Pushing the boundaries of crowd-enabled databases with query-driven schema expansion
Joachim Selke,Christoph Lofi,Wolf-Tilo Balke +2 more
- Vol. 5, Iss: 6, pp 538-549
TLDR
This paper extends crowd-enabled databases by flexible query-driven schema expansion, allowing the addition of new attributes to the database at query time, and leverages the usergenerated data found in the Social Web to build perceptual spaces.Abstract:
By incorporating human workers into the query execution process crowd-enabled databases facilitate intelligent, social capabilities like completing missing data at query time or performing cognitive operators. But despite all their flexibility, crowd-enabled databases still maintain rigid schemas. In this paper, we extend crowd-enabled databases by flexible query-driven schema expansion, allowing the addition of new attributes to the database at query time. However, the number of crowd-sourced mini-tasks to fill in missing values may often be prohibitively large and the resulting data quality is doubtful. Instead of simple crowd-sourcing to obtain all values individually, we leverage the usergenerated data found in the Social Web: By exploiting user ratings we build perceptual spaces, i.e., highly-compressed representations of opinions, impressions, and perceptions of large numbers of users. Using few training samples obtained by expert crowd sourcing, we then can extract all missing data automatically from the perceptual space with high quality and at low costs. Extensive experiments show that our approach can boost both performance and quality of crowd-enabled databases, while also providing the flexibility to expand schemas in a query-driven fashion.read more
Citations
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Dissertation
Pay-as-you-go instance-level integration
TL;DR: In this paper, the authors proposed a method to improve the accuracy of 6.8×6.8.0.0% and 6.5×5.8% respectively.
Perceptual Perspectives for Experience Items: Representation and Query Processing
TL;DR: This paper discusses how to use unstructured reviews to build a structured semantic representation of such items, enabling the implementation of user-driven queries, and addresses one of the central challenges of Big Data: making sense of huge collections of unstructuring user feedback.
Dissertation
Database Content Exploration and Exploratory Analysis of User Queries
TL;DR: This thesis develops a framework for organizing and summarizing keyword search results based on their textual content and temporal data and introduces a new type of query, the eXploratory Top-k Join (XTJk) query, which creates object combinations that are better suited to user preferences than single objects, and presents algorithms for the efficient processing of XTJk queries.
Intelligent Information Processing - Chances of Crowdsourcing (NII Shonan Meeting 2013-15).
TL;DR: The relationship between crowdsourcing and social computing and its potential social impact is discussed and the main components of a crowdsourcing platform are defined and a classification of challenges can be solved through crowdsourcing.
References
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Journal ArticleDOI
Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
Journal ArticleDOI
A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
Journal ArticleDOI
Learning from Imbalanced Data
Haibo He,E.A. Garcia +1 more
TL;DR: A critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario is provided.
Proceedings Article
Support Vector Regression Machines
TL;DR: This work compares support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space and expects that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space.
BookDOI
Semi-Supervised Learning
TL;DR: Semi-supervised learning (SSL) as discussed by the authors is the middle ground between supervised learning (in which all training examples are labeled) and unsupervised training (where no label data are given).