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Search Computing Engineering SeCo: Liquid Queries Marco Brambilla, Stefano Ceri SeCo workshop, Como, June 17th-19th, 2009.

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Presentation on theme: "Search Computing Engineering SeCo: Liquid Queries Marco Brambilla, Stefano Ceri SeCo workshop, Como, June 17th-19th, 2009."— Presentation transcript:

1 Search Computing Engineering SeCo: Liquid Queries Marco Brambilla, Stefano Ceri SeCo workshop, Como, June 17th-19th, 2009

2 Brambilla, Ceri Search Computing: LIQUID QUERIES Agenda 1.Overview of the SeCo architecture –Development and experimentation roadmap 2.Application development approach: LIQUID QUERIES –Configurability of the interface, strong parameter typing, static mapping to services –Continuous query processes –Exploitation of user intelligence (interactive query process – user feedback), BPM –Automatic code generation of user interface and interaction steps –Adaptivity and customization of the query interaction 3.Support to the developer in the various design phases –Service marts specification –Query specification –User interface specification 4.SeCo extensions –High-level queries -- General almost-NL query NLP, wordnet, query splitting, and mapping to services 2

3 1. Overview and roadmap of the SeCo architecture SeCo workshop, Como, June 17th-19th, 2009

4 Brambilla, Ceri Search Computing: LIQUID QUERIES Search Computing architecture: overall view 4 Main Query flow relation High level query Where can I attend a DB scientific conference close to a beautiful beach reachable with cheap flights? Sub query 1 Where can I attend a DB scientific conference? Sub query 2 place close to a beautiful beach? Sub query 3 place reachable with cheap flight? Low level query 1 ConfSearch(DB,placeX,dateY) Low level query 2 TourSearch(Beach,PlaceX) Low level query 3 Flight(cost<200,PlaceX,DateY) Query plan Services invocations and operators execution Results Presented results MSVVEIS08 - Barcelona – Iberia LID08 – Rome - Alitalia RCIS08- Marrakech- AirFrance

5 Brambilla, Ceri Search Computing: LIQUID QUERIES Search Computing architecture: configurability of the implementation 5 Main Query flow relation

6 Brambilla, Ceri Search Computing: LIQUID QUERIES Search Computing architecture: development roadmap 6 Prototype 1: Core behaviour of the system. Engine-based execution of queries Domain repository Service repository Coarse result presentation relation Prototype 2: Planning Automatic optimized query planning Prototype 3: Mapping and presentation mapping to domains presentation of results Prototype 4: High level queries

7 2. Application development approach: LIQUID QUERIES SeCo workshop, Como, June 17th-19th, 2009

8 Brambilla, Ceri Search Computing: LIQUID QUERIES LIQUID QUERY A level above the optimization: –Forcing the query flow LIQUID QUERY: A query with flexible boundaries Control is –on the user –at query time –on the evolution Contextual/recommended direction could be proposed In line with current trends in search (and others!) 8 Forward-looking and (a little bit) far-fetched ideas Open to discussion

9 Brambilla, Ceri Search Computing: LIQUID QUERIES Microsoft Bing Contextual step-by-step evolution of the query 9

10 Brambilla, Ceri Search Computing: LIQUID QUERIES Google Squared Multi-content, resizable, reshapeable query 10

11 Brambilla, Ceri Search Computing: LIQUID QUERIES Not a search: Hunch Just a big decision tree Perceived as great value by today users 11

12 Brambilla, Ceri Search Computing: LIQUID QUERIES Yahoo! research Web of pages vs. Web of objects Understand the need behind the user query Exploiting user intelligence –Tags –Folksonomies Multi-step queries Multi-technology queries –Annotations –Content-based 12

13 Brambilla, Ceri Search Computing: LIQUID QUERIES LIQUID QUERY Moving from "one time query" to a process-based approach Continuation of queries based on exploitation of relations between service marts A query with flexible boundaries, that can be –Reshaped/refined: asking for different information on the results –Expanded: asking for additional information on the results adding new domains –Extended: asking for more results by the user at runtime Contextual/recommended direction could be proposed Relies on the SeCo query machine –Every user interaction could trigger recalculations 13

14 Brambilla, Ceri Search Computing: LIQUID QUERIES Liquid query navigation Liquid- what? –Liquid data (BEA & San Diego) –Liquid publications and docs (Fabio & Trento) –(old-style) Liquid queries (Heer & Berkeley) Somehow similar to Google Squared, but: –Multi-domain – Multi-purpose –More flexible 14 Upon first query cycle, various options to the user: Refinement of the query Extension of the query results (give me more) Expansion of the query (add more domains) Choosing a different connection between services (i.e., changing the adopted access pattern) Clustering, re-ranking,... What does it imply at the query machine level?

15 Brambilla, Ceri Search Computing: LIQUID QUERIES Liquid query navigation 15 Conference Photo Description Date Hotel Photo Description Address Services

16 Brambilla, Ceri Search Computing: LIQUID QUERIES Liquid query: clustering/unclustering 16 At the query machine level? Probably nothing, just a presentation issue, if...

17 Brambilla, Ceri Search Computing: LIQUID QUERIES Liquid query: ranking/reranking For unclustered data or cluster representatives 17 At the query machine level? If multi-ranking service available, recompute the query. If not, just re-sort the query result at presentation level.

18 Brambilla, Ceri Search Computing: LIQUID QUERIES Liquid query: refinment Adding additional constraints –E.g., on this timeframe... More or less results Search again Refined search... At the query machine level? Rebuild the plan, possibly. And re-execute the query. If pieces can be reused... (caching)

19 Brambilla, Ceri Search Computing: LIQUID QUERIES Liquid query: extend the query 19 At the query machine level? Run again the machine on further data Gimme more Asking for more results... More results...

20 Brambilla, Ceri Search Computing: LIQUID QUERIES Asking for more results on a specific service... More results... Liquid query: zooming in (service-wise) 20 At the query machine level? Run again the machine on that service. Or: Change the throughput of the machine Clock branches Gimme more

21 Brambilla, Ceri Search Computing: LIQUID QUERIES Asking for more results on a specific item... More results... Liquid query: zooming in 21 At the query machine level? Run again the machine on services joined to that item. Or: Change the throughput of the machine Clock branches Gimme more

22 Brambilla, Ceri Search Computing: LIQUID QUERIES Liquid query: expand (shrink) the query 22 At the query machine level? Changing the plan. If something can be reused... (caching) Additional subquery Asking for more columns (or remove existing ones)... Results... ?

23 Brambilla, Ceri Search Computing: LIQUID QUERIES Changing the used access paths Liquid query: change join conditions 23 At the query machine level? Changing the plan. If something can be reused... (caching)

24 Brambilla, Ceri Search Computing: LIQUID QUERIES Horizontal and vertical multi-domain search Structure of the interface automatically generated based on the structure of the access plan Additional feature: save the resulting inteface (for query and results) for canned vertical applications Apply a stylesheet for making the application real –Set of default stylesheets that can be painted upon the inteface –Possibility of defining custom stylesheets 24

25 3. Support to the designer. SETTING UP THE LIQUID QUERY ENVIRONMENT SeCo workshop, Como, June 17th-19th, 2009

26 Brambilla, Ceri Search Computing: LIQUID QUERIES Registration Time The role of the designer is at registration time! Low development cost Higher cost of registration –Description of services –Description of default interfaces for services inputs and results 26

27 Brambilla, Ceri Search Computing: LIQUID QUERIES The tools Strong parameter typing –UI fields are typed Static mapping to services –UI fields are directly mapped to search services BPM-like modeling of the user interaction and query processing steps Automatic generation of UI Adaptivity and customization of the query interaction 27

28 Brambilla, Ceri Search Computing: LIQUID QUERIES The hard task: Registration time Building access patterns Building binding Defining the (lightweight) semantics –Domains –Keywords Defining the (default) presentation –Forms –Results 28

29 4. SeCo Extensions: High level queries SeCo workshop, Como, June 17th-19th, 2009

30 Brambilla, Ceri Search Computing: LIQUID QUERIES High level queries Almost NL-specified queries –Conjunctive noun phrases Need to be decomposed and mapped to semantic domains need of domain repository Require NLP and semantization of phrase contents need of NL analysis

31 Brambilla, Ceri Search Computing: LIQUID QUERIES Domain repository Storage of –domain definitions taxonomy (e.g., Dewey classification) –mappings of NL words to domains –mappings of services to domains Shallow approach based on –Wordnet (synsets Sx) –Wordnet-Domains (domains Dx) 31 D1 D2 D3 S1 S2... S6 (.2,.8) Service Repository ss1 (.4,.6)

32 Brambilla, Ceri Search Computing: LIQUID QUERIES Domain Repository: API Three main interfaces: Domain query: used to extract a domain (or a list of domains), and their corresponding properties, that relate to a specific string Service extraction: used to extract the list of services associated to the domain Domain hierarchy update: used to update the domain hierarchy

33 Brambilla, Ceri Search Computing: LIQUID QUERIES Query Analyser Starts from almost- natural language specification of the user request tries to determine a decomposition in subqueries that can fit the problem of mapping on a domain E.g.: scientific conference reachable with a cheap flight, with a beautiful beach nearby Target splitting –q1=scientific conference ", –q2=reachable with a cheap flight ", and –q3=with a beautiful beach nearby ".

34 Brambilla, Ceri Search Computing: LIQUID QUERIES Query Analyser For NLP, we exploit an open source tool developed by the Stanford Natural Language Processing Group The outcome is a tree representation of the query Definition of euristics for query splitting To optimize the recognition of query-subquery relations: –iterative invocation of the NLP tool based on various arguments (feedback from user, feedfwd/back from other components,...); – exploitation of knowledge/services available in other components. E.g., knowledge –about the available services, domains, and so on; – syntax/logic analysis results on the sentence.

35 Brambilla, Ceri Search Computing: LIQUID QUERIES Back to the example - 2 scientific conference reachable with a cheap flight, with a beautiful beach nearby Very coarse euristics: –Subqueries = first level subtrees Obtained splitting –q1=scientific conference reachable", –q2=with a cheap flight ", and –q3=with a beautiful beach nearby ". 35

36 Brambilla, Ceri Search Computing: LIQUID QUERIES Back to the example - 3 Still not exact, but rather close (being the first shot:) Further information can be extracted –Association between words: e.g., cheap_flight –Meaning of phrase connectives And... –What about negation? –What about join attributes between phrases? –... 36

37 Brambilla, Ceri Search Computing: LIQUID QUERIES Query Analyser Tasklist –Extraction of a corpus of queries from Yahoo! Answers –Definition of concrete options for optimization of the extractor –Training? –Validation of the approach on the corpus –Mapping: currently could be trivial on keywords of domains

38 Q uestions? Search Computing


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