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  • Speeding up a search .net 4.0

    - by user231465
    Wondering if I can speed up the search. I need to build a functionality that has to be used by many UI screens The one I have got works but I need to make sure I am implementing a fast algoritim if you like It's like an incremental search. User types a word to search for eg const string searchFor = "Guinea"; const char nextLetter = ' ' It looks in the list and returns 2 records "Guinea and Guinea Bissau " User types a word to search for eg const string searchFor = "Gu"; const char nextLetter = 'i' returns 3 results. This is the function but I would like to speed it up. Is there a pattern for this kind of search? class Program { static void Main() { //Find all countries that begin with string + a possible letter added to it //const string searchFor = "Guinea"; //const char nextLetter = ' '; //returns 2 results const string searchFor = "Gu"; const char nextLetter = 'i'; List<string> result = FindPossibleMatches(searchFor, nextLetter); result.ForEach(x=>Console.WriteLine(x)); //returns 3 results Console.Read(); } /// <summary> /// Find all possible matches /// </summary> /// <param name="searchFor">string to search for</param> /// <param name="nextLetter">pretend user as just typed a letter</param> /// <returns></returns> public static List<string> FindPossibleMatches (string searchFor, char nextLetter) { var hashedCountryList = new HashSet<string>(CountriesList()); var result=new List<string>(); IEnumerable<string> tempCountryList = hashedCountryList.Where(x => x.StartsWith(searchFor)); foreach (string item in tempCountryList) { string tempSearchItem; if (nextLetter == ' ') { tempSearchItem = searchFor; } else { tempSearchItem = searchFor + nextLetter; } if(item.StartsWith(tempSearchItem)) { result.Add(item); } } return result; } /// <summary> /// Returns list of countries. /// </summary> public static string[] CountriesList() { return new[] { "Afghanistan", "Albania", "Algeria", "American Samoa", "Andorra", "Angola", "Anguilla", "Antarctica", "Antigua And Barbuda", "Argentina", "Armenia", "Aruba", "Australia", "Austria", "Azerbaijan", "Bahamas", "Bahrain", "Bangladesh", "Barbados", "Belarus", "Belgium", "Belize", "Benin", "Bermuda", "Bhutan", "Bolivia", "Bosnia Hercegovina", "Botswana", "Bouvet Island", "Brazil", "Brunei Darussalam", "Bulgaria", "Burkina Faso", "Burundi", "Byelorussian SSR", "Cambodia", "Cameroon", "Canada", "Cape Verde", "Cayman Islands", "Central African Republic", "Chad", "Chile", "China", "Christmas Island", "Cocos (Keeling) Islands", "Colombia", "Comoros", "Congo", "Cook Islands", "Costa Rica", "Cote D'Ivoire", "Croatia", "Cuba", "Cyprus", "Czech Republic", "Czechoslovakia", "Denmark", "Djibouti", "Dominica", "Dominican Republic", "East Timor", "Ecuador", "Egypt", "El Salvador", "England", "Equatorial Guinea", "Eritrea", "Estonia", "Ethiopia", "Falkland Islands", "Faroe Islands", "Fiji", "Finland", "France", "Gabon", "Gambia", "Georgia", "Germany", "Ghana", "Gibraltar", "Great Britain", "Greece", "Greenland", "Grenada", "Guadeloupe", "Guam", "Guatemela", "Guernsey", "Guiana", "Guinea", "Guinea Bissau", "Guyana", "Haiti", "Heard Islands", "Honduras", "Hong Kong", "Hungary", "Iceland", "India", "Indonesia", "Iran", "Iraq", "Ireland", "Isle Of Man", "Israel", "Italy", "Jamaica", "Japan", "Jersey", "Jordan", "Kazakhstan", "Kenya", "Kiribati", "Korea, South", "Korea, North", "Kuwait", "Kyrgyzstan", "Lao People's Dem. Rep.", "Latvia", "Lebanon", "Lesotho", "Liberia", "Libya", "Liechtenstein", "Lithuania", "Luxembourg", "Macau", "Macedonia", "Madagascar", "Malawi", "Malaysia", "Maldives", "Mali", "Malta", "Mariana Islands", "Marshall Islands", "Martinique", "Mauritania", "Mauritius", "Mayotte", "Mexico", "Micronesia", "Moldova", "Monaco", "Mongolia", "Montserrat", "Morocco", "Mozambique", "Myanmar", "Namibia", "Nauru", "Nepal", "Netherlands", "Netherlands Antilles", "Neutral Zone", "New Caledonia", "New Zealand", "Nicaragua", "Niger", "Nigeria", "Niue", "Norfolk Island", "Northern Ireland", "Norway", "Oman", "Pakistan", "Palau", "Panama", "Papua New Guinea", "Paraguay", "Peru", "Philippines", "Pitcairn", "Poland", "Polynesia", "Portugal", "Puerto Rico", "Qatar", "Reunion", "Romania", "Russian Federation", "Rwanda", "Saint Helena", "Saint Kitts", "Saint Lucia", "Saint Pierre", "Saint Vincent", "Samoa", "San Marino", "Sao Tome and Principe", "Saudi Arabia", "Scotland", "Senegal", "Seychelles", "Sierra Leone", "Singapore", "Slovakia", "Slovenia", "Solomon Islands", "Somalia", "South Africa", "South Georgia", "Spain", "Sri Lanka", "Sudan", "Suriname", "Svalbard", "Swaziland", "Sweden", "Switzerland", "Syrian Arab Republic", "Taiwan", "Tajikista", "Tanzania", "Thailand", "Togo", "Tokelau", "Tonga", "Trinidad and Tobago", "Tunisia", "Turkey", "Turkmenistan", "Turks and Caicos Islands", "Tuvalu", "Uganda", "Ukraine", "United Arab Emirates", "United Kingdom", "United States", "Uruguay", "Uzbekistan", "Vanuatu", "Vatican City State", "Venezuela", "Vietnam", "Virgin Islands", "Wales", "Western Sahara", "Yemen", "Yugoslavia", "Zaire", "Zambia", "Zimbabwe" }; } } } Any suggestions? Thanks

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  • Fraud Detection with the SQL Server Suite Part 1

    - by Dejan Sarka
    While working on different fraud detection projects, I developed my own approach to the solution for this problem. In my PASS Summit 2013 session I am introducing this approach. I also wrote a whitepaper on the same topic, which was generously reviewed by my friend Matija Lah. In order to spread this knowledge faster, I am starting a series of blog posts which will at the end make the whole whitepaper. Abstract With the massive usage of credit cards and web applications for banking and payment processing, the number of fraudulent transactions is growing rapidly and on a global scale. Several fraud detection algorithms are available within a variety of different products. In this paper, we focus on using the Microsoft SQL Server suite for this purpose. In addition, we will explain our original approach to solving the problem by introducing a continuous learning procedure. Our preferred type of service is mentoring; it allows us to perform the work and consulting together with transferring the knowledge onto the customer, thus making it possible for a customer to continue to learn independently. This paper is based on practical experience with different projects covering online banking and credit card usage. Introduction A fraud is a criminal or deceptive activity with the intention of achieving financial or some other gain. Fraud can appear in multiple business areas. You can find a detailed overview of the business domains where fraud can take place in Sahin Y., & Duman E. (2011), Detecting Credit Card Fraud by Decision Trees and Support Vector Machines, Proceedings of the International MultiConference of Engineers and Computer Scientists 2011 Vol 1. Hong Kong: IMECS. Dealing with frauds includes fraud prevention and fraud detection. Fraud prevention is a proactive mechanism, which tries to disable frauds by using previous knowledge. Fraud detection is a reactive mechanism with the goal of detecting suspicious behavior when a fraudster surpasses the fraud prevention mechanism. A fraud detection mechanism checks every transaction and assigns a weight in terms of probability between 0 and 1 that represents a score for evaluating whether a transaction is fraudulent or not. A fraud detection mechanism cannot detect frauds with a probability of 100%; therefore, manual transaction checking must also be available. With fraud detection, this manual part can focus on the most suspicious transactions. This way, an unchanged number of supervisors can detect significantly more frauds than could be achieved with traditional methods of selecting which transactions to check, for example with random sampling. There are two principal data mining techniques available both in general data mining as well as in specific fraud detection techniques: supervised or directed and unsupervised or undirected. Supervised techniques or data mining models use previous knowledge. Typically, existing transactions are marked with a flag denoting whether a particular transaction is fraudulent or not. Customers at some point in time do report frauds, and the transactional system should be capable of accepting such a flag. Supervised data mining algorithms try to explain the value of this flag by using different input variables. When the patterns and rules that lead to frauds are learned through the model training process, they can be used for prediction of the fraud flag on new incoming transactions. Unsupervised techniques analyze data without prior knowledge, without the fraud flag; they try to find transactions which do not resemble other transactions, i.e. outliers. In both cases, there should be more frauds in the data set selected for checking by using the data mining knowledge compared to selecting the data set with simpler methods; this is known as the lift of a model. Typically, we compare the lift with random sampling. The supervised methods typically give a much better lift than the unsupervised ones. However, we must use the unsupervised ones when we do not have any previous knowledge. Furthermore, unsupervised methods are useful for controlling whether the supervised models are still efficient. Accuracy of the predictions drops over time. Patterns of credit card usage, for example, change over time. In addition, fraudsters continuously learn as well. Therefore, it is important to check the efficiency of the predictive models with the undirected ones. When the difference between the lift of the supervised models and the lift of the unsupervised models drops, it is time to refine the supervised models. However, the unsupervised models can become obsolete as well. It is also important to measure the overall efficiency of both, supervised and unsupervised models, over time. We can compare the number of predicted frauds with the total number of frauds that include predicted and reported occurrences. For measuring behavior across time, specific analytical databases called data warehouses (DW) and on-line analytical processing (OLAP) systems can be employed. By controlling the supervised models with unsupervised ones and by using an OLAP system or DW reports to control both, a continuous learning infrastructure can be established. There are many difficulties in developing a fraud detection system. As has already been mentioned, fraudsters continuously learn, and the patterns change. The exchange of experiences and ideas can be very limited due to privacy concerns. In addition, both data sets and results might be censored, as the companies generally do not want to publically expose actual fraudulent behaviors. Therefore it can be quite difficult if not impossible to cross-evaluate the models using data from different companies and different business areas. This fact stresses the importance of continuous learning even more. Finally, the number of frauds in the total number of transactions is small, typically much less than 1% of transactions is fraudulent. Some predictive data mining algorithms do not give good results when the target state is represented with a very low frequency. Data preparation techniques like oversampling and undersampling can help overcome the shortcomings of many algorithms. SQL Server suite includes all of the software required to create, deploy any maintain a fraud detection infrastructure. The Database Engine is the relational database management system (RDBMS), which supports all activity needed for data preparation and for data warehouses. SQL Server Analysis Services (SSAS) supports OLAP and data mining (in version 2012, you need to install SSAS in multidimensional and data mining mode; this was the only mode in previous versions of SSAS, while SSAS 2012 also supports the tabular mode, which does not include data mining). Additional products from the suite can be useful as well. SQL Server Integration Services (SSIS) is a tool for developing extract transform–load (ETL) applications. SSIS is typically used for loading a DW, and in addition, it can use SSAS data mining models for building intelligent data flows. SQL Server Reporting Services (SSRS) is useful for presenting the results in a variety of reports. Data Quality Services (DQS) mitigate the occasional data cleansing process by maintaining a knowledge base. Master Data Services is an application that helps companies maintaining a central, authoritative source of their master data, i.e. the most important data to any organization. For an overview of the SQL Server business intelligence (BI) part of the suite that includes Database Engine, SSAS and SSRS, please refer to Veerman E., Lachev T., & Sarka D. (2009). MCTS Self-Paced Training Kit (Exam 70-448): Microsoft® SQL Server® 2008 Business Intelligence Development and Maintenance. MS Press. For an overview of the enterprise information management (EIM) part that includes SSIS, DQS and MDS, please refer to Sarka D., Lah M., & Jerkic G. (2012). Training Kit (Exam 70-463): Implementing a Data Warehouse with Microsoft® SQL Server® 2012. O'Reilly. For details about SSAS data mining, please refer to MacLennan J., Tang Z., & Crivat B. (2009). Data Mining with Microsoft SQL Server 2008. Wiley. SQL Server Data Mining Add-ins for Office, a free download for Office versions 2007, 2010 and 2013, bring the power of data mining to Excel, enabling advanced analytics in Excel. Together with PowerPivot for Excel, which is also freely downloadable and can be used in Excel 2010, is already included in Excel 2013. It brings OLAP functionalities directly into Excel, making it possible for an advanced analyst to build a complete learning infrastructure using a familiar tool. This way, many more people, including employees in subsidiaries, can contribute to the learning process by examining local transactions and quickly identifying new patterns.

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  • Oracle bleibt auch 2011 Spitzenreiter im Bereich Datenbanken

    - by Anne Manke
    Mit der Veröffentlichung der aktuellen Ausgabe "Market Share: All Software Markets, Worldwide 2011" bestätigt das weltweit führende Marktanalyseunternehmen Gartner Oracle's Marktführerschaft im Bereich der Relationellen Datenbank Management Systeme (RDBMS). Oracle konnte innerhalb des letzten Jahres seinen Abstand zu seinen Marktbegleitern im Bereich der RDBMS mit einem stabilen Wachstum von 18% sogar ausbauen: der Marktanteil stieg im Jahr 2010 von 48,2% auf 48,8% im Jahr 2011. Damit ist der Abstand zu Oracle's stärkstem Verfolger IBM auf 28,6%.   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-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:12.0pt; mso-para-margin-left:0cm; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} table.MsoTableLightListAccent2 {mso-style-name:"Light List - Accent 2"; mso-tstyle-rowband-size:1; mso-tstyle-colband-size:1; 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mso-style-priority:61; mso-style-unhide:no; mso-tstyle-border-top:2.25pt double #C0504D; mso-tstyle-border-top-themecolor:accent2; mso-tstyle-border-left:1.0pt solid #C0504D; mso-tstyle-border-left-themecolor:accent2; mso-tstyle-border-bottom:1.0pt solid #C0504D; mso-tstyle-border-bottom-themecolor:accent2; mso-tstyle-border-right:1.0pt solid #C0504D; mso-tstyle-border-right-themecolor:accent2; mso-para-margin-top:0cm; mso-para-margin-bottom:0cm; mso-para-margin-bottom:.0001pt; line-height:normal; mso-ansi-font-weight:bold; mso-bidi-font-weight:bold;} table.MsoTableLightListAccent2FirstCol {mso-style-name:"Light List - Accent 2"; mso-table-condition:first-column; mso-style-priority:61; mso-style-unhide:no; mso-ansi-font-weight:bold; mso-bidi-font-weight:bold;} table.MsoTableLightListAccent2LastCol {mso-style-name:"Light List - Accent 2"; mso-table-condition:last-column; mso-style-priority:61; mso-style-unhide:no; mso-ansi-font-weight:bold; mso-bidi-font-weight:bold;} table.MsoTableLightListAccent2OddColumn {mso-style-name:"Light List - Accent 2"; mso-table-condition:odd-column; mso-style-priority:61; mso-style-unhide:no; mso-tstyle-border-top:1.0pt solid #C0504D; mso-tstyle-border-top-themecolor:accent2; mso-tstyle-border-left:1.0pt solid #C0504D; mso-tstyle-border-left-themecolor:accent2; mso-tstyle-border-bottom:1.0pt solid #C0504D; mso-tstyle-border-bottom-themecolor:accent2; mso-tstyle-border-right:1.0pt solid #C0504D; mso-tstyle-border-right-themecolor:accent2;} table.MsoTableLightListAccent2OddRow {mso-style-name:"Light List - Accent 2"; mso-table-condition:odd-row; mso-style-priority:61; mso-style-unhide:no; mso-tstyle-border-top:1.0pt solid #C0504D; mso-tstyle-border-top-themecolor:accent2; mso-tstyle-border-left:1.0pt solid #C0504D; mso-tstyle-border-left-themecolor:accent2; mso-tstyle-border-bottom:1.0pt solid #C0504D; mso-tstyle-border-bottom-themecolor:accent2; mso-tstyle-border-right:1.0pt solid #C0504D; mso-tstyle-border-right-themecolor:accent2;} Revenue 2010 ($USM) Revenue 2011 ($USM) Growth 2010 Growth 2011 Share 2010 Share 2011 Oracle 9,990.5 11,787.0 10.9% 18.0% 48.2% 48.8% IBM 4,300.4 4,870.4 5.4% 13.3% 20.7% 20.2% Microsoft 3,641.2 4,098.9 10.1% 12.6% 17.6% 17.0% SAP/Sybase 744.4 1,101.1 12.8% 47.9% 3.6% 4.6% Teradata 754.7 882.3 16.9% 16.9% 3.6% 3.7% Source: Gartner’s “Market Share: All Software Markets, Worldwide 2011,” March 29, 2012, By Colleen Graham, Joanne Correia, David Coyle, Fabrizio Biscotti, Matthew Cheung, Ruggero Contu, Yanna Dharmasthira, Tom Eid, Chad Eschinger, Bianca Granetto, Hai Hong Swinehart, Sharon Mertz, Chris Pang, Asheesh Raina, Dan Sommer, Bhavish Sood, Marianne D'Aquila, Laurie Wurster and Jie Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; 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mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:12.0pt; mso-para-margin-left:0cm; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} table.MsoTableLightListAccent2 {mso-style-name:"Light List - Accent 2"; mso-tstyle-rowband-size:1; mso-tstyle-colband-size:1; mso-style-priority:61; mso-style-unhide:no; border:solid #C0504D 1.0pt; mso-border-themecolor:accent2; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; 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mso-tstyle-border-right-themecolor:accent2; mso-para-margin-top:0cm; mso-para-margin-bottom:0cm; mso-para-margin-bottom:.0001pt; line-height:normal; mso-ansi-font-weight:bold; mso-bidi-font-weight:bold;} table.MsoTableLightListAccent2FirstCol {mso-style-name:"Light List - Accent 2"; mso-table-condition:first-column; mso-style-priority:61; mso-style-unhide:no; mso-ansi-font-weight:bold; mso-bidi-font-weight:bold;} table.MsoTableLightListAccent2LastCol {mso-style-name:"Light List - Accent 2"; mso-table-condition:last-column; mso-style-priority:61; mso-style-unhide:no; mso-ansi-font-weight:bold; mso-bidi-font-weight:bold;} table.MsoTableLightListAccent2OddColumn {mso-style-name:"Light List - Accent 2"; mso-table-condition:odd-column; mso-style-priority:61; mso-style-unhide:no; mso-tstyle-border-top:1.0pt solid #C0504D; mso-tstyle-border-top-themecolor:accent2; mso-tstyle-border-left:1.0pt solid #C0504D; mso-tstyle-border-left-themecolor:accent2; mso-tstyle-border-bottom:1.0pt solid #C0504D; mso-tstyle-border-bottom-themecolor:accent2; mso-tstyle-border-right:1.0pt solid #C0504D; mso-tstyle-border-right-themecolor:accent2;} table.MsoTableLightListAccent2OddRow {mso-style-name:"Light List - Accent 2"; mso-table-condition:odd-row; mso-style-priority:61; mso-style-unhide:no; mso-tstyle-border-top:1.0pt solid #C0504D; mso-tstyle-border-top-themecolor:accent2; mso-tstyle-border-left:1.0pt solid #C0504D; mso-tstyle-border-left-themecolor:accent2; mso-tstyle-border-bottom:1.0pt solid #C0504D; mso-tstyle-border-bottom-themecolor:accent2; mso-tstyle-border-right:1.0pt solid #C0504D; mso-tstyle-border-right-themecolor:accent2;}

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  • Google Maps API v3 - Different markers/labels on different zoom levels

    - by krikara
    I was wondering if it is possible that Google has a feature to view different markers on different zoom levels. For example, on zoom level 1, I want one marker over China with the label saying "5". And as the user zooms in, lets say on zoom level 4, I want the previous marker and label to disappear. And I want to have 5 new markers/labels, each on a different city in China all saying "1". Thus China will say a number and all the cities in China will say numbers adding up to China's number. The key concept I am trying to figure out here is how to hide markers and labels based on zoom levels. A constraint for me is that I am living in China currently where google is censored, so a lot of online documents are censored for me, including many of google's documentations. Here is my code thus far <!DOCTYPE html> <html> <head> <meta name="viewport" content="initial-scale=1.0, user-scalable=no" /> <title>TM China</title> <style type="text/css"> html, body, #map_canvas { margin: 0; padding: 0; height: 100% } .labels { color: red; background-color: white; font-family: "Lucida Grande", "Arial", sans-serif; font-size: 10px; font-weight: bold; text-align: center; width: 60px; border: 2px solid black; white-space: nowrap; } </style> <script type="text/javascript" src="http://maps.googleapis.com/maps/api/js?key=AIzaSyDV0lcdK7C2GHbQAmdkBID70Uppuf-D030&sensor=true"> </script> <script type="text/javascript"> eval(function(p,a,c,k,e,r){e=function(c){return(c<a?'':e(parseInt(c/a)))+((c=c%a)>35?String.fromCharCode(c+29):c.toString(36))};if(!''.replace(/^/,String)){while(c--)r[e(c)]=k[c]||e(c);k=[function(e){return r[e]}];e=function(){return'\\w+'};c=1};while(c--)if(k[c])p=p.replace(new RegExp('\\b'+e(c)+'\\b','g'),k[c]);return p}('7 m(a){2.3=a;2.8=V.1E("1u");2.8.4.C="I: 1m; J: 1g;";2.k=V.1E("1u");2.k.4.C=2.8.4.C}m.l=E 6.5.22();m.l.1Y=7(){n c=2;n h=t;n f=t;n j;n b;n d,K;n i;n g=7(e){p(e.1v){e.1v()}e.2b=u;p(e.1t){e.1t()}};2.1s().24.G(2.8);2.1s().20.G(2.k);2.11=[6.5.9.w(V,"1o",7(a){p(f){a.s=j;i=u;6.5.9.r(c.3,"1n",a)}h=t;6.5.9.r(c.3,"1o",a)}),6.5.9.o(c.3.1P(),"1N",7(a){p(h&&c.3.1M()){a.s=E 6.5.1J(a.s.U()-d,a.s.T()-K);j=a.s;p(f){6.5.9.r(c.3,"1i",a)}F{d=a.s.U()-c.3.Z().U();K=a.s.T()-c.3.Z().T();6.5.9.r(c.3,"1e",a)}}}),6.5.9.w(2.k,"1d",7(e){c.k.4.1c="2i";6.5.9.r(c.3,"1d",e)}),6.5.9.w(2.k,"1D",7(e){c.k.4.1c=c.3.2g();6.5.9.r(c.3,"1D",e)}),6.5.9.w(2.k,"1C",7(e){p(i){i=t}F{g(e);6.5.9.r(c.3,"1C",e)}}),6.5.9.w(2.k,"1A",7(e){g(e);6.5.9.r(c.3,"1A",e)}),6.5.9.w(2.k,"1z",7(e){h=u;f=t;d=0;K=0;g(e);6.5.9.r(c.3,"1z",e)}),6.5.9.o(2.3,"1e",7(a){f=u;b=c.3.1b()}),6.5.9.o(2.3,"1i",7(a){c.3.O(a.s);c.3.D(2a)}),6.5.9.o(2.3,"1n",7(a){f=t;c.3.D(b)}),6.5.9.o(2.3,"29",7(){c.O()}),6.5.9.o(2.3,"28",7(){c.D()}),6.5.9.o(2.3,"27",7(){c.N()}),6.5.9.o(2.3,"26",7(){c.N()}),6.5.9.o(2.3,"25",7(){c.16()}),6.5.9.o(2.3,"23",7(){c.15()}),6.5.9.o(2.3,"21",7(){c.13()}),6.5.9.o(2.3,"1Z",7(){c.L()}),6.5.9.o(2.3,"1X",7(){c.L()})]};m.l.1W=7(){n i;2.8.1r.1q(2.8);2.k.1r.1q(2.k);1p(i=0;i<2.11.1V;i++){6.5.9.1U(2.11[i])}};m.l.1T=7(){2.15();2.16();2.L()};m.l.15=7(){n a=2.3.z("Y");p(H a.1S==="P"){2.8.W=a;2.k.W=2.8.W}F{2.8.G(a);a=a.1R(u);2.k.G(a)}};m.l.16=7(){2.k.1Q=2.3.1O()||""};m.l.L=7(){n i,q;2.8.S=2.3.z("R");2.k.S=2.8.S;2.8.4.C="";2.k.4.C="";q=2.3.z("q");1p(i 1L q){p(q.1K(i)){2.8.4[i]=q[i];2.k.4[i]=q[i]}}2.1l()};m.l.1l=7(){2.8.4.I="1m";2.8.4.J="1g";p(H 2.8.4.B!=="P"){2.8.4.1k="1j(B="+(2.8.4.B*1I)+")"}2.k.4.I=2.8.4.I;2.k.4.J=2.8.4.J;2.k.4.B=0.1H;2.k.4.1k="1j(B=1)";2.13();2.O();2.N()};m.l.13=7(){n a=2.3.z("X");2.8.4.1h=-a.x+"v";2.8.4.1f=-a.y+"v";2.k.4.1h=-a.x+"v";2.k.4.1f=-a.y+"v"};m.l.O=7(){n a=2.1G().1F(2.3.Z());2.8.4.12=a.x+"v";2.8.4.M=a.y+"v";2.k.4.12=2.8.4.12;2.k.4.M=2.8.4.M;2.D()};m.l.D=7(){n a=(2.3.z("14")?-1:+1);p(H 2.3.1b()==="P"){2.8.4.A=2h(2.8.4.M,10)+a;2.k.4.A=2.8.4.A}F{2.8.4.A=2.3.1b()+a;2.k.4.A=2.8.4.A}};m.l.N=7(){p(2.3.z("1a")){2.8.4.Q=2.3.2f()?"2e":"1B"}F{2.8.4.Q="1B"}2.k.4.Q=2.8.4.Q};7 19(a){a=a||{};a.Y=a.Y||"";a.X=a.X||E 6.5.2d(0,0);a.R=a.R||"2c";a.q=a.q||{};a.14=a.14||t;p(H a.1a==="P"){a.1a=u}2.1y=E m(2);6.5.18.1x(2,1w)}19.l=E 6.5.18();19.l.17=7(a){6.5.18.l.17.1x(2,1w);2.1y.17(a)};',62,143,'||this|marker_|style|maps|google|function|labelDiv_|event|||||||||||eventDiv_|prototype|MarkerLabel_|var|addListener|if|labelStyle|trigger|latLng|false|true|px|addDomListener|||get|zIndex|opacity|cssText|setZIndex|new|else|appendChild|typeof|position|overflow|cLngOffset|setStyles|top|setVisible|setPosition|undefined|display|labelClass|className|lng|lat|document|innerHTML|labelAnchor|labelContent|getPosition||listeners_|left|setAnchor|labelInBackground|setContent|setTitle|setMap|Marker|MarkerWithLabel|labelVisible|getZIndex|cursor|mouseover|dragstart|marginTop|hidden|marginLeft|drag|alpha|filter|setMandatoryStyles|absolute|dragend|mouseup|for|removeChild|parentNode|getPanes|stopPropagation|div|preventDefault|arguments|apply|label|mousedown|dblclick|none|click|mouseout|createElement|fromLatLngToDivPixel|getProjection|01|100|LatLng|hasOwnProperty|in|getDraggable|mousemove|getTitle|getMap|title|cloneNode|nodeType|draw|removeListener|length|onRemove|labelstyle_changed|onAdd|labelclass_changed|overlayMouseTarget|labelanchor_changed|OverlayView|labelcontent_changed|overlayImage|title_changed|labelvisible_changed|visible_changed|zindex_changed|position_changed|1000000|cancelBubble|markerLabels|Point|block|getVisible|getCursor|parseInt|pointer'.split('|'),0,{})) var map; var mapOptions = { center: new google.maps.LatLng(35, 105), zoom: 3, mapTypeId: google.maps.MapTypeId.ROADMAP }; var locations = [ ['Hong Kong', 22.39, 114.10, 1885], ['Shanghai', 31.232, 121.47, 5885], ['Beijing', 39.88, 116.40, 6426], ['Guangzhou', 23.129, 113.264, 4067], ['Shenzhen', 22.54, 114.05, 3089], ['Hangzhou', 30.27, 120.15, 954] ]; var infowindow = new google.maps.InfoWindow(); var i; /* for (i = 0; i < locations.length; i++) { marker = new google.maps.Marker({ position: new google.maps.LatLng(locations[i][1], locations[i][2]), map: map }); google.maps.event.addListener(marker, 'click', (function(marker, i) { return function() { infowindow.setContent(locations[i][0]); infowindow.open(map, marker); } })(marker, i)); } */ function myMarker(options) { if(!options.labelAnchor) { options.labelAnchor = new google.maps.Point(30, 50); } if(!options.labelClass) { options.labelClass = "labels"; } options.map = map; return new MarkerWithLabel(options); } function initialize() { map = new google.maps.Map(document.getElementById("map_canvas"), mapOptions); for (i = 0; i < locations.length; i++) { var marker = new MarkerWithLabel({ position: new google.maps.LatLng(locations[i][1], locations[i][2]), draggable: false, map: map, labelContent: locations[i][3], labelAnchor: new google.maps.Point(30, 0), labelClass: "labels", // the CSS class for the label labelStyle: {opacity: 0.75} }); } /* var marker2 = new myMarker({ position: new google.maps.LatLng(20,20), draggable: true, labelContent: "second" }); */ } google.maps.event.addDomListener(window, 'load', initialize); </script> </head> <body onload="initialize()"> <div id="map_canvas" style="width:85%; height:85%"></div> <script type="text/javascript"> </script> </body> </html> EDIT I have been trying to experiment with the MarkerManager, but I can't get the markers to create successfully on different zoom levels. First, I changed my default zoom level to 1, and then I changed my code to what is shown below. function initialize() { map = new google.maps.Map(document.getElementById("map_canvas"), mapOptions); /* for (i = 0; i < locations.length; i++) { var marker = new MarkerWithLabel({ position: new google.maps.LatLng(locations[i][1], locations[i][2]), draggable: false, map: map, labelContent: locations[i][3], labelAnchor: new google.maps.Point(30, 0), labelClass: "labels", // the CSS class for the label labelStyle: {opacity: 0.75} }); } */ var listener = google.maps.event.addListener(map, 'bounds_changed', function(){ setupMarkers(); google.maps.event.removeListener(listener); }); } function createCityMarkers() { for (i = 0; i < locations.length; i++) { var marker = new MarkerWithLabel({ position: new google.maps.LatLng(locations[i][1], locations[i][2]), draggable: false, map: map, labelContent: locations[i][3], labelAnchor: new google.maps.Point(30, 0), labelClass: "labels", // the CSS class for the label labelStyle: {opacity: 0.75} }); } } function setupMarkers() { mgr = new MarkerManager(map); google.maps.event.addListener(mgr, 'loaded', function(){ mgr.addMarkers(createCityMarkers(), 4); mgr.refresh(); }); } I have also tried applying the source code of this link as well, but nothing is working out. And when I copy the source code directly to my computer and replace all the icons with markers, the markers still don't appear. I can't seem to figure how to make markers appear using the marker Manager. http://google-maps-utility-library-v3.googlecode.com/svn/tags/markermanager/1.0/examples/weather_map.html

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  • Generating a drop down list of timezones with PHP

    - by Xeoncross
    Most sites need some way to show the dates on the site in the users preferred timezone. Below are two lists that I found and then one method using the built in PHP DateTime class in PHP 5. I need help knowing which of these would be the best to attempt to use when trying to get the UTC offset from the user on register. One: <option value="-12">[UTC - 12] Baker Island Time</option> <option value="-11">[UTC - 11] Niue Time, Samoa Standard Time</option> <option value="-10">[UTC - 10] Hawaii-Aleutian Standard Time, Cook Island Time</option> <option value="-9.5">[UTC - 9:30] Marquesas Islands Time</option> <option value="-9">[UTC - 9] Alaska Standard Time, Gambier Island Time</option> <option value="-8">[UTC - 8] Pacific Standard Time</option> <option value="-7">[UTC - 7] Mountain Standard Time</option> <option value="-6">[UTC - 6] Central Standard Time</option> <option value="-5">[UTC - 5] Eastern Standard Time</option> <option value="-4.5">[UTC - 4:30] Venezuelan Standard Time</option> <option value="-4">[UTC - 4] Atlantic Standard Time</option> <option value="-3.5">[UTC - 3:30] Newfoundland Standard Time</option> <option value="-3">[UTC - 3] Amazon Standard Time, Central Greenland Time</option> <option value="-2">[UTC - 2] Fernando de Noronha Time, South Georgia &amp; the South Sandwich Islands Time</option> <option value="-1">[UTC - 1] Azores Standard Time, Cape Verde Time, Eastern Greenland Time</option> <option value="0" selected="selected">[UTC] Western European Time, Greenwich Mean Time</option> <option value="1">[UTC + 1] Central European Time, West African Time</option> <option value="2">[UTC + 2] Eastern European Time, Central African Time</option> <option value="3">[UTC + 3] Moscow Standard Time, Eastern African Time</option> <option value="3.5">[UTC + 3:30] Iran Standard Time</option> <option value="4">[UTC + 4] Gulf Standard Time, Samara Standard Time</option> <option value="4.5">[UTC + 4:30] Afghanistan Time</option> <option value="5">[UTC + 5] Pakistan Standard Time, Yekaterinburg Standard Time</option> <option value="5.5">[UTC + 5:30] Indian Standard Time, Sri Lanka Time</option> <option value="5.75">[UTC + 5:45] Nepal Time</option> <option value="6">[UTC + 6] Bangladesh Time, Bhutan Time, Novosibirsk Standard Time</option> <option value="6.5">[UTC + 6:30] Cocos Islands Time, Myanmar Time</option> <option value="7">[UTC + 7] Indochina Time, Krasnoyarsk Standard Time</option> <option value="8">[UTC + 8] Chinese Standard Time, Australian Western Standard Time, Irkutsk Standard Time</option> <option value="8.75">[UTC + 8:45] Southeastern Western Australia Standard Time</option> <option value="9">[UTC + 9] Japan Standard Time, Korea Standard Time, Chita Standard Time</option> <option value="9.5">[UTC + 9:30] Australian Central Standard Time</option> <option value="10">[UTC + 10] Australian Eastern Standard Time, Vladivostok Standard Time</option> <option value="10.5">[UTC + 10:30] Lord Howe Standard Time</option> <option value="11">[UTC + 11] Solomon Island Time, Magadan Standard Time</option> <option value="11.5">[UTC + 11:30] Norfolk Island Time</option> <option value="12">[UTC + 12] New Zealand Time, Fiji Time, Kamchatka Standard Time</option> <option value="12.75">[UTC + 12:45] Chatham Islands Time</option> <option value="13">[UTC + 13] Tonga Time, Phoenix Islands Time</option> <option value="14">[UTC + 14] Line Island Time</option> Or using PHP friendly values: <option value="Pacific/Midway">(GMT-11:00) Midway Island, Samoa</option> <option value="America/Adak">(GMT-10:00) Hawaii-Aleutian</option> <option value="Etc/GMT+10">(GMT-10:00) Hawaii</option> <option value="Pacific/Marquesas">(GMT-09:30) Marquesas Islands</option> <option value="Pacific/Gambier">(GMT-09:00) Gambier Islands</option> <option value="America/Anchorage">(GMT-09:00) Alaska</option> <option value="America/Ensenada">(GMT-08:00) Tijuana, Baja California</option> <option value="Etc/GMT+8">(GMT-08:00) Pitcairn Islands</option> <option value="America/Los_Angeles">(GMT-08:00) Pacific Time (US & Canada)</option> <option value="America/Denver">(GMT-07:00) Mountain Time (US & Canada)</option> <option value="America/Chihuahua">(GMT-07:00) Chihuahua, La Paz, Mazatlan</option> <option value="America/Dawson_Creek">(GMT-07:00) Arizona</option> <option value="America/Belize">(GMT-06:00) Saskatchewan, Central America</option> <option value="America/Cancun">(GMT-06:00) Guadalajara, Mexico City, Monterrey</option> <option value="Chile/EasterIsland">(GMT-06:00) Easter Island</option> <option value="America/Chicago">(GMT-06:00) Central Time (US & Canada)</option> <option value="America/New_York">(GMT-05:00) Eastern Time (US & Canada)</option> <option value="America/Havana">(GMT-05:00) Cuba</option> <option value="America/Bogota">(GMT-05:00) Bogota, Lima, Quito, Rio Branco</option> <option value="America/Caracas">(GMT-04:30) Caracas</option> <option value="America/Santiago">(GMT-04:00) Santiago</option> <option value="America/La_Paz">(GMT-04:00) La Paz</option> <option value="Atlantic/Stanley">(GMT-04:00) Faukland Islands</option> <option value="America/Campo_Grande">(GMT-04:00) Brazil</option> <option value="America/Goose_Bay">(GMT-04:00) Atlantic Time (Goose Bay)</option> <option value="America/Glace_Bay">(GMT-04:00) Atlantic Time (Canada)</option> <option value="America/St_Johns">(GMT-03:30) Newfoundland</option> <option value="America/Araguaina">(GMT-03:00) UTC-3</option> <option value="America/Montevideo">(GMT-03:00) Montevideo</option> <option value="America/Miquelon">(GMT-03:00) Miquelon, St. Pierre</option> <option value="America/Godthab">(GMT-03:00) Greenland</option> <option value="America/Argentina/Buenos_Aires">(GMT-03:00) Buenos Aires</option> <option value="America/Sao_Paulo">(GMT-03:00) Brasilia</option> <option value="America/Noronha">(GMT-02:00) Mid-Atlantic</option> <option value="Atlantic/Cape_Verde">(GMT-01:00) Cape Verde Is.</option> <option value="Atlantic/Azores">(GMT-01:00) Azores</option> <option value="Europe/Belfast">(GMT) Greenwich Mean Time : Belfast</option> <option value="Europe/Dublin">(GMT) Greenwich Mean Time : Dublin</option> <option value="Europe/Lisbon">(GMT) Greenwich Mean Time : Lisbon</option> <option value="Europe/London">(GMT) Greenwich Mean Time : London</option> <option value="Africa/Abidjan">(GMT) Monrovia, Reykjavik</option> <option value="Europe/Amsterdam">(GMT+01:00) Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna</option> <option value="Europe/Belgrade">(GMT+01:00) Belgrade, Bratislava, Budapest, Ljubljana, Prague</option> <option value="Europe/Brussels">(GMT+01:00) Brussels, Copenhagen, Madrid, Paris</option> <option value="Africa/Algiers">(GMT+01:00) West Central Africa</option> <option value="Africa/Windhoek">(GMT+01:00) Windhoek</option> <option value="Asia/Beirut">(GMT+02:00) Beirut</option> <option value="Africa/Cairo">(GMT+02:00) Cairo</option> <option value="Asia/Gaza">(GMT+02:00) Gaza</option> <option value="Africa/Blantyre">(GMT+02:00) Harare, Pretoria</option> <option value="Asia/Jerusalem">(GMT+02:00) Jerusalem</option> <option value="Europe/Minsk">(GMT+02:00) Minsk</option> <option value="Asia/Damascus">(GMT+02:00) Syria</option> <option value="Europe/Moscow">(GMT+03:00) Moscow, St. Petersburg, Volgograd</option> <option value="Africa/Addis_Ababa">(GMT+03:00) Nairobi</option> <option value="Asia/Tehran">(GMT+03:30) Tehran</option> <option value="Asia/Dubai">(GMT+04:00) Abu Dhabi, Muscat</option> <option value="Asia/Yerevan">(GMT+04:00) Yerevan</option> <option value="Asia/Kabul">(GMT+04:30) Kabul</option> <option value="Asia/Yekaterinburg">(GMT+05:00) Ekaterinburg</option> <option value="Asia/Tashkent">(GMT+05:00) Tashkent</option> <option value="Asia/Kolkata">(GMT+05:30) Chennai, Kolkata, Mumbai, New Delhi</option> <option value="Asia/Katmandu">(GMT+05:45) Kathmandu</option> <option value="Asia/Dhaka">(GMT+06:00) Astana, Dhaka</option> <option value="Asia/Novosibirsk">(GMT+06:00) Novosibirsk</option> <option value="Asia/Rangoon">(GMT+06:30) Yangon (Rangoon)</option> <option value="Asia/Bangkok">(GMT+07:00) Bangkok, Hanoi, Jakarta</option> <option value="Asia/Krasnoyarsk">(GMT+07:00) Krasnoyarsk</option> <option value="Asia/Hong_Kong">(GMT+08:00) Beijing, Chongqing, Hong Kong, Urumqi</option> <option value="Asia/Irkutsk">(GMT+08:00) Irkutsk, Ulaan Bataar</option> <option value="Australia/Perth">(GMT+08:00) Perth</option> <option value="Australia/Eucla">(GMT+08:45) Eucla</option> <option value="Asia/Tokyo">(GMT+09:00) Osaka, Sapporo, Tokyo</option> <option value="Asia/Seoul">(GMT+09:00) Seoul</option> <option value="Asia/Yakutsk">(GMT+09:00) Yakutsk</option> <option value="Australia/Adelaide">(GMT+09:30) Adelaide</option> <option value="Australia/Darwin">(GMT+09:30) Darwin</option> <option value="Australia/Brisbane">(GMT+10:00) Brisbane</option> <option value="Australia/Hobart">(GMT+10:00) Hobart</option> <option value="Asia/Vladivostok">(GMT+10:00) Vladivostok</option> <option value="Australia/Lord_Howe">(GMT+10:30) Lord Howe Island</option> <option value="Etc/GMT-11">(GMT+11:00) Solomon Is., New Caledonia</option> <option value="Asia/Magadan">(GMT+11:00) Magadan</option> <option value="Pacific/Norfolk">(GMT+11:30) Norfolk Island</option> <option value="Asia/Anadyr">(GMT+12:00) Anadyr, Kamchatka</option> <option value="Pacific/Auckland">(GMT+12:00) Auckland, Wellington</option> <option value="Etc/GMT-12">(GMT+12:00) Fiji, Kamchatka, Marshall Is.</option> <option value="Pacific/Chatham">(GMT+12:45) Chatham Islands</option> <option value="Pacific/Tongatapu">(GMT+13:00) Nuku'alofa</option> <option value="Pacific/Kiritimati">(GMT+14:00) Kiritimati</option> Or just using PHP it's self $timezones = DateTimeZone::listAbbreviations(); $cities = array(); foreach( $timezones as $key => $zones ) { foreach( $zones as $id => $zone ) { /** * Only get timezones explicitely not part of "Others". * @see http://www.php.net/manual/en/timezones.others.php */ if ( preg_match( '/^(America|Antartica|Arctic|Asia|Atlantic|Europe|Indian|Pacific)\//', $zone['timezone_id'] ) && $zone['timezone_id']) { $cities[$zone['timezone_id']][] = $key; } } } // For each city, have a comma separated list of all possible timezones for that city. foreach( $cities as $key => $value ) $cities[$key] = join( ', ', $value); // Only keep one city (the first and also most important) for each set of possibilities. $cities = array_unique( $cities ); // Sort by area/city name. ksort( $cities ); It seems like the last one would be the safest as it would grow with the PHP release being used. You could also flip that array around when needed to tie timezones to city names.

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