SQL QueRIE recommendations

Abstract

This demonstration presents QueRIE, a recommender system that supports interactive database exploration. This system aims at assisting non-expert users of scientific databases by tracking their querying behavior and generating personalized query recommendations. The system is supported by two recommendation engines and the underlying recommendation algorithms. The first identifies potentially "interesting" parts of the database related to the corresponding data analysis task by locating those database parts that were accessed by similar users in the past. The second identifies structurally similar queries to the ones posted by the current user. Both approaches result in a recommendation set of SQL queries that is provided to the user to modify, or directly post to the database. The demonstrated system will enable users to query and get real-time recommendations from the SkyServer database, using user traces collected from the SkyServer query log.

A knowledge-based framework for a mobile OLAP system

“…The challenges of applying data mining techniques to the database query logs are introduced in Khoussainova et al (2009). QueRIE (Akbarnejad et al, 2010) is the first framework to address the problem of generating SQL query recommendations proposing a fully-fledged solution (Akbarnejad et al, 2010). But in both proposed approaches (Khoussainova et al, 2009;Akbarnejad et al, 2010), the recommendations are given without taking into consideration the context.…”

Describing Analytical Sessions Using a Multidimensional Algebra

“…More elaborated management was not needed as operational systems mainly issue canned (i.e., known in advance) queries. To our knowledge, only two works go beyond and propose a framework to manage the knowledge captured in the issued analytical queries (i.e., the DBMS log) to support query recommendation [3,4] or query completion [11] for interactive analysis of relational sources. The first approach follows the idea of recommender systems in the exploration of Web data.…”

“…Later, by means of a distance function they compute how similar two queries are. In their second approach [3], given the time-consuming task of generating these matrixes and compare them, the authors extend their previous work to incorporate two recommendation engines, a tuple-based one that recommends queries that touch similar parts of the database, and a query fragment-based one that recommends structurally similar queries (i.e., a syntactical approach).…”

“…This trend is rather evident for scientists, who are increasingly using relational databases and SQL for conducting analytical sessions over huge repositories of data [11]. For this reason, novel works have focused on supporting analytical tasks over relational sources [3,4,10,11,14,17]. Specifically, it has already been pointed out the necessity to come up with flexible, powerful means for analyzing the issued queries (the keystone of these systems, usually stored in the DB query log), and decompose, store and handle them in a dedicated subsystem in order to better support any decisional task with the knowledge captured in the analytical queries [10].…”

“…Specifically, it has already been pointed out the necessity to come up with flexible, powerful means for analyzing the issued queries (the keystone of these systems, usually stored in the DB query log), and decompose, store and handle them in a dedicated subsystem in order to better support any decisional task with the knowledge captured in the analytical queries [10]. As examples of such tasks, the system should be able to support the user when formulating new queries and leverage his / her knowledge with other users (query recommendation) [3,4,11] and, at the same time, database administrators should tune their databases to cope with the evolution of queries issued (physical as well as conceptual design) [14,17].…”