CDAS: A Crowdsourcing Data Analytics System (1207.0143v1)

Abstract: Some complex problems, such as image tagging and natural language processing, are very challenging for computers, where even state-of-the-art technology is yet able to provide satisfactory accuracy. Therefore, rather than relying solely on developing new and better algorithms to handle such tasks, we look to the crowdsourcing solution -- employing human participation -- to make good the shortfall in current technology. Crowdsourcing is a good supplement to many computer tasks. A complex job may be divided into computer-oriented tasks and human-oriented tasks, which are then assigned to machines and humans respectively. To leverage the power of crowdsourcing, we design and implement a Crowdsourcing Data Analytics System, CDAS. CDAS is a framework designed to support the deployment of various crowdsourcing applications. The core part of CDAS is a quality-sensitive answering model, which guides the crowdsourcing engine to process and monitor the human tasks. In this paper, we introduce the principles of our quality-sensitive model. To satisfy user required accuracy, the model guides the crowdsourcing query engine for the design and processing of the corresponding crowdsourcing jobs. It provides an estimated accuracy for each generated result based on the human workers' historical performances. When verifying the quality of the result, the model employs an online strategy to reduce waiting time. To show the effectiveness of the model, we implement and deploy two analytics jobs on CDAS, a twitter sentiment analytics job and an image tagging job. We use real Twitter and Flickr data as our queries respectively. We compare our approaches with state-of-the-art classification and image annotation techniques. The results show that the human-assisted methods can indeed achieve a much higher accuracy. By embedding the quality-sensitive model into crowdsourcing query engine, we effectiv...[truncated].

Overview of CDAS: A Crowdsourcing Data Analytics System

In their paper, Liu et al. present the Crowdsourcing Data Analytics System (CDAS), a robust framework designed to leverage the capabilities of human intelligence via crowdsourcing to enhance the efficacy of complex computational tasks. The system primarily targets problems in domains such as image tagging and NLP, where current algorithms often fall short in achieving desired accuracy levels.

The CDAS framework is underpinned by a quality-sensitive answering model that predicts, processes, and evaluates human-provided task results to meet specified accuracy requirements while minimizing costs. This model is split into two sub-models: a prediction model and a verification model. The prediction model estimates the number of human workers necessary for achieving a certain accuracy level, while the verification model employs a probability-based approach to refine and select the best possible answers.

Prediction and Verification Models

The prediction model, a crucial component of CDAS, determines the quantity of workers needed to achieve predefined accuracy thresholds. Utilizing statistical methods, the system analyzes the historical performance of workers to make informed predictions about the number of workers required for a given task, thereby ensuring efficient resource allocation. A conservative estimate is complemented by a binary search optimization technique to derive a tighter lower bound, reducing the required number of workers without compromising accuracy.

In contrast, the verification model discards traditional voting strategies in favor of a probability-based approach that evaluates the quality of answers by considering each worker's previous accuracy. By embedding sampling-based methods, CDAS effectively estimates workers' performance, which is crucial for adjusting the reliability of crowdsourced answers.

System Architecture

CDAS is architecturally composed of three principal components: the job manager, the crowdsourcing engine, and the program executor. The interplay of these components translates analytics jobs into human- and computer-oriented tasks, allowing for efficient processing. The system exploits the parallel nature of human intelligence tasks, employing strategies such as online processing to provide approximate results, thus substantially improving response times.

Experimental Evidence and Implications

The paper details experiments involving Twitter sentiment analysis (TSA) and image tagging (IT), demonstrating the system's efficacy in employing human intelligence to enhance computational tasks. Through comparative analysis against traditional algorithms such as LIBSVM in sentiment analysis and a labeling tool like ALIPR for image tagging, CDAS shows superior accuracy, underscoring the potential benefits of human augmentation in data-driven tasks.

The implications of this research are profound; CDAS not only addresses current limitations in AI and machine learning workflows but also proposes a scalable method for integrating human judgment where algorithmic solutions are insufficient. By improving both cost-effectiveness and result accuracy, CDAS lays the groundwork for more sophisticated hybrid systems where human input remains an integral component of data analytics.

Future Directions

Looking ahead, the framework proposed by CDAS can drive significant advancements in various domains beyond social media and image processing. The ability to seamlessly integrate human input with machine-driven processes may spur innovations in areas reliant on subjective decision-making or intricate pattern recognition. Substantive research efforts may also focus on refining the accuracy prediction models to adapt dynamically to the evolving proficiency of crowdsourced workers, further optimizing costs and improving the overall efficacy of this promising approach.

In conclusion, Liu et al. provide a compelling case for the integration of crowdsourcing into data analytics systems, advancing the state of the art in combining human intelligence with automated processes. CDAS represents a significant step toward harnessing collective human expertise to overcome barriers faced by current computational systems.