Benchmarking Interactive Benchmarking Interactive Social Networking Actions Social Networking Actions Shahram Ghandeharizadeh Shahram Ghandeharizadeh Director of Database Lab Director of Database Lab Computer Science Department Computer Science Department University of Southern California University of Southern California
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Benchmarking Interactive Social Networking Actions
Benchmarking Interactive Social Networking Actions. Shahram Ghandeharizadeh Director of Database Lab Computer Science Department University of Southern California. Outline. Motivation Research questions Survey use cases BG Benchmark FORSEE Future research. Motivation. Data Stores - PowerPoint PPT Presentation
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Benchmarking Interactive Social Benchmarking Interactive Social Networking ActionsNetworking Actions
Shahram GhandeharizadehShahram GhandeharizadehDirector of Database LabDirector of Database LabComputer Science DepartmentComputer Science DepartmentUniversity of Southern CaliforniaUniversity of Southern California
Outline
Motivation Research questions
Survey use cases BG Benchmark FORSEE Future research
Motivation Data Stores
Cloud Services
Person-to-person cloud services
Research Questions
What is the tradeoff between alternative data models? E.g., Is JSON superior to the relational
data model?
How do alternative architectures compare with one another? E.g., Is cache augmented SQL as good as
a document/extensible store?
Do NewSQL data stores scale as well as NoSQL data stores?
Survey Use Case
S. Barahmand and S. Ghandeharizadeh. BG: A Benchmark to Evaluate Interactive Social Networking Actions. CIDR ‘13, Asilomar, CA.
Data Model
Accounts
Friend
MembersPages
Follow
Resources Own
Share
Share
News Feed Displays Ownd
BG Architecture
Scalable
Emulates User Behavior
Service Level AgreementQuick and Efficient Rating
Visualization Tool
S. Barahmand and S. Ghandeharizadeh. Expedited Benchmarking of Social Networking Actions with Agile Data Loading Techniques. CIKM ‘13, SF, CA.
http://bgbenchmark.org
Good Benchmark = FORSEE
Focus on an important debate & provide relevant metrics to facilitate progress.
One number to describe alternative designs/solution.
Runs in a reasonable amount of time.
Scalable.
Effective abstraction with meaningful requests.
Extendible.
Good Benchmark = FORSEE
F
One number to describe alternative designs/solution.
Runs in a reasonable amount of time.
Scalable.
Effective abstraction with meaningful requests.
Extendible.
+ Unpredictable data
Good Benchmark = FORSEE
F
O
Runs in a reasonable amount of time.
Scalable.
Effective abstraction with meaningful requests.
Extendible.
+ Unpredictable data
SoAR
Good Benchmark = FORSEE
F
O
R
Scalable.
Effective abstraction with meaningful requests.
Extendible.
+ Unpredictable data
SoAR
4 months to rate =1 Week to rate =
Good Benchmark = FORSEE
F
O
R
S
Effective abstraction with meaningful requests.
Extendible.
+ Unpredictable data
SoAR
4 months to rate =1 Week to rate =
Good Benchmark = FORSEE
F
O
R
S
E
Extendible.
+ Unpredictable data
SoAR
4 months to rate =1 Week to rate =
Only when two members are NOT friends!
Good Benchmark = FORSEE
F
O
R
S
E
E
+ Unpredictable data
SoAR
4 months to rate =1 Week to rate =
Only when two members are NOT friends!
FORSEE = PREDICT
Good Benchmark = FORSEE
F
O
R
S
E
E
+ Unpredictable data
SoAR
4 months to rate =1 Week to rate =
Only when two members are NOT friends!
A good benchmark helps settle debates
quickly to enable its discipline to make rapid progress.
Future Research: Data Sciences
Challenge: Wide variety of science applications with diverse debates.