Suggesting Assess Queries for Interactive Analysis of Multidimensional Data
Authors: 
Matteo Francia, Matteo Golfarelli, Patrick Marcel, Stefano Rizzi, 
Panos VassiliadisAuthors Info & Claims
Pages 6421 - 6434
Abstract
Assessment is the process of comparing the actual to the expected behavior of a business phenomenon and judging the outcome of the comparison. The <inline-formula><tex-math notation="LaTeX">${{\sf assess}}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">assess</mml:mi></mml:math><inline-graphic xlink:href="rizzi-ieq1-3171516.gif"/></alternatives></inline-formula> querying operator has been recently proposed to support assessment based on the results of a query on a data cube. This operator requires (i) the specification of an OLAP query to determine a target cube; (ii) the specification of a reference cube of comparison (benchmark), which represents the expected performance; (iii) the specification of how to perform the comparison, and (iv) a labeling function that classifies the result of this comparison. Despite the adoption of a SQL-like syntax that hides the complexity of the assessment process, writing a complete assess statement is not easy. In this paper we focus on making the user experience more comfortable by letting the system suggest suitable completions for partially-specified statements. To this end we propose two interaction modes: progressive refinement and auto-completion, both starting from an assess statement partially declared by the user. These two modes are evaluated both in terms of scalability and user experience, with the support of two experiments made with real users.
References
[1]
V. L. O’Day and R. Jeffries, “Orienteering in an information landscape: How information seekers get from here to there,” in Proc. Conf. Hum. Factors Comput. Syst., 1993, pp. 438–445.
[2]
S. Kandel, A. Paepcke, J. M. Hellerstein, and J. Heer, “Enterprise data analysis and visualization: An interview study,” IEEE Trans. Vis. Comput. Graph., vol. 18, no. 12, pp. 2917–2926, Dec. 2012.
[3]
K. Wongsuphasawat, Y. Liu, and J. Heer, “Goals, process, and challenges of exploratory data analysis: A interview study,” CoRR, vol. abs/1911.00568, pp. 1–10, 2019.
[4]
T. Blount, L. Koesten, Y. Zhao, and E. Simperl, “Understanding the use of narrative patterns by novice data storytellers,” in Proc. Int. Conf. Comput.-Hum. Interaction Res. Appl., 2020, pp. 128–138.
[5]
J. Hullman, R. Kosara, and H. Lam, “Finding a clear path: Structuring strategies for visualization sequences,” Comput. Graph. Forum, vol. 36, no. 3, pp. 365–375, 2017.
[6]
P. Vassiliadis, P. Marcel, and S. Rizzi, “Beyond roll-up’s and drill-down’s: An intentional analytics model to reinvent OLAP,” Inf. Syst., vol. 85, pp. 68–91, 2019.
[7]
M. Francia, M. Golfarelli, P. Marcel, S. Rizzi, and P. Vassiliadis, “Assess queries for interactive analysis of data cubes,” in Proc. Int. Conf. Extending Database Technol., 2021, pp. 121–132.
[8]
M. R. Vieira et al., “On query result diversification,” in Proc. Int. Conf. Data Eng., 2011, pp. 1163–1174.
[9]
O. B. El, T. Milo, and A. Somech, “Automatically generating data exploration sessions using deep reinforcement learning,” in Proc. Int. Conf. Manage. Data, 2020, pp. 1527–1537.
[10]
X. Jin and J. Han, “K-Medoids Clustering,” in Encyclopedia of Machine Learning, C. Sammut and G. I. Webb Eds., Boston, MA, USA: Springer US, 2010, pp. 564–565.
[11]
M. Francia, P. Marcel, V. Peralta, and S. Rizzi, “Enhancing cubes with models to describe multidimensional data,” Inf. Syst. Front., vol. 24, pp. 31–48, 2022.
[12]
T. Haslwanter, An Introduction to Statistics With Python: With Applications in the Life Sciences. Berlin, Germany: Springer, 2016.
[13]
R. Fagin, R. Kumar, M. Mahdian, D. Sivakumar, and E. Vee, “Comparing and aggregating rankings with ties,” in Proc. Symp. Princ. Database Syst., 2004, pp. 47–58.
[14]
K. Börner, Atlas of Knowledge: Anyone Can Map. Cambridge, MA, USA: MIT Press, 2015.
[15]
E. Tufte, The Visual Display of Quantitative Information. Cheshire, CT, USA: Graphics Press Cheshire, 1983, vol. 2.
[16]
M. Francia, E. Gallinucci, and M. Golfarelli, “COOL: A framework for conversational OLAP,” Inf. Syst., vol. 104, 2022, Art. no.
[17]
P. E. O’Neil, E. J. O’Neil, X. Chen, and S. Revilak, “The star schema benchmark and augmented fact table indexing,” in Proc. TPC Technol. Conf. Perform. Eval. Benchmarking, 2009, pp. 237–252.
[18]
O. Romero and A. Abelló, “On the need of a reference algebra for OLAP,” in Proc. Int. Conf. Data Warehousing Knowl. Discov., 2007, pp. 99–110.
[19]
L. Golab and D. Srivastava, “Exploring data using patterns: A survey and open problems,” in Proc. Int. Workshop Des., Optim., Lang. Anal. Process. Big Data, 2021, pp. 116–120.
[20]
L. Golab, H. J. Karloff, F. Korn, and D. Srivastava, “Data auditor: Exploring data quality and semantics using pattern tableaux,” Proc. VLDB Endow., vol. 3, no. 2, pp. 1641–1644, 2010.
[21]
A. Chédin, M. Francia, P. Marcel, V. Peralta, and S. Rizzi, “The tell-tale cube,” in Proc. Eur. Conf. Adv. Databases Inf. Syst., 2020, pp. 204–218.
[22]
S. Sarawagi, “Explaining differences in multidimensional aggregates,” in Proc. 25th Int. Conf. Very Large Data Bases, 1999, pp. 42–53.
[23]
F. Abuzaid et al., “DIFF: A relational interface for large-scale data explanation,” VLDB J., vol. 30, no. 1, pp. 45–70, 2021.
[24]
R. Subramonian, “Defining diff as a data mining primitive,” in Proc. Conf. Knowl. Discov. Data Mining, 1998, pp. 334–338.
[25]
B. Tang, S. Han, M. L. Yiu, R. Ding, and D. Zhang, “Extracting top-k insights from multi-dimensional data,” in Proc. ACM Int. Conf. Manage. Data, 2017, pp. 1509–1524.
[26]
G. Sathe and S. Sarawagi, “Intelligent rollups in multidimensional OLAP data,” in Proc. 27th Int. Conf. Very Large Data Bases, 2001, pp. 531–540.
[27]
E. Zgraggen, Z. Zhao, R. C. Zeleznik, and T. Kraska, “Investigating the effect of the multiple comparisons problem in visual analysis,” in Proc. CHI Conf. Hum. Factors Comput. Syst., 2018, Art. no.
[28]
D. Gkesoulis, P. Vassiliadis, and P. Manousis, “CineCubes: Aiding data workers gain insights from OLAP queries,” Inf. Syst., vol. 53, pp. 60–86, 2015.
[29]
T. Siddiqui, S. Chaudhuri, and V. R. Narasayya, “COMPARE: Accelerating groupwise comparison in relational databases for data analytics,” Proc. VLDB Endow., vol. 14, no. 11, pp. 2419–2431, 2021.
[30]
L. Song, J. Gan, Z. Bao, B. Ruan, H. V. Jagadish, and T. Sellis, “Incremental preference adjustment: A graph-theoretical approach,” VLDB J., vol. 29, no. 6, pp. 1475–1500, 2020.
[31]
S. Sarawagi, “User-adaptive exploration of multidimensional data,” in Proc. Int. Conf. Very Large Data Bases, 2000, pp. 307–316.
[32]
J. Flores, S. Nadal, and O. Romero, “Towards scalable data discovery,” in Proc. 24th Int. Conf. Extending Database Technol., 2021, pp. 433–438.
[33]
M. Paganelli, P. Sottovia, A. Maccioni, M. Interlandi, and F. Guerra, “Explaining data with descriptions,” Inf. Syst., vol. 92, 2020, Art. no.
[34]
K. Wongsuphasawat et al., “Voyager 2: Augmenting visual analysis with partial view specifications,” in Proc. Conf. Hum. Factors Comput. Syst., 2017, pp. 2648–2659.
[35]
T. Siddiqui, A. Kim, J. Lee, K. Karahalios, and A. G. Parameswaran, “Effortless data exploration with zenvisage: An expressive and interactive visual analytics system,” Proc. VLDB Endow., vol. 10, no. 4, pp. 457–468, 2016.
[36]
M. Francia, M. Golfarelli, and S. Rizzi, “A-BI: A framework for augmented business intelligence,” Inf. Syst., vol. 92, 2020, Art. no.
[37]
A. Giacometti, P. Marcel, E. Negre, and A. Soulet, “Query recommendations for OLAP discovery-driven analysis,” Int. J. Data Warehousing Mining, vol. 7, no. 2, pp. 1–25, 2011.
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Published: 01 June 2023
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