A clear and elaborate summary of 45 paper articles, books such as "Data-Intensive Applications", "Fundamentals of database systems", "Hadoop The definitive guide" and "The Business Blockchain". Chapters within this summary include Relational Data Modelling, SQL Query Language, Map-Reduce, Hadoop, ...
Advanced Data Systems for Smart Services Summary
A summary of 45+ articles and book chapters in advanced data systems
Author: Martijn C. Paulussen
University: Maastricht University School of Business and Economics
Master: MSc Business Intelligence & Smart Services
Course: [EBC4224] Advanced Data Systems for Smart Services
School of Business and Economics
MSc Business Intelligence & Smart Services
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,Table of Contents
1. Introduction to Advanced Data Systems for Smart Services (lecture) ...................................................2
1.1. Presentation: Advanced Data Systems for Smart Services & Conceptualizing smart service
systems & [Book Chapter 1] Designing Data Intensive Applications by Martin Klepmann .....................2
2. Relational Data Model and SQL Query Language .................................................................................4
2.1. Presentation: Databases ..................................................................................................................4
2.2. Database Fundamentals ..................................................................................................................7
3. Map-Reduce and Hadoop .......................................................................................................................8
3.1. MapReduce Simplified Data Processing on Large Clusters ...........................................................8
3.2. Parallel Data Processing with MapReduce A Survey.....................................................................9
3.3. The Hadoop Distributed File System ...........................................................................................10
3.4. Presentation: Examples and Exercise ...........................................................................................11
4. Some notions of distributed systems ....................................................................................................13
4.1. Eventually Consistent ...................................................................................................................13
4.2. Consistency in DS ........................................................................................................................14
4.3. Time and State in DS ....................................................................................................................15
4.4. Consistent Hashing (Rings) ..........................................................................................................17
4.5. Presentation: Replication in Distributed Systems.........................................................................18
5. No-SQL databases ................................................................................................................................22
5.1. NoSQL Systems for Big Data Management .................................................................................22
5.2. Scalable SQL and NoSQL Data Stores ........................................................................................24
5.3. Security Issues in NoSQL Databases ...........................................................................................25
5.4. Dynamo Amazon's Highly Available Key-value Store ................................................................25
6. Semantic Web.......................................................................................................................................27
6.1. The Semantic Web .......................................................................................................................27
6.2. A New Look at the Semantic Web ...............................................................................................28
6.3. The FAIR Guiding Principles for scientific data management and stewardship ..........................28
6.4. Semantic Web in Data Mining and knowledge discovery A compressive survey .......................29
7. Blockchain ............................................................................................................................................31
7.1. Bitcoin A Peer-to-Peer Electronic Cash System ..........................................................................31
7.2. Blockchains and Smart Contracts for the Internet of Things .......................................................32
7.3. A Case Study for Blockchain in Healthcare - MedRec ...............................................................33
8. m-Health and Mobile Cloud Computing ..............................................................................................34
8.1. A survey of mobile cloud computing architecture, applications, and approaches .......................34
8.2. Mobile Health Technology Evaluation.........................................................................................37
8.3. COMMODITY A Smart e-Health Environment for Diabetes Management ................................37
8.4. Presentation: Autonomous systems and Multi-Agent Systems (MAS) ........................................38
1
, 1. Introduction to Advanced Data Systems for Smart Services (lecture)
Papers discussed are:
- Presentation: Advanced Data Systems For Smart Services
- Conceptualizing smart service systems
- Book Chapter: Data-Intensive Applications
1.1. Presentation: Advanced Data Systems for Smart Services &
Conceptualizing smart service systems &
[Book Chapter 1] Designing Data Intensive Applications by Martin Klepmann
Advanced Data Systems are:
- Data intensive
o The amount of data
o The complexity of data
o The speed at which it is changing
o Databases: Store data for later use
o Caches: Remember results for faster reads
o Search indexes: search by keywords
o Stream processing: Handle processes asynchronously
o Batch processing: Large amounts of accumulated data
- Distributed solutions
- Multiple actors
Advanced Data Systems should be:
- Reliable: The system works correctly even in the face of adversity, tolerates user mistakes,
performance and prevents abuse.
o Fault: A component deviating from its specifications, caused by poor error handling. You
can prevent faults by including tests with faults to ensure correct handling when it
happens. (e.g. Hardware, Software, Human)
o Failure: The system as a whole stops providing the required service to the user
o Systems that anticipate faults are called fault-tolerant or resilient.
o Why? Safety, Loss of productivity, Damage to reputation
- Scalable: As the system grows (in data, traffic or complexity), there is a way of dealing with that
growth or load increase.
o Keep data in cache for faster and cheaper reads and requests of for example timelines
(twitter).
o Percentiles are best to understand the average performance (median = 50%)
o Scaling up: Vertical scaling more powerful machines
o Scaling out: Horizontal scaling more and multiple smaller machines
o Elastic systems: can automatically increase computing resources.
- Maintainable: Maintaining current behavior and adapting the system to new use cases.
o The cost of software is not in its initial development, but in its ongoing maintenance.
o Design Principles:
Operability: Easy for teams to keep the system running smoothly.
Simplicity: Easy for new engineers to understand the system/remove complexity.
Extensibility: Make it easy for engineers to make changes to the system in the
future, adapting it for unanticipated use cases.
2
, Advanced Data Systems questions can be:
- How to ensure that the data remains correct and complete, even when things go wrong?
- How do you provide consistently good performance to clients?
- How do you scale to handle an increase in load?
- What does a good API for the service look like?
Smart Systems:
- Smart service systems bring a customer’s and provider’s perspective on value creation with a
smart product. Which can consist of connected sensors or Internet of Things devices.
- Key features:
o Unique identifier: addressable by other products.
o Localized: “know” their location and can be localized and traced.
o Connected: share data and functionality with other devices.
o Sensors: obtain physical data in their proximity.
o Storage: store data locally which can be status, usage or contextual.
o Computation: Compute and give results for autonomous behavior.
o Actuators: share data and functionality with other IoT devices.
o Interfaces: Intractable with humans (not only desktop-wise).
- Architecture and smart services:
o Core properties: Awareness and connectivity.
o Basic properties: Single shared identity and different interpretability.
o Offer contextual and preemptive services based on intelligence.
o Create value in use energy consumption optimizer
3
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