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Big Data by Mind Map: Big Data

1. how does the culture of information and data processing processing change when data is available in real-time?

2. social media and privacy issues, companies go to your private isolation. they attac you directly - wwhat is your information what you can prvoide

3. Velocity, Volume, Variety, Veracity

4. what are applicaiton areas of big data?

4.1. start with the Big Data issues and break it down to case studies

5. How and What informaiton can you really get out of all the social media tools? Marketing, consumer interest, what needs to be produced that it would directly impact on the production or production volume

6. what directly impacts the production line - practical success cases - and if they are true? what are people promising?

7. reality check of big data - where does it come from - what is fact and fiction? e.g. key-performance indicators

8. Grouping of case studies

8.1. cost saving

8.2. business processes

8.3. how it is implemented in a practical case

8.4. what is big data - 4Vs

8.5. ERP integration with social media tools

9. Cases

9.1. Traffic

9.2. Finance sector

9.3. Business

10. Problems & Missing Themes

10.1. Use Cases

10.2. In General: Lack of use case examples, other overview papers re-iterate prominent examples

10.3. What are the expections of the case studies/ Goals & Benefits of Big Data

11. Big Data Promises & Expectionats

11.1. Cost Saving

11.2. benefits for companies

11.3. New Insights in Community Research

11.4. integration of IT Systems

11.5. Select case studies according the Big Theme Problems & Expections?

12. How to approach the paper

12.1. starting by case-studies and continue with the details

12.2. Guiding Ideas for Paper

12.2.1. Show Use Cases "off the beaten tracks", beyond stereotypical landmark examples

13. Covered Themes and Topics

13.1. Corporate Context

13.2. Relevant Technologies

13.2.1. Pattern recognition

13.2.2. Information Visualization

13.3. Philosophical Papers

13.4. Social Phenomena

13.5. Theoretical Concepts

14. Core questions of the paper

14.1. what is big data? - we can't make a definition of big data that others did not - we are not able to do that

14.2. Big Data as marketing tool or sales argument of services (e.g. selling potential) - what is fact and what is science fiction

14.3. what are issues of big data?

14.4. Business aspects


15.1. data quality

15.2. risk issues of big data

15.3. - what can you mesure? relating data accross systems, how does big data increase the potentials of errors (while combining unstructured data)

15.4. definition of big data - is it really due to big data? is it just a hype word? Where is the line between big and small data?


16.1. definition of big data - is it really due to big data? is it just a hype word? Where is the line between big and small data?

16.2. practical cases & applicability of big data issues and how to improve peoples' daily lives

16.3. * tradition systems made transactions as e.g. orders as well as the information was structued like that. These are facts that are there. The data has happned and accroding this the future of a company has been made. But in reality the plan does not go like planned. in big Data you have a time-lag. Big Data is promising because of hte real-time facts. This is something what should really influence business processes from purchace, produciton, and processes.... and how social media information could be utilized

16.4. Big Data is not really new - the challenges where always there

16.5. in history there came things collected, but now things come in milliseconds, seconds, and at any time there. this makes it more challenging.


17.1. real-time - acting it out now and narrow down the whcih consequences do these decissions have - and you see an immediate reaction, and then again an immediate connection, how does the system of behavior change? e.g. traffic


18.1. Cybernetics

18.2. Neuro theory

18.3. "algorithmic regulation" (O'Reilly)

18.4. Case Study (eg. IBM Watson)

19. Paper - Structure

19.1. 3 Case Studies

19.1.1. Traffic

19.1.2. Social Media

19.1.3. Business

19.2. Big Data Promises

19.3. Big Data - Definitions

19.4. state of the art

19.5. Cross-Domain Applications

19.5.1. Social Media & BI Systems

19.5.2. Social Media & Traffic

19.5.3. Traffic & Business

19.6. Big Data Process design

19.7. Approach

19.7.1. 3 case studies - and then from there we go to the literature and then we pair the defintiion iwht hte use cases - what applies to all 3 use-cases as an example. Iteratively, and deductive approach

19.7.2. traditional use-cases from traditional big data applications. eventually use-case themes. 3 domains of applications. eventually gets to much split thematically if it is not focused the paper should be readable and understandable