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VIQS: Visual Interactive Exploration of Query Semantics

Published: 13 March 2017 Publication History

Abstract

Analytics platforms such as IBM's Watson Analytics TM are collecting metadata about their use, including user queries on uploaded datasets. The analysis of this metadata may be valuable in improving services, such as query recommendation and automatic data visualization. However, analysis of metadata is difficult not only in terms of scale but also in terms of complexity. Generalizing and exploring query patterns across users and datasets is challenging. Abstractions are likely to help bridge differences in specifics (e.g., column names and query details), particularly in semantics. For example, a single query, "What is the trend of sales over year?" could be abstracted in many different ways (e.g., "What is the trend of financial gain over time?"). In this paper, we describe our process of creating a dataset of query semantics, starting from initial metadata extraction from query logs to semantic expansion using WordNet. To help system designers effectively browse and understand patterns of use, we developed VIQS (Visual Interactive Query Semantics), a system that extracts query semantics from query logs over multiple datasets, and allows users to explore underlying patterns visually. We present results from an informal interview study along with specific insights regarding popular query patterns from 3-months of data. We believe the analytic process, as well as the specific insights on query patterns, will benefit the design of analytics platforms.

References

[1]
J. Akbarnejad, G. Chatzopoulou, M. Eirinaki, S. Koshy, S. Mittal, D. On, N. Polyzotis, and J. S. V. Varman. Sql querie recommendations. Proc. of VLDB Endowment '10, 3(1--2):1597--1600, 2010.
[2]
J. Aligon, M. Golfarelli, P. Marcel, S. Rizzi, and E. Turricchia. Similarity measures for olap sessions. KAIS, 39(2):463--489, 2014.
[3]
D. A. Benson, M. Cavanaugh, K. Clark, I. Karsch-Mizrachi, D. J. Lipman, J. Ostell, and E. W. Sayers. Genbank. Nucleic Acids Research, 41(D1):D36--D42, 2013.
[4]
M. Bostock, V. Ogievetsky, and J. Heer. D3: Data-driven documents. 2011.
[5]
G. Chatzopoulou, M. Eirinaki, S. Koshy, S. Mittal, N. Polyzotis, and J. S. V. Varman. The querie system for personalized query recommendations. IEEE Data Eng. Bull., 34(2):55--60, 2011.
[6]
F. Chevalier, D. Auber, and A. Telea. Structural analysis and visualization of c
[7]
code evolution using syntax trees. In IWPSE'07, pages 90--97. ACM, 2007.
[8]
C. Collins, M. S. T. Carpendale, and G. Penn. Docuburst: Visualizing document content using language structure. volume 28, pages 1039--1046, 2009.
[9]
Data.gov. http://www.data.gov/.
[10]
M. Drosou and E. Pitoura. Redrive: result-driven database exploration through recommendations. In Proc. of ACM CIKM'11, pages 1547--1552, 2011.
[11]
C. Fellbaum. Wordnet: An electronic lexical database. 1998.
[12]
A. Giacometti, P. Marcel, and E. Negre. A framework for recommending OLAP queries. In Proc. of DOLAP 2008, pages 73--80, 2008.
[13]
M. Golfarelli, S. Rizzi, and P. Biondi. myolap: An approach to express and evaluate OLAP preferences. IEEE TKDE'11, 23(7):1050--1064, 2011.
[14]
H. Guo, S. R. Gomez, C. Ziemkiewicz, and D. H. Laidlaw. A case study using visualization interaction logs and insight metrics to understand how analysts arrive at insights. Proc. of IEEE TVCG'16, 22(1):51--60, 2016.
[15]
E. Kandogan, M. Roth, P. M. Schwarz, J. Hui, I. Terrizzano, C. Christodoulakis, and R. J. Miller. Labbook: Metadata-driven social collaborative data analysis. In IEEE Big Data, pages 431--440, 2015.
[16]
R. Kosara, F. Bendix, and H. Hauser. Parallel sets: Interactive exploration and visual analysis of categorical data. IEEE TVCG'06, 12(4):558--568, 2006.
[17]
V. A. Kulyukin, K. J. Hammond, and R. D. Burke. Answering questions for an organization online. In Proc. of AAAI and IAAI '98, pages 532--537, 1998.
[18]
V. Nastase. Topic-driven multi-document summarization with encyclopedic knowledge and spreading activation. In Proc. of EMNLP'08, pages 763--772. Association for Computational Linguistics, 2008.
[19]
W. B. Paley. Textarc: Showing word frequency and distribution in text. 2002.
[20]
A. Pease, I. Niles, and J. Li. The suggested upper merged ontology: A large ontology for the semantic web and its applications. In AAAI-2002 workshop on ontologies and the semantic web, volume 28, 2002.
[21]
C. S. Ray, D. H. Goh, and S. Foo. The Effect of Lexical Relationships on the Quality of Query Clusters. 2006.
[22]
C. Sapia. PROMISE: predicting query behavior to enable predictive caching strategies for OLAP systems. In Proc. of DaWaK'00, pages 224--233, 2000.
[23]
S. Scott and S. Matwin. Text classification using wordnet hypernyms. In Proc. ofCOLING/ACL Workshop on Usage of WordNet in NLP Systems, pages 38--44, 1998.
[24]
A. Simitsis, G. Koutrika, and Y. E. Ioannidis. Précis: from unstructured keywords as queries to structured databases as answers. VLDB J., 17(1):117--149, 2008.
[25]
F. M. Suchanek, G. Kasneci, and G. Weikum. YAGO: A large ontology from wikipedia and wordnet. Journal of Web Semantics, 6(3):203--217, 2008.
[26]
F. van Ham, M. Wattenberg, and F. B. Viégas. Mapping text with phrase nets. IEEE TVCG'09, 15(6):1169--1176, 2009.
[27]
Watson analytics. http://www.ibm.com/analytics/watson-analytics/.
[28]
M. Wattenberg and F. B. Viégas. The word tree, an interactive visual concordance. IEEE TVCG'08, 14(6):1221--1228, 2008.
[29]
J. Wen, J. Nie, and H. Zhang. Query clustering using user logs. ACM TOIS'02, 20(1):59--81, 2002.
[30]
D. Yang, E. A. Rundensteiner, and M. O. Ward. Nugget discovery in visual exploration environments by query consolidation. In Proc. of ACM CIKM'07, pages 603--612, 2007.
[31]
X. Yang, C. M. Procopiuc, and D. Srivastava. Recommending join queries via query log analysis. In Proc. of IEEE ICDE'09, pages 964--975, 2009.
[32]
Q. Yao, A. An, and X. Huang. Finding and analyzing database user sessions. In Proc. of DASFAA'05, pages 851--862, 2005.

Cited By

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  • (2019)Mining Precision Interfaces From Query LogsProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3319872(988-1005)Online publication date: 25-Jun-2019
  • (2019)Metadata-Based Ontological Framework for Semantic Query in Multilingual DatabasesFrontiers in Intelligent Computing: Theory and Applications10.1007/978-981-32-9186-7_32(310-318)Online publication date: 4-Oct-2019
  • (2018)Contextual Intelligence for Unified Data GovernanceProceedings of the First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management10.1145/3211954.3211955(1-9)Online publication date: 10-Jun-2018
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    cover image ACM Conferences
    ESIDA '17: Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics
    March 2017
    82 pages
    ISBN:9781450349031
    DOI:10.1145/3038462
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 13 March 2017

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    Author Tags

    1. information seeking & search
    2. user interface design

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    View all
    • (2019)Mining Precision Interfaces From Query LogsProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3319872(988-1005)Online publication date: 25-Jun-2019
    • (2019)Metadata-Based Ontological Framework for Semantic Query in Multilingual DatabasesFrontiers in Intelligent Computing: Theory and Applications10.1007/978-981-32-9186-7_32(310-318)Online publication date: 4-Oct-2019
    • (2018)Contextual Intelligence for Unified Data GovernanceProceedings of the First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management10.1145/3211954.3211955(1-9)Online publication date: 10-Jun-2018
    • (2018)Context Analytics: Vision, Architecture, Opportunity2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW.2018.00007(1-8)Online publication date: Apr-2018
    • (2018)Metadata-Based Semantic Query in Multilingual DatabasesInformation and Decision Sciences10.1007/978-981-10-7563-6_26(249-255)Online publication date: 14-Apr-2018
    • (2017)Exploratory Search and Interactive Data AnalyticsProceedings of the 22nd International Conference on Intelligent User Interfaces Companion10.1145/3030024.3040246(9-11)Online publication date: 7-Mar-2017

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