Learning and verifying quantified boolean queries by example

Abouzied, Azza; Angluin, Dana; Papadimitriou, Christos H.; Hellerstein, Joseph M.; Silberschatz, Abraham

doi:10.1145/2463664.2465220

Cited by 60 publications

(43 citation statements)

References 28 publications

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“…These works do not focus on predicting user interests or "similar" data tuples. In [20] they require the user to specify in advance the attributes and value assignments used to learn queries, which are assumptions we cannot make in our work. Finally, our vision for automatic, interactive navigation in databases was first introduced in [21].…”

Section: Related Workmentioning

confidence: 99%

Automatic user steering for interactive data exploration

Dimitriadou

2014

2014 IEEE 30th International Conference on Data Engineering Workshops

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The amount of data that have flooded databases during the last few years have created several new problems for the data management community to address. One of the most prominent is the discovery of new and interesting information that is hidden in the underlying big data sets. As of now, in order to explore these data sets users begin with a few general queries, study their output and iteratively issue more specific ones until they discover interesting information. This is an onerous process that requires time and effort. In this thesis we are developing an automatic data exploration framework that will make the discovery of new information in a vast data space both effective and efficient. Our system asks users for their relevance feedback on strategically collected samples in an interactive manner, steers them towards the interesting parts of the database and eventually formulates the query that retrieves their data of interest. Our preliminary results are encouraging and allow us to persevere in the development of such a system.

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Section: Related Workmentioning

confidence: 99%

Automatic user steering for interactive data exploration

Dimitriadou

2014

2014 IEEE 30th International Conference on Data Engineering Workshops

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“…Query learning: Database community has proposed several systems that help the DBMS learn the user's information need by showing examples to the user and collecting her feedback [2,8,19,41,61]. In these systems, a user explicitly teaches the system by labeling a set of examples potentially in several steps without getting any answer to her information need.…”

Section: Related Workmentioning

confidence: 99%

A Signaling Game Approach to Databases Querying and Interaction

Termehchy

Touri

2015

Proceedings of the 2015 International Conference on the Theory of Information Retrieval

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As most users do not precisely know the structure and/or the content of databases, their queries do not exactly reflect their information needs. The database management systems (DBMS) may interact with users and use their feedback on the returned results to learn the information needs behind their queries. Current query interfaces assume that users do not learn and modify the way way they express their information needs in form of queries during their interaction with the DBMS. Using a real-world interaction workload, we show that users learn and modify how to express their information needs during their interactions with the DBMS and their learning is accurately modeled by a well-known reinforcement learning mechanism. As current data interaction systems assume that users do not modify their strategies, they cannot discover the information needs behind users' queries effectively. We model the interaction between users and DBMS as a game with identical interest between two rational agents whose goal is to establish a common language for representing information needs in form of queries. We propose a reinforcement learning method that learns and answers the information needs behind queries and adapts to the changes in users' strategies and prove that it improves the effectiveness of answering queries stochastically speaking. We propose two efficient implementation of this method over large relational databases. Our extensive empirical studies over real-world query workloads indicate that our algorithms are efficient and effective.

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“…The instantiation of our paradigm for learning relational queries [11,12] follows a very recent line of research [39,34,18] from which we differ in two ways: (i) we assume no knowledge of the database schema, and (ii) we do not have an initial query output to start with and we discover it from user interactions. Another work strongly related to ours is [1,2], which focuses on learning quantified Boolean queries and also uses the framework of learning with membership queries [6]. The goal of their system is somewhat different from our paradigm, in that their goal is to disambiguate a natural language specification of the query, whereas we focus on raw data to guess the "unknown" query that the user has in mind.…”

Section: Related Workmentioning

confidence: 99%

A Paradigm for Learning Queries on Big Data

Bonifati

Ciucanu

Lemay

et al. 2014

Proceedings of the First International Workshop on Bringing the Value of "Big Data" to Users (Data4U 2014)

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International audienceSpecifying a database query using a formal query language is typically a challenging task for non-expert users. In the context of big data, this problem becomes even harder as it requires the users to deal with database instances of big sizes and hence difficult to visualize. Such instances usually lack a schema to help the users specify their queries, or have an incomplete schema as they come from disparate data sources. In this paper, we propose a novel paradigm for interactive learning of queries on big data, without assuming any knowledge of the database schema. The paradigm can be applied to different database models and a class of queries adequate to the database model. In particular, in this paper we present two instantiations that validated the proposed paradigm for learning relational join queries and for learning path queries on graph databases. Finally, we discuss the challenges of employing the paradigm for further data models and for learning cross-model schema mappings

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Learning and verifying quantified boolean queries by example

Cited by 60 publications

References 28 publications

Automatic user steering for interactive data exploration

Automatic user steering for interactive data exploration

A Signaling Game Approach to Databases Querying and Interaction

A Paradigm for Learning Queries on Big Data

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