QueRIE : System for Personalized Query Recommendation (original) (raw)

The QueRIE system for Personalized Query Recommendations

Data …, 2011

QueRIE: A Query Recommender System supporting Interactive Database Exploration

… Conference on Data …, 2010

This demonstration presents QueRIE, a recommender system that supports interactive database exploration. This system aims at assisting non-expert users of scientific databases by generating personalized query recommendations. Drawing inspiration from Web recommender systems, QueRIE tracks the querying behavior of each user and 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. It then generates and recommends the queries that cover those parts to the user.

A case for a collaborative query management system

2009

Over the past 40 years, database management systems (DBMSs) have evolved to provide a sophisticated variety of data management capabilities. At the same time, tools for managing queries over the data have remained relatively primitive. One reason for this is that queries are typically issued through applications. They are thus debugged once and re-used repeatedly. This mode of interaction, however, is changing. As scientists (and others) store and share increasingly large volumes of data in data centers, they need the ability to analyze the data by issuing exploratory queries. In this paper, we argue that, in these new settings, data management systems must provide powerful query management capabilities, from query browsing to automatic query recommendations. We first discuss the requirements for a collaborative query management system. We outline an early system architecture and discuss the many research challenges associated with building such an engine.

Personalization of queries in database systems

Proceedings. 20th International Conference on Data Engineering, 2004

As information becomes available in increasing amounts to a wide spectrum of users, the need for a shift towards a more user-centered information access paradigm arises. We develop a personal-ization framework for database systems based on user profiles and identify the ...

ACCESS ENABLED DATABASE QUERY RECOMMENDATION SYSTEM

Extensive exploration of the database is essential for an effective information mining process. Massive volumes of complex data from various sources or platforms that are integrated and available in datamarts have to be analyzed and studied for any form of knowledge discovery. For example, users like scientists need to query large databases for scientific data analysis and exploration. However, all users may not possess the competencies in Structured Query Language that is in general required, to query relevant data from relational database. Inexperienced users will seldom be proficient with the structural details of the schema and databases. This knowledge is required for all database related activities like generating reports for ad-hoc business requests. A database query recommendation system is a system that assists users by presenting personalized database query recommendations. The framework attempts to identify similarities with previous users\\\' information needs and recommend queries to the current user. An access enabled recommendation engine is an extension to the query recommendation system that supports the capability of recommending queries based on the user\\\'s privileges to access a particular database object. For example, the system will refrain from suggesting a query that refers to a database table with confidential data, to a user with insufficient privileges, regardless of whether the query qualifies with highest similarity for recommendation.

SQL QueRIE Recommendations: a query fragment-based approach

Proceedings of the …, 2010

Relational database systems are becoming increasingly popular in the scientific community to support the interactive exploration of large volumes of data. In this scenario, users employ a query interface (typically, a web-based client) to issue a series of SQL queries that aim to analyze the data and mine it for interesting information. First-time users, however, may not have the necessary knowledge to know where to start their exploration. Other times, users may simply overlook queries that retrieve important information. In this work we describe a framework to assist non-expert users by providing personalized query recommendations. The querying behavior of the active user is represented by a set of query fragments, which are then used to identify similar query fragments in the recorded sessions of other users. The identified fragments are then transformed to interesting queries that are recommended to the active user. An experimental evaluation using real user traces shows that the generated recommendations can achieve high accuracy.

QueRIE: Collaborative Database Exploration

IEEE Transactions on Knowledge and Data Engineering, 2014

Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user's querying behavior and finds matching patterns in the system's query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these "similar" users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user's session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach.

Integration of information retrieval and database management systems

Information Processing & Management, 1988

The problem of integrating database management systems and information retrieval systems has received increasing attention in recent years. In a database management environment, the records are formatted. The attributes with which the record characteristics and the user needs are described are precise. In contrast, an information retrieval system provides facilities for identifying references to documents, usually in textual form, from which the user information needs can be satisfied. The descriptors used to represent the content of the documents for this purpose are normally imprecise. The motivation for integrating these two types of systems is presented. Several features of such an integrated system in terms of what kinds of retrieval options such an integrated system should provide are identified. In particular, it is argued that the integrated system should allow the selection of the degree of structuredness of the search, depending on how precisely the user can specify his or her needs. A formulation of the underlying retrieval problem is presented. It is observed that the kind of integrated system considered has a broader scope of application than either one of its components.

Implementation of Interactive Database Exploration

2016

ARTICLE INFO Database Management Systems interact with the user to capture and to analyze data. The non-expert user of SQL or the user who is not familiar with database schema face great difficulties in analyzing and mining interesting information from this system. In this paper we have taken a review of Query Recommender System to help these users. This system tracks the querying behavior of each user and identifies current user 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. It then generates and recommends the queries that cover those parts to the user.

QueRIE : System for Personalized Query Recommendation (original) (raw)

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