Bid data applied field

1.E-commerce Industry
The E-commerce industry has involved big data to exact marketing from the very beginning, the data production and logistics management will be processed in advance considering the customer’s consumption habits, which benefits the fine social mass production. Due to the data of e-commerce is centralized, large, and various, the future of e-commerce application has more potential space which includes key factors such as forecasting trends, consumption trends, regional consumption characteristics, customer consumption habits, correlation of various consumption behaviors, consumption hot spots, and influence consumers.

2.The Financial Industry
The range of financial industry in big data is wide, it is more applied to trade, lots of equity transactions are carried out by big data algorithm, and these algorithms are more and more concerned the social media and web news, which may impact the trading actions.

3.Medical Industry

Medical data
The data is from medical processes, which includes the patient's basic data, electronic medical records, diagnosis and treatment data, medical imaging data, medical management, economic data, medical equipment and instrument data, etc. The patient can be regarded as the main resource of medical data.

Medical data source
Most of the medical sources are mainly from 4 perspectives: patient, research, equipment, and life science.

  • ・Patient
    Patient's physical signs data, patient's test data, patient's description, patient's hospital data, doctor's consultation data, doctor's clinical diagnosis and treatment, medication, operation data.
  • ・Research
    The data is from the experiment as well as the patient’s data, with no strict standard.
  • ・Equipment
    The data is from wearable equipment such as hand rings, pacemakers, and glasses, which can collect body signs.
  • ・Life Science
    The data is from a medical experiment related to the data of genomics such as dosage, duration, composition, reaction time, symptom improvement.

The characters of medical data
As a kind of data, medical data also associated some characters with big data: Large scale, various structures, increases rapidly, high value; however, it also associated with some medical characters: polymorphism, redundancy, timeliness, and privacy.

  • ・Polymorphism:
    Medical data consist of pure data which has created from production and laboratory; meanwhile, medical data also can be transferred by doctor analyzed image data such as electrocardiogram; besides, the medical data can be collected from voice as well such as heartbeat, cry, cough; moreover, the medical data also can be extracted from video resources such as the image from B-scan ultrasonography in a modern hospital.
  • ・Imperfection:
    Various reasons may lead the medical data imperfect, such as the subjective judgment of the doctor and its description imperfect, patient stop the treatment, and patient’s description unclear.
  • ・Redundancy:
    It is difficult to analyze and filter the data considering it's huge and complex which brings lots of needless data daily.
  • ・Timeliness:
    Most of the medical data associated with timeliness and continuity such as electrocardiogram, quickening metal map, are related to time.
  • ・Privacy:
    As the significant character of medical data, most of the hospitals do not want their medical data; even some hospitals use their independent network.

Data processing
The processing of data generally can be divided into 6 steps; it consists of data mining, collection, analysis, reserve, and utility.

The potential chances of marketing can be found in each step.

  • ・From the point of view of medical data mining: although there is no effective method to deal with and analyze those data, medical institutes such as hospitals and their relevant sectors are pay more and more attention to the medical data.
  • ・From the perspective of data collection: the main medical data has been collected from medical institutes such as hospital, however, some medical equipment factories also collect data, as well as its modalities, amounts, and kinds, become more and more various, which can be regarded as a necessary part of medical big data.
  • ・In the terms of medical data analysis: there are lots of medical data analysis companies existing in the market already, such as iCarbonX and 23mofang (which will be noticed later).
  • ・Data reserve: considering some characters of big data such as large scale, various structures, rapid growth, there are some issues which relevant to data reserve will be involved. Some internet giant such as BAT and IBM are building their medical big data nowadays.
  • ・Medical big data utility: some mobile medical companies make customized big data as the slogan; however, it is necessary to find an effective data analysis method and medical big data source to make medical data utility.

Medical Big Data Application
The main purpose of medical big data includes medication analysis, etiological analysis, mobile medical treatment, genomics, illness prevention, and wearable medical equipment. As the analysis development as well as AI technology reformation, more and more analysis and forecast scene will be benefited with accurate utility via medical big data. Medical big data will become the significant evidence to support the medical decision and the path of the decision will be changed as well. Experience is a decision turned to data support decision now and data will be a decision in the future.

4.Traffic Industry
For now, traffic data can be mainly applied in two fields. The traffic density can be known via the big data sensor, which can provide a reasonable route plan. On the other hand, it can raise traffic efficiency via data-based traffic lights real-time management. Effective traffic lights management is a complex project, a reasonable plan cannot be created without a big data calculation platform. Reasonable traffic lights management raised 30% of traffic capacity, reduced more than 50% of traffic accidents. Additionally, it also raises efficiency and reduces the cost in the airline business and transportation industry.

5.Communication Industry

6.Retail Industry
There are huge human resources, material resources and vigor will be involved in the market development. Accurate market positioning should be regarded as a significant part of market development, or there may cause huge damage even destruction to investigator and company. Accurate market positioning also helps companies to provide products that can satisfy the marketing needs and keeps the competitiveness. However, it required huge amounts of information data which will be provided to the relevant researchers to conduct data analysis and estimate. Traditionally, the collection of data analysis is mainly from statistical yearbook, industry management department, related industry report, industry expert opinion, and local market research. Those data mainly exist various issues such as lack of enough samples, late delivery, low accuracy, and lack of effective information, which can be regarded as the data bottleneck of market positioning. Thanks to the age of big data, data mining and collection not only provide researchers enough data and samples but forecast the future market as well. Certainly, traditional data collection and statistics cannot meet the requirement in a big data environment, thus, it needs support from some automatic data mining tools.

Data Utility Scene

Intelligent Traffic Data Application
Driving video collection, road information extraction, including 3D point cloud obstacle, traffic light, lane light, and high precision map. Provide accurate training data for pedestrian identification, vehicle identification, traffic light identification, lane line identification, and other technologies.

Face recognition data application
Multi-age, multi-angle, multi-expression, multi-light face image acquisition, face key information point tagging, provide data protection for promoting face recognition technology.

E-business data application
Production information, production sales, and customer feedback will be involved to collect and analyze the e-business data to well understand the e-business.

Intelligent voice data application
Main languages collection, voice content processing, emotional judgment, and voice-text transport. To provide high-quality voice data to ASR, TTS, etc.

OCR data application
Identify and extract the license plate, lottery, business card, ID card, bank card, handwriting track to improve the accuracy of the smart device.

Network public opinion monitoring
Provide data collection and analysis of articles, comments, and likes of those social media such as news, BBS, blog, post-bar. More accurate and faster public opinion data can make monitoring easier.