TY  - GEN
AB  - Analyzing big data is a task encountered across disciplines. Addressing the challenges inherent in dealing with big data necessitate solutions that cover its three defining properties: volume, variety, and velocity. However, what is less understood is the treatment of the data that must be completed even before any analysis can begin. Specifically, there is often a non-trivial amount of time and resources that are utilized to the end of retrieving and preprocessing big data. This problem, known collectively as data integration, is a term frequently used for the general problem of taking data in some initial form and transforming it into a desired form. Examples of this include the rearranging of fields, changing the form of expression of one or more fields, altering the boundary notation of records and/or fields, encrypting or decrypting records and/or fields, parsing non-record data and organizing it into a record-oriented form, etc. In this work, we present our progress in creating a benchmarking suite that characterizes a diverse set of data integration applications.
AD  - Washington University in St. Louis
AD  - Washington University in St. Louis
AD  - Washington University in St. Louis
AD  - Washington University in St. Louis
AD  - Washington University in St. Louis
AD  - Washington University in St. Louis
AD  - Washington University in St. Louis
AD  - Washington University in St. Louis
AU  - Cabrera, Anthony M
AU  - Faber, Clayton
AU  - Cepeda, Kyle
AU  - Deber, Robert
AU  - Epstein, Cooper
AU  - Zheng, Jason
AU  - Cytron, Ron K
AU  - Chamberlain, Roger
DA  - 2018-02-18
DO  - 10.7936/K7NZ8715
DO  - DOI
ID  - 9
KW  - Computer and information sciences
KW  - data integration
KW  - format normalization
KW  - Application-Specific Instruction Processor
L1  - https://data.library.wustl.edu/record/9/files/DIBS.zip
L1  - https://data.library.wustl.edu/record/9/files/README.txt
L2  - https://data.library.wustl.edu/record/9/files/DIBS.zip
L2  - https://data.library.wustl.edu/record/9/files/README.txt
L4  - https://data.library.wustl.edu/record/9/files/DIBS.zip
L4  - https://data.library.wustl.edu/record/9/files/README.txt
LA  - eng
LK  - https://data.library.wustl.edu/record/9/files/DIBS.zip
LK  - https://data.library.wustl.edu/record/9/files/README.txt
N2  - Analyzing big data is a task encountered across disciplines. Addressing the challenges inherent in dealing with big data necessitate solutions that cover its three defining properties: volume, variety, and velocity. However, what is less understood is the treatment of the data that must be completed even before any analysis can begin. Specifically, there is often a non-trivial amount of time and resources that are utilized to the end of retrieving and preprocessing big data. This problem, known collectively as data integration, is a term frequently used for the general problem of taking data in some initial form and transforming it into a desired form. Examples of this include the rearranging of fields, changing the form of expression of one or more fields, altering the boundary notation of records and/or fields, encrypting or decrypting records and/or fields, parsing non-record data and organizing it into a record-oriented form, etc. In this work, we present our progress in creating a benchmarking suite that characterizes a diverse set of data integration applications.
PY  - 2018-02-18
T1  - Data Integration Benchmark Suite v1
TI  - Data Integration Benchmark Suite v1
UR  - https://data.library.wustl.edu/record/9/files/DIBS.zip
UR  - https://data.library.wustl.edu/record/9/files/README.txt
Y1  - 2018-02-18
ER  -