BDTC2014 China Big Data Technology Conference meeting records, bdtc2014 meeting records

BDTC2014 China Big Data Technology Conference meeting records, bdtc2014 meeting records
1. Implementation, cross-border, efficiency, and big data when leaders meet each other;
2. It takes 5 to 10 years for big data to become a mainstream industry;
3. Data is the appearance, and the essence is the problem;
4. Doug Cutting: Fuel for change: data, EDH, Style catches on: ecosySystem, the Data Multi-Tool, Cloudera;

1. White Paper and development trends;
2. Integration, cross-border, basic, and breakthrough;
3. Multi-Disciplinary integration, worrying about security and privacy;
4. Deep Learning, visual analysis, and open-source systems;
5. Data from equipment monitoring and monitoring has become a hot topic;
6. Tianjin Binhai New Area;
7. Big Data creative competition, student achievement prediction;

1. Store Anything, Object Stonge, Scalable;
2. Real time bidding;
3. How to perform data analysis in a non-programmed manner;
4. Watson perception computing;
5. DeepQA in-depth question answering;

1. How to use big data;
2. Waiting for Communication Collaboration will waste the performance of multiple machines;
3. Hadoop 90% is waiting for a node model;
4. ubiquitous software devices, middle layers, and middleware;
5. high risks and low returns; not every company can play;
6. machine learning requires repeated iterations;
7. Hard Disk reading costs are high;
8. Two Directions of data and model;
9. ML Machine Learning
10. Spark: Faster MapR on Data Parallel;
11. GraphLab, PETUUM, and semi-sync;
12. Structure-based parallelism of SAP;

1. understand people's minds;
2. gartner Prediction;
3. Perception, thinking, and control;
4. The ability to learn is the essence of intelligence;
5. Experience is data; Baidu experience;
6. Humans (each person) need to connect 1000 devices-Internet of everything;
7. Internet of everything-intelligence of everything;
8. Promotion error-decomposition-hypothesis-model;
There is no perfect assumption;
9. Imperfect Data: Noisy, incomplete, and limited;
10. Imperfect Algorithms;
11. Intelligence consumes a lot of energy;