CUbRIK is a framework for the development of search-based applications

Multimedia search processing done in CUbRIK (across query content analysis and relevance feedback processing) benefit from:

  • high level metadata gathered from crowdsourcing and as a by-product of playing from games with a purpose
  • techniques for social web and community activity analysis and trust evaluation
  • a library of new and existing content processing components and algorithms certified and integrated on the platform
  • a reference data model for the logical structure of data to be processed, entities and relationships,configuration of the components in the system, information model for content providers, content owners, users and developers of search-based applications
  • entity-based time and space awareness, supported by a knowledge base of spatio-temporal entities (locations, events, trends) correlated with rich semantic associations. 

Read more about:

the Framework Architecture , Query processing, Content Analysis and Relevance Feedback Processing

Human search

CUbRIK incorporates human and social capabilities

CUbRIK works on enhancing the quality of search experience by augmenting the precision and the relevance of results when machine intelligence fails or is unable to remove uncertainty. The approach is not to emulate, but rather to incorporate human and social capabilities from feature extraction to search and validation of multimedia content: because we, the humans, still have a lot to teach machines  about semantic understanding of multimedia content!

CUbRIK search-based applications are able to mix Crowd-based, GWAP-basedand pure-machine computation thanks to pipelines that describe programmable workflow of tasks allocated to executors (e.g. software components for data analysis, metadata indexing, search engines, presentation of results, or others) and able to combine different types of intelligence, according to the specific application requirements.

The mechanics of this relies on two specific Frameworks, part of the CUbRIK architecture:

      • the CrowdSearcher Framework, supporting design, execution and verification of crowdsourced tasks and managing core aspects dealing with  People to Task matching, Task assignment, Task execution, Executor evaluation and Output aggregation. 
        CrowdSearcher method for query management, jointly developed
        #SeCo and #CUbRIKproject, has now obtained a US Patent   CUbRIK Project (@cubrikproject) September 6, 2014
      • the Gaming Framework, providing easy development, improvement and maintenance of existing and new Games with a Purpose (GWAPs) and using these results to train machine interpretation of multimedia content.

CUbRIK for you

CUbRIK has proven the feasibility of its approach and the benefits of the integration of machine, human and social computation for multimedia search by showcasing reference ASSETS for the exploitation of its technologies. 

: guidelines about how to embed GWAP, Gamification and Crowdsourcing

APPLICATIONS assets: reference examples of applications, demonstrating how the human touch makes the difference between ambiguity and precision of results by problem areas and by domains.

FRAMEWORKS assets: software infrastructures, ready to use for combining conventional computation with the power of the crowd-based processing, enablers of the development of this new class of multimedia search applications

COMPONENTS, ALGORITHMS, SERVICES and LIBRARIES: technologies used and developed in CUbRIK, reference functionalities that can be invoked in pipelines of this new class of multimedia search applications.

Innovators are welcome to freely explore this paradigm shift: successful testimonials here Many vertical domains can benefit, wherever precision in interpreting structured and unstructured data can make a difference: the vision for Textile is an example. 


The inspiring principle of CUbRIK is about creating a “white-box” version of a multimedia content & query processing system.

The functionalities of query processing, content analysis and relevance feedback processing are unbundled into a set of search processing orchestrations (referred to as Pipelinesable to mix open source and third-party components, to instantiate algorithms and to aggregate Automatic Computation Jobs (automatic workflow or SMILA Workflow) and Human Computation (Crowd-enabled  or GWAP-enabled)Tasks.

Metadata are extracted from media collections, using the software mix that best fits the need, and  specific components can be included to process multimodal queries or to analyze user’s feedback in novel ways.

The architecture is an example of differential design: based on a SMILA underlying framework, modified to support programmable workflows and asynchronous task execution as it is required to mix automatic operations (Jobs) and human activities (Tasks) chained in a sequence. CUbRIK inherits and extends SMILA capabilities of easy integration of data source connectors, search engines, sophisticated analysis methods and other components by gaining scalability and reliability out-of-the-box.

This open approach is accompanied by releasing CUbRIK resultsfreely in the public domain.

CUbRIK is social collaboration

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FP7 logo and EU emblem

The CUbRIK project has received research funding from the European Union.

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