Data model is a key challenge for crowd-powered data applications. However, current data
model cannot effectively express semantic relations to support big data management …

With the huge amount of data continuously accumulated and shared by individuals and
organizations, it has become necessary to meet the emerging processing and information …

The explosive growth in volume, velocity, and diversity of data produced by mobile devices
and cloud applications has contributed to the abundance of data or 'big data.'Available …

Crowdsourcing database systems have been proposed to leverage crowd-powered
operations to encapsulate the complexities of interacting with the crowd. Existing systems …

In order to efficiently retrieve information from highly huge and complicated datasets with
dispersed storage in cloud computing, indexing methods are continually used on big data …

Crowd-powered database systems can leverage the crowd's ability to address machine-
hard problems, eg, data integration. Existing crowdsourcing systems adopt the traditional …

Large-scale crowd behavior data is the group image data of pedestrians in public places
collected by high-definition cameras. The collected original data need further data analysis …

Index structures are one of the main strategies for effective data access applied for indexing
data. According to expansion of data, traditional indexing strategies on big data meets …

Efficient response to search queries is very crucial for data analysts to obtain timely results
from big data spanned over heterogeneous machines. Currently, a number of big-data …

Based on the distributed decision tree algorithm, this paper first proposes a method of
vertically partitioning datasets and synchronously updating the hash table to establish an …