Reverse engineering complex join queries

Zhang, Meihui; Elmeleegy, Hazem; Procopiuc, Cecilia M.; Srivastava, Divesh

doi:10.1145/2463676.2465320

Cited by 54 publications

(42 citation statements)

References 11 publications

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“…It would be interesting to see how our meta-path-based similarity metrics can be used to enhance their solutions. In [23,26], the problem of generating queries from user-provided example query results in a relational database was investigated.…”

Section: Related Workmentioning

confidence: 99%

Discovering Meta-Paths in Large Heterogeneous Information Networks

Meng

Cheng

Maniu

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

103

105

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The Heterogeneous Information Network (HIN) is a graph data model in which nodes and edges are annotated with class and relationship labels. Large and complex datasets, such as Yago or DBLP, can be modeled as HINs. Recent work has studied how to make use of these rich information sources. In particular, meta-paths, which represent sequences of node classes and edge types between two nodes in a HIN, have been proposed for such tasks as information retrieval, decision making, and product recommendation. Current methods assume meta-paths are found by domain experts. However, in a large and complex HIN, retrieving meta-paths manually can be tedious and difficult. We thus study how to discover meta-paths automatically. Specifically, users are asked to provide example pairs of nodes that exhibit high proximity. We then investigate how to generate meta-paths that can best explain the relationship between these node pairs. Since this problem is computationally intractable, we propose a greedy algorithm to select the most relevant meta-paths. We also present a data structure to enable efficient execution of this algorithm. We further incorporate hierarchical relationships among node classes in our solutions. Extensive experiments on real-world HIN show that our approach captures important meta-paths in an efficient and scalable manner.

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Section: Related Workmentioning

confidence: 99%

Discovering Meta-Paths in Large Heterogeneous Information Networks

Meng

Cheng

Maniu

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

103

105

View full text Add to dashboard Cite

show abstract

“…One way to verify whether a candidate query is valid is to execute it and check whether its output contains all the rows in the ET [23]. This is typically very expensive, hence we do not follow this approach.…”

Section: Baseline Algorithm (Verifyall)mentioning

confidence: 99%

“…Our work is also related to approaches for query by output [22,23]. These approaches take a table as input (needs to be completely filled) and return the query that produces that exact table.…”

Section: Related Workmentioning

confidence: 99%

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Discovering queries based on example tuples

Shen

Chakrabarti

Chaudhuri

et al. 2014

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

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An enterprise information worker is often aware of a few example tuples (but not the entire result) that should be present in the output of the query. We study the problem of discovering the minimal project join query that contains the given example tuples in its output. Efficient discovery of such queries is challenging. We propose novel algorithms to solve this problem. Our experiments on reallife datasets show that the proposed solution is significantly more efficient compared with naïve adaptations of known techniques.

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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].…”

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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Reverse engineering complex join queries

Cited by 54 publications

References 11 publications

Discovering Meta-Paths in Large Heterogeneous Information Networks

Discovering Meta-Paths in Large Heterogeneous Information Networks

Discovering queries based on example tuples

A Paradigm for Learning Queries on Big Data

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