, T10.hundred, T10.ten             FROM T_5K T5, T_10K T10             WHERE T5.unique3 = T10.unique3             AND T4.unique3 = V.hundred(+)             AND T4.ten = V.ten(+)) V WHERE T4.thousand = 5; The execution plan for query B is shown below. In the execution plan BX, note the keyword 'VIEW PUSHED PREDICATE' indicates that the view has undergone the join predicate pushdown transformation. The join predicates (shown here in red) have been moved into the view V; these join predicates open up index access paths thereby enabling index-based nested-loop join of the view. With join predicate pushdown, the cost of query A has come down from 62 to 32.  As mentioned earlier, the join predicate pushdown transformation is cost-based, and a join predicate pushed-down plan is selected only when it reduces the overall cost. Consider another example of a query C, which contains a view with the UNION ALL set operator.C: SELECT R.unique1, V.unique3 FROM T_5K R,            (SELECT T1.unique3, T2.unique1+T1.unique1             FROM T_5K T1, T_10K T2             WHERE T1.unique1 = T2.unique1             UNION ALL             SELECT T1.unique3, T2.unique2             FROM G_4K T1, T_10K T2             WHERE T1.unique1 = T2.unique1) V WHERE R.unique3 = V.unique3 and R.thousand < 1; The execution plan of query C is shown below. In the above, 'VIEW UNION ALL PUSHED PREDICATE' indicates that the UNION ALL view has undergone the join predicate pushdown transformation. As can be seen, here the join predicate has been replicated and pushed inside every branch of the UNION ALL view. The join predicates (shown here in red) open up index access paths thereby enabling index-based nested loop join of the view. Consider query D as an example of join predicate pushdown into a distinct view. We have the following cardinalities of the tables involved in query D: Sales (1,016,271), Customers (50,000), and Costs (787,766).  D: SELECT C.cust_last_name, C.cust_city FROM customers C,            (SELECT DISTINCT S.cust_id             FROM sales S, costs CT             WHERE S.prod_id = CT.prod_id and CT.unit_price > 70) V WHERE C.cust_state_province = 'CA' and C.cust_id = V.cust_id; The execution plan of query D is shown below. As shown in XD, when query D undergoes join predicate pushdown transformation, the expensive DISTINCT operator is removed and the join is converted into a semi-join; this is possible, since all the SELECT list items of the view participate in an equi-join with the outer tables. Under similar conditions, when a group-by view undergoes join predicate pushdown transformation, the expensive group-by operator can also be removed. With the join predicate pushdown transformation, the elapsed time of query D came down from 63 seconds to 5 seconds. Since distinct and group-by views are mergeable views, the cost-based transformation framework also compares the cost of merging the view with that of join predicate pushdown in selecting the most optimal execution plan. Summary We have tried to illustrate the basic ideas behind join predicate pushdown on different types of views by showing example queries that are quite simple. Oracle can handle far more complex queries and other types of views not shown here in the examples. Again many thanks to Rafi Ahmed for the content of this blog post.

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  • Java EE 6 and NoSQL/MongoDB on GlassFish using JPA and EclipseLink 2.4 (TOTD #175)

    - by arungupta
    TOTD #166 explained how to use MongoDB in your Java EE 6 applications. The code in that tip used the APIs exposed by the MongoDB Java driver and so requires you to learn a new API. However if you are building Java EE 6 applications then you are already familiar with Java Persistence API (JPA). Eclipse Link 2.4, scheduled to release as part of Eclipse Juno, provides support for NoSQL databases by mapping a JPA entity to a document. Their wiki provides complete explanation of how the mapping is done. This Tip Of The Day (TOTD) will show how you can leverage that support in your Java EE 6 applications deployed on GlassFish 3.1.2. Before we dig into the code, here are the key concepts ... A POJO is mapped to a NoSQL data source using @NoSQL or <no-sql> element in "persistence.xml". A subset of JPQL and Criteria query are supported, based upon the underlying data store Connection properties are defined in "persistence.xml" Now, lets lets take a look at the code ... Download the latest EclipseLink 2.4 Nightly Bundle. There is a Installer, Source, and Bundle - make sure to download the Bundle link (20120410) and unzip. Download GlassFish 3.1.2 zip and unzip. Install the Eclipse Link 2.4 JARs in GlassFish Remove the following JARs from "glassfish/modules": org.eclipse.persistence.antlr.jar org.eclipse.persistence.asm.jar org.eclipse.persistence.core.jar org.eclipse.persistence.jpa.jar org.eclipse.persistence.jpa.modelgen.jar org.eclipse.persistence.moxy.jar org.eclipse.persistence.oracle.jar Add the following JARs from Eclipse Link 2.4 nightly build to "glassfish/modules": org.eclipse.persistence.antlr_3.2.0.v201107111232.jar org.eclipse.persistence.asm_3.3.1.v201107111215.jar org.eclipse.persistence.core.jpql_2.4.0.v20120407-r11132.jar org.eclipse.persistence.core_2.4.0.v20120407-r11132.jar org.eclipse.persistence.jpa.jpql_2.0.0.v20120407-r11132.jar org.eclipse.persistence.jpa.modelgen_2.4.0.v20120407-r11132.jar org.eclipse.persistence.jpa_2.4.0.v20120407-r11132.jar org.eclipse.persistence.moxy_2.4.0.v20120407-r11132.jar org.eclipse.persistence.nosql_2.4.0.v20120407-r11132.jar org.eclipse.persistence.oracle_2.4.0.v20120407-r11132.jar Start MongoDB Download latest MongoDB from here (2.0.4 as of this writing). Create the default data directory for MongoDB as: sudo mkdir -p /data/db/sudo chown `id -u` /data/db Refer to Quickstart for more details. Start MongoDB as: arungup-mac:mongodb-osx-x86_64-2.0.4 <arungup> ->./bin/mongod./bin/mongod --help for help and startup optionsMon Apr  9 12:56:02 [initandlisten] MongoDB starting : pid=3124 port=27017 dbpath=/data/db/ 64-bit host=arungup-mac.localMon Apr  9 12:56:02 [initandlisten] db version v2.0.4, pdfile version 4.5Mon Apr  9 12:56:02 [initandlisten] git version: 329f3c47fe8136c03392c8f0e548506cb21f8ebfMon Apr  9 12:56:02 [initandlisten] build info: Darwin erh2.10gen.cc 9.8.0 Darwin Kernel Version 9.8.0: Wed Jul 15 16:55:01 PDT 2009; root:xnu-1228.15.4~1/RELEASE_I386 i386 BOOST_LIB_VERSION=1_40Mon Apr  9 12:56:02 [initandlisten] options: {}Mon Apr  9 12:56:02 [initandlisten] journal dir=/data/db/journalMon Apr  9 12:56:02 [initandlisten] recover : no journal files present, no recovery neededMon Apr  9 12:56:02 [websvr] admin web console waiting for connections on port 28017Mon Apr  9 12:56:02 [initandlisten] waiting for connections on port 27017 Check out the JPA/NoSQL sample from SVN repository. The complete source code built in this TOTD can be downloaded here. Create Java EE 6 web app Create a Java EE 6 Maven web app as: mvn archetype:generate -DarchetypeGroupId=org.codehaus.mojo.archetypes -DarchetypeArtifactId=webapp-javaee6 -DgroupId=model -DartifactId=javaee-nosql -DarchetypeVersion=1.5 -DinteractiveMode=false Copy the model files from the checked out workspace to the generated project as: cd javaee-nosqlcp -r ~/code/workspaces/org.eclipse.persistence.example.jpa.nosql.mongo/src/model src/main/java Copy "persistence.xml" mkdir src/main/resources cp -r ~/code/workspaces/org.eclipse.persistence.example.jpa.nosql.mongo/src/META-INF ./src/main/resources Add the following dependencies: <dependency> <groupId>org.eclipse.persistence</groupId> <artifactId>org.eclipse.persistence.jpa</artifactId> <version>2.4.0-SNAPSHOT</version> <scope>provided</scope></dependency><dependency> <groupId>org.eclipse.persistence</groupId> <artifactId>org.eclipse.persistence.nosql</artifactId> <version>2.4.0-SNAPSHOT</version></dependency><dependency> <groupId>org.mongodb</groupId> <artifactId>mongo-java-driver</artifactId> <version>2.7.3</version></dependency> The first one is for the EclipseLink latest APIs, the second one is for EclipseLink/NoSQL support, and the last one is the MongoDB Java driver. And the following repository: <repositories> <repository> <id>EclipseLink Repo</id> <url>http://www.eclipse.org/downloads/download.php?r=1&amp;nf=1&amp;file=/rt/eclipselink/maven.repo</url> <snapshots> <enabled>true</enabled> </snapshots> </repository>  </repositories> Copy the "Test.java" to the generated project: mkdir src/main/java/examplecp -r ~/code/workspaces/org.eclipse.persistence.example.jpa.nosql.mongo/src/example/Test.java ./src/main/java/example/ This file contains the source code to CRUD the JPA entity to MongoDB. This sample is explained in detail on EclipseLink wiki. Create a new Servlet in "example" directory as: package example;import java.io.IOException;import java.io.PrintWriter;import javax.servlet.ServletException;import javax.servlet.annotation.WebServlet;import javax.servlet.http.HttpServlet;import javax.servlet.http.HttpServletRequest;import javax.servlet.http.HttpServletResponse;/** * @author Arun Gupta */@WebServlet(name = "TestServlet", urlPatterns = {"/TestServlet"})public class TestServlet extends HttpServlet { protected void processRequest(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { response.setContentType("text/html;charset=UTF-8"); PrintWriter out = response.getWriter(); try { out.println("<html>"); out.println("<head>"); out.println("<title>Servlet TestServlet</title>"); out.println("</head>"); out.println("<body>"); out.println("<h1>Servlet TestServlet at " + request.getContextPath() + "</h1>"); try { Test.main(null); } catch (Exception ex) { ex.printStackTrace(); } out.println("</body>"); out.println("</html>"); } finally { out.close(); } } @Override protected void doGet(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { processRequest(request, response); } @Override protected void doPost(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { processRequest(request, response); }} Build the project and deploy it as: mvn clean packageglassfish3/bin/asadmin deploy --force=true target/javaee-nosql-1.0-SNAPSHOT.war Accessing http://localhost:8080/javaee-nosql/TestServlet shows the following messages in the server.log: connecting(EISLogin( platform=> MongoPlatform user name=> "" MongoConnectionSpec())) . . .Connected: User: Database: 2.7  Version: 2.7 . . .Executing MappedInteraction() spec => null properties => {mongo.collection=CUSTOMER, mongo.operation=INSERT} input => [DatabaseRecord( CUSTOMER._id => 4F848E2BDA0670307E2A8FA4 CUSTOMER.NAME => AMCE)]. . .Data access result: [{TOTALCOST=757.0, ORDERLINES=[{DESCRIPTION=table, LINENUMBER=1, COST=300.0}, {DESCRIPTION=balls, LINENUMBER=2, COST=5.0}, {DESCRIPTION=rackets, LINENUMBER=3, COST=15.0}, {DESCRIPTION=net, LINENUMBER=4, COST=2.0}, {DESCRIPTION=shipping, LINENUMBER=5, COST=80.0}, {DESCRIPTION=handling, LINENUMBER=6, COST=55.0},{DESCRIPTION=tax, LINENUMBER=7, COST=300.0}], SHIPPINGADDRESS=[{POSTALCODE=L5J1H7, PROVINCE=ON, COUNTRY=Canada, CITY=Ottawa,STREET=17 Jane St.}], VERSION=2, _id=4F848E2BDA0670307E2A8FA8,DESCRIPTION=Pingpong table, CUSTOMER__id=4F848E2BDA0670307E2A8FA7, BILLINGADDRESS=[{POSTALCODE=L5J1H8, PROVINCE=ON, COUNTRY=Canada, CITY=Ottawa, STREET=7 Bank St.}]}] You'll not see any output in the browser, just the output in the console. But the code can be easily modified to do so. Once again, the complete Maven project can be downloaded here. Do you want to try accessing relational and non-relational (aka NoSQL) databases in the same PU ?

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  • What’s Your Tax Strategy? Automate the Tax Transfer Pricing Process!

    - by tobyehatch
    Does your business operate in multiple countries? Well, whether you like it or not, many local and international tax authorities inspect your tax strategy.  Legal, effective tax planning is perceived as a “moral” issue. CEOs are being asked to testify on their process of tax transfer pricing between multinational legal entities.  Marc Seewald, Senior Director of Product Management for EPM Applications specializing in all tax subjects and Product Manager for Oracle Hyperion Tax Provisioning, and Bart Stoehr, Senior Director of Product Strategy for Oracle Hyperion Profitability and Cost Management joined me for a discussion/podcast on this interesting subject.  So what exactly is “tax transfer pricing”? Marc defined it this way. “Tax transfer pricing is a profit allocation methodology required to be used by multinational corporations. Specifically, the ultimate goal of the transfer pricing is to ensure that the global multinational pays their fair share of income tax in each of their local markets. Specifically, it prevents companies from unfairly moving profit from ‘high tax’ countries to ‘low tax’ countries.” According to Marc, in today’s global economy, profitability can be significantly impacted by goods and services exchanged between the related divisions within a single multinational company.  To ensure that these cost allocations are done fairly, there are rules that govern the process. These rules ensure that intercompany allocations fairly represent the actual nature of the businesses activity- as if two divisions were unrelated - and provide a clear audit trail of how the costs have been allocated to prove that allocations fall within reasonable ranges.  What are the repercussions of improper tax transfer pricing? How important is it? Tax transfer pricing allocations can materially impact the amount of overall corporate income taxes paid by a company worldwide, in some cases by hundreds of millions of dollars!  Since so much tax revenue is at stake, revenue agencies like the IRS, and international regulatory bodies like the Organization for Economic Cooperation and Development (OECD) are pushing to reform and clarify reporting for tax transfer pricing. Most recently the OECD announced an “Action Plan for Base Erosion and Profit Shifting”. As Marc explained, the times are changing and companies need to be responsive to this issue. “It feels like every other week there is another company being accused of avoiding taxes,” said Marc. Most recently, Caterpillar was accused of avoiding billions of dollars in taxes. In the last couple of years, Apple, GE, Ikea, and Starbucks, have all been accused of tax avoidance. It’s imperative that companies like these have a clear and auditable tax transfer process that enables them to justify tax transfer pricing allocations and avoid steep penalties and bad publicity. Transparency and efficiency are what is needed when it comes to the tax transfer pricing process. Bart explained that tax transfer pricing is driving a deeper inspection of profit recognition specifically focused on the tax element of profit.  However, allocations needed to support tax profitability are nearly identical in process to allocations taking place in other parts of the finance organization. For example, the methods and processes necessary to arrive at tax profitability by legal entity are no different than those used to arrive at fully loaded profitability for a product line. In fact, there is a great opportunity for alignment across these two different functions.So it seems that tax transfer pricing should be reflected in profitability in general. Bart agreed and told us more about some of the critical sub-processes of an overall tax transfer pricing process within the Oracle solution for tax transfer pricing.  “First, there is a ton of data preparation, enrichment and pre-allocation data analysis that is managed in the Oracle Hyperion solution. This serves as the “data staging” to the next, critical sub-processes.  From here, we leverage the Oracle EPM platform’s ability to re-use dimensions and legal entity driver data and financial data with Oracle Hyperion Profitability and Cost Management (HPCM).  Within HPCM, we manage the driver data, define the legal entity to legal entity allocation rules (like cost plus), and have the option to test out multiple, simultaneous tax transfer pricing what-if scenarios.  Once processed, a tax expert can evaluate the effectiveness of any one scenario result versus another via a variance analysis configured with HPCM’s pre-packaged reporting capability known as Oracle Hyperion SmartView for Office.”   Further, Bart explained that the ability to visibly demonstrate how a cost or revenue has been allocated is really helpful and auditable.  “HPCM’s Traceability Maps are that visual representation of all allocation flows that have been executed and is the tax transfer analyst’s best friend in maintaining clear documentation for tax transfer pricing audits. Simply click and drill as you inspect the chain of allocation definitions and results. Once final, the post-allocated tax data can be compared to the GL to create invoices and journal entries for posting to your GL system of choice.  Of course, there is a framework for overall governance of the journal entries, allocation percentages, and reporting to include necessary approvals.” Lastly, Marc explained that the key value in using the Oracle Hyperion solution for tax transfer pricing is that it keeps everything in alignment in one single place. Specifically, Oracle Hyperion effectively becomes the single book of record for the GAAP, management, and the tax set of books. There are many benefits to having one source of the truth. These include EFFICIENCY, CONTROLS and TRANSPARENCY.So, what’s your tax strategy? Why not automate the tax transfer pricing process!To listen to the entire podcast, click here.To learn more about Oracle Hyperion Profitability and Cost Management (HPCM), click here.

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  • HPCM 11.1.2.2.x - How to find data in an HPCM Standard Costing database

    - by Jane Story
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} When working with a Hyperion Profitability and Cost Management (HPCM) Standard Costing application, there can often be a requirement to check data or allocated results using reporting tools e.g Smartview. To do this, you are retrieving data directly from the Essbase databases related to your HPCM model. For information, running reports is covered in Chapter 9 of the HPCM User documentation. The aim of this blog is to provide a quick guide to finding this data for reporting in the HPCM generated Essbase database in v11.1.2.2.x of HPCM. In order to retrieve data from an HPCM generated Essbase database, it is important to understand each of the following dimensions in the Essbase database and where data is located within them: Measures dimension – identifies Measures AllocationType dimension – identifies Direct Allocation Data or Genealogy Allocation data Point Of View (POV) dimensions – there must be at least one, maximum of four. Business dimensions: Stage Business dimensions – these will be identified by the Stage prefix. Intra-Stage dimension – these will be identified by the _Intra suffix. Essbase outlines and reporting is explained in the documentation here:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/ch09s02.html For additional details on reporting measures, please review this section of the documentation:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/apas03.html Reporting requirements in HPCM quite often start with identifying non balanced items in the Stage Balancing report. The following documentation link provides help with identifying some of the items within the Stage Balancing report:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/generatestagebalancing.html The following are some types of data upon which you may want to report: Stage Data: Direct Input Assigned Input Data Assigned Output Data Idle Cost/Revenue Unassigned Cost/Revenue Over Driven Cost/Revenue Direct Allocation Data Genealogy Allocation Data Stage Data Stage Data consists of: Direct Input i.e. input data, the starting point of your allocation e.g. in Stage 1 Assigned Input Data i.e. the cost/revenue received from a prior stage (i.e. stage 2 and higher). Assigned Output Data i.e. for each stage, the data that will be assigned forward is assigned post stage data. Reporting on this data is explained in the documentation here:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/ch09s03.html Dimension Selection Measures Direct Input: CostInput RevenueInput Assigned Input (from previous stages): CostReceivedPriorStage RevenueReceivedPriorStage Assigned Output (to subsequent stages): CostAssignedPostStage RevenueAssignedPostStage AllocationType DirectAllocation POV One member from each POV dimension Stage Business Dimensions Any members for the stage business dimensions for the stage you wish to see the Stage data for. All other Dimensions NoMember Idle/Unassigned/OverDriven To view Idle, Unassigned or Overdriven Costs/Revenue, first select which stage for which you want to view this data. If multiple Stages have unassigned/idle, resolve the earliest first and re-run the calculation as differences in early stages will create unassigned/idle in later stages. Dimension Selection Measures Idle: IdleCost IdleRevenue Unassigned: UnAssignedCost UnAssignedRevenue Overdriven: OverDrivenCost OverDrivenRevenue AllocationType DirectAllocation POV One member from each POV dimension Dimensions in the Stage with Unassigned/ Idle/OverDriven Cost All the Stage Business dimensions in the Stage with Unassigned/Idle/Overdriven. Zoom in on each dimension to find the individual members to find which members have Unassigned/Idle/OverDriven data. All other Dimensions NoMember Direct Allocation Data Direct allocation data shows the data received by a destination intersection from a source intersection where a direct assignment(s) exists. Reporting on direct allocation data is explained in the documentation here:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/ch09s04.html You would select the following to report direct allocation data Dimension Selection Measures CostReceivedPriorStage AllocationType DirectAllocation POV One member from each POV dimension Stage Business Dimensions Any members for the SOURCE stage business dimensions and the DESTINATION stage business dimensions for the direct allocations for the stage you wish to report on. All other Dimensions NoMember Genealogy Allocation Data Genealogy allocation data shows the indirect data relationships between stages. Genealogy calculations run in the HPCM Reporting database only. Reporting on genealogy data is explained in the documentation here:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/ch09s05.html Dimension Selection Measures CostReceivedPriorStage AllocationType GenealogyAllocation (IndirectAllocation in 11.1.2.1 and prior versions) POV One member from each POV dimension Stage Business Dimensions Any stage business dimension members from the STARTING stage in Genealogy Any stage business dimension members from the INTERMEDIATE stage(s) in Genealogy Any stage business dimension members from the ENDING stage in Genealogy All other Dimensions NoMember Notes If you still don’t see data after checking the above, please check the following Check the calculation has been run. Here are couple of indicators that might help them with that. Note the size of essbase cube before and after calculations ensure that a calculation was run against the database you are examing. Export the essbase data to a text file to confirm that some data exists. Examine the date and time on task area to see when, if any, calculations were run and what choices were used (e.g. Genealogy choices) If data does not exist in places where they are expecting, it could be that No calculations/genealogy were run No calculations were successfully run The model/data at feeder location were either absent or incompatible, resulting in no allocation e.g no driver data. Smartview Invocation from HPCM From version 11.1.2.2.350 of HPCM (this version will be GA shortly), it is possible to directly invoke Smartview from HPCM. There is guided navigation before the Smartview invocation and it is then possible to see the selected value(s) in SmartView. Click to Download HPCM 11.1.2.2.x - How to find data in an HPCM Standard Costing database (Right click or option-click the link and choose "Save As..." to download this pdf file)

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  • Developing Schema Compare for Oracle (Part 6): 9i Query Performance

    - by Simon Cooper
    All throughout the EAP and beta versions of Schema Compare for Oracle, our main request was support for Oracle 9i. After releasing version 1.0 with support for 10g and 11g, our next step was then to get version 1.1 of SCfO out with support for 9i. However, there were some significant problems that we had to overcome first. This post will concentrate on query execution time. When we first tested SCfO on a 9i server, after accounting for various changes to the data dictionary, we found that database registration was taking a long time. And I mean a looooooong time. The same database that on 10g or 11g would take a couple of minutes to register would be taking upwards of 30 mins on 9i. Obviously, this is not ideal, so a poke around the query execution plans was required. As an example, let's take the table population query - the one that reads ALL_TABLES and joins it with a few other dictionary views to get us back our list of tables. On 10g, this query takes 5.6 seconds. On 9i, it takes 89.47 seconds. The difference in execution plan is even more dramatic - here's the (edited) execution plan on 10g: -------------------------------------------------------------------------------| Id | Operation | Name | Bytes | Cost |-------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 108K| 939 || 1 | SORT ORDER BY | | 108K| 939 || 2 | NESTED LOOPS OUTER | | 108K| 938 ||* 3 | HASH JOIN RIGHT OUTER | | 103K| 762 || 4 | VIEW | ALL_EXTERNAL_LOCATIONS | 2058 | 3 ||* 20 | HASH JOIN RIGHT OUTER | | 73472 | 759 || 21 | VIEW | ALL_EXTERNAL_TABLES | 2097 | 3 ||* 34 | HASH JOIN RIGHT OUTER | | 39920 | 755 || 35 | VIEW | ALL_MVIEWS | 51 | 7 || 58 | NESTED LOOPS OUTER | | 39104 | 748 || 59 | VIEW | ALL_TABLES | 6704 | 668 || 89 | VIEW PUSHED PREDICATE | ALL_TAB_COMMENTS | 2025 | 5 || 106 | VIEW | ALL_PART_TABLES | 277 | 11 |------------------------------------------------------------------------------- And the same query on 9i: -------------------------------------------------------------------------------| Id | Operation | Name | Bytes | Cost |-------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 16P| 55G|| 1 | SORT ORDER BY | | 16P| 55G|| 2 | NESTED LOOPS OUTER | | 16P| 862M|| 3 | NESTED LOOPS OUTER | | 5251G| 992K|| 4 | NESTED LOOPS OUTER | | 4243M| 2578 || 5 | NESTED LOOPS OUTER | | 2669K| 1440 ||* 6 | HASH JOIN OUTER | | 398K| 302 || 7 | VIEW | ALL_TABLES | 342K| 276 || 29 | VIEW | ALL_MVIEWS | 51 | 20 ||* 50 | VIEW PUSHED PREDICATE | ALL_TAB_COMMENTS | 2043 | ||* 66 | VIEW PUSHED PREDICATE | ALL_EXTERNAL_TABLES | 1777K| ||* 80 | VIEW PUSHED PREDICATE | ALL_EXTERNAL_LOCATIONS | 1744K| ||* 96 | VIEW | ALL_PART_TABLES | 852K| |------------------------------------------------------------------------------- Have a look at the cost column. 10g's overall query cost is 939, and 9i is 55,000,000,000 (or more precisely, 55,496,472,769). It's also having to process far more data. What on earth could be causing this huge difference in query cost? After trawling through the '10g New Features' documentation, we found item 1.9.2.21. Before 10g, Oracle advised that you do not collect statistics on data dictionary objects. From 10g, it advised that you do collect statistics on the data dictionary; for our queries, Oracle therefore knows what sort of data is in the dictionary tables, and so can generate an efficient execution plan. On 9i, no statistics are present on the system tables, so Oracle has to use the Rule Based Optimizer, which turns most LEFT JOINs into nested loops. If we force 9i to use hash joins, like 10g, we get a much better plan: -------------------------------------------------------------------------------| Id | Operation | Name | Bytes | Cost |-------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 7587K| 3704 || 1 | SORT ORDER BY | | 7587K| 3704 ||* 2 | HASH JOIN OUTER | | 7587K| 822 ||* 3 | HASH JOIN OUTER | | 5262K| 616 ||* 4 | HASH JOIN OUTER | | 2980K| 465 ||* 5 | HASH JOIN OUTER | | 710K| 432 ||* 6 | HASH JOIN OUTER | | 398K| 302 || 7 | VIEW | ALL_TABLES | 342K| 276 || 29 | VIEW | ALL_MVIEWS | 51 | 20 || 50 | VIEW | ALL_PART_TABLES | 852K| 104 || 78 | VIEW | ALL_TAB_COMMENTS | 2043 | 14 || 93 | VIEW | ALL_EXTERNAL_LOCATIONS | 1744K| 31 || 106 | VIEW | ALL_EXTERNAL_TABLES | 1777K| 28 |------------------------------------------------------------------------------- That's much more like it. This drops the execution time down to 24 seconds. Not as good as 10g, but still an improvement. There are still several problems with this, however. 10g introduced a new join method - a right outer hash join (used in the first execution plan). The 9i query optimizer doesn't have this option available, so forcing a hash join means it has to hash the ALL_TABLES table, and furthermore re-hash it for every hash join in the execution plan; this could be thousands and thousands of rows. And although forcing hash joins somewhat alleviates this problem on our test systems, there's no guarantee that this will improve the execution time on customers' systems; it may even increase the time it takes (say, if all their tables are partitioned, or they've got a lot of materialized views). Ideally, we would want a solution that provides a speedup whatever the input. To try and get some ideas, we asked some oracle performance specialists to see if they had any ideas or tips. Their recommendation was to add a hidden hook into the product that allowed users to specify their own query hints, or even rewrite the queries entirely. However, we would prefer not to take that approach; as well as a lot of new infrastructure & a rewrite of the population code, it would have meant that any users of 9i would have to spend some time optimizing it to get it working on their system before they could use the product. Another approach was needed. All our population queries have a very specific pattern - a base table provides most of the information we need (ALL_TABLES for tables, or ALL_TAB_COLS for columns) and we do a left join to extra subsidiary tables that fill in gaps (for instance, ALL_PART_TABLES for partition information). All the left joins use the same set of columns to join on (typically the object owner & name), so we could re-use the hash information for each join, rather than re-hashing the same columns for every join. To allow us to do this, along with various other performance improvements that could be done for the specific query pattern we were using, we read all the tables individually and do a hash join on the client. Fortunately, this 'pure' algorithmic problem is the kind that can be very well optimized for expected real-world situations; as well as storing row data we're not using in the hash key on disk, we use very specific memory-efficient data structures to store all the information we need. This allows us to achieve a database population time that is as fast as on 10g, and even (in some situations) slightly faster, and a memory overhead of roughly 150 bytes per row of data in the result set (for schemas with 10,000 tables in that means an extra 1.4MB memory being used during population). Next: fun with the 9i dictionary views.

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  • Answers to Conference Revenue Tweet Questions

    - by D'Arcy Lussier
    Originally posted on: http://geekswithblogs.net/dlussier/archive/2014/05/27/156612.aspxI tweeted this the other day… …and I had some people tweet back questioning/asking about the profit number. So here’s how I came to that figure. Total Revenue Let’s talk total revenue first. This conference has a huge list of companies/organizations paying some amount for sponsorship. Platinum ($1500) x 5 = $7500 Gold ($1000) x 3 = $3000 Silver ($500) x 9 = $4500 Bronze ($250) x 13 = $3250 There’s also a title sponsor level but there’s no mention of how much that is…more than $1500 though, so let’s just say $2500. Total Sponsorship Revenue: $20750.00 For registrations, this conference is claiming over 300 attendees. We’ll just calculate at 300 and the discounted “member rate” – $249. Total Registration Revenue: $74700.00 Booth space is also sold for a vendor area, but let’s just leave that out of the calculation. Total Event Revenue: $95450.00 Now that we know how much money we’re playing with, let’s knock out the costs for the event. Total Costs Hard Costs Audio/Visual Services $2000 Conference Rooms (4 Breakouts + Plenary) $2500 Insurance $700 Printing/Signage $1500 Travel/Hotel Rooms $2000 Keynotes $2000 So let’s talk about these hard costs first. First you may be asking about the Audio Visual. Yes those services can be that high, actually higher. But since there’s an A/V company touted as the official A/V provider, I gotta think there’s some discount for being branded as such. Conference rooms are actually an inflated amount of $500 per. Venues make money on the food they sell at events, not on room rentals. The more food, the cheaper the rooms tend to be offered at. Still, for the sake of argument, let’s set the rooms at $500 each knowing that they could be lower. For travel and hotel rooms…it appears that most of the speakers at this conference are local, meaning there’s no travel or hotel cost. But a few of them I wasn’t too sure…so let’s factor in enough to cover two outside speakers (airfare and hotel). There are two keynotes for this event and depending on the event those may be paid gigs. I’m not sure if they are or not, but considering the closing one is a comedian I’m going to add some funds here for that just in case. Total Hard Costs: $10700 Now that the hard costs are out of the way, let’s talk about the food costs. Food Costs The conference is providing a continental breakfast (YEEEESH!), some level of luncheon, and I have to assume coffee breaks in between. Let’s look at those costs. Continental Breakfast $12 per person Lunch Buffet $18 per person Coffee Breaks (2) $6 per person (or $3 a cup) Snacks (2) $10 per person (or $5 each) Note that the lunch buffet assumes a *good* lunch buffet – two entrees, starch, vegetable, salads, and bread. Not sure if there’ll be snacks during coffee breaks but let’s assume so. Total Food Cost Per Person: $46 Food Cost: $14950 Gratuity: $2691 Total Food Cost: $17641 Total food cost is based on the $46 per person cost x 325. 300 for attendance, 12 for speakers, extra 13 for volunteers/organizers. Gratuity is 18%. Grand Totals So let’s sum things up here. Total Costs Hard Costs: $10700.00 Food Costs: $17641.00 Total:          $28341.00 Taxes:         $3685.00 Grand Total  $32026.00 Total Revenue Sponsorship  $20750 Registration   $74700 Grand Total   $95450.00 Total Profit $63424.00 Now what if the registration numbers were lower and they only got 100 people to show up. In that scenario there’d still be a profit of just under $26000. Closing Comments A couple of things to note: - I haven’t factored in anything for prizes. Not sure if any will be given out - We didn’t add in the booth space revenue - We’re assuming speakers aren’t getting paid, but even if they were at the high end its $12000 ($1000 per session), which is probably an inflated number for local speakers. - Note that all registrations were set to the “member” discounted price. The non-member registration price is higher. There is also an option for those that just want to show up for the opening keynote. There you have it! Let me know if you have any questions. D

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  • Need a CDN with SSL

    - by Till
    We currently use Edgecast through Speedyrails. Back when I did my research they were both fast and very cost-effective. I haven't looked in a while, but now we need SSL on our assets as well. I reached out to our current provider and they want a setup fee and something like 260 USD per host per month (we use multiple hosts currently). I looked at AWS Cloudfront and it seems the most cost affective way to get SSL, but it's not a custom domain then (e.g. cdn.example.org), which I could live with. Has any else researched this lately and has any providers to get in touch with - can be resellers or direct buys. I'm not looking for a bargain, I just want to get an idea what these things cost. Edit, 2012-08-23: Must have is custom origin. E.g. I don't want to manually upload files somewhere else. Edgecast and Cloudfront both support this.

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  • Announcing StorageTek VSM 6

    - by uwes
    On 23rd of October Oracle announced the 6th generation StorageTek Virtual Storage Manager system (StorageTek VSM 6). StorageTek VSM 6 provides customers simple, flexible and mainframe class reliability all while reducing a customer’s total cost of ownership: Simple – Efficiently manages data and storage resources according to customer-defined rules, while streamlining overall tape operations Flexible – Engineered with flexibility in mind, can be deployed to meet each enterprise’s unique business requirements  Reliable – Reduces a customer’s exposure by providing superior data protection, end-to-end high availability architecture and closed loop data integrity checking Low Total Cost of Ownership and Investment Protection – Low asset acquisition cost, high-density data center footprint and physical tape energy efficiency keeps customers storage spending within budget For More Information Go To: Oracle.com Tape PageOracle Technology Network Tape Page

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  • What is the best free or low-cost Java reporting library (e.g. BIRT, JasperReports, etc.) for making

    - by Max3000
    I want to print, email and write to PDF very simple reports. The reports are basically a list of items, divided in various sections/columns. The sections are not necessarily identical. Think newspaper. I just wasted a solid 2 days of work trying to make this kind of reports using JasperReports. I find that Jasper is great for outputing "normalized" data. The kind that would come out of a database for instance, each row neatly describing an item and each item printed on a line. I'm simplifying a bit but that's the idea. However, given what I want to do I always ended up completely lost. Data not being displayed for no apparent reason, columns of texts never the correct size, column positioning always ending up incorrect, pagination not sanely possible (I was never able to figure it out; the FAQ gives an obscure workaround), etc. I came to the conclusion that Jasper is really not built to make the kind of reports I want. Am I missing something? I'm ready to pay for a tool, as long as the price is reasonable. By reasonable I mean a few $100s. Thanks. EDIT: To answer cetus, here is more information about the report I made in Jasper. What I want is something like this: text text text text ------------------- text | text text |---------- text | text text | text --------| text text |---------- text | text What I made in jasper is this: (detail band) subreport | subreport ------------------------------------ subreport | subreport ------------------------------------ subreport | subreport The subreports are all the same actual report. This report has one field (called "field") and basically just prints this field in a detail band. Hence, running a single subreport simply lists all items from the datasource. The datasource itself is a simple custom JRDatasource containing a collection of strings in the field "field". The datasource iterates over the collection until there are no more strings. Each subreport has its own datasource. I tried many different variations of the above, with all sorts of different properties for the report, subreports, etc. IMO, this is fairly simple stuff. However, the problems I encounter are as follows: Subreports starting from the 3rd don't show up when their position type is 'float'. They do show up when they have 'fix relative to top'. However, I don't want to do this because the first two subreports can be of any length. I can't make each subreport to stretch according to its own length. Instead, they either don't stretch at all (which is not desirable because they have different lenghts) or they stretch according to the longest subreport. This makes a weird layout for sure. Pagination doesn't happen. If some subreports fall outside the page, they simple don't show. One alternative is to increase the 'page height' considerably and the 'detail band height' accordingly. However, in this case it is not really possibly to know the total height in advance. So I'm stuck with calculating/guessing it myself, before the report is even generated. More importantly, long reports end up on one page and this is not acceptable (the printout text is too small, it's ugly/non-professional to have different reports with different PDF page lengths, etc.). BTW, I used iReport so it's possibly limitations of iReport I'm listing here and not of Jasper itself. That's one of the things I'm trying to find out asking this question here. One alternative would be to generate the jrxml myself with just static text but I'm afraid I'll encounter the very same limitations. Anyway, I just generally wasted so much time getting anything done with Jasper that I can't help thinking its not the right tool for the job. (Not to say that Jasper doesn't excel in what it's good at).

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  • Understanding the value of Customer Experience & Loyalty for the Telecommunications Industry

    - by raul.goycoolea
    Worried by economic woes and market forces, especially in mature markets, communications service providers (CSPs) increasingly focus on improving customer experience. In fact, it seems difficult to find a major message by a C-level executive in the developed world that does not include something on "meeting and exceeding customers' needs". Frequently in customer satisfaction studies by prominent firms, CSPs fall short of the leadership demonstrated by other industries that take customer-centric approaches to their bottom-line strategies. Consider the following:Despite the continued impact of global economic crisis, in July 2010, Apple Computer posted record revenue and net quarterly profit. Those who attribute the results primarily to the iPhone 4 launch should note that Apple also shipped around 30% more Macintosh computers than the same period the previous year. Even sales of the iPod line increased by 8% in a highly commoditized, shrinking media player market. Finally, Apple began selling iPads during the quarter, with total sales of more than 3 million units. What does Apple have that the others lack? Well, some great products (and services) to be sure, but it also excels at customer service and support, marketing, and distribution, and has one of the strongest brands globally. Its products are useful, simple to use, easy to acquire and augment, high quality, and considered very cool. They also evoke such an emotional response from many of Apple's customers, which they turn up their noses at competitive products.In other words, Apple appears to have mastered virtually every aspect of customer experience and the resultant loyalty of its customer base - even in difficult financial times. Through that unwavering customer focus, Apple continues to drive its revenues and profits to new heights. Other customer loyalty leaders like Wal-Mart, Google, Toyota and Honda are also doing well by focusing on customer experience as an essential driver of profitability. Service providers should note this performance and ask themselves how they might leverage the same principles to increase their own profitability. After all, that is what customer experience and loyalty are all about: profitability.To successfully manage all the critical touch points of customer experience, CSPs must shun the one-size-fits-all approach. They can no longer afford to view customer service fundamentally as an act of altruism - which mentality dates back to the industry's civil service days, when CSPs were typically government organizations that were critical to economic development and public safety.As regulators and public officials have pushed, and continue to push, service providers to new heights of reliability - using incentives and punishments - most CSPs already have some of the fundamental building blocks of customer service in place. Yet despite that history and experience, service providers still lag other industries in providing what is seen as good customer service.As we observed in the TMF's 2009 Insights Research report, Customer Experience Management: Driving Loyalty & Profitability there has been resurgence in interest by CSPs. More and more of them have stated ambitions to catch up other industries, and they are realizing that good customer service is a powerful strategy for increasing business performance and profitability, not an act of good will.CSPs are recognizing the connection between customer experience and profitability, as demonstrated in many studies. For example, according to research by Bain & Company, a 5 percent improvement in customer retention rates can yield as much as a 75 percent increase in profits for companies across a range of industries.After decades of customer experience strategy formulation, Bain partner and business author, Frederick Reichheld, considers "would you recommend us to a friend?" as the ultimate question for a customer. How many times have you or your friends recommended an iPod, iPhone or a Mac? What do your children recommend to their peers? Their peers to them?There are certain steps service providers have to take to create more personalized relationships with their customers, as well as reduce churn and increase profitability, all while becoming leaner and more agile. First, they have to define customer experience, we define it as the result of the sum of observations, perceptions, thoughts and feelings arising from interactions and relationships between customers and their service provider(s). Virtually every customer touch point - whether directly or indirectly linked to service providers and their partners - contributes to customer perception, satisfaction, loyalty, and ultimately profitability. Gaining leadership in customer experience and satisfaction will not be a simple task, as it is affected by virtually every customer-facing aspect of the service provider, and in turn impacts the service provider deeply - especially on the all-important bottom line. The scope of issues affecting customer experience is complex and dynamic.With new services, devices and applications extending the basis of customer experience to domains beyond the direct control of the service provider, it is likely to increase in complexity and dynamism.Customer loyalty = increased profitsAs stated earlier, customer experience programs are not fundamentally altruistic exercises, but a strategic means of improving competitiveness and profitability in the short and long term. Loyalty is essential to deriving long term profits from customers.Some of the earliest loyalty programs date back to the 1930s, when packaged goods companies offered embedded coupons for rewards to buyers, and eventually retail chains began offering reward programs to frequent shoppers. These programs continued for decades but were leapfrogged in the 1980s by more aggressive programs from the airlines.This movement was led by American Airlines, which launched the first full-scale loyalty marketing program of the modern era with the AAdvantage frequent flyer scheme. It was the first to reward frequent fliers with notional air miles that could be accumulated and later redeemed for free travel. Figure 1: Opportunities example of Customer loyalty driven profitOther airlines and travel providers were quick to grasp the incredible value of providing customers with an incentive to use their company exclusively. Within a few years, dozens of travel industry companies launched similar initiatives and now loyalty programs are achieving near-ubiquity in many service industries, especially those in which it is difficult to differentiate offerings by product attributes.The belief is that increased profitability will result from customer retention efforts because:•    The cost of acquisition occurs only at the beginning of a relationship: the longer the relationship, the lower the amortized cost;•    Account maintenance costs decline as a percentage of total costs, or as a percentage of revenue, over the lifetime of the relationship;•    Long term customers tend to be less inclined to switch and less price sensitive which can result in stable unit sales volume and increases in dollar-sales volume;•    Long term customers may initiate word-of-mouth promotions and referrals, which cost the company nothing and arguably are the most effective form of advertising;•    Long-term customers are more likely to buy ancillary products and higher margin supplemental products;•    Long term customers tend to be satisfied with their relationship with the company and are less likely to switch to competitors, making market entry or competitors gaining market share difficult;•    Regular customers tend to be less expensive to service, as they are familiar with the processes involved, require less 'education', and are consistent in their order placement;•    Increased customer retention and loyalty makes the employees' jobs easier and more satisfying. In turn, happy employees feed back into higher customer satisfaction in a virtuous circle. Figure 2: The virtuous circle of customer loyaltyFigure 2 represents a high-level example of a virtuous cycle driven by customer satisfaction and loyalty, depicting how superiority in product and service offerings, as well as strong customer support by competent employees, lead to higher sales and ultimately profitability. As stated above, this is not a new concept, but succeeding with it is difficult. It has eluded many a company driven to achieve profitability goals. Of course, for this circle to be virtuous, the customer relationship(s) must be profitable.Trying to maintain the loyalty of unprofitable customers is not a viable business strategy. It is, therefore, important that marketers can assess the profitability of each customer (or customer segment), and either improve or terminate relationships that are not profitable. This means each customer's 'relationship costs' must be understood and compared to their 'relationship revenue'. Customer lifetime value (CLV) is the most commonly used metric here, as it is generally accepted as a representation of exactly how much each customer is worth in monetary terms, and therefore a determinant of exactly how much a service provider should be willing to spend to acquire or retain that customer.CLV models make several simplifying assumptions and often involve the following inputs:•    Churn rate represents the percentage of customers who end their relationship with a company in a given period;•    Retention rate is calculated by subtracting the churn rate percentage from 100;•    Period/horizon equates to the units of time into which a customer relationship can be divided for analysis. A year is the most commonly used period for this purpose. Customer lifetime value is a multi-period calculation, often projecting three to seven years into the future. In practice, analysis beyond this point is viewed as too speculative to be reliable. The model horizon is the number of periods used in the calculation;•    Periodic revenue is the amount of revenue collected from a customer in a given period (though this is often extended across multiple periods into the future to understand lifetime value), such as usage revenue, revenues anticipated from cross and upselling, and often some weighting for referrals by a loyal customer to others; •    Retention cost describes the amount of money the service provider must spend, in a given period, to retain an existing customer. Again, this is often forecast across multiple periods. Retention costs include customer support, billing, promotional incentives and so on;•    Discount rate means the cost of capital used to discount future revenue from a customer. Discounting is an advanced method used in more sophisticated CLV calculations;•    Profit margin is the projected profit as a percentage of revenue for the period. This may be reflected as a percentage of gross or net profit. Again, this is generally projected across the model horizon to understand lifetime value.A strong focus on managing these inputs can help service providers realize stronger customer relationships and profits, but there are some obstacles to overcome in achieving accurate calculations of CLV, such as the complexity of allocating costs across the customer base. There are many costs that serve all customers which must be properly allocated across the base, and often a simple proportional allocation across the whole base or a segment may not accurately reflect the true cost of serving that customer;  This is made worse by the fragmentation of customer information, which is likely to be across a variety of product or operations groups, and may be difficult to aggregate due to different representations.In addition, there is the complexity of account relationships and structures to take into consideration. Complex account structures may not be understood or properly represented. For example, a profitable customer may have a separate account for a second home or another family member, which may appear to be unprofitable. If the service provider cannot relate the two accounts, CLV is not properly represented and any resultant cancellation of the apparently unprofitable account may result in the customer churning from the profitable one.In summary, if service providers are to realize strong customer relationships and their attendant profits, there must be a very strong focus on data management. This needs to be coupled with analytics that help business managers and those who work in customer-facing functions offer highly personalized solutions to customers, while maintaining profitability for the service provider. It's clear that acquiring new customers is expensive. Advertising costs, campaign management expenses, promotional service pricing and discounting, and equipment subsidies make a serious dent in a new customer's profitability. That is especially true given the rising subsidies for Smartphone users, which service providers hope will result in greater profits from profits from data services profitability in future.  The situation is made worse by falling prices and greater competition in mature markets.Customer acquisition through industry consolidation isn't cheap either. A North American service provider spent about $2,000 per subscriber in its acquisition of a smaller company earlier this year. While this has allowed it to leapfrog to become the largest mobile service provider in the country, it required a total investment of more than $28 billion (including assumption of the acquiree's debt).While many operating cost synergies clearly made this deal more attractive to the acquiring company, this is certainly an expensive way to acquire customers: the cost per subscriber in this case is not out of line with the prices others have paid for acquisitions.While growth by acquisition certainly increases overall revenues, it often creates tremendous challenges for profitability. Organic growth through increased customer loyalty and retention is a more effective driver of profit, as well as a stronger predictor of future profitability. Service providers, especially those in mature markets, are increasingly recognizing this and taking steps toward a creating a more personalized, flexible and satisfying experience for their customers.In summary, the clearest path to profitability for companies in virtually all industries is through customer retention and maximization of lifetime value. Service providers would do well to recognize this and focus attention on profitable customer relationships.

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  • CBO????????

    - by Liu Maclean(???)
    ???Itpub????????CBO??????????, ????????: SQL> create table maclean1 as select * from dba_objects; Table created. SQL> update maclean1 set status='INVALID' where owner='MACLEAN'; 2 rows updated. SQL> commit; Commit complete. SQL> create index ind_maclean1 on maclean1(status); Index created. SQL> exec dbms_stats.gather_table_stats('SYS','MACLEAN1',cascade=>true); PL/SQL procedure successfully completed. SQL> explain plan for select * from maclean1 where status='INVALID'; Explained. SQL> set linesize 140 pagesize 1400 SQL> select * from table(dbms_xplan.display()); PLAN_TABLE_OUTPUT --------------------------------------------------------------------------- Plan hash value: 987568083 ------------------------------------------------------------------------------ | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------ | 0 | SELECT STATEMENT | | 11320 | 1028K| 85 (0)| 00:00:02 | |* 1 | TABLE ACCESS FULL| MACLEAN1 | 11320 | 1028K| 85 (0)| 00:00:02 | ------------------------------------------------------------------------------ Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter("STATUS"='INVALID') 13 rows selected. 10053 trace Access path analysis for MACLEAN1 *************************************** SINGLE TABLE ACCESS PATH   Single Table Cardinality Estimation for MACLEAN1[MACLEAN1]   Column (#10): STATUS(     AvgLen: 7 NDV: 2 Nulls: 0 Density: 0.500000   Table: MACLEAN1  Alias: MACLEAN1     Card: Original: 22639.000000  Rounded: 11320  Computed: 11319.50  Non Adjusted: 11319.50   Access Path: TableScan     Cost:  85.33  Resp: 85.33  Degree: 0       Cost_io: 85.00  Cost_cpu: 11935345       Resp_io: 85.00  Resp_cpu: 11935345   Access Path: index (AllEqRange)     Index: IND_MACLEAN1     resc_io: 185.00  resc_cpu: 8449916     ix_sel: 0.500000  ix_sel_with_filters: 0.500000     Cost: 185.24  Resp: 185.24  Degree: 1   Best:: AccessPath: TableScan          Cost: 85.33  Degree: 1  Resp: 85.33  Card: 11319.50  Bytes: 0 ?????10053????????????,?????Density = 0.5 ?? 1/ NDV ??? ??????????????STATUS='INVALID"???????????, ????????????????? ????”STATUS”=’INVALID’ condition???2?,?status??????,??????dbms_stats?????????????,???CBO????INDEX Range ind_maclean1,???????,??????opitimizer?????? ?????????????????????????,????????,??????????status=’INVALID’???????card??,????????: [oracle@vrh4 ~]$ sqlplus / as sysdba SQL*Plus: Release 11.2.0.2.0 Production on Mon Oct 17 19:15:45 2011 Copyright (c) 1982, 2010, Oracle. All rights reserved. Connected to: Oracle Database 11g Enterprise Edition Release 11.2.0.2.0 - 64bit Production With the Partitioning, OLAP, Data Mining and Real Application Testing options SQL> select * from v$version; BANNER -------------------------------------------------------------------------------- Oracle Database 11g Enterprise Edition Release 11.2.0.2.0 - 64bit Production PL/SQL Release 11.2.0.2.0 - Production CORE 11.2.0.2.0 Production TNS for Linux: Version 11.2.0.2.0 - Production NLSRTL Version 11.2.0.2.0 - Production SQL> show parameter optimizer_fea NAME TYPE VALUE ------------------------------------ ----------- ------------------------------ optimizer_features_enable string 11.2.0.2 SQL> select * from global_name; GLOBAL_NAME -------------------------------------------------------------------------------- www.oracledatabase12g.com & www.askmaclean.com SQL> drop table maclean; Table dropped. SQL> create table maclean as select * from dba_objects; Table created. SQL> update maclean set status='INVALID' where owner='MACLEAN'; 2 rows updated. SQL> commit; Commit complete. SQL> create index ind_maclean on maclean(status); Index created. SQL> exec dbms_stats.gather_table_stats('SYS','MACLEAN',cascade=>true, method_opt=>'FOR ALL COLUMNS SIZE 2'); PL/SQL procedure successfully completed. ???????2?bucket????, ??????????????? ???Quest???Guy Harrison???????FREQUENCY????????,??????: rem rem Generate a histogram of data distribution in a column as recorded rem in dba_tab_histograms rem rem Guy Harrison Jan 2010 : www.guyharrison.net rem rem hexstr function is from From http://asktom.oracle.com/pls/asktom/f?p=100:11:0::::P11_QUESTION_ID:707586567563 set pagesize 10000 set lines 120 set verify off col char_value format a10 heading "Endpoint|value" col bucket_count format 99,999,999 heading "bucket|count" col pct format 999.99 heading "Pct" col pct_of_max format a62 heading "Pct of|Max value" rem col endpoint_value format 9999999999999 heading "endpoint|value" CREATE OR REPLACE FUNCTION hexstr (p_number IN NUMBER) RETURN VARCHAR2 AS l_str LONG := TO_CHAR (p_number, 'fm' || RPAD ('x', 50, 'x')); l_return VARCHAR2 (4000); BEGIN WHILE (l_str IS NOT NULL) LOOP l_return := l_return || CHR (TO_NUMBER (SUBSTR (l_str, 1, 2), 'xx')); l_str := SUBSTR (l_str, 3); END LOOP; RETURN (SUBSTR (l_return, 1, 6)); END; / WITH hist_data AS ( SELECT endpoint_value,endpoint_actual_value, NVL(LAG (endpoint_value) OVER (ORDER BY endpoint_value),' ') prev_value, endpoint_number, endpoint_number, endpoint_number - NVL (LAG (endpoint_number) OVER (ORDER BY endpoint_value), 0) bucket_count FROM dba_tab_histograms JOIN dba_tab_col_statistics USING (owner, table_name,column_name) WHERE owner = '&owner' AND table_name = '&table' AND column_name = '&column' AND histogram='FREQUENCY') SELECT nvl(endpoint_actual_value,endpoint_value) endpoint_value , bucket_count, ROUND(bucket_count*100/SUM(bucket_count) OVER(),2) PCT, RPAD(' ',ROUND(bucket_count*50/MAX(bucket_count) OVER()),'*') pct_of_max FROM hist_data; WITH hist_data AS ( SELECT endpoint_value,endpoint_actual_value, NVL(LAG (endpoint_value) OVER (ORDER BY endpoint_value),' ') prev_value, endpoint_number, endpoint_number, endpoint_number - NVL (LAG (endpoint_number) OVER (ORDER BY endpoint_value), 0) bucket_count FROM dba_tab_histograms JOIN dba_tab_col_statistics USING (owner, table_name,column_name) WHERE owner = '&owner' AND table_name = '&table' AND column_name = '&column' AND histogram='FREQUENCY') SELECT hexstr(endpoint_value) char_value, bucket_count, ROUND(bucket_count*100/SUM(bucket_count) OVER(),2) PCT, RPAD(' ',ROUND(bucket_count*50/MAX(bucket_count) OVER()),'*') pct_of_max FROM hist_data ORDER BY endpoint_value; ?????,??????????FREQUENCY?????: ??dbms_stats ?????STATUS=’INVALID’ bucket count=9 percent = 0.04 ,??????10053 trace????????: SQL> explain plan for select * from maclean where status='INVALID'; Explained. SQL>  select * from table(dbms_xplan.display()); PLAN_TABLE_OUTPUT ------------------------------------- Plan hash value: 3087014066 ------------------------------------------------------------------------------------------- | Id  | Operation                   | Name        | Rows  | Bytes | Cost (%CPU)| Time     | ------------------------------------------------------------------------------------------- |   0 | SELECT STATEMENT            |             |     9 |   837 |     2   (0)| 00:00:01 | |   1 |  TABLE ACCESS BY INDEX ROWID| MACLEAN     |     9 |   837 |     2   (0)| 00:00:01 | |*  2 |   INDEX RANGE SCAN          | IND_MACLEAN |     9 |       |     1   (0)| 00:00:01 | ------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): ---------------------------------------------------    2 - access("STATUS"='INVALID') ??????????????CBO???????STATUS=’INVALID’?cardnality?? , ??????????? ,??index range scan??Full table scan? ????????????????10053 trace: SQL> alter system flush shared_pool; System altered. SQL> oradebug setmypid; Statement processed. SQL> oradebug event 10053 trace name context forever ,level 1; Statement processed. SQL> explain plan for select * from maclean where status='INVALID'; Explained. SINGLE TABLE ACCESS PATH Single Table Cardinality Estimation for MACLEAN[MACLEAN] Column (#10): NewDensity:0.000199, OldDensity:0.000022 BktCnt:22640, PopBktCnt:22640, PopValCnt:2, NDV:2 ???NewDensity= bucket_count / SUM(bucket_count) /2 Column (#10): STATUS( AvgLen: 7 NDV: 2 Nulls: 0 Density: 0.000199 Histogram: Freq #Bkts: 2 UncompBkts: 22640 EndPtVals: 2 Table: MACLEAN Alias: MACLEAN Card: Original: 22640.000000 Rounded: 9 Computed: 9.00 Non Adjusted: 9.00 Access Path: TableScan Cost: 85.30 Resp: 85.30 Degree: 0 Cost_io: 85.00 Cost_cpu: 10804625 Resp_io: 85.00 Resp_cpu: 10804625 Access Path: index (AllEqRange) Index: IND_MACLEAN resc_io: 2.00 resc_cpu: 20763 ix_sel: 0.000398 ix_sel_with_filters: 0.000398 Cost: 2.00 Resp: 2.00 Degree: 1 Best:: AccessPath: IndexRange Index: IND_MACLEAN Cost: 2.00 Degree: 1 Resp: 2.00 Card: 9.00 Bytes: 0 ???????????2 bucket?????CBO????????????,???????????????????,???dbms_stats.DEFAULT_METHOD_OPT????????????????????? ???dbms_stats?????????????????????col_usage$??????predicate???????,??col_usage$??<????????SMON??(?):??col_usage$????>? ??????????dbms_stats????????,col_usage$????????????predicate???,??dbms_stats??????????????????, ?: SQL> drop table maclean; Table dropped. SQL> create table maclean as select * from dba_objects; Table created. SQL> update maclean set status='INVALID' where owner='MACLEAN'; 2 rows updated. SQL> commit; Commit complete. SQL> create index ind_maclean on maclean(status); Index created. ??dbms_stats??method_opt??maclean? SQL> exec dbms_stats.gather_table_stats('SYS','MACLEAN'); PL/SQL procedure successfully completed. @histogram.sql Enter value for owner: SYS old  12:    WHERE owner = '&owner' new  12:    WHERE owner = 'SYS' Enter value for table: MACLEAN old  13:      AND table_name = '&table' new  13:      AND table_name = 'MACLEAN' Enter value for column: STATUS old  14:      AND column_name = '&column' new  14:      AND column_name = 'STATUS' no rows selected ????col_usage$?????,????????status????? declare begin for i in 1..500 loop execute immediate ' alter system flush shared_pool'; DBMS_STATS.FLUSH_DATABASE_MONITORING_INFO; execute immediate 'select count(*) from maclean where status=''INVALID'' ' ; end loop; end; / PL/SQL procedure successfully completed. SQL> select obj# from obj$ where name='MACLEAN';       OBJ# ----------      97215 SQL> select * from  col_usage$ where  OBJ#=97215;       OBJ#    INTCOL# EQUALITY_PREDS EQUIJOIN_PREDS NONEQUIJOIN_PREDS RANGE_PREDS LIKE_PREDS NULL_PREDS TIMESTAMP ---------- ---------- -------------- -------------- ----------------- ----------- ---------- ---------- ---------      97215          1              1              0                 0           0          0          0 17-OCT-11      97215         10            499              0                 0           0          0          0 17-OCT-11 SQL> exec dbms_stats.gather_table_stats('SYS','MACLEAN'); PL/SQL procedure successfully completed. @histogram.sql Enter value for owner: SYS Enter value for table: MACLEAN Enter value for column: STATUS Endpoint        bucket         Pct of value            count     Pct Max value ---------- ----------- ------- -------------------------------------------------------------- INVALI               2     .04 VALIC3           5,453   99.96  *************************************************

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  • Proving What You are Worth

    - by Ted Henson
    Here is a challenge for everyone. Just about everyone has been asked to provide or calculate the Return on Investment (ROI), so I will assume everyone has a method they use. The problem with stopping once you have an ROI is that those in the C-Suite probably do not care about the ROI as much as Return on Equity (ROE). Shareholders are mostly concerned with their return on the money the invested. Warren Buffett looks at ROE when deciding whether to make a deal or not. This article will outline how you can add more meaning to your ROI and show how you can potentially enhance the ROE of the company.   First I want to start with a base definition I am using for ROI and ROE. Return on investment (ROI) and return on equity (ROE) are ways to measure management effectiveness, parts of a system of measures that also includes profit margins for profitability, price-to-earnings ratio for valuation, and various debt-to-equity ratios for financial strength. Without a set of evaluation metrics, a company's financial performance cannot be fully examined by investors. ROI and ROE calculate the rate of return on a specific investment and the equity capital respectively, assessing how efficient financial resources have been used. Typically, the best way to improve financial efficiency is to reduce production cost, so that will be the focus. Now that the challenge has been made and items have been defined, let’s go deeper. Most research about implementation stops short at system start-up and seldom addresses post-implementation issues. However, we know implementation is a continuous improvement effort, and continued efforts after system start-up will influence the ultimate success of a system.   Most UPK ROI’s I have seen only include the cost savings in developing the training material. Some will also include savings based on reduced Help Desk calls. Using just those values you get a good ROI. To get an ROE you need to go a little deeper. Typically, the best way to improve financial efficiency is to reduce production cost, which is the purpose of implementing/upgrading an enterprise application. Let’s assume the new system is up and running and all users have been properly trained and are comfortable using the system. You provide senior management with your ROI that justifies the original cost. What you want to do now is develop a good base value to a measure the current efficiency. Using usage tracking you can look for various patterns. For example, you may find that users that are accessing UPK assistance are processing a procedure, such as entering an order, 5 minutes faster than those that don’t.  You do some research and discover each minute saved in processing a claim saves the company one dollar. That translates to the company saving five dollars on every transaction. Assuming 100,000 transactions are performed a year, and all users improve their performance, the company will be saving $500,000 a year. That $500,000 can be re-invested, used to reduce debt or paid to the shareholders.   With continued refinement during the life cycle, you should be able to find ways to reduce cost. These are the type of numbers and productivity gains that senior management and shareholders want to see. Being able to quantify savings and increase productivity may also help when seeking a raise or promotion.

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  • Alternative to Amazon’s S3 service?

    - by Cory
    Just wondering if there is good alternative to Amazon's S3 service? I like S3 but the bandwidth cost is high. I looked at CouldFiles from Rackspace but the cost is even higher. I don't mind prepaying or having monthly payment in order to reduce the bandwidth cost greatly. Thank you for any help

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  • Updating a Minimum spanning tree when a new edge is inserted

    - by Lynette
    Hello, I've been presented the following problem in University: Let G = (V, E) be an (undirected) graph with costs ce = 0 on the edges e € E. Assume you are given a minimum-cost spanning tree T in G. Now assume that a new edge is added to G, connecting two nodes v, tv € V with cost c. a) Give an efficient algorithm to test if T remains the minimum-cost spanning tree with the new edge added to G (but not to the tree T). Make your algorithm run in time O(|E|). Can you do it in O(|V|) time? Please note any assumptions you make about what data structure is used to represent the tree T and the graph G. b)Suppose T is no longer the minimum-cost spanning tree. Give a linear-time algorithm (time O(|E|)) to update the tree T to the new minimum-cost spanning tree. This is the solution I found: Let e1=(a,b) the new edge added Find in T the shortest path from a to b (BFS) if e1 is the most expensive edge in the cycle then T remains the MST else T is not the MST It seems to work but i can easily make this run in O(|V|) time, while the problem asks O(|E|) time. Am i missing something? By the way we are authorized to ask for help from anyone so I'm not cheating :D Thanks in advance

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  • Neo4j Reading data / performing shortest path calculations on stored data

    - by paddydub
    I'm using the Batch_Insert example to insert Data into the database How can i read this data back from the database. I can't find any examples of how i do this. public static void CreateData() { // create the batch inserter BatchInserter inserter = new BatchInserterImpl( "var/graphdb", BatchInserterImpl.loadProperties( "var/neo4j.props" ) ); Map<String,Object> properties = new HashMap<String,Object>(); properties.put( "name", "Mr. Andersson" ); properties.put( "age", 29 ); long node1 = inserter.createNode( properties ); properties.put( "name", "Trinity" ); properties.remove( "age" ); long node2 = inserter.createNode( properties ); inserter.createRelationship( node1, node2, DynamicRelationshipType.withName( "KNOWS" ), null ); inserter.shutdown(); } I would like to store graph data in the database, graph.makeEdge( "s", "c", "cost", (double) 7 ); graph.makeEdge( "c", "e", "cost", (double) 7 ); graph.makeEdge( "s", "a", "cost", (double) 2 ); graph.makeEdge( "a", "b", "cost", (double) 7 ); graph.makeEdge( "b", "e", "cost", (double) 2 ); Dijkstra<Double> dijkstra = getDijkstra( graph, 0.0, "s", "e" ); What is the best method to store this kind data with 10000's of edges. Then run the Dijskra algorighm to find shortest path calculations using the stored graph data.

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  • Approximate string matching with a letter confusion matrix?

    - by zigglenaut
    I'm trying to model a phonetic recognizer that has to isolate instances of words (strings of phones) out of a long stream of phones that doesn't have gaps between each word. The stream of phones may have been poorly recognized, with letter substitutions/insertions/deletions, so I will have to do approximate string matching. However, I want the matching to be phonetically-motivated, e.g. "m" and "n" are phonetically similar, so the substitution cost of "m" for "n" should be small, compared to say, "m" and "k". So, if I'm searching for [mein] "main", it would match the letter sequence [meim] "maim" with, say, cost 0.1, whereas it would match the letter sequence [meik] "make" with, say, cost 0.7. Similarly, there are differing costs for inserting or deleting each letter. I can supply a confusion matrix that, for each letter pair (x,y), gives the cost of substituting x with y, where x and y are any letter or the empty string. I know that there are tools available that do approximate matching such as agrep, but as far as I can tell, they do not take a confusion matrix as input. That is, the cost of any insertion/substitution/deletion = 1. My question is, are there any open-source tools already available that can do approximate matching with confusion matrices, and if not, what is a good algorithm that I can implement to accomplish this?

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  • How to optimize my PostgreSQL DB for prefix search?

    - by asmaier
    I have a table called "nodes" with roughly 1.7 million rows in my PostgreSQL db =#\d nodes Table "public.nodes" Column | Type | Modifiers --------+------------------------+----------- id | integer | not null title | character varying(256) | score | double precision | Indexes: "nodes_pkey" PRIMARY KEY, btree (id) I want to use information from that table for autocompletion of a search field, showing the user a list of the ten titles having the highest score fitting to his input. So I used this query (here searching for all titles starting with "s") =# explain analyze select title,score from nodes where title ilike 's%' order by score desc; QUERY PLAN ----------------------------------------------------------------------------------------------------------------------- Sort (cost=64177.92..64581.38 rows=161385 width=25) (actual time=4930.334..5047.321 rows=161264 loops=1) Sort Key: score Sort Method: external merge Disk: 5712kB -> Seq Scan on nodes (cost=0.00..46630.50 rows=161385 width=25) (actual time=0.611..4464.413 rows=161264 loops=1) Filter: ((title)::text ~~* 's%'::text) Total runtime: 5260.791 ms (6 rows) This was much to slow for using it with autocomplete. With some information from Using PostgreSQL in Web 2.0 Applications I was able to improve that with a special index =# create index title_idx on nodes using btree(lower(title) text_pattern_ops); =# explain analyze select title,score from nodes where lower(title) like lower('s%') order by score desc limit 10; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------------ Limit (cost=18122.41..18122.43 rows=10 width=25) (actual time=1324.703..1324.708 rows=10 loops=1) -> Sort (cost=18122.41..18144.60 rows=8876 width=25) (actual time=1324.700..1324.702 rows=10 loops=1) Sort Key: score Sort Method: top-N heapsort Memory: 17kB -> Bitmap Heap Scan on nodes (cost=243.53..17930.60 rows=8876 width=25) (actual time=96.124..1227.203 rows=161264 loops=1) Filter: (lower((title)::text) ~~ 's%'::text) -> Bitmap Index Scan on title_idx (cost=0.00..241.31 rows=8876 width=0) (actual time=90.059..90.059 rows=161264 loops=1) Index Cond: ((lower((title)::text) ~>=~ 's'::text) AND (lower((title)::text) ~<~ 't'::text)) Total runtime: 1325.085 ms (9 rows) So this gave me a speedup of factor 4. But can this be further improved? What if I want to use '%s%' instead of 's%'? Do I have any chance of getting a decent performance with PostgreSQL in that case, too? Or should I better try a different solution (Lucene?, Sphinx?) for implementing my autocomplete feature?

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  • How can I work around SQL Server - Inline Table Value Function execution plan variation based on par

    - by Ovidiu Pacurar
    Here is the situation: I have a table value function with a datetime parameter ,lest's say tdf(p_date) , that filters about two million rows selecting those with column date smaller than p_date and computes some aggregate values on other columns. It works great but if p_date is a custom scalar value function (returning the end of day in my case) the execution plan is altered an the query goes from 1 sec to 1 minute execution time. A proof of concept table - 1K products, 2M rows: CREATE TABLE [dbo].[POC]( [Date] [datetime] NOT NULL, [idProduct] [int] NOT NULL, [Quantity] [int] NOT NULL ) ON [PRIMARY] The inline table value function: CREATE FUNCTION tdf (@p_date datetime) RETURNS TABLE AS RETURN ( SELECT idProduct, SUM(Quantity) AS TotalQuantity, max(Date) as LastDate FROM POC WHERE (Date < @p_date) GROUP BY idProduct ) The scalar value function: CREATE FUNCTION [dbo].[EndOfDay] (@date datetime) RETURNS datetime AS BEGIN DECLARE @res datetime SET @res=dateadd(second, -1, dateadd(day, 1, dateadd(ms, -datepart(ms, @date), dateadd(ss, -datepart(ss, @date), dateadd(mi,- datepart(mi,@date), dateadd(hh, -datepart(hh, @date), @date)))))) RETURN @res END Query 1 - Working great SELECT * FROM [dbo].[tdf] (getdate()) The end of execution plan: Stream Aggregate Cost 13% <--- Clustered Index Scan Cost 86% Query 2 - Not so great SELECT * FROM [dbo].[tdf] (dbo.EndOfDay(getdate())) The end of execution plan: Stream Aggregate Cost 4% <--- Filter Cost 12% <--- Clustered Index Scan Cost 86%

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  • GridView's NewValues and OldValues empty in the OnRowUpdating event.

    - by Abe Miessler
    I have the GridView below. I am binding to a custom datasource in the code behind. It gets into the "OnRowUpdating" event just fine, but there are no NewValues or OldValues. Any suggestions as to how I can get these values? <asp:GridView ID="gv_Personnel" runat="server" OnRowDataBound="gv_Personnel_DataBind" OnRowCancelingEdit="gv_Personnel_CancelEdit" OnRowEditing="gv_Personnel_EditRow" OnRowUpdating="gv_Personnel_UpdateRow" AutoGenerateColumns="false" ShowFooter="true" DataKeyNames="BudgetLineID" AutoGenerateEditButton="true" AutoGenerateDeleteButton="true" > <Columns> <asp:BoundField HeaderText="Level of Staff" DataField="LineDescription" /> <%--<asp:BoundField HeaderText="Hrs/Units requested" DataField="NumberOfUnits" />--%> <asp:TemplateField HeaderText="Hrs/Units requested"> <ItemTemplate> <%# Eval("NumberOfUnits")%> </ItemTemplate> <EditItemTemplate> <asp:TextBox ID="tb_NumUnits" runat="server" Text='<%# Bind("NumberOfUnits")%>' /> </EditItemTemplate> </asp:TemplateField> <asp:BoundField HeaderText="Hrs/Units of Applicant Cost Share" DataField="" NullDisplayText="0" /> <asp:BoundField HeaderText="Hrs/Units of Partner Cost Share" DataField="" NullDisplayText="0" /> <asp:BoundField FooterStyle-Font-Bold="true" FooterText="TOTAL PERSONNEL SERVICES:" HeaderText="Rate" DataFormatString="{0:C}" DataField="UnitPrice" /> <asp:TemplateField HeaderText="Amount Requested" ItemStyle-HorizontalAlign="Right" FooterStyle-HorizontalAlign="Right" FooterStyle-BorderWidth="2" FooterStyle-Font-Bold="true"/> <asp:TemplateField HeaderText="Applicant Cost Share" ItemStyle-HorizontalAlign="Right" FooterStyle-HorizontalAlign="Right" FooterStyle-BorderWidth="2" FooterStyle-Font-Bold="true"/> <asp:TemplateField HeaderText="Partner Cost Share" ItemStyle-HorizontalAlign="Right" FooterStyle-HorizontalAlign="Right" FooterStyle-BorderWidth="2" FooterStyle-Font-Bold="true"/> <asp:TemplateField HeaderText="Total Projet Cost" ItemStyle-HorizontalAlign="Right" FooterStyle-HorizontalAlign="Right" FooterStyle-BorderWidth="2" FooterStyle-Font-Bold="true"/> </Columns> </asp:GridView>

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  • Python code formatting

    - by Curious2learn
    In response to another question of mine, someone suggested that I avoid long lines in the code and to use PEP-8 rules when writing Python code. One of the PEP-8 rules suggested avoiding lines which are longer than 80 characters. I changed a lot of my code to comply with this requirement without any problems. However, changing the following line in the manner shown below breaks the code. Any ideas why? Does it have to do with the fact that what follows return command has to be in a single line? The line longer that 80 characters: def __str__(self): return "Car Type \n"+"mpg: %.1f \n" % self.mpg + "hp: %.2f \n" %(self.hp) + "pc: %i \n" %self.pc + "unit cost: $%.2f \n" %(self.cost) + "price: $%.2f "%(self.price) The line changed by using Enter key and Spaces as necessary: def __str__(self): return "Car Type \n"+"mpg: %.1f \n" % self.mpg + "hp: %.2f \n" %(self.hp) + "pc: %i \n" %self.pc + "unit cost: $%.2f \n" %(self.cost) + "price: $%.2f "%(self.price)

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  • Dijkstras Algorithm exaplination java

    - by alchemey89
    Hi, I have found an implementation for dijkstras algorithm on the internet and was wondering if someone could help me understand how the code works. Many thanks private int nr_points=0; private int[][]Cost; private int []mask; private void dijkstraTSP() { if(nr_points==0)return; //algorithm=new String("Dijkstra"); nod1=new Vector(); nod2=new Vector(); weight=new Vector(); mask=new int[nr_points]; //initialise mask with zeros (mask[x]=1 means the vertex is marked as used) for(int i=0;i<nr_points;i++)mask[i]=0; //Dijkstra: int []dd=new int[nr_points]; int []pre=new int[nr_points]; int []path=new int[nr_points+1]; int init_vert=0,pos_in_path=0,new_vert=0; //initialise the vectors for(int i=0;i<nr_points;i++) { dd[i]=Cost[init_vert][i]; pre[i]=init_vert; path[i]=-1; } pre[init_vert]=0; path[0]=init_vert; pos_in_path++; mask[init_vert]=1; for(int k=0;k<nr_points-1;k++) { //find min. cost in dd for(int j=0;j<nr_points;j++) if(dd[j]!=0 && mask[j]==0){new_vert=j; break;} for(int j=0;j<nr_points;j++) if(dd[j]<dd[new_vert] && mask[j]==0 && dd[j]!=0)new_vert=j; mask[new_vert]=1; path[pos_in_path]=new_vert; pos_in_path++; for(int j=0;j<nr_points;j++) { if(mask[j]==0) { if(dd[j]>dd[new_vert]+Cost[new_vert][j]) { dd[j]=dd[new_vert]+Cost[new_vert][j]; } } } } //Close the cycle path[nr_points]=init_vert; //Save the solution in 3 vectors (for graphical purposes) for(int i=0;i<nr_points;i++) { nod1.addElement(path[i]); nod2.addElement(path[i+1]); weight.addElement(Cost[path[i]][path[i+1]]); } }

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  • Maximum float value in php

    - by Alex Deem
    Is there a way to programmatically retrieve the maximum float value for php. Akin to FLT_MAX or std::numeric_limits< float >::max() in C / C++? I am using something like the following: $minimumCost = MAXIMUM_FLOAT_VALUE??; foreach ( $objects as $object ) { $cost = $object->CalculateCost(); if ( $cost < $minimumCost ) { $minimumCost = $cost; } } (using php 5.2)

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  • Dijkstra's Algorithm explanation java

    - by alchemey89
    Hi, I have found an implementation for dijkstras algorithm on the internet and was wondering if someone could help me understand how the code works. Many thanks private int nr_points=0; private int[][]Cost; private int []mask; private void dijkstraTSP() { if(nr_points==0)return; //algorithm=new String("Dijkstra"); nod1=new Vector(); nod2=new Vector(); weight=new Vector(); mask=new int[nr_points]; //initialise mask with zeros (mask[x]=1 means the vertex is marked as used) for(int i=0;i<nr_points;i++)mask[i]=0; //Dijkstra: int []dd=new int[nr_points]; int []pre=new int[nr_points]; int []path=new int[nr_points+1]; int init_vert=0,pos_in_path=0,new_vert=0; //initialise the vectors for(int i=0;i<nr_points;i++) { dd[i]=Cost[init_vert][i]; pre[i]=init_vert; path[i]=-1; } pre[init_vert]=0; path[0]=init_vert; pos_in_path++; mask[init_vert]=1; for(int k=0;k<nr_points-1;k++) { //find min. cost in dd for(int j=0;j<nr_points;j++) if(dd[j]!=0 && mask[j]==0){new_vert=j; break;} for(int j=0;j<nr_points;j++) if(dd[j]<dd[new_vert] && mask[j]==0 && dd[j]!=0)new_vert=j; mask[new_vert]=1; path[pos_in_path]=new_vert; pos_in_path++; for(int j=0;j<nr_points;j++) { if(mask[j]==0) { if(dd[j]>dd[new_vert]+Cost[new_vert][j]) { dd[j]=dd[new_vert]+Cost[new_vert][j]; } } } } //Close the cycle path[nr_points]=init_vert; //Save the solution in 3 vectors (for graphical purposes) for(int i=0;i<nr_points;i++) { nod1.addElement(path[i]); nod2.addElement(path[i+1]); weight.addElement(Cost[path[i]][path[i+1]]); } }

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