ATLaS: A Native Extension of SQL for Data Mining

ATLaS: A Native Extension of SQL for Data Mining and Stream Computations

A lack of power and extensibility in their query languages has seriously limited the generality of DBMSs and hampered their ability to support new application domains. Considerable efforts by database researchers and commercial DBMS vendors have led to major extensions; yet there remain important applications—particularly data mining—that are not supported well in SQL-3. Thus, there is a pressi...

ATLaS: a Turing-Complete Extension of SQL for Data Mining Applications and Streams

ATLAS: A Small but Complete SQL Extension for Data Mining and Data Streams

DBMSs have long suffered from SQL’s lack of power and extensibility. We have implemented ATLaS [1], a powerful database language and system that enables users to develop complete data-intensive applications in SQL—by writing new aggregates and table functions in SQL, rather than in procedural languages as in current Object-Relational systems. As a result, ATLaS’ SQL is Turing-complete [7], and ...

Extending SQL for Decision Support Applications

Extended Abstract The challenge of extending database systems for decision support applications has been the topic of much recent research—a very incomplete list of previous work includes [11, 8, 12, 4, 10, 5]. Yet, there is no generally accepted solution for the problem, which remains a critical one, since the inability of current DBMSs to support data mining applications is well-tested and cl...

SQL-Like Language for Database Mining

Data mining, also referred to as database mining or knowledge discovery in databases (KDD), is a new research area that aims at the discovery of useful information from large datasets. One of the most interesting and important research problems is discovering of different types of rules (e.g. association, characteristic, discriminant, etc.) from data. In this work we propose the new SQL-like la...