Patent Publication Number: US-8972336-B2

Title: System and method for mapping source columns to target columns

Description:
CLAIM OF PRIORITY 
     This application claims priority from U.S. Provisional Patent Application No. 61/642,069 entitled A System and Method for Mapping Source Columns to Target Columns, by Arun Kumar Jagota, filed May 3, 2012. This application is also a continuation-in-part of commonly owned, co-pending U.S. patent application Ser. No. 13/773,141 entitled System and Method for Using a Statistical Classifier to Score Contact Entities, by Arun Kumar Jagota, filed Feb. 21, 2013, which is incorporated herein by reference. 
    
    
     COPYRIGHT NOTICE 
     A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the records of the United States Patent and Trademark Office, but otherwise reserves all rights. 
     TECHNICAL FIELD 
     The subject matter disclosed herein relates generally to computer-based methods for evaluating character strings, and more particularly, to application of these methods for mapping columns from a source file to a target file. 
     BACKGROUND 
     The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which may be unique on their own. 
     In conventional database systems, users access their data resources in a single logical database. A user of such a conventional database system typically retrieves data from and stores data on the database system using the user&#39;s own computing systems or devices. For example, a user system might remotely access one of a plurality of server systems that might in turn access the database system. Data retrieval from the system might include the issuance of a query from the user system to the database system. The database system might process the request for information received in the query and send to the user system information relevant to the request. The retrieval of accurate information and subsequent delivery of this information to the user system in a timely and efficient manner has been and continues to be a goal of administrators of database systems. 
     However, in a multi-tenant database system (“MTS”), various elements of hardware and software of the database system may be shared by one or more customers. The notion of “cloud computing” has recently developed whereby service providers offer access to hardware/software systems through a network, such as the Internet. For example, a specified application server may simultaneously process requests for many customers, and a specified database table may store rows of data for many customers. 
     In the MTS environment, customers demand up-to-date, comprehensive data. For example, sales and marketing organizations need accurate and up-to-date contact information in order to maintain good relations with existing customers as well as solicit leads for new customers. 
     Unfortunately, conventional database approaches rely on individuals to maintain their own contact information, and therefore, even within a single organization, individuals may have inconsistent and/or inaccurate contact information. 
     An advantage of the MTS system is that business enterprises may share data resources, and rely on the MTS provider to maintain accurate data, such as personal and/or business contact information. Another advantage is that all personnel within a single enterprise have access to the exact same contact information, so that inaccuracies or inconsistencies within the organization no longer exist. New or updated information is typically received by the MTS as one or more files or character strings, and it can be difficult to accurately identifying the entity that the character string is associated with. In particular, files with structured data can present challenges in recognizing entities in order to map the data in columns from a source file to the database model. 
     Accordingly, it is desirable to provide techniques for accurately correlating columns in a source file with defined entities of the database model in order to transform the source file for importation into the data model of the MTS. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the following drawings, like reference numbers are used to refer to like elements. Although the following figures depict various examples, the one or more implementations are not limited to the examples depicted in the figures. 
         FIG. 1  is a simplified block diagram illustrating a multi-tenant database system (“MTS”). 
         FIG. 2A  illustrates a block diagram of an example of an environment wherein an on-demand database service might be used. 
         FIG. 2B  illustrates a block diagram of an embodiment of elements of  FIG. 2A  and various possible interconnections between those elements. 
         FIG. 3  is a block diagram illustrating a contact record. 
         FIG. 4  is a block diagram illustrating the operational flow of handling a character string through the MTS. 
         FIGS. 5A &amp; 5B  are flow charts illustrating a process for applying a statistical classifier to the problem of identifying an entity associated with a character string. 
         FIG. 6  is a software code listing for the process of  FIG. 5 . 
         FIG. 7  is a flow chart illustrating a process for mapping source columns to target columns. 
         FIG. 8  is a flow chart illustrating post-processing steps for the process of  FIG. 7 . 
         FIG. 9  is a flow chart illustrating the step of evaluating contact from the process of  FIG. 7 . 
         FIG. 10  is a flow chart illustrating score adjustment steps for the process of  FIG. 7 . 
     
    
    
     DETAILED DESCRIPTION 
     1. Simplified System Overview 
     As used herein, the term multi-tenant database system refers to those systems in which various elements of hardware and software of the database system may be shared by one or more customers. For example, a given application server may simultaneously process requests for a great number of customers, and a given database table may store rows for a potentially much greater number of customers. As used herein, the term query refers to a set of steps used to access information in a database system. 
       FIG. 1  illustrates an embodiment of an on-demand, multi-tenant database service (“MTS”)  16  operating in a computing environment  10 . User devices or systems  12  access and communicate with the MTS  16  through network  14  in a known manner. More detailed examples for implementing the MTS  16  are shown in  FIGS. 2A-2B , described below. For example, user devices  12  may be any computing device, such as a desktop, laptop, table, smartphone, etc. Network  14  may be any type of computing network, such as the Internet, local area network, wide area network, etc. The operation of the MTS  16  is controlled by a processor system  17  running an operating system. 
     The MTS  16  provides the users of user systems  12  with access to many features and applications available on or through MTS  16 . In particular, the MTS  16  includes tenant data storage  22 , which is configured through the MTS to maintain tenant data for multiple users/tenants, as further described below and in the patent references mentioned above. Tenant data storage  22  (shown in dashed lines) may be physically incorporated within MTS  16 , or may alternatively be remote storage  22   a  (shown in dashed lines), likewise accessible and useful to the MTS to support user systems  12 . 
     Many different types of tenant data may be maintained in tenant data storage  22 , including, in particular, contact data, i.e., information about an individual, including name, company, address, telephone number, etc.  FIG. 3  illustrates a typical database record  300  for a contact. In this embodiment, the contact record  300  has 10 defined fields or entities: first_name  310 , last_name  311 , (job) title  312 , company_name  313 , email  314 , address  315 , city  316 , state  317 , zip  318 , and country  319 . Other fields or other formats may be used. 
     An important objective for any user of MTS  16  is to maintain up-to-date and detailed information for all contacts. A unique feature of MTS  16  described herein is one or more applications  19 , managed and operated by the MTS through its application platform  18 , that facilitate this objective by providing one or more sets of program instructions providing features to help maintain and secure the accuracy and reliability of the tenant data, whether it is contact data, company data, etc. For example, instructions may be provided for crawling the Internet or any other available network or document repository to find relevant information; for extracting a string of characters; and for making a probabilistic determination as to which entity the string belongs. Alternatively, instructions may be provided for receiving, normalizing and importing data files to fit the data model of the MTS. 
     The block diagram shown in  FIG. 4  illustrates the operation of applications  19   a  and  19   b . Application  19   a  handles simple receipt and pre-processing of a source file having data in CSV format or similar. Application  19   b  handles processing including heuristic and probabilistic analysis of the CSV file, mapping the source file to the data model, then transforming the CSV file in accord with the mapping. 
     In application  19   a , a first program module  30  has program code suitable to receive and place into temporary storage data files from an external source, the files having data arranged in row and columns. For example, a user may upload one or more CSV files having contact records or company records. The creation of program code suitable to retrieve and store CSV file or the like in module  30  is routine for those skilled in these arts and need not be described in further detail herein. 
     In application  19   b , the source file is first subjected to a heuristic analysis by program module  35  to evaluate whether column headers of the source file are recognized structurally as defined entities of the data model. The contents of the columns are then subjected to a probabilistic analysis by program module  36  to evaluate whether the contents help correlate the columns to defined entities of the data model. The results of the heuristic analysis and the probabilistic analysis lead to the creation through program module  37  of a plausible mapping scheme of the columns of the source file to the entities of the data model. Finally, the columns of the source file are transformed in accord with the mapping scheme and stored in tenant storage of the MTS. 
     2. Entity Recognition in Character Strings 
     A probabilistic scoring method incorporates a Bayes classifier to compute a posterior probability distribution over various defined entities of a contact record for any given input. The Bayes classifier algorithm is well known. See, for example, T. Mitchell,  Machine Learning  (McGraw-Hill 1997). As an example, consider the string “Cisco.” The probability that the string is one of the defined entities of the contact record can be written as P(first_name|“Cisco”), P(last_name|“Cisco”), P(title|“Cisco”), etc. These probabilities quantify the likelihood that the character string “Cisco” is a first name vs. a last name vs. a job title, etc. Accordingly, the posterior probabilities can be written as P(e|s), where e denotes the entity and s denotes the input string. However, the method described herein uses advanced features of the Bayes method, described below, to help recognize the defined entities as accurately as possible. Thus, the posterior probabilities are not computed directly from string s, but instead from a certain set of features extracted from string s. 
     Let f denote a certain feature of the Bayes classifier, and therefore f(s) is its value in s. Then let F(s)={f→f(s)} denote the set of (feature-name, feature-value) pairs in string s. Therefore, P(e|F(s)) is a succinct and precise way of saying that the posterior probably that the character string is entity e is computed from the features extracted from s. However, rather than using only the strict probabilistic formulation, an heuristic, iterative method is described which looks at the character string s and decides which features to use for that particular character string s, thereby providing a reasonable approximation of the posterior probability. 
     A. Features for String Recognition 
     There are many applications that have a need to recognize the entity of a given character string s. For example, the string “Los Angeles” should be recognized as a city, the string “94512” is probably a zip, and the string “John” is probably a first name. These type of determinations can be made by finding the entity which has the highest approximate posterior probability for the given string s. This entity is then guessed to be the correct entity, and its posterior probability is the confidence in that prediction. Further, if the confidence is not high enough, then the guess may be abandoned and no prediction made. 
     Other applications may use the posterior probabilities in a more elaborate scoring scheme. For instance, when checking spreadsheets for fraudulent or bad data, each value in a column is scored against the entity the column represents. For example, if the column has a header that indicates that the column stores “job titles,” and one of the values in this column is “XYZ Inc,” then this value should get a low score because it is clearly a company name, not a title. This score can simply be the approximate posterior probability of the column&#39;s entity given that value. The posterior probabilities of the column&#39;s entity are calculated one by one for all values in the column. The overall score can then be defined to be the average of these posterior probabilities. The score then quantifies how well the data in the column fits the column&#39;s entity. 
     The Bayes classifier can be used to define an advanced set of features which help discriminate among entities. The relatively harder cases are distinguishing person names from titles; distinguishing titles from company names; distinguishing person names, titles, and company names from address lines; and distinguishing phone numbers from zip codes. 
     The advanced features are described below. All of the features change the string to lower-case and remove any flanking white space from it. 
     The last word feature, denoted as lw(s), returns the last word in the input string. 
     The digit-blurring feature, denoted as db(s), replaces every numerical digit in the input string by the character ‘d.’ 
     The words feature, denoted as w(s), returns a set of one or more words in the string, i.e. substrings, separated by white space. The last word is excluded from this feature because it is already in the feature lw(s). 
     The full string feature, denoted as fs(s), returns a version of the original string s in lower case characters with flanking white space removed. 
     The address line feature is a fairly elaborate feature. This feature is used to accurately discriminate between strings that form address lines and strings that form other entities, such as title, company name, city, first name, etc. For example, let s 1 s 2  . . . s k  denote the token sequence of the string s obtained by splitting the string on white space. The feature is the string f(s) formed by concatenating f 1 , f 2 , . . . , f k , joined by a single blank, where each f i  is a string, and f i  equals “d” if s i  is all digits; f i  equals “w” if s i  is all non-digits; f i  equals “wd” if s i  is a mix of digits and non-digits; f i  equals n(s i ) if s i  is a normalizable word; else f i  equals “ws”. 
     The term n(s i ) needs additional explanation. The desired normalizations may be represented as a map n: string→string, i.e. a map of (key, value) pairs. If s i  (after changing to lower-case and stripping away flanking white spaces) is a key in this map, then f i  is set to n(s i ). The example below shows a few (key, value) pairs that are put into the map n. 
     avenue→road, road→road, boulevard→road, blvd→road 
     These key/value pairs are chosen since they help discriminate strings in address lines from strings in other entities. Normalizing different ways of saying the same thing, e.g. all of these terms can be treated as “road,” leads to a more compact model, and compact models tend to generalize better. One way to think about this normalization is the following: take any address line having the word “avenue” (or “boulevard” etc.) in it, and replace the word “avenue” with the word “road.” This action does not change the prediction that that string is an address line (and not a title, or a company name, etc.). 
     B. Last Word Feature 
     The last word feature is a strong predictor that the string s is a company name. A few examples are presented in Table I below. 
     
       
         
           
               
               
               
             
               
                 TABLE I 
               
               
                   
               
               
                   
                 String 
                 Feature value 
               
               
                   
               
             
            
               
                   
                 “XYZ Corp” 
                 “corp” 
               
               
                   
                 “Johnson Computers” 
                 “computers” 
               
               
                   
                 “VP of Sales” 
                 “sales” 
               
               
                   
               
            
           
         
       
     
     For example, the string “XYZ Corp” will get predicted as a company name because it ends with the word “corp” as detected by the last word feature. This will happen even if “XYZ Corp” is not in the training set. Thus, the last word feature helps the algorithm to generalize, i.e., to be able to detect companies as company names even when not in the training set. 
     The effect of this feature could be partly emulated by maintaining a dictionary of common company name suffixes, such as inc, corp, etc., and applying a rule “If the last word in the string is one of these suffixes, then predict that the string is a company name.” However, the statistical classifier employing the last word feature works much better, provided that a large and representative training set of the various entities is available. 
     The statistical classifier is more effective works better than a dictionary look-up because the statistical classifier automatically extracts words from the training set that, when occurring as last words in strings, are strong predictors that the string is a company or organization name. Beyond the common company suffixes that would get picked up automatically, these include words such as systems, trust, board, computers, agency, etc. Reconstructing these “soft” company name suffixes manually would be a daunting task. 
     No less importantly, even if such a set could be assembled somehow, the rule makes a hard decision just based on this feature, and such a decision would be too risky in many cases. The statistical classifier, by contrast, uses the predictions and their strengths made by this feature in conjunction with those from other features and is thus more accurate. To illustrate, consider the string “XYZ Systems.” The statistical classifier would conclude that this is highly likely to be a company name because it ends with “Systems” and “XYZ” does not predict some other entity. (Some other features could come into play as well, if warranted.) Consider a different string VP of Information Systems. Although Systems predicts company name strongly, it predicts title at least weakly under the (reasonable) assumption that titles ending with the word Systems are also in the training set of titles. Furthermore other features extracted from the string (e.g. VP) predict title much more strongly than company name. So one would expect the aggregate of the evidence from the various features to favor title, as it should. 
     C. Digit-Blurring Feature 
     The digit-blurring feature is a strong predictor that the string is a phone or a zip, and which of the two. The numerical specific digits of the string are blurred out, but the position of the digits is retained, as well as the identity of the other characters. Some examples are presented in Table II below. 
     
       
         
           
               
               
               
             
               
                 TABLE II 
               
               
                   
               
               
                   
                 String 
                 Feature value 
               
               
                   
               
             
            
               
                   
                 “346 4928” 
                 “ddd dddd” 
               
               
                   
                 “+1 (592) 346 4928” 
                 “+d (ddd) ddd dddd” 
               
               
                   
                 “95128” 
                 “ddddd” 
               
               
                   
                 “95128-4209” 
                 “ddddd-dddd” 
               
               
                   
                 “John Van” 
                 “john van” 
               
               
                   
                 “SW1A 0AA” 
                 “swda daa” 
               
               
                   
                 “+61 418 123456” 
                 “+dd ddd dddddd” 
               
               
                   
               
            
           
         
       
     
     Blurring out the specific digits allows generalization, i.e., recognition of strings that are phones or zips, but that are not in the training set as such. Replacing each of the numerical digits with the letter ‘d’ and keeping the rest of the non-numerical characters intact retains just the information to distinguish phones from zips and other entities. For example, dddd−dddd is more likely to be a zip than a phone, and “+dd ddd dddddd” is more likely to be a phone than a zip. 
     This feature allows a system to ‘learn’ many different formats of phones and zips without having to explicitly model each one of them. All it requires is that the training set be rich enough. For example, to recognize phone numbers from different countries, the training set should have examples of phone numbers from different countries. Also, phone numbers from the same country with different formats could be included simply by including examples of each format in the training set. Thus, a training set could have U.S. phone numbers with the long-distance prefix “+1” missing, or with the “+” missing, that use parentheses, hyphens, or periods, etc. Similarly, the training set could have 5-digit U.S. zip codes and the more comprehensive format of (5+4)-digit U.S. zip codes. 
     D. Words Feature 
     This discussion applies to the combination of the words features and the last word feature (described above). Several examples are shown in Table III below. 
     
       
         
           
               
               
               
             
               
                 TABLE III 
               
               
                   
               
               
                   
                 String 
                 Values of features 
               
               
                   
               
             
            
               
                   
                 “Vice President of Sales” 
                 {“vice”, ”president”, ”of”} 
               
               
                   
                 “John Van” 
                 {“john”} 
               
               
                   
                 “123 Market St” 
                 {“123”, ”market”} 
               
               
                   
                 “346 4928” 
                 {“346”} 
               
               
                   
                 “New York” 
                 {“new”} 
               
               
                   
                 “john” 
                 { } 
               
               
                   
               
            
           
         
       
     
     These two features work well to discriminate titles from other entities. This is because certain words, such as ‘president,’ ‘vice,’ ‘officer,’ ‘chief,’ and many others, almost always occur in titles, but not in other entities. These two features also help with generalization in multi-word first and last names. As an example, if “John Van” is labeled as a first name in the training set, these features predict that “John” and “Van” are also plausible first names. 
     E. Full String Feature 
     This discussion applies to the combination of the words features and the last word feature (described above). Several examples are shown in Table IV below. 
     
       
         
           
               
               
               
             
               
                 TABLE IV 
               
               
                   
               
               
                   
                 String 
                 Feature value 
               
               
                   
               
             
            
               
                   
                 “Los Angeles” 
                 “los angeles” 
               
               
                   
                 “California” 
                 “california” 
               
               
                   
                 “123 Market St” 
                 {“123”, ”market”} 
               
               
                   
                 “346 4928” 
                 {“346”} 
               
               
                   
                 “New York” 
                 {“new”} 
               
               
                   
                 “john” 
                 { } 
               
               
                   
               
            
           
         
       
     
     The full string feature is useful for recognition of entities whose values tend to be from a controlled vocabulary, namely city and state. Consider the string “California.” The method should predict that the entity is state with 100% confidence. Now consider the string “California Security Systems.” The method should now predict that the entity is company name instead. The full string feature ensures that the former happens, while the words feature makes the latter happen. 
     F. Address Line Feature 
     The address line feature is a strong predictor that the string is an address line. Several examples are shown in Table IV below. 
     
       
         
           
               
               
               
             
               
                 TABLE V 
               
               
                   
               
               
                   
                 String 
                 Feature value 
               
               
                   
               
             
            
               
                   
                 “1 Market St” 
                 “d w road” 
               
               
                   
                 “One Broadway Blvd” 
                 “d w road” 
               
               
                   
                 “Intel” 
                 “w” 
               
               
                   
                 “L5 Inc” 
                 “wd w” 
               
               
                   
                 “New York” 
                 “w w” 
               
               
                   
                 “408 345-1234” 
                 “d ws” 
               
               
                   
                 “Vice President of Sales” 
                 “w w w w” 
               
               
                   
               
            
           
         
       
     
     G. Training Set 
     The aim of training is to learn, from a training set, the correlations between the values of the various features and the defined entities. Table VI below is a small sample training set. (note—an actual training set to produce an accurate classifier would have a lot more examples, perhaps several million.). 
     
       
         
           
               
               
               
             
               
                 TABLE VI 
               
               
                   
               
               
                   
                 String 
                 Correct entity 
               
               
                   
               
             
            
               
                   
                 “Vice President of Sales” 
                 title 
               
               
                   
                 “XYZ Inc” 
                 Company name 
               
               
                   
                 “123 Market St” 
                 Address line 
               
               
                   
                 “+1 921 346 4928” 
                 phone 
               
               
                   
                 “95316” 
                 zip 
               
               
                   
                 “New York” 
                 state 
               
               
                   
                 “New York” 
                 city 
               
               
                   
                 “New York” 
                 state 
               
               
                   
                 “Australia” 
                 Country 
               
               
                   
                 “Los Angeles” 
                 state 
               
               
                   
                 “New York City” 
                 City 
               
               
                   
                 “John” 
                 First name 
               
               
                   
                 “Smith” 
                 Last name 
               
               
                   
                 “Cleveland” 
                 city 
               
               
                   
                 “Cleveland” 
                 Last name 
               
               
                   
               
            
           
         
       
     
     Note that the same example(s) can appear multiple times in the training set. This can be useful—the classifier uses frequency (among several other factors) to strengthen the learned correlations between the values of various features and the entities. Also note that the training set can contain the same string mapped to different entities. For example, the string “Cleveland” is a city but it can also be a last name. In a rich training set, one would expect (“Cleveland”, city) to occur much more frequently than (“Cleveland”, last name). This and the fact that there is much more diversity in last names than cities allows the classifier to automatically learn that “Cleveland” is a city or a last name, with the former being more likely in the absence of other information. 
     H. Training Process 
     Let (s, e) denote an example in the training set, where s is the string and e its correct entity. In this notation, e is called the label of s. First, the set of all features F(s) are extracted from s. Then, each of two counters is incremented by one (both counters are initialized to 0 at the beginning of the training). n e , is the number of examples in the training set labeled e. n e (f, f(s)), is the number of examples in the training set labeled e whose feature f has value f(s). 
     From these counts, we get 
                 P   ⁡     (       f   ⁡     (   s   )       |   e     )       =         n   e     ⁡     (     f   ,     f   ⁡     (   s   )         )         n   e         ,         
which is used for computing the posterior probabilities, as further described below. An example of a string and the various features extracted from it is shown in Table VII below.
 
     
       
         
           
               
               
               
             
               
                 TABLE VII 
               
               
                   
               
               
                   
                 String 
                 “123 market St” 
               
               
                   
               
             
            
               
                   
                 Last word feature 
                 “st” 
               
               
                   
                 Word features 
                 {“123”, ”market”} 
               
               
                   
                 Full string feature 
                 “123 market St” 
               
               
                   
                 Address line feature 
                 “d w road” 
               
               
                   
               
            
           
         
       
     
     3. Heuristic Approximation of Posterior Probabilities 
       FIGS. 5A and 5B  illustrate a process  400  that provides a heuristic approximation of the posterior probabilities that a character string s is one or more of the defined entities. The process  400  is preferably provided as an integrated web service, for example, as application  19  of MTS  16  in  FIG. 4 , but could be provided in other forms as well. 
     In step  402 , an input character string is received. In step  404 , the string is evaluated to see if it is recognized structurally as a particular instance of entity e. If so, then in step  406 , a score of 1 is returned for entity e, i.e., the probability is 100% that the string is actually entity e. The structural recognition step is done first because it is only successful where there is a near certainty that the structure of the string is correctly identified as representing the entity e. There must be no ambiguity, i.e., the structure must be well-defined. For example, an email address typically has the format &lt;name@domain&gt;. Structural rules may be developed from the training set. 
     If the string is not structurally recognized in step  404 , then the string is evaluated in step  408  for dictionary-based recognition. If so, then in step  410 , a score of 1 is returned for entity e, i.e., again, the probability is 100% that the string is actually entity e. As with structural recognition, there must be a near certainty that the structure of the string is correctly identified as representing the entity e. Dictionary-based rules may also be developed from the training set. 
     If dictionary-based recognition does not occur in step  408 , then the string is evaluated in step  412  for a “softer” version of dictionary-based recognition. More specifically, the full string feature is applied to the string, and if there is at least one example of that string in the training set, i.e., if n e (fs, fs(s))&gt;0, then in step  414 , the posterior probabilities are calculated for each of the defined entities over the full string feature, i.e., P(·|{fs→fs(s)}), and the score is returned in step  416 . If the score for any entity is high enough in step  418 , then characterizing the string as that entity may be accepted. If the score is not high enough, then characterizing the string as that entity may be rejected, and the process continues. 
     Take s=cleveland as an example. n e (fs, fs(cleveland)) is greater than 0 for e being {city, last_name, . . . }. Therefore, the posteriors of various entities are computed over this feature value. This should inform us that cleveland has a high probability of being a city, and a lower (but greater than zero) probability of being a person&#39;s last name. 
     If there is not at least one example of the string in the training set in step  412 , or if the string characterization is rejected in step  418 , then the digits blurring feature will be evaluated. More specifically, the digit blurring feature is applied to the string in step  420 , and if there is at least one example of that string in the training set, i.e., n e (db, db(s))&gt;0, then in step  422 , the posterior probabilities are calculated for each of the defined entities over the digit blurring feature, i.e., P(·{db→db(s)}), and the score is returned in step  424 . If the score for any entity is high enough in step  426 , then characterizing the string as that entity may be accepted. If the score is not high enough, then characterizing the string as that entity may be rejected, and the process continues. 
     The digit blurring step accurately scores phone versus zip. For example, the string “ddddd−dddd” will be scored high for being a zip and much lower for being a phone; v the string “ddddddddd” will be scored roughly the same for being a zip or a phone; and “+1 ddd ddd−dddd” will be scored very high for being a phone and zero or near-zero for being a zip. 
     If there is not at least one example of the string in the training set in step  420 , or if the string characterization is rejected in step  426 , then the string is probably an address line, a company name, or an unknown first name or last name of a person. Step  428  calculates posterior probability for each of the three remaining features (address_line, company_name, last_word) over all entities. The score is then used to discriminate between these entities. 
     Generally, for any subset F′(s) of F(s), P(·|F′(s)) denotes the distribution of posterior probabilities restricted to the features in F′(s). P(·|F′(s)) is computed in the usual way, by applying the Bayes rule under the assumption of conditional independence of the features in F′(s) given the entity. That is: 
     
       
         
           
             
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     Because certain values of lw strongly predict that the entity is company name, in step  430 , if the score of lw is greater than x, than the entity is accepted as company name in step  432 . Because certain values of address_line strongly predict that the entity is address_line, in step  434 , if the score of address_line is greater than y, than the entity is accepted as address_line in step  436 . Because certain values of w strongly predict that the entity is first_name, in step  438 , if the score of w is greater than z, than the entity is accepted as first_name in step  440 . If none of the above, then the entity is first_name in step  442 . Process  400  may be coded as shown in  FIG. 6 . 
     4. Application to Column Mapping 
     A. Normalization of Column Names 
     A database may be used to maintain many different types of structured data other than just contact data, including company data, financial data, product data, health data, manufacturing data, etc. Structured data is often stored in a format suitable for spreadsheet analysis, such as comma-separated values (“CSV”), tab-delimited or tab-separated values (“TSV”), or an Excel file, for convenience of creation, analysis, and distribution. However, before importing such source data into the database, it needs to be normalized, i.e., the columns in the source data need to be properly mapped to the attributes of the target data model for the database. More specifically, this means that a sequence of target columns in the database represent predefined entities of the data model. For example, the primary attributes of interest, or predefined entities, of a contact record in one embodiment of the database are: first_name, last_name, job_title, email address, phone_number, company_name, street, city, state, zip, and country. In this embodiment, a contact record is considered complete if at least the first four columns, namelyfirst_name, last_name, job_title and email, are non-empty. 
     Structured data frequently has issues that cause difficulty in mapping data from source columns to target columns. For example, the header or column names for the source data may be non-standard, e.g., “First” used for first_name, or “Last” for last_name, etc., and such names must be correlated to the appropriate target entities if possible. Some columns in the source data may be irrelevant and need to be ignored, such as “Hobbies” or “Alternate Fax.” Other columns may have data that needs to be merged or split. An example of the former is a pair of source columns named “street1” and “street2,” which need to be mapped into a single target column street. An example of the latter is a source column “name” which contains the full name of a person, and which needs to be split to be mapped into the pair of predefined target columns first_name and last_name. 
     In a significant number of files, even the header row is missing for the source data. In such cases, the only way to accurately identify the columns of interest and infer their correct names is to analyze the content of the file. For example, if a column seems to contain first names, it is probably the target entity first_name, especially if other evidence corroborates it, e.g., it is immediately to the left of the last_name column. 
     Content-based analysis can be useful even when the file has a header row. For example, sometimes the name of a source column is ambiguous, e.g., title could mean job title or salutation. Another example is that the source column name is not recognized as a known alias for one of the target entities. In these examples, evaluation of the content of the source column provides additional information that can help make a decision as to what entity the source column represents. 
     The evaluation of content in a source column uses the statistical classifier described previously to score the value of each cell against each possible entity name for a selected number of rows in the source file. The scores returned on the individual calls to the classifier for each cell are then used by a more elaborate probabilistic scoring engine to make the final guess as to column name. 
     B. Method for Mapping Columns 
     Referring now to  FIG. 7 , a process  500  is illustrated for mapping columns in a source file to columns in a target file. The source data is modeled generally as (S, D) where S i  is the name of the i th  source column and D i  is the data in this column. The objective is to find the most likely sequence of target column names T for this source data (S, D), where T i  is the target column for the mapping of source column S i , and to apply the mapping to transform the data. 
     In step  501 , the first column of the source file is retrieved into temporary storage. In step  502 , the top of the column is evaluated—does it have a header, i.e., a column name? If so, then in step  504 , the header of the source column is compared with the names of the target columns/entities to see if there is an exact match. If so, then in step  506 , the name of the source column is initially identified as being mapped to the matching target column, and in step  507 , this mapping is weighted heavily, i.e., a high score, since the match is exact. 
     If there is no actual match in step  504 , then the name of the source column is compared to a table of known aliases of the actual names in step  508 . If the alias is recognized, then the name of the source column is initially identified as being mapped to the target column matching the alias in step  510 . In step  511 , this mapping is weighted moderately, i.e., a medium score, not as high as an exact match but higher than no match. 
     If the name of the source column is not recognized as an alias in step  508 , or the source column does not have a header at all in step  502 , then the content of the source column is evaluated in step  512 . Further, even if there is a match and initial mapping, content analysis is still useful to corroborate and support the initial mapping to provide a higher (or lower) confidence score. As explained in more detail below, evaluating the content in step  512  involves looking at each cell in a column for a number of rows, for example, twenty rows, and evaluating the likelihood that the content of the cell corresponds to one or more of the defined target entities. Ultimately, a score may be derived representing the likelihood for each defined entity in step  514 . These entity likelihood scores may be influenced by the weight assigned to an initial mapping in either step  507  or  511 . 
     If the score for a specific mapping of the source column S, to a defined target entity T i  exceeds a defined threshold in step  516 , for example, 80%, then the mapping is created or updated to reflect that correspondence in step  518 . If the score is not adequate in step  516 , then the mapping is updated in step  519  to associate the source column S i  with a previously undefined target column T x , for example, adopting the name of the source column. Such a result likely means that the source column is irrelevant and therefore will be ignored in a post-processing step when the data is actually converted. 
     If all the source columns have not yet been evaluated in step  520 , then the system gets the next column in step  522  and returns to step  502  to evaluate the next column. If all the source columns have been handled in step  520 , then post-processing and conversion of the columns is accord with the mapping will proceed as in process  600 , as will now be described in connection with  FIG. 8 . 
     The question of whether any post-processing steps are required is considered in step  602 . For example, if one or more source columns need to be ignored, merged or split, then post-processing is required. If post-processing is not required, then in step  604 , the source columns are transformed in accord with the mapping scheme and stored in the database. Note this might be an unusual result since one would expect to find some differences between the source file and the target data model. 
     If post-processing is required in step  602 , then in step  606 , the system will check to see if any source columns should be ignored at the actual data transformation stage. Source columns to be ignored will have been mapped to a target column not previously defined in the analysis stage, and after considering (through content analysis) and rejecting that such columns might contain relevant content to be split or merged, an indicator is set, such as a program flag, to tell the process this column should be ignored. In step  608 , the mapping is updated to include an instruction to specifically exclude or remove all columns marked “ignore” when transformation of the data occurs. 
     In step  610 , the system checks for any columns that need to be split. Recall the prior example of a source column containing a full name, and thus needing to be split into the defined entities first_name and last_name for proper transformation. Columns requiring a split will be recognized as such during content analysis, and will be identified for special handling. For example, a split routine may be performed in step  612  to split the source column into its constituent parts, which may then be mapped to the corresponding defined entities in step  614 . 
     In step  616 , the system checks for any columns that need to be merged. Recall the prior example of a pairs of source columns called street1 and street2, thus needing to be merged into the single defined entity address. Columns requiring merger will also be recognized as such during content analysis, and will be identified for special handling. For example, a merge routine may be performed in step  618  to merge a pair of source columns into a single column, which may then be mapped to the corresponding defined entity in step  620 . 
     Once all post-processing steps have been considered, then the source columns are transformed in accord with the updated mapping scheme and stored in the database. 
     The evaluation of content of a source column in step  512  is further illustrated in  FIG. 9 . In step  702 , initialization for this column takes place, setting a counter row=1 and setting the maximum count for a representative sample size of rows, for example, max=20. In step  704 , the content of the cell is received into temporary storage for processing. The structure of the cell content, e.g. a character string, is evaluated in step  706 , as described previously. If the structure is recognized in step  708 , e.g., it matches the structure of a defined entity with a near certainty, or it matches a known alias for a defined entity stored in a lookup table, then in step  710 , the column is associated with the defined entity as an educated guess. Further, as noted above, a relatively high confidence score is assigned to this guess in step  712  since it is associated with a structural match. The score for this cell is stored in step  714 . 
     If the structure of the cell contents is not recognized in step  708 , then the cell contents, i.e., the character string, is evaluated in step  716  using a probabilistic approximation. As described above, the probabilistic approximation uses advanced string features of a statistical classifier in an ordered approach to try and recognize the string as one of the defined entities. This probabilistic approach results in a confidence score in step  718 , and the score for this cell is stored in step  714 . 
     In step  720 , the row counter is incremented by one, namely, row=row+1. In step  722 , the row counter is compared to the maximum count. If the row count is not greater than the maximum, then the process  700  returns to step  704  to get and evaluate the contents of the next cell in the column from the next row. If the row count is greater than the maximum in step  722 , then an adequate representative sample of rows of this column have been evaluated. 
     Before going on to the next column, however, additional scoring adjustments may be made for the current column through process  800  shown in  FIG. 10 . In step  802 , an aggregate score for the entire column is evaluated. The scores of all cells in the column for the various entities are combined together in a way that yields both a high recall and a high precision. For example, if the content of the first cell in a column is not recognized as a first name, but the content for 15 of the next 19 cells in the column are recognized as a first name, then the guess that the column is a first_name column is highly plausible. More specifically, the statistical classifier is called multiple times for the same cell contents in order to evaluate the probability for all defined entities. A single aggregated probability is then derived for the entire column. 
     In step  804 , the aggregated score for the column is compared to a defined threshold, for example 80%. If the aggregated score is greater than the threshold, then the confidence score can be increased in step  806 . If the aggregated score is less than the threshold, then the confidence score can be decreased in step  808 . 
     In step  810 , the proximity of this column relative to other columns is evaluated. Some entities typically occur in a specific order. For example, the column first_name is usually immediately to the left of the column last_name. Likewise, the columns address_line, city, state, zip and country usually occur together, and in that order. Such proximity indicators are well-known for the data model of the database, and can also influence the confidence score. As an example, if a column scores high for first_name, and the column to its immediate right scores high for last_name, this proximity is used to increase the scores of both column mappings even more. Thus, if the proximity indicators support the guess in step  812 , then the confidence score can be increased in step  814 . If the proximity indicators do not support the guess, then the confidence score can be decreased in step  816 . 
     C. Formulation of the Probabilistic Approximation 
     In probabilistic terms, the objective is to find a T* that maximizes the posterior probability P(T|S, D) that a given source column S with data D maps to target column T. The calculation of this posterior probability cannot be solved efficiently without introducing approximations or simplifying assumptions. Thus, the first approximation is:
 
 P ( T|S,D )≈Π i=1   n   P ( T   i   |S   i   ,D   i   ,f ( T   1 . . . i-1 ))  (1)
 
     In Equation (1), n is the number of source columns. Equation (1) is a reasonable approximation of the posterior probability, in effect saying that the score of a particular target column name for source column i is influenced by (a) the name of the source column, (b) the data of the source column, (c) the name of the target column of to the immediate left of the most likely target column, and (d) a function of the first i−1 target column names in T, explained in more detail below. 
     Table VIII below shows examples where the target name of the immediate left neighbor helps support a guess as to the target column: 
     
       
         
           
               
               
               
             
               
                 TABLE VIII 
               
               
                   
               
               
                   
                 T i−1   
                 most likely T i   
               
               
                   
               
             
            
               
                   
                 First name 
                 Last name 
               
               
                   
                 Address line 
                 City 
               
               
                   
                 City 
                 State 
               
               
                   
                 State 
                 Zip 
               
               
                   
                 Zip 
                 Country 
               
               
                   
                 Address_line1 
                 Address_line2 
               
               
                   
               
            
           
         
       
     
     A higher-order dependency does not add much value, so the approximation of Equation (1) stops at the first order dependency. However, note that Equation (1) does allow T i  to be influenced by some function of T 1 . . . i-1 . The term f(T 1 . . . i-1 ) is there in Equation (1) to ensure a low score is assigned to a T in which the same target name appears too often. For example, one would expect the column first_name to appear only once in a source file. If a probabilistic determination of T finds that the entity first_name appears multiple times, it is likely that at least some of these mappings are wrong, and so this solution for T should have a low score. On the other hand, for certain target columns, such as phone and email, a few occurrences may be acceptable since a person may have multiple telephone numbers and/or multiple email addresses. Thus, it is better to model this constraint probabilistically, and not use a hard and fast rule, for example, that no target name can occur multiple times in T. 
     For simplicity, the term f(T 1 . . . i-1 ) will be ignored in most of the discussion below. As an example, consider the simplified approximation of P(T i |T i-1 , S i , D i ). Let S i  be “Business City,” let D i  contain city names, and let T i-1  be the entity address_line. All three of these indicators individually point to and support the conclusion that the target column T i  is the entity city: the source column is named “Business City;” the data of this column is city names; and the column to the immediate left is address_line. Thus, the target column T i  will get a very high score for this S i . 
     In another example of the simplified approximation of P(T i |T i-1 , S i , D i ), a heuristic algorithm as described above can be used to evaluate the structure of the data in the cell. Consider the target column T i =email as an example. If the content D i  of the cell contains syntactically well-formed email addresses in most values, then the probability P(email|T i-1 , S i , D i ) should be high. Further, if the name of source column S i  is a known alias for the entity email, this supports the guess and the score can be increased. However, if the target column to the immediately left T i-1  is a column which expects some other column to follow it—for example, the entityfirst_name expects to be followed by either middle_name or last_name.—this contradicts the guess and the score can be decreased. 
     For all other entities, we use probabilistic scoring. Specifically:
 
 P ( T   i   |T   i-1   ,S   i   ,D   i )∝ P ( T   i-1   ,S   i   ,D   i   |T   i ) P ( T   i )  (2)
 
     Assuming that T i-1 , S i , and D i  are all conditionally independent given T i , yields:
 
 P ( T   i-1   ,S   i   ,D   i   |T   i )= P ( T   i-2   |T   i ) P ( S   i   |T   i ) P ( D   i   |T   i )  (3)
 
     Thus, for any given (S, D), the problem is to find the T that maximizes P(T|S, D). One approach would be to generate and score all plausible T&#39;s, but such an effort would be prohibitively slow. Therefore, in one embodiment, a variant of the Viterbi algorithm for a Hidden Markov Model (“HMM”) is used. See generally, L. Rabiner,  A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition  (1989 Proceedings of the IEEE, pp. 257-86; and A. McCallum and D. Freitag,  Maximun Entropy Markov Models for Information Extraction and Segmentation  (2000 Morgan Kaufmann, pp. 591-98). 
     Let τ denote the set of target column names. Applying the Viterbi algorithm the problem of identifying how to map source columns to defined entities of the data model involves computing Equation (5) for all tετ and all i:
 
 v   ti =max T     1 . . . i-1     P ( T   i   =t,T   1 . . . i-1   |S   1 . . . i )  (5)
 
     Equation (5) can be computed efficiently and recursively as:
 
 v   ti =max t′ετ−{t} ( v   t′(i-1)   P ( t|t′,S   i   ,D   i ))  (6)
 
     In Equation (5), P(t|t′, S i , D i ) is computed via Equations (2) and (3) for most t&#39;s, and via heuristic analysis for other t&#39;s, as discussed earlier. For convenience, a new ‘target column name’ b can be added denoting the BEGIN state in the corresponding probability model. The initial conditions are then v b0 =1 and v t0 =0 for all tετ. 
     Computing the entire matrix v using Equation (6) is a time consuming effort, impacting real-time mapping. Therefore, to make this algorithm more efficient, the following preprocessing step is added:
 
 T   c ( i )={ t|P ( t|S   i   ,D   i )≧δ}  (7)
 
     where δ&gt;0 is a defined parameter. For the t&#39;s that are scored probabilistically, P(t|S i , D i ) is computed through versions of Equations (2) and (3) with setting P(t) as the uniform prior 1/|τ|, and in which the term P(T i-1 |T i ) is dropped. That is:
 
 P ( t|S   i   ,D   i )∝ P ( S   i   |T   i ) P ( D   i   |T   i )
 
     For the rest of the t&#39;s, P(t|S i , D i ), is computed via heuristic analysis. 
     T c (i) is the subset of T that can be a plausible mapping for source column i. In most cases, one would expect |T c (i)|&lt;&lt;|τ|. This may be leveraged in two ways to improve the speed of the processing steps. First, v ti  is computed only for tεT c  rather than for all tετ. Second, v ti  may be computed more efficiently by replacing t′ετ−{t} in (6) by t′εT c (i−1)−{t}. 
     C. Training Sets 
     For entities that are scored probabilistically, two types of training sets are generally available for the estimation of the probability distributions in Equations (2) and (3), namely (i) a training set of (entity_name, entity_value) pairs, and (ii) a training set of (source header row, target header row) pairs. 
     The probability distributions for P(S i |T i ), P(T i ), and P(T i-1 |T i ) will be estimated from the type ii training set. The probability distributions for P(D i |T i ) distributions will be estimated from the type i training set. Specifically, the statistical classifier will be trained on a type i data set, and it will return the likelihood P(e|T i ) when given an entity value e and target entity T i  as input. As mentioned above, the probability is scored on content in the first twenty rows or so of column i, namely:
 
 P ( D   i   |T   i )≈Π j=1   20   P ( d   ij   |T   i )  (4)
 
     where d ij  denotes the jth value in D i . Please also note the entity values are conditionally independent given the entity name. 
     Both types of training sets are split because the type i training sets are concerned only with entity names and entity values. Very large training sets of this type can often be assembled with virtually no human effort. For example, type i training sets having more than 100 million such pairs may be easily and programmatically constructed from contact databases and company databases. 
     A type ii training set will typically be constructed manually since it involves examining a large number of source files for a particular object, e.g., a contact, and manually mapping the source row to a target row. One example of such a training set for contacts is shown in Table IX below. 
     
       
         
           
               
               
               
             
               
                 TABLE IX 
               
               
                   
               
               
                   
                 Source Header Row 
                 Target Header Row 
               
               
                   
               
             
            
               
                   
                 First 
                 first_name 
               
               
                   
                 Last 
                 last_name 
               
               
                   
                 Job Title 
                 title 
               
               
                   
                 Account Name 
                 company 
               
               
                   
                 Business email 
                 email 
               
               
                   
                 Fax # 
                 fax 
               
               
                   
               
            
           
         
       
     
     It is also convenient to be able to specify (source column name, target column name) pairs directly. Although such pairs do not have the proximity information that can be gleaned from multiple columns, a large and diverse set of (alias, target column name) pairs can be quickly assembled. One example is shown in Table X below. 
     
       
         
           
               
               
               
             
               
                 TABLE X 
               
               
                   
               
               
                   
                 Source Column Name 
                 Target Column Name 
               
               
                   
               
             
            
               
                   
                 First 
                 first_name 
               
               
                   
                 job position 
                 title 
               
               
                   
                 business email 
                 email 
               
               
                   
                 title 
                 title 
               
               
                   
                 title 
                 salutation 
               
               
                   
                 postal code 
                 zip 
               
               
                   
                 account name 
                 company 
               
               
                   
                 business city 
                 city 
               
               
                   
                 firm name 
                 company 
               
               
                   
                 Telephone 
                 phone 
               
               
                   
                 street address 
                 address 
               
               
                   
                 organization 
                 company 
               
               
                   
                 division 
                 department 
               
               
                   
               
            
           
         
       
     
     P(S i |T i ) will be learned from a type ii training set, which means that the common aliases for standard (target) column names can be learned from the training set, but there can also be a probabilistic component, which is useful in ambiguous cases. For example, the source column “title” could possibly mean the target column (job) title or the target column salutation, and thus, P(title|job title) and P(title|salutation) will both be greater than zero. This ambiguity may be resolved, in a particular case, by looking probabilistically at the content of this column in addition to P(S i |T i ). Pseudo-counts may be used to ensure that P(S i |T i ) is always greater than 0 in order to avoid an unknown source column name (e.g., gibberish) forcing P(S i |T i ) to 0 for every T i , thus making it impossible to find any target column name for that S i . 
     It should be evident that the richer the training set, the more accurate the column mapper is likely to be. Consider a type ii training set. There are typically many source files available to model. The hard part of training set construction is labeling the source rows, i.e., constructing the target rows to be paired with the source rows. This process can be made more efficient by first training a bootstrap version of the column mapper on a simple (type i plus type ii) training set, using this column mapper to label a source row in a source file with a target row, and manually correcting any labeling errors. If the accuracy is high, then the time taken to manually correct the labeling errors is significantly lower than the time it takes to label all the sources from scratch. 
     6. Detailed System Description 
       FIG. 2A  is a block diagram of an exemplary environment  110  for use of an on-demand database service. Environment  110  may include user systems  112 , network  114  and system  116 . Further, the system  116  can include processor system  117 , application platform  118 , network interface  120 , tenant data storage  122 , system data storage  124 , program code  126  and process space  128 . In other embodiments, environment  110  may not have all of the components listed and/or may have other elements instead of, or in addition to, those listed above. 
     User system  112  may be any machine or system used to access a database user system. For example, any of the user systems  112  could be a handheld computing device, a mobile phone, a laptop computer, a work station, and/or a network of computing devices. As illustrated in  FIG. 2A  (and in more detail in  FIG. 2B ), user systems  112  might interact via a network  114  with an on-demand database service, which in this embodiment is system  116 . 
     An on-demand database service, such as system  116 , is a database system that is made available to outside users that are not necessarily concerned with building and/or maintaining the database system, but instead, only that the database system be available for their use when needed (e.g., on the demand of the users). Some on-demand database services may store information from one or more tenants into tables of a common database image to form a multi-tenant database system (MTS). Accordingly, the terms “on-demand database service  116 ” and “system  116 ” will be used interchangeably in this disclosure. A database image may include one or more database objects or entities. A database management system (DBMS) or the equivalent may execute storage and retrieval of information against the database objects or entities, whether the database is relational or graph-oriented. Application platform  118  may be a framework that allows the applications of system  116  to run, such as the hardware and/or software, e.g., the operating system. In an embodiment, on-demand database service  116  may include an application platform  118  that enables creation, managing and executing one or more applications developed by the provider of the on-demand database service, users accessing the on-demand database service via user systems  112 , or third party application developers accessing the on-demand database service via user systems  112 . 
     The users of user systems  112  may differ in their respective capacities, and the capacity of a particular user system  112  might be entirely determined by permission levels for the current user. For example, where a salesperson is using a particular user system  112  to interact with system  116 , that user system has the capacities allotted to that salesperson. However, while an administrator is using that user system to interact with system  116 , that user system has the capacities allotted to that administrator. In systems with a hierarchical role model, users at one permission level may have access to applications, data, and database information accessible by a lower permission level user, but may not have access to certain applications, database information, and data accessible by a user at a higher permission level. Thus, different users will have different capabilities with regard to accessing and modifying application and database information, depending on a user&#39;s security or permission level. 
     Network  114  is any network or combination of networks of devices that communicate with one another. For example, network  114  can be any one or any combination of a LAN (local area network), WAN (wide area network), telephone network, wireless network, point-to-point network, star network, token ring network, hub network, or other appropriate configuration. As the most common type of computer network in current use is a TCP/IP (Transfer Control Protocol and Internet Protocol) network, such as the global network of networks often referred to as the Internet, that network will be used in many of the examples herein. However, it should be understood that the networks that the one or more implementations might use are not so limited, although TCP/IP is a frequently implemented protocol. 
     User systems  112  might communicate with system  116  using TCP/IP and, at a higher network level, use other common Internet protocols to communicate, such as HTTP, FTP, AFS, WAP, etc. In an example where HTTP is used, user system  112  might include an HTTP client commonly referred to as a browser for sending and receiving HTTP messages to and from an HTTP server at system  116 . Such an HTTP server might be implemented as the sole network interface between system  116  and network  114 , but other techniques might be used as well or instead. In some implementations, the interface between system  116  and network  114  includes load sharing functionality, such as round-robin HTTP request distributors to balance loads and distribute incoming HTTP requests evenly over a plurality of servers. At least as for the users that are accessing that server, each of the plurality of servers has access to the data stored in the MTS; however, other alternative configurations may be used instead. 
     In one embodiment, system  116  implements a web-based customer relationship management (CRM) system. For example, in one embodiment, system  116  includes application servers configured to implement and execute CRM software applications as well as provide related data, code, forms, web pages and other information to and from user systems  112  and to store to, and retrieve from, a database system related data, objects, and Web page content. With a multi-tenant system, data for multiple tenants may be stored in the same physical database object; however, tenant data typically is arranged so that data of one tenant is kept logically separate from that of other tenants so that one tenant does not have access to another tenant&#39;s data, unless such data is expressly shared. In certain embodiments, system  116  implements applications other than, or in addition to, a CRM application. For example, system  116  may provide tenant access to multiple hosted (standard and custom) applications, including a CRM application. User (or third party developer) applications, which may or may not include CRM, may be supported by the application platform  118 , which manages creation, storage of the applications into one or more database objects and executing of the applications in a virtual machine in the process space of the system  116 . 
     One arrangement for elements of system  116  is shown in  FIG. 2A , including a network interface  120 , application platform  118 , tenant data storage  122  for tenant data  123 , system data storage  124  for system data  125  accessible to system  116  and possibly multiple tenants, program code  126  for implementing various functions of system  116 , and a process space  128  for executing MTS system processes and tenant-specific processes, such as running applications as part of an application hosting service. Additional processes that may execute on system  116  include database indexing processes. 
     Several elements in the system shown in  FIG. 2A  include conventional, well-known elements that are explained only briefly here. For example, each user system  112  could include a desktop personal computer, workstation, laptop, PDA, cell phone, or any wireless access protocol (WAP) enabled device or any other computing device capable of interfacing directly or indirectly to the Internet or other network connection. User system  112  typically runs an HTTP client, e.g., a browsing program, such as Microsoft&#39;s Internet Explorer browser, Netscape&#39;s Navigator browser, Opera&#39;s browser, or a WAP-enabled browser in the case of a cell phone, PDA or other wireless device, or the like, allowing a user (e.g., subscriber of the multi-tenant database system) of user system  112  to access, process and view information, pages and applications available to it from system  116  over network  114 . Each user system  112  also typically includes one or more user interface devices, such as a keyboard, a mouse, trackball, touch pad, touch screen, pen or the like, for interacting with a graphical user interface (GUI) provided by the browser on a display (e.g., a monitor screen, LCD display, etc.) in conjunction with pages, forms, applications and other information provided by system  116  or other systems or servers. For example, the user interface device can be used to access data and applications hosted by system  116 , and to perform searches on stored data, and otherwise allow a user to interact with various GUI pages that may be presented to a user. As discussed above, embodiments are suitable for use with the Internet, which refers to a specific global internetwork of networks. However, it should be understood that other networks can be used instead of the Internet, such as an intranet, an extranet, a virtual private network (VPN), a non-TCP/IP based network, any LAN or WAN or the like. 
     According to one embodiment, each user system  112  and all of its components are operator configurable using applications, such as a browser, including computer code run using a central processing unit such as an Intel Pentium® processor or the like. Similarly, system  116  (and additional instances of an MTS, where more than one is present) and all of their components might be operator configurable using application(s) including computer code to run using a central processing unit such as processor system  117 , which may include an Intel Pentium® processor or the like, and/or multiple processor units. A computer program product embodiment includes a machine-readable storage medium (media) having instructions stored thereon/in which can be used to program a computer to perform any of the processes of the embodiments described herein. Computer code for operating and configuring system  116  to intercommunicate and to process web pages, applications and other data and media content as described herein are preferably downloaded and stored on a hard disk, but the entire program code, or portions thereof, may also be stored in any other volatile or non-volatile memory medium or device as is well known, such as a ROM or RAM, or provided on any media capable of storing program code, such as any type of rotating media including floppy disks, optical discs, digital versatile disk (DVD), compact disk (CD), microdrive, and magneto-optical disks, and magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data. Additionally, the entire program code, or portions thereof, may be transmitted and downloaded from a software source over a transmission medium, e.g., over the Internet, or from another server, as is well known, or transmitted over any other conventional network connection as is well known (e.g., extranet, VPN, LAN, etc.) using any communication medium and protocols (e.g., TCP/IP, HTTP, HTTPS, Ethernet, etc.) as are well known. It will also be appreciated that computer code for implementing embodiments can be implemented in any programming language that can be executed on a client system and/or server or server system such as, for example, C, C++, HTML, any other markup language, Java™, JavaScript, ActiveX, any other scripting language, such as VBScript, and many other programming languages as are well known may be used. (Java™ is a trademark of Sun Microsystems, Inc.). 
     According to one embodiment, each system  116  is configured to provide web pages, forms, applications, data and media content to user (client) systems  112  to support the access by user systems  112  as tenants of system  116 . As such, system  116  provides security mechanisms to keep each tenant&#39;s data separate unless the data is shared. If more than one MTS is used, they may be located in close proximity to one another (e.g., in a server farm located in a single building or campus), or they may be distributed at locations remote from one another (e.g., one or more servers located in city A and one or more servers located in city B). As used herein, each MTS could include one or more logically and/or physically connected servers distributed locally or across one or more geographic locations. Additionally, the term “server” is meant to include a computer system, including processing hardware and process space(s), and an associated storage system and database application (e.g., OODBMS or RDBMS) as is well known in the art. It should also be understood that “server system” and “server” are often used interchangeably herein. Similarly, the database object described herein can be implemented as single databases, a distributed database, a collection of distributed databases, a database with redundant online or offline backups or other redundancies, etc., and might include a distributed database or storage network and associated processing intelligence. 
       FIG. 2B  also illustrates environment  110 . However, in  FIG. 2B  elements of system  116  and various interconnections in an embodiment are further illustrated.  FIG. 2B  shows that user system  112  may include processor system  112 A, memory system  112 B, input system  112 C, and output system  112 D.  FIG. 2B  shows network  114  and system  116 .  FIG. 2B  also shows that system  116  may include tenant data storage  122 , tenant data  123 , system data storage  124 , system data  125 , User Interface (UI)  230 , Application Program Interface (API)  232 , PL/SOQL  234 , save routines  236 , application setup mechanism  238 , applications servers  200   1 - 200   N , system process space  202 , tenant process spaces  204 , tenant management process space  210 , tenant storage area  212 , user storage  214 , and application metadata  216 . In other embodiments, environment  110  may not have the same elements as those listed above and/or may have other elements instead of, or in addition to, those listed above. 
     User system  112 , network  114 , system  116 , tenant data storage  122 , and system data storage  124  were discussed above in  FIG. 2A . Regarding user system  112 , processor system  112 A may be any combination of one or more processors. Memory system  112 B may be any combination of one or more memory devices, short term, and/or long term memory. Input system  112 C may be any combination of input devices, such as one or more keyboards, mice, trackballs, scanners, cameras, and/or interfaces to networks. Output system  112 D may be any combination of output devices, such as one or more monitors, printers, and/or interfaces to networks. 
     As shown by  FIG. 2B , system  116  may include a network interface  115  (of  FIG. 2A ) implemented as a set of HTTP application servers  200 , an application platform  118 , tenant data storage  122 , and system data storage  124 . Also shown is system process space  202 , including individual tenant process spaces  204  and a tenant management process space  210 . Each application server  200  may be configured to tenant data storage  122  and the tenant data  123  therein, and system data storage  124  and the system data  125  therein to serve requests of user systems  112 . The tenant data  123  might be divided into individual tenant storage areas  212 , which can be either a physical arrangement and/or a logical arrangement of data. Within each tenant storage area  212 , user storage  214  and application metadata  216  might be similarly allocated for each user. For example, a copy of a user&#39;s most recently used (MRU) items might be stored to user storage  214 . Similarly, a copy of MRU items for an entire organization that is a tenant might be stored to tenant storage area  212 . A UI  230  provides a user interface and an API  232  provides an application programmer interface to system  116  resident processes to users and/or developers at user systems  112 . The tenant data and the system data may be stored in various databases, such as one or more Oracle™ databases, or in distributed memory as described herein. 
     Application platform  118  includes an application setup mechanism  238  that supports application developers&#39; creation and management of applications, which may be saved as metadata into tenant data storage  122  by save routines  236  for execution by subscribers as one or more tenant process spaces  204  managed by tenant management process  210  for example. Invocations to such applications may be coded using PL/SOQL  234  that provides a programming language style interface extension to API  232 . A detailed description of some PL/SOQL language embodiments is discussed in commonly owned, co-pending U.S. Provisional Patent App. No. 60/828,192, entitled Programming Language Method And System For Extending APIs To Execute In Conjunction With Database APIs, filed Oct. 4, 2006, which is incorporated in its entirety herein for all purposes. Invocations to applications may be detected by one or more system processes, which manages retrieving application metadata  216  for the subscriber making the invocation and executing the metadata as an application in a virtual machine. 
     Each application server  200  may be coupled for communications with database systems, e.g., having access to system data  125  and tenant data  123 , via a different network connection. For example, one application server  200   1  might be coupled via the network  114  (e.g., the Internet), another application server  200   N-1  might be coupled via a direct network link, and another application server  200   N  might be coupled by yet a different network connection. Transfer Control Protocol and Internet Protocol (TCP/IP) are typical protocols for communicating between application servers  200  and the database system. However, it will be apparent to one skilled in the art that other transport protocols may be used to optimize the system depending on the network interconnect used. 
     In certain embodiments, each application server  200  is configured to handle requests for any user associated with any organization that is a tenant. Because it is desirable to be able to add and remove application servers from the server pool at any time for any reason, there is preferably no server affinity for a user and/or organization to a specific application server  200 . In one embodiment, an interface system implementing a load balancing function (e.g., an F5 Big-IP IP load balancer) is coupled for communication between the application servers  200  and the user systems  112  to distribute requests to the application servers  200 . In one embodiment, the load balancer uses a “least connections” algorithm to route user requests to the application servers  200 . Other examples of load balancing algorithms, such as round robin and observed response time, also can be used. For example, in certain embodiments, three consecutive requests from the same user could hit three different application servers  200 , and three requests from different users could hit the same application server  200 . In this manner, system  116  is multi-tenant and handles storage of, and access to, different objects, data and applications across disparate users and organizations. 
     As an example of storage, one tenant might be a company that employs a sales force where each salesperson uses system  116  to manage their sales process. Thus, a user might maintain contact data, leads data, customer follow-up data, performance data, goals and progress data, etc., all applicable to that user&#39;s personal sales process (e.g., in tenant data storage  122 ). In an example of a MTS arrangement, since all of the data and the applications to access, view, modify, report, transmit, calculate, etc., can be maintained and accessed by a user system having nothing more than network access, the user can manage his or her sales efforts and cycles from any of many different user systems. For example, if a salesperson is visiting a customer and the customer has Internet access in their lobby, the salesperson can obtain critical updates as to that customer while waiting for the customer to arrive in the lobby. 
     While each user&#39;s data might be separate from other users&#39; data regardless of the employers of each user, some data might be shared organization-wide or accessible by a plurality of users or all of the users for a given organization that is a tenant. Thus, there might be some data structures managed by system  116  that are allocated at the tenant level while other data structures might be managed at the user level. Because an MTS might support multiple tenants including possible competitors, the MTS should have security protocols that keep data, applications, and application use separate. Also, because many tenants may opt for access to an MTS rather than maintain their own system, redundancy, up-time, and backup are additional functions that may be implemented in the MTS. In addition to user-specific data and tenant specific data, system  116  might also maintain system level data usable by multiple tenants or other data. Such system level data might include industry reports, news, postings, and the like that are sharable among tenants. 
     In certain embodiments, user systems  112  (which may be client systems) communicate with application servers  200  to request and update system-level and tenant-level data from system  116  that may require sending one or more queries to tenant data storage  122  and/or system data storage  124 . System  116  (e.g., an application server  200  in system  116 ) automatically generates one or more SQL statements (e.g., one or more SQL queries) that are designed to access the desired information. System data storage  124  may generate query plans to access the requested data from the database. 
     Each database can generally be viewed as a collection of objects, such as a set of logical tables, containing data fitted into predefined categories. A “table” is one representation of a data object, and may be used herein to simplify the conceptual description of objects and custom objects. It should be understood that “table” and “object” may be used interchangeably herein. Each table generally contains one or more data categories logically arranged as columns or fields in a viewable schema. Each row or record of a table contains an instance of data for each category defined by the fields. For example, a CRM database may include a table that describes a customer with fields for basic contact information such as name, address, phone number, fax number, etc. Another table might describe a purchase order, including fields for information such as customer, product, sale price, date, etc. In some multi-tenant database systems, standard entity tables might be provided for use by all tenants. For CRM database applications, such standard entities might include tables for Account, Contact, Lead, and Opportunity data, each containing pre-defined fields. It should be understood that the word “entity” may also be used interchangeably herein with “object” and “table.” 
     In some multi-tenant database systems, tenants may be allowed to create and store custom objects, or they may be allowed to customize standard entities or objects, for example by creating custom fields for standard objects, including custom index fields. U.S. Pat. No. 7,779,039, entitled Custom Entities and Fields in a Multi-Tenant Database System, is hereby incorporated herein by reference, and teaches systems and methods for creating custom objects as well as customizing standard objects in a multi-tenant database system. In certain embodiments, for example, all custom entity data rows are stored in a single multi-tenant physical table, which may contain multiple logical tables per organization. It is transparent to customers that their multiple “tables” are in fact stored in one large table or that their data may be stored in the same table as the data of other customers. 
     While one or more implementations have been described by way of example and in terms of the specific embodiments, it is to be understood that one or more implementations are not limited to the disclosed embodiments. To the contrary, it is intended to cover various modifications and similar arrangements as would be apparent to those skilled in the art. Therefore, the scope of the appended claims should be accorded the broadest interpretation so as to encompass all such modifications and similar arrangements.