[SEBD2021] Conversational OLAP
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The democratization of data access and the adoption of OLAP in scenarios requiring hand-free interfaces push towards the creation of smart OLAP interfaces. We describe COOL, a framework devised for COnversational OLap applications. COOL interprets and translates a natural language dialog into an OLAP session that starts with a GPSJ (Generalized Projection, Selection, and Join) query and continues with the application of OLAP operators. The interpretation relies on a formal grammar and on a repository storing metadata and values from a multidimensional cube. In case of ambiguous text description, COOL can obtain the correct query either through automatic inference or user interactions to disambiguate the text.
![SEBD 2021
Motivation
Goal: query multidimensional cubes through natural language
Natural language enables analytics in hand-free scenarios [1]
- Augmented reality or with smart assistants
OLAP is based on standard operators [2]
- No help in query construction and natural language disambiguation
We introduce COOL (COnversational OLap) [3]
Matteo Francia – University of Bologna 2
Introduction
[1] Matteo Francia, Matteo Golfarelli, Stefano Rizzi: A-BI+: A framework for Augmented Business Intelligence. Information Systems. (2020)
[2] Panos Vassiliadis, Patrick Marcel, Stefano Rizzi: Beyond roll-up's and drill-down's: An intentional analytics model to reinvent OLAP. Information Systems. (2019)
[3] Matteo Francia, Enrico Gallinucci, Matteo Golfarelli: COOL: A Framework for Conversational OLAP. Information Systems. (2021)](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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[SEBD2021] Conversational OLAP
- 1. SEBD 2021 Conversational OLAP (discussion paper) Matteo Francia, Enrico Gallinucci, Matteo Golfarelli University of Bologna, Italy 29th Italian Symposium on Advanced Database Systems (SEBD 2021)
- 2. SEBD 2021 Motivation Goal: query multidimensional cubes through natural language Natural language enables analytics in hand-free scenarios [1] - Augmented reality or with smart assistants OLAP is based on standard operators [2] - No help in query construction and natural language disambiguation We introduce COOL (COnversational OLap) [3] Matteo Francia – University of Bologna 2 Introduction [1] Matteo Francia, Matteo Golfarelli, Stefano Rizzi: A-BI+: A framework for Augmented Business Intelligence. Information Systems. (2020) [2] Panos Vassiliadis, Patrick Marcel, Stefano Rizzi: Beyond roll-up's and drill-down's: An intentional analytics model to reinvent OLAP. Information Systems. (2019) [3] Matteo Francia, Enrico Gallinucci, Matteo Golfarelli: COOL: A Framework for Conversational OLAP. Information Systems. (2021)
- 3. SEBD 2021 COOL: architecture Matteo Francia – University of Bologna 3 COOL: overview Automatic KB feeding Manual KB enrichment KB DW Metadata & values Synonyms Offline Online Synonyms Ontology
- 4. SEBD 2021 COOL: architecture Matteo Francia – University of Bologna 4 COOL: overview Speech- to-Text OLAP operator Full query Disambiguation & Enhancement Execution & Visualization Automatic KB feeding Manual KB enrichment Raw text Annotated parse forest Parse tree Metadata & values Synonyms Log Interpretation Offline Online Synonyms Ontology SQL generation SQL Sales by Customer and Month Parse tree Statistics KB DW
- 5. SEBD 2021 Robustness: given a text T, we allow several mappings - E.g., by matching each n-gram to a set of similar entities from the KB T = «return the average sales for the product NY in each region» M1 = avg, UnitSales, where, Product, =, New York, group by, Region M2 = avg, UnitSales, where, Product, =, New York, group by, Regin ⟨GPSJ⟩ ::= ⟨MC⟩⟨GC⟩⟨SC⟩ ⟨MC⟩ ::= (⟨Agg⟩⟨Mea⟩ | ⟨Cnt⟩⟨Fct⟩)+ ⟨GC⟩ ::= “𝑔𝑟𝑜𝑢𝑝 𝑏𝑦” ⟨Attr⟩+ ⟨SC⟩ ::= “𝑤ℎ𝑒𝑟𝑒” ⟨SCA⟩ ⟨SCA⟩ ::= ⟨SCN⟩ “𝑎𝑛𝑑” ⟨SCA⟩ | ⟨SCN⟩ ⟨SCN⟩ ::= “𝑛𝑜𝑡” ⟨SSC⟩ | ⟨SSC⟩ ⟨SSC⟩ ::= ⟨Attr⟩⟨Cop⟩⟨Val⟩ | ⟨Attr⟩⟨Val⟩ | ⟨Val⟩ ⟨Cop⟩ ::= “=” | “<>” | “>” | “<” | “≥” | “≤” ⟨Agg⟩ ::= “𝑠𝑢𝑚” | “𝑎𝑣𝑔” | “𝑚𝑖𝑛” | “𝑚𝑎𝑥” ⟨Cnt⟩ ::= “𝑐𝑜𝑢𝑛𝑡” | “𝑐𝑜𝑢𝑛𝑡 𝑑𝑖𝑠𝑡𝑖𝑛𝑐𝑡” ⟨Fct⟩ ::= Domain-specific facts ⟨Mea⟩ ::= Domain-specific measures ⟨Attr⟩ ::= Domain-specific attributes ⟨Val⟩ ::= Domain-specific values COOL: interpretation Matteo Francia – University of Bologna 5 COOL: interpretation M1 = avg, UnitSales, where, Product, =, New York, group by, Region Mea Agg “where” MC SC GPSJ SCA SCN SSC Val Cop Attr “group by”Attr GC T = «return the average sales for the product NY in each region»
- 6. SEBD 2021 COOL: ambiguities Not all syntactically-correct clauses are "valid" - E.g., New York is not a Product - Annotate it - Ask a question for each ambiguity Matteo Francia – University of Bologna 6 M1 = avg, UnitSales, where, Product, =, New York, group by, Region Mea Agg “where” MC SC GPSJ SCA SCN SSC Val Cop Attr “group by”Attr GC AVM COOL: interpretation T = «return the average sales for the product NY in each region» New York is a not a product, could you pick a product among ...?
- 7. SEBD 2021 COOL: ambiguities Some clauses could be excluded from the GPSJ query - Annotate them for (possible) addiction Matteo Francia – University of Bologna 7 COOL: interpretation M2 = avg, UnitSales, where, Product, =, New York, group by, Regin Mea Agg “where” “group by” MC SC GPSJ Val SCA SCN SSC SCA SCN SSC Val SC Attr Cop AVM unparsed T = «return the average sales for the product NY in each region» Do you want to add the selection predicate "Regin"?
- 8. SEBD 2021 COOL: scoring function Return the forest with the highest score Matteo Francia – University of Bologna 8 COOL: interpretation M1 = avg, UnitSales, where, Product, =, New York, group by, Region Mea Agg “where” MC SC GPSJ SCA SCN SSC Val Cop Attr “group by”Attr GC AVM M2 = avg, UnitSales, where, Product, =, New York, group by, Regin Mea Agg “where” “group by” MC SC GPSJ Val SCA SCN SSC SCA SCN SSC Val SC Attr Cop AVM unparsed Score(PFM1) Score(PFM2) Score(M1) Score(M2) Score(PFM1) > Score(PFM2)
- 9. SEBD 2021 COOL: scoring function Also, use the score for pruning purpose - Sort all the mappings by descending score Score(M) - First, parse the mapping with the highest Score(M) - Then, parse only the mappings s.t. Score(M) > Score(PFM) Matteo Francia – University of Bologna 9 COOL: interpretation M1 = avg, UnitSales, where, Product, =, New York, group by, Region Mea Agg “where” MC SC GPSJ SCA SCN SSC Val Cop Attr “group by”Attr GC AVM M3 = avg, UnitSales, where, Product, =, New York Score(PFM1) Score(M3) Score(M1)
- 10. SEBD 2021 Experimental Evaluation Top-𝑘 accuracy by varying the similarity 𝛼 to build the mappings - Real-world dataset from [1] - Accuracy is stable wrt to 𝑘 (up to 94%) - 𝛼 depends on the inaccuracies in the text Matteo Francia – University of Bologna 10 Results [1] K. Drushku, J. Aligon, N. Labroche, P. Marcel, V. Peralta, Interest-based recommendations for business intelligence users, Inf. Syst. 86 (2019)
- 11. SEBD 2021 User Evaluation 40 users with heterogeneous OLAP skills - Asked to translate (Italian) analytic goals into English - Users provided good feedback on the interface... - ... as well as on the interpretation accuracy Matteo Francia – University of Bologna 11 Results Full Query OLAP operator OLAP Familiarity Accuracy Time (s) Accuracy Time (s) Low 0.91 141 0.86 102 High 0.91 97 0.92 71
- 12. SEBD 2021 In Action! Matteo Francia, Enrico Gallinucci, Matteo Golfarelli: Conversational OLAP in Action! EDBT (Best demo award). (2021)
- 13. SEBD 2021 In Action! Matteo Francia, Enrico Gallinucci, Matteo Golfarelli: Conversational OLAP in Action! EDBT (Best demo award). (2021)
- 14. SEBD 2021 In Action! Matteo Francia, Enrico Gallinucci, Matteo Golfarelli: Conversational OLAP in Action! EDBT (Best demo award). (2021)
- 15. SEBD 2021 In Action! Matteo Francia, Enrico Gallinucci, Matteo Golfarelli: Conversational OLAP in Action! EDBT (Best demo award). (2021)
- 16. SEBD 2021 In Action! Matteo Francia, Enrico Gallinucci, Matteo Golfarelli: Conversational OLAP in Action! EDBT (Best demo award). (2021)
- 17. SEBD 2021 Questions? Matteo Francia – University of Bologna 17 Thank you. Full paper: Matteo Francia, Enrico Gallinucci, Matteo Golfarelli: COOL: A framework for conversational OLAP. Information Systems. (2021) Best demo award: Matteo Francia, Enrico Gallinucci, Matteo Golfarelli: Conversational OLAP in Action! EDBT. (2021)
Editor's Notes
- DIFF: [17] returns tuples that maximize difference between cells of a cube given as input Profile user exploration to recommend which unvisited parts of the cube RELAXoperator allows toverify whether a pattern observed at a certain level of detail ispresent at a coarser level of detail too [19] Alternative operators have also been proposed in theCinecubes method [7,8]. The goal of this effort is to facilitateautomated reporting, given an original OLAP query as input.To achieve this purpose two operators (expressed asacts) areproposed, namely, (a)put-in-context, i.e., compare the result ofthe original query to query results over similar, sibling values;and (b)give-details, where drill-downs of the original query’sgroupers are performed.
- DIFF: [17] returns tuples that maximize difference between cells of a cube given as input Profile user exploration to recommend which unvisited parts of the cube RELAXoperator allows toverify whether a pattern observed at a certain level of detail ispresent at a coarser level of detail too [19] Alternative operators have also been proposed in theCinecubes method [7,8]. The goal of this effort is to facilitateautomated reporting, given an original OLAP query as input.To achieve this purpose two operators (expressed asacts) areproposed, namely, (a)put-in-context, i.e., compare the result ofthe original query to query results over similar, sibling values;and (b)give-details, where drill-downs of the original query’sgroupers are performed.