Abstract:
A method and computer program for automatic mapping of Extensible Business Reports Language (XBRL) Data to corresponding locations in an initial business document. The program takes XBRL filing, together with text of the initial report, and starts a data mapping engine based on Evolutionary Optimization. The engine searches for the most plausible locations in the document for every data item. After the data locations have been identified, the program tags them in the document and creates visualization forms so a user could easily see and verify correspondence between 2 formats of the same data: saved in XBRL filing and presented in the document.

Description:
FIELD OF INVENTION 
       [0001]    The present invention relates to XBRL (eXtensible Business Reporting Language) and, in particular, to an XBRL application or program. 
       DESCRIPTION OF PRIOR ART 
       [0002]    The present invention is directed to a method that applies Evolutionary Optimization algorithm to the task of automated XBRL data mapping and to a computer program that manages the following processing steps:
       Loading of XBRL instance and structure XML, files and creation of in-memory objects for manipulations on data   Initialization of automatic data mapping process   Creation of visual representations for XBRL presentation and validation structures linked to the document text       
 
         [0006]    The use of Evolutionary Optimization for the task of XBRL Data Mapping is the core of the invention. The search for document locations of data values presented in XBRL filings can be interpreted as a task of combinatorial optimization. Most of the values presented in XBRL Instance documents can correspond to more than one text object in the initial document. Average XBRL filing contains over a hundred data items. This makes the number of variations of mapping huge and inaccessible for the complete enumeration. 
         [0007]    Evolutionary Data Mapping algorithm proposed in this invention allows reaching the best possible variant of data localization in several hundred steps. With the support of in-memory data caching the algorithm manages to find the required mapping solution in minutes, even at a personal computer with modest processing power. 
         [0008]    The method starts from random mapping solution generation. According to generic Evolutionary Optimization schema, it is required to generate an initial population of random solutions. Using the XBRL and HTML Utilities we create a list of possible document locations for every XBRL data item. A Random mapping solutions generator produces complete variants of data mapping, combining random locations for every data item. 
         [0009]    Population plays a very important role in the Evolutionary Optimization process. It maintains a restricted set of the best variants of a solution, and thus serves as a store of features that have proved their usefulness as higher than average. 
         [0010]    After creating the initial population of random solutions, an algorithm starts the main loop of Evolutionary Optimization. At every step of the main loop the algorithm creates a new variant of mapping solution, combining locations of data items from parents, two randomly selected members of the population. Two mutually complimentary modification methods provide a transformation of the best parent solutions&#39; features to a new offspring solution and the restoration of missed features. They are crossover and mutation. 
         [0011]    Crossover takes two solutions and combines their features that are document locations for the same data items in our case. The whole purpose of the crossover is propagation of the promising features found at the prior steps of Evolutionary Process and saved in population. In order to enhance the productivity of crossover, we calculate and save individual estimations for every data link in the solution. The estimations allow selecting better links with higher probability. Thus, crossover presents the conservative side of optimization, saving and passing to new generations the best findings of the past trials. 
         [0012]    Mutation does quite the opposite. It provides new solutions with minor random deviations from the mainstream of the features existing in the population. The idea behind the mutation is the following: crossover alone is capable of combining parents&#39; features only. Thus, it would never be able to include into a new solution a link that is missed in the population. Mutation closes the gap, providing new solutions with all the variations of links existing for the corresponding data items. It uses individual link plausibility estimations for convergence optimization. The links with the worst estimations get mutated more frequently. 
         [0013]    In order to support XBRL Data mapping, the program comprises all the classes and utility components required for input and output format conversions and in-memory processing, in addition to Evolutionary Mapping classes. Among them, specialized classes and utility methods for loading the XBRL document schema and basic taxonomy presentations and calculations structures referenced from the schema. Taxonomy structures are presented in multiple XML files saved on internet sites. The structures loading classes traverse through them, load and save the structures as a collection of in-memory objects for further use. 
         [0014]    The program further comprises data, presentation and calculations conversion classes and utility methods for XBRL instance files. They support the creation of in-memory instance objects and structures from instance XML files and basic structures loaded, as reviewed above. 
         [0015]    One more part of the program essential for the mapping process is HTML conversion utility. It provides the successful mapping of data items to initial document locations, it is absolutely required to be able to:
       Find the position of every HTML tag and every word of text in the initial document   Save structure relations (part-of) between the parts of the initial document   Identify clusters of words corresponding to such text objects as paragraphs, tables and parts of tables: columns, rows and cells   Modify document&#39;s text, Inserting marking tags around required text element       
 
         [0020]    HTML Utility supports all these actions by creation of in-memory presentation of the HTML document and providing methods for loading, manipulations and modifications. 
         [0021]    The last part of the program to be mentioned is the Mapping Request class that plays the role of interface between the user or automatic script and the program. It allows specifying files containing all parts of the instance filing:
       Schema XSD file   Instance data XML file   Instance presentation XML file   Instance calculations XML file       
 
       BACKGROUND OF THE INVENTION 
       [0026]    XBRL (eXtensible Business Reporting Language) has become a de facto standard for business and financial data representation (http://xbrl.org/frontend.aspx?clk=LK&amp;val=20). It normalizes data hidden in report texts providing unified semantic tags for data items and a structure covering relations between data categories. It is hard to overestimate the importance of such standardization, as it allows the collection and fast processing of financial data from various sources. 
         [0027]    At the same time, the step to XBRL representation doesn&#39;t come free. Text representation of financial data is more habitual for human readers and it takes a substantial effort for those making preparations to create appropriate mapping of the data to the more computer-oriented XBRL representation. The size of the XBRL structure (over 13,000 categories) and the subjective interpretation of data elements makes mapping highly tedious and imprecise. 
         [0028]    One of the filing process problems is the lack of visibility. XBRL format doesn&#39;t save links to the data location in the initial business report document and thus the user loses the ability to verify the correctness of data extraction. 
     
    
     
       DESCRIPTION OF DRAWINGS 
         [0029]      FIG. 1  is a block diagram of a computer environment in which XBRL Data Mapping Builder program can be employed 
           [0030]      FIG. 2  is a high level static UML class diagram of XBRL Data Mapping Builder program 
           [0031]      FIG. 3  contains a high level static UMI, class diagram of Evolutionary XBRL Mapping components 
           [0032]      FIG. 4  illustrates random mapping solution generation 
           [0033]      FIG. 5  illustrates crossover of parent solutions during the Evolutionary XBRL Mapping process 
           [0034]      FIG. 6  is a diagram of conversion utilities interaction 
           [0035]      FIG. 7  illustrates process of HTML document conversion by HTML Container 
           [0036]      FIG. 8  demonstrates a fragment of sample visualization of final XBRL Data Mapping solution 
           [0037]      FIG. 9  illustrates interaction between XBRL Data Request, instance data files, document HTML and Evolutionary XBRL Data Mapping processor 
       
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENT 
       [0038]    With reference to  FIG. 1  a typical computer environment within which XBRL Data Mapping Builder program manages to build links between filing data and document text Is illustrated. The program is hereinafter referred to as data mapping application. The environment comprises a computer  100  comprising:
       a processor   a random access memory capable of storing the mapping application and data from XBRL filing and HTML document   a hard drive capable of storing a copy of the mapping application, XBRL taxonomies, XBRL instance files, HTML document and resulting output forms as well as operating system program and data files       
 
         [0042]    In the course of building a mapping solution, the mapping application first loads essential parts of XBRL Taxonomy  102  consisting of:
       a set of inter-referenced XBRL schema (XSD) files   a set of XBRL presentation files   a set of XBRL calculations files       
 
         [0046]    After basic Taxonomy structures had been loaded, the mapping application loads XBRL Instance files  104  and converts them into in-memory structures. The instance files include:
       an XBRL schema file (XSD)   an XBRL presentation file   an XBRL calculations file   an instance data XML file       
 
         [0051]    The next data source required for building a mapping solution is HTML document file  106 . The mapping application loads the HTML document and converts it into an in-memory structure. It saves links between the parts of in-memory structure and the HTML document for further use at output forms generation time. 
         [0052]    Statistical models  108  help to better identify the most plausible locations of data items. The models contain statistical relations between text objects built on review of multiple precedents of XBRL data locations. The mapping application loads statistical models for every data item category, including end terms and abstract text objects. 
         [0053]    After processing the mapping application converts the resulting solution into output forms  110 . Depending on the input parameters, output forms can be created as a set of linked HTML files or a combination of HTML and Microsoft Excel files 
         [0054]    With reference to  FIG. 2  the mapping application is further comprised of an HTML Utility  200  that provides the user with the ability to import a business report  210  in HTML format and convert it into in-memory structure for text object separation and identification. 
         [0055]    Additionally, the XBRL Utility  204  provides the ability to import XBRL taxonomy  216  and instance XBRL files  214 . The utility is able to browse through multiple inter-linked schema, presentations and calculations files, load the required ones, and convert them into in-memory objects. 
         [0056]    Mapping Request Manager  202  controls processing of other parts of the data mapping application by loading names of XBRL Instance and HTML document files. 
         [0057]    Consequently, the Mapping Request Manager checks the availability and correctness of all specified data files, and in successful cases starts the Evolutionary Mapping Engine  206 . The Evolutionary Mapping Engine, in its turn, imports statistical Text Mining models and performs the Evolutionary Mapping Algorithm in a separate thread. 
         [0058]    After the optimal mapping has been built, the Output forms generator  208  creates output forms  220  as a set of interlinked HTML files for source business document, presentation and calculations. 
         [0059]    With reference to  FIG. 3  the classes comprising the Evolutionary XBRL Mapping Engine. The engine represents an implementation of the Evolutionary Search algorithm (http://www.ev-soft.com). Evolutionary Software, Inc. provides a library of Java classes that includes generic classes that need to be specialized for a particular optimization task. Class XBRLDataProcessor  300  implements generic interface Processor  306  that serves as a controller for the Evolutionary Optimization process. An instance of XBRLDataProcess performs the following actions:
       initializes all the objects required for successful optimization   connects active and controlling elements using events exchange mechanism   provides a client application with the ability to check out the readiness of the processor to start optimization process   starts optimization session   returns the best solution found during the optimization session       
 
         [0065]    Next class XBRLDataSolution  302  extends generic abstract class EvSolution  308 . Each instance of this class contains a complete variant of the mapping of instance data items to locations in the document text. In the course of optimization, Evolutionary Search generates several thousand of such variants. The first several hundred of them serve as a source of random features that should be generated as uniformly as possible. XBRLDataSolution generates random variants at the initial stage of search in the method fillRandompy( ). Further convergence of the search to the best variant depends on the way variants of the solution selected to population are used for the creation of new solutions. XBRLDataSolution combines features of a couple of selected population members in method crossover( ). One more method requiring implementation is mutation( ). It updates variants created by crossover( ), supplying them with random deviations. 
         [0066]    One more class that requires implementation for the given optimization problem is EvTask  310 . It is meant for the calculation of optimization criteria. XBRLDataTask  304  implements the estimation of data mapping variant. Composed estimation criteria for the mapping data optimization combines the following partial estimations:
       consistency of co-location of the data items associated with the same statement inside the same HTML Table   consistency of co-location of the data items associated with the same statement and context inside the same HTML Table column   consistency of co-location of the data items with the same name and different contexts inside the same HTML Table row   Number of data items with missed locations   Number of locations linked to more than one data item   Results of statistical classification models estimations for individual data as well as for financial statement tables as wholes       
 
         [0073]    With reference to  FIG. 4  a general schema of random data mapping is comprised of a set on XBRL Instance files  400  containing data records, presentation and calculations structures. Each data item contains a value that can be linked to a number of locations in the initial document, as shown in schema by links between a fragment of presentation structure  402  and a fragment of the initial document  404 . generation of random mapping solutions implemented in method XBRLDataSolution.fillRandomly( ) takes one link per data item, using a random number generator with a uniform distribution function. 
         [0074]    With reference to  FIG. 5  an illustration of crossover of two parent XDRLDataSolution  500  and  502  containing different mapping links for the same data item “LiabilitiesNdStckholdersEquity” demonstrates the links in a fragment of visualization  504 . The Crossover algorithm compares individual estimations of both links and selects one of them for incorporation into the offspring solution. The probability that a link is selected for inclusion into an offspring is proportional to its individual estimation. 
         [0075]    With reference to  FIG. 6  a diagram of interactions between data conversion utilities and data sources is comprised of a core data class XBRLContainer  600  that holds data arrays and structures imported from instance files: a Presentation XML  604 , Calculations XML  606  and Instance XML  602 . XBRLPresentation  608  specializes in the conversion of presentation XMLs into in-memory presentation objects. Another utility class XBRLCalculations  610  loads calculations XMLs and converts them into in-memory calculations objects. 
         [0076]    XBRLUtility  612  provides a set of utility methods used by other conversion utilities. 
         [0077]    With reference to  FIG. 7  illustration of the process of HTML document conversion by the HTML Container consists of a fragment of initial HTML file  700  and a utility class  702  that loads the document and converts it into an internal tree-like object,  704  which contains all HTML tags as branches and saves the coordinates of each tag&#39;s location in the initial document. 
         [0078]    With reference to  FIG. 8  a fragment of sample visualization of final XBRL Data Mapping solution contains the final XBRL Data Solution  800  found by the Evolutionary Mapping algorithm taken by a utility class XBRLContainer  802 . The utility inserts reference tags around the data items locations into the initial HTML documents and generates separate HTMLs for presentation and calculations structures. The fourth frame HTML combines these three resulting HTMLs in joined view  804 . HTML links inserted into in the generated HTMLs provides a user with the ability to move from one HTML panel to another by simple mouse clicks on the data representations. 
         [0079]    With reference to  FIG. 9  instance data file  902 , presentation file  904  and document HTML  906  get loaded and converted under supervision of XBRL Data Request  900 . Then, the request manager passes all the created in-memory objects to the Evolutionary XBRL Data Mapping Processor  908  which builds optimal mapping from them.