人工智能私人助理的研发与应用

Development and Implementation of Automated Intelligent Personal Assistants

姓名（Name）: Dr. Adrian Ho?yk

毕业院校（University）:

克拉科夫AGH科技大学

AGH University of Science and Technology

研究领域(Experience/Expertise):

人工智能

Artificial Intelligence (AI）

开发和实现：

?数据挖掘，利用组合图形数据结构自动判定所收集到的数据并为大数据应用快速挖掘有效数据。 

?基于采购成本最低的数据特征的自动选择子集的分级的智能关联分类。 

?使用于互联网上各种虚拟实景应用的自动智能个人助理。 

?用于加速大数据应用程序的数据库操作的关联图数据库（AGDB）模型的引擎和其他模型的转换机制。

Development and implementation of:

?data mining algorithms using associative graph data structures to automate inferences on gathered data and speed up mining processes for Big Data applications. 

?a smart associative classifier for intelligent defining of classes based on an automatically selected subset of data features of the lowest purchase costs.

?automated intelligent personal assistants for various uses in the virtual reality on the Internet. 

?an engine for the model of associative graph databases AGDB and conversion mechanisms for other databases to accelerate database operations for Big Data applications.

1.设想描述: 

Today, data are stored and operated by various database systems and engines. The most of them are SQL DBMS, however, some NO-SQL DBMS also exist. According to my latest research on associative approaches inspired by brain associative processes, I have proved that it is possible to store data together with a great subset of their relations which normally must be searched in classic relational databases. In result, we can create a specific associative data structure and organize stored data in such a way that they are always sorted for all data features, aggregated (i.e. there are no duplicates), and very quickly available thanks to newly developed AVB+trees. Moreover, such a structure represents an enriched subset of relations that do not need to be searched, butthey are available from stock in constant time. These features outperform other solutions and accelerate data access and operations many times and simultaneously reduces costs of data storage. 

2. 目标: Creation of an associative graph database engine based on the associative approach and data structure to compete with other solutions in the market. 

3. 创新点: 

? A smart model for Big Data storage and operation inspired by brain structures 

? Significant acceleration of all operations on data stored in new associative graph databases 

? Reduction of costs of data storage and operations. 

? Ability to use this kind of databases for faster and automatic data mining. 

? Ability to successfully compete with all database solutions available on the market. 

4. 预算: 

? Dependent on a level and scale of implementation of those mechanisms. 

5. 项目团队: 

? Dependent on a level and scale of implementation of those mechanisms. 

? From at least a few developers and database professionals. 

6.项目实施时间: 

? Dependent on a level and scale of implementation of those mechanisms. 

? At least three years. 

7.项目盈利: 

? Dependent on a level and scale of implementation of those mechanisms. 

? Production of the professional AGDB Engine can allow for huge profitability on the data market and the ability to master or taking over the database market.