Suggesting Assess Queries for Interactive Analysis of Multidimensional Data

Article Computer Science, Information Systems

COOL: A framework for conversational OLAP

Matteo Francia et al.

Summary: The paper introduces the COOL framework for Conversational OLap applications, which interprets and translates natural language dialogues into OLAP sessions. It relies on formal grammar and a repository to obtain the correct query through automatic inference or user interactions, achieving an average accuracy of up to 94%.

INFORMATION SYSTEMS (2022)

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Article Computer Science, Information Systems

Enhancing Cubes with Models to Describe Multidimensional Data

Matteo Francia et al.

Summary: IAM is a new model paradigm that allows users to explore data by expressing analysis intentions and returns annotated multidimensional data. Research challenges include automatically tuning model size, estimating model component interestingness, selecting effective visualizations, and designing visual metaphors for interaction. Method effectiveness is evaluated based on user effort, efficiency, and scalability.

INFORMATION SYSTEMS FRONTIERS (2022)

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Article Computer Science, Hardware & Architecture

DIFF: a relational interface for large-scale data explanation

Firas Abuzaid et al.

Summary: Existing explanation engines have limitations in applicability and extensibility, hence the proposal of the DIFF operator to unify the core functionality of these engines. The DIFF operator can provide the same semantics as existing explanation engines and perform well in industrial production use cases.

VLDB JOURNAL (2021)

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Article Computer Science, Information Systems

COMPARE: Accelerating Groupwise Comparison in Relational Databases for Data Analytics

Tarique Siddiqui et al.

Summary: The paper introduces a new logical operator COMPARE for relational databases that simplifies the comparison between subsets of data and significantly improves query performance. By extending the database engine with optimization techniques based on the semantics of COMPARE, the approach outperforms existing methods in terms of speed and efficiency. Extensive evaluations on synthetic and real-world datasets demonstrate the effectiveness of COMPARE in achieving significant speedups.

PROCEEDINGS OF THE VLDB ENDOWMENT (2021)

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Article Computer Science, Information Systems