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This book provides an overview of crowdsourced data management. Covering all aspects including the workflow, algorithms and research potential, it particularly focuses on the latest techniques and recent advances. The authors identify three key aspects in determining the performance of crowdsourced data management: quality control, cost control and latency control. By surveying and synthesizing a wide spectrum of studies on crowdsourced data management, the book outlines important factors that need to be considered to improve crowdsourced data management. It also introduces a practical crowdsourced-database-system design and presents a number of crowdsourced operators. Self-contained and covering theory, algorithms, techniques and applications, it is a valuable reference resource for researchers and students new to crowdsourced data management with a basic knowledge of data structures and databases.

Database management --- Automation. --- Big data. --- Database management. --- Software engineering. --- Data mining. --- Mobile computing. --- Big Data. --- Database Management. --- Special Purpose and Application-Based Systems. --- Data Mining and Knowledge Discovery. --- Mobile Computing. --- Electronic data processing --- Context-aware computing --- Portable computers --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Computer software engineering --- Engineering --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Data sets, Large --- Large data sets --- Data sets --- Special purpose computers. --- Special purpose computers --- Computers