Filters








8 Hits in 0.86 sec

OASSIS

Yael Amsterdamer, Susan B. Davidson, Tova Milo, Slava Novgorodov, Amit Somech
2014 Proceedings of the 2014 ACM SIGMOD international conference on Management of data - SIGMOD '14  
Our approach is based on (1) a simple generic model that captures both ontological knowledge as well as the individual history or habits of crowd members from which frequent patterns are mined; (2) a query  ...  language in which users can declaratively specify their information needs and the data patterns of interest; (3) an efficient query evaluation algorithm, which enables mining semantically concise answers  ...  OASSIS extends previous query-driven crowdsourcing platforms, e.g. [19, 21, 25, 31, 34] , by enabling users to mine the crowd for significant data patterns.  ... 
doi:10.1145/2588555.2610514 dblp:conf/sigmod/AmsterdamerDMNS14 fatcat:36jmfq3fdfdlndr5vkznneza5q

Ontology assisted crowd mining

Yael Amsterdamer, Susan B. Davidson, Tova Milo, Slava Novgorodov, Amit Somech
2014 Proceedings of the VLDB Endowment  
We will demonstrate OASSIS using a couple of real-life scenarios, showing how users can formulate and execute queries through the OASSIS UI and how the relevant data is mined from the crowd.  ...  We present OASSIS (for Ontology ASSISted crowd mining), a prototype system which allows users to declaratively specify their information needs, and mines the crowd for answers.  ...  For the latter, we employ techniques developed in our previous work [1] , but query-driven crowd mining is a new development.  ... 
doi:10.14778/2733004.2733039 fatcat:fk3votc25nclnpip3hw3ieruwy

A Hybrid Approach to Perform Efficient and Effective Query Execution Against Public SPARQL Endpoints

Maribel Acosta
2015 Proceedings of the 24th International Conference on World Wide Web - WWW '15 Companion  
Linked Open Data initiatives have fostered the publication of Linked Data sets, as well as the deployment of publicly available SPARQL endpoints as client-server querying infrastructures to access these  ...  We tackle these problems and propose a novel hybrid architecture that relies on shipping policies to improve the performance of SPARQL endpoints, and exploits human and machine query processing computation  ...  OASSIS provides a SPARQLlike query language where users specify sub-queries that will be evaluated against the ontology and the ones that will be mined from the crowd.  ... 
doi:10.1145/2740908.2741750 dblp:conf/www/Acosta15 fatcat:ee4wsnedv5fcdelsajhaoooflq

RDF-Hunter: Automatically Crowdsourcing the Execution of Queries Against RDF Data Sets [article]

Maribel Acosta, Elena Simperl, Fabian Flöck, Maria-Esther Vidal, Rudi Studer
2015 arXiv   pre-print
crowd.  ...  We develop a novel quality model and query engine in order to enable RDF-Hunter to on the fly decide which parts of a query should be executed through conventional technology or crowd computing.  ...  OASSIS provides a SPARQL-like query language where users specify sub-queries that will be evaluated against the ontology and the ones that will be mined from the crowd.  ... 
arXiv:1503.02911v1 fatcat:hvnwe7d4jnebjkfx3rmsxzmkt4

Crowdsourced Data Management: A Survey

Guoliang Li, Jiannan Wang, Yudian Zheng, Michael J. Franklin
2016 IEEE Transactions on Knowledge and Data Engineering  
., the crowd) to apply human computation for such tasks. Thus, crowdsourced data management has become an area of increasing interest in research and industry.  ...  important problems in crowdsourced data management. (1) Quality Control: Workers may return noisy or incorrect results so effective techniques are required to achieve high quality; (2) Cost Control: The crowd  ...  OASSIS contains a query language called OASSIS-QL (based on the SPARQL), and it asks requesters to formulate the query using the provided tools.  ... 
doi:10.1109/tkde.2016.2535242 fatcat:sit3comyvra4djqkl4xre3kj24

Enhancing Answer Completeness of SPARQL Queries via Crowdsourcing

Maribel Acosta, Elena Simperl, Fabian Flock, Maria-Esther Vidal
2017 Social Science Research Network  
crowd answers about missing values in the RDF dataset; (3) a query engine that combines on-the-fly crowd knowledge and estimates provided by the RDF completeness model, to decide upon the sub-queries  ...  We acquire query answer completeness by capturing knowledge collected from the crowd, and propose a novel hybrid query processing engine that brings together machine and human computation to execute SPARQL  ...  OASSIS [7] is a recommendation system that mines frequent patterns from personal data collected via crowdsourcing. Patterns to mine are specified in OASSIS-QL, a SPARQL-like language.  ... 
doi:10.2139/ssrn.3199306 fatcat:72qyebbyibgkjnxoziy5touyni

Enhancing answer completeness of SPARQL queries via crowdsourcing

Maribel Acosta, Elena Simperl, Fabian Flöck, Maria-Esther Vidal
2017 Journal of Web Semantics  
crowd answers about missing values in the RDF dataset; (3) a query engine that combines on-the-fly crowd knowledge and estimates provided by the RDF completeness model, to decide upon the sub-queries  ...  We acquire query answer completeness by capturing knowledge collected from the crowd, and propose a novel hybrid query processing engine that brings together machine and human computation to execute SPARQL  ...  OASSIS [7] is a recommendation system that mines frequent patterns from personal data collected via crowdsourcing. Patterns to mine are specified in OASSIS-QL, a SPARQL-like language.  ... 
doi:10.1016/j.websem.2017.07.001 fatcat:kowyty7zh5aezevgw6z5akcbxm

Dagstuhl Reports, Volume 6, Issue 4, April 2016, Complete Issue [article]

2016
Crowd-driven annotation? We experimented with crowd-driven annotation of the Parliamentary debates to create training and test data.  ...  This tutorial introduces the notion of crowd mining and describes a generic architecture for crowd mining applications.  ...  This will make it possible to shift from a supply driven to a demand driven public transport system. Several questions are raised (e.g. How many vehicles are necessary? How to route the vehicles?  ... 
doi:10.4230/dagrep.6.4 fatcat:27bnit22ive7hplns57eoojqtm