Parser translator system and method

Details Parser translator system and method Parser translator system and method

1999-02-19

2003-02-18

Dam, Tuan Q. (Department: 2124)

Data processing: software development, installation, and managem

Software program development tool

Translation of code

C717S112000, C704S009000, C707S793000, C707S793000

Reexamination Certificate

active

06523172

ABSTRACT:

BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to specification of data transformation statements, and in particular, to systems, methods and computer program products for interactively defining data transformations.
2. Description of the Related Art
Automated transformation of data from one or more source formats, systems, database environments, or schemas, to one or more targets has become an important enabling technology for managing heterogeneous data environments, for migrating data from legacy systems to newer data environments, for creating so-called “application islands,” and for data warehousing or otherwise maintaining multiple instances of the same semantic information. The need for such automated data transformation is driven in part by rapid changes in computing and information systems technology, but also by business changes such as the acquisition and merging of business units, by year 2000 (Y2K) issues, by growth and/or change of business focus, by the use of enterprise planning, front office and back office systems, by increasing demands by customers, suppliers, and business partners for real-time or near-real time data, and by competitive pressures that demand timely operational data to support management decisions.
Large information technology departments face formidable challenges in transforming data. For example, even when incorporating off-the-shelf packaged applications, the level of effort required to integrate the off-the-shelf application into a pre-existing data environment can be substantial. Indeed, some estimates place the level of effort associated with data conversion alone at 40% of overall effort for integrating enterprise-wide software applications. Companies frequently address the data conversion challenge by building teams of programmers and consultants to hand-code programs to convert and integrate the required data. Unfortunately, converting large amounts of data can be a time-consuming and difficult process, even for skilled programmers. Automated tools are a practical necessity.
The need for automated data conversion tools goes well beyond simple, one-time, transformation of data from a legacy source format to that required by a new software and/or hardware system. Data conversion programs, sometimes called data bridge programs, are often written when an organization does any of the following.
1. Integration of a new application that uses a different data format into a pre-existing computing environment. For example, suppose a company becomes self-insured and buys a flexible benefits software package. To use the package, the company must extract data from a personnel database, a payroll database (perhaps in a flat file form), a claim system (which could be maintained out of house), a general ledger, and other financial data stores. It is very likely that the systems reside on different platforms and use different data formats. Moreover, once the new system has been implemented and integrated, the company will have an ongoing need to access data from these original sources, as well as to apply changes to some of these databases to reflect changes in the flexible benefits system.
2. Modification of a data structure definition (schema) used by a database management system. When a software support organization develops a new application (perhaps to reflect new equipment being deployed in the field), it is often necessary to modify the schema definition of various databases to reflect the new information. This process can require dumping the data content of the original database and reloading the data content into the new record types, along with information about the new equipment or application, which may be stored in a flat file or database.
3. Integration of data from multiple sources or reports for use with decision support tools. Companies typically store related data on a variety of different systems in different data formats. For example, business data are typically stored in an environment dominated by older applications and storage formats, whereas engineering data may be stored in an object-oriented database on a workstation and simulation data may be stored in a flat file on a mainframe. Periodic planning often requires that data from the various systems be collected and integrated.
Although brute force, hand-coding techniques have been, and continue to be, used in support of the above, data-driven techniques are preferable because of their flexibility and because they can allow non-technical personnel to define appropriate data transformations. One such data-driven environment is the ETI·EXTRACT® Tool Suite available from Evolutionary Technologies International, Inc. of Austin, Texas (ETI·EXTRACT is a registered trademark of Evolutionary Technologies International, Inc). The ETI·EXTRACT Tool Suite is an extensible set of software tools for automating the generation and execution of data conversion programs. Because the tool suite is data-driven, functionality can be added without changing the ETI·EXTRACT code itself. In general, the data that drives the tool suite includes (1) internal databases that store metadata, i.e., data about data, describing the structure and format of source and target data sets and (2) transformation rules. Using the ETI·EXTRACT Tool Suite, transformational rules are developed using grammars that define the universe of legal transformation statements. In this way, transformational rules developed using the ETI·EXTRACT Tool Suite are correct by construction.
In the information systems arts, the use of metadata is emerging as a critical element in effective information resource management. Vendors and users alike recognize the value of metadata; however, the rapid proliferation of data manipulation and management tools has resulted in information technology products that represent and process metadata differently and without consideration for sharing of metadata. To enable full-scale enterprise data management, different information tools must be able to freely and easily access, update, and share metadata. One viable mechanism to enable disparate tools from different vendors to exchange metadata is a common metadata interchange specification. Another is through repositories (e.g., a Microsoft Repository with its Open Information Model and Oracle's Repository with its Common Warehouse Model) with business models and tools to interchange information about these business models with other applications.
Although there is currently no generally deployed standard for metadata exchange, a number of products (e.g., CASE tools and data repositories) do have the ability to exchange metadata with a subset of other products. A proposed Meta Data Interchange Specification (MDIS) created by the Meta Data Coalition, is a file-based exchange format currently supported by a variety of vendors. Unfortunately, metadata exchange is only part of the solution. While having a standard metadata exchange mechanism is critical, with one exception, it is relatively easy to relate the metamodels used by different tools. The exception is business rules, i.e., the functional logic used to test and transform data values.
In general, users today specify business rules in one of three ways: (1) SQL or some proprietary control language (2) fragments of code such as of COBOL, C++ or BASIC, (3) code fragments with embedded SQL or (4) merely as documentation strings. These representations are unfortunate for two reasons. First, for products that require code blocks or some Fourth Generation Language (4GL), the user of the product must have some technical training. Second, and more importantly, such encodings make it extremely difficult for products to exchange business rules. Systems and methods are desired that allow interoperability and exchange of business rules or test and transformation logic (e.g., as part of metadata exchange) are desired.
SUMMARY OF THE INVENTION
Accordingly, a parser-translator technology has been developed to allow a user to specify complex test and/or transfo

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