Paper Info
Title
Management and analytic of biomedical big data with cloud-based in-memory database and dynamic querying: a hands-on experience with real-world data
Abstract
Analyzing Biomedical Big Data (BBD) is computationally expensive due to high dimensionality and large data volume. Performance and scalability issues of traditional database management systems (DBMS) often limit the usage of more sophisticated and complex data queries and analytic models. Moreover, in the conventional setting, data management and analysis use separate software platforms. Exporting and importing large amounts of data across platforms require a significant amount of computational and I/O resources, as well as potentially putting sensitive data at a security risk. In this tutorial, the participants will learn the difference between in-memory DBMS and traditional DBMS through hands-on exercises using SAP's cloud-based HANA in-memory DBMS in conjunction with the Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) dataset. MIMIC is an open-access critical care EHR archive (over 4TB in size) and consists of structured, unstructured and waveform data. Furthermore, this tutorial will seek to educate the participants on how a combination of dynamic querying, and in-memory DBMS may enhance the management and analysis of complex clinical data.
Year
DOI
Venue
2014
10.1145/2623330.2630806
KDD
Keywords
Field
DocType
health,big data,biomedical data,in-memory dbms,data storage representations,dynamic querying
Data science,Data mining,Computer science,In-memory database,Component-oriented database,Software,Data management,Big data,Intensive care,Database,Cloud computing,Scalability
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Mengling Feng117516.57
Mohammad Ghassemi2124.07
Thomas Brennan300.34
John Ellenberger400.34
Ishrar Hussain5776.38
Roger G. Mark624330.74