Title: Research on Key Technologies of Time Series, Spatio-temporal and Graphic Databases


Technical Area: Database



With the rapid development of Internet, IoT and Edge computing, Surveying and Social Sensing, the time series and spatio-temporal data will be enormously enriched and the cross-media linkage of information will become increasingly complex. New types of data such as 3D-scene data, spatio-temporal trajectory data, IoT sensing data(time+location+value, spatio-temporal media data and complex relational network data will be used in various industries.


New type of businesses like IoT, new retail, traveling/shared trip, automatic driving, intelligent logistics, intelligent transportation will make time series computing, spatio-temporal computing and graph computing ubiquitous. Therefore, the time series/spatio-temporal/graph computing power of database will become the core requirements to support these emerging industries, and also act as a new key driving factor for cloud computing business.



In combination with business scenario requirements, this study aims to design a new time series model, a spatio-temporal model, or a graph model of the database and implement it from the perspective of the database kernel engine, and achieve the following goals:


Related Research Topics

1. Real time non-structured data processing.

Combined with stream computing system, we need to establish a real-time access method, efficient compression storage and analysis framework for large-scale time-series / location / graph data. Write up to tens of millions of sequential data points per second.

2. Graph modeling and application based on spatio-temporal constraints.

In combination with related applications, we need to design and build a Graph model with dynamic temporal and spatial semantic constraints, which can support data compression, fast path/relational search and analysis under large-scale scenarios.

3. Hierarchical multidimensional efficient database index.

Design/optimize temporal indexes, spatial indexes, graph indexes and their combinations; combined with time series / graph new data models and query features, studies to implement pre-aggregated indexes, correlation indexes, and approximate query indexes.

Hardware and software acceleration for graphics and images. Studies to implement graphic and image query processing operators based on hardware acceleration and algorithm optimization. Performance will be improved by an order of magnitude.