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Iterative data analysis enabled through query result abstraction

Iterative data analysis enabled through query result abstraction description/claims

This is a continuation of co-pending U.S. patent application Ser. No. 10/431,916 filed May 8, 2003, which is herein incorporated by reference.

1. Field of the Invention

The present invention generally relates to data processing, and more particularly, to the accessing data through a logical framework.

2. Description of the Related Art

Databases are computerized information storage and retrieval systems. The most prevalent type of database is the relational database, a tabular database in which data is defined so that it can be reorganized and accessed in a number of different ways. A relational database management system (DBMS) is a database management system that uses relational techniques for storing and retrieving data.

Regardless of the particular architecture, in a DBMS, a requesting entity (e.g., an application, the operating system or a user) demands access to a specified database by issuing a database access request. Such requests may include, for instance, simple catalog lookup requests or transactions and combinations of transactions that operate to read, change and add specified records in the database. These requests are made using high-level query languages such as the Structured Query Language (SQL). Illustratively, SQL is used to make interactive queries for getting information from and updating a database such as International Business Machines' (IBM) DB2, Microsoft's SQL Server, and database products from Oracle, Sybase, and Computer Associates. The term “query” denominates a set of commands for retrieving data from a stored database. Queries take the form of a command language that lets programmers and programs select, insert, update, find out the location of data, and so forth.

In many cases, particularly in research-oriented environments, the task of analyzing information is typically a multi-step process involving generation of an initial set of query results that is further reduced through subsequent queries. For example, in a medical research environment, an initial query could be posed to find a set of research candidates meeting a particular diagnosis or test result profile. In a large data warehouse environment, it may take a lot of time and resources to process a query of this nature, in particular if the criteria specified is complex. Once the query results are returned, it may be desirable to apply other criteria to further reduce or subset the results returned. While this could be accomplished by extending the original query with additional logic, it would be more efficient to perform the subsequent query against the results of the original query. Unfortunately, current methods to address this scenario are typically rather manual and error prone. Specifically, the user is required to go through several steps to save the results from the original query in a form that would allow for subsequent queries to be performed against it. In a relational environment, this would require a table to be created to hold the results of the original query. Similarly, in an XML-based repository, query result data would need to be stored in the repository in order to be re-queried. In either case, the user would then need to understand the physical schema used to represent the initial query results and expresses subsequent query taking this schema into account.

Another problem encountered in the iterative query scenario is that a subsequent query may not return a desired set of results. The user may then wish to go back to a prior point in the progression of intermediate query results. Using conventional techniques the user must consciously save the results from each query in order to re-establish a prior query iteration.

Therefore, what is needed is a query framework providing flexibility in data analysis.

The present invention provides a method, system and article of manufacture for accessing physical data through an abstraction model. The abstraction model includes metadata describing and defining a plurality of logical fields.

In one aspect, a method of using a logical framework to query data is provided. The method includes at least defining a logical view of the data; and iteratively restricting the logical view of the data for each successive abstract query, based on at least one result criterion of an immediately previous abstract query.

In another aspect, a method for constructing abstract queries defined by logical fields is provided. The method includes at least providing an initial abstract data model defining a plurality of logical fields mapped to physical data having a particular schema; receiving a first abstract query comprising at least one condition and a result criterion comprising at least one of the plurality of logical fields; transforming the first abstract query into an executable query with reference to the initial abstract data model; executing the executable query; returning results produced by execution of the executable query; and generating another abstract data model comprising only the at least one of the plurality of logical fields of the result criterion, wherein the at least one of the plurality of logical fields is mapped to the results.

In another aspect, a method for constructing abstract queries defined by logical fields is provided. The method includes at least providing an initial abstract data model defining a plurality of logical fields and mapping each logical field to physical data; receiving a first abstract query comprising at least two logical fields defined by the initial abstract data model; transforming the first abstract query into an executable query with reference to the initial abstract data model; executing the executable query; returning results produced by execution of the executable query; and generating another abstract data model. Generating another abstract data model includes, for each of the at least two logical fields of the first abstract query, (i) retrieving, from the initial abstract data model, a logical field definition for the logical field; (ii) updating the logical field definition to refer to a portion of the results; and (iii) adding the updated logical field definition to the another abstract data model.

Yet another aspect provides a computer-readable medium containing a program which, when executed by a processor, performs an operation for each abstract query in a succession of abstract queries. The operation includes at least initializing an abstract data model; adding to the abstract data model only those one or more logical fields defined as result fields in the abstract query; mapping each logical field of the abstract data model to a different portion of results returned for the abstract query as a result of being executed; making the abstract data model available for construction of a next abstract query in the succession of abstract queries, whereby logical fields defined by a given abstract data model are limited to those defined as result fields in a last-executed abstract query of the succession of abstract queries.

Still another aspect provides a computer comprising a memory and at least one processor, and further comprising a framework for defining and processing abstract queries for accessing physical data. The framework includes at least an abstract data model generator configured to generate abstract data models based on (i) a logical result field of a last-executed abstract query in a series of abstract queries; and (ii) results returned for the last-executed abstract query. Each abstract data model defines a logical view of the data and includes at least (a) a logical field definition only for each logical result field of the last-executed abstract query; and (b) mapping information for each of the one or more logical field definitions, which maps each of the logical field definitions to a separate portion of the results returned for the last-executed abstract query.

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