C ROWD S EARCHING (And Beyond)

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CROWDSEARCHING(AND BEYOND)

Stefano CeriPolitecnico di Milano

Dipartimento di Elettronica, Informazione e BioIngegneria

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Crowd-based Applications• Emerging crowd-based applications:

• opinion mining • localized information gathering • marketing campaigns • expert response gathering

• General structure: • the requestor poses some questions • a wide set of responders are in charge of providing answers

(typically unknown to the requestor)• the system organizes a response collection campaign

• Include crowdsourcing and crowdsearching

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The “system” is a wide concept • Crowd-based applications may use social networks and Q&A

websites in addition to crowdsourcing platforms• Our approach: a coordination engine which keeps an overall

control on the application deployment and execution

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CrowdSearcher

AP

I Access

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A simple example of crowdsearching

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Example: Find your job (social invitation)

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Example: Find your job (social invitation)

Selected data items can be transferred to the crowd question

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Find your job (response submission)

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Crowdsearcher results (in the loop)Crowdsearcher

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Deployment alternatives • Multi-platform deployment

Embedded application

Social/ Crowd platformNative

behaviours

External application

Standalone application

API

Embedding

Community / Crowd

Generated query template

Native

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Deployment: search on a social network• Multi-platform deployment

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Deployment: search on the social network• Multi-platform deployment

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THE MODEL ANDTHE PROCESS

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CrowdSearcher• Combines a conceptual framework, a specification

paradigm and a reactive execution control environment • Supports designing, deploying, and monitoring

applications on top of crowd-based systems• Design is top-down, platform-independent• Deployment turns declarative specifications into platform-specific

implementations which include social networks and crowdsourcing platforms

• Monitoring provides reactive control, which guarantees applications’ adaptation and interoperability

• Developed in the context of Search Computing (SeCo, ERC Advanced Grant, 2008-2013)

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• A simple task design and deployment process, based on specific data structures• created using model-driven transformations• driven by the task specification

The Design Process

Task Specification Task Planning Control

Specification

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• Task Specification: task operations, objects, and performers• Task Planning: work distribution• Control Specification: task control policies

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DEMO !

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Valuable ideas: 1. Operation types• In a Task, performers are required to execute logical operations on input objects

• e.g. Locate the faces of the people appearing in the following 5 images

• CrowdSearcher offers pre-defined operation types:• Like: Ask a performer to express a preference (true/false)

• e.g. Do you like this picture?• Comment: Ask a performer to write a description / summary / evaluation

• e.g. Can you summarize the following text using your own words?• Tag: Ask a performer to annotate an object with a set of tags

• e.g. How would you label the following image?• Classify: Ask a performer to classify an object within a closed-set of alternatives

• e.g. Would you classify this tweet as pro-right, pro-left, or neutral? • Add: Ask a performer to add a new object conforming to the specified schema

• e.g. Can you list the name and address of good restaurants nearby Politecnico di Milano?• Modify: Ask a performer to verify/modify the content of one or more input object

• e.g. Is this wine from Cinque Terre? If not, where does it come from? • Order: Ask a performer to order the input objects

• e.g. Order the following books according to your taste

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2. Platform-independent Meta-Model

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3. Reactive Crowdsourcing• A conceptual framework for controlling the execution of

crowd-based computations. Based on: • Control Marts• Active Rules

• Classical forms of controls:• Majority control (to close object computations)• Quality control (to check that quality constraints are met)• Spam detection (to detect / eliminate some performers)• Multi-platform adaptation (to change the deployment platform) • Social adaptation (to change the community of performers)

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Why Active Rules?• Ease of Use: control is easily expressible

• Simple formalism, simple computation• Power: arbitrarily complex controls is supported

• Extensibility mechanisms• Automation: active rules can be system-generated

• Well-defined semantics• Flexibility: localized impact of changes on the rules set

• Control isolation• Known formal properties descending from known theory

• Termination, confluence

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4. Control Mart• Data structure for controlling application execution, inspired by data

marts (for data warehousing); content is automatically built from task specification & planning

• Central entity: MicroTask Object Execution

• Dimensions: Task / Operations, Performer, Object

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Task Specification Task Planning Control Specification

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Auxiliary Structures• Object : tracking object responses• Performer: tracking performer behavior (e.g. spammers)• Task: tracking task status

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Task Specification Task Planning Control Specification

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Active Rules Language• Active rules are expressed on the previous data structures• Event-Condition-Action paradigm

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• Events: data updates / timer• ROW-level granularity

• OLD before state of a row• NEW after state of a row

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e: UPDATE FOR μTaskObjectExecution[ClassifiedParty]

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• Condition: a predicate that must be satisfied (e.g. conditions on control mart attributes)

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c: NEW.ClassifiedParty == ’Republican’

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• Condition: a predicate that must be satisfied (e.g. conditions on control mart attributes)

• Actions: updates on data structures (e.g. change attribute value, create new instances), special functions (e.g. replan)

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a: SET ObjectControl[oID == NEW.oID].#Eval+= 1

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Rule Example 1

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Rule Example 1

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Rule Example 1

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5. Rule Programming Best Practices• We define three classes of rules

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Rule Programming Best Practice

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• We define three classes of rules• Control rules: modifying the control tables;

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• We define three classes of rules• Control rules: modifying the control tables; • Result rules: modifying the dimension tables (object, performer, task);

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• Top-to-bottom, left-to-right, evaluation• Guaranteed termination

• We define three classes of rules• Control rules: modifying the control tables; • Result rules: modifying the dimension tables (object, performer, task);

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Rule Programming Best Practice• We define three classes of rules

• Control rules: modifying the control tables; • Result rules: modifying the dimension tables (object, performer, task); • Execution rules: modifying the execution table, either directly or through re-planning

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• Termination must be proven (rule precedence graph has cycles)

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6. Dealing with interoperability• Adaptation is any change of allocation of the application

to crowd-based systems or to their performers.• Migration is the moving of the application from a given

system to a different one. (Migration is a special case of adaptation)

• Cross-Platform Interoperability: applications change the underlying social network or crowdsourcing platforms, e.g., from Facebook to Twitter or to AMT.

• Cross-Community Interoperability: applications change the performers' community, e.g., from the students to the professors of a university.

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Adaptation optionsAdaptation may require: • Re-planning: the process of generating new micro-tasks. • Re-invitation: the process of generating new invitation

messages for existing or re-planned micro-tasks, with the aim of getting new performers for them.

Adaptation occurs at different levels of granularity• Task granularity: re-planning or re-invitation occurs for the

whole task• Object granularity: re-planning or re-invitation is focused on

one (or a few) objects (for instance, objects on which it is harder to achieve an agreement among performers, with a majority-based decision mechanisms).

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EXPERIMENTS

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Politician Affiliation• Given the picture and name of a politician, specify his/her political

affiliation• No time limit• Performers are encouraged to look up online

• 2 set of rules• Majority Evaluation• Spammer Detection

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Movie Scenes• users can select the screenshot timeframe and whether it is

a spoiler or not• 20 still images each from 16 popular movies• each micro-task consists of evaluating one image• Results are accepted, and the corresponding request is closed, when an agreement between 5 performers is reached both on the temporal category and the spoiler option, independently on the number of executions.

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Professors’ images• 16 professors within two

research groups in our department (DB and AI groups)

• The top 50 images returned by the Google Image API for each query

• Each microtask consisted of evaluating 5 images regarding a professor.

• Results are accepted (and thus the corresponding object is closed) when enough agreement on the class of the image is reached

• Closed objects are removed from new executions.

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SINGLE PLATFORM

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Query Type• Engagement depends on the difficulty of the task• Like vs. Add tasks:

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Comparison of Execution Platforms• Facebook vs. Doodle

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Posting Time• Facebook vs. Doodle

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Majority Evaluation_1/3

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30 object; object redundancy = 9; Final object classification as simple majority after 7 evaluations

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Final object classification as total majority after 3 evaluationsOtherwise, re-plan of 4 additional evaluations. Then simple majority at 7

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Final object classification as total majority after 3 evaluationsOtherwise, simple majority at 5 or at 7 (with replan)

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Spammer Detection_1/2

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New rule for spammer detection without ground truthPerformer correctness on final majority. Spammer if > 50% wrong classifications

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Spammer Detection_1/2

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New rule for spammer detection without ground truthPerformer correctness on current majority. Spammer if > 50% wrong classifications

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MULTI-PLATFORM &MULTICOMMUNITY

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Number of Executions per Platform• Immediate engagement and then plateau. • Higher engagement on AMT (paid) then SN (unpaid and limited by #

of contacts of inviter)

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Precision of Performers per Platform

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• AMT significantly lower in precision

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Precision on Closed Objects • Precision decreases on crowdsourcing platforms• Agreement increases precision wrt single performers

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Number of performers per community

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Precision for different engagement strategies

• Precision decreases with less expert communities• Inside-out strategy (from expert to generic users)

outperforms outside-in strategy (from generic to expert)

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EXPERT FINDING IN CROWDSEARCHER

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Problem• Ranking the members of a social group according to the level of knowledge that they have about a given topic

• Application: crowd selection (for Crowd Searching or Sourcing)

• Available data• User profile • behavioral trace that users leave behind them through

their social activities

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Most interesting aspect:Feature Organization Meta-Model

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Main Results• Profiles are less effective than level-1 resources

• Resources produced by others help in describing each individual’s expertise

• Twitter is the most effective social network for expertise matching – sometimes it outperforms the other social networks• Twitter most effective in Computer Engineering, Science, Technology &

Games, Sport• Facebook effective in Locations, Sport, Movies & TV, Music• Linked-in never very helpful in locating expertise

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CONCLUSIONS

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Summary• Results

• An integrated framework for crowdsourcing task design and control• Well-structured control rules with guarantees of termination• Support for cross-platform crowd interoperability• A working prototype crowdsearcher.search-computing.org

• Forthcoming• Publication of Web Interface + API• Support of declarative options for automatic rule generation• Integration with more social networks and human computation

platforms • Providing vertical solutions for specific markets• More applications and experiments (e.g. in Expo 2015)

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APPENDIX

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Current «other» interest:Genomic Computing

• NGS changes biology & medicine Massive DNA testing becoming available DNA-based personalized medicine approaching

• NGS data management looks to me as the biggest and most important big-data problem, but:

• No high-level view of genome data supporting high-level query and search

• No scalable method for NGS data analysis

-> Data management research for NGS data has very good potential for impact!

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Single-Gene Disease Mutations

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Our results so far:

1. Intuition: genomic data management requires a «genometric space data processing system»

2. Data model for profiling NGS data

3. System Architecture for managing genometric queries

4. Genometric Data Model (GQM) and Genometric Query Language (GQL) + mapping to PIG LATIN (Query Language for Hadoop).

Long-Term Goal: INTERNET OF GENOMES

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QUESTIONS?

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C ROWD S EARCHING (And Beyond)

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