ATLAS: A Small but Complete SQL Extension for Data Mining and Data Streams
نویسندگان
چکیده
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 is very suitable for advanced data-intensive applications, such as data mining and stream queries. The ATLaS system is now available for download along with a suite of applications [1] including various data mining functions, that have been coded in ATLaS’ SQL, and execute with a modest (20–40%) performance overhead with respect to the same applications written in C/C++. Our proposed demo will illustrate the key features and applications of ATLaS. In particular, we will demonstrate:
منابع مشابه
ATLaS: A Native Extension of SQL for Data Mining
A lack of power and extensibility in their query languages has seriously limited the generality of DBMSs and hampered their ability to support data mining applications. Thus, there is a pressing need for more general mechanisms for extending DBMSs to support efficiently database-centric data mining appliacations. To satisfy this need, we propose a new extensibility mechanism for SQL-compliant D...
متن کاملATLaS: 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...
متن کاملAn Introduction to the Expressive Stream Language ( ESL ) 1 WEB
ESL is the application language of the Stream Mill system that supports2: • Continuous queries on data streams, • Ad hoc queries on (i) database tables and (ii) on concrete views created from streaming data, • Spanning applications that combine and compare incoming live data with stored data. ESL is based on SQL to help users to learn it, and use it on spanning applications. For the same reason...
متن کاملMining Frequent Patterns in Uncertain and Relational Data Streams using the Landmark Windows
Todays, in many modern applications, we search for frequent and repeating patterns in the analyzed data sets. In this search, we look for patterns that frequently appear in data set and mark them as frequent patterns to enable users to make decisions based on these discoveries. Most algorithms presented in the context of data stream mining and frequent pattern detection, work either on uncertai...
متن کامل