1. Non-Traditional Databases

2. Reading
Required:
Oscar Garcia-Morchon and Klaus Wehrle Modular context-aware access control for medical sensor networks. In Proceedings of the 15th ACM symposium on Access control models and technologies (SACMAT '10). ACM, New York, NY, USA,
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3. Reading
Recommended:
M. Stonebaker, U. Cetintemel, One Size Fits All": An Idea Whose Time Has Come and Gone, in Proceeding of CDE '05 Proceedings of the 21st International Conference on Data Engineering, IEEE Computer Society Washington, DC, USA, 2005,
Scientific data management at the Johns Hopkins institute for data intensive engineering and science Yanif Ahmad, Randal Burns, Michael Kazhdan, Charles Meneveau, Alex Szalay, Andreas Terzis, February 2011 SIGMOD Record , Volume 39 Issue 3 ,
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4. Traditional Database Management Systems
Focus on business data management
Provide uniform capabilities regardless of the data characteristics
Need: capabilities to meet new application requirements
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5. Examples of New Needs Stream Data Processing
Large scale scientific databases
Data warehousing
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6. Streaming Data Sensor-based applications
Real-time systems: sophisticated alerting, location-based services,
Historical data
Financial applications
Support applications, such as electronic trading, legal compliance, real-time marker analysis, etc.
Performance requirements
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7. Performance SDMS vs. RDMS
Empirical results (see reference paper #3)
Issues:
Inbound processing model
Correct primitives for stream processing (aggregates, “timeout,” “slack”)
Seamless integration of DBMS processing with application processing (client-server vs. embedded applications)
Transactional behavior (weaker notion of recovery, tolerance, no ACID requirements)
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8. Security for Streaming Data?
What is the difference between the security needs of streaming vs. traditional (e.g., relational) data?
How to enforce security?
Security punctuation
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9. Medical Sensor Data Management
Home medical care
Sensitive patient data
Collect
Transit
Use
Store
Medical professionals (doctors, nurses)
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10. Cyber Attacks Eavesdropping Data modification Data replay Fabrication
How can data stream security be supported?
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11. Encrypted Sensor Communication
Challenges:
Limited computational power
Limited power supply
Data integration
Calibration errors
Missing data
Distorted data
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12. A solution Local wireless collection point (computer) Collect data
Organize data
Protect data
Sends data
What are the vulnerabilities of the collection point?
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13. Context-Aware Processing
Patient Area Network
Sensors associated with a patient
Medical Sensor Networks
Thousands of sensors
Hundreds of patients
Diverse organizations
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14. Medical Sensor Networks
Interoperation
Reliability
Security
Privacy
What are the limitations of Access Control Systems for MSN?
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15. Access Control Models RBAC DAC MAC Which of these is applicable?
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16. Access Control Challenges
High number of users
Hierarchy, situation-aware, role-aware
Enforcement of access control on sensors
What to enforce
How to enforce
Resource constraints
Power, computational power
Access rights depend on medical context
Normal usage vs. elevated scenarios
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17. Granular Access Control
High Acuteness
Critical situation
life in danger
No need for authentication
Emergency situation
Serious injury
Reduced authentication
Normal situation
Normal authentication and AC
High Safety T R A D E O F
Low Acuteness
High Privacy
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18. Scientific Databases Massive amount of data Heterogeneous data
Sensor data, satellite, scientific simulation data, etc.
Goal: better understanding of physical phenomena
Genomic database, geological exploration, astronomy, etc.
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19. Scientific Databases Need efficient analysis and querying capabilities
Multi-dimensional indexing (e.g., genomic sequence indexing)
Specific applications (e.g., visualization of seismic data)
Specific aggregations (e.g., data mining for biological correlation)
Efficient data archiving, staging, lineage, and error propagation techniques
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20. Example Scientific Data Management
Basic research:
Formation of hypotheses and theories
Designing experiments for their validation
Collecting data by experimentation
Analyzing data to guide new insights for further research
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21. Scientific Computing Steps 3 and 4 are data intensive
Need to improve computational power
Parallel processing
Grid and supercomputers
Special application logic
Preservation of scientific data
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22. Next Class
Semester Review
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