Chapter 2 - Querying

Abstract

This chapter focuses on the issues that are common to all query scenarios, and Structured Query Language (SQL) as a query solution. It reviews querying nontraditional data, ways to extract traditional and structured data and addresses the different challenges in querying traditional and non-traditional data. The most popular way of querying data has been SQL, the SQL Query Language. A relational database (a SQL database) stores data in tables and allows search across those tables with SQL. The Relational Model includes the notion of tables, columns, and rows (or relations, attributes, and tuples). It also defines a relational algebra, with operations on tuples (rows in a table, or intermediate query results). However, one of the major criticisms of the Relational Model is that its data model imposes a rather simplistic structure on the data. While much of the world's data fits quite neatly into this model, a lot of data simply does not fit.. A lot of the data that is stored and represented as Extensible Markup Language (XML) today is traditional data and SQL is particularly good at querying such data. But, at least 90% of the data in the world is nontraditional data, and a great deal of valuable information is locked up in Word files, nontraditional data informally as data that cannot be represented naturally as numbers and dates and strings, such as documents and pictures and movie clips. There are three approaches to query such data: metadata, objects, and markup. The other approach is to store the nontraditional data as an opaque chunk of data and add metadata. In a database this opaque chunk is often called a binary large object (BLOB).